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Christ, Emily Hall. "Optimizing yield with agricultural climate and weather forecasts." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54952.
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Weather affects agriculture more than any other variable. For centuries, growers had to depend upon small bits and pieces of local climatological data collected and passed down in almanacs. Over the last 100 years, however, scientists have developed complex Numerical Weather Prediction (NWP) models that are able to forecast weather with increasing accuracy. The objective of this work was to use a probabilistic NWP model (the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS)) as a component to couple with agricultural decision-making tools and models. First, customized ECMWF EPS forecasts were used as an irrigation scheduling aid for a field trial. Next, the CROPGRO Cotton Model was used to simulate the field experiment as well as an additional irrigation scheduling strategy. Finally, a cotton canopy temperature model was developed and coupled with customized ECMWF EPS forecasts to generate hourly canopy temperature forecasts. These forecasts were used to create a heat stress warning system. Results from the field trial indicate that using precipitation forecasts to schedule irrigation could provide a convenient alternative relative to a standard method. Results from the simulated field trial suggest using precipitation forecasts issued on the day of irrigation could be more efficient than using forecasts issued one to two days prior. Last, results from the heat stress project indicate forecasts were skillful to 10 days, allowing enough time for growers to protect crops if needed. In light of the above, implications for the agricultural community could be significant. Coupled atmospheric-agricultural models have the ability to put weather forecasts in terms producers can understand and can quickly use to make strategic on-farm decisions, therefore, possessing the potential to make a large positive global impact.
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Bohn, Louise Eleanor. "Seasonal climate forecasts in Swaziland : the producer-user interface." Thesis, University of East Anglia, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405705.
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Wood, Andrew W. "Using climate model ensemble forecasts for seasonal hydrologic prediction /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/10205.
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Sedig, Victoria, Evelina Samuelsson, Nils Gumaelius, and Andrea Lindgren. "Greenhouse Climate Optimization using Weather Forecasts and Machine Learning." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-391045.
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It is difficult for a small scaled local farmer to support him- or herself. In this investigation a program was devloped to help the small scaled farmer Janne from Sala to keep an energy efficient greenhouse. The program applied machine learning to make predictions of future temperatures in the greenhouse. When the temperature was predicted to be dangerously low for the plants and crops Janne was warned via a HTML web page. To make an as accurate prediction as possible different machine learning algorithm methods were evaluated. XGBoost was the most efficient and accurate method with an cross validation value at 2.33 and was used to make the predictions. The data to train the method with was old data inside and outside the greenhouse provided from the consultancy Bitroot and SMHI. To make predictions in real time weather forecast was collectd from SMHI via their API. The program can be useful for a farmer and can be further developed in the future.
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NETRANANDA, SAHU. "Impacts of Climate Variations on Seasonal Streamflows and Probabilistic Forecasts." 京都大学 (Kyoto University), 2012. http://hdl.handle.net/2433/161004.
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Rowe, Scott Thomas. "The predictability of Iowa's hydroclimate through analog forecasts." Thesis, University of Iowa, 2014. https://ir.uiowa.edu/etd/1390.
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Iowa has long been affected by periods characterized by extreme drought and flood. In 2008, Cedar Rapids, Iowa was devastated by a record flood with damages around $3 billion. Several years later, Iowa was affected by severe drought in 2012, causing upwards of $30 billion in damages and losses across the United States. These climatic regimes can quickly transition from one regime to another, as was observed in the June 2013 major floods to the late summer 2013 severe drought across eastern Iowa. Though it is not possible to prevent a natural disaster from occurring, we explore how predictable these events are by using forecast models and analogs.
Iowa's climate records are analyzed from 1950 to 2012 to determine if there are specific surface and upper-air pressure patterns linked to climate regimes (i.e., cold/hot and dry/wet conditions for a given month). We found that opposing climate regimes in Iowa have reversed anomalies in certain geographical regions of the northern hemisphere. These defined patterns and waves suggested to us that it could be possible to forecast extreme temperature and precipitation periods over Iowa if given a skillful forecast system. We examined the CMC, COLA, and GFDL models within the National Multi-Model Ensemble suite to create analog forecasts based on either surface or upper-air pressure forecasts. The verification results show that some analogs have predictability skill at the 0.5-month lead time exceeding random chance, but our overall confidence in the analog forecasts is not high enough to allow us to issue statewide categorical temperature and precipitation climate forecasts.
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Forsee, William Joel. "Implementation of a Hybrid Weather Generator and Creating Sets of Synthetic Weather Series Consistent with Seasonal Climate Forecasts in the Southeastern United States." Scholarly Repository, 2008. http://scholarlyrepository.miami.edu/oa_theses/215.
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Stochastic weather generators create multiple series of synthetic daily weather (precipitation, maximum temperature, etc.), and ideally these series will have statistical properties similar to those of the input historical data. The synthetic output has many applications and for example, can be used in sectors such as agriculture and hydrology. This work used a ?hybrid? weather generator which consists of a parametric Markov chain for generating precipitation occurrence and a nonparametric k-nearest neighbor method for generating values of maximum temperature, minimum temperature, and precipitation. The hybrid weather generator was implemented and validated for use at 11 different locations in the Southeastern United States. A total of 36 graphic diagnostics were used to assess the model?s performance. These diagnostics revealed that the weather generator successfully created synthetic series with most statistical properties of the historical data including extreme wet and dry spell lengths and days of first and last freeze. Climate forecasts are typically provided for seasons or months. Alternatively, process models used for risk assessment often operate at daily time scales. If climate forecasts were incorporated into the daily weather input for process models, stakeholders could then use these models to assess possible impacts on their sector of interest due to anticipated changes in climate conditions. In this work, an ?ad hoc? resampling approach was developed to create sets of daily synthetic weather series consistent with seasonal climate forecasts in the Southeastern United States. In this approach, the output of the hybrid weather generator was resampled based on forecasts in two different formats: the commonly used tercile format and a probability distribution function. This resampling approach successfully created sets of synthetic series which reflected different forecast scenarios (i.e. wetter or drier conditions). Distributions of quarterly total precipitation from the resampled synthetic series were found to be shifted with respect to the corresponding historical distributions, and in some cases, the occurrence and intensity statistics of precipitation in the new weather series had changed with respect to the historical values.
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Cavicchioli, Niccolò. "Preparing for a future satellite mission to measure wind and improve climate forecasts." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23037/.
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Stratospheric Inferred Winds (SIW) is a future satellite mission, which has been selected by the Swedish Space Agency to become the next Swedish research satellite, to be launched in 2024. It will consist of a sub-millimetre radiometer instrument, optimised for wind measurements in the middle atmosphere, and orbiting the Earth aboard a microsatellite platform. The goal of this master thesis was to carry out a preliminary study to assess the potential of the mission to contribute to a better understanding of the middle atmospheric dynamical events, and thus to improve weather and climate forecasts. The analysis of zonal mean eastward wind from two five-year-long reanalysis data sets, namely ERA5 and MERRA-2, is described and compared to SIW estimated performances. The areas of major disagreement are investigated in details. It appears that the models have important difficulties to accurately reproduce the dynamical phenomena in the regions out of geostrophic balance due to wave forcing processes. The results show that a significant contribution can be provided by the SIW mission particularly at low latitudes, where the effects related to the Semi-Annual Oscillation can be studied, and at high latitudes during winter-time, where the effects of Sudden Stratospheric Warming events can be investigated. In those regions, at mesospheric altitudes, SIW estimated precision is most of the time significantly lower than the observed differences.
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Dickerson, Susan E. Mitchell Robert. "Modeling the effects of climate change forecasts on streamflow in the Nooksack River Basin /." Online version, 2010. http://content.wwu.edu/cdm4/item_viewer.php?CISOROOT=/theses&CISOPTR=366&CISOBOX=1&REC=1.
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Che, Him Norziha. "Potential for using climate forecasts in spatio-temporal prediction of Dengue fever incidence in Malaysia." Thesis, University of Exeter, 2015. http://hdl.handle.net/10871/23205.
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Dengue fever is a viral infection transmitted by the bite of female \textit{Aedes aegypti} mosquitoes. It is estimated that nearly 40\% of the world's population is now at risk from Dengue in over 100 endemic countries including Malaysia. Several studies in various countries in recent years have identified statistically significant links between Dengue incidence and climatic factors. There has been relatively little work on this issue in Malaysia, particularly on a national scale. This study attempts to fill that gap. The primary research question is `to what extent can climate variables be used to assist predictions of dengue fever incidence in Malaysia?'. The study proposes a potential framework of modelling spatio-temporal variation in dengue risk on a national scale in Malaysia using both climate and non-climate information. Early chapters set the scene by discussing Malaysia and Climate in Malaysia and reviewing previous work on dengue fever and dengue fever in Malaysia. Subsequent chapters focus on the analysis and modelling of annual dengue incidence rate (DIR) for the twelve states of Peninsular Malaysia for the period 1991 to 2009 and monthly DIR for the same states in the period 2001 to 2009. Exploratory analyses are presented which suggest possible relationships between annual and monthly DIR and climate and other factors. The variables that were considered included annual trend, in year seasonal effects, population, population density and lagged dengue incidence rate as well as climate factors such as average rainfall and temperature, number of rainy days, ENSO and lagged values of these climate variables. Findings include evidence of an increasing annual trend in DIR in all states of Malaysia and a strong in-year seasonal cycle in DIR with possible differences in this cycle in different geographical regions of Malaysia. High population density is found to be positively related to monthly DIR as is the DIR in the immediately preceding months. Relationships between monthly DIR and climate variables are generally quite weak, nevertheless some relationships may be able to be usefully incorporated into predictive models. These include average temperature and rainfall, number of rainy days and ENSO. However lagged values of these variables need to be considered for up to 6 months in the case of ENSO and from 1-3 months in the case of other variables. These exploratory findings are then more formally investigated using a framework where dengue counts are modelled using a negative binomial generalised linear model (GLM) with a population offset. This is subsequently extended to a negative binomial generalised additive model (GAM) which is able to deal more flexibly with non-linear relationships between the response and certain of the explanatory variables. The model successfully accounts for the large amount of overdispersion found in the observed dengue counts. Results indicated that there are statistically significant relationships with both climate and non-climate covariates using this modelling framework. More specifically, smooth functions of year and month differentiated by geographical areas of the country are significant in the model to allow for seasonality and annual trend. Other significant covariates included were mean rainfall at lag zero month and lag 3 months, mean temperature at lag zero month and lag 1 month, number of rainy days at lag zero month and lag 3 months, sea surface temperature at lag 6 months, interaction between mean temperature at lag 1 month and sea surface temperature at lag 6 months, dengue incidence rate at lag 3 months and population density. Three final competing models were selected as potential candidates upon which an early warning system for dengue in Malaysia might be able to be developed. The model fits for the whole data set were compared using simulation experiments to allow for both parameter and negative binomial model uncertainty and a single model preferred from the three models was identified. The `out of sample' predictive performance of this model was then compared and contrasted for different lead times by fitting the model to the first 7 years of the 9 years monthly data set covering 2001-2009 and then analysing predictions for the subsequent 2 years for lead time of 3, 6 12 and 24 months. Again simulation experiments were conducted to allow for both parameter and model uncertainty. Results were mixed. There does seem to be predictive potential for lead times of up to six months from the model in areas outside of the highly urbanised South Western states of Kuala Lumpur and Selangor and such a model may therefore possibly be useful as a basis for developing early warning systems for those areas. However, none of the models developed work well for Kuala Lumpur and Selangor where there are clearly more complex localised influences involved which need further study. This study is one of the first to look at potential climatic influences on dengue incidence on a nationwide scale in Malaysia. It is also one of the few studies worldwide to explore the use of generalised additive models in the spatio-temporal modelling of dengue incidence. Although, the results of the study show a mixed picture, hopefully the framework developed will be able to be used as a starting point to investigate further if climate information can valuably be incorporated in an early warning system for dengue in Malaysia.
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Selato, Janet Chatanga. "Credibility and scale as barriers to uptake and use of seasonal climate forecasts in Bobirwa Sub-District, Botswana." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27526.
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Seasonal climate forecasts (SCF) can play a crucial role in reducing vulnerability to climate variability, particularly for rural populations reliant on agriculture for their livelihood. The use of disseminated SCF by farmers in decision-making could reduce losses and maximise benefits in agriculture. Despite the potential usefulness of SCF, incorporating them into farming decisions is a complex process that navigates through several barriers which constrain their effective use. The first two barriers, namely credibility (trust on SCF) and scale (relevance of SCF in geographical space and time), originate from the limitations of SCF associated with the form in which they are produced. In this study, credibility and scale are investigated as limitations of SCF, which potentially bar the uptake and use of SCF in Bobirwa sub-district. The second group of barriers are beyond the SCF themselves but limit their effective use and emanate from biophysical, socio-cultural and economic factors. This study examines whether credibility and scale are barriers to the use of SCF in Bobirwa farmers' decision-making, investigates how SCF are used in decision-making, and seeks to find out how the barriers are overcome. To make these investigations, qualitative data was collected from subsistence agro-pastoral farmers in eight villages in Bobirwa sub-district of Botswana using semi-structured interviews. Data was collected considering gender to allow for gendered analysis. Themes related to the main study questions were identified from the data and analysed for the number of people who mentioned the themes. It was found that all 47 farmers interviewed coincidentally had access to SCF and the majority used SCF in their decision-making, while only a handful of farmers were non-users of SCF. The results show that scale (both temporal and spatial) is a barrier for users of SCF, whereas credibility is a major constraint for non-users of SCF in Bobirwa. To cope with the barriers, farmers mainly use local knowledge to complement SCF. Additionally, farmers apply advice from Ministry of Agriculture (MoA) and use economic information in their decisions to deal with the barriers. Despite the barriers, some farmers indicated that using SCF was beneficial in increasing harvests, providing warnings and minimising losses of crops and livestock. However, disadvantages of using SCF were also highlighted, including lost crops, seeds and harvest, and missed opportunities to plant because of lack of temporal and geographical detail in the forecasts. The barrier of credibility has contributed to a few non-users resorting to using traditional planting, possibly making them vulnerable to the impacts of climate variability. A gendered analysis shows that almost equal proportions of both males and females use SCF. Moreover, women use SCF for crop farming while men use it for livestock management, which is aligned to traditional roles in Botswana. It is also revealed that, unlike women who only use local knowledge and MoA advice to overcome SCF limitations, a few men also use economic ventures, which could make men less vulnerable than women farmers. Strong networks between scientists and farmers can reduce the perceived credibility barrier, and innovative ways of reducing the scale barrier can be devised. Therefore, recommendations from the study include continuous engagement with farmers to understand their decisionmaking context in order to tailor the information to their local context as much as science permits. Government programmes should be designed to integrate SCF to build farmers' resilience to climate variabilities. The impacts on livestock farming, which is dominated by men, need to be given as much prominence in SCF information as arable farming. Forecasters should continue to improve credibility and scale without compromising either factor to avoid chances of contributing to the vulnerability of farmers particularly women, who mostly rely on SCF for crop production.
