Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Development of a visibility retrieval for the GOES-R Advanced Baseline Imager R. Bradley Pierce 1 (GOVERNMENT PRINCIPAL INVESTIGATOR), Allen Lenzen 2, Mike Pavolonis 1, Andrew Heidinger 1, Shobha Kondragunta 1 1 NOAA/NESDIS/STAR, 2 University of Wisconsin Space Science and Engineering Center (SSEC) Requirement: Provide accurate, timely, and integrated weather information to meet air and surface transportation needs Science: Can GOES-R ABI aerosol optical depth (AOD) and Low Cloud/Fog cloud optical thickness (COT) retrievals be used to provide a satellite based estimate of boundary layer extinction to augment existing Automated Surface Observing System (ASOS) visibility measurements? Benefit: Reduced runway visibility results in loss of visual references and can lead to loss of control and therefore requires increased separation between air traffic. GOES-R visibility retrievals will augment existing ASOS network. Stakeholders include the Department of Transportation (DOT), Federal Aviation Administration (FAA), Department of Defense (DOD), the NOAA Aviation Weather Center (AWC) and National Weather Service (NWS), as well as Private Industry, General Aviation (GA), and International Civil Aviation Organization (ICAO) global aviation standards. Science Challenges: Introduction of errors due to: Uncertainties in the PBL height estimates Inhomogeneous distributions of aerosols within the PBL Elevated aerosol layers in the free troposphere Next Steps: Development of the fog/low cloud component of the ABI visibility algorithm. Regression analyses will be conducted to establish statistical relationship (seasonal and categorical) between ASOS measurements and GOES-12 based fog visibility estimates. Transition Path: GOES-R visibility Algorithm Package (algorithm description, ancillary data requirements, sample software, test data, and test results) will be delivered to the GOES-R Algorithm Integration Team (AIT) for independent review and then delivered to the Ground System Contractor for implementation For retrieval of daytime visibility, Koschmieder’s Law is used: V = 3./σ Where V is the visibility (in kilometers) and σ is the extinction coefficient which is assumed to be associated with aerosols/fog within the planetary boundary layer: σ pbl = AOD or COT/ZPBL The ABI visibility product utilizes low-cloud/fog detection, cloud optical thickness (COT), and aerosol optical depth (AOD) retrievals to estimate horizontal visibility within the planetary boundary layer (PBL). Conversion from AOD or COT to extinction requires knowledge of the depth of the aerosol or fog/low-cloud layer, which is assumed to be determined by the depth of the planetary boundary layer (ZPBL). Thresholds for Poor, Low, Moderate, and Clear visibilities are developed based on statistical regression of proxy satellite AOD and COT measurements against Automated Surface Observing System (ASOS) extinction measurements. Required accuracy: 80% correct classification Required precision: 1.5 categories Developmental System: CIMSS Geostationary Cloud Algorithm Testbed (GEOCAT). Clear-sky categorical validation studies using MODIS AOD showed a 58% success rate and an estimated precision of 0.72 for non-bias corrected aerosol visibility during The non-bias corrected MODIS visibility was found to significantly overestimate the frequency of Moderate, Low and Poor visibility events. Monthly bias corrections (intercept) and scale factors (slope) based on linear regression analyses between MODIS proxy and ASOS visibility resulted in a 78% categorical success rate and an estimated precision of However, the bias-corrected MODIS visibility significantly underestimated the frequency of Moderate, Low and Poor visibility. Sensitivity studies were conducted to determine optimal weighting for blended MODIS aerosol visibility retrieval. Weighting determines the relative contribution between the non-bias corrected (first guess) and bias corrected MODIS aerosol visibility in the final aerosol visibility estimate. Results of Heidke Skill tests and showed that a 60% weighting resulted in the largest improvement relative to chance for both Clear and Moderate aerosol visibility and also minimized false alarm rates for low visibility conditions. The 40/60 blended aerosol visibility retrieval results in an 75.8% categorical success rate and an estimated precision of It captures the frequency of clear and moderate visibility very well and improves the prediction of low visibility compared to the bias corrected MODIS visibility alone.