Cloud Radiative Effect by Cloud Types Based on Radiative Transfer Model Calculations and Collocated A-Train Data

Abstract:

Cloud radiative effect is sensitive to both cloud types and the atmospheric conditions that are correspondent with the clouds. It is important to separate the radiative effects due to the microphysical and radiative properties of clouds and the impact of clouds on clear atmosphere radiation. To better quantify these components of cloud radiative effects, we construct a data record of water vapor, temperature, TOA shortwave and long-wave radiations, and cloud properties from collocated A-Train satellite observations and NASA MERRA reanalysis, stratified according to cloud types determined by MODIS observations. The sensitivity of cloud radiative effects on the properties of cloud is investigated in this study using the observation data. The cloud masking effect is quantified for different cloud types using the Fu and Liou radiative transfer model and the observed cloudy and clear atmospheric conditions. The sampling biases of the satellite observed temperature and water vapor vertical distributions are quantified based on comparisons between satellite observations and reanalysis, and then incorporated into the radiative transfer calculations to study the impact of these observational biases on cloud radiative effect estimation from the temperature and water vapor profiles obtained from satellite.