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Lowe, Rachel. "Spatio-temporal modelling of climate-sensitive disease risk : towards an early warning system for dengue in Brazil." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/120070.
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The transmission of many infectious diseases is affected by climate variations, particularly for diseases spread by arthropod vectors such as malaria and dengue. Previous epidemiological studies have demonstrated statistically significant associations between infectious disease incidence and climate variations. Such research has highlighted the potential for developing climate-based epidemic early warning systems. To establish how much variation in disease risk can be attributed to climatic conditions, non-climatic confounding factors should also be considered in the model parameterisation to avoid reporting misleading climate-disease associations. This issue is sometimes overlooked in climate related disease studies. Due to the lack of spatial resolution and/or the capability to predict future disease risk (e.g. several months ahead), some previous models are of limited value for public health decision making. This thesis proposes a framework to model spatio-temporal variation in disease risk using both climate and non-climate information. The framework is developed in the context of dengue fever in Brazil. Dengue is currently one of the most important emerging tropical diseases and dengue epidemics impact heavily on Brazilian public health services. A negative binomial generalised linear mixed model (GLMM) is adopted which makes allowances for unobserved confounding factors by including spatially structured and unstructured random effects. The model successfully accounts for the large amount of overdispersion found in disease counts. The parameters in this spatio-temporal Bayesian hierarchical model are estimated using Markov Chain Monte Carlo (MCMC). This allows posterior predictive distributions for disease risk to be derived for each spatial location and time period (month/season). Given decision and epidemic thresholds, probabilistic forecasts can be issued, which are useful for developing epidemic early warning systems. The potential to provide useful early warnings of future increased and geographically specific dengue risk is investigated. The predictive validity of the model is evaluated by fitting the GLMM to data from 2001-2007 and comparing probabilistic predictions to the most recent out-of-sample data in 2008-2009. For a probability decision threshold of 30% and the pre-defined epidemic threshold of 300 cases per 100,000 inhabitants, successful epidemic alerts would have been issued for 94% of the 54 microregions that experienced high dengue incidence rates in South East Brazil, during February - April 2008.
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Thornes, Tobias. "Investigating the potential for improving the accuracy of weather and climate forecasts by varying numerical precision in computer models." Thesis, University of Oxford, 2018. http://ora.ox.ac.uk/objects/uuid:038874a3-710a-476d-a9f7-e94ef1036648.
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Accurate forecasts of weather and climate will become increasingly important as the world adapts to anthropogenic climatic change. Forecasts' accuracy is limited by the computer power available to forecast centres, which determines the maximum resolution, ensemble size and complexity of atmospheric models. Furthermore, faster supercomputers are increasingly energy-hungry and unaffordable to run. In this thesis, a new means of making computer simulations more efficient is presented that could lead to more accurate forecasts without increasing computational costs. This 'scale-selective reduced precision' technique builds on previous work that shows that weather models can be run with almost all real numbers represented in 32 bit precision or lower without any impact on forecast accuracy, challenging the paradigm that 64 bits of numerical precision are necessary for sufficiently accurate computations. The observational and model errors inherent in weather and climate simulations, combined with the sensitive dependence on initial conditions of the atmosphere and atmospheric models, renders such high precision unnecessary, especially at small scales. The 'scale-selective' technique introduced here therefore represents smaller, less influential scales of motion with less precision. Experiments are described in which reduced precision is emulated on conventional hardware and applied to three models of increasing complexity. In a three-scale extension of the Lorenz '96 toy model, it is demonstrated that high resolution scale-dependent precision forecasts are more accurate than low resolution high-precision forecasts of a similar computational cost. A spectral model based on the Surface Quasi-Geostrophic Equations is used to determine a power law describing how low precision can be safely reduced as a function of spatial scale; and experiments using four historical test-cases in an open-source version of the real-world Integrated Forecasting System demonstrate that a similar power law holds for the spectral part of this model. It is concluded that the scale-selective approach could be beneficially employed to optimally balance forecast cost and accuracy if utilised on real reduced precision hardware.
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Makaudze, Ephias M. "Do seasonal climate forecasts and crop insurance really matter for smallholder farmers in Zimbabwe? Using contingent valuation method and remote sensing applications." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1110389049.
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Thesis (Ph. D.)--Ohio State University, 2005.Title from first page of PDF file. Document formatted into pages; contains xiii, 155 p.; also includes map, graphics (some col.) Includes bibliographical references (p. 149-155). Available online via OhioLINK's ETD Center
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DeChant, Caleb Matthew. "Quantifying the Impacts of Initial Condition and Model Uncertainty on Hydrological Forecasts." Thesis, Portland State University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3628148.
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Forecasts of hydrological information are vital for many of society's functions. Availability of water is a requirement for any civilization, and this necessitates quantitative estimates of water for effective resource management. The research in this dissertation will focus on the forecasting of hydrological quantities, with emphasis on times of anomalously low water availability, commonly referred to as droughts. Of particular focus is the quantification of uncertainty in hydrological forecasts, and the factors that affect that uncertainty. With this focus, Bayesian methods, including ensemble data assimilation and multi-model combinations, are utilized to develop a probabilistic forecasting system. This system is applied to the upper Colorado River Basin for water supply and drought forecast analysis.
This dissertation examines further advancements related to the identification of drought intensity. Due to the reliance of drought forecasting on measures of the magnitude of a drought event, it is imperative that these measures be highly accurate. In order to quantify drought intensity, hydrologists typically use statistical indices, which place observed hydrological deficiencies within the context of historical climate. Although such indices are a convenient framework for understanding the intensity of a drought event, they have obstacles related to non-stationary climate, and non-uniformly distributed input variables. This dissertation discusses these shortcomings, demonstrates some errors that conventional indices may lead to, and then proposes a movement towards physically-based indices to overcome these issues.
A final advancement in this dissertation is an examination of the sensitivity of hydrological forecasts to initial conditions. Although this has been performed in many recent studies, the experiment here takes a more detailed approach. Rather than determining the lead time at which meteorological forcing becomes dominant with respect to initial conditions, this study quantifies the lead time at which the forecast becomes entirely insensitive to initial conditions, and estimating the rate at which the forecast loses sensitivity to initial conditions. A primary goal with this study is to examine the recovery of drought, which is related to the loss of sensitivity to below average initial moisture conditions over time. Through this analysis, it is found that forecasts are sensitive to initial conditions at greater lead times than previously thought, which has repercussions for development of forecast systems.
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AlMutairi, Bandar Saud. "Statistical Models for Characterizing and Reducing Uncertainty in Seasonal Rainfall Pattern Forecasts to Inform Decision Making." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/940.
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Uncertainty in rainfall forecasts affects the level of quality and assurance for decisions made to manage water resource-based systems. However, eliminating uncertainty in a complete manner could be difficult, decision-makers thus are challenged to make decisions in the light of uncertainty. This study provides statistical models as an approach to cope with uncertainty, including: a) a statistical method relying on a Gaussian mixture (GM) model to assist in better characterize uncertainty in climate model projections and evaluate their performance in matching observations; b) a stochastic model that incorporates the El Niño–Southern Oscillation (ENSO) cycle to narrow uncertainty in seasonal rainfall forecasts; and c) a statistical approach to determine to what extent drought events forecasted using ENSO information could be utilized in the water resources decision-making process. This study also investigates the relationship between calibration and lead time on the ability to narrow the interannual uncertainty of forecasts and the associated usefulness for decision making. These objectives are demonstrated for the northwest region of Costa Rica as a case study of a developing country in Central America. This region of Costa Rica is under an increasing risk of future water shortages due to climate change, increased demand, and high variability in the bimodal cycle of seasonal rainfall. First, the GM model is shown to be a suitable approach to compare and characterize long-term projections of climate models. The GM representation of seasonal cycles is then employed to construct detailed comparison tests for climate models with respect to observed rainfall data. Three verification metrics demonstrate that an acceptable degree of predictability can be obtained by incorporating ENSO information in reducing error and interannual variability in the forecast of seasonal rainfall. The predictability of multicategory rainfall forecasts in the late portion of the wet season surpasses that in the early portion of the wet season. Later, the value of drought forecast information for coping with uncertainty in making decisions on water management is determined by quantifying the reduction in expected losses relative to a perfect forecast. Both the discrimination ability and the relative economic value of drought-event forecasts are improved by the proposed forecast method, especially after calibration. Positive relative economic value is found only for a range of scenarios of the cost-loss ratio, which indicates that the proposed forecast could be used for specific cases. Otherwise, taking actions (no-actions) is preferred as the cost-loss ratio approaches zero (one). Overall, the approach of incorporating ENSO information into seasonal rainfall forecasts would provide useful value to the decision-making process - in particular at lead times of one year ahead.
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Sewe, Maquins Odhiambo. "Towards Climate Based Early Warning and Response Systems for Malaria." Doctoral thesis, Umeå universitet, Epidemiologi och global hälsa, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-130169.
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Background: Great strides have been made in combating malaria, however, the indicators in sub Saharan Africa still do not show promise for elimination in the near future as malaria infections still result in high morbidity and mortality among children. The abundance of the malaria-transmitting mosquito vectors in these regions are driven by climate suitability. In order to achieve malaria elimination by 2030, strengthening of surveillance systems have been advocated. Based on malaria surveillance and climate monitoring, forecasting models may be developed for early warnings. Therefore, in this thesis, we strived to illustrate the use malaria surveillance and climate data for policy and decision making by assessing the association between weather variability (from ground and remote sensing sources) and malaria mortality, and by building malaria admission forecasting models. We further propose an economic framework for integrating forecasts into operational surveillance system for evidence based decisionmaking and resource allocation. Methods: The studies were based in Asembo, Gem and Karemo areas of the KEMRI/CDC Health and Demographic Surveillance System in Western Kenya. Lagged association of rainfall and temperature with malaria mortality was modeled using general additive models, while distributed lag non-linear models were used to explore relationship between remote sensing variables, land surface temperature(LST), normalized difference vegetation index(NDVI) and rainfall on weekly malaria mortality. General additive models, with and without boosting, were used to develop malaria admissions forecasting models for lead times one to three months. We developed a framework for incorporating forecast output into economic evaluation of response strategies at different lead times including uncertainties. The forecast output could either be an alert based on a threshold, or absolute predicted cases. In both situations, interventions at each lead time could be evaluated by the derived net benefit function and uncertainty incorporated by simulation. Results: We found that the environmental factors correlated with malaria mortality with varying latencies. In the first paper, where we used ground weather data, the effect of mean temperature was significant from lag of 9 weeks, with risks higher for mean temperatures above 250C. The effect of cumulative precipitation was delayed and began from 5 weeks. Weekly total rainfall of more than 120 mm resulted in increased risk for mortality. In the second paper, using remotely sensed data, the effect of precipitation was consistent in the three areas, with increasing effect with weekly total rainfall of over 40 mm, and then declined at 80 mm of weekly rainfall. NDVI below 0.4 increased the risk of malaria mortality, while day LST above 350C increased the risk of malaria mortality with shorter lags for high LST weeks. The lag effect of precipitation was more delayed for precipitation values below 20 mm starting at week 5 while shorter lag effect for higher precipitation weeks. The effect of higher NDVI values above 0.4 were more delayed and protective while shorter lag effect for NDVI below 0.4. For all the lead times, in the malaria admissions forecasting modelling in the third paper, the boosted regression models provided better prediction accuracy. The economic framework in the fourth paper presented a probability function of the net benefit of response measures, where the best response at particular lead time corresponded to the one with the highest probability, and absolute value, of a net benefit surplus. Conclusion: We have shown that lagged relationship between environmental variables and malaria health outcomes follow the expected biological mechanism, where presentation of cases follow the onset of specific weather conditions and climate variability. This relationship guided the development of predictive models showcased with the malaria admissions model. Further, we developed an economic framework connecting the forecasts to response measures in situations with considerable uncertainties. Thus, the thesis work has contributed to several important components of early warning systems including risk assessment; utilizing surveillance data for prediction; and a method to identifying cost-effective response strategies. We recommend economic evaluation becomes standard in implementation of early warning system to guide long-term sustainability of such health protection programs.
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Pagano, Thomas Christopher, and Thomas Christopher Pagano. "The role and usability of climate forecasts for flood control and water supply agencies in Arizona: a case study of the 1997-98 El Nino." Thesis, The University of Arizona, 1999. http://hdl.handle.net/10150/626891.
