An analysis of the difference between the multiple linear regression approach and the multimodel ensemble mean |
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Authors: | Zongjian Ke Wenjie Dong Peiqun Zhang Jin Wang Tianbao Zhao |
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Institution: | [1]National Climate Center, China Meteorological Administration, Beijing 100081 [2]Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 [3]State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875 [4]National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081 |
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Abstract: | An investigation of the difference in seasonal
precipitation forecast skills between the multiple linear regression
(MLR) ensemble and the simple multimodel ensemble mean (EM) was
based on the forecast quality of individual models. The possible
causes of difference in previous studies were analyzed. In order to
make the simulation capability of studied regions relatively
uniform, three regions with different temporal correlation
coefficients were chosen for this study. Results show the causes
resulting in the incapability of the MLR approach vary among
different regions. In the Nino3.4 region, strong co-linearity
within individual models is generally the main reason. However, in
the high latitude region, no significant co-linearity can be found
in individual models, but the abilities of single models are so poor
that it makes the MLR approach inappropriate for superensemble
forecasts in this region. In addition, it is important to note that
the use of various score measurements could result in some
discrepancies when we compare the results derived from different
multimodel ensemble approaches. |
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Keywords: | precipitation multimodel ensemble seasonal prediction difference analysis co-linearity diagnosis |
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