A New Technique for Estimation of Surface Latent Heat Fluxes Using Satellite-Based Observations

Abstract

Monthly mean surface latent heat fluxes (LHFