Improving Aerosol and Visibility Forecasting Capabilities Using Current and Future Generations of Satellite Observations
Abstract
Critical to both military and civilian applications, the Navy Aerosol Analysis and Prediction System (NAAPS) is the worlds only truly operational global aerosol and visibility forecasting model. Recent studies indicate that the assimilation of satellite observations significantly improves NAAPS aerosol forecasting capability and reliability. To fully utilize the wide breadth and depth of various current satellite observations, and prepare for future reductions in aerosol sensing satellites over the next decade, we propose to construct a multi-channel, multi-sensor, and multi-task assimilation system to improve NAAPS forecasts for both current and future applications. The specific objectives are to: 1. Finalize over-land and over-ocean aerosol assimilation methods using operational data assimilation quality Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging SpectroRadiometer (MISR) aerosol products, and develop a framework for considering current and future satellite aerosol products. 2. Develop forward models to enable a radiance assimilation capability by: 1) improving forecast performance over cloudy regions using the Ozone Monitoring Instrument (OMI) Aerosol Index; and 2) preparing for the post-MODIS/MISR era using the Geostationary Operational Environmental Satellite (GOES). 3. Improve model representations of aerosol vertical profiles and the accuracy of aerosol speciation in NAAPS through the use of a 3-D aerosol assimilation method and a generalized Angstrom exponent assimilation scheme. 4. Develop an improved 3-D parameterization for satellite observation and model forecasting error matrices using ground observations from the Aerosol Robotic Network (AERONET) and the Micropulse Lidar Network (MPLNET).
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 30, 2013
- Accession Number
- ADA599087
Entities
People
- Jianglong Zhang
Organizations
- University of North Dakota