4.7 Article

Using constructed analogs to improve the skill of National Multi-Model Ensemble March-April-May precipitation forecasts in equatorial East Africa

Journal

ENVIRONMENTAL RESEARCH LETTERS
Volume 9, Issue 9, Pages -

Publisher

IOP Publishing Ltd
DOI: 10.1088/1748-9326/9/9/094009

Keywords

rainfall forecast; East Africa; NMME

Funding

  1. Postdoc Applying Climate Expertise (PACE) Fellowship Program - NOAA Climate Program Office
  2. USGS [G09AC000001]
  3. NASA SERVIR award [NNX13AQ95A]
  4. USAID Office of Food for Peace [AID-FFP-P-10-00002]
  5. USGS Land Change Science Program
  6. NASA [NNX13AQ95A, 465175] Funding Source: Federal RePORTER

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In this study we implement and evaluate a simple 'hybrid' forecast approach that uses constructed analogs (CA) to improve the National Multi-Model Ensemble's (NMME) March-April-May (MAM) precipitation forecasts over equatorial eastern Africa (hereafter referred to as EA, 2 degrees S to 8 degrees N and 36 degrees E to 46 degrees E). Due to recent declines in MAM rainfall, increases in population, land degradation, and limited technological advances, this region has become a recent epicenter of food insecurity. Timely and skillful precipitation forecasts for EA could help decision makers better manage their limited resources, mitigate socio-economic losses, and potentially save human lives. The 'hybrid approach' described in this study uses the CA method to translate dynamical precipitation and sea surface temperature (SST) forecasts over the Indian and Pacific Oceans (specifically 30 degrees S to 30 degrees N and 30 degrees E to 270 degrees E) into terrestrial MAM precipitation forecasts over the EA region. In doing so, this approach benefits from the post-1999 teleconnection that exists between precipitation and SSTs over the Indian and tropical Pacific Oceans (Indo-Pacific) and EA MAM rainfall. The coupled atmosphere-ocean dynamical forecasts used in this study were drawn from the NMME. We demonstrate that while the MAM precipitation forecasts (initialized in February) skill of the NMME models over the EA region itself is negligible, the ranked probability skill score of hybrid CA forecasts based on Indo-Pacific NMME precipitation and SST forecasts reach up to 0.45.

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