92nd American Meteorological Society Annual Meeting (January 22-26, 2012)

Tuesday, 24 January 2012: 2:30 PM
Data Assimilation for the Whole Atmosphere Model (WAM)
Room 252/253 (New Orleans Convention Center )
Houjun Wang, NOAA SWPC and CIRES Univ. of Colorado, Boulder, CO; and T. J. Fuller-Rowell, R. A. Akmaev, M. Hu, D. T. Kleist, and M. Iredell

The Gridpoint Statistical Interpolation (GSI) analysis system is used at NCEP for both regional and global operational data assimilations. It uses the 3-dimensional variational (3DVAR) analysis technique. The Developmental Testbed Center (DTC) support of the community GSI has expanded its community users, such as users of the Weather Research and Forecasting (WRF) model for regional numerical weather prediction applications. The community GSI has added features that make it flexible in adding state and control variables. GSI has also been used for chemical/aerosol and cloud data assimilation.

WAM is an extension of NCEP's Global Forecast System (GFS) model from 64 model levels (with the model top at about 60 km) to 150 model levels (with the model top at about 600 km). It covers the regions of important ionospheric processes and their variability. WAM includes basic ionospheric effects on neutral atmosphere, i.e., ion drag and Joule heating. Free annual run with WAM produced comparable climatology of tidal wave variability in the mesosphere and low thermosphere (MLT) region.

GSI was extended for data assimilation for WAM. The first simulations with the data assimilation (DA) system have produced realistic dynamic and electrodynamic responses to real large-scale sudden stratospheric warming (SSW) events. In this talk, some on-going works with the DA system will be presented. First, update of the background error statistics derived from yearlong short-term forecasts from the current DA system will be described. Then, the effects of assimilating observations in the MLT regions, such as SABER temperature and TIDI wind data from the TIMED satellite will be assessed. Assimilation of other observational data, such as CHAMP temperature/density data, SSM/IS upper air channel radiance, will also be discussed. Assimilation and error/bias quantification of these new data sources provides new opportunities to validate WAM simulations and forecasts.

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