Incorporation of Observed Precipitation Estimates in Four-Dimensional Data Assimilation Schemes for Mesoscale Models

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A potential for significant improvement in mesoscale numerical weather prediction lies in finding the optimal use for the new data sets (from next generation radar, or NEXRAD, satellites, automated surface observations, wind profilers, aircraft reports, etc.) that will become available operationally during the next decade. These observations are incorporated into a four-dimensional analysis process that produces an initial state compatible with the forecast model. The research under this award specifically addresses the issue of how to include precipitation measurements in this analysis process so that the model's rainfall rates will resemble the atmosphere's as closely as possible as the forecast begins. The principal investigators will explore methods to using raingauge, satellite and radar data to determine the vertical distribution of heating in the lower atmosphere. This information is then incorporated into the atmospheric analysis during a pre-forecast integration in which the mass and wind fields are brought into dynamical balance with the imposed condensational heating. This approach should greatly reduced the problem of "spin-up" in the model prediction. This project is part of a joint program between NOAA's National Meteorological Center (NMC) and NSF. An important aspect of the research will be to test the new algorithms that are developed in the NMC operational models.