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Statistical downscaling of monthly forecasts
Author(s) -
Landman Willem A.,
Tennant Warren J.
Publication year - 2000
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/1097-0088(20001115)20:13<1521::aid-joc558>3.0.co;2-n
Subject(s) - downscaling , climatology , gcm transcription factors , environmental science , canonical correlation , sea surface temperature , general circulation model , meteorology , geography , precipitation , climate change , geology , mathematics , oceanography , statistics
Canonical correlation analysis (CCA) is used to downscale large‐scale circulation forecasts by the Centre for Ocean–Land–Atmosphere studies (COLA) T30 general circulation model (GCM) statistically to regional rainfall in South Africa. Monthly GCM ensemble forecasts available from 1979 to 1995 have been generated using NCEP reanalysis data as initial input and globally observed sea‐surface temperature (SST) data at the lower boundary. Altogether, 51 30‐day cases of GCM simulations, spanning 17 years, within the target season of December–February (DJF), are produced. This period is very important for agriculture and maize, in particular. A model output statistics (MOS) procedure is used to downscale GCM forecast sea‐level pressure and 500 hPa height fields to regional rainfall for 30‐day periods over South Africa. The CCA model is trained on the first 31 cases (up to February 1989) and forecasts are subsequently made for the remaining 20 cases. These retro‐active real‐time forecasts have a high potential (correlations >0.5) over most of the interior of South Africa and, furthermore, the prediction of extreme events seems feasible. CCA diagnostics of the GCM‐output against rainfall reveal that favourable rainfall over most of the interior is associated with low pressure systems at the surface over the west coast, with an associated ridging high. This is supported by other observational studies. Copyright © 2000 Royal Meteorological Society