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The 1997-98 El Nino provided a unique opportunity for climate information and
forecasts to be utilized by water management agencies in the Southwestern U.S. While
Arizona has experienced high streamflow associated with previous El Nino events, never
before had an event of such magnitude been predicted with advance warning of several
months. Likewise, the availability of information, including Internet sources and
widespread media coverage, was higher than ever before. Insights about use of this
information in operational water management decision processes are developed through a
series of semi-structured in-depth interviews with key personnel from a broad array of
agencies responsible for emergency management and water supply, with jurisdictions
ranging from urban to rural and local to regional. The interviews investigate where
information was acquired, how it was interpreted and how it was incorporated into
specific decisions and actions. The interviews also investigate agency satisfaction with
the products available to them, their operational decisions, and intentions to utilize
forecast products in the future. Study fmdings lead to recommendations about how to
more effectively provide intended users of forecasts with information required to enact
mitigation measures and utilize opportunities that some climatic events present.
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Vamborg, Freja S. E. "Linguistic uncertainty in meteorological forecastsfor Russian speaking audiences : A comparative study between televised weather forecastsand seasonal outlooks of the Northern Eurasian ClimateOutlook Forum." Thesis, Högskolan Dalarna, Ryska, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:du-27832.
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In order to make informed decisions, we need to resort to various types of information and we need to know how uncertain this information is. A commonly used source for information and subsequent action is weather forecasts. The communication of uncertainty in weather forecasts has been widely studied for English speaking audiences, resulting in a number of guidelines that practitioners can follow. For forecasts aimed at Russian speaking audiences there are very few, if no, such studies. The aim of this study is to extend previous research on the communication of uncertainties in weather forecasts to the Russian-speaking domain. The underlying hypothesis for this study is that it should be possible to distinguish texts from different types of forecasts, with different inherent uncertainty, by analysing the linguistic uncertainty markers in the text-based section of these forecasts. If this is not the case, this could in a first step be solved by applying the recommendations in the available guidelines, in a second step the guidelines themselves might need to be extended to meet the needs of the practitioners. To test the hypothesis, I analyse the expressed linguistic uncertainty in two different sources of meteorological information: weather forecasts and seasonal outlooks. The analysis shows that the original hypothesis can be confirmed: the differences between these two sources of information can be detected by analysing linguistic uncertainty markers. Further, the recommendations from the guidelines were met to a large extent, but both type of forecasts, in particular the seasonal outlooks, would benefit from a more consolidated approach. The analysis also shows that these guidelines could be improved by placing an increased emphasis on text-based forecasts, highlighting which linguistic means should be used for what purpose. The guidelines could be extended with language-specific best-practise examples. This way the guidelines would cater for a much larger user-base than they do at present.
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Thakur, Balbhadra. "HYDROLOGIC VARIABILITY WITHIN THE CLIMATE REGIONS OF CONTINENTAL UNITED STATES AND ITS TELECONNECTION WITH CLIMATE VARIABLES." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1844.
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The entropy of all systems is supposed to increase with time, this is also observed in the hydroclimatic records as increased variability. The current dissertation is primarily focused on the hydrologic variability of the hydrologic records in the climate regions across Continental United States. The study evaluated the effects of serial correlation in the historical streamflow records on both gradual trend and abrupt shift in streamflow. The study also evaluated the trend before and after the shift occurrence to validate whether the observed changes in streamflow is a result of long-term variability or climate regime shift. Secondly, the current dissertation evaluated the variability within western US hydrology which is highly driven by the oscillation of Pacific Ocean such as El Niño – Southern Oscillation (ENSO). The dissertation evaluated the variability in snow water equivalent (SWE) of western US as the winter snow accumulation of the region drives the spring-summer streamflow in the region which contributes to the major portion of yearly streamflow. The SWE variability during the individual phases of ENSO were analyzed to reveal the detailed influence of ENSO on historic snow accumulations. The study is not solely limited to the hydrologic variability evaluation rather; it also delves into obtaining the time lagged spatiotemporal teleconnections between large scale climate variables and streamflow and forecast the later based on the obtained teleconnections. To accomplish the research goals the current dissertation was subdivided into three research tasks. First task dealt with the streamflow records of 419 unimpaired streamflow records which were grouped into seven climate regions based on National Climate Assessment, to evaluate the regional changes in both seasonal streamflow and yearly streamflow percentiles. Non-parametric Mann-Kendall test and Pettitt’s test were utilized to evaluate the streamflow variability as gradual trend and abrupt shift, respectively. Walker test was performed to test the global significance of the streamflow variability within each climate regions based on local trend and shift significance of each streamflow stations. The task also evaluated the presence of serial correlation in the streamflow records and its effects on both trend and shift within the climate regions of continental United States for the first time. Maximum variability in terms of both trend and shift were observed for summer as compared to other seasons. Similarly, greater number of stations showed streamflow variability for 5th and 50th percentile streamflow as compared to 95th and 100th percentile streamflow. It was also observed that serial correlation affected both trend and step while, accounting for the lag-1 autocorrelation improved shift results. The results indicated that the streamflow variability has more likely occurred as shift as compared to the gradual trend. The outcomes of the current result detailing historic variability may help to envision future changes in streamflow. The second task evaluated the spatiotemporal variability of western US SWE over 58 years (1961–2018) as a trend and a shift. The task tested whether the SWE is consistent during ENSO phases utilizing the Kolmogorov – Smirnov (KS) test. Trend analysis was performed on the SWE data of each ENSO phase. Shift analysis was performed in the entire time series of 58 years. Additionally, the trend in the SWE data was evaluated before and after shift years. Mann- Kendal and Pettit's tests were utilized for the detection of trend and shift, respectively. The serial correlation was considered during the trend evaluation, while Thiel-Sen approach was used for the evaluation of the trend magnitude. The serial correlation in time series which is the potential cause of overestimation and underestimation of the trend evaluation was found to be absent in the SWE data. The results suggested a negative trend and a shift during the study period. The negative trend was absent during neutral years and present during El Niño and La Niña years. The trend magnitudes were maximum during La Niña years followed by those during El Niño years and the entire length of the data. It was also observed that if the presence of negative shift in the SWE was considered, then most of the stations did not show a significant trend before and after the occurrence of a shift. The third task forecasted the streamflow at a regional scale within Sacramento San Joaquin (SSJ) River Basin with largescale climate variables. SSJ is an agricultural watershed located in the drought sensitive region of California. The forecast techniques involved a hybrid statistical framework that eliminates the bias resulting from predefined indices at regional scale. The study was performed for eight unimpaired streamflow stations from 1962 to 2016. First, the Singular Valued Decomposition (SVD) teleconnections of the streamflow corresponding to 500 mbar geopotential height, sea surface temperature, 500 mbar specific humidity (SHUM500), and 500 mbar U-wind (U500) were obtained. Second, the skillful SVD teleconnections were screened non-parametrically. Finally, the screened teleconnections were used as the streamflow predictors in the non-linear regression models (K-nearest neighbor regression and data-driven support vector machine). The SVD results identified new spatial regions that have not been included in existing predefined indices. The nonparametric model indicated the teleconnections of SHUM500 and U500 being better streamflow predictors compared to other climate variables. The regression models were capable to apprehend most of the sustained low flows, proving the model to be effective for drought-affected regions. It was also observed that the forecasting approach showed better forecasting skills with preprocessed large-scale climate variables rather than using the predefined indices. The techniques involved in this task was simple, yet robust in providing qualitative streamflow forecasts that may assist water managers in making policy-related decisions when planning and managing watersheds.
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Coelho, Caio Augusto dos Santos. "Forecast calibration and combination : Bayesian assimilation of seasonal climate predictions." Thesis, University of Reading, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417353.
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Yano, Jun-Ichi, Jean-François Geleyn, Martin Köller, Dmitrii Mironov, Johannes Quaas, Pedro M. M. Soares, Vaughan T. J. Phillips, et al. "Basic concepts for convection parameterization in weather forecast and climate models." Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-177427.
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The research network “Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models” was organized with European funding (COST Action ES0905) for the period of 2010–2014. Its extensive brainstorming suggests how the
subgrid-scale parameterization problem in atmospheric modeling, especially for convection, can be examined and developed from the point of view of a robust theoretical basis. Our main cautions are current emphasis on massive observational data analyses and process studies. The closure and the entrainment–detrainment problems are identified as the two highest priorities for convection parameterization under the mass–flux formulation. The need for
a drastic change of the current European research culture as concerns policies and funding in order not to further deplete the visions of the European researchers focusing on those basic issues is emphasized.
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Ryu, Jae Hyeon. "The management of water resources using a mid-range climate forecast model /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/10118.
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Ziervogel, Gina. "Seasonal climate forecast applications : a case study of smallholder farmers in Lesotho." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270168.
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Mohammadipour, Gishani Azadeh. "An Introduction to Application of Statistical Methods in Modeling the Climate Change." Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175770.
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There are many unsolved questions about the future of climate, and most of them are due to lack of knowledgeabout the complex system of atmosphere, but still there are models that produce relatively realistic projectionsof the future although there are uncertainties in the presentation of them, and that's where statistical methodscould be of help. Here a short introduction is given to the projection of future climate with GCM ensembles andthe uncertainties about them, the emerging probabilistic approach, as well as the REA (Reliability EnsembleAverage) method for measuring the reliability of the model projections. In order to have an impression of theresults of the GCM ensemble results and their uncertainties the results of the weather forecast over a time periodof one year in three dierent cities of Sweden is studied as well.
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Yano, Jun-Ichi, Jean-François Geleyn, Martin Köller, Dmitrii Mironov, Johannes Quaas, Pedro M. M. Soares, Vaughan T. J. Phillips, et al. "Basic concepts for convection parameterization in weather forecast and climate models: COST Action ES0905 final report." MDPI AG, 2014. https://ul.qucosa.de/id/qucosa%3A12408.
Full textAbstract:
The research network “Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models” was organized with European funding (COST Action ES0905) for the period of 2010–2014. Its extensive brainstorming suggests how the
subgrid-scale parameterization problem in atmospheric modeling, especially for convection, can be examined and developed from the point of view of a robust theoretical basis. Our main cautions are current emphasis on massive observational data analyses and process studies. The closure and the entrainment–detrainment problems are identified as the two highest priorities for convection parameterization under the mass–flux formulation. The need for
a drastic change of the current European research culture as concerns policies and funding in order not to further deplete the visions of the European researchers focusing on those basic issues is emphasized.
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Thomas, Arthur. "The Econometrics of Energy Demand : identification and Forecast." Thesis, Nantes, 2020. http://www.theses.fr/2020NANT3021.
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La prévention du changement climatique est l'une des priorités de la politique énergétique mondiale qui vise à réduire massivement les émissions de gaz à effet de serre. Face à ces défis, il est frappant de constater que notre connaissance de la modélisation de la demande énergétique demeure imparfaite car elle repose en grande partie sur des travaux empiriques anciens et des méthodologies aujourd'hui dépassées. L'objectif scientifique de cette thèse est double : analyser quantitativement les déterminants économiques de la demande énergétique et développer de nouveaux modèles de prévision. Cette thèse est structurée en quatre chapitres. Le premier chapitre montre que la consommation de gaz naturel en France peut être prédite à l'aide d'un modèle simple utilisant seulement les informations disponibles pour les acteurs du marché. Ce chapitre prouve l'existence d'une relation à long terme entre la demande de gaz naturel et les prix des autres énergies et il fournit des estimations de leurs impacts marginaux sur les niveaux de demande observés. Le deuxième chapitre étudie empiriquement le rôle de la température dans la prévision des prix du gaz aux États-Unis. Il développe une méthodologie de construction d’un nouvel indice mensuel basé sur la température. Cet indice capture les variations de la demande résiduelle de gaz naturel en temps réel. Il est utilisé comme variable exogène supplémentaire dans des modèles structurels VAR afin d’améliorer les prévisions ; et nous montrons que ces modèles prédictifs dérivés de modèles structurels sont améliorés en s’appuyant sur des données en temps réelles (non sujettes à révision). Le troisième chapitre propose d’utiliser dans le cas du pétrole, un modèle structurel capturant les anticipations à l’aide de VAR non causaux et d’identifier correctement les réactions des variables clés du pétrole à un choc d’actualité. Le quatrième chapitre réexamine le pouvoir prédictif de la structure par terme des prix, dite « convenience yield », du pétrole et du gaz en intégrant les anticipations dans une spécification empirique, par le biais d’un VAR non causal basé sur la théorie du stockage qui fournit des prévisions de prix très compétitives dans un cadre bivarié simpleThe prevention of climate change is one of the priorities of the world energy policy that aims to massively reduce greenhouse gas emissions. Faced with these challenges, it is striking to note that our knowledge of energy demand modeling remains limited because it is largely based on old empirical work and methodologies that are now dated. Therefore, the objective of our work is twofold. First, we analyze quantitatively the economic determinants of energy demand. Second, we develop new forecasting models. This thesis is structured in four chapters. The first chapter shows that natural gas consumption in France can be predicted using a simple model which only includes public information that is available to market's participants. This chapter proves the existence of a long-term relationship between demand and prices of other energies and provides estimates of their marginal impacts on observed demand levels. The second chapter empirically investigates the role of temperature in forecasting gas prices in the US. It develops a methodology to build a new monthly index based on temperature. This index captures variations in residual demand for natural gas in real time. It is used as an additional exogenous variable in structural models (VAR) to improve forecasts and we show that, in our case, predictive models derived from a structural model are enhanced relying on true real-time (not subject to revisions) data. The third chapter proposes to use, in the case of oil market, a structural model capturing expectations in a noncausal VAR framework, and to properly identify the reactions of oil key variables to supply news shock. The fourth chapter revisits the predictive power of oil and gas convenience yield by incorporating expectations into an empirical specification through non-causal VAR based on the theory of storage which delivers very competitive price predictions in a simple bivariate setting
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Mudelsee, Manfred. "XTREND: A computer program for estimating trends in the occurrence rate of extreme weather and climate events." Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-217157.
