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1
Lavaysse, C., J. Vogt, and F. Pappenberger. "Early warning of drought in Europe using the monthly ensemble system from ECMWF." Hydrology and Earth System Sciences 19, no. 7 (July 28, 2015): 3273–86. http://dx.doi.org/10.5194/hess-19-3273-2015.
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Abstract. Timely forecasts of the onset or possible evolution of droughts are an important contribution to mitigate their manifold negative effects. In this paper we therefore analyse and compare the performance of the first month of the probabilistic extended range forecast and of the seasonal forecast from the European Centre for Medium-range Weather Forecasts (ECMWF) in predicting droughts over the European continent. The Standardized Precipitation Index (SPI-1) is used to quantify the onset or likely evolution of ongoing droughts for the next month. It can be shown that on average the extended range forecast has greater skill than the seasonal forecast, whilst both outperform climatology. No significant spatial or temporal patterns can be observed, but the scores are improved when focussing on large-scale droughts. In a second step we then analyse several different methods to convert the probabilistic forecasts of SPI into a Boolean drought warning. It can be demonstrated that methodologies which convert low percentiles of the forecasted SPI cumulative distribution function into warnings are superior in comparison with alternatives such as the mean or the median of the ensemble. The paper demonstrates that up to 40 % of droughts are correctly forecasted one month in advance. Nevertheless, during false alarms or misses, we did not find significant differences in the distribution of the ensemble members that would allow for a quantitative assessment of the uncertainty.
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Lepore, Chiara, Michael K. Tippett, and John T. Allen. "CFSv2 Monthly Forecasts of Tornado and Hail Activity." Weather and Forecasting 33, no. 5 (September 24, 2018): 1283–97. http://dx.doi.org/10.1175/waf-d-18-0054.1.
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Abstract Climate Forecast System, version 2, predictions of monthly U.S. severe thunderstorm activity are analyzed for the period 1982–2016. Forecasts are based on a tornado environmental index and a hail environmental index, which are functions of monthly averaged storm relative helicity (SRH), convective precipitation (cPrcp), and convective available potential energy (CAPE). Overall, forecast indices reproduce well the annual cycle of tornado and hail events. Forecast index biases are mostly negative and caused by environment values that are low east of the Rockies, although forecast CAPE is higher than the reanalysis values over the High Plains. Skill is diagnosed spatially for the indices and their constituents separately. SRH is more skillfully forecast than cPrcp and CAPE, especially during December–June. The spatial patterns of forecast skill for CAPE and cPrcp are similar, with higher skill for CAPE and less spatial coherence for cPrcp. The indices are forecast with substantially less skill than the environmental parameters. Numbers of tornado and hail events are forecast with modest but statistically significant skill in some NOAA regions and months of the year. Skill tends to be relatively higher for hail events and in climatologically active seasons and regions. Much of the monthly skill appears to be derived from the first 2 weeks of the forecast. El Niño–Southern Oscillation (ENSO) modulates forecasts and, to a lesser extent, forecast skill, during March–May, with more activity and higher skill during cool ENSO conditions.
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Lavaysse, C., J. Vogt, and F. Pappenberger. "Early warning of drought in Europe using the monthly ensemble system from ECMWF." Hydrology and Earth System Sciences Discussions 12, no. 2 (February 13, 2015): 1973–2009. http://dx.doi.org/10.5194/hessd-12-1973-2015.
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Abstract. Timely forecasts of the onset or possible evolution of droughts are an important contribution to mitigate their manifold negative effects. In this paper we therefore analyse and compare the performance of the first month of the probabilistic extended range forecast and of the seasonal forecast from ECMWF in predicting droughts over the European continent. The Standardized Precipitation Index (SPI) is used to quantify the onset and severity of droughts. It can be shown that on average the extended range forecast has greater skill than the seasonal forecast whilst both outperform climatology. No significant spatial or temporal patterns can be observed but the scores are improved when focussing on large-scale droughts. In a second step we then analyse several different methods to convert the probabilistic forecasts of SPI into a Boolean drought warning. It can be demonstrated that methodologies which convert low percentiles of the forecasted SPI cumulative distribution function into warnings are superior in comparison with alternatives such as the mean or the median of the ensemble. The paper demonstrates that up to 40% of droughts are correctly forecasted one month in advance. Nevertheless, during false alarms or misses, we did not find significant differences in the distribution of the ensemble members that would allow for a quantitative assessment of the uncertainty.
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Lee, Cai Lin, and Dong Mei Wang. "Monthly Runoff Probabilistic Forecast Model Based on Similar Process Derivations." Applied Mechanics and Materials 737 (March 2015): 710–14. http://dx.doi.org/10.4028/www.scientific.net/amm.737.710.
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In this paper, a runoff forecast model combining similar process derivation with probabilistic forecasts is proposed. Certain forecast result is computed by similar processes derivations, and on the basis of certain results, a confidence interval under given confidence coefficient is worked out by probabilistic forecast part. The model is simple in structure, easy in establishing and unnecessary to concern for predictor selections. Applying above model in simulation experiments, the results show the forecast model have excellent forecast accuracy and can be used in monthly runoff forecast effectively.
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Tippett, Michael K., Laurie Trenary, Timothy DelSole, Kathleen Pegion, and Michelle L. L’Heureux. "Sources of Bias in the Monthly CFSv2 Forecast Climatology." Journal of Applied Meteorology and Climatology 57, no. 5 (May 2018): 1111–22. http://dx.doi.org/10.1175/jamc-d-17-0299.1.
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AbstractForecast climatologies are used to remove systematic errors from forecasts and to express forecasts as departures from normal. Forecast climatologies are computed from hindcasts by various averaging, smoothing, and interpolation procedures. Here the Climate Forecast System, version 2 (CFSv2), monthly forecast climatology provided by the NCEP Environmental Modeling Center (EMC) is shown to be biased in the sense of systematically differing from the hindcasts that are used to compute it. These biases, which are unexpected, are primarily due to fitting harmonics to hindcast data that have been organized in a particular format, which on careful inspection is seen to introduce discontinuities. Biases in the monthly near-surface temperature forecast climatology reach 2°C over North America for March targets and start times at the end of January. Biases in the monthly Niño-3.4 forecast climatology are also largest for start times near calendar-month boundaries. A further undesirable consequence of this fitting procedure is that the EMC forecast climatology varies discontinuously with lead time for fixed target month. Two alternative methods for computing the forecast climatology are proposed and illustrated. The proposed methods more accurately fit the hindcast data and provide a clearer representation of the CFSv2 model climate drift toward lower Niño-3.4 values for starts in March and April and toward higher Niño-3.4 values for starts in June, July, and August.
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Aptukov, Valery N., and Victor Yu Mitin. "STATISTICAL MODELS FOR FORECASTING AVERAGE MONTHLY TEMPERATURE AND MONTHLY PRECIPITATION AMOUNT IN PERM." Географический вестник = Geographical bulletin, no. 2(57) (2021): 84–95. http://dx.doi.org/10.17072/2079-7877-2021-2-84-95.
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The article proposes an approach to forecasting mean temperature and total precipitation for the upcoming month, based on the study of the regularities of the influence of statistical characteristics of temperature and precipitation of previous periods on them. Among the predictors, along with the basic statistical characteristics, we use the fractality index which is an indicator of the randomness/ determinism of the climate series. Within the framework of this approach, we have developed models of different levels to predict the temperature and total precipitation amount in the upcoming month. The main parameters of these models are described and the possibilities of their variation are indicated. Examples are given to illustrate the forecasting methodology using various types of models and include the results of quality control of the models, calculation of forecast accuracy and dependence of forecast accuracy of average temperature and precipitation on the month (climate season). When tested in 2020, models for forecasting temperature and precipitation for the upcoming month give good results: 9 correct forecasts of temperature anomalies out of 10 (90%) and 7 correct forecasts of precipitation anomalies out of 9 (77,7%).
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Fundel, F., S. Jörg-Hess, and M. Zappa. "Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices." Hydrology and Earth System Sciences 17, no. 1 (January 29, 2013): 395–407. http://dx.doi.org/10.5194/hess-17-395-2013.
