%0 Journal Article %1 Yoshimura2013Incremental %A Yoshimura, Kei %A Kanamitsu, Masao %B Monthly Weather Review %D 2013 %I American Meteorological Society %J Mon. Wea. Rev. %K statistics 20CR ensembles downscaling %R 10.1175/mwr-d-12-00271.1 %T Incremental Correction for the Dynamical Downscaling of Ensemble Mean Atmospheric Fields %U http://dx.doi.org/10.1175/mwr-d-12-00271.1 %X Abstract This research was motivated by the need for an improved method compared to the conventional brute force approach to ensemble downscaling. That method simply applies dynamical downscaling to each ensemble member. It obtains a reliable forecast by taking the ensemble average of all the downscaled ensemble members. This approach, although straightforward, has a problem in that the computational cost is too large for an operational environment. We propose a method for downscaling ensemble mean forecasts. Although this method does not provide probabilistic forecasts, it will provide the regional-scale detail at minimum cost. In this product, all of the predicted parameters are dynamically and physically consistent, that is most likely to occur on a seasonal time scale. We believe that such a product has great utility for regional climate forecast and application products. The method applies a correction to one of the global forecast members in such a way that the seasonal mean is equal to that of the ensemble mean, and it then downscales the corrected global forecast. This method was tested for a 140-year period by using the 20th Century Reanalysis, which is a product of ensemble Kalman filtering data assimilation. Use of the method clearly improves the downscaling skill compared to the case of using only a single member; the skill becomes equivalent to that achieved when 2?6 members are used directly.

@article{Yoshimura2013Incremental, abstract = {Abstract This research was motivated by the need for an improved method compared to the conventional brute force approach to ensemble downscaling. That method simply applies dynamical downscaling to each ensemble member. It obtains a reliable forecast by taking the ensemble average of all the downscaled ensemble members. This approach, although straightforward, has a problem in that the computational cost is too large for an operational environment. We propose a method for downscaling ensemble mean forecasts. Although this method does not provide probabilistic forecasts, it will provide the regional-scale detail at minimum cost. In this product, all of the predicted parameters are dynamically and physically consistent, that is most likely to occur on a seasonal time scale. We believe that such a product has great utility for regional climate forecast and application products. The method applies a correction to one of the global forecast members in such a way that the seasonal mean is equal to that of the ensemble mean, and it then downscales the corrected global forecast. This method was tested for a 140-year period by using the 20th Century Reanalysis, which is a product of ensemble Kalman filtering data assimilation. Use of the method clearly improves the downscaling skill compared to the case of using only a single member; the skill becomes equivalent to that achieved when 2?6 members are used directly.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Yoshimura, Kei and Kanamitsu, Masao}, biburl = {https://www.bibsonomy.org/bibtex/2c5294b648f4f18ddd7f8b0b6d66ad843/pbett}, booktitle = {Monthly Weather Review}, citeulike-article-id = {12243251}, citeulike-linkout-0 = {http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00271.1}, citeulike-linkout-1 = {http://dx.doi.org/10.1175/mwr-d-12-00271.1}, day = 22, doi = {10.1175/mwr-d-12-00271.1}, interhash = {8204854d4bfa2ccf2b2776c53cff3d8a}, intrahash = {c5294b648f4f18ddd7f8b0b6d66ad843}, journal = {Mon. Wea. Rev.}, keywords = {statistics 20CR ensembles downscaling}, month = mar, posted-at = {2013-04-05 16:57:40}, priority = {2}, publisher = {American Meteorological Society}, timestamp = {2018-06-22T18:33:17.000+0200}, title = {Incremental Correction for the Dynamical Downscaling of Ensemble Mean Atmospheric Fields}, url = {http://dx.doi.org/10.1175/mwr-d-12-00271.1}, year = 2013 }