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USING METEOROLOGICAL MODEL OUTPUT AS A SURROGATE FOR ON-SITE OBSERVATIONS TO PREDICT DEPOSITION VELOCITY
Citation:
Schwede, D B., S K. LeDuc, AND T L. Otte. USING METEOROLOGICAL MODEL OUTPUT AS A SURROGATE FOR ON-SITE OBSERVATIONS TO PREDICT DEPOSITION VELOCITY. Presented at Sixth International Conference on Air Surface Exchange of Gases and Particles, Edinburgh, England, July 3-7, 2000.
Impact/Purpose:
This task objective is the development and improvement of state-of-the-science meteorology models and contributing process parameterizations for use in advanced air quality simulation model systems such as the Community Multi-scale Air Quality (CMAQ) modeling system and for other modeling studies and situations involving transport and dispersion of pollutants. Components of this work include: (a) improved meteorological and transport modeling, (b) improved meteorological modeling physics, (c) physical modeling of flows- building wakes, complex terrain, urban canyons, (d) modeling of transport and dispersion of specialized situations and (e) develop AERMOD (AMS/EPA Regulatory MODel).
Description:
The National Oceanic and Atmospheric Administration's Multi-Layer Model (NOAA-MLM) is used by several operational dry deposition networks for estimating the deposition velocity of O , SO , HNO , and particles. The NOAA-MLM requires hourly values of meteorological variables and since collection of on-site meteorology can be expensive, a study was performed to evaluate NOAA-MLM predicted deposition velocities using modeled meteorological data in lieu of on-site data. NOAA-MLM was run for three sites in the Clean Air Status and Trends Network using on-site data as well as the output of two mesoscale meteorological models, Eta and MM5. Atmospheric turbulence and local precipitation proved particularly difficult to match between the on-site and modeled data. Some modifications to the NOAA-MLM and the manner in which the Eta and MM5 data are processed my improve the usefulness of these data as a surrogate for the on-site observations.