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Abstract. Streamflow droughts, characterized by low runoff as consequence of a drought event, affect numerous aspects of life. Economic sectors that are impacted by low streamflow are, e.g., power production, agriculture, tourism, water quality management and shipping. Those sectors could potentially benefit from forecasts of streamflow drought events, even of short events on the monthly time scales or below. Numerical hydrometeorological models have increasingly been used to forecast low streamflow and have become the focus of recent research. Here, we consider daily ensemble runoff forecasts for the river Thur, which has its source in the Swiss Alps. We focus on the evaluation of low streamflow and of the derived indices as duration, severity and magnitude, characterizing streamflow droughts up to a lead time of one month. The ECMWF VarEPS 5-member ensemble reforecast, which covers 18 yr, is used as forcing for the hydrological model PREVAH. A thorough verification reveals that, compared to probabilistic peak-flow forecasts, which show skill up to a lead time of two weeks, forecasts of streamflow droughts are skilful over the entire forecast range of one month. For forecasts at the lower end of the runoff regime, the quality of the initial state seems to be crucial to achieve a good forecast quality in the longer range. It is shown that the states used in this study to initialize forecasts satisfy this requirement. The produced forecasts of streamflow drought indices, derived from the ensemble forecasts, could be beneficially included in a decision-making process. This is valid for probabilistic forecasts of streamflow drought events falling below a daily varying threshold, based on a quantile derived from a runoff climatology. Although the forecasts have a tendency to overpredict streamflow droughts, it is shown that the relative economic value of the ensemble forecasts reaches up to 60%, in case a forecast user is able to take preventive action based on the forecast.
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Qiao, Guangchao, Mingxiang Yang, and Xiaoling Zeng. "Monthly-scale runoff forecast model based on PSO-SVR." Journal of Physics: Conference Series 2189, no. 1 (February 1, 2022): 012016. http://dx.doi.org/10.1088/1742-6596/2189/1/012016.
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Abstract The current methods used in the Lubbog reservoir runoff forecast generally have shortcomings such as low forecast accuracy and low stability. Aiming at these problems, this paper constructs a PSO-SVR mid-and-long term forecast model, and it uses the particle swarm optimization algorithm (PSO) to find the penalty coefficient C, the insensitivity coefficient ε and the gamma parameter of the Gaussian radial basis kernel function of the support vector regression machine (SVR). The results demonstrates that the average relative errors of the PSO-SVR forecast model is relatively small, which are all within a reasonable range; the qualification rates for most monthly forecasts are above 80%. Experimental results indicate that compared with multiple regression analysis, the PSO-SVR model has a higher forecast accuracy, a stronger stability, and a higher credibility. It has a certain practical value and provides a reference for related research.
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Wang, Ting, and Xi Miao Jia. "Monthly Load Forecasting Based on Optimum Grey Model." Advanced Materials Research 230-232 (May 2011): 1226–30. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.1226.
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Due to the variety and the randomicity of its influencing factors, the monthly load forecasting is a difficult problem for a long time. In order to improve the forecast accuracy, the paper proposes a new load forecast model based on improved GM (1, 1).First, the GM (1, 1) is used to forecast the load data, which takes the longitude historical data as original series, the increment trend of load was forecasted and takes the crosswise historical data as original series, the fluctuation trend of load was forecasted. On this basis the optimum method is led in. An optimal integrated forecasting model is built up. The case calculation results show that the proposed method can remarkably improve the accuracy of monthly load forecasting, and decrease the error. The integrated model this paper describes for short-term load forecasting is available and accurate.
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Milléo, Carla, and Ricardo Carvalho de Almeida. "Application of RBF artificial neural networks to precipitation and temperature forecasting in Paraná, Brazil." Ciência e Natura 43 (March 1, 2021): e40. http://dx.doi.org/10.5902/2179460x43258.
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Precipitation and temperature have an impact on various sectors of society, such as agriculture, power generation, water availability, so it is essential to develop accurate monthly forecasts. The objective of this study is to develop an artificial neural network (ANN) model for monthly temperature and precipitation forecasts for the state of Paraná, Brazil. An important step in the ANN model is the selection of input variables, for which the forward stepwise regression method was used. After identifying the predictor variables for the forecasting model, the Radial Basis Function (RBF) ANN was developed with 50 neurons in the hidden layer and one neuron in the output layer. For the precipitation forecasting models, better performances were obtained for forecasting the data smoothed by the three-month moving average, since noisy data, such as monthly precipitation, are more difficult to be simulated by the neural network. For the temperature forecasts, the ANN model performed well both in the monthly temperature forecast and in the 3-month moving average forecast. This study showed the suitability of forecasting precipitation and temperature with the use of RBF ANNs, especially in the forecast of the monthly temperature.
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Woldemeskel, Fitsum, David McInerney, Julien Lerat, Mark Thyer, Dmitri Kavetski, Daehyok Shin, Narendra Tuteja, and George Kuczera. "Evaluating post-processing approaches for monthly and seasonal streamflow forecasts." Hydrology and Earth System Sciences 22, no. 12 (December 6, 2018): 6257–78. http://dx.doi.org/10.5194/hess-22-6257-2018.
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Abstract. Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological forecasts, hydrological model structure, and parameterization, as well as in the observed rainfall and streamflow data used to calibrate the models. Statistical streamflow post-processing is an important technique available to improve the probabilistic properties of the forecasts. This study evaluates post-processing approaches based on three transformations – logarithmic (Log), log-sinh (Log-Sinh), and Box–Cox with λ=0.2 (BC0.2) – and identifies the best-performing scheme for post-processing monthly and seasonal (3-months-ahead) streamflow forecasts, such as those produced by the Australian Bureau of Meteorology. Using the Bureau's operational dynamic streamflow forecasting system, we carry out comprehensive analysis of the three post-processing schemes across 300 Australian catchments with a wide range of hydro-climatic conditions. Forecast verification is assessed using reliability and sharpness metrics, as well as the Continuous Ranked Probability Skill Score (CRPSS). Results show that the uncorrected forecasts (i.e. without post-processing) are unreliable at half of the catchments. Post-processing of forecasts substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme substantially outperforms the Log and Log-Sinh schemes. Overall, the BC0.2 scheme achieves reliable and sharper-than-climatology forecasts at a larger number of catchments than the Log and Log-Sinh schemes. The improvements in forecast reliability and sharpness achieved using the BC0.2 post-processing scheme will help water managers and users of the forecasting service make better-informed decisions in planning and management of water resources. Highlights. Uncorrected and post-processed streamflow forecasts (using three transformations, namely Log, Log-Sinh, and BC0.2) are evaluated over 300 diverse Australian catchments. Post-processing enhances streamflow forecast reliability, increasing the percentage of catchments with reliable predictions from 50 % to over 90 %. The BC0.2 transformation achieves substantially better forecast sharpness than the Log-Sinh and Log transformations, particularly in dry catchments.
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So, Jae-Min, Joo-Heon Lee, and Deg-Hyo Bae. "Development of a Hydrological Drought Forecasting Model Using Weather Forecasting Data from GloSea5." Water 12, no. 10 (October 6, 2020): 2785. http://dx.doi.org/10.3390/w12102785.
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This study developed a hydrological drought forecasting framework linked to the meteorological model and land surface model (LSM) considering hydrologic facilities and evaluated the feasibility of the Modified Surface Water Supply Index (MSWSI) for drought forecasts in South Korea. The Global Seasonal Forecast System version 5 (GloSea5) and variable infiltration capacity (VIC) models were adapted for meteorological and hydrological models for ensemble weather forecasts and corresponding hydrologic river and dam inflow forecasts, respectively. Instead of direct use for weather and runoff forecasts, the anomaly between the ensemble forecast and hindcast data for each month was computed. Then, the monthly forecasted weather and runoff were obtained by adding this anomaly and the statistical nominal values obtained from the average monthly runoff during the last 30 years. For the selection of drought index duration, past historical observation data and drought records were used, and the 3-month period of the MSWSI outperformed any other durations in the study area. In addition, the simulated monthly river and dam inflows agreed well with the observed inflows; therefore, the model-driven runoff data from the VIC model were usable for hydrological drought forecasts. A case study result for the 2015–2016 drought event demonstrated that the hydrological drought forecasting framework suggested in this study is reliable for drought forecasting up to a 2-month forecast lead time. It is therefore concluded that the proposed framework linked with GloSea5, the VIC model and MSWSI(3) provides useful information for supporting decision-making related to water supply and management.
