A First Evaluation of ERA-20CM over ChinaSource: Monthly Weather Review:;2015:;volume( 144 ):;issue: 001::page 45DOI: 10.1175/MWR-D-15-0195.1Publisher: American Meteorological Society
Abstract: s an important global data resource, reanalysis is widely applied for climate impact studies of the past several decades. For the first time, monthly mean temperature and monthly total precipitation derived from the newest generation reanalysis product?the ECMWF twentieth-century reanalysis dataset (ERA-20CM)?is quantitatively evaluated based on probability density functions and 702 meteorological stations during the period of 1960?2009 across China. This study attempts to investigate how well each member ensemble prediction of ERA-20CM performs for different regions. Generally, all ensemble predictions in ERA-20CM are able to recreate the real conditions on a comparable level. More than 90% of the observed probability for temperature and more than 80% of the probabilities for precipitation could be captured by ERA-20CM over China. However, the performance changes significantly from region to region because of different topographical features and climate characteristics. The Tibetan Plateau is the most difficult to model for all member ensembles. The Jianhuai region is the area with the best performance for both temperature and precipitation. Although the best and worst ensembles for temperature and precipitation for each region were selected according to the skill scores, the differences among the 10-member ensemble predictions are negligible. This evaluation would be helpful for the potential users of reanalysis data, such as ERA-20CM for local climate impact assessments in China.
|
Collections
Show full item record
contributor author | Gao, Lu | |
contributor author | Bernhardt, Matthias | |
contributor author | Schulz, Karsten | |
contributor author | Chen, Xingwei | |
contributor author | Chen, Ying | |
contributor author | Liu, Meibing | |
date accessioned | 2017-06-09T17:33:10Z | |
date available | 2017-06-09T17:33:10Z | |
date copyright | 2016/01/01 | |
date issued | 2015 | |
identifier issn | 0027-0644 | |
identifier other | ams-87130.pdf | |
identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4230765 | |
description abstract | s an important global data resource, reanalysis is widely applied for climate impact studies of the past several decades. For the first time, monthly mean temperature and monthly total precipitation derived from the newest generation reanalysis product?the ECMWF twentieth-century reanalysis dataset (ERA-20CM)?is quantitatively evaluated based on probability density functions and 702 meteorological stations during the period of 1960?2009 across China. This study attempts to investigate how well each member ensemble prediction of ERA-20CM performs for different regions. Generally, all ensemble predictions in ERA-20CM are able to recreate the real conditions on a comparable level. More than 90% of the observed probability for temperature and more than 80% of the probabilities for precipitation could be captured by ERA-20CM over China. However, the performance changes significantly from region to region because of different topographical features and climate characteristics. The Tibetan Plateau is the most difficult to model for all member ensembles. The Jianhuai region is the area with the best performance for both temperature and precipitation. Although the best and worst ensembles for temperature and precipitation for each region were selected according to the skill scores, the differences among the 10-member ensemble predictions are negligible. This evaluation would be helpful for the potential users of reanalysis data, such as ERA-20CM for local climate impact assessments in China. | |
publisher | American Meteorological Society | |
title | A First Evaluation of ERA-20CM over China | |
type | Journal Paper | |
journal volume | 144 | |
journal issue | 1 | |
journal title | Monthly Weather Review | |
identifier doi | 10.1175/MWR-D-15-0195.1 | |
journal fristpage | 45 | |
journal lastpage | 57 | |
tree | Monthly Weather Review:;2015:;volume( 144 ):;issue: 001 | |
contenttype | Fulltext |