Two overlooked biases of the advanced research wrf (arw) model in geopotential height and temperature

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Title:	Two overlooked biases of the advanced research wrf (arw) model in geopotential height and temperature
Authors:	陳舒雅;Wee, Tae-Kwon;Kuo, Ying-Hwa;Lee, Dong-Kyou;Liu, Zhiquan;Wang, Wei;Chen, Shu-Ya
Contributors:	全球大氣觀測與資料應用研究中心
Keywords:	Bias;Climatology;Data collection;Earth, ocean, space;Exact sciences and technology;External geophysics;Geopotential;Hydrostatics;Initial conditions;Mathematical analysis;Mathematical models;Meteorology;Studies;Weather;Weather forecasting
Date:	2012-12-01
Issue Date:	2026-04-23 11:25:25 (UTC+8)
Publisher:	American Meteorological Society;Boston, MA: American Meteorological Society
Abstract:	摘要： The authors have discovered two sizeable biases in the Weather Research and Forecasting (WRF) model: a negative bias in geopotential and a warm bias in temperature, appearing both in the initial condition and the forecast. The biases increase with height and thus manifest themselves at the upper part of the model domain. Both biases stem from a common root, which is that vertical structures of specific volume and potential temperature are convex functions. The geopotential bias is caused by the particular discrete hydrostatic equation used in WRF and is proportional to the square of the thickness of model layers. For the vertical levels used in this study, the bias far exceeds the gross 1-day forecast bias combining all other sources. The bias is fixed by revising the discrete hydrostatic equation. WRF interpolates potential temperature from the grids of an external dataset to the WRF grids in generating the initial condition. Associated with the Exner function, this leads to the marked bias in temperature. By interpolating temperature to the WRF grids and then computing potential temperature, the bias is removed. The bias corrections developed in this study are expected to reduce the disparity between the forecast and observations, and eventually to improve the quality of analysis and forecast in the subsequent data assimilation. The bias corrections might be especially beneficial to assimilating height-based observations (e.g., radio occultation data). 出版者： Boston, MA: American Meteorological Society 出版日期： 2012-12-01 出處： Monthly weather review, 2012-12, Vol.140 (12), p.3907-3918 資源來源： EBSCOhost OmniFile Full Text Select 版權： 2014 INIST-CNRS 版權： Copyright American Meteorological Society Dec 2012 識別號： ISSN: 0027-0644 識別號： EISSN: 1520-0493 識別號： DOI: 10.1175/MWR-D-12-00045.1 識別號： CODEN: MWREAB
Appears in Collections:	[Global Atmosphere Observation and Data Application Research Center] journal & Dissertation

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