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Harrison, Matthew T., Karen M. Christie, and Richard P. Rawnsley. "Assessing the reliability of dynamical and historical climate forecasts in simulating hindcast pasture growth rates." Animal Production Science 57, no. 7 (2017): 1525. http://dx.doi.org/10.1071/an16492.
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A priori knowledge of seasonal pasture growth rates helps livestock farmers plan with pasture supply and feed budgeting. Longer forecasts may allow managers more lead time, yet inaccurate forecasts could lead to counterproductive decisions and foregone income. By using climate forecasts generated from historical archives or the global circulation model (GCM) called the Predictive Ocean Atmosphere Model for Australia (POAMA), we simulated pasture growth rates in a whole-farm model and compared growth-rate forecasts with growth-rate hindcasts (viz. retrospective forecasts). Hindcast pasture growth rates were generated using posterior weather data measured at two sites in north-western Tasmania, Australia. Forecasts were made on a monthly basis for durations of 30, 60 and 90 days. Across sites, forecasting approaches and durations, there were no significant differences between simulated growth-rate forecasts and hindcasts when our statistical inference was conducted using either the Kolmogorov–Smirnov statistic or empirical cumulative distribution functions. However, given that both of these tests were calculated by comparing growth-rate hindcasts with monthly distributions of forecasts, we also examined linear correlations between monthly hindcast values and median monthly growth-rate forecasts. Using this approach, we found a higher correlation between hindcasts and median monthly forecasts for 30 days than for 60 or 90 days, suggesting that monthly growth-rate forecasts provide more skilful predictions than forecast durations of 2 or 3 months. The range in monthly growth-rate forecasts at 30 days was less than that at 60 or 90 days, further reinfocing the aforementioned result. The strength of the correlation between growth-rate hindcasts and median monthly forecasts from the historical approach was similar to that generated using POAMA data. Overall, the present study found that (1) statistical methods of comparing forecast data with hindcast data are important, particularly if the former is a distribution whereas the latter is a single value, (2) 1-month growth-rate forecasts have less uncertainty than forecast durations of 2 or 3 months, and (3) there is little difference between pasture growth rates simulated using climate data from either historical records or from GCMs. To test the generality of these conclusions, the study should be extended to other dairy regions. Including more regions would both enable studies of sites with greater intra-seasonal climate variability, but also better highlight the impact of seasonal and regional variation in forecast skill of POAMA as applied in our forecasting methods.
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Koster, R. D., and G. K. Walker. "Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts." Journal of Hydrometeorology 16, no. 4 (July 29, 2015): 1456–65. http://dx.doi.org/10.1175/jhm-d-14-0205.1.
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Abstract The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations—as well as quantifying the inherent predictability of temperature within each ensemble—shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.
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Hou, Zhaolu, Jianping Li, and Bin Zuo. "Correction of Monthly SST Forecasts in CFSv2 Using the Local Dynamical Analog Method." Weather and Forecasting 36, no. 3 (June 2021): 843–58. http://dx.doi.org/10.1175/waf-d-20-0123.1.
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AbstractNumerical seasonal forecasts in Earth science always contain forecast errors that cannot be eliminated by improving the ability of the numerical model. Therefore, correction of model forecast results is required. Analog correction is an effective way to reduce model forecast errors, but the key question is how to locate analogs. In this paper, we updated the local dynamical analog (LDA) algorithm to find analogs and depicted the process of model error correction as the LDA correction scheme. The LDA correction scheme was first applied to correct the operational seasonal forecasts of sea surface temperature (SST) over the period 1982–2018 from the state-of-the-art coupled climate model named NCEP Climate Forecast System, version 2. The results demonstrated that the LDA correction scheme improves forecast skill in many regions as measured by the correlation coefficient and root-mean-square error, especially over the extratropical eastern Pacific and tropical Pacific, where the model has high simulation ability. El Niño–Southern Oscillation (ENSO) as the focused physics process is also improved. The seasonal predictability barrier of ENSO is in remission, and the forecast skill of central Pacific ENSO also increases due to the LDA correction method. The intensity of the ENSO mature phases is improved. Meanwhile, the ensemble forecast results are corrected, which proves the positive influence from this LDA correction scheme on the probability forecast of cold and warm events. Overall, the LDA correction scheme, combining statistical and model dynamical information, is demonstrated to be readily integrable with other advanced operational models and has the capability to improve forecast results.
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Rodwell, Mark J., and Francisco J. Doblas-Reyes. "Medium-Range, Monthly, and Seasonal Prediction for Europe and the Use of Forecast Information." Journal of Climate 19, no. 23 (December 1, 2006): 6025–46. http://dx.doi.org/10.1175/jcli3944.1.
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Abstract Operational probabilistic (ensemble) forecasts made at ECMWF during the European summer heat wave of 2003 indicate significant skill on medium (3–10 day) and monthly (10–30 day) time scales. A more general “unified” analysis of many medium-range, monthly, and seasonal forecasts confirms a high degree of probabilistic forecast skill for European temperatures over the first month. The unified analysis also identifies seasonal predictability for Europe, which is not yet realized in seasonal forecasts. Interestingly, the initial atmospheric state appears to be important even for month 2 of a coupled forecast. Seasonal coupled model forecasts capture the general level of observed European deterministic predictability associated with the persistence of anomalies. A review is made of the possibilities to improve seasonal forecasts. This includes multimodel and probabilistic techniques and the potential for “windows of opportunity” where better representation of the effects of boundary conditions (e.g., sea surface temperature and soil moisture) may improve forecasts. “Perfect coupled model” potential predictability estimates are sensitive to the coupled model used and so it is not yet possible to estimate ultimate levels of seasonal predictability. The impact of forecast information on different users with different mitigation strategies (i.e., ways of coping with a weather or climate event) is investigated. The importance of using forecast information to reduce volatility as well as reducing the expected expense is highlighted. The possibility that weather forecasts can affect the cost of mitigating actions is considered. The simplified analysis leads to different conclusions about the usefulness of forecasts that could guide decisions about the development of “end-to-end” (forecast-to-user decision) systems.
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Smith, Thomas M., Samuel S. P. Shen, and Ralph R. Ferraro. "Superensemble Statistical Forecasting of Monthly Precipitation over the Contiguous United States, with Improvements from Ocean-Area Precipitation Predictors." Journal of Hydrometeorology 17, no. 10 (October 1, 2016): 2699–711. http://dx.doi.org/10.1175/jhm-d-16-0018.1.
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Abstract Extended precipitation forecasts, with leads of weeks to seasons, are valuable for planning water use and are produced by the U.S. National Weather Service. Forecast skill tends to be low and any skill improvement could be valuable. Here, methods are discussed for improving statistical precipitation forecasting over the contiguous United States. Monthly precipitation is forecast using predictors from the previous month. Testing shows that improvements are obtained from both improved statistical methods and from the use of satellite-based ocean-area precipitation predictors. The statistical superensemble method gives higher skill compared to traditional statistical forecasting. Ensemble statistical forecasting combines individual forecasts. The proposed superensemble is a weighted mean of many forecasts or of forecasts from different prediction systems and uses the forecast reliability estimate to define weights. The method is tested with different predictors to show its skill and how skill can be improved using additional predictors. Cross validation is used to evaluate the skill. Although predictions are strongly influenced by ENSO, in the superensemble other regions contribute more to the forecast skill. The superensemble optimally combines forecasts based on different predictor regions and predictor types. The contribution from multiple predictor regions improves skill and reduces the ENSO spring barrier. Adding satellite-based ocean-area precipitation predictors noticeably increases forecast skill. The resulting skill is comparable to that from dynamic-model forecasts, but the regions with best forecast skill may be different. This paper shows that the statistical superensemble forecasts may be complementary to dynamic forecasts and that combining them may further increase forecast skill.
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Williams, Daniel W., and Shayne C. Kavanagh. "Local government revenue forecasting methods: competition and comparison." Journal of Public Budgeting, Accounting & Financial Management 28, no. 4 (March 1, 2016): 488–526. http://dx.doi.org/10.1108/jpbafm-28-04-2016-b004.
