PNNL

Abstract

Accurate simulations of boundary layer cloud processes remain a challenge in the Earth System modeling. Observations are essential to evaluate and improve models of such processes. This study tests whether a satellite detection algorithm of continental shallow cumulus (ShCu) clouds developed using ground-based observations at the the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) [Tian et al., 2021] can be applied to the Southeastern United States (SEUS), where ShCu populations are more prevalent. This study firstly generates surface reflectance maps at the SEUS and then identifies a ShCu pixel with a reflectance detection threshold using Geostationary Operational Environmental Satellite (GOES) data. Cloud fractions (CFs) for ShCu cases from this algorithm are compared against CFs from ground-based ceilometers. Factors (e.g., different observed areas, satellite parallax issue, systemic biases) that contribute to the discrepancies between CFs from satellite and ceilometer are taken into consideration. In conclusion, we find that it is feasible to apply the detection algorithm of continental ShCu clouds developed at SGP [Tian et al., 2021] to the SEUS with a detection threshold as 0.055, which allows for the reproduction of half-hourly and hourly CFs at the SEUS using GOES satellite data. Based on such algorithm, a valuable dataset of ShCu cloud mask can be produced for the SEUS region, which is beneficial for investigations related to cloud morphology and land-atmosphere-cloud interactions.