8A.2 Application of mixed-layer theory to satellite stratocumulus cloud assimilation in WRF

Wednesday, 1 July 2015: 8:15 AM
Salon A-2 (Hilton Chicago)
Handa Yang, University of California, La Jolla, CA; and J. Kleissl

A preprocessing procedure for the Weather Research and Forecasting (WRF) model was developed based on satellite cloud data assimilation and mixed-layer theory in order to more accurately forecast marine layer stratocumulus lifetime and spatial coverage over coastal southern California.

First, low cloud coverage is obtained from geostationary satellite images. Following, cloud tops are defined at the temperature inversion base height or, in the absence of an inversion, the intersection of the WRF-simulated vertical temperature profile and satellite cloud-top temperature. Cloud base height is then determined from an empirical function of cloud top height (derived from radiosonde observations during summer months between 2008 and 2011). Finally, saturation water vapor mixing ratio at cloud base is assumed to represent the total water mixing ratio within the cloud deck. Cloud liquid water is then inserted into initial conditions based on the difference between the total water mixing ratio and saturation water vapor mixing ratio.

Preprocessed simulations were compared against standard WRF simulations, and consistently showed longer cloud lifetimes and farther inland cloud coverage, in agreement with satellite observations. The global horizontal irradiance (GHI) output from preprocessed simulations were validated against GHI measurements from four ground stations (two coastal, two inland) over the month of June, 2013. Preliminary results for nine days have shown the preprocessed WRF simulations to consistently outperform 24-hour persistence GHI forecasts for both coastal sites and one inland site.

Additionally, the preprocessing procedure reduces microphysics scheme cloud “spin-up” time from 6-12 hours to 1-3 hours. This spin-up period is due to lack of cloud water information from North American Mesoscale (NAM) model initial conditions. Apart from the obvious benefit of cheaper computational cost, the impact of incorrectly modeled surface radiative fluxes during spin-up is lessened.

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