Does improved SSTA prediction ensure better seasonal rainfall forecasts?

2015 ◽  
Vol 51 (5) ◽  
pp. 3370-3383 ◽  
Author(s):  
Mohammad Zaved Kaiser Khan ◽  
Ashish Sharma ◽  
Rajeshwar Mehrotra ◽  
Andrew Schepen ◽  
Q. J. Wang
Keyword(s):  
Seasonal Rainfall ◽  
Seasonal Rainfall Forecasts

Can Farmers of Burkina Faso Use Seasonal Rainfall Forecasts?

2000 ◽  
Vol 22 (4) ◽  
pp. 24-28 ◽  
Author(s):  
Carla Roncoli ◽  
Keith Ingram ◽  
Paul Kirshen
Keyword(s):  
West Africa ◽  
Burkina Faso ◽  
Agricultural Production ◽  
Scientific Information ◽  
Seasonal Rainfall ◽  
Seasonal Forecasts ◽  
Recent Event ◽  
Livelihood Security ◽  
Research Findings ◽  
Seasonal Rainfall Forecasts

In this article we bring anthropological reflections to bear on a recent event we participated in, whereby farmers and scientists came together to discuss the possibility of applying rainfall seasonal forecasts to improve agricultural production and livelihood security in West Africa. In so doing, We also report on the research findings from the project that organized this encounter and that we have been working with for the last three years. Our intent is to highlight the complexities and challenges inherent in this process of integrating scientific information and farmers' production decisions, while also pointing to practical issues to be considered in implementing such initiatives.

scholarly journals Merging Seasonal Rainfall Forecasts from Multiple Statistical Models through Bayesian Model Averaging

2012 ◽  
Vol 25 (16) ◽  
pp. 5524-5537 ◽  
Author(s):  
Q. J. Wang ◽  
Andrew Schepen ◽  
David E. Robertson
Keyword(s):  
Statistical Models ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Seasonal Rainfall ◽  
Multiple Models ◽  
Forecast Uncertainty ◽  
Spatial Coverage ◽  
Forecast Lead Time ◽  
Seasonal Rainfall Forecasts

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.

Sorghum yield prediction from seasonal rainfall forecasts in Burkina Faso

2008 ◽  
Vol 148 (11) ◽  
pp. 1798-1814 ◽  
Author(s):  
Ashok Mishra ◽  
James W. Hansen ◽  
Michael Dingkuhn ◽  
Christian Baron ◽  
Seydou B. Traoré ◽  
...  
Keyword(s):  
Burkina Faso ◽  
Seasonal Rainfall ◽  
Yield Prediction ◽  
Seasonal Rainfall Forecasts

scholarly journals Evaluation of the NCEP Climate Forecast System and Its Downscaling for Seasonal Rainfall Prediction over Vietnam

2018 ◽  
Vol 33 (3) ◽  
pp. 615-640 ◽  
Author(s):  
Tan Phan-Van ◽  
Thanh Nguyen-Xuan ◽  
Hiep Van Nguyen ◽  
Patrick Laux ◽  
Ha Pham-Thanh ◽  
...  
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Seasonal Rainfall ◽  
Monsoon Circulation ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Global Precipitation Climatology Centre ◽  
Ncep Climate Forecast System ◽  
Seasonal Rainfall Forecasts

Abstract This study investigates the ability to apply National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) products and their downscaling by using the Regional Climate Model version 4.2 (RegCM4.2) on seasonal rainfall forecasts over Vietnam. First, the CFS hindcasts (CFS_Rfc) from 1982 to 2009 are used to assess the ability of the CFS to predict the overall circulation and precipitation patterns at forecast lead times of up to 6 months. Second, the operational CFS forecasts (CFS_Ope) and its RegCM4.2 downscaling (RegCM_CFS) for the period 2012–14 are used to derive seasonal rainfall forecasts over Vietnam. The CFS_Rfc and CFS_Ope are validated against the ECMWF interim reanalysis, the Global Precipitation Climatology Centre (GPCC) analyzed rainfall, and observations from 150 meteorological stations across Vietnam. The results show that the CFS_Rfc can capture the seasonal variability of the Asian monsoon circulation and rainfall distribution. The higher-resolution RegCM_CFS product is advantageous over the raw CFS in specific climatic subregions during the transitional, dry, and rainy seasons, particularly in the northern part of Vietnam in January and in the country’s central highlands during July.

scholarly journals Potential value of GCM-based seasonal rainfall forecasts for maize management in semi-arid Kenya

2009 ◽  
Vol 101 (1-2) ◽  
pp. 80-90 ◽  
Author(s):  
James W. Hansen ◽  
Ashok Mishra ◽  
K.P.C. Rao ◽  
Matayo Indeje ◽  
Robinson Kinuthia Ngugi
Keyword(s):  
Seasonal Rainfall ◽  
Potential Value ◽  
Semi Arid ◽  
Seasonal Rainfall Forecasts

Case studies of seasonal rainfall forecasts for Hong Kong and its vicinity using a regional climate model

2007 ◽  
Vol 42 (1) ◽  
pp. 193-207 ◽  
Author(s):  
David Tai-Wai Hui ◽  
Karen Kit-Ying Shum ◽  
Ji Chen ◽  
Shyh-Chin Chen ◽  
Jack Ritchie ◽  
...  
Keyword(s):  
Hong Kong ◽  
Regional Climate Model ◽  
Case Studies ◽  
Climate Model ◽  
Regional Climate ◽  
Seasonal Rainfall ◽  
Seasonal Rainfall Forecasts
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