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Atmosphere 2018, 9(3), 101; https://doi.org/10.3390/atmos9030101

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

1
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
2
Tianjin Institute of Meteorological Science, Tianjin 300074, China
3
State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China
*
Authors to whom correspondence should be addressed.
Received: 22 December 2017 / Revised: 22 February 2018 / Accepted: 7 March 2018 / Published: 11 March 2018
(This article belongs to the Special Issue Regional Climate Modeling)
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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. View Full-Text
Keywords: WRF; dynamical downscaling; spectral nudging; re-initialization; land use; vegetation fraction WRF; dynamical downscaling; spectral nudging; re-initialization; land use; vegetation fraction
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Liu, P.; Qiu, X.; Yang, Y.; Ma, Y.; Jin, S. Assessment of the Performance of Three Dynamical Climate Downscaling Methods Using Different Land Surface Information over China. Atmosphere 2018, 9, 101.

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