Modeling atmospheric dust for respiratory health alerts

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Tuesday, 19 January 2010: 9:15 AM
B301 (GWCC)
Stanley A. Morain, University of New Mexico, Albuquerque, NM; and A. M. Budge and W. A. Sprigg

Presentation PDF (961.0 kB)

Modeling atmospheric lifting of mineral dust from arid land surfaces has been accomplished by nesting a regional model with NCEP/eta and with NCEP/NMM at their respective horizontal resolutions. The Dust Regional Atmospheric Model (DREAM) provides the dust simulator used by both NWS models to produce an hourly surface dust forecast based on frequently updated, synoptic land surface observations obtained from NASA sensors. Model dust concentrations have been, and continue to be, verified and validated using three different techniques: indices of agreement between modeled and observed patterns generated from METAR and radiosonde reports; comparisons between modeled and observed AIRNow and other ground-based CAM networks; and by calculating model skill and threat scores using the Point-Stat tool developed for use in WRF. There is now a four year archive of daily model runs for V&V analysis. These daily model runs have attracted the attention of health communities in the Southwest from AZ, NM and West TX. The communities include: (1) school nurses and public school districts that must develop Asthma Action Plans for pending dust episodes; (2) print and broadcast media; and (3), epidemiologists from university hospitals and State departments of health who need access to archived dust datasets for use in longitudinal and etiological analyses. Visualizations of dust storm movements are interesting to the first two communities because they can see the forecasted distribution and generation of dust patterns. These animations help build user-confidence, especially if they confirm user experiences. Once confidence and confirmation are achieved, these communities would rather have information delivered to them via print and broadcast media or by fax, twitter, or text messaging for broader distribution to affected populations. The third community is less interested in daily forecasts. Their statistical analyses are based on long-term data sets aggregated into summaries for analysis against reported health outcomes.