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XTREND consists of the following methodical Parts. Time interval extraction (Part 1) to analyse different parts of a time series; extreme events detection (Part 2) with robust smoothing; magnitude classification (Part 3) by hand; occurrence rate estimation (Part 4) with kernel functions; bootstrap simulations (Part 5) to estimate confidence bands around the occurrence rate. You work interactively with XTREND (parameter adjustment, calculation, graphics) to acquire more intuition for your data. Although, using “normal” data sizes (less than, say, 1000) and modern machines, the computing time seems to be acceptable (less than a few minutes), parameter adjustment should be done carefully to avoid spurious results or, on the other hand, too long computing times. This Report helps you to achieve that. Although it explains the statistical concepts used, this is generally done with less detail, and you should consult the given references (which include some textbooks) for a deeper understanding.
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Maldonado, Philip Pasqual. "Low Flow Variations in Source Water Supply for the Occoquan Reservoir System Based on a 100-Year Climate Forecast." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/35203.
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The reliability of future water supplies comes into question with the onset of global climate change and the variations in local weather patterns that it brings. Changes in temperature, precipitation, soil moisture, and sea level can all have an impact on drinking water storage and supply. As these impacts are realized, it is increasingly important to use forward projecting estimates of future supply through the use of general circulation models (GCMs). GCMs can be used to predict changes in local weather over the next century. Using GCM data as input to a hydrologic model of local water supplies, water supply managers can assess and be better prepared for the impact of these possible changes.
Land use/demand in particular has an impact on runoff characteristics within a watershed. By incorporating changes in land use/demand into hydrologic model simulations, a more complete picture can be generated of the possible runoff characteristics, and thereby source water supply. The four land use scenarios used in this study are: 1) present day land use/demand; 2) projected land use/demand to 2040; 3) projected land use/demand to 2070; and 4) projected land use/demand to 2100.
This study uses established techniques to incorporate both climate and land use/demand change into a hydrologic model of the Occoquan watershed, which encompasses an area of approximately 1,550 square kilometers in Northern Virginia, U.S.A., and is part of the drinking water supply to approximately 1.7 million residents.
Master of Science
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Vieira, Julio Cesar de Azevedo. "Forecast dengue fever cases using time series models with exogenous covariates: climate, effective reproduction number, and twitter data." reponame:Repositório Institucional do FGV, 2018. http://hdl.handle.net/10438/24308.
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Dengue é uma doença infecciosa que afeta países subtropicais. Autoridades de saúde locais utilizam informações sobre o número de notificações para monitorar e prever epidemias. Este trabalho foca na modelagem do número de casos de dengue semanal em quatro cidades do estado do Rio de Janeiro: Rio de Janeiro, São Gonçalo, Campos dos Goytacazes, e Petrópolis. Modelos de séries temporais são frequentemente utilizados para prever o número de casos de dengue nos próximos ciclos (semanas ou meses), particularmente, modelos SARIMA (Modelo Sazonal Autorregressivo Integrado de Médias Móveis) apresentam uma boa performance em situações distintas. Modelagens alternativas ainda incluem informação sobre o clima da região para melhorar a performance preditiva. Apesar disso, modelos que usam apenas dados históricos e de clima podem não possuir informações suficientes para capturar mudanças entre os regimes de não-epidemia e epidemia. Duas razões para isso são o atraso na notificação dos casos e que possivelmente não houveram epidemias nos anos anteriores. Baseando-se no sistema de monitoramento InfoDengue, esperasse que incluindo dados sobre ”numero de reprodução efetiva dos mosquitos”(RT) e ”número de tweets se referindo a dengue”(tweets) possam melhorar a qualidade das previsões no curto (1 semana) e longo (8 semanas) prazo. Foi possível mostrar que modelos de séries temporais incluindo RT e informações climáticas frequentemente performam melhor do que o modelo SARIMA em termos do erro preditivo quadrático médio (RMSE). Incluir a variável sobre o twitter não mostrou uma melhora no RMSE.
Dengue fever is an infectious disease affecting subtropical countries. Local health departments use the number of notified cases to monitor and predict epidemics. This work focus on modeling weekly incidence of dengue fever in four cities of the state of Rio de Janeiro: Rio de Janeiro, São Gonçalo, Campos dos Goytacazes, and Petrópolis. Time series models are often used to predict the number of cases in the next cycles (weeks, months), in particular, SARIMA (Seazonal Auto-Regressive Integrated Moving Average) models are shown to perform well in distinct settings. Alternative models also include climate covariates to improve the quality of the forecasts. However, models that only use historical and climate data may no have sufficient information to capture changes from non-epidemic to an epidemic regime. Two reasons are that there is a delay in the notification of cases and there might not have had epidemics in the previous years. Based on the INFODENGUE monitoring system we argue data including the "effective reproduction number of mosquitoes" (RT) and "number tweets referring to dengue" (tweets) may improve the quality of forecasts in the short (1 week) to long (8 weeks) range. We show that time series models including RT and climate information often outperform SARIMA models in terms of mean squared predictive error (RMSE). Inclusion of twitter did not improve the RMSE.
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Junior, Pedro Abel Vieira. "Previsão de atributos do clima e do rendimento de grãos de milho na região Centro-Sul do Brasil." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/11/11136/tde-06032007-144956/.
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A Previsão de Safras tem se constituído em importante ferramenta para o estabelecimento de políticas agrícolas públicas e privadas. Em geral, a Previsão de Safras consiste na previsão do clima e na estimativa do rendimento das partes de interesse econômico de uma cultura. A previsão do clima pode ser realizada pela análise de séries históricas dos parâmetros climáticos e dos efeitos de fenômenos conhecidos, a exemplo do El Niño Oscilação Sul (ENSO), o qual pode ser medido pelo Índice de Oscilação Sul (IOS). Também pode ser realizada pela integração numérica das equações diferenciais que regem os movimentos da atmosfera no planeta Terra, também conhecida como previsão numérica. A estimativa do rendimento das culturas também pode ser realizada pela análise estatística de séries históricas ou pela integração numérica de equações diferenciais que regem a fisiologia e o desenvolvimento das plantas, ambos conhecidos como modelo de culturas. O principal objetivo deste trabalho foi propor uma metodologia para a Previsão de Safras no Brasil, tendo como ponto de partida e protótipo o estudo do rendimento de grãos de milho na região Centro-Sul do país. Para tanto, séries históricas com 60 anos de precipitação pluvial em 24 locais da região Centro-Sul do Brasil foram comparadas aos Índices de Oscilação Sul medidos no mesmo período, inferindo-se que o fenômeno ENSO apresenta efeito marcante, e distinto, apenas em locais mais ao Sul e a Nordeste da região Centro-Sul. Concluiu-se pela impossibilidade de utilização do IOS para a previsão de parâmetros climáticos diários, o que também é prejudicado pela carência de séries históricas dos parâmetros climáticos com 60 ou mais anos no Brasil. Ainda quanto à previsão do clima, as previsões de radiação solar, precipitação pluvial, temperaturas máxima e mínima e umidade relativa do ar, geradas pelo modelo Eta a cada seis horas entre os dias 16/7/1997 e 15/6/2002, foram comparadas às respectivas medidas diárias desses parâmetros climáticos, concluindo-se pela possibilidade da aplicação das previsões geradas pelo modelo Eta na Previsão de Safras, à exceção dos locais mais ao Sul e mais a Nordeste da região Centro-Sul do Brasil. Acerca da estimativa do rendimento de grãos de milho, foi proposto um modelo de cultura baseado na integração das equações que regem a fisiologia e o desenvolvimento das plantas. Comparando-se os rendimentos de grãos de milho estimados nos 24 locais durante as safras 1997/98 a 2001/02, conclui-se pela possibilidade da estimativa do rendimento de grãos de milho na região Centro-Sul pelo modelo proposto. Porém, as discrepâncias entre os rendimentos estimados e os respectivos rendimentos medidos nos locais mais ao Sul e nos locais com textura de solo arenosa apontam a necessidade de correção da estimativa da dinâmica de água realizada pelo modelo de cultura proposto. Como conclusão geral, verificou-se que a metodologia proposta para a Previsão de Safras tem virtudes que devem ser exploradas no sentido de sua implementação no Brasil. Porém, essa implementação depende substancialmente da gestão dos trabalhos, de modo a propiciar as condições necessárias. Cabe destacar que o país tem realizado notáveis avanços nesse setor, caso da implementação da rede meteorológica nacional e do conhecimento gerado pelo Centro de Estudos e Previsões do Clima e pela Empresa Brasileira de Pesquisa Agropecuária, entre outras instituições. Ainda assim, essa área do conhecimento, fundamental para um país agrícola como o Brasil, carece de estudos.Crop forecast has become an important tool for the private and public agricultural policies to be established. Generally, crop forecast is composed by climatic forecast and the yield estimative of growth of economically interesting parts of crops. The climatic forecast can be performed through the analyses of historical series of the climatic features and of the known phenomena, such as the El Niño Southern Oscillation (ENSO), which can be measured by the Southern Oscillation Index (IOS). It can also be done through a numerical integration of differential equations that rule the atmospheric movements of the Earth, a.k.a. numerical forecast. The estimate of crop yields can also be done through the statistical analysis of historical series or through the integration of differential equations that rule the plant physiology and development, both known as crop models. The main objective of this study was to indicate a methodology for Crop Forecast in Brazil, having as a starting point and prototype the study of corn grain yield in the Center-South region of Brazil. Thus, historical series of 60 years of precipitation in 24 sites of the studied region were compared to the IOS measured in the same period, inferring that the phenomenon ENSO has a remarkable effect, distinctly in the most southern and northeast portions of the studied region. One concluded due to the impossibility of using the IOS for daily climatic forecast, which is threatened by the lack of historical series of climatic features with 60 or more years in Brazil. Regarding the climatic forecast, the forecasts of solar radiation maximum and minimum temperatures and air moisture generated by the model Eta on every 6 hours between July 16, 1997 and June 15, 2002 were compared to the respective daily measurements of these climatic parameters. This provided subsidies for the conclusion that the data generated by the model Eta could be used in the Crop Forecast, except for the most southern and northeast regions in the Center-South region of Brazil. For the estimate of corn grain yield, a model based in the integration of equations that rule the plant physiology and development was proposed. Comparing corn grain yields estimated in 24 sites from the agricultural year 1997/98 to 2001/02, one concluded the possibility of estimating the corn grain yield for the studied region by the proposed model. Although the differences between the estimated and the measured yields in the most southern sites and in those with sandy soils indicate the demand for correction of the estimative of water dynamics performed by the proposed model. As a general conclusion, the methodology proposed for crop forecasting brings positive points which should be explored in the sense of its implementation in Brazil. On the other hand, this implementation depends substantially on the work management, propitiating the necessary conditions. One should highlight that the country has developed notably in this sector, such as the cases of the implementation of the national meteorological net and of the knowledge broadcasted by the Center of Climatic Studies and Forecasting and by the The Brazilian Agricultural Research Corporation (EMBRAPA), among other institutions. Even though, this area of knowledge - vital to an agricultural country as Brazil - demands more research.
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Mkuhlani, Siyabusa. "Integration of seasonal forecast information and crop models to enhance decision making in small-scale farming systems of South Africa." Thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/32708.
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Climate variability threatens agricultural productivity and household food security, amongst small-scale farmers of South Africa. Managing climate variability is challenging due to the variation of climate parameters and the difficulty in making decisions under such conditions. Integrated seasonal forecast information and crop models have been used as a tool that enhances decision making in some countries. Utilization of such an approach in South Africa can enhance decision making in climate variability management. The study therefore sought to formulate a decision-making approach to enhance climate variability management in small-scale farming systems of South Africa through integrating seasonal forecast information and crop models. Current practices, challenges and opportunities for climate variability management by different small-scale farmer types were identified using focus group discussions and local agricultural extension officers. The Climate Forecast System version 2 (CFSv2) model-based forecasts were integrated with the Decision Support System for Agrotechnology Transfer (DSSAT) v4.7, a mechanistic crop model based on the Global Climate Model (GCM) approach. The GCM approach was the most appropriate technique for integrating seasonal forecast information and the crop model due to the compatibility in the forecast and crop model format. The decision-making process was formulated through assessing the simulation yield patterns under a range of farm management practices and seasonal forecasts for different cropping seasons, crops and farmer types for Limpopo and Eastern Cape, South Africa for 2017/18 season. The study assessed 48 different potential combinations of farm management practices: organic amendments, varieties, fertilizers and irrigation. Benefits of the decision formulation process and specific seasonal forecast-based recommendations were then assessed in the context of the performance of the practices under historical measured data for the conditions; 2011-2017, using percentile ranking. Assessing the yield response patterns under different farm management practices and seasonal forecasts (2017/2018), the study realized a range of decision scenarios. These are (1) low decision capacity and low climate sensitivity where there is low value for decision due to the homogeneous performance of the different management practices given climate forecasts. (2) high decision capacity and low climate sensitivity, where there is higher potential value for decision making as the different practices have uniform performance across climate forecasts. (3) High decision capacity and high climate sensitivity, where the good response to change in practices under changing climate forecasts. Confidence in the decision formulation process v was re-enforced as some of the decision scenarios were also realized under different conditions in the period; 2011-17. The scenario (2): High decision capacity and low climate sensitivity was predominant in locations with low forecast skill. In contrast the scenario (3): High decision capacity and high climate sensitivity was predominant in locations with high forecast skill. The decision formulation process allows for assessment of farm management practices in the seasonal forecast decision space. Although the case study realized some scenarios ahead of others, the process is robust and repeatable under any conditions. Although the process does not always offer recommendation with improved value for decision making, the value of recommendations is greater under decision scenarios with greater decision capacity. Such benefits are crop and location dependent. Improved seasonal forecasting skill increases reliability of the decision-making process, decision scenarios and associated recommendations. Such assertions need to be tested on the field scale to assess their practical feasibility.
