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Literatura académica sobre el tema "Climate Forecasts"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 11 de febrero de 2022

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Artículos de revistas sobre el tema "Climate Forecasts"

Shao, Yawen, Quan J. Wang, Andrew Schepen y Dongryeol Ryu. "Going with the Trend: Forecasting Seasonal Climate Conditions under Climate Change". Monthly Weather Review 149, n.º 8 (agosto de 2021): 2513–22. http://dx.doi.org/10.1175/mwr-d-20-0318.1.

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AbstractFor managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision-making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast postprocessing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical postprocessing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast postprocessing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting postprocessed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast postprocessing is expected to boost user confidence in seasonal climate forecasts.

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Steinemann, Anne C. "Using Climate Forecasts for Drought Management". Journal of Applied Meteorology and Climatology 45, n.º 10 (1 de octubre de 2006): 1353–61. http://dx.doi.org/10.1175/jam2401.1.

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Abstract Drought hazards, and the ability to mitigate them with advance warning, offer potentially valuable applications of climate forecast products. Yet the value is often untapped, owing to the gap between climate science and societal decisions. This study bridged that gap; it determined forecast needs among water managers, translated forecasts to meet those needs, and shaped drought decision making to take advantage of forecasts. NOAA Climate Prediction Center (CPC) seasonal precipitation outlooks were converted into a forecast precipitation index (FPI) tailored for water managers in the southeastern United States. The FPI expresses forecasts as a departure from the climatological normal and is consistent with other drought indicators. Evaluations of CPC seasonal forecasts issued during 1995–2000 demonstrated positive skill for drought seasons in the Southeast. In addition, using evaluation criteria of water managers, 88% of forecasts for drought seasons would have appropriately prompted drought responses. Encouraged by these evaluations, and the understandability of the FPI, state water managers started using the forecasts in 2001 for deciding whether to pay farmers to suspend irrigation. Economic benefits of this forecast information were estimated at $100–$350 million in a state-declared drought year (2001, 2002) and $5–$30 million in the other years (2003, 2004). This study provides four main contributions: 1) an investigation of the needs and potential benefits of seasonal forecast information for water management, 2) a method for translating the CPC forecasts into a format needed by water managers, 3) the integration of forecast information into agency decision making, and 4) the economic valuation of that forecast information.

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Tippett, Michael K., Timothy DelSole y Anthony G. Barnston. "Reliability of Regression-Corrected Climate Forecasts". Journal of Climate 27, n.º 9 (23 de abril de 2014): 3393–404. http://dx.doi.org/10.1175/jcli-d-13-00565.1.

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Abstract Regression is often used to calibrate climate model forecasts with observations. Reliability is an aspect of forecast quality that refers to the degree of correspondence between forecast probabilities and observed frequencies of occurrence. While regression-corrected climate forecasts are reliable in principle, the estimated regression parameters used in practice are affected by sampling error. The low skill and small sample sizes typically encountered in climate prediction imply substantial sampling error in the estimated regression parameters. Here the reliability of regression-corrected climate forecasts is analyzed for the case of joint-Gaussian distributed ensemble forecasts and observations with regression parameters estimated by least squares. Hypothesis testing of the regression parameters provides direct information about the skill and reliability of the uncorrected ensemble-based probability forecasts. However, the regression-corrected probability forecasts with estimated parameters are systematically “overconfident” because sampling error causes a positive bias in the regression forecast signal variance, despite the fact that the estimates of the regression parameters are themselves unbiased. An analytical description of the reliability diagram of a generic regression-corrected climate forecast is derived and is shown to depend on sample size and population correlation skill, with small sample size and low skill being factors that increase overconfidence. The analytical reliability estimate is shown to capture the effect of sampling error in synthetic data experiments and in a 29-yr dataset of NOAA Climate Forecast System version 2 predictions of seasonal precipitation totals over the Americas. The impact of sampling error on the reliability of regression-corrected forecast has been previously unrecognized and affects all regression-based forecasts. The use of regression parameters estimated by shrinkage methods such as ridge regression substantially reduces overconfidence.

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Pasternack, Alexander, Jonas Bhend, Mark A. Liniger, Henning W. Rust, Wolfgang A. Müller y Uwe Ulbrich. "Parametric decadal climate forecast recalibration (DeFoReSt 1.0)". Geoscientific Model Development 11, n.º 1 (25 de enero de 2018): 351–68. http://dx.doi.org/10.5194/gmd-11-351-2018.

