Improved Seasonal Prediction of Rainfall over East Africa for Application in Agriculture: Statistical Downscaling of CFSv2 and GFDL-FLOR

Improved Seasonal Prediction of Rainfall over East Africa for Application in Agriculture:... AbstractWe evaluate statistically downscaled forecasts of October-November-December (OND) rainfall over East Africa from two General Circulation Model (GCM) seasonal prediction systems. The methodology uses Canonical Correlation Analysis (CCA) to relate variability in predicted large-scale rainfall (characterizing, for example, predicted ENSO and IOD variability) to observed local variability over Kenya and Tanzania. Evaluation is performed for the period 1982-2011 and for the real-time forecast for OND 2015, a season when a strong El Nino was active.The seasonal forecast systems used are the National Centres for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) and the Geophysical Fluid Dynamics Laboratory - Forecast-Oriented Low Ocean Resolution (GFDL-FLOR). The Climate Hazards Infrared Precipitation with Station data (CHIRPS) rainfall dataset - a blend of in-situ station observations and satellite estimates - was used at 5x5 km resolution over Kenya and Tanzania as benchmark data for the downscaling.Results for the case study forecast for OND 2015 show that downscaled output from both models adds realistic spatial detail relative to the coarser raw model output – albeit with some overestimation of rainfall that may have been derived from the downscaling procedure introducing a wet response to El Nino more typical of historical cases. Assessment of the downscaled forecasts over the 1982-2011 period shows positive long-term skill better than that documented in previous studies of unprocessed GCM forecasts for the region. Climate forecast downscaling is thus a key undertaking worldwide in the generation of more reliable products for sector specific application including agricultural planning and decision making. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Meteorology and Climatology American Meteorological Society

Improved Seasonal Prediction of Rainfall over East Africa for Application in Agriculture: Statistical Downscaling of CFSv2 and GFDL-FLOR

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1558-8432
D.O.I.
10.1175/JAMC-D-16-0365.1
Publisher site
See Article on Publisher Site

Abstract

AbstractWe evaluate statistically downscaled forecasts of October-November-December (OND) rainfall over East Africa from two General Circulation Model (GCM) seasonal prediction systems. The methodology uses Canonical Correlation Analysis (CCA) to relate variability in predicted large-scale rainfall (characterizing, for example, predicted ENSO and IOD variability) to observed local variability over Kenya and Tanzania. Evaluation is performed for the period 1982-2011 and for the real-time forecast for OND 2015, a season when a strong El Nino was active.The seasonal forecast systems used are the National Centres for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) and the Geophysical Fluid Dynamics Laboratory - Forecast-Oriented Low Ocean Resolution (GFDL-FLOR). The Climate Hazards Infrared Precipitation with Station data (CHIRPS) rainfall dataset - a blend of in-situ station observations and satellite estimates - was used at 5x5 km resolution over Kenya and Tanzania as benchmark data for the downscaling.Results for the case study forecast for OND 2015 show that downscaled output from both models adds realistic spatial detail relative to the coarser raw model output – albeit with some overestimation of rainfall that may have been derived from the downscaling procedure introducing a wet response to El Nino more typical of historical cases. Assessment of the downscaled forecasts over the 1982-2011 period shows positive long-term skill better than that documented in previous studies of unprocessed GCM forecasts for the region. Climate forecast downscaling is thus a key undertaking worldwide in the generation of more reliable products for sector specific application including agricultural planning and decision making.

Journal

Journal of Applied Meteorology and ClimatologyAmerican Meteorological Society

Published: Sep 28, 2017

References

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