%0 Journal Article %1 Gong2016Methods %A Gong, Zhiqiang %A Hutin, Clément %A Feng, Guolin %D 2016 %I American Meteorological Society %J Wea. Forecasting %K statistics skill seasonal precip China %R 10.1175/waf-d-16-0007.1 %T Methods for Improving the Prediction Skill of Summer Precipitation over East Asia-West Pacific %U http://dx.doi.org/10.1175/waf-d-16-0007.1 %X AbstractThe performance of summer precipitation prediction skill of the Beijing Climate Center Climate System Model(CSM) over the East Asia-West Pacific (EA-WP) region indicates the need for further development in order to improve model prediction skill. Two methods, namely the statistic alone prediction and the statistic and model combined prediction are proposed, to improve prediction skill. For the former, more than 2000 combinations of precursory predictors are considered in the predictive model, both most similar and dissimilar information are considered in the prediction process. For the latter, both the statistical analogue information and the CSM's forecast are considered in the predictive model, this new prediction method is based on merging information of both dynamical model and historical analogues. Cross validation of summer precipitation over EA-WP for the years 1991-2010 shows that the average spatial anomaly correlation coefficient (ACC) of the statistical alone prediction is 0.16 and the average spatial root-mean-standard-error (RMSE) is 22.7, which are much improved compared to the systematic error correction with ACC and RMSE being 0.09 and 25.0 respectively. The ACC and RMSE of the statistic and CSM combined prediction are 0.22 and 21.8 respectively, which also indicates an improvement compare to the statistical alone prediction. Independent sample validation for the year 2011 to 2013 indicate that the average ACCs of the statistic alone prediction, the statistic and CSM combined prediction (0.25 and 0.24) are enhanced compared to the systematic error correction (-0.02). The average RMSE is also improved from 25.0 to 23.6 and 22.7. Therefore, the two new prediction methods proposed in this paper both demonstrate the possibilities for operational prediction of summer precipitation over EA-WP.

@article{Gong2016Methods, abstract = {AbstractThe performance of summer precipitation prediction skill of the Beijing Climate Center Climate System Model(CSM) over the East Asia-West Pacific (EA-WP) region indicates the need for further development in order to improve model prediction skill. Two methods, namely the statistic alone prediction and the statistic and model combined prediction are proposed, to improve prediction skill. For the former, more than 2000 combinations of precursory predictors are considered in the predictive model, both most similar and dissimilar information are considered in the prediction process. For the latter, both the statistical analogue information and the CSM's forecast are considered in the predictive model, this new prediction method is based on merging information of both dynamical model and historical analogues. Cross validation of summer precipitation over EA-WP for the years 1991-2010 shows that the average spatial anomaly correlation coefficient (ACC) of the statistical alone prediction is 0.16 and the average spatial root-mean-standard-error (RMSE) is 22.7, which are much improved compared to the systematic error correction with ACC and RMSE being 0.09 and 25.0 respectively. The ACC and RMSE of the statistic and CSM combined prediction are 0.22 and 21.8 respectively, which also indicates an improvement compare to the statistical alone prediction. Independent sample validation for the year 2011 to 2013 indicate that the average ACCs of the statistic alone prediction, the statistic and CSM combined prediction (0.25 and 0.24) are enhanced compared to the systematic error correction (-0.02). The average RMSE is also improved from 25.0 to 23.6 and 22.7. Therefore, the two new prediction methods proposed in this paper both demonstrate the possibilities for operational prediction of summer precipitation over EA-WP.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Gong, Zhiqiang and Hutin, Cl\'{e}ment and Feng, Guolin}, biburl = {https://www.bibsonomy.org/bibtex/2be490ad04b0a845f24eb8773f886c7d3/pbett}, citeulike-article-id = {14087837}, citeulike-linkout-0 = {http://journals.ametsoc.org/doi/abs/10.1175/WAF-D-16-0007.1}, citeulike-linkout-1 = {http://dx.doi.org/10.1175/waf-d-16-0007.1}, day = 29, doi = {10.1175/waf-d-16-0007.1}, interhash = {0b0166071eb3782dc52a3c73f9a8b11d}, intrahash = {be490ad04b0a845f24eb8773f886c7d3}, journal = {Wea. Forecasting}, keywords = {statistics skill seasonal precip China}, month = jun, posted-at = {2016-07-01 09:03:36}, priority = {2}, publisher = {American Meteorological Society}, timestamp = {2018-08-22T09:32:01.000+0200}, title = {Methods for Improving the Prediction Skill of Summer Precipitation over East Asia-West Pacific}, url = {http://dx.doi.org/10.1175/waf-d-16-0007.1}, year = 2016 }