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Bayesian regression model for seasonal forecast of precipitation over Korea

Bayesian regression model for seasonal forecast of precipitation over Korea Abstract In this paper, we apply three different Bayesian methods to the seasonal forecasting of the precipitation in a region around Korea (32.5°N–42.5°N, 122.5°E-132.5°E). We focus on the precipitation of summer season (June–July–August; JJA) for the period of 1979–2007 using the precipitation produced by the Global Data Assimilation and Prediction System (GDAPS) as predictors. Through cross-validation, we demonstrate improvement for seasonal forecast of precipitation in terms of root mean squared error (RMSE) and linear error in probability space score (LEPS). The proposed methods yield RMSE of 1.09 and LEPS of 0.31 between the predicted and observed precipitations, while the prediction using GDAPS output only produces RMSE of 1.20 and LEPS of 0.33 for CPC Merged Analyzed Precipitation (CMAP) data. For station-measured precipitation data, the RMSE and LEPS of the proposed Bayesian methods are 0.53 and 0.29, while GDAPS output is 0.66 and 0.33, respectively. The methods seem to capture the spatial pattern of the observed precipitation. The Bayesian paradigm incorporates the model uncertainty as an integral part of modeling in a natural way. We provide a probabilistic forecast integrating model uncertainty. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Asia-Pacific Journal of Atmospheric Sciences" Springer Journals

Bayesian regression model for seasonal forecast of precipitation over Korea

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References (20)

Publisher
Springer Journals
Copyright
2012 Korean Meteorological Society and Springer Netherlands
ISSN
1976-7633
eISSN
1976-7951
DOI
10.1007/s13143-012-0021-7
Publisher site
See Article on Publisher Site

Abstract

Abstract In this paper, we apply three different Bayesian methods to the seasonal forecasting of the precipitation in a region around Korea (32.5°N–42.5°N, 122.5°E-132.5°E). We focus on the precipitation of summer season (June–July–August; JJA) for the period of 1979–2007 using the precipitation produced by the Global Data Assimilation and Prediction System (GDAPS) as predictors. Through cross-validation, we demonstrate improvement for seasonal forecast of precipitation in terms of root mean squared error (RMSE) and linear error in probability space score (LEPS). The proposed methods yield RMSE of 1.09 and LEPS of 0.31 between the predicted and observed precipitations, while the prediction using GDAPS output only produces RMSE of 1.20 and LEPS of 0.33 for CPC Merged Analyzed Precipitation (CMAP) data. For station-measured precipitation data, the RMSE and LEPS of the proposed Bayesian methods are 0.53 and 0.29, while GDAPS output is 0.66 and 0.33, respectively. The methods seem to capture the spatial pattern of the observed precipitation. The Bayesian paradigm incorporates the model uncertainty as an integral part of modeling in a natural way. We provide a probabilistic forecast integrating model uncertainty.

Journal

"Asia-Pacific Journal of Atmospheric Sciences"Springer Journals

Published: Aug 1, 2012

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