Share Email Print
cover

Proceedings Paper

Evaluation of a regional assimilation system coupled with the WRF-chem model
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Air quality has become a social issue that is causing great concern to humankind across the globe, but particularly in developing countries. Even though the Weather Research and Forecasting with Chemistry (WRF-Chem) model has been applied in many regions, the resolution for inputting meteorology field analysis still impacts the accuracy of forecast. This article describes the application of the CIMSS Regional Assimilation System (CRAS) in East China, and its capability to assimilate the direct broadcast (DB) satellite data for obtaining more detailed meteorological information, including cloud top pressure (CTP) and total precipitation water (TPW) from MODIS. Performance evaluation of CRAS is based on qualitative and quantitative analyses. Compared with data collected from ERA-Interim, Radiosonde, and the Tropical Rainfall Measuring Mission (TRMM) precipitation measurements using bias and Root Mean Square Error (RMSE), CRAS has a systematic error due to the impact of topography and other factors; however, the forecast accuracy of all elements in the model center area is higher at various levels. The bias computed with Radiosonde reveals that the temperature and geopotential height of CRAS are better than ERA-Interim at first guess. Moreover, the location of the 24 h accumulated precipitation forecast are highly consistent with the TRMM retrieval precipitation, which means that the performance of CRAS is excellent. In summation, the newly built Vtable can realize the function of inputting the meteorology field from CRAS output into WRF, which couples the CRAS with WRF-Chem. Therefore, this study not only provides for forecast accuracy of CRAS, but also increases the capability of running the WRF-Chem model at higher resolutions in the future.

Paper Details

Date Published: 24 September 2013
PDF: 7 pages
Proc. SPIE 8869, Remote Sensing and Modeling of Ecosystems for Sustainability X, 88690K (24 September 2013); doi: 10.1117/12.2024387
Show Author Affiliations
Yan-an Liu, East China Normal Univ. (China)
Univ. of Wisconsin-Madison (United States)
Wei Gao, East China Normal Univ. (China)
Colorado State Univ. (United States)
Hung-lung Huang, Univ. of Wisconsin-Madison (United States)
Kathleen Strabala, Univ. of Wisconsin-Madison (United States)
Chaoshun Liu, East China Normal Univ. (China)
Runhe Shi, East China Normal Univ. (China)


Published in SPIE Proceedings Vol. 8869:
Remote Sensing and Modeling of Ecosystems for Sustainability X
Wei Gao; Thomas J. Jackson; Jinnian Wang; Ni-Bin Chang, Editor(s)

© SPIE. Terms of Use
Back to Top