A21F-3102:
A Synergistic Approach to Estimating Aerosol Optical Depth over Ocean Using GOES Observations and Goddard Earth Observing System (GEOS-5) Model Forecasts

Tuesday, 16 December 2014
Cecilia Fleeger1, Patrick Minnis2, Douglas Spangenberg1, Rabindra Palikonda1 and William Smith Jr3, (1)Science Systems and Applications, Inc. Hampton, Hampton, VA, United States, (2)Nasa Larc, Hampton, VA, United States, (3)NASA Langley Research Ctr, Hampton, VA, United States
Abstract:
Aerosols directly and indirectly affect Earth’s energy balance. The aerosol indirect effect remains the largest uncertainty in global radiative forcing (IPCC, 2013). Various satellite sensors have been employed to estimate aerosol properties globally using multi-channel, multi-angle, polarization and/or Lidar approaches. Sensors able to portray the spatial and temporal dynamics of aerosol loading, such as MODIS and VIIRS, often have low revisiting frequency, i.e. two images per day for the same region. While sensors capable of observing columnar aerosol properties (e.g. CALIPSO, MISR, POLDER) have limited usage for climate study and operational air quality monitoring and forecast because of their narrow swath and sparse revisiting frequency. Imagers onboard Geostationary (GEO) satellite GOES East and West are able to provide half-hourly observations with wide coverage and moderate spatial resolution. However, their use for estimating aerosol optical depth is largely limited because only the visible channel (0.53 – 0.75 μm) can be applied with large uncertainties on spectral properties related to aerosol types. Toward the goal of optimizing aerosol assimilation and forecast in the GEOS-5 systems, we developed a synergistic approach to estimate aerosol optical depth (AOD) over ocean. This algorithm utilizes GOES visible channel observations, look-up tables (LUTs) derived from a coupled ocean-atmosphere radiative transfer model (COART), and is constrained by MODIS-assimilated and GOCART-based GEOS-5 aerosol type information. In this presentation, we describe this algorithm; show the preliminary product, including AOD, aerosol type, and quality assurance; and present validations using AERONET observations and MODIS aerosol product.