Session 11A.6 On the Role of Atmospheric Data Assimilation and Model Resolution on Model Forecast Accuracy for the Torino Winter Olympics

Thursday, 28 June 2007: 5:15 PM
Summit A (The Yarrow Resort Hotel and Conference Center)
David R. Stauffer, Pennsylvania State University, University Park, PA; and G. K. Hunter, A. Deng, J. R. Zielonka, K. Tinklepaugh, and P. Hayes

Presentation PDF (1.0 MB)

The 2006 Winter Olympic Games in Torino, Italy represented a potential high value target for disruption by terrorist individuals and organizations. Penn State worked with the Defense Threat Reduction Agency (DTRA) to provide realtime modeling support and high resolution weather products to drive hazard prediction and consequence assessment activities. Local and regional scale atmospheric conditions strongly influence atmospheric transport and dispersion (AT&D) processes in the boundary layer, and the extent and scope of the spread of dangerous materials in the lower levels of the atmosphere. Managing the consequences of chemical-biological-radiative-nuclear (CBRN) incidents requires accurate current and future weather conditions to model potential effects. Thus the role of continuous four-dimensional data assimilation and mesoscale model resolutions of 36 km, 12 km, 4 km and 1.3 km on meteorological model accuracy over northern Italy's varying terrain conditions and scales is investigated.

ARPA-Piemonte's special weather observations network in the Alps and Torino plains is used for model initialization within a “running start” data assimilation strategy, and for verification of the resulting 24-h forecasts. Mesoscale models are run with and without data assimilation to determine the added value of various meteorological input data. The observational data were also used for dynamic analysis where they are continuously assimilated within a mesoscale model throughout the 24-h periods to produce a more complete and dynamically consistent meteorological analysis than that provided by the observations alone.

The meteorological model forecasts for six cases representing the range of weather conditions observed over the study region during February 2006 generally show improved predictive skill when using data assimilation and increasing model resolution, especially at the surface and in the boundary layer. Statistical differences were relatively small between the 4-km and 1.3-km grids, although subjective analysis revealed greater mesoscale details using 1.3-km resolution. This result poses important questions regarding the input meteorological data requirements for AT&D models and the allocation of computational resources for numerical weather prediction.

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