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This study examines forecast accuracy associated with the forecast of 55 revenue data series of 18 local governments. The last 18 months (6 quarters; or 2 years) of the data are held-out for accuracy evaluation. Results show that forecast software, damped trend methods, and simple exponential smoothing methods perform best with monthly and quarterly data; and use of monthly or quarterly data is marginally better than annualized data. For monthly data, there is no advantage to converting dollar values to real dollars before forecasting and reconverting using a forecasted index. With annual data, naïve methods can outperform exponential smoothing methods for some types of data; and real dollar conversion generally outperforms nominal dollars. The study suggests benchmark forecast errors and recommends a process for selecting a forecast method.
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Mastrangelo, D., P. Malguzzi, C. Rendina, O. Drofa, and A. Buzzi. "First outcomes from the CNR-ISAC monthly forecasting system." Advances in Science and Research 8, no. 1 (April 16, 2012): 77–82. http://dx.doi.org/10.5194/asr-8-77-2012.
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Abstract. A monthly probabilistic forecasting system is experimentally operated at the ISAC institute of the National Council of Research of Italy. The forecasting system is based on GLOBO, an atmospheric general circulation model developed at the same institute. The model is presently run on a monthly basis to produce an ensemble of 32 forecasts initialized with GFS-NCEP perturbed analyses. Reforecasts, initialized with ECMWF ERA-Interim reanalyses of the 1989–2009 period, are also produced to determine modelled climatology of the month to forecast. The modelled monthly climatology is then used to calibrate the ensemble forecast of daily precipitation, geopotential height and temperature on standard pressure levels. In this work, we present the forecasting system and a preliminary evaluation of the model systematic and forecast errors in terms of non-probabilistic scores of the 500-hPa geopotential height. Results show that the proposed forecasting system outperforms the climatology in the first two weeks of integrations. The adopted calibration based on weighted bias correction is found to reduce the systematic and the forecast errors.
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Mohd-Lair, Noor Ajian, Jia Kit Hong, and Mohd Suffian Misaran. "The Linear Regression vs. Additive Forecast Techniques in Predicting Palm Oil Estate Monthly Delivery Quantity." Applied Mechanics and Materials 465-466 (December 2013): 1127–32. http://dx.doi.org/10.4028/www.scientific.net/amm.465-466.1127.
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The quantity of palm oil fruits supplied from palm oil estates often affects the number of workers required and the area to be harvested. Thus, the ultimate objective of this research is to develop a system to forecast monthly delivery quantities such that the companys profit will increase through proper balance between supply and demand. This research is limited to 10 years of monthly deliveries from a palm oil estates deliver to only one palm oil mill as the case study. Two forecast techniques were chosen; the linear regression and additive forecast methods. Based on theories and formulations of the selected forecast techniques, forecast software was developed. For this software, user only needs to specify the year to be forecasted and choose one forecast technique to be used. Then, the forecasted values and errors were calculated and the results were displayed on the GUI. The performance of each technique was compared based on the mean absolute percentage error (MAPE). The generated results showed that the additive method produced lower MAPE compared to the linear regression method. This proved that the additive method is a better technique to predict the monthly delivery quantities of the palm fruits by the estate.
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Lee, Y. W., K. G. Tay, and Y. Y. Choy. "Forecasting Electricity Consumption Using Time Series Model." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 218. http://dx.doi.org/10.14419/ijet.v7i4.30.22124.
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Electricity demand forecasting is important for planning and facility expansion in the electricity sector. Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development. Universiti Tun Hussein Onn Malaysia (UTHM) which is a developing university in Malaysia has been growing since its formation in 1993. Thus, it is important for UTHM to forecast the electricity consumption in future so that the future development can be determined. Hence, UTHM electricity consumption was forecasted by using the simple moving average (SMA), weighted moving average (WMA), simple exponential smoothing (SES), Holt linear trend (HL), Holt-Winters (HW) and centered moving average (CMA). The monthly electricity consumption from January 2011 to December 2017 was used to forecast January to December 2018 monthly electricity consumption. HW gives the smallest mean absolute error (MAE) and mean absolute percentage error (MAPE), while CMA produces the lowest mean square error (MSE) and root mean square error (RMSE). As there is a decreasing population of UTHM after the moving of four faculties to Pagoh and HW forecasted trend is decreasing whereas CMA is increasing, hence HW might forecast better in this problem.
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Schepen, Andrew, Tongtiegang Zhao, Q. J. Wang, Senlin Zhou, and Paul Feikema. "Optimising seasonal streamflow forecast lead time for operational decision making in Australia." Hydrology and Earth System Sciences 20, no. 10 (October 10, 2016): 4117–28. http://dx.doi.org/10.5194/hess-20-4117-2016.
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Abstract. Statistical seasonal forecasts of 3-month streamflow totals are released in Australia by the Bureau of Meteorology and updated on a monthly basis. The forecasts are often released in the second week of the forecast period, due to the onerous forecast production process. The current service relies on models built using data for complete calendar months, meaning the forecast production process cannot begin until the first day of the forecast period. Somehow, the bureau needs to transition to a service that provides forecasts before the beginning of the forecast period; timelier forecast release will become critical as sub-seasonal (monthly) forecasts are developed. Increasing the forecast lead time to one month ahead is not considered a viable option for Australian catchments that typically lack any predictability associated with snowmelt. The bureau's forecasts are built around Bayesian joint probability models that have antecedent streamflow, rainfall and climate indices as predictors. In this study, we adapt the modelling approach so that forecasts have any number of days of lead time. Daily streamflow and sea surface temperatures are used to develop predictors based on 28-day sliding windows. Forecasts are produced for 23 forecast locations with 0–14- and 21-day lead time. The forecasts are assessed in terms of continuous ranked probability score (CRPS) skill score and reliability metrics. CRPS skill scores, on average, reduce monotonically with increase in days of lead time, although both positive and negative differences are observed. Considering only skilful forecast locations, CRPS skill scores at 7-day lead time are reduced on average by 4 percentage points, with differences largely contained within +5 to −15 percentage points. A flexible forecasting system that allows for any number of days of lead time could benefit Australian seasonal streamflow forecast users by allowing more time for forecasts to be disseminated, comprehended and made use of prior to the commencement of a forecast season. The system would allow for forecasts to be updated if necessary.
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Xu, Hui Jun. "Wavelet ANN Based Monthly Runoff Forecast." Applied Mechanics and Materials 421 (September 2013): 803–7. http://dx.doi.org/10.4028/www.scientific.net/amm.421.803.
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A wavelet artificial neural network to forecasting monthly runoff is proposed. The monthly runoff series is firstly decomposed to sub-series on different time scales, and each sub-series is modeled. The weights of the network are replaced by wavelet functions and are corrected by conjugate gradient method in the training iteration. Then the proposed network is trained with 49 years (1952-2000) actual data of one hydro power plant of Jiangxi province and is tested for target year (2001-2003). Finally, some actual results for mid and long term water inflow forecasting are obtained and which show the proposed method has a good precision for forecasting.
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何, 绍坤. "Probability Forecast of Monthly Reservoir Inflow." Journal of Water Resources Research 06, no. 01 (2017): 1–8. http://dx.doi.org/10.12677/jwrr.2017.61001.
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Reichler, Thomas, and John O. Roads. "Long-Range Predictability in the Tropics. Part I: Monthly Averages." Journal of Climate 18, no. 5 (March 1, 2005): 619–33. http://dx.doi.org/10.1175/jcli-3294.1.
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Abstract The sensitivity to initial and boundary conditions of monthly mean tropical long-range forecasts (1–14 weeks) during Northern Hemisphere winter is studied with a numerical model. Five predictability experiments with different combinations of initial conditions and prescribed ocean boundary conditions are conducted in order to investigate the temporal and spatial characteristics of the perfect model forecast skill. It is shown that initial conditions dominate a tropical forecast during the first 3 weeks and that they influence a forecast for at least 8 weeks. The initial condition effect is strongest over the Eastern Hemisphere and during years when the El Niño–Southern Oscillation (ENSO) phenomenon is weak. The relatively long sensitivity to initial conditions is related to a complex combination of dynamic and thermodynamic effects, and to positive internal feedbacks of large-scale convective anomalies. At lead times of more than 3 weeks, boundary forcing is the main contributor to tropical predictability. This effect is particularly strong over the Western Hemisphere and during ENSO. Using persisted instead of observed sea surface temperatures leads to useful forecast results only over the Western Hemisphere and during ENSO.
