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Near Real-time Data Assimilation for Aerosol Dispersion Model

Near Real-time Data Assimilation for Aerosol Dispersion Model Kostas Kalpakis , Yaacov Yesha , and Milt Halem , Shiming Yang @ CSEE.UMBC Bob Szabo , Eric Bouillet , and Lisa Amini @ IBM. Prediction of smoke dispersion. Meteorology data. Motivation. Data Assimilation. Experiments.

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Near Real-time Data Assimilation for Aerosol Dispersion Model

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  1. Near Real-time Data Assimilation for Aerosol Dispersion Model Kostas Kalpakis, YaacovYesha, and Milt Halem,Shiming Yang @CSEE.UMBC Bob Szabo, Eric Bouillet, and Lisa Amini @ IBM Prediction of smoke dispersion Meteorology data Motivation Data Assimilation Experiments Data Assimilation MODIS observation HPCs Demand for real-time processing of high volume dynamic sensor data streams from scientific areas for forecasting applications Demand for more accurate forecast; better initial condition for the prediction model; utilization of information from observation Natural hazard control and environment protection The CAPTEX (1983) of DATEM archive Data assimilation are techniques to fuse observation and model data while considering uncertainties in both, as well as consistency constraints due to the underlying physical system Fig: A feedback system using HYSPLIT as model for wildfire smoke concentration NOAA manual release of tracing material in Sep/Oct 1983 . On ground concentration observation HYSPLIT Algorithm: Evaluation metrics include the normalized mean square error (NMSE), average bias, model rank, etc.. The Local Ensemble Transform Kalman Filter (LETKF) shows efficiency and good parallelism. It involves many matrix / vector operations that have large proportion of data parallelism. Especially, its local analysis favors many-core programming with massive threads on GPUs. Results show that the with data assimilation using data from the CAPTEX experiment, the NMSE has average 18.9%, 17.6%, and 13.0% improvement for the first three emission phases. Besides, assimilated results are less biased, and increase the overall model ranking. Goal Observation: Develop efficient data assimilation system using multiple near real-time observation More accurate aerosol dispersion forecast Apply to wildfire smoke prediction and monitoring Quality assured observational data are collected from multiple sources. First, the daily Aerosol Optical Depth (AOD) is from the LAADS Terra. It can also be obtained from the GOES Aerosol Smoke Products (GASP) for hourly record. Second, the Air Quality Service (AQS) provides the ground concentration. Model: The Hybrid Single Particle Lagrangian Integrated Trajectory Model (Hysplit) is developed by ARL NOAA for computing atmospheric tra- jectorys, complex dispersion, and concentratioin simulations. Feature Computational intensive: Global weather forecast involving hundreds of physical variables Computational Intensive: 1000Km scale tracer study refined concentration grid of 0.01 degree hourly sampling 27 members in the ensemble of system states Near real-time: Forecast; fast response for recent hazards September, 2010

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