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Pennesi, Karen. "The Predicament of Prediction: Rain Prophets and Meteorologists in Northeast Brazil." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/194313.
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Meteorologists working for the state government in Ceara, Northeast Brazil claim that the kinds of forecasts they can currently produce are not useful for subsistence farmers, who lack resources to act on forecast-based decisions. I argue that scientific predictions do have meaning and consequences in rural communities. Official forecasts inform policies that affect farmers; therefore, farmers hold government accountable for predictions, even if they do not directly influence the farmers' own decision-making.My investigation takes the discussion beyond notions of "usefulness" as I demonstrate that prediction is more than a projection of the future based on the past and the present. In prediction discourse, people create understandings of their place in the social world, including their relationship to government. While government discourse constructs farmers as "non-users" and removes its responsibility to them, traditional "rain prophets" motivate farmers with optimistically-framed predictions and encourage autonomy from government.Prediction is a meaning-making endeavor―not just of ecological and atmospheric processes, but of who people are and how they live. Drawing on linguistic theories of performance and performativity, I analyze strategic language use within a cultural models framework, taking into account the emotions and motivations associated with experiences of living in a particular environment (both natural and material), and how these are crucial to understanding the meanings of prediction. Through prediction, people test the limits of their knowledge, judgement and faith. My examination of the connections between cultural models of 'prediction' and 'lie' explains how traditional predictions motivate farmers and build solidarity in opposition to exclusionary systems of government and science.This research furthers our understanding of how locally marginalized groups engage with government and the knowledge systems it privileges. After tracing constructions of "rain prophet" and "scientist" in the media, I show how rain prophets both oppose themselves to and align themselves with media representations of science, as they establish their authority and challenge meteorologists' expertise. Meanwhile, meteorologists work to authenticate science as the only legitimate authority. Thus, in prediction performances, meteorologists and rain prophets position themselves within local and global discourses about science and traditional knowledge.
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Mudelsee, Manfred. "XTREND: A computer program for estimating trends in the occurrence rate of extreme weather and climate events." Wissenschaftliche Mitteilungen des Leipziger Instituts für Meteorologie ; 26 = Meteorologische Arbeiten aus Leipzig ; 7 (2002), S. 149-196, 2002. https://ul.qucosa.de/id/qucosa%3A15228.
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XTREND consists of the following methodical Parts. Time interval extraction (Part 1) to analyse different parts of a time series; extreme events detection (Part 2) with robust smoothing; magnitude classification (Part 3) by hand; occurrence rate estimation (Part 4) with kernel functions; bootstrap simulations (Part 5) to estimate confidence bands around the occurrence rate. You work interactively with XTREND (parameter adjustment, calculation, graphics) to acquire more intuition for your data. Although, using “normal” data sizes (less than, say, 1000) and modern machines, the computing time seems to be acceptable (less than a few minutes), parameter adjustment should be done carefully to avoid spurious results or, on the other hand, too long computing times. This Report helps you to achieve that. Although it explains the statistical concepts used, this is generally done with less detail, and you should consult the given references (which include some textbooks) for a deeper understanding.
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White, Megan L. "ASSOCIATING SEVERE THUNDERSTORM WARNINGS WITH DEMOGRAPHIC AND LANDSCAPE VARIABLES: A GEOGRAPHICALLY WEIGHTED REGRESSION-BASED MAPPING OF FORECAST BIAS." UKnowledge, 2014. http://uknowledge.uky.edu/geography_etds/20.
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Severe thunderstorm warnings (SVTs) are released by meteorologists in the local forecast offices of the National Weather Service (NWS). These warnings are issued with the intent of alerting areas in the path of severe thunderstorms that human and property risk are elevated, and that appropriate precautionary measures should be taken. However, studies have shown that the spatial distribution of severe storm warnings demonstrates bias. Greater numbers of severe thunderstorm warnings sometimes are issued where population is denser. By contrast, less populated areas may be underwarned. To investigate the spatial patterns of these biases for the central and southeastern United States, geographically weighted regression was implemented on a set of demographic and land cover descriptors to ascertain their patterns of spatial association with counts of National Weather Service severe thunderstorm warnings. GWR was performed for each our independent variables (total population, median income, and percent impervious land cover) and for all three of these variables as a group. Global R2 values indicate that each individual variable as well as all three collectively explain approximately 60% of the geographical variation in severe thunderstorm warning counts. Local R2 increased in the vicinity of several urban regions, notably Atlanta, Washington, D.C., St. Louis, and Nashville. However, the independent variables did not exhibit the same spatial patterning of R2. Some cities had high local R2 for all variables. Other cities exhibited high local R2 for only one or two of these independent variables. Median income had the highest local R2 values overall. Standardized residuals confirmed significant differences among several NWS forecast offices in the number and pattern of severe thunderstorm warnings. Overall, approximately half of the influences on the distribution of severe thunderstorm warnings across the study area are related to underlying land cover and demographics. Future studies may find it productive to investigate the extent to which the spatial bias mapped in this study is an artifact of forecast culture, background thunderstorm regime, or a product of urban anthropogenic weather modification.
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Ancell, Trueba Rafael. "Aportaciones de las redes bayesianas en meteorología.Predicción probabilística de precipitación. Applications of Bayesian Networks in Meteorology. Probabilistic Forecast of Precipitation." Doctoral thesis, Universidad de Cantabria, 2009. http://hdl.handle.net/10803/113596.
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Esta tesis está dirigida principalmente a investigadores interesados en la aplicación de técnicas de minera de datos en Meteorología y otras ciencias medioambientales afines. De forma genérica, trata de la modelización probabilística de sistemas definidos por muchas variables, cuyas relaciones de dependencia son inferidas a partir de un conjunto representativo de datos.
La idea es resolver algunos problemas prácticos relacionados con el diagnóstico y la predicción probabilística local en Meteorología, considerando el problema de la coherencia espacial. En concreto, el eje central de esta tesis ha sido el desarrollo de redes Bayesianas, para su aplicación en la predicción probabilística local.This thesis is mainly oriented to researchers interested in the data mining techniques applied to Meteorology and other related environmental sciences. It uses probabilistic models to describe systems defined by many variables whose dependencies have to be inferred from a set of representative data. The main purpose is solve practical problems related to the diagnosis and probabilistic local forecasting Meteorology, considering the problem of spatial coherence. Specifically, the focus of this thesis has been the development of Bayesian networks to be applied in the local probabilistic forecasting.
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Lemos, Wictor Edney Dajtenko. "PrevisÃo climÃtica sazonal do regime tÃrmico e hidrodinÃmico de reservatÃrio." Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=14582.
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Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgicoA dinÃmica dos processos relacionados à qualidade da Ãgua em reservatÃrios à funÃÃo da sua morfologia, da aÃÃo das variÃveis meteorolÃgicas e das afluÃncias e defluÃncias, em maior grau. Prever o comportamento hidrodinÃmico de reservatÃrios e o impacto causado por mudanÃas ou variabilidades na forÃante meteorolÃgica à essencial ao gerenciamento da qualidade da Ãgua e foi o objetivo principal desta tese. Para tanto foram utilizados modelos climÃticos, hidrolÃgicos, hidrodinÃmicos e de balanÃo de energia, em cascata. O comportamento da hidrodinÃmica resultante da modelagem mostrou resultados consonantes com reservatÃrios de regiÃes tropicais, representando os padrÃes diÃrios de circulaÃÃo e a formaÃÃo de estratificaÃÃes tÃrmicas no reservatÃrio modelado. As principais variaÃÃes hidrodinÃmicas sazonais puderam ser modeladas, ainda que com um alto Ãndice de incerteza. Foi realizado um monitoramento no reservatÃrio Pereira de Miranda que forneceu meios para dar inÃcio ao ciclo de modelagem e monitoramento integrado. Foi apresentada a tÃcnica de downscaling dinÃmico para a obtenÃÃo das variÃveis meteorolÃgicas de previsÃo regionalizadas, demostrando algumas possibilidades de aplicaÃÃo dos resultados dos modelos climÃticos na modelagem hidrodinÃmica de reservatÃrios, indispensÃvel na modelagem da qualidade da Ãgua. Os resultados mostraram a possibilidade de calibraÃÃo e validaÃÃo do modelo hidrodinÃmico CE-QUAL-W2 com o uso de dados de reanÃlise atmosfÃrica, aplicaÃÃo de tÃcnicas de previsÃo climÃtica na avaliaÃÃo e previsÃo dos padrÃes hidrodinÃmicos de reservatÃrios e a necessidade de um sistema de monitoramento como subsidiÃrio de informaÃÃes relevantes à modelagem, no sentido de melhorar os sistemas existentes e aumentar o nÃvel de conhecimento sobre a dinÃmica de reservatÃrios localizados no semiÃrido.
The dynamics of water quality related processes in reservoirs is a function of its morphology, the action of meteorological variables and defluÃncias inflows and, to a greater extent. Predict the hydrodynamic behavior of reservoirs and the impact of changes or variability in weather forcing is essential to the management of water quality and was the main objective of this thesis. Therefore, we used climate models, hydrological, hydrodynamic and energy balance in cascade. The behavior of the resulting hydrodynamic modeling showed results in line with tropical reservoirs, representing the daily patterns of movement and the formation of thermal stratification in modeled reservoir. The main hydrodynamic seasonal variations could be modeled, albeit with a high level of uncertainty. Monitoring on a Miranda Pereira reservoir that provided a means to begin the modeling and integrated monitoring cycle was performed. The dynamic downscaling technique to obtain the meteorological variables of regionalized forecast was presented, showing some application possibilities of the results of climate models in hydrodynamic modeling of reservoirs, essential in modeling of water quality. The results showed the possibility of calibration and validation of the hydrodynamic model CE-QUAL-W2 using atmospheric reanalysis data, application of climate prediction techniques in assessing and predicting the hydrodynamic patterns of tanks and the need for a monitoring system as Subsidiary information relevant to modeling, to improve existing systems and increase the level of knowledge about the dynamics of reservoirs located in the semiarid.
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Tchedná, João Lona. "Dinâmica da monção oeste africana (moa) e avariabilidade de precipitação sazonal no SAHEL: impactos sobre as populações e sobre os ecossistemas." Master's thesis, Universidade de Évora, 2006. http://hdl.handle.net/10174/16157.
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O presente trabalho analisa a dinâmica da monção oeste africana e a variabilidade da precipitação sazonal no Sahel e os impactos sobre as populações e sobre os ecossistemas, estando organizado em seis capítulos que se complementam. Consiste num estudo diagnóstico da dinâmica da monção oeste africana, analisando o comportamento dos campos meteorológicos (energéticos, dinâmicos e higrométricos), durante as fases do início, de intensificação e do fim da monção no Sahel, nos dois anos de referência: 1984 (ano seco) e 1994 (ano chuvoso), com a finalidade de perceber a inerência dos parâmetros meteorológicos na perturbação da monção oeste africana e consequentes impactos sobre as populações e sobre os ecossistemas. A variabilidade de precipitação sazonal foi avaliada através dos métodos de análise estatística para a região Oeste e a região Este do Sahel. Destas análises destacam-se: os modelos de tendência, de sazonalidade e da periodicidade feita através dos espectros de frequência de precipitação anual, pelo método de entropia máxima. As previsões sazonais e a modelação climática, consistem nas previsões sazonais para a África Ocidental (PRESA-AO) e recorrem ao Modelo de Previsão Climática, CPT - Climate Predictability Tools; como exemplo de aplicação foi avaliado o desempenho do modelo CPT utilizando as saldas dos Modelos Dinâmicos de Circulação Global para melhorar as previsões climáticas sazonais, baseados nos métodos estatísticos. Para este estudo foram utilizados os dados de Reanálises do NCEP/NCAR, os dados climatológicos de precipitação do CMAP e os dados de observações (precipitação no Sahel, 1960-2000), obtidos na base de dados do Centro Regional AGRHYMET, espacializados nos pontos de grelha de 1° por 1° e 2.5° por 2.5° graus. Conjugando a perturbação da monção oeste africana e a variabilidade da precipitação sazonal no Sahel com a tendência de evolução demográfica nos países do Sahel e estado de degradação dos ecossistemas, destaca-se os impactos sobre as populações e sobre os ecossistemas. /ABSTRACT - The present work aims at studying the dynamics of the West African monsoon and the variability of the seasonal precipitation in the Sahel as well as the impacts on the populations and ecosystems. It is organized in six chapters A diagnostic study of the dynamics of the west African monsoon is done by analyzing the behavior of the meteorological fields (energetic, dynamic and hygrometric), during the phases of the beginning, of intensification and the end of the monsoon in the Sahel for the reference years of 1984 (dry year) and of 1994 (rainy year), with the purpose of understanding the influence of the meteorological parameters in the disturbance of the west African monsoon and its impacts on the populations and ecosystems. The seasonal precipitation variability was evaluated through the statistical methods of analyses both for the West and the East sectors of the Sahel region. Among different components of variability one can distinguish the following ones: trend, seasonal and periodic analyzed via the annual precipitation spectra, using the maximum entropy method. The seasonal forecasts and the climate modeling focus on the Seasonal Forecasts for West Africa (PRESA-AO) and are based on the Model of Climatic Forecast, CPT - Climate Predictability Tools; as an example of application the performance of CPT was analyzed using the output of the Global Circulation Dynamic Models for improving the seasonal climatic forecasts, based on statistical methods. For this study the Reanalysis of the NCEP/NCAR data, the CMAP precipitation climate data and the precipitation data in the Sahel, for the 1960-2000 period from the Regional Center AGRHYMET database have been used, and specialized in the points of grid of 1 ° by 1 ° and 2.5 ° by 2.5 ° degrees. Conjugating the disturbance of the West African monsoon and the variability of the seasonal precipitation in the Sahel region with the trend of demographic evolution in the countries of the Sahel and state of degradation of ecosystems, the impacts on the populations and ecosystems are emphasized.
