12.6a
Assimilation of multi-satellite precipitation data for improving quantitative precipitation forecasts (Formerly paper number P1.29)
Zhaoxia Pu, University of Maryland, Baltimore County and NASA/GSFC, Greenbelt, MD; and W. K. Tao
Compared to other meteorological fields, improvement in the forecast skill of quantitative precipitation forecasts (QPFs) has been slow. This could be attributed to uncertainties in model physics parameterization and initial conditions. With the rapid increase of satellite data, the skill of QPFs is expected to be improved by using these data.
In this study, three-hourly TRMM real-time multi-satellite precipitation data from the NASA Goddard Space Flight Center (NASA/GSFC) will be assimilated into mesoscale model (MM5) to improve the forecast skill of QPFs over the tropical region. The TRMM real-time multi-satellite precipitation data are available at quarter degree by quarter degree resolution and are generated by merging the rainfall products from TMI, SSM/I and IR all together. The data cover the most of areas between 50S~50N. A 1+1D scheme, which was originally developed for GEOS global analysis at data assimilation office (DAO) of NASA/GSFC, will be re-designed and applied for mesoscale assimilation of precipitation data.
The feasibility of 1+1D scheme for mesoscale data assimilation, and the impact of multi-satellite data on the forecast skill of QPFs will be evaluated. Further study will also be conducted to evaluate the model physics parameterizations that are highly related to the skill of QPFs.
Session 12, Mesoscale Numerical Models
Thursday, 26 June 2003, 4:00 PM-5:30 PM
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