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Dirmeyer, Paul A., and Trent W. Ford. "A Technique for Seamless Forecast Construction and Validation from Weather to Monthly Time Scales." Monthly Weather Review 148, no. 9 (August 12, 2020): 3589–603. http://dx.doi.org/10.1175/mwr-d-19-0076.1.
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Abstract Seamless prediction means bridging discrete short-term weather forecasts valid at a specific time and time-averaged forecasts at longer periods. Subseasonal predictions span this time range and must contend with this transition. Seamless forecasts and seamless validation methods go hand-in-hand. Time-averaged forecasts often feature a verification window that widens in time with growing forecast leads. Ideally, a smooth transition across daily to monthly time scales would provide true seamlessness—a generalized approach is presented here to accomplish this. We discuss prior attempts to achieve this transition with individual weighting functions before presenting the two-parameter Hill equation as a general weighting function to blend discrete and time-averaged forecasts, achieving seamlessness. The Hill equation can be tuned to specify the lead time at which the discrete forecast loses dominance to time-averaged forecasts, as well as the swiftness of the transition with lead time. For this application, discrete forecasts are defined at any lead time using a Kronecker delta weighting, and any time-averaged weighting approach can be used at longer leads. Time-averaged weighting functions whose averaging window widens with lead time are used. Example applications are shown for deterministic and ensemble forecasts and validation and a variety of validation metrics, along with sensitivities to parameter choices and a discussion of caveats. This technique aims to counterbalance the natural increase in uncertainty with forecast lead. It is not meant to construct forecasts with the highest skill, but to construct forecasts with the highest utility across time scales from weather to subseasonal in a single seamless product.
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Schneider, J. M., J. D. Garbrecht, and D. A. Unger. "A Heuristic Method for Time Disaggregation of Seasonal Climate Forecasts." Weather and Forecasting 20, no. 2 (April 1, 2005): 212–21. http://dx.doi.org/10.1175/waf839.1.
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Abstract To be immediately useful in practical applications that employ daily weather generators, seasonal climate forecasts issued for overlapping 3-month periods need to be disaggregated into a sequence of 1-month forecasts. Direct linear algebraic approaches to disaggregation produce physically unrealistic sequences of monthly forecasts. As an alternative, a heuristic method has been developed to disaggregate the NOAA/Climate Prediction Center (CPC) probability of exceedance seasonal precipitation forecasts, and tested on observed precipitation data for 1971–2000 for the 102 forecast divisions covering the contiguous United States. This simple method produces monthly values that replicate the direction and amplitude of variations on the 3-month time scale, and approach the amplitude of variations on the 1-month scale, without any unrealistic behavior. Root-mean-square errors between the disaggregated values and the actual precipitation over the 30-yr test period and all forecast divisions averaged 0.94 in., which is 39% of the mean monthly precipitation, and 58% of the monthly standard deviation. This method performs equally well across widely different precipitation regimes and does a reasonable job reproducing the sudden onset of strong seasonal variations such as the southwest U.S. monsoon.
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Koster, Randal D., Max J. Suarez, Ping Liu, Urszula Jambor, Aaron Berg, Michael Kistler, Rolf Reichle, Matthew Rodell, and Jay Famiglietti. "Realistic Initialization of Land Surface States: Impacts on Subseasonal Forecast Skill." Journal of Hydrometeorology 5, no. 6 (December 1, 2004): 1049–63. http://dx.doi.org/10.1175/jhm-387.1.
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Abstract Forcing a land surface model (LSM) offline with realistic global fields of precipitation, radiation, and near-surface meteorology produces realistic fields (within the context of the LSM) of soil moisture, temperature, and other land surface states. These fields can be used as initial conditions for precipitation and temperature forecasts with an atmospheric general circulation model (AGCM). Their usefulness is tested in this regard by performing retrospective 1-month forecasts (for May through September, 1979–93) with the NASA Global Modeling and Assimilation Office (GMAO) seasonal prediction system. The 75 separate forecasts provide an adequate statistical basis for quantifying improvements in forecast skill associated with land initialization. Evaluation of skill is focused on the Great Plains of North America, a region with both a reliable land initialization and an ability of soil moisture conditions to overwhelm atmospheric chaos in the evolution of the meteorological fields. The land initialization does cause a small but statistically significant improvement in precipitation and air temperature forecasts in this region. For precipitation, the increases in forecast skill appear strongest in May through July, whereas for air temperature, they are largest in August and September. The joint initialization of land and atmospheric variables is considered in a supplemental series of ensemble monthly forecasts. Potential predictability from atmospheric initialization dominates over that from land initialization during the first 2 weeks of the forecast, whereas during the final 2 weeks, the relative contributions from the two sources are of the same order. Both land and atmospheric initialization contribute independently to the actual skill of the monthly temperature forecast, with the greatest skill derived from the initialization of both. Land initialization appears to contribute the most to monthly precipitation forecast skill.
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M.N, Harinarayanan, Manivannan V, Ga Dheebakaran, and Guna M. "Usability of monthly ERFS (Extended Range Forecast System) to predict maize yield using DSSAT (Decision Support System for Agro-technology Transfer) model over Erode District of Tamil Nadu." Journal of Applied and Natural Science 14, SI (July 15, 2022): 244–50. http://dx.doi.org/10.31018/jans.v14isi.3709.
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Extended Range of Forecast Service (ERFS) is highly useful for planning of cropping season and midterm correction at the farm level. The medium-range and long-range forecast validation have many studies, whereas ERF has less that needs to be studied. Maize is an important field crop in India after rice and wheat. Therefore, the prediction of maize yield has significant importance. In the present study, ERFS data were validated by correlation analysis using monthly observed rainfall frequency and intensity. This data was imported to DSSAT (Decision Support System for Agro-technology Transfer) to simulate maize yield of Erode district of Tamil Nadu. The model output and actual yield data from Erode were compared. Forecasted monthly total rainfall was correlated at a rate of 0.97r value with that observed. Yield simulation of maize was done using DSSAT by integrating ERFS data and the observed monthly data. Mean per cent deviation among the yields of observed weather and the disaggregated one tended to be -15.7 %. The average deviation between the yields of ERF forecasted weather data and actual yield was very high ( -29.7 % ) for Erode. Mean % deviation between the yields of observed weather and the actual yield was -14.7 %. Downscaled and accurate weather forecasts could be facilitated for yield prediction of crops by DSSAT model. Yield prediction by the model under observed weather was convenient and usable. Model under-predicted the yields when using ERF data. Both model and ERF forecast need to be improved further for higher resolution.
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Gu, X., and J. Jiang. "A complex autoregressive model and application to monthly temperature forecasts." Annales Geophysicae 23, no. 10 (November 30, 2005): 3229–35. http://dx.doi.org/10.5194/angeo-23-3229-2005.
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Abstract. A complex autoregressive model was established based on the mathematic derivation of the least squares for the complex number domain which is referred to as the complex least squares. The model is different from the conventional way that the real number and the imaginary number are separately calculated. An application of this new model shows a better forecast than forecasts from other conventional statistical models, in predicting monthly temperature anomalies in July at 160 meteorological stations in mainland China. The conventional statistical models include an autoregressive model, where the real number and the imaginary number are separately disposed, an autoregressive model in the real number domain, and a persistence-forecast model.
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Graf, Renata, and Viktor Vyshnevskyi. "Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions." Resources 11, no. 12 (November 30, 2022): 111. http://dx.doi.org/10.3390/resources11120111.
