Extended Abstract (556K)P3.1
GOES Data Assimilation in MM5: Application for Texas Air Quality Study 2000
Arastoo Pour Biazar, University of Alabama, Huntsville, AL; and R. McNider, K. Doty, S. L. Haines, W. Lapenta, R. J. Suggs, and G. Jedlovec
Surface moisture availability and surface heat capacity are critical to accurate temperature and boundary layer characteristics. In the current study we have utilized GOES-8 Imager data for skin temperature, surface albedo, and insolation retrievals, to infer surface moisture availability and heat capacity compatible with model parameterization.
MM5 simulations span over the period of August 23, to September 2, 2000 (period of Texas Air Quality Study 2000 modeling activities) and are performed over four domains from 108- to 4-km resolution. Moisture availability is adjusted over the mid-morning period and the heat capacity in the evening period.
The inferred moisture availability exhibits the overall drying of the surface during the period of study. Compared to the control simulations the assimilation runs improve the model predictions of temperature where the control exhibits cool bias, but exacerbate the warm bias in the control run. The assimilation runs also show more improvement over the control runs as the model resolution increases (since the impact of land-use inhomogeneity become more pronounced and comparable to the satellite pixel area).
Poster Session 3, Wednesday Posters
Wednesday, 14 January 2004, 2:30 PM-4:00 PM, Room 4AB
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