Open AccessAssimilation of soil moisture and temperature in the GRAPES_Meso model using an ensemble Kalman filter
Author(s) -
Wang Lili
Publication year - 2019
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1777
Subject(s) - data assimilation , ensemble kalman filter , assimilation (phonology) , environmental science , relative humidity , precipitation , humidity , kalman filter , meteorology , water content , atmospheric sciences , moisture , climatology , extended kalman filter , mathematics , statistics , geography , geology , linguistics , philosophy , geotechnical engineering
Soil moisture and temperature are significant variables in numerical weather prediction systems and land surface models, controlling the partitioning of moisture and energy fluxes at the surface. The ensemble Kalman filter (EnKF) is an approximation to the Kalman filter in that background error covariances are estimated from a finite ensemble of forecasts. The EnKF technique is now widely applied in data assimilation of the atmosphere, ocean and land surface. In the current GRAPES_Meso model version V4.0, the land surface soil assimilation method has not been integrated for land surface assimilation. Therefore, in this work, an EnKF has been introduced in the GRAPES_Meso model using air temperature at 2 m and the relative humidity at 2 m and its performance has been evaluated in land surface assimilation. The results show that the land surface assimilation method can effectively improve the performance skill of air temperature at 2 m and it has little effect on precipitation.