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Saunier-Batté, Lauriane. "Prévisions d'ensemble à l'échelle saisonnière : mise en place d'une dynamique stochastique." Phd thesis, Université Paris-Est, 2013. http://pastel.archives-ouvertes.fr/pastel-00795478.
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La prévision d'ensemble à l'échelle saisonnière avec des modèles de circulation générale a connu un essor certain au cours des vingt dernières années avec la croissance exponentielle des capacités de calcul, l'amélioration de la résolution des modèles, et l'introduction progressive dans ceux-ci des différentes composantes (océan, atmosphère, surfaces continentales et glace de mer) régissant l'évolution du climat à cette échelle. Malgré ces efforts, prévoir la température et les précipitations de la saison à venir reste délicat, non seulement sur les latitudes tempérées mais aussi sur des régions sujettes à des aléas climatiques forts comme l'Afrique de l'ouest pendant la saison de mousson. L'une des clés d'une bonne prévision est la prise en compte des incertitudes liées à la formulation des modèles (résolution, paramétrisations, approximations et erreurs). Une méthode éprouvée est l'approche multi-modèle consistant à regrouper les membres de plusieurs modèles couplés en un seul ensemble de grande taille. Cette approche a été mise en œuvre notamment dans le cadre du projet européen ENSEMBLES, et nous montrons qu'elle permet généralement d'améliorer les rétro-prévisions saisonnières des précipitations sur plusieurs régions d'Afrique par rapport aux modèles pris individuellement. On se propose dans le cadre de cette thèse d'étudier une autre piste de prise en compte des incertitudes du modèle couplé CNRM-CM5, consistant à ajouter des perturbations stochastiques de la dynamique du modèle d'atmosphère ARPEGE-Climat. Cette méthode, baptisée "dynamique stochastique", consiste à introduire des perturbations additives de température, humidité spécifique et vorticité corrigeant des estimations d'erreur de tendance initiale du modèle. Dans cette thèse, deux méthodes d'estimation des erreurs de tendance initiale ont été étudiées, basées sur la méthode de nudging (guidage) du modèle vers des données de référence. Elles donnent des résultats contrastés en termes de scores des rétro-prévisions selon les régions étudiées. Si on estime les corrections d'erreur de tendance initiale par une méthode de nudging itéré du modèle couplé vers les réanalyses ERA-Interim, on améliore significativement les scores sur l'hémisphère Nord en hiver en perturbant les prévisions saisonnières en tirant aléatoirement parmi ces corrections. Cette amélioration est accompagnée d'une nette réduction des biais de la hauteur de géopotentiel à 500 hPa. Une rétro-prévision en utilisant des perturbations dites"optimales" correspondant aux corrections d'erreurs de tendance initiale du mois en cours de prévision montre l'existence d'une information à l'échelle mensuelle qui pourrait permettre de considérablement améliorer les prévisions. La dernière partie de cette thèse explore l'idée d'un conditionnement des perturbations en fonction de l'état du modèle en cours de prévision, afin de se rapprocher si possible des améliorations obtenues avec ces perturbations optimales
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Desroches, Sabrina. "Fostering Anticipatory Action via Social Protection Systems : A Case Study of the Climate Vulnerability of Flood-Exposed Social Security Allowance Beneficiaries in Bardiya District, Nepal." Thesis, Uppsala universitet, Teologiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-415293.
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Rationale – Climate disasters represent a significant and growing proportion of the humanitarian burden and are a key factor in increasing poverty and insecurity. A myriad of studies demonstrate that aid delivered in an ex-ante fashion can be effective in mitigating losses of life, assets and livelihoods associated with climate hazards. This inquiry supplements the nascent body of research and empirical evidence base pertaining to the building of anticipatory capacity into large-scale national systems, namely via linking a Forecast-based Financing mechanism to an existing social protection system. Research question – Using the case of flood disasters in Bardiya district, Nepal, the research inquired the following: How can social protection be combined with Forecast-based Financing in order to optimise anticipatory humanitarian relief for climate-related disasters? Sub-questions – Research sub-questions guided the inquiry: (1) To what extent are current social protection beneficiaries exposed to climate-related disasters? (2) What is the specific climate vulnerability of social protection beneficiaries? (3) What are the anticipatory relief needs of climate vulnerable social protection beneficiaries? Methodology – Grounded in empirical research via the conduct of a qualitative single case study, the inquiry adopted a conceptual perspective and an exploratory design. A remote data collection strategy was applied, which included (1) a thorough desk review of key scientific literature and secondary data provided by in-field humanitarian organisations; and (2) semi-structured interviews with key informants. Key findings – The data demonstrated that the exposure of social protection beneficiaries to flood hazards is comparable to the general population. Nevertheless, an elevated climate vulnerability is evident secondary to an increased sensitivity and diminished adaptive capacity. The flood anticipatory relief needs/preferences identified include cash-based assistance, food provisions, evacuation assistance and/or enhanced Early Warning Systems. Conclusion – The research supports the utilisation of the proposed conceptual model for an integrated social protection and Forecast-based Financing mechanism, inclusive of vertical and horizontal expansion, in order to effectively identify the most climate vulnerable groups and to guide the provision of targeted anticipatory actions. The mechanism is optimised when a people-centred approach is utilised, with reference to the idiosyncratic, lifecycle and corresponding intersectional vulnerabilities of the targeted population. These findings will contribute to prospective programming in Nepal; additionally, the extent to which they can be generalised will be informed by future applied efficacy studies and comparative analyses with research from differing contexts.
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Barreiro, Susana Miguel. "Development of forest simulation tools for assessing the impact of different management strategies and climatic changes on wood production and carbon sequestration for Eucalyptus in Portugal." Doctoral thesis, ISA/UTL, 2012. http://hdl.handle.net/10400.5/5216.
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Doutoramento em Engenharia Florestal e dos Recursos Florestais - Instituto Superior de AgronomiaThe present work had as main objective developing tools capable of simulating the evolution of Eucalyptus globulus forests in Portugal taking into account disturbance factors, such as market demands, hazards occurrence, land use changes, forest management and/or climate changes. Some conceptual work was done concerning the definition of different forest management alternatives while at the same time the E. globulus current management was described. SIMPLOT, a regional simulator based on national forest inventory plots was developed and validated. This simulation tool, mainly driven by wood and biomass demands, takes into account the occurrence of hazards, land use changes and the changes between different forest management alternatives allowing accessing its long-term impacts, namely on wood production and carbon sequestration. Some of the empirical growth models available for this species in Portugal were integrated into this simulator. However, the need to forecast the growth of highly stocked stands managed for bioenergy lead to the development of a new model. In order to account for climate changes, a process-based model was required. Therefore, the applicability of 3PG process-based model at a regional scale was tested for planted and coppice stands. Two forest level simulators, 3PG-Out+ and GLOBULUS, were developed along this study.
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Diallo, Mouhamet. "Estimation et prédiction de l’ensoleillement en zone intertropicale Improving the Heliosat-2 Method for Surface Solar Irradiation Estimation Under Cloudy Sky Areas Assessing GFS and IFS global weather preduction and numerical model forecast accuracy in the intertropical zone and for tropical climates Calibration of WRF irradiance in French Guiana and comparison with AROME forecasts." Thesis, Guyane, 2018. http://www.theses.fr/2018YANE0009.
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La Guyane est un territoire d’outre-mer, situé en zone intertropicale (ZIT). Cette zone est le lieu de phénomènes de convections intenses. De ce fait, l’énergie solaire incidente au sol est très variable ce qui constitue un frein à son exploitation à grande échelle. La question de recherche étudiée dans ce manuscrit est : comment peut-on améliorer les estimations et prédictions de rayonnement au sol en ZIT de façon à augmenter le taux de pénétration dans le réseau électrique de cette énergie renouvelable intermittente ? Afin de répondre à cette question, nous avons utilisé deux outils. Le code Héliosat-II (HII) et le modéle de prévisions météorologiques Weather and research forecast (WRF). Nous avons utilisé ces outils de manière à améliorer les estimations et prévisions de rayonnement global au sol (IGH) dans la ZIT. La première partie de ce manuscrit présente le contexte de la thèse. La seconde présente une modification d’H-II permettant d’améliorer les estimations d’IGH par une modélisation explicite de l’absorption de nuages. Ces estimations améliorées donnent ainsi des outils décisionnels permettant de situer au mieux une centrale solaire en fonction du potentiel solaire du site et des systèmes services avoisinants. La seconde partie traite dans un premier temps de la précision des prévisions des modèles globaux IFS et GFS (i.e integrated forecast system, global forecast system GFS) en ZIT. Ces produits téléchargés sont validés par comparaison avec des mesures in situ de trois pays situés dans la ZIT et caractérisés par des climats tropicaux. Cette étude permet de combler un vide dans l’étude des prévisions d’IGH des modèles globaux en ZIT. Nous proposons ensuite une méthode générique permettant de calibrer le modèle WRF en ZIT. Cette méthodologie vise à limiter le nombre de simulations à effectuer en sélectionnant et en faisant varier uniquement les paramètres ayant le plus d’influence sur le rayonnement au sol en ZIT. Pour valider cette méthodologie nous avons comparé les prévisions d’IGH du modelé WRF calibré avec celle du modelé AROME ainsi qu’avec des mesures in situ en Guyane. La quatrième partie présente l’utilisation d’une méthode hybride ensembliste variationnelle d’assimilation de donnée permettant d’améliorer les prévisions de rayonnements en ZIT. Cette méthode initialement utilisée pour améliorer la description de phénomènes convectifs extrêmes tels que prévision de la trajectoire des cyclones est pour la première fois appliquée pour améliorer les prévisions d’IGH. Cette méthodologie appliquée à la ZIT fournie alors des prévisions améliorées d’IGH permettant ainsi une gestion améliorée de centrale solaireFrench Guiana is a French territory located in the inter-tropical zone (ITZ). The ITZ is an area with highly variable dynamic in which we encounter significant amounts of convective clouds. Consequently the solar energy available at the ground is highly variable. This variability causes economical and technical challenges to fully exploit this resource. This thesis dissertation aims to answer the following scientific issue: How could the solar irradiance be assessed and forecast accurately in the ITZ to increase the penetration rate of this intermittent renewable energy into the electricity grid? To answer this scientific issue, we use two tools: Heliosat-II (H-II) and Weather and research forecast (WRF). We used these tools in order to produce improved GHI estimates in the inter-tropical zone. The first chapter introduces the thesis and the research issue. The second chapter presents a modification to H-II; with this modification H-II can account for cloud absorption. The GHI estimates from modified H-II provide therefore tools for decision making in the ITZ. These tools allow one identifying the most suitable locations to install solar facilities in the ITZ with respect to both solar potential and surrounding facilities that favor grid stability. In the third chapter we study first the accuracy in the ITZ of the GHI forecasts from integrated forecast system (IFS) and global forecast system (GFS) numerical weather prediction model (NWP). We validate the accuracy of these downloaded products by comparison with ground measurements from three countries located in the ITZ that have tropical climate. This study aims to fill the gap with regard to the accuracy of global NWP model in the ITZ. Second we propose a methodology to calibrate WRF to produce improved GHI forecasts in the ITZ. The goal is to restrain and select the minimum number of simulations to run, to obtain improved GHI forecasts compared to a non-calibrated model. This methodology to calibrate WRF is validated in French Guiana by comparison with the GHI forecasts of AROME NWP model and ground measurements. The fourth chapter deals with the use of an hybrid 3D variational (3D-Var) ensemble transform Kalman filter (ENTKF) to further improve the GHI forecasts of calibrated WRF in the ITZ. This methodology originally used in the tracking of extreme convection events such as cyclones is applied for the first time for GHI forecasts. This methodology applied to the ITZ therefore allows obtaining improved GHI forecasts which makes easier monitoring the electricity production from solar facilities
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Pires, Camilla Leimann. "Metodologia para previsão de carga e geração no horizonte de curtíssimo prazo." Universidade Federal de Santa Maria, 2016. http://repositorio.ufsm.br/handle/1/8601.
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Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorLoad forecasting is a very important activity on electric power system operation and planning, because many studies on electricity sector depend on future behavior of the system, requiring the electricity demand forecast for its realization. The very short-term load forecasting has a horizon of few minutes to a few hours and it seeks to translate more accurately the instantaneous profile of load. There are several factors that should be considered in forecasting methods, climatic variables have a major influence on demand trends in the very short term, therefore, they should be incorporated into the projection model. In Brazil, has been growing use of electricity production through the photovoltaic generation, so, for this feature to be used efficiently, energy produced by the solar panels forecast is a tool that contributes to this type of energy act reliably. The main objective of this work is to develop a methodology for load and solar power generation forecasting in the very short-term considering the influence the climatic variables. The methodology for load, wind and solar power generation forecasting considers the climatic variables: temperature, relative humidity, wind speed, solar radiation and atmospheric pressure. The study presents data load for a typical year of a substation of the metropolitan region of Rio Grande do Sul, analyzed with data from a weather station in the region. For calculate the solar power generation forecasting the method uses a model that considers the solar radiation and the temperature to calculate the power produced by the photovoltaic module. The method for the forecast was performed using Excel VBA tool, by grouping the load and climate variables data of history and is based on multiple linear regression. The projection algorithm was tested and compared computationally, based on actual data, presenting significant results, because as it is projected to hours ahead, the data is updated with the actual data every hour, reducing forecast errors, confirming that the considered climatic variables are very important to refine load and generation forecasting methods, essential for system planning. Compared to other existing methods, the proposed method stands out by the fact to consider climatic variables for the projection, and uses the methodology to perform the projection of solar power generation.
