Development of statistical models for at-site probabilistic seasonal rainfall forecast

2011 ◽  
Vol 32 (14) ◽  
pp. 2197-2212 ◽  
Author(s):  
Gabriele Villarini ◽  
Francesco Serinaldi
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Itesh Dash ◽  
Masahiko Nagai ◽  
Indrajit Pal

A Multi-Model Ensemble (MME) based seasonal rainfall forecast customization tool called FOCUS was developed for Myanmar in order to provide improved seasonal rainfall forecast to the country. The tool was developed using hindcast data from 7 Global Climate Models (GCMs) and observed rainfall data from 49 meteorological surface observatories for the period of 1982 to 2011 from the Department of Meteorology and Hydrology. Based on the homogeneity in terms of the rainfall received annually, the country was divided into six climatological zones. Three different operational MME techniques, namely, (a) arithmetic mean (AM-MME), (b) weighted average (WA-MME), and (c) supervised principal component regression (PCR-MME), were used and built-in to the tool developed. For this study, all 7 GCMs were initialized with forecast data of May month to predict the rainfall during June to September (JJAS) period, which is the predominant rainfall season for Myanmar. The predictability of raw GCMs, bias-corrected GCMs, and the MMEs was evaluated using RMSE, correlation coefficients, and standard deviations. The probabilistic forecasts for the terciles were also evaluated using the relative operating characteristics (ROC) scores, to quantify the uncertainty in the GCMs. The results suggested that MME forecasts have shown improved performance (RMSE = 1.29), compared to the raw individual models (ECMWF, which is comparatively better among the selected models) with RMSE = 4.4 and bias-corrected RMSE = 4.3, over Myanmar. Specifically, WA-MME (CC = 0.64) and PCR-MME (CC = 0.68) methods have shown significant improvement in the high rainfall (delta) zone compared with WA-MME (CC = 0.57) and PCR-MME (CC = 0.56) techniques for the southern zone. The PCR method suggests higher predictability skill for the upper tercile (ROC = 0.78) and lower tercile categories (ROC = 0.85) for the delta region and is less skillful over lower rainfall zones like dry zones with ROC = 0.6 and 0.63 for upper and lower terciles, respectively. The model is thus suggested to perform relatively well over the higher rainfall (Wet) zones compared to the lower (Dry) zone during the JJAS period.


Climate ◽  
2015 ◽  
Vol 3 (3) ◽  
pp. 727-752 ◽  
Author(s):  
Abdouramane Djibo ◽  
Harouna Karambiri ◽  
Ousmane Seidou ◽  
Ketvara Sittichok ◽  
Nathalie Philippon ◽  
...  

2012 ◽  
Vol 25 (16) ◽  
pp. 5524-5537 ◽  
Author(s):  
Q. J. Wang ◽  
Andrew Schepen ◽  
David E. Robertson

Abstract Merging forecasts from multiple models has the potential to combine the strengths of individual models and to better represent forecast uncertainty than the use of a single model. This study develops a Bayesian model averaging (BMA) method for merging forecasts from multiple models, giving greater weights to better performing models. The study aims for a BMA method that is capable of producing relatively stable weights in the presence of significant sampling variability, leading to robust forecasts for future events. The BMA method is applied to merge forecasts from multiple statistical models for seasonal rainfall forecasts over Australia using climate indices as predictors. It is shown that the fully merged forecasts effectively combine the best skills of the models to maximize the spatial coverage of positive skill. Overall, the skill is low for the first half of the year but more positive for the second half of the year. Models in the Pacific group contribute the most skill, and models in the Indian and extratropical groups also produce useful and sometimes distinct skills. The fully merged probabilistic forecasts are found to be reliable in representing forecast uncertainty spread. The forecast skill holds well when forecast lead time is increased from 0 to 1 month. The BMA method outperforms the approach of using a model with two fixed predictors chosen a priori and the approach of selecting the best model based on predictive performance.


2021 ◽  
Vol 130 (1) ◽  
Author(s):  
Bathsheba Musonda ◽  
Yuanshu Jing ◽  
Matthews Nyasulu ◽  
Lucia Mumo

2013 ◽  
Vol 49 (11) ◽  
pp. 7681-7697 ◽  
Author(s):  
Diriba Korecha ◽  
Asgeir Sorteberg

2015 ◽  
Vol 36 (1) ◽  
pp. 439-454 ◽  
Author(s):  
Ester Salimun ◽  
Fredolin Tangang ◽  
Liew Juneng ◽  
Francis W. Zwiers ◽  
William J Merryfield

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