An Overview of the Assimilation of Remote Sensing and Observational Data for Improving Meteorological and Air Quality Modeling in the Coastal Areas

This study is an overview of the assimilation of the remote sensing and observational data into a Weather Research and Forecastng (WRF) meteorological model, including in-situ observational data. The goals of this study are to improve the accuracy of the WRF meteorological model in the coastal areas and to reduce the model uncertainty. We will evaluate and verify the model, and assess the model's performance. This study will also investigate the diurnal variation of the meteorological phenomena and the flow characteristics in the coastal environment. Furthermore, understanding the atmospheric processes in the atmospheric boundary layer is important to assess the impact of air quality onshore in the coastal areas. The result of air quality modeling strongly depends on the accuracy of the meteorological model. The advantages of the WRF model are that one can directly extract the land use and boundary layer parameters from the WRF mode and the WRF model can provide the comprehensive data at the specified source location for air quality modeling and regulatory applications. A suite of meteorological instruments has been deployed on an oil platform in the Gulf of Mexico. The unprecedented dataset of wind-wave measurements, temperature, moisture, and wind profile has been collected; this dataset can be used for model evaluation and verification. Time series and sensitivity analysis of the model are also performed; the model results are compared with the observational data.