A previsão de carga é uma atividade de grande importância inserida na operação e no planejamento do sistema elétrico de potência, pois muitos estudos referentes ao setor elétrico dependem do comportamento futuro do sistema, sendo necessária a previsão de demanda de energia elétrica para sua realização. A previsão de demanda de eletricidade para curtíssimo prazo possui um horizonte de poucos minutos até algumas horas e ela procura traduzir com maior exatidão o perfil instantâneo da carga. Há vários fatores que devem ser considerados nos métodos de previsão, as variáveis climáticas apresentam grande influência na evolução de demanda no curtíssimo prazo, portanto, devem ser incorporadas no modelo de projeção. No Brasil, tem sido crescente a utilização da produção de energia elétrica através da geração fotovoltaica, sendo assim, para que esse recurso seja utilizado de forma eficiente, a previsão da energia produzida pelos painéis solares é uma ferramenta que contribui para que esse tipo de energia atue de forma confiável. O objetivo principal deste trabalho é o desenvolvimento de uma metodologia para previsão de carga e geração de energia solar para o horizonte de curtíssimo prazo, considerando a influência das variáveis climáticas. A metodologia para previsão de carga e geração de energia solar considera as variáveis climáticas: temperatura ambiente, umidade relativa do ar, velocidade do vento, radiação solar e pressão atmosférica. O estudo apresenta dados de carga de uma subestação da região metropolitana do estado do Rio Grande do Sul, analisados com dados de uma estação meteorológica da região. Para o cálculo da previsão da geração solar o método utiliza um modelo que considera a radiação solar e a temperatura para o cálculo da potência produzida pelo módulo fotovoltaico. O método para a previsão foi realizado utilizando a ferramenta VBA do Excel, através do agrupamento dos dados de carga e das variáveis climáticas do histórico e baseia-se na regressão linear múltipla. O algoritmo de previsão foi testado e comparado computacionalmente com base nos dados reais, apresentando resultados significativos, pois como a projeção é para horas a frente, os dados são atualizados com os dados reais a cada hora, diminuindo os erros da previsão, confirmando que as variáveis climáticas consideradas tem grande importância para refinar métodos de previsão de carga e geração de energia solar, fundamental para o planejamento do sistema elétrico. Em relação aos demais métodos já existentes, o método proposto se destaca pelo fato de considerar variáveis climáticas para a projeção de carga, e utiliza a metodologia para realizar a projeção da geração solar.
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Fischer, Graciela Redies, and Graciela Redies Fischer. "Estudo das relações preditivas entre o número de dias de chuva e a Temperatura da Superfície do Mar (TSM) para o Rio Grande do Sul." Universidade Federal de Pelotas, 2007. http://guaiaca.ufpel.edu.br:8080/handle/prefix/3991.
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A precipitação pluvial, medida em estações meteorológicas, nem sempre é um bom parâmetro para avaliar-se a disponibilidade hídrica em determinado período. Os totais da precipitação pluvial correspondem a todo o período considerado, não sendo levado em conta se foram bem ou mal distribuídos. Com o Número de Dias de Chuva, tem-se uma idéia da intensidade da precipitação pluvial, pois ao se analisar o mesmo total de chuva, em intervalos de tempo distintos, obtém-se qual a intensidade, bem como a variabilidade quantitativa da mesma. Conhecer e poder prever grandezas meteorológicas tem sido objeto de estudo de pesquisadores de todo globo: os prognósticos devem contemplar tanto a escala temporal quanto a espacial. Partindo da hipótese de que as escalas dos modelos de previsão de longo prazo, atualmente existentes, não contemplam as diversidades climáticas regionais do Estado do Rio Grande do Sul e que estudos regionalizados podem melhorar as informações demandadas pela sociedade, este trabalho teve como objetivo principal determinar as relações preditivas entre o Número de Dias de Chuva (NDC) de algumas estações meteorológicas do Rio Grande do Sul e as Temperaturas da Superfície do Mar (TSM). Nesta pesquisa foram usados dois conjuntos de dados: o primeiro formado por dados mensais de Número de Dias de Chuva de 17 estações meteorológicas do Estado, para o período de 1982 a 2005; o segundo, composto por dados de Temperatura da Superfície do Mar, para o período de 1982 a 2005. A série foi dividida em dois períodos: o dependente, compreendendo o intervalo de 1982 a 2002, para determinação das equações preditivas, bem como os coeficientes de regressão, e o período independente, cujo intervalo foi de 2003 a 2005, para validação do modelo. Os dados de TSM foram utilizados para, através das equações de regressão, estabelecer as relações entre as variáveis. Depois de estabelecidas as equações, foram calculados os valores previstos de NDC, e então comparados com valores observados, a fim de se verificar a eficiência do modelo. Para todas as regiões e para os meses analisados, obtiveram-se bons resultados na previsão de NDC. A série de dados prevista e a observada seguem um mesmo padrão de distribuição desta variável, embora existam alguns valores previstos que apresentam diferenças dos observados, essas não são significativas. No período independente, a série prevista mostra as maiores diferenças em relação aos valores observados. A região em que o modelo apresenta melhor destreza é a região ecoclimática da Campanha (R9) e o mês de melhor previsão é julho.
A pluvial precipitation measured in meteorologic stations, is not always a good parameter to evaluate the hydric availability in a determined period. The total pluvial precipitation corresponds the whole period considered, not taking into account if they were distributed well or badly. With the Number of Rainfall Days, there is an idea of the intensity of the pluvial precipitation, as analysing the same total of rain in intervals of distinct time, obtaining the intensity as well as the quantitative variability of the same. To know and be able to predict grandeur meteorologics has been the purpose of researchers in the whole world. The forecast must contemplate the temporal scale as much as the spatial. Starting from the theory that the scales of model prediction at long term, now existing, does not contemplate the diverse climatical regions State of Rio Grande do Sul and the regional studies can improve the information demanded by society, this study had as main objective to determine the predicted relations between the Number of Rainfall Days (NRD) of some meteorologic stations of Rio Grande do Sul and the Sea Surface Temperature (SST). In this research, were used two sets of data; the first formed by monthly datas of Number of Rainfall Days in 17 meteorologic stations in the State, from the period of 1982 to 2005. The second, composed of datas of the Sea Surface Temperature, from period 1982 to 2005. The series were divided into two periods, the dependent, comprehending the gap from 1982 to 2002, for determination of predicted equations as well as the factor of regression, and the independent period, which gap was from 2003 to 2005, for validation of the model. The datas of SST were used to, through the equations of regression, establish the relations between the variables. After establishing the equations, the values predicted of NRD were calculated, and them compared with the values observed, in order to verify the efficiency of the model. For all the regions analysed, were obtained good results in the prediction of Number of Rainfall Days for all the months analysed. The series of observed data, proceeds the same standard of distribution of this variable, although there are some foreseen values that present differences in observed values, but are not significant. In the independent period, the foreseen series show the biggest differences in relation to the observed values. The region in which the model presents the best dexterity is the echoclimatic region of Campanha (R9) and the month of best prediction is July.
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Cunha, Juliana Bilecki da. "Sistema de suporte à decisão para previsão de crises em mananciais." reponame:Repositório Institucional da UFABC, 2016.
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Orientador: Prof. Dr. Patrícia Belfiore FáveroDissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, 2016.
As dificuldades para gerenciar recursos hídricos aumentaram por conta dos efeitos das mudanças climáticas. É por meio da água que a maioria dos impactos destas mudanças, como secas e inundações, são sentidos. Como consequência destes impactos em mananciais destinados ao abastecimento, a população pode ser prejudicada tanto pela diminuição na oferta de água potável quanto pelas inundações em regiões de barragens. A ocorrência destes eventos meteorológicos extremos vem se intensificando nos últimos anos. Para o Brasil, estudos preveem modificações nos padrões de chuvas com a possibilidade de ocorrência de fenômenos naturais severos. Um exemplo é o caso do Sistema Cantareira, responsável por abastecer parcialmente a Região Metropolitana da cidade de São Paulo, que experimentou ambas as situações críticas de inundação e estiagem entre os anos de 2010 e 2015. Como estes fenômenos naturais são difíceis de prever e não podem ser evitados, este estudo pretende investigar medidas que envolvam informação e comunicação para auxiliar gestores e reduzir os danos causados à população. Esta pesquisa incluiu a elaboração de um modelo de simulação que foi incorporado a um Sistema de Suporte à Decisão. Desta forma, serão apresentadas as contribuições desta ferramenta que propõe uma abordagem diferente para avaliar riscos, simulando cenários de crise e emitindo alertas com antecedência. Esta ferramenta foi testada para as crises ocorridas no Sistema Cantareira entre 2010 e 2015 e apresentou resultados satisfatórios. A conclusão deste trabalho indica que novas formas de abordar este problema podem ser avaliadas e propostas para se tentar reduzir as incertezas que envolvem os impactos das mudanças climáticas no gerenciamento de recursos hídricos.
The difficulties to manage water resources increased due to the effects of climate changes. It is through water that most impacts of these changes, such as droughts and floods, are felt. As a result of these impacts on water sources for the supply, the population may be affected by both the decrease in the supply of drinking water as flood in dams regions. The occurrence of these extreme weather events has intensified in recent years. For Brazil, studies predict changes in rainfall patterns with the possibility of severe natural phenomena. An example is the case of the "Sistema Cantareira" (Cantareira System), responsible for partially supply the metropolitan region of São Paulo, who experienced both critical situations of flood and drought between 2010 and 2015. As these natural phenomena are difficult to predict and can't be avoided, this study aims investigate measures involving information and communication to assist managers and reduce population damage. This research included the development of a simulation model that was incorporated into a Support System Decision. Thus, this paper presents the contributions of this tool that proposes a different approach to assessing risks, simulating crisis scenarios and sending advance alerts. This tool has been tested for the "Sistema Cantareira" crises occurred between 2010 and 2015 and showed good mresults. The conclusion of this study indicates that new ways of approaching this problem can be evaluated and proposed to try to reduce uncertainties surrounding the impacts of climate changes on water resources management.
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Larmérus, Alexander. "Styrning av värmesystem i kontorsbyggnader : Jämförelse mellan prognosstyrning, styrning som utnyttjar byggnadens värmetröghet, samt traditionell styrning." Thesis, KTH, Tillämpad termodynamik och kylteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146975.
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En stor del av Sveriges energianvändning går till bostäder och lokaler. Ur en nationell synvinkel är energieffektiviseringar i befintliga byggnader därför en potentiellt viktig del för att kunna nå de satta klimatmålen till år 2020. I ett traditionellt styr- och reglersystem styrs framledningstemperaturen i ett vätskeburet värmesystem efter en kurva som beror på utomhustemperaturen. En del nya styr- och reglersystem tar även hänsyn till andra parametrar, såsom byggnaders värmetröghet och lokala väderprognoser. Ett exempel på ett sådant system är Ecopilot, utvecklat av Kabona. Nuvarande kunskap angående hur stor energibesparing som styr- och reglersystem med prognosstyrning och styrning som utnyttjar byggnadens värmetröghet ger upphov till består till största del av referensfall som jämför byggnaders energianvändning före och efter installationen. I detta examensarbete undersöktes hur energianvändning och inomhusklimat påverkades av prognosstyrning och styrning som utnyttjar byggnaders värmetröghet. Mätningar utfördes på två kontorsbyggnader vid namn Fräsaren 10 och Fräsaren 11. Båda byggnaderna är belägna i Sundbyberg och har Kabona Ecopilot installerat. Mätdata loggades genom redan utsatta givare och en enklare form av validering av dessa gjordes. I Fräsaren 10 och Fräsaren 11 jämfördes Ecopilot i normal drift med driftfallet då prognosstyrningsfunktionen stängdes av i Ecopilot. Även ett tredje driftfall undersöktes i Fräsaren 10. Under detta driftfall stängdes Ecopilot av och framledningstemperaturen styrdes med hjälp av reglerkurvor. I luftbehandlingsaggregaten sattes tilluftstemperaturens börvärde, till 19-20 °C. Varje driftfall hade en mätperiod på minst 14 dagar. Energisignaturer användes för att jämföra energianvändningen och en osäkerhetsanalys av de anpassade linjerna gjordes. En egen modell för att undersöka toppbelastningar i radiatorsystemet, VS1, i Fräsaren 10 togs fram. Även en modell för att undersöka hur temperaturen varierat inomhus mellan de olika mätperioderna togs fram. Energisignaturer för radiatorsystemen VS1 och VS2 i Fräsaren 10 visade på att likvärdiga energisignaturer kunde fås för samtliga av de undersökta driftfallen under det temperaturintervall som undersöktes. Energisignaturer för värmeanvändning i luftbehandlingsaggregatet, LB2601, visade på att en konstant tillufttemperatur på 19 °C som användes då Ecopilot var avstängd, kunde ge en högre värmeanvändning jämfört med fallen då Ecopilot var i normal drift och då Ecopilot hade sin prognosstyrning avstängd. Från jämförelse mellan fallen då Ecopilot var i normal drift och då Ecopilots prognosstyrning var avstängd kunde inga substantiella skillnader hittas mellan energisignaturerna. Det betyder dock inte att prognosstyrningen inte ger upphov till energibesparingar, utan att eventuella energibesparingarna var för små relativt mätningarnas osäkerhet vid en konfidensnivå på 65 % eller 95 %. Osäkerheten kan minskas om mätningar utförs över en längre tidsperiod än som var möjligt under detta examensarbete. Värmetoppbelastningar som undersöktes i radiatorsystemet i VS1 Fräsaren 10 visade inte på att några signifikanta skillnader mellan antalet uppmätta värmeeffekttoppar under de olika mätperioderna. Det förekom dock en viss indikation att det kan leda till fler värmeeffekttoppar om prognosstyrningen stängs av i Ecopilot. För att få ett mer tillförlitligt resultat behöver mätningar göras under en längre tidsperiod. Inomhustemperaturen undersöktes i Fräsaren 10 och Fräsaren 11. I Fräsaren 10 uppgick medeltemperatur till 21,5 °C för fallen då Ecopilot var i normal drift och då prognosstyrningen var avstängd. Då Ecopilot var avstängd var medeltemperaturen 22,1 °C. Under mätperioderna uppmättes en variation som understeg ± 1 °C från medelvärdet för respektive mätperiod. Baserat på resultaten presenterade i detta examensarbete antaganden angående hur stor besparing av värme som Ecopilot ger upphov till revideras. Att jämföra energianvändning före och efter installation av styrsystem såsom Ecopilot kan ge en dålig bild av hur stor del av energibesparingen som orsakats av Ecopilot, speciellt om reglerkurvorna i det gamla systemet var dåligt intrimmade.
