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An evaluation of parametric sensitivities of different meteorological variables simulated by the WRF model
Author(s) -
Quan Jiping,
Di Zhenhua,
Duan Qingyun,
Gong Wei,
Wang Chen,
Gan Yanjun,
Ye Aizhong,
Miao Chiyuan
Publication year - 2016
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.2885
Subject(s) - weather research and forecasting model , parametrization (atmospheric modeling) , precipitable water , environmental science , wind speed , meteorology , sensitivity (control systems) , numerical weather prediction , model output statistics , parametric statistics , cloud cover , precipitation , mathematics , computer science , statistics , cloud computing , geography , engineering , physics , radiative transfer , quantum mechanics , electronic engineering , operating system
The specification of model parameters in numerical weather prediction ( NWP ) models has great influence on model performance. However, how to specify model parameters properly is not a trivial task because a typical NWP model like the Weather Research and Forecasting ( WRF ) model contains many model parameters and many model outputs. This article presents the results of an investigation into the sensitivities of different WRF model outputs to the specification of its model parameters. Using a global sensitivity analysis method, the sensitivities are evaluated for surface meteorological variables such as precipitation, surface air temperature, humidity and wind speed, as well as for atmospheric variables such as total precipitable water, cloud cover, boundary‐layer height and outgoing long‐wave radiation at the top of the atmosphere, all simulated by the WRF model using different model parameters. The goal of this study is to identify the parameters that exert most influence on the skill of short‐range meteorological forecasts. The study was performed over the Greater Beijing Region of China. A total of 23 adjustable parameters from seven different physical parametrization schemes were considered. The results indicate that parameter sensitivities vary with different model outputs. However, some of the 23 model parameters considered are shown to be sensitive to all model outputs evaluated, while other parameters may be sensitive to a particular output. The sensitivity results from this research are a basis for further optimizations of the WRF model parameters.