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River-flow forecasts are important for the management and planning of water resources and their rational use. The present study, based on direct multistep-ahead forecasting with multiple time series specific to the XGBoost algorithm, estimates the long-term changes and forecast monthly flows of selected rivers in Ukraine. In a new, applied approach, a single multioutput model was proposed that forecasts over both short- and long-term horizons using grouped or hierarchical data series. Three forecast stages were considered: using train and test subsets, using a model with train-test data, and training with all data. The historical period included the measurements of the monthly flows, precipitation, and air temperature in the period 1961–2020. The forecast horizons of 12, 60, and 120 months into the future were selected for this dataset, i.e., December 2021, December 2025, and December 2030. The research was conducted for diverse hydrological systems: the Prut, a mountain river; the Styr, an upland river; and the Sula, a lowland river in relation to the variability and forecasts of precipitation and air temperature. The results of the analyses showed a varying degree of sensitivity among rivers to changes in precipitation and air temperature and different projections for future time horizons of 12, 60, and 120 months. For all studied rivers, variable dynamics of flow was observed in the years 1961–2020, yet with a clearly marked decrease in monthly flows during in the final, 2010–2020 decade. The last decade of low flows on the Prut and Styr rivers was preceded by their noticeable increase in the earlier decade (2000–2010). In the case of the Sula River, a continuous decrease in monthly flows has been observed since the end of the 1990s, with a global minimum in the decade 2010–2020. Two patterns were obtained in the forecasts: a decrease in flow for the rivers Prut (6%) and the Styr (12–14%), accompanied by a decrease in precipitation and an increase in air temperature until 2030, and for the Sula River, an increase in flow (16–23%), with a slight increase in precipitation and an increase in air temperature. The predicted changes in the flows of the Prut, the Styr, and the Sula rivers correspond to forecasts in other regions of Ukraine and Europe. The performance of the models over a variety of available datasets over time was assessed and hyperparameters, which minimize the forecast error over the relevant forecast horizons, were selected. The obtained RMSE parameter values indicate high variability in hydrological and meteorological data in the catchment areas and not very good fit of retrospective data regardless of the selected horizon length. The advantages of this model, which was used in the work for forecasting monthly river flows in Ukraine, include modelling multiple time series simultaneously with a single model, the simplicity of the modelling, potentially more-robust results because of pooling data across time series, and solving the “cold start” problem when few data points were available for a given time series. The model, because of its universality, can be used in forecasting hydrological and meteorological parameters in other catchments, irrespective of their geographic location.
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Preisler, Haiganoush K., and Anthony L. Westerling. "Statistical Model for Forecasting Monthly Large Wildfire Events in Western United States." Journal of Applied Meteorology and Climatology 46, no. 7 (July 1, 2007): 1020–30. http://dx.doi.org/10.1175/jam2513.1.
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Abstract The ability to forecast the number and location of large wildfire events (with specified confidence bounds) is important to fire managers attempting to allocate and distribute suppression efforts during severe fire seasons. This paper describes the development of a statistical model for assessing the forecasting skills of fire-danger predictors and producing 1-month-ahead wildfire-danger probabilities in the western United States. The method is based on logistic regression techniques with spline functions to accommodate nonlinear relationships between fire-danger predictors and probability of large fire events. Estimates were based on 25 yr of historic fire occurrence data (1980–2004). The model using the predictors monthly average temperature, and lagged Palmer drought severity index demonstrated significant improvement in forecasting skill over historic frequencies (persistence forecasts) of large fire events. The statistical models were particularly amenable to model evaluation and production of probability-based fire-danger maps with prespecified precisions. For example, during the 25 yr of the study for the month of July, an area greater than 400 ha burned in 3% of locations where the model forecast was low; 11% of locations where the forecast was moderate; and 76% of locations where the forecast was extreme. The statistical techniques may be used to assess the skill of forecast fire-danger indices developed at other temporal or spatial scales.
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Delgado, José Miguel, Sebastian Voss, Gerd Bürger, Klaus Vormoor, Aline Murawski, José Marcelo Rodrigues Pereira, Eduardo Martins, Francisco Vasconcelos Júnior, and Till Francke. "Seasonal drought prediction for semiarid northeastern Brazil: verification of six hydro-meteorological forecast products." Hydrology and Earth System Sciences 22, no. 9 (September 28, 2018): 5041–56. http://dx.doi.org/10.5194/hess-22-5041-2018.
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Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.
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PAI, D. S., O. P. SREEJITH, S. G. NARGUND, MADHURI MUSALE, and AJIT TYAGI. "Present operational long range forecasting system for southwest monsoon rainfall over India and its performance during 2010." MAUSAM 62, no. 2 (December 14, 2021): 179–96. http://dx.doi.org/10.54302/mausam.v62i2.283.
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At present, India Meteorological Department (IMD) issues various monthly and seasonal operational forecasts for the south-west monsoon season using models based on latest statistical techniques with useful skill. Operational models are reviewed regularly and improved through in house research activities. For the forecasting of the south-west monsoon season (June – September) rainfall over the country as a whole, a newly introduced statistical ensemble forecasting system is used. In addition, models have been developed for the forecast of the monsoon season rainfall over four geographical regions (NW India, NE India, Central India and South Peninsula) of the country and forecast for the rainfall over the second half of the monsoon season over the country as a whole. Models have also been developed for issuing operational forecast for the monthly rainfall for the months of July, August & September over the country as a whole. Operational forecasts issued by IMD for 2010 south-west monsoon rainfall have been discussed and verified. In addition, the experimental forecasts for the season rainfall over the country as a whole based on bothstatistical and dynamical models received from various climate research institutes within the country other than IMD arealso discussed.
The operational monthly and seasonal long range forecasts issued for the 2010 southwest monsoon season for the country as a whole were accurate. However, forecasts for the season rainfall over the 4 geographical regions (Northwest India, Central India, Northeast India and south Peninsular India) were not accurate as the forecast for South Peninsular India overestimated the actual rainfall and that for northeast India underestimated the actual rainfall. The experimental forecasts for the season rainfall over the country as whole from various climate research institutes within the country showed large variance (91 % - 112% of LPA).
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Chen, Mingyue, Wanqiu Wang, and Arun Kumar. "Prediction of Monthly-Mean Temperature: The Roles of Atmospheric and Land Initial Conditions and Sea Surface Temperature." Journal of Climate 23, no. 3 (February 1, 2010): 717–25. http://dx.doi.org/10.1175/2009jcli3090.1.
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Abstract Using the retrospective forecasts from the National Centers for Environmental Prediction (NCEP) coupled atmosphere–ocean Climate Forecast System (CFS) and the Atmospheric Model Intercomparison Project (AMIP) simulations from its uncoupled atmospheric component, the NCEP Global Forecast System (GFS), the relative roles of atmospheric and land initial conditions and the lower boundary condition of sea surface temperatures (SSTs) for the prediction of monthly-mean temperature are investigated. The analysis focuses on the lead-time dependence of monthly-mean prediction skill and its asymptotic value for longer lead times, which could be attributed the atmospheric response to the slowly varying SST. The results show that the observed atmospheric and land initial conditions improve the skill of monthly-mean prediction in the extratropics but have little influence in the tropics. However, the influence of initial atmospheric and land conditions in the extratropics decays rapidly. For 30-day-lead predictions, the global-mean forecast skill of monthly means is found to reach an asymptotic value that is primarily determined by the SST anomalies. The lead time at which initial conditions lose their influence varies spatially. In addition, the initial atmospheric and land conditions are found to have longer impacts in northern winter and spring than in summer and fall. The relevance of the results for constructing lagged ensemble forecasts is discussed.
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Bennett, James C., Quan J. Wang, David E. Robertson, Andrew Schepen, Ming Li, and Kelvin Michael. "Assessment of an ensemble seasonal streamflow forecasting system for Australia." Hydrology and Earth System Sciences 21, no. 12 (November 30, 2017): 6007–30. http://dx.doi.org/10.5194/hess-21-6007-2017.
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Abstract. Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean–land–atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall–runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( < 4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall–runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall–runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.
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PURI, SR, SN KATHURIA, DS UPADHYAY, and SURENDRA KUMAR. "A stochastic approach for monthly streamflow forecast." MAUSAM 39, no. 1 (January 1, 1988): 65–70. http://dx.doi.org/10.54302/mausam.v39i1.3188.
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A multiplicative seasonal ARIMA model of order (0, 1, 1) X (0, 1, 1) has been applied for the prediction of monthly flow (x) in Sutlej at Bhakra dam site, using 40 years (1925-64) discharge data.
The accuracy of prediction has been tested by using seven years (1965-71) observations. The root mean square error for the forecast period was calculated. It is found that the root mean square error varies from 3 per cent 1n December to about 43 per cent In September.
