Validating ABI and VIIRS Cloud Cover Layers with Surface and Space-borne Active Sensor Measurements

Recent advancements in satellite remote sensing have enabled satellites to provide greatly advanced high-resolution cloud observations in both time and space. Information from cloud retrieval products is often biased toward locations at/near cloud top due to the natural limitations of passive sensor observations, but a statistical cloud base height algorithm that our team has developed allows derivation of more vertically extended cloud information. Cloud Cover Layers (CCL) is a level-2 satellite product that provides information on the depth of clouds in addition to their height. CCL provides cloud fraction and layer information at predefined atmospheric levels when a cloudy pixel is present, determined by cloud mask. This product is operational for the Advanced Baseline Imager (ABI) aboard GOES-16 and GOES-18 and the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard JPSS polar satellite series including NOAA-21. The current atmospheric levels correspond to five common flight levels (surface – 5kft – 10kft – 18kft – 24kft – TOA) used in aviation as requested by the NWS Operational Advisory Team (NOAT), where the lowest two levels can refer to low clouds, and the highest level can refer to high clouds. Validation of the performance of the operational product is necessary to continue its improvement, and further the goal of helping NOAA deliver quality cloud products to users. The CCL products from both sensors are compared to ground-based measurements from the Atmospheric Radiation Measurement (ARM) sites and CloudSat/CALIPSO data. The ARM measurements provide a broad range of intensive observational data for targeted areas such as the Southern Great Plains and North Slope of Alaska sites. CloudSat radar and CALIPSO lidar have provided many years of vertical cloud structure observations which are invaluable for retrospective case evaluations. This validation study will test the performance of the CCL algorithm for single-layer clouds, as well as machine learning approaches newly developed for determining the lowest ‘ceiling’ in multi-layered cloudy scenes.