Open AccessAn Operational Dynamical Downscaling Prediction System for Nordeste Brazil and the 2002–04 Real-Time Forecast Evaluation
Author(s) -
Liqiang Sun,
David Ferran Moncunill,
Huilan Li,
Antônio Divino Moura,
Francisco D. A. D. S. Filho,
Stephen E. Zebiak
Publication year - 2006
Publication title -
journal of climate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.315
H-Index - 287
eISSN - 1520-0442
pISSN - 0894-8755
DOI - 10.1175/jcli3715.1
Subject(s) - downscaling , climatology , predictability , environmental science , forcing (mathematics) , forecast skill , meteorology , global forecast system , climate model , sea surface temperature , general circulation model , climate change , precipitation , numerical weather prediction , geography , statistics , mathematics , geology , oceanography
The International Research Institute for Climate Prediction (IRI) and Ceará Foundation for Meteorology and Water Resources (FUNCEME) in Brazil have developed a dynamical downscaling prediction system for Northeast Brazil (the Nordeste) and have been issuing seasonal rainfall forecasts since December 2001. To the authors’ knowledge, this is the first operational climate dynamical downscaling prediction system. The ECHAM4.5 AGCM and the NCEP Regional Spectral Model (RSM) are the core of this prediction system. This is a two-tiered prediction system. SST forecasts are produced first, which then serve as the lower boundary condition forcing for the ECHAM4.5 AGCM–NCEP RSM nested system. Hindcasts for January–June 1971–2000 with the nested model, using observed SSTs, provided estimates of model potential predictability and characteristics of the model climatology. During 2002–04, the overall rainfall forecast skill, measured by the ranked probability skill score (RPSS), is positive over a majority of the Nordeste. Higher skill is found for the March–May (MAM) and April–June (AMJ) seasons with forecast lead times up to 3 months. The skill of the downscaled forecasts is generally higher than that of the driving global model forecasts.