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Bai, Yun, Pu Wang, Jingjing Xie, Jiangtao Li, and Chuan Li. "Additive Model for Monthly Reservoir Inflow Forecast." Journal of Hydrologic Engineering 20, no. 7 (July 2015): 04014079. http://dx.doi.org/10.1061/(asce)he.1943-5584.0001101.
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FUDZI, FAIQAH MOHAMAD, ZAHAYU MD YUSOF, and MASNITA MISIRAN. "RAINFALL FORECASTING WITH TIME SERIES MODEL IN ALOR SETAR, KEDAH." Universiti Malaysia Terengganu Journal of Undergraduate Research 3, no. 1 (January 31, 2021): 37–44. http://dx.doi.org/10.46754/umtjur.v3i1.190.
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The prediction of rainfall on monthly and seasonal time scales is not only scientifically challenging but is also important for planning and devising agricultural strategies. In this paper, the study is conducted to examine the pattern of monthly rainfall in Alor Setar, Kedah within ten years which is from 2008 to 2018. This paper considered a model based on real data that obtained from Department of Meteorology Malaysia. This study indicates that the monthly rainfall in Alor Setar has a seasonal and trend pattern based on yt vs t plotting, autocorrelation function and Kruskal Wallis Test for seasonality. The examined rainfall time-series modelling approaches include Naïve Model, Decomposition Method, Holt-Winter’s and Box-Jenkins ARIMA. Multiplicative Decomposition Method was identified as the best model to forecast rainfall for the year of 2019 by analysing the previous ten-year’s data (2008-2018). As a result from the forecast of 2019, October is the wettest month with highest forecasted rainfall of 276.15mm while the driest month is in February with lowest forecasted rainfall of 50.55mm. The model is therefore adequate and appropriate to forecast future monthly rainfall values in the catchment which can help farmers to plan their farming activities ahead of time.
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Gibbs, Matthew S., David McInerney, Greer Humphrey, Mark A. Thyer, Holger R. Maier, Graeme C. Dandy, and Dmitri Kavetski. "State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application." Hydrology and Earth System Sciences 22, no. 1 (February 1, 2018): 871–87. http://dx.doi.org/10.5194/hess-22-871-2018.
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Abstract. Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall–runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.
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Lee Drbohlav, Hae-Kyung, and V. Krishnamurthy. "Spatial Structure, Forecast Errors, and Predictability of the South Asian Monsoon in CFS Monthly Retrospective Forecasts." Journal of Climate 23, no. 18 (September 15, 2010): 4750–69. http://dx.doi.org/10.1175/2010jcli2356.1.
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Abstract The spatial structure of the boreal summer South Asian monsoon in the ensemble mean of monthly retrospective forecasts by the Climate Forecast System of the National Centers for Environmental Prediction is examined. The forecast errors and predictability of the model are assessed. Systematic errors in the forecasts consist of deficient rainfall over India, excess rainfall over the Arabian Sea, and a dipole structure over the equatorial Indian Ocean. On interannual time scale during 1981–2003, two different characteristics of the monsoon are recognized—both in observation and forecasts. One feature seems to indicate that the monsoon is regionally controlled, while the other shows a strong relation with El Niño–Southern Oscillation (ENSO). The spatial structure of the regional monsoon can be characterized by the dominant rainfall between the latitudes of 15°N and 5°S in the western Indian Ocean. The maximum precipitation anomalies in the northern Arabian Sea are associated with the cyclonic circulation, while the precipitation anomalies in the equatorial western Indian Ocean accompany the easterlies over the equatorial Indian Ocean. In the ENSO-related monsoon, strong positive precipitation anomalies prevail from the equatorial eastern Indian Ocean to the western Pacific, inducing westerlies over the equatorial Indian Ocean. The spatial structure of the forecast error shows that the model is inclined to predict the ENSO-related feature more accurately than the regional feature. The predictability is found to be lower over certain areas in the northern and equatorial eastern Indian Ocean. The predictability errors in the northern Indian Ocean diminish for longer forecast leads, presumably because the impact of different initial conditions dissipates with time. On the other hand, predictability errors over the equatorial eastern Indian Ocean grow as the forecast lead increases.
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Chowdhury, Shahadat, and Ashish Sharma. "Global Sea Surface Temperature Forecasts Using a Pairwise Dynamic Combination Approach." Journal of Climate 24, no. 7 (April 1, 2011): 1869–77. http://dx.doi.org/10.1175/2010jcli3632.1.
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Abstract This paper dynamically combined three multivariate forecasts where spatially and temporally variant combination weights are estimated using a nearest-neighbor approach. The case study presented combines forecasts from three climate models for the period 1958–2001. The variables of interest here are the monthly global sea surface temperature anomalies (SSTA) at a 5° × 5° latitude–longitude grid, predicted 3 months in advance. The forecast from the static weight combination is used as the base case for comparison. The forecasted sea surface temperature using the dynamic combination algorithm offers consistent improvements over the static combination approach for all seasons. This improved skill is achieved over at least 93% of the global grid cells, in four 10-yr independent validation segments. Dynamically combined forecasts reduce the mean-square error of the SSTA by at least 25% for 72% of the global grid cells when compared against the best-performing single forecast among the three climate models considered.
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Choudhary, A., P. Kumar, S. K. Sahu, C. Pradhan, S. K. Singh, M. Gašparović, A. Shukla, and A. K. Singh. "Time Series Simulation and Forecasting of Air Quality Using In-situ and Satellite-Based Observations Over an Urban Region." Nature Environment and Pollution Technology 21, no. 3 (September 6, 2022): 1137–48. http://dx.doi.org/10.46488/nept.2022.v21i03.018.
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Air quality is directly associated with the health of society. So, it becomes essential to forecast air pollution, which assumes an imperative part in air pollution warnings and control. A time-series simulation approach was adapted for the forecasting of monthly mean ambient air pollutants (PM2.5, O3, NO2) concentration and Aerosol Optical Depth (AOD) at an urban traffic site (Mathura Road, CSIR-CRRI) in New Delhi, India. Satellite-based aerosol loading (AOD550) retrieved from the Terra MODIS (Collection 6) enhanced Deep Blue (DB) algorithm was used for further analysis. The analysis considered the average monthly mean concentration of air pollutants and AOD between 2012-2017 and, simulates the concentrations of PM2.5, O3, NO2, and AOD for the same period and then forecasts air quality for the years 2020-2023. The forecasted results were validated with 24 months of in-situ and satellite data from 2018-to and 2019. In the year 2020, observed and simulated results are in lower agreement due to the shutdown of anthropogenic activities to combat pandemic situations. Otherwise, modeled and forecasted results are in good harmony with the in-situ and satellite observations. The results also signify that the time series Autoregressive Integrated Moving Average (ARIMA) modeling approach can be an effective and simple tool for air pollution simulation and future forecast. The results are evocative concerning the forecast of near future aerosol loading information and will also be profound to address the problems.
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McInerney, David, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Fitsum Woldemeskel, Narendra Tuteja, and George Kuczera. "Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too." Hydrology and Earth System Sciences 26, no. 21 (November 10, 2022): 5669–83. http://dx.doi.org/10.5194/hess-26-5669-2022.
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Abstract. Subseasonal streamflow forecasts inform a multitude of water management decisions, from early flood warning to reservoir operation. Seamless forecasts, i.e. forecasts that are reliable and sharp over a range of lead times (1–30 d) and aggregation timescales (e.g. daily to monthly) are of clear practical interest. However, existing forecast products are often non-seamless, i.e. developed and applied for a single timescale and lead time (e.g. 1 month ahead). If seamless forecasts are to be a viable replacement for existing non-seamless forecasts, it is important that they offer (at least) similar predictive performance at the timescale of the non-seamless forecast. This study compares forecasts from two probabilistic streamflow post-processing (QPP) models, namely the recently developed seamless daily Multi-Temporal Hydrological Residual Error (MuTHRE) model and the more traditional (non-seamless) monthly QPP model used in the Australian Bureau of Meteorology's dynamic forecasting system. Streamflow forecasts from both post-processing models are generated for 11 Australian catchments, using the GR4J hydrological model and pre-processed rainfall forecasts from the Australian Community Climate and Earth System Simulator – Seasonal (ACCESS-S) numerical weather prediction model. Evaluating monthly forecasts with key performance metrics (reliability, sharpness, bias, and continuous ranked probability score skill score), we find that the seamless MuTHRE model achieves essentially the same performance as the non-seamless monthly QPP model for the vast majority of metrics and temporal stratifications (months and years). As such, MuTHRE provides the capability of seamless daily streamflow forecasts with no loss of performance at the monthly scale – the modeller can proverbially “have their cake and eat it too”. This finding demonstrates that seamless forecasting technologies, such as the MuTHRE post-processing model, are not only viable but also a preferred choice for future research development and practical adoption in streamflow forecasting.
