83rd Annual

Thursday, 13 February 2003: 11:30 AM
Forecasting Dust Storms using CARMA-Dust Model and MM5 Weather Data
B. H. Barnum, Johns Hopkins University Applied Physics Laboratory, Laurel, MD; and N. S. Winstead, J. Wesely, L. A. Hakola, O. B. Toon, P. Colarco, P. Ginoux, G. W. Brooks, L. Hasselbarth, and B. Toth
Poster PDF (1.4 MB)
Dust storms throughout Saharan Africa, Middle East and Asia are estimated to place more than 200 to 5000 million tons of mineral dust into the earth's atmosphere each year (Tegen and Fung 1994). Dust storms directly affect visibility and impact daily commercial and military operations in dust prone regions. The United States Air Force Weather Agency (AFWA) has supported the development of a dust forecast model with a 72 hour forecast capability. The dust model called CARMA (Community Aerosol Research Model from Ames) was developed by Professor Owen Toon and Dr. Pete Colarco at the University of Colorado, Boulder. The CARMA model has been modified by Johns Hopkins Applied Physics Laboratory to use daily Mesoscale Model 5th generation (MM5) weather forecasts run by the United States Air Force Weather Agency. The latest version of the CARMA MM5 dust model can make 72 hour forecasts of surface and airborne dust concentrations in 3 different mesoscale theaters covering Saharan Africa and the Middle East, Southwest Asia and China. A new global dust source database developed by Dr. Paul Ginoux is used in the CARMA model. The dust source model is based on topographical features associated with dust sources and has been further supplemented with TOMS and AVHRR satellite data. The forecast ability of the dust model was evaluated over a 3 month period for two of the AFWA MM5 forecast theaters; African Sahara and Middle East/Southwest Asia. The Middle East has been grouped with Southwest Asia for this evaluation. The model forecasts were compared with DMSP satellite imagery and ground observations. Each theater was broken into sub-regions for detailed evaluation of the short (6-12 hour), mid (30-36 hour) and long term (54-60 hour) forecast ability of the model. Results of the study show the dust model has good skill in forecasting dust conditions for short and medium range forecast periods.

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