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Abstract. Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

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Block, P. "Tailoring seasonal climate forecasts for hydropower operations". Hydrology and Earth System Sciences 15, n.º 4 (29 de abril de 2011): 1355–68. http://dx.doi.org/10.5194/hess-15-1355-2011.

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Abstract. Integration of seasonal precipitation forecasts into water resources operations and planning is practically nonexistent, even in regions of scarcity. This is often attributable to water manager's tendency to act in a risk averse manner, preferring to avoid consequences of poor forecasts, at the expense of unrealized benefits. Convincing demonstrations of forecast value are therefore desirable to support assimilation into practice. A dynamically linked system, including forecast, rainfall-runoff, and hydropower models, is applied to the upper Blue Nile basin in Ethiopia to compare benefits and reliability generated by actual forecasts against a climatology-based approach, commonly practiced in most water resources systems. Processing one hundred decadal sequences demonstrates superior forecast-based benefits in 68 cases, a respectable advancement, however benefits in a few forecast-based sequences are noticeably low, likely to dissuade manager's adoption. A hydropower sensitivity test reveals a propensity toward poor-decision making when forecasts over-predict wet conditions. Tailoring the precipitation forecast to highlight critical dry forecasts minimizes this inclination, resulting in 97% of the sequences favoring the forecast-based approach. Considering managerial risk preferences for the system, even risk-averse actions, if coupled with forecasts, exhibit superior benefits and reliability compared with risk-taking tendencies conditioned on climatology.

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Weisheimer, A. y T. N. Palmer. "On the reliability of seasonal climate forecasts". Journal of The Royal Society Interface 11, n.º 96 (6 de julio de 2014): 20131162. http://dx.doi.org/10.1098/rsif.2013.1162.

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Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time.

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Goddard, L., A. G. Barnston y S. J. Mason. "Evaluation of the IRI'S “Net Assessment” Seasonal Climate Forecasts: 1997–2001". Bulletin of the American Meteorological Society 84, n.º 12 (1 de diciembre de 2003): 1761–82. http://dx.doi.org/10.1175/bams-84-12-1761.

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The International Research Institute for Climate Prediction (IRI) net assessment seasonal temperature and precipitation forecasts are evaluated for the 4-yr period from October–December 1997 to October–December 2001. These probabilistic forecasts represent the human distillation of seasonal climate predictions from various sources. The ranked probability skill score (RPSS) serves as the verification measure. The evaluation is offered as time-averaged spatial maps of the RPSS as well as area-averaged time series. A key element of this evaluation is the examination of the extent to which the consolidation of several predictions, accomplished here subjectively by the forecasters, contributes to or detracts from the forecast skill possible from any individual prediction tool. Overall, the skills of the net assessment forecasts for both temperature and precipitation are positive throughout the 1997–2001 period. The skill may have been enhanced during the peak of the 1997/98 El Niño, particularly for tropical precipitation, although widespread positive skill exists even at times of weak forcing from the tropical Pacific. The temporally averaged RPSS for the net assessment temperature forecasts appears lower than that for the AGCMs. Over time, however, the IRI forecast skill is more consistently positive than that of the AGCMs. The IRI precipitation forecasts generally have lower skill than the temperature forecasts, but the forecast probabilities for precipitation are found to be appropriate to the frequency of the observed outcomes, and thus reliable. Over many regions where the precipitation variability is known to be potentially predictable, the net assessment precipitation forecasts exhibit more spatially coherent areas of positive skill than most, if not all, prediction tools. On average, the IRI net assessment forecasts appear to perform better than any of the individual objective prediction tools.

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Krakauer, Nir Y., Michael D. Grossberg, Irina Gladkova y Hannah Aizenman. "Information Content of Seasonal Forecasts in a Changing Climate". Advances in Meteorology 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/480210.

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We study the potential value to stakeholders of probabilistic long-term forecasts, as quantified by the mean information gain of the forecast compared to climatology. We use as a case study the USA Climate Prediction Center (CPC) forecasts of 3-month temperature and precipitation anomalies made at 0.5-month lead time since 1995. Mean information gain was positive but low (about 2% and 0.5% of the maximum possible for temperature and precipitation forecasts, resp.) and has not increased over time. Information-based skill scores showed similar patterns to other, non-information-based, skill scores commonly used for evaluating seasonal forecasts but tended to be smaller, suggesting that information gain is a particularly stringent measure of forecast quality. We also present a new decomposition of forecast information gain into Confidence, Forecast Miscalibration, and Climatology Miscalibration components. Based on this decomposition, the CPC forecasts for temperature are on average underconfident while the precipitation forecasts are overconfident. We apply a probabilistic trend extrapolation method to provide an improved reference seasonal forecast, compared to the current CPC procedure which uses climatology from a recent 30-year period. We show that combining the CPC forecast with the probabilistic trend extrapolation more than doubles the mean information gain, providing one simple avenue for increasing forecast skill.