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Vo Van, Hoa, Tien Du Duc, Hung Mai Khanh, Lars Robert Hole, Duc Tran Anh, Huyen Luong Thi Thanh, and Quan Dang Dinh. "Assessment of Seasonal Winter Temperature Forecast Errors in the RegCM Model over Northern Vietnam." Climate 8, no. 6 (June 14, 2020): 77. http://dx.doi.org/10.3390/cli8060077.
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This study verified the seasonal six-month forecasts for winter temperatures for northern Vietnam in 1998–2018 using a regional climate model (RegCM4) with the boundary conditions of the climate forecast system Version 2 (CFSv2) from the National Centers for Environmental Prediction (NCEP). First, different physical schemes (land-surface process, cumulus, and radiation parameterizations) in RegCM4 were applied to generate 12 single forecasts. Second, the simple ensemble forecasts were generated through the combinations of those different physical formulations. Three subclimate regions (R1, R2, R3) of northern Vietnam were separately tested with surface observations and a reanalysis dataset (Japanese 55-year reanalysis (JRA55)). The highest sensitivity to the mean monthly temperature forecasts was shown by the land-surface parameterizations (the biosphere−atmosphere transfer scheme (BATS) and community land model version 4.5 (CLM)). The BATS forecast groups tended to provide forecasts with lower temperatures than the actual observations, while the CLM forecast groups tended to overestimate the temperatures. The forecast errors from single forecasts could be clearly reduced with ensemble mean forecasts, but ensemble spreads were less than those root-mean-square errors (RMSEs). This indicated that the ensemble forecast was underdispersed and that the direct forecast from RegCM4 needed more postprocessing.
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Koster, Randal D., Thomas L. Bell, Rolf H. Reichle, Max J. Suarez, and Siegfried D. Schubert. "Using Observed Spatial Correlation Structures to Increase the Skill of Subseasonal Forecasts." Monthly Weather Review 136, no. 6 (June 1, 2008): 1923–30. http://dx.doi.org/10.1175/2007mwr2255.1.
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Abstract Model deficiencies limit a subseasonal or seasonal forecast system’s ability to produce accurate predictions. In this paper, an approach for transforming the output of a forecast system into a revised forecast is presented; it is designed to correct for some of the deficiencies in the system (particularly those associated with the spatial correlation structures of the forecasted fields) and thereby increase forecast skill. The approach, based on the joint consideration of the correlation structures present in the observational record and the inherent potential predictability of the model, is tested on a preexisting subseasonal forecast experiment. It is shown to produce modest but significant increases in the accuracy of forecasted precipitation and near-surface air temperature at monthly time scales.
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SHUKLA, A. K., Y. A. GARDE, and INA JAIN. "Forecast of weather parameters using time series data." MAUSAM 65, no. 4 (December 28, 2021): 509–20. http://dx.doi.org/10.54302/mausam.v65i4.1185.
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The present study is undertaken to develop area specific weather forecasting models based on time series data for Pantnagar, Uttarakhand. The study was carried out by using time series secondary monthly weather data of 27 years (from 1981-82 to 2007-08). The trend analysis of weather parameters was done by Mann-Kendall test statistics. The methodologies adopted to forecast weather parameters were the winter’s exponential smoothing model and Seasonal Autoregressive Integrated Moving Average (SARIMA). Comparative study has been carried out by using forecast error percentage and mean square error. The study showed that knowledge of this trend is likely to be helpful in planning and production of enterprises/crops. The study of forecast models revealed that SARIMA model is the most efficient model for forecasting of monthly maximum temperature, monthly minimum temperature and monthly humidity I. The Winter’s model was found to be the most efficient model for forecasting Monthly Humidity II but no model was found to be appropriate to forecast monthly total rainfall.
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Bai, Yun, Jingjing Xie, Xiaoxue Wang, and Chuan Li. "Model fusion approach for monthly reservoir inflow forecasting." Journal of Hydroinformatics 18, no. 4 (January 4, 2016): 634–50. http://dx.doi.org/10.2166/hydro.2016.141.
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Considering the complexity of reservoir systems, a model fusion approach is proposed in this paper. According to different inflow information represented, the historical monthly data can be constructed as two time series, namely, yearly-scale series and monthly-scale series. Even grey model (EGM) and adaptive neuro-fuzzy inference system (ANFIS) are adopted for the forecasts at the two scales, respectively. Grey relational analysis (GRA) is subsequently used as a scale-normalized model fusion tool to integrate the two scales' results. The proposed method is evaluated using the data of the Three Gorges reservoir ranging from January 2000 to December 2012. The forecast performances of the individual-scale models are improved substantially by the suggested method. For comparison, two peer models, back-propagation neural network and autoregressive integrated moving average model, are also involved. The results show that, having combined together the small-sample forecast ability of the EGM in the yearly-scale, the nonlinearity of the ANFIS in the monthly-scale, and the grey fusion capability of the GRA, the present approach is more accurate for holistic evaluation than those models in terms of mean absolute percentage error, normalized root-mean-square error, and correlation coefficient criteria, and also for peak inflow forecasting in accordance with peak percent threshold statistics.
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Tay, K. G., Y. Y. Choy, and C. C. Chew. "Forecasting Electricity Consumption Using Fuzzy Time Series." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 342. http://dx.doi.org/10.14419/ijet.v7i4.30.22305.
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Electricity consumption forecasting is important for effective operation, planning and facility expansion of power system. Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development. There is a great development of Universiti Tun Hussein Onn Malaysia (UTHM) infrastructure since its formation in 1993. The development will be accompanied with the increasing demand of electricity. Hence, there is a need to forecast the UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. Therefore, in this study, the Fuzzy time series (FTS) with trapezoidal membership function was implemented on the UTHM monthly electricity consumption from January 2011 to December 2017 to forecast January to December 2018 monthly electricity consumption. The procedure of the FTS and trapezoidal membership function was described together with January data. FTS is able to forecast UTHM electricity consumption quite well.
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Menda, Dhally M., Mukumbuta Nawa, Rosemary K. Zimba, Catherine M. Mulikita, Jim Mwandia, Henry Mwaba, and Karen Sichinga. "Forecasting Confirmed Malaria Cases in Northwestern Province of Zambia: A Time Series Analysis Using 2014–2020 Routine Data." Advances in Public Health 2021 (October 13, 2021): 1–8. http://dx.doi.org/10.1155/2021/6522352.
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Background. Malaria remains a significant public health problem, especially in resource-poor settings. We aimed to forecast the year 2021 monthly confirmed malaria cases in the northwestern province of Zambia. Methods. The total number of confirmed monthly malaria cases recorded at health facilities over the past 7-years period (January 2014 to December 2020) was taken from the District Health Information System version 2 (DHIS.2) database. Box–Jenkins autoregressive integrated moving average (ARIMA) was used to forecast monthly confirmed malaria cases for 2021. STATA software version 16 was used for analyzing the time series data. Results. Between 2014 and 2020, there were 3,795,541 confirmed malaria cases in the northwestern province with a monthly mean of 45,185 cases. ARIMA (2, 1, 2) (0, 1, 1)12 was the best fit and the most parsimonious model. The forecasted mean monthly confirmed malaria cases were 60,284 (95%CI 30,969–121,944), and the total forecasted confirmed malaria cases were 723,413 (95%CI 371,626–1,463,322) for the year 2021. Conclusion. The forecasted confirmed malaria cases suggest that there will be an increase in the number of confirmed malaria cases for the year 2021 in the northwestern province. Therefore, there is a need for concerted efforts to prevent and eliminate the disease if the goal to eliminate malaria in Zambia by 2030 is to be realized.