Pan XD, Li X, Shi XK, Han XJ, Luo LH, Wang LX. (2012). Dynamic downscaling of near-surface air temperature at the basin scale using WRF–a case study in the Heihe River Basin, China. Frontiers of Earth Science. 6(3): 314-323, doi: 10.1007/s11707-012-0306-2.
|Type Of Reference||JOUR|
|Title||Dynamic downscaling of near-surface air temperature at the basin scale using WRF–a case study in the Heihe River Basin, China|
|Authors||Pan, Xiaoduo| Li, Xin| Shi, Xiaokang| Han, Xujun| Luo, Lihui| Wang, Liangxu||
|Secondary Title||Frontiers of Earth Science|
|Abstract||The spatial resolution of general circulation models (GCMs) is too coarse to represent regional climate variations at the regional, basin, and local scales required for many environmental modeling and impact assessments. Weather research and forecasting model (WRF) is a next-generation, fully compressible, Euler non-hydrostatic mesoscale forecast model with a run-time hydrostatic option. This model is useful for downscaling weather and climate at the scales from one kilometer to thousands of kilometers, and is useful for deriving meteorological parameters required for hydrological simulation too. The objective of this paper is to validate WRF simulating 5 km/1 h air temperatures by daily observed data of China Meteorological Administration (CMA) stations, and by hourly in-situ data of the Watershed Allied Telemetry Experimental Research Project. The daily validation shows that the WRF simulation has good agreement with the observed data; the R 2 between the WRF simulation and each station is more than 0.93; the absolute of meanbias error (MBE) for each station is less than 2°C; and the MBEs of Ejina, Mazongshan and Alxa stations are near zero, with R 2 is more than 0.98, which can be taken as an unbiased estimation. The hourly validation shows that the WRF simulation can capture the basic trend of observed data, the MBE of each site is approximately 2°C, the R 2 of each site is more than 0.80, with the highest at 0.95, and the computed and observed surface air temperature series show a significantly similar trend.|
|Keywords||Earth and Environmental Science|
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