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Assessment of the Performance of Three Dynamical Climate Downscaling Methods Using Different Land Surface Information over China.

Authors :
Liu, Peng
Qiu, Xiaobin
Yang, Yi
Ma, Yuanyuan
Jin, Shuanglong
Source :
Atmosphere; Mar2018, Vol. 9 Issue 3, p101, 22p
Publication Year :
2018

Abstract

This study aims to assess the performance of different dynamical downscaling methods using updated land surface information. Particular attention is given to obtaining high-resolution climate information over China by the combination of an appropriate dynamical downscaling method and updated land surface information. Two group experiments using two land surface datasets are performed, including default Weather Research and Forecasting (WRF) land surface data (OLD) and accurate dynamically accordant MODIS data (NEW). Each group consists of three types of experiments for the summer of 2014, including traditional continuous integration (CT), spectral nudging (SN), and re-initialization (Re) experiments. The Weather Research and Forecasting (WRF) model is used to dynamically downscale ERA-Interim (reanalysis of the European Centre for Medium-Range Weather Forecast, ECMWF) data with a grid spacing of 30 km over China. The simulations are evaluated via comparison with observed conventional meteorological variables, showing that the CT method, which notably overestimates 2 m temperature and underestimates 2 m relative humidity across China, performs the worst; the SN and Re runs outperform the CT method, and the Re shows the smallest RMSE (root means square error). A comparison of observed and simulated precipitation shows that the SN simulation is closest to the observed data, while the CT and Re simulations overestimate precipitation south of the Yangtze River. Compared with the OLD group, the RMSE values of temperature and relative humidity are significantly improved in CT and SN, and there is smaller improved in Re. However, obvious improvements in precipitation are not evident. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20734433
Volume :
9
Issue :
3
Database :
Complementary Index
Journal :
Atmosphere
Publication Type :
Academic Journal
Accession number :
128646333
Full Text :
https://doi.org/10.3390/atmos9030101