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Multimodel Ensemble Forecasts for Precipitations in China in 1998


doi: 10.1007/s00376-008-0072-y

  • Different multimodel ensemble methods are used to forecast precipitations in China, 1998, and their forecast skills are compared with those of individual models. Datasets were obtained from monthly simulations of eight models during the period of January 1979 to December 1998 from the ``Climate of the 20th Century Experiment" (20C3M) for the Fourth IPCC Assessment Report. Climate Research Unit (CRU) data were chosen for the observation analysis field. Root mean square (RMS) error and correlation coefficients (R) are used to measure the forecast skills. In addition, superensemble forecasts based on different input data and weights are analyzed. Results show that for original data, superensemble forecasting based on multiple linear regression (MLR) performs best. However, for bias-corrected data, the superensemble based on singular value decomposition (SVD) produces a lower RMS error and a higher $R$ than in the MLR superensemble. It is an interesting result that the SVD superensemble based on bias-corrected data performs better than the MLR superensemble, but that the SVD superensemble based on original data is inferior to the corresponding MLR superensemble. In addition, weights calculated by different data formats are shown to affect the forecast skills of the superensembles. In comparison with the MLR superensemble, a slightly significant effect is present in the SVD superensemble. However, both the SVD and MLR superensembles based on different weight formats outperform the ensemble mean of bias-corrected data.
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    [3] JIE Weihua, WU Tongwen, WANG Jun, LI Weijing, LIU Xiangwen, 2014: Improvement of 6-15 Day Precipitation Forecasts Using a Time-Lagged Ensemble Method, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 293-304.  doi: 10.1007/s00376-013-3037-8
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Manuscript History

Manuscript received: 10 January 2008
Manuscript revised: 10 January 2008
通讯作者: 陈斌, bchen63@163.com
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Multimodel Ensemble Forecasts for Precipitations in China in 1998

  • 1. Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081;Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081

Abstract: Different multimodel ensemble methods are used to forecast precipitations in China, 1998, and their forecast skills are compared with those of individual models. Datasets were obtained from monthly simulations of eight models during the period of January 1979 to December 1998 from the ``Climate of the 20th Century Experiment" (20C3M) for the Fourth IPCC Assessment Report. Climate Research Unit (CRU) data were chosen for the observation analysis field. Root mean square (RMS) error and correlation coefficients (R) are used to measure the forecast skills. In addition, superensemble forecasts based on different input data and weights are analyzed. Results show that for original data, superensemble forecasting based on multiple linear regression (MLR) performs best. However, for bias-corrected data, the superensemble based on singular value decomposition (SVD) produces a lower RMS error and a higher $R$ than in the MLR superensemble. It is an interesting result that the SVD superensemble based on bias-corrected data performs better than the MLR superensemble, but that the SVD superensemble based on original data is inferior to the corresponding MLR superensemble. In addition, weights calculated by different data formats are shown to affect the forecast skills of the superensembles. In comparison with the MLR superensemble, a slightly significant effect is present in the SVD superensemble. However, both the SVD and MLR superensembles based on different weight formats outperform the ensemble mean of bias-corrected data.

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