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Pirret, Jennifer S. R., Joseph D. Daron, Philip E. Bett, Nicolas Fournier y Andre Kamga Foamouhoue. "Assessing the Skill and Reliability of Seasonal Climate Forecasts in Sahelian West Africa". Weather and Forecasting 35, n.º 3 (6 de mayo de 2020): 1035–50. http://dx.doi.org/10.1175/waf-d-19-0168.1.

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Abstract Seasonal climate forecasts have the potential to support planning decisions and provide advanced warning to government, industry, and communities to help reduce the impacts of adverse climatic conditions. Assessing the reliability of seasonal forecasts, generated using different models and methods, is essential to ensure their appropriate interpretation and use. Here we assess the reliability of forecasts for seasonal total precipitation in Sahelian West Africa, a region of high year-to-year climate variability. Through digitizing forecasts issued from the regional climate outlook forum in West Africa known as Prévisions Climatiques Saisonnières en Afrique Soudano-Sahélienne (PRESASS), we assess their reliability by comparing them to the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) project observational data over the past 20 years. The PRESASS forecasts show positive skill and reliability, but a bias toward lower forecast probabilities in the below-normal precipitation category. In addition, we assess the reliability of seasonal precipitation forecasts for the same region using available global dynamical forecast models. We find all models have positive skill and reliability, but this varies geographically. On average, NCEP’s CFS and ECMWF’s SEAS5 systems show greater skill and reliability than the Met Office’s GloSea5, and in turn than Météo-France’s Sys5, but one key caveat is that model performance might depend on the meteorological situation. We discuss the potential for improving use of dynamical model forecasts in the regional climate outlook forums, to improve the reliability of seasonal forecasts in the region and the objectivity of the seasonal forecasting process used in the PRESASS regional climate outlook forum.

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Hu, Qi, Lisa M. Pytlik Zillig, Gary D. Lynne, Alan J. Tomkins, William J. Waltman, Michael J. Hayes, Kenneth G. Hubbard, Ikrom Artikov, Stacey J. Hoffman y Donald A. Wilhite. "Understanding Farmers’ Forecast Use from Their Beliefs, Values, Social Norms, and Perceived Obstacles*". Journal of Applied Meteorology and Climatology 45, n.º 9 (1 de septiembre de 2006): 1190–201. http://dx.doi.org/10.1175/jam2414.1.

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Abstract Although the accuracy of weather and climate forecasts is continuously improving and new information retrieved from climate data is adding to the understanding of climate variation, use of the forecasts and climate information by farmers in farming decisions has changed little. This lack of change may result from knowledge barriers and psychological, social, and economic factors that undermine farmer motivation to use forecasts and climate information. According to the theory of planned behavior (TPB), the motivation to use forecasts may arise from personal attitudes, social norms, and perceived control or ability to use forecasts in specific decisions. These attributes are examined using data from a survey designed around the TPB and conducted among farming communities in the region of eastern Nebraska and the western U.S. Corn Belt. There were three major findings: 1) the utility and value of the forecasts for farming decisions as perceived by farmers are, on average, around 3.0 on a 0–7 scale, indicating much room to improve attitudes toward the forecast value. 2) The use of forecasts by farmers to influence decisions is likely affected by several social groups that can provide “expert viewpoints” on forecast use. 3) A major obstacle, next to forecast accuracy, is the perceived identity and reliability of the forecast makers. Given the rapidly increasing number of forecasts in this growing service business, the ambiguous identity of forecast providers may have left farmers confused and may have prevented them from developing both trust in forecasts and skills to use them. These findings shed light on productive avenues for increasing the influence of forecasts, which may lead to greater farming productivity. In addition, this study establishes a set of reference points that can be used for comparisons with future studies to quantify changes in forecast use and influence.

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Tesis sobre el tema "Climate Forecasts"

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 y 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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Más fuentes

Libros sobre el tema "Climate Forecasts"

Allen, M. R. Uncertainty in forecasts of anthropogenic climate change. Chilton: Rutherford Appleton Laboratory, 1999.

