scholarly journals Direct and indirect seasonal rainfall forecasts for East Africa using global dynamical models

2019 ◽  
Vol 40 (2) ◽  
pp. 1132-1148
Author(s):  
Andrew W. Colman ◽  
Richard J. Graham ◽  
Michael K. Davey
Keyword(s):  
East Africa ◽  
Seasonal Rainfall ◽  
Dynamical Models ◽  
Seasonal Rainfall Forecasts

Relationships between seasonal rainfall in East Africa and the Southern Oscillation

1988 ◽  
Vol 8 (1) ◽  
pp. 31-43 ◽  
Author(s):  
L. J. Ogallo
Keyword(s):  
East Africa ◽  
Southern Oscillation ◽  
Seasonal Rainfall

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.

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

scholarly journals Development and Assessment of Localized Seasonal Rainfall Prediction Models: Mapping and Characterizing Rift Valley Fever Hotspot Areas in the Southern and Southeastern Ethiopia

2018 ◽  
Vol 11 (5) ◽  
pp. 102
Author(s):  
Ephrem Weledekidane
Keyword(s):  
East Africa ◽  
Rift Valley ◽  
Rift Valley Fever ◽  
Large Scale ◽  
Prediction Models ◽  
Rainfall Anomaly ◽  
Seasonal Rainfall ◽  
Valley Fever ◽  
Average Rainfall ◽  
Early Indicators

Rift Valley Fever disease has been recognized as being among permanent threats for the sustainability of livestock production in Ethiopia, owing to shared boarders with RVF endemic countries in East Africa. Above-normal and widespread rainfall have outweighed as immediate risk factor that facilitated historical outbreaks of the disease in the East Africa. The objective of the present study, thus, was to develop prospective localized seasonal rainfall anomaly prediction models, and assess their skills as early indicators to map high risk localized rift valley fever disease outbreak areas (hotspots) over the southern and southeastern part of Ethiopia. 21 years of daily rainfall data; for five meteorological stations, was employed in diagnosing existences of any anomalous patterns of rainfall, along with a cumulative rainfall analysis to determine if there were ideal conditions for potential flooding. The results indicated that rainfall in the region is highly variable; with non-significant trends, and attributed to be the results of the effects of large-scale climatic-teleconnection. The moderate to strong positive correlations found between the regional average rainfall and large scale teleconnection variables (r ≥ 0.48), indicated some potentials for early prediction of seasonal patterns of rainfall. Accordingly, models developed, based on the regional average rainfall and emerging developments of El Niño/Southern Oscillation and other regional climate forcings, showed maximum skills (ROC scores ≥ 0.7) and moderate reliability. Deterministically, most of the positive rainfall anomaly patterns, corresponding to El Niño years, were portrayed with some skills. The study demonstrated that localized climate prediction models are invaluable as early indicators to skillfully map climatically potential RVF hotspot areas.

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.

scholarly journals Seasonal rainfall forecasting for the Yangtze River basin using statistical and dynamical models

2019 ◽  
Vol 40 (1) ◽  
pp. 361-377 ◽  
Author(s):  
Shuni Qian ◽  
Jie Chen ◽  
Xiangquan Li ◽  
Chong‐Yu Xu ◽  
Shenglian Guo ◽  
...  
Keyword(s):  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Seasonal Rainfall ◽  
The Yangtze River ◽  
Dynamical Models ◽  
Rainfall Forecasting ◽  
The Yangtze River Basin

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 Seasonal Rainfall Forecasts for the Yangtze River Basin in Summer 2019 from an Improved Climate Service

2020 ◽  
Author(s):  
Philip Bett ◽  
Nicola Martin ◽  
Adam Scaife ◽  
Nick Dunstone ◽  
Gill Martin ◽  
...  
Keyword(s):  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Seasonal Rainfall ◽  
The Yangtze River ◽  
Climate Service ◽  
The Yangtze River Basin ◽  
Seasonal Rainfall Forecasts
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