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Cloud Data Assimilation

Improve mesoscale weather analysis by incorporating hydrologic variables into data assimilation. Utilize 4D variational assimilation of satellite radiance with a cloud-resolving model for accurate weather predictions. Assess the assimilation of visible and IR radiance for different cloud types and evaluate model performance using ARM sounding data.

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Cloud Data Assimilation

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  1. Cloud Data Assimilation • Motivation: Measoscale weather analysis requires inclusion of hydrologic variables consistent with the dynamics in the data assimilation to advance short term weather forecast and understanding of mesoscale atmospheric processes. • Method: 4D variational data assimilation of clear and cloudy satellite radiance with a mesoscale, cloud resolving model T. Vukicevic

  2. Studies performed • Algorithms: • Regional Atmospheric Modeling and Data Assimilation System (RAMDAS) developed for RAMS using 4DVAR approach • Observational operator developed for assimilation of visible and IR radiance • Applications: • Assimilation of GOES imager visible and IR observations for a case of continental stratus cloud evolution • Sensitivity of visible and IR radiance measurements to cloud parameters • Assimilation of GOES imager IR observations for a case of cirrus cloud evolution (sample in this presentation) T. Vukicevic

  3. Sample of results from cirrus cloud study T. Vukicevic

  4. Good convergence Model domain bias eliminated Error variance significantly reduced Iteration number Iteration number Comparison with independent data: ARM sounding of T T. Vukicevic Temperature Temperature

  5. Vertical extent of cirrus cloud and radar reflectivity values in good agreement with the observations within 1 h window of assimilation T. Vukicevic

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