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Global Climate Observing System. Working Group on Socio-economic Benefits. The socio-economic benefits of climate forecasts: Literature review and recommendations. Geneva, Switzerland: Joint Planning Office, Global Climate Observing System, c/o World Meteorological Organization, 1995.

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Short Course on Capturing the Benefits of Seasonal Climate Forecasts in Agricultural Management (2001 Tamil Nadu Agricultural University). Short Course on Capturing the Benefits of Seasonal Climate Forecasts in Agricultural Management. Coimbatore: Dept. of Agricultural Meteorology, 2001.

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Hamilton, Clive. Requiem for a species: Why we resist the truth about climate change. London: Earthscan, 2010.

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Hamilton, Clive. Requiem for a species: Why we resist the truth about climate change. London: Earthscan, 2010.

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Beyond smoke and mirrors: Climate change and energy in the 21st century. Cambridge, UK: Cambridge University Press, 2010.

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Beyond smoke and mirrors: Climate change and energy in the 21st century. New York: Cambridge University Press, 2010.

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Doomsday denied: A survivor's guide to the 21st century. Golden, Colo: Fulcrum Pub., 1997.

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Korzun, V. A. Izmenenie klimata: Prichiny, prognozy, vozmozhnye posledstvii︠a︡ dli︠a︡ mirovoĭ ėkonomiki = The Climate change : Causes, Forecasts and Consequences for the World Economy. Moskva: IMĖMO, 2012.

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Más fuentes

Capítulos de libros sobre el tema "Climate Forecasts"

Simon, J. Mason y C. Thomson Madeleine. "Weather Forecasts". En Climate Information For Public Health Action, 147–74. Abingdon, Oxon; New York, NY: Routledge, 2019. | Series: Routledge studies in environment and health: Routledge, 2018. http://dx.doi.org/10.4324/9781315115603-7.

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Livezey, Robert E. "The Evaluation of Forecasts". En Analysis of Climate Variability, 177–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03167-4_10.

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Livezey, Robert E. "The Evaluation of Forecasts". En Analysis of Climate Variability, 179–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03744-7_10.

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Pagano, T. C. y D. C. Garen. "Integration of Climate Information and Forecasts into Western US Water Supply Forecasts". En Climate Variations, Climate Change, and Water Resources Engineering, 86–102. Reston, VA: American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/9780784408247.ch06.

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Simon, J. Mason. "Climate Forecasts for Early Warning". En Climate Information For Public Health Action, 175–98. Abingdon, Oxon; New York, NY: Routledge, 2019. | Series: Routledge studies in environment and health: Routledge, 2018. http://dx.doi.org/10.4324/9781315115603-8.

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Kelly-Hope, Louise y Madeleine C. Thomson. "Climate and Infectious Diseases". En Seasonal Forecasts, Climatic Change and Human Health, 31–70. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6877-5_3.

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Nkuba, Michael Robert, Raban Chanda, Gagoitseope Mmopelwa, Akintayo Adedoyin, Margaret Najjingo Mangheni, David Lesolle y Edward Kato. "Indigenous and Scientific Forecasts on Climate Change Perceptions of Arable Farmers: Rwenzori Region, Western Uganda". En African Handbook of Climate Change Adaptation, 1685–703. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_113.

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AbstractDespite the dissemination of climate information from national meteorological systems, arable farmers still have challenges of dealing with climate-related risks. This study investigated the effect of using indigenous knowledge-based forecasts (IFs) and scientific knowledge-based forecasts (SFs) on the climate change perceptions of arable farmers in the Rwenzori region, Western Uganda. Data on socio-economic characteristics, use of forecasts, and climate change perceptions was collected from 580 arable farmers and the probit model was used in the analysis. The findings indicated that use of IFs only increased the likelihood of perceiving increase in the frequency in occurrences of droughts and floods. Using both SFs and IFs had a significant positive effect on perception of unpredictable rains and the increase in drought incidence among arable farmers. Although forecasts are important drivers of perceptions, other factors, such as gender, social capital, and dissemination of climate change information by radio, enhance climate change perceptions. Active participation of arable farmers in the dissemination of forecasts by national meteorological services could improve perceptions of climate related risks.

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Croley, T. E. "Using Climate Predictions in Great Lakes Hydrologic Forecasts". En Climate Variations, Climate Change, and Water Resources Engineering, 166–87. Reston, VA: American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/9780784408247.ch12.

