A Comparison of the Clouds from AVHRR Extended (CLAVR-x) Cloud Phase Algorithm with CALIPSO Cloud Phase

Cloud mask and cloud property retrievals are part of the operational suite of products from operational geostationary- and polar-orbiting satellite imagers. Cloud phase retrievals are used to identify glaciation of clouds and assess convective cloud growth in nowcasting applications. Furthermore, cloud phase is an important variable to constrain the earth’s radiative balance in climate assessments and modeling. Misclassifications of cloud phase in the tropics and mid-latitudes from passive IR imagers (such as the Advanced Baseline Imager, ABI, on GOES-16, 17, and 18) occur most frequently over snow cover and along coast lines. The operational requirement for cloud phase retrievals for the GOES-R series is 80% minimum accuracy. The Cloud-Aerosol Lidar Pathfinder Satellite Observations (CALIPSO) cloud phase algorithm provides an important independent data source from an active sensor for validating the cloud phase retrievals from IR imagers.

The Clouds from AVHRR Extended (CLAVR-x) processing system is a developmental testbed for many of NOAA’s operational cloud property retrieval algorithms including cloud mask and cloud properties retrievals. CLAVR-x is maintained and developed at the Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin-Madison (publicly available here: http://cimss.ssec.wisc.edu/clavrx/documentation.html). This presentation will compare the current enterprise cloud phase algorithm used in operations by the National Oceanic and Atmospheric Administration (NOAA) and the CLAVR-x cloud phase from the GOES-16 ABI to the CALIPSO determined cloud phase. Preliminary analyses reveal that both imager algorithms easily meet the 80% accuracy threshold. The majority of misclassified cloud phase pixels are likely caused by the horizontal mismatch between the CALIPSO and ABI pixels used for comparison. It has been found that these misclassified pixels tend to be in more heterogeneous scenes than pixels that have matching cloud phases.