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Article
Article Type:
Research Article

Statistical Downscaling Multimodel Forecasts for Seasonal Precipitation and Surface Temperature over the Southeastern United States

Di Tian Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida

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Christopher J. Martinez Agricultural and Biological Engineering Department, University of Florida, Gainesville, Florida

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Wendy D. Graham Agricultural and Biological Engineering Department, and Water Institute, University of Florida, Gainesville, Florida

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Syewoon Hwang Agricultural Engineering Department, Gyeongsang National University, Jinju, South Korea

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Print Publication:
15 Nov 2014
DOI:
https://doi.org/10.1175/JCLI-D-13-00481.1
Page(s):
8384–8411
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Abstract

This study compared two types of approaches to downscale seasonal precipitation (P) and 2-m air temperature (T2M) forecasts from the North American Multimodel Ensemble (NMME) over the states of Alabama, Georgia, and Florida in the southeastern United States (SEUS). Each NMME model forecast was evaluated. Two multimodel ensemble (MME) schemes were tested by assigning equal weight to all forecast members (SuperEns) or by assigning equal weights to each model’s ensemble mean (MeanEns). One type of downscaling approach used was a model output statistics (MOS) method, which was based on direct spatial disaggregation and bias correction of the NMME P and T2M forecasts using the quantile mapping technique [spatial disaggregation with bias correction (SDBC)]. The other type of approach used was a perfect prognosis (PP) approach using nonparametric locally weighted polynomial regression (LWPR) models, which used the NMME forecasts of Niño-3.4 sea surface temperatures (SSTs) to predict local-scale P and T2M. Both SDBC and LWPR downscaled P showed skill in winter but no skill or limited skill in summer at all lead times for all NMME models. The SDBC downscaled T2M were skillful only for the Climate Forecast System, version 2 (CFSv2), model even at far lead times, whereas the LWPR downscaled T2M showed limited skill or no skill for all NMME models. In many cases, the LWPR method showed significantly higher skill than the SDBC. After bias correction, the SuperEns mostly showed higher skill than the MeanEns and most of the single models, but its skill did not outperform the best single model.

Current affiliation: Civil and Environmental Engineering Department, Princeton University, Princeton, New Jersey.

Corresponding author address: Di Tian, Civil and Environmental Engineering Department, Princeton University, Engineering Quadrangle, 59 Olden Street, Princeton, NJ 08544. E-mail: dtian@princeton.edu

Abstract

This study compared two types of approaches to downscale seasonal precipitation (P) and 2-m air temperature (T2M) forecasts from the North American Multimodel Ensemble (NMME) over the states of Alabama, Georgia, and Florida in the southeastern United States (SEUS). Each NMME model forecast was evaluated. Two multimodel ensemble (MME) schemes were tested by assigning equal weight to all forecast members (SuperEns) or by assigning equal weights to each model’s ensemble mean (MeanEns). One type of downscaling approach used was a model output statistics (MOS) method, which was based on direct spatial disaggregation and bias correction of the NMME P and T2M forecasts using the quantile mapping technique [spatial disaggregation with bias correction (SDBC)]. The other type of approach used was a perfect prognosis (PP) approach using nonparametric locally weighted polynomial regression (LWPR) models, which used the NMME forecasts of Niño-3.4 sea surface temperatures (SSTs) to predict local-scale P and T2M. Both SDBC and LWPR downscaled P showed skill in winter but no skill or limited skill in summer at all lead times for all NMME models. The SDBC downscaled T2M were skillful only for the Climate Forecast System, version 2 (CFSv2), model even at far lead times, whereas the LWPR downscaled T2M showed limited skill or no skill for all NMME models. In many cases, the LWPR method showed significantly higher skill than the SDBC. After bias correction, the SuperEns mostly showed higher skill than the MeanEns and most of the single models, but its skill did not outperform the best single model.

Current affiliation: Civil and Environmental Engineering Department, Princeton University, Princeton, New Jersey.

Corresponding author address: Di Tian, Civil and Environmental Engineering Department, Princeton University, Engineering Quadrangle, 59 Olden Street, Princeton, NJ 08544. E-mail: dtian@princeton.edu
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