9.2 Effective Assimilation of Global Precipitation: Simulation Experiments

Wednesday, 9 January 2013: 1:45 PM
Room 9C (Austin Convention Center)
Guo-Yuan Lien, University of Maryland, College Park, MD; and E. Kalnay and T. Miyoshi
Manuscript (728.9 kB)

Past attempts to assimilate precipitation by modifying the moisture and sometimes temperature profiles have succeeded in forcing the model precipitation to be close to the observed values. However, this is not an efficient way to modify the potential vorticity field that the model would remember, thus model forecasts tend to lose their additional skill after few forecast hours. In this study, the ensemble Kalman filter (EnKF) method is used to effectively change the potential vorticity field by allowing ensemble members with better precipitation to receive higher weights. In addition, two other changes in the precipitation assimilation process are proposed to solve the problems related to the highly non-Gaussian nature of the precipitation variable: a) transform precipitation into a Gaussian distribution based on its climatological distribution, and b) only assimilate precipitation at the location where some ensemble members have positive precipitation.

The idea is first tested by observing system simulation experiments (OSSEs) using SPEEDY, a simplified but realistic general circulation model. When the global precipitation is assimilated in addition to conventional rawinsonde observations, both the analyses and the medium range forecasts are significantly improved as compared to only having rawinsonde observations. The improvement is much reduced when only modifying the moisture field with the same approach, which shows the importance of the error covariance between precipitation and all other model variables. The effect of precipitation assimilation is larger in the Southern Hemisphere than that in the Northern Hemisphere because the Northern Hemisphere analyses are already accurate by denser rawinsonde stations. Assigning smaller horizontal localization scales for precipitation observations also helps for the EnKF analysis. Assimilation of precipitation using a more comprehensive global model and with real satellite data is ongoing work. The practical use of the precipitation assimilation in real global analyses will be investigated at this stage.

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