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Waliser, Duane E. y Jason M. Cordeira. "Atmospheric River Modeling: Forecasts, Climate Simulations, and Climate Projections". En Atmospheric Rivers, 179–99. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-28906-5_6.

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Schneider, J. M. y J. D. Garbrecht. "Utility of Recent NOAA/CPC Forecasts of Seasonal Precipitation". En Climate Variations, Climate Change, and Water Resources Engineering, 51–64. Reston, VA: American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/9780784408247.ch04.

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Actas de conferencias sobre el tema "Climate Forecasts"

"The role of climate drift in seasonal forecasts". En 23rd International Congress on Modelling and Simulation (MODSIM2019). Modelling and Simulation Society of Australia and New Zealand, 2019. http://dx.doi.org/10.36334/modsim.2019.h8.osbrough.

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Rienecker, M., M. Suarez, R. Koster, R. Reichle, C. Keppenne, D. Adamec y S. Schubert. "Improving Short-term Climate Forecasts with Satellite Observations". En 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.897.

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Schneider, J. M. y J. D. Garbrecht. "Regional Performance of NOAA/CPC Seasonal Climate Temperature Forecasts". En World Water and Environmental Resources Congress 2004. Reston, VA: American Society of Civil Engineers, 2004. http://dx.doi.org/10.1061/40737(2004)150.

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Schneider, J. M. y J. D. Garbrecht. "Regional Utility of NOAA/CPC Seasonal Climate Precipitation Forecasts". En World Water and Environmental Resources Congress 2003. Reston, VA: American Society of Civil Engineers, 2003. http://dx.doi.org/10.1061/40685(2003)370.

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Guihan, Rebecca, Austin Polebitski, Richard Palmer y Casey Brown. "Integrating Climate Forecasts and Reforecast Products into Reservoir Operations Management". En World Environmental and Water Resources Congress 2013. Reston, VA: American Society of Civil Engineers, 2013. http://dx.doi.org/10.1061/9780784412947.154.

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Robertson, Andrew, Upmanu Lall, Stephen Zebiak y Balaji Rajagopalan. "Categorical Climate Forecasts through Optimal Combination of Multiple GCM Ensembles". En World Water and Environmental Resources Congress 2003. Reston, VA: American Society of Civil Engineers, 2003. http://dx.doi.org/10.1061/40685(2003)184.

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"A software architecture for seasonal climate forecasts in the tropical Pacific". En 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.c4.charles.

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O'Connell, Sara M. y David W. Watkins, Jr. "Probabilistic Forecasts Using Climate Information for Water Management in Central Texas". En World Water and Environmental Resources Congress 2003. Reston, VA: American Society of Civil Engineers, 2003. http://dx.doi.org/10.1061/40685(2003)368.

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Pagano, T. C. y D. C. Garen. "Use of Climate Information in Official Western US Water Supply Forecasts". En World Water and Environmental Resources Congress 2003. Reston, VA: American Society of Civil Engineers, 2003. http://dx.doi.org/10.1061/40685(2003)377.

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Wang, Hui, Sankarasubramanian Arumugam y Ranji S. Ranjithan. "Seamless Integration of Weather and Climate Information in Developing Operational Streamflow Forecasts". En World Environmental and Water Resources Congress 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41114(371)470.

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Informes sobre el tema "Climate Forecasts"

Severen, Christopher, Christopher Costello y Olivier Deschenes. A Forward Looking Ricardian Approach: Do Land Markets Capitalize Climate Change Forecasts? Cambridge, MA: National Bureau of Economic Research, julio de 2016. http://dx.doi.org/10.3386/w22413.

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Schattman, Rachel. How Do Farmers Think about Climate Risk? A Study of On-Farm Decision Making in an Era of Climate Change. USDA Northeast Climate Hub, agosto de 2017. http://dx.doi.org/10.32747/2017.6956535.ch.

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climate forecasts suggest farmers in the northeast will be faced with both challenges and opportunities as the climate changes. currently farmers and other land stewards manage the risks created by changing weather patterns in many different ways.

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Tjernstrom, Michael. Description of Mixed-Phase Clouds in Weather Forecast and Climate Models. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2012. http://dx.doi.org/10.21236/ada572578.

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Tjernstrom, Michael. Description of Mixed-Phase Clouds in Weather Forecast and Climate Models. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2013. http://dx.doi.org/10.21236/ada601216.

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Tjernstroem, Michael. Description of Mixed-Phase Clouds in Weather Forecast and Climate Models. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2014. http://dx.doi.org/10.21236/ada616540.

