Abstract
Deep convective clouds (DCCs) are used as vicarious calibration targets because of their stability, brightness, and relatively small angular and spectral variation. DCCs at visible channels usually have relatively sharp histogram distributions with high reflectance. However, as a calibration target, there remains relatively large uncertainty in identifying uniform cold cloud top of DCCs at near infrared (NIR) channels. The main objective of this study is to reduce DCC reflectance uncertainty at NIR channels for GOES-R Advanced Baseline Imager (ABI). The DCC pixels information from Visible Infrared Imaging Radiometer Suite (VIIRS) Level1B and Level2 product are used as proxy data and analyzed to determine where convective cloud core locates and to characterize the internal structure of DCCs. This work uses DCC calibration technique suggested by Doelling et al. (2004) to get DCC pixels from VIIRS Level1B data and the target region is over GOES-R ABI check-out spatial domain (20°N-20°S and 109.5°W-69.5°W). Identification of DCC pixels from VIIRS Level2 product is followed by International Satellite Cloud Climatology Project cloud classification based on cloud optical depth and cloud top pressure (Rossow and Schiffer 1999). The overlapped Level1B and Level2 DCC pixels show different patterns of cloud top properties between the visible channels and NIR channels. The preliminary results show that the patterns of DCC properties are well correlated with DCC reflectance and their relationship with DCC reflectance are differentiated between the visible and NIR channel. Among the DCC microphysical parameters, optical thickness seems to be most important variable to characterize the overshooting part of DCCs. This work provides useful guidance toward finding core and anvil part of DCCs and thus helps to reduce the DCC NIR reflectance uncertainty from Level1B data. We will use these results to validate the algorithm with the collocated DCC area from AHI and VIIRS data.
Analysis of Deep Convective Clouds (DCC) for Near Infrared Channels
Deep convective clouds (DCCs) are used as vicarious calibration targets because of their stability, brightness, and relatively small angular and spectral variation. DCCs at visible channels usually have relatively sharp histogram distributions with high reflectance. However, as a calibration target, there remains relatively large uncertainty in identifying uniform cold cloud top of DCCs at near infrared (NIR) channels. The main objective of this study is to reduce DCC reflectance uncertainty at NIR channels for GOES-R Advanced Baseline Imager (ABI). The DCC pixels information from Visible Infrared Imaging Radiometer Suite (VIIRS) Level1B and Level2 product are used as proxy data and analyzed to determine where convective cloud core locates and to characterize the internal structure of DCCs. This work uses DCC calibration technique suggested by Doelling et al. (2004) to get DCC pixels from VIIRS Level1B data and the target region is over GOES-R ABI check-out spatial domain (20°N-20°S and 109.5°W-69.5°W). Identification of DCC pixels from VIIRS Level2 product is followed by International Satellite Cloud Climatology Project cloud classification based on cloud optical depth and cloud top pressure (Rossow and Schiffer 1999). The overlapped Level1B and Level2 DCC pixels show different patterns of cloud top properties between the visible channels and NIR channels. The preliminary results show that the patterns of DCC properties are well correlated with DCC reflectance and their relationship with DCC reflectance are differentiated between the visible and NIR channel. Among the DCC microphysical parameters, optical thickness seems to be most important variable to characterize the overshooting part of DCCs. This work provides useful guidance toward finding core and anvil part of DCCs and thus helps to reduce the DCC NIR reflectance uncertainty from Level1B data. We will use these results to validate the algorithm with the collocated DCC area from AHI and VIIRS data.