A Fast Radiative Transfer Parameterization Under Cloudy Condition in Solar Spectral Region

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) system, which is proposed and developed by NASA, will directly measure the Earth's thermal infrared spectrum (IR), the spectrum of solar radiation reflected by the Earth and its atmosphere (RS), and radio occultation (RO). IR, RS, and RO measurements provide information on the most critical but least understood climate forcings, responses, and feedbacks associated with the vertical distribution of atmospheric temperature and water vapor, broadband reflected and emitted radiative fluxes, cloud properties, surface albedo, and surface skin temperature. To perform Observing System Simulation Experiments (OSSE) for long term climate observations, accurate and fast radiative transfer models are needed. The principal component-based radiative transfer model (PCRTM) is one of the efforts devoted to the development of fast radiative transfer models for simulating radiances and reflecatance observed by various hyperspectral instruments. Retrieval algorithm based on PCRTM forward model has been developed for AIRS, NAST, IASI, and CrIS. It is very fast and very accurate relative to the training radiative transfer model. In this work, we are extending PCRTM to UV-VIS-near IR spectral region. To implement faster cloudy radiative transfer calculations, we carefully investigated the radiative transfer process under cloud condition. The cloud bidirectional reflectance was parameterized based on off-line 36-stream multiple scattering calculations while few other lookup tables were generated to describe the effective transmittance and reflectance of the cloud-clear-sky coupling system in solar spectral region. The bidirectional reflectance or the irradiance measured by satellite may be calculated using a simple fast radiative transfer model providing the type of cloud (ice or water), optical depth of the cloud, optical depth of both atmospheric trace gases above and below clouds, particle size of the cloud, as well as the surface albedo of the earth are given. The fast model is about 3 orders faster than 36-stream DISORT. In the 11,700,000 cases we investigated for ice and water cloud, respectively, the mean and standard deviation of the fast model errors were 0.86% and 1.51% for ice cloud, and 1.93% and 3.71% for water cloud, respectively.