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Glascoe, L. y H. Chin. Modeling of High-altitude Atmospheric Dispersion Using Climate and Meteorological Forecast Data. Office of Scientific and Technical Information (OSTI), marzo de 2005. http://dx.doi.org/10.2172/15015929.

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Backus, George, Thomas Lowry, Shannon Jones, La Walker, Barry Roberts y Leonard Malczynski. County-Level Climate Uncertainty for Risk Assessments: Volume 9 Appendix H - Forecast Precipitation. Office of Scientific and Technical Information (OSTI), mayo de 2017. http://dx.doi.org/10.2172/1813909.

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Venäläinen, Ari, Sanna Luhtala, Mikko Laapas, Otto Hyvärinen, Hilppa Gregow, Mikko Strahlendorff, Mikko Peltoniemi et al. Sää- ja ilmastotiedot sekä uudet palvelut auttavat metsäbiotaloutta sopeutumaan ilmastonmuutokseen. Finnish Meteorological Institute, enero de 2021. http://dx.doi.org/10.35614/isbn.9789523361317.

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Climate change will increase weather induced risks to forests, and thus effective adaptation measures are needed. In Säätyö project funded by the Ministry of Agriculture and Forestry, we have summarized the data that facilitate adaptation measures, developed weather and climate services that benefit forestry, and mapped what kind of new weather and climate services are needed in forestry. In addition, we have recorded key further development needs to promote adaptation. The Säätyö project developed a service product describing the harvesting conditions of trees based on the soil moisture assessment. The output includes an analysis of the current situation and a 10-day forecast. In the project we also tested the usefulness of long forecasts beyond three months. The weather forecasting service is sidelined and supplemented by another co-operation project between the Finnish Meteorological Institute and Metsäteho called HarvesterSeasons (https://harvesterseasons.com/). The HarvesterSeasons service utilizes long-term forecasts of up to 6 months to assess terrain bearing conditions. A test version of a wind damage risk tool was developed in cooperation with the Department of Forest Sciences of the University of Eastern Finland and the Finnish Meteorological Institute. It can be used to calculate the wind speeds required in a forest area for wind damage (falling trees). It is currently only suitable for researcher use. In the Säätyö project the possibility of locating the most severe wind damage areas immediately after a storm was also tested. The method is based on the spatial interpolation of wind observations. The method was used to analyze storms that caused forest damages in the summer and fall of 2020. The produced maps were considered illustrative and useful to those responsible for compiling the situational picture. The accumulation of snow on tree branches, can be modeled using weather data such as rainfall, temperature, air humidity, and wind speed. In the Säätyö project, the snow damage risk assessment model was further developed in such a way that, in addition to the accumulated snow load amount, the characteristics of the stand and the variations in terrain height were also taken into account. According to the verification performed, the importance of abiotic factors increased under extreme snow load conditions (winter 2017-2018). In ordinary winters, the importance of biotic factors was emphasized. According to the comparison, the actual snow damage could be explained well with the tested model. In the interviews and workshop, the uses of information products, their benefits, the conditions for their introduction and development opportunities were mapped. According to the results, diverse uses and benefits of information products and services were seen. Information products would make it possible to develop proactive forest management, which would reduce the economic costs caused by wind and snow damages. A more up-to-date understanding of harvesting conditions, enabled by information products, would enhance the implementation of harvesting and harvesting operations and the management of timber stocks, as well as reduce terrain, trunk and root damage. According to the study, the introduction of information is particularly affected by the availability of timeliness. Although the interviewees were not currently willing to pay for the information products developed in the project, the interviews highlighted several suggestions for the development of information products, which could make it possible to commercialize them.

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Backus, George A., Thomas Stephen Lowry, Shannon M. Jones, La Tonya Nicole Walker, Barry L. Roberts y Leonard A. Malczynski. County-Level Climate Uncertainty for Risk Assessments: Volume 17 Appendix P - Forecast Soil Moisture. Office of Scientific and Technical Information (OSTI), abril de 2017. http://dx.doi.org/10.2172/1365516.

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Backus, George A., Thomas Stephen Lowry, Shannon M. Jones, La Tonya Nicole Walker, Barry L. Roberts y Leonard A. Malczynski. County-Level Climate Uncertainty for Risk Assessments: Volume 27 Appendix Z - Forecast Ridging Rate. Office of Scientific and Technical Information (OSTI), junio de 2017. http://dx.doi.org/10.2172/1365546.

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