The LAPS Cloud Analysis: Validation with All-sky imagery and development of a variational cloud assimilation

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Thursday, 8 January 2015: 8:30 AM
231ABC (Phoenix Convention Center - West and North Buildings)
Steve Albers, NOAA & CIRA, Boulder, CO; and K. Holub, Y. Xie, Z. Toth, H. Jiang, and J. Zhou

The Local Analysis and Prediction (LAPS) system is being used to produce rapid update, high resolution analyses and forecasts of various meteorological fields, including cloud and precipitating hydrometeors. LAPS is highly portable and can be run onsite, particularly when high-resolution and rapid updating is needed. This allows the user to assimilate their own observational data merged with centrally available observations and to set up the analysis/forecast configuration to their liking.The cloud analysis uses satellite (including IR and 1-km resolution visible imagery, updated every 15-min), METARs, radar, aircraft and model first guess information to produce 3-D fields of cloud fraction, cloud liquid, cloud ice, rain, snow, and precipitating ice. This analysis is currently being done by the long-standing, largely sequential data insertion procedure developed within LAPS, though we are presently developing a variational version of this procedure. To help visualize and validate the cloud analysis a ray-tracing procedure was developed to construct simulated all-sky imagery. The visualization procedure efficiently considers various aspects of radiative transfer in clouds, precipitation, aerosols. The terrain is also visualized from vantage points located on the Earth's surface. The all-sky imagery, in either a polar or cylindrical projection can show either analysis or forecast fields. We will present ongoing results of this simulated imagery, along with comparisons to actual CCD images produced by an all-sky camera that is located within our Colorado 500m resolution domain. These comparisons check the skill of the existing analysis at high-resolution, and help communicate the model results in a visual manner to scientific and lay audiences. Our plans include using all-sky cameras directly in data assimilation to help fill observational gaps at small-scales.

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