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Bordignon, Sérgio. "Metodologia para previsão de carga de curtíssimo prazo considerando variáveis climáticas e auxiliando na programação de despacho de pequenas centrais hidrelétricas." Universidade Federal do Pampa, 2012. http://dspace.unipampa.edu.br:8080/xmlui/handle/riu/241.
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A previsão de carga é uma atividade de grande importância no Setor Elétrico, tendo em vista que a maioria dos estudos de planejamento e operação dos sistemas elétricos necessita de uma boa estimativa da carga a ser atendida. Na literatura encontram-se diversas metodologias para projeção de carga elétrica nos distintos horizontes de planejamento, porém limitadas a sistemas elétricos de médio e grande porte e poucas são as propostas de projeção de demanda no horizonte de curtíssimo prazo, principalmente para pequenas empresas do Setor Elétrico. O objetivo deste trabalho é apresentar uma metodologia inovadora de previsão de carga, a curtíssimo prazo, que considere as influências das condições climáticas e que possa auxiliar na programação do regime de operação de uma Pequena Central Hidrelétrica (PCH), principalmente em épocas de estiagem, quando a disponibilidade de água é restrita. A metodologia proposta envolve a criação de um modelo probabilístico discreto (cadeia de Markov) a partir da classificação dos dados históricos em um Mapa Auto-Organizável (SOM). Assim, é possível se estimar a probabilidade de um determinado nível de demanda acontecer dada uma condição climática atual, bem como o número de intervalos de tempo (horas) até que isso aconteça. Com estas informações é possível elaborar a melhor agenda de funcionamento da PCH de forma que a mesma esteja em funcionamento nos momentos em que a demanda atingir os valores máximos. O método proposto apresenta como diferencial em relação aos demais métodos existentes o fato de considerar a influência das variáveis climáticas (temperatura, umidade relativa do ar e velocidade do vento) para a previsão de demanda de energia elétrica no curtíssimo prazo, além de que os valores de entrada de demanda de energia e das variáveis climáticas (temperatura e umidade relativa do ar) são obtidos em tempo real, através de um sistema SCADA. Esta metodologia foi aplicada utilizando-se os dados reais de uma pequena concessionária de distribuição de energia elétrica do Rio Grande do Sul, mostrando resultados satisfatórios, suficientes para permitir a sua aplicação prática.
The electrical charge forecast is an activity of great importance in the Electricity Sector, considering that most studies of electrical systems planning and operation require a good estimative of the charge to be fulfilled. In books, there are various methodologies to have the electrical charge projection in different planning horizons, but limited to medium and large electrical systems. Furthermore, there are only a few demand projection proposals in the very short-term horizon, especially for small Electricity Sector companies. The aim of this paper is to present an innovative methodology in order to have the charge forecast, in a very short-term, which considers the climatic conditions influence and is able to assist the operation system programming of a Small Hydroelectric Power Plant, particularly in times of drought when water availability is restricted. The proposed methodology involves creating a discrete probabilistic pattern (Markov chain) from the historical data classification in a Self-Organizing Map (SOM). It is therefore possible to estimate the probability of reaching a certain demand level, taking the current climatic condition, as well as the periods of time (hours) until it happens. With this information it is possible to develop the best plant operation schedule so that it operates when the demand reaches its maximum numbers. The proposed method presents as differentials upon the other existing methods, the fact of considering the climatic variables influence (temperature, air humidity and wind speed) to forecast electricity demand in the very short-term, as well as the energy demand input values and climate variables obtainment (temperature and air humidity) in real time via a SCADA system. This methodology was applied using real data from a small electricity distribution plant in Rio Grande do Sul, showing satisfactory results, enough to allow their practical application.
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Han, Wan-rong, and 韓宛容. "Apply Statistical-Downscaling Climate Forecasts for Estimating Shihmen Reservoir Inflows." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/66191775238781910522.
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碩士國立中央大學
水文與海洋科學研究所
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Resources in Taiwan not only are impotant for water resources management, but also paly as retention measures against flooding. In recent years, the need for domestic and industrial water have increased rapidly because of economic vigorous development, which result in rising stress of water supply especially in drought periods. Therefore, if reservoir inflows can be quantitatively forecasted beforehand, it will be helpful for issuing drought wqrning and making properly decision for water allocations. The Central Weather Bureau (CWB) issued short-term climate forecasts by statistical downscaling for precipitation and temperature with lead time of 5 months in a 1-month moving window. The objective of this study is to apply the short-term climate forecasts by integrating with a weather generator and a watershed hydrological model to predict inflows of the Shihmen Reservoir with the maximum lead time of 5 months. Both probabilistic flow forecasts and deterministic flow forecasts were produced in this approach, as well as the associated potential economic values of two flow forecasts. The sampling techniques, including maximum probability, weighted probability, and bias correction probability, were applied to retrieve monthoy mean values of precipitation and temperature from the climate forecast. Then a weather generator was applied to generate daily temperature and precipitation to drive a hydrological model for inflow predictions of the Shimen Reservoir. The skill scores (RPSS, LEPS and MSE) of three sampling results were all greater than climatology skill. The maximum probability approach has the highest predictive ability. Results of both probabilistic flow forecasts and deterministic flow forecasts are also greater than climatology skill, and show certain economic values from June to October. From January to May, the deterministic flow forecasts possess greater economic benefits than that of the probabilistic flow forecasts for cases of observed inflows at blow normal outlooks; while, the probabilistic flow forecasts possess greater economic benefits that than of the deterministic flow forecasts for cases of observed inflows at above normal outlooks.
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Dai, Shih-Jhong, and 戴世忠. "The Research of Applying Short-term Climate Forecasts to Strategic Planning." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/50137733468857898642.
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碩士國防大學中正理工學院
應用物理研究所
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The predictability of short-term climate in east Asia during winter time was investigated from 1999 to 2002 by using global spectral model (GSM) which was developed by National Centers of Environmental Prediction, USA. The model was forced with observed sea surface temperature during the time integration. The ensemble members were produced by using analysis field at 00UTC everyday in October of the target year for initial field, and the time integrations were carried out from this initial time to February of next year. The skill of probabilistic forecasts for 850 hPa temperature, 500 hPa height, zonal and meridional wind anomalies at 850 hPa and 300 hPa were evaluated by the Brier skill score and its algebraic decomposition to reliability, resolution and uncertainty, and relative operating characteristics (ROC). It is revealed that the best forecast produced by 29 ensemble members among 31 kinds of ensemble size. However, if we consider the efficiency of operational forecasting, the forecasts of the last fifteen members of October can also reach to about 97% skill of the best ensemble size. In the study of prior forecast start time, the effect of five, seven, ten, fifteen ensemble members were examined respectively. It is showed that the forecasts of the first seven members of October for 850 hPa temperature and 500 hPa height anomaly have improved with the climatological forecast, and that of the first fifteen members has reached to about 70% skill of the best ensemble size. In the reliability diagram, predicted probabilities are overestimated for the forecast probability greater than 0.4 and are underestimated for the forecast probability less than 0.4. This conclusion is contributive for adjusting the forecast in operations. Otherwise, the skill for 850 hPa temperature and 500 hPa height anomaly is better than other variables. According to the above-mentioned, we can certainly use the ensemble scheme to produce short-term climate prediction one month ago or prior, and provide significant information for the air force strategy in operations or maneuvers objectively.
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Kgakatsi, Ikalafeng Ben. "The contribution of seasonal climate forecasts to the management of agricultural disaster-risk in South Africa." Thesis, 2015. http://hdl.handle.net/10539/16916.
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A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. July 2014.South Africa’s climate is highly variable, implying that the national agricultural sector should make provision to have early warning services in place in order to reduce the risks of disasters. More than 70% of natural disasters worldwide are caused by weather and climate or weather and climate related hazards. Reliable Seasonal Climate Forecasting (SCF) for South Africa would have the potential to be of great benefit to users in addressing disaster risk reduction. A disaster is a serious disruption of the functioning of a community or a society, causing widespread human, material, economic or environmental losses, which exceed the ability of the affected community or society to cope when using their own resources. The negative impacts on agricultural production in South Africa due to natural disasters including disasters due to increasing climate variability and climate change are critical to the sector. The hypothesis assumed in the study is the improved early warning service and better SCF dissemination lead to more effective and better decision making for subsequent disaster risk reduction in the agricultural sector. The most important aspect of knowledge management in early warning operations is that of distributing the most useful service to the target group that needs it at the right time. This will not only ensure maximum performance of the entity responsible for issuing the early warnings, but will also ensure the maximum benefit to the target group. South Africa is becoming increasingly vulnerable to natural disasters that are afflicted by localised incidents of seasonal droughts, floods and flash floods that have devastating impacts on agriculture and food security. Such disasters might affect agricultural production decisions, as well as agricultural productivity. Planting dates and plant selection are decisions that depend on reliable and accurate meteorological and climatological knowledge and services for agriculture. Early warning services that could be used to facilitate informed decision making includes advisories on iv future soil moisture conditions in order to determine estimated planting times, on future grazing capacity, on future water availability and on forecasts of the following season’s weather and climate, whenever that is possible. The involvement of government structures, obviously, is also critical in immediate responses and long term interventions. The importance of creating awareness, of offering training workshops on climate knowledge and SCF, and of creating effective early warning services dissemination channels is realized by government. This is essential in order to put effective early warning services in place as a disaster-risk coping tool. Early warning services, however, can only be successful if the end-users are aware of what early warning systems, structures and technologies are in place, and if they are willing that those issuing the early warning services become involved in the decision-making process. Integrated disaster-risk reduction initiatives in government programmes, effective dissemination structures, natural resource-management projects and communityparticipation programmes are only a few examples of actions that will contribute to the development of effective early warning services, and the subsequent response to and adoption of the advices/services strategies by the people most affected. The effective distribution of the most useful early warning services to the end-user, who needs it at the right time through the best governing structures, may significantly improve decision making in the agricultural, food security and other water-sensitive sectors. Developed disaster-risk policies for extension and farmers as well as other disaster prone sectors should encourage self-reliance and the sustainable use of natural resources, and will reduce the need for government intervention. The SCF producers (e.g. the South African Weather Service (SAWS)) have issued new knowledge to intermediaries for some years now, and it is important to determine whether this knowledge has been used in services, and if so whether these services were applied effectively in coping with disaster-risks and in disaster v reduction initiatives and programmes. This study for that reason also intends to do an evaluation of the knowledge communication processes between forecasters, and intermediaries at national and provincial government levels. It therefore, aims to assess and evaluate the current knowledge communication structures within the national agricultural sector, seeking to improve disaster-risk reduction through effective early warning services. A boundary organisation is an organization which crosses the boundary between science, politics and end-users as they draw on the interests and knowledge of agencies on both sides to facilitate evidence base and socially beneficial policies and programmes. Reducing uncertainty in SCF is potentially of enormous economic value especially to the rural communities. The potential for climate science to deliver reduction in total SCF uncertainty is associated entirely with the contributions from internal variability and model uncertainty. The understanding of the limitations of the SCFs as a result of uncertainties is very important for decision making and to end-users during planning. Disappointing, however, is that several studies have shown a fairly narrow group of potential users actually receive SCFs, with an even a smaller number that makes use of these forecasts In meeting the objectives of the study the methodology to be followed is based on knowledge communication. For that reason two types of questionnaires were drafted. Open and closed questionnaires comprehensively review the knowledge, understanding, interpretation of SCFs and in early warning services distribution channels. These questionnaires were administered among the SCF producers and intermediaries and results analysed. Lastly the availability of useful SCFs knowledge has important implications for agricultural production and food security. Reliable and accurate climate service, as one of the elements of early warning services, will be discussed since they may be used to improve agricultural practices such as crop diversification, time of planting vi and changes in cultivation practices. It was clear from the conclusions of the study that critical elements of early warning services need to receive focused attention such as the SCF knowledge feedback programme should be improved by both seasonal climate producers and intermediaries, together with established structures through which reliable, accurate and timely early warning services can be disseminated. Also the relevant dissemination channels of SCFs are critical to the success of effective implementation of early warning services including the educating and training of farming communities. The boundary organisation and early warning structures are important in effective implementation of risk reduction measures within the agricultural sector and thus need to be prioritised. Enhancing the understandability and interpretability of SCF knowledge by intermediaries will assist in improving action needed to respond to SCFs. Multiple media used by both SCF producers and intermediaries in disseminating of SCFs should be accessible by all users and end-users. The Government should ensure that farming communities are educated, trained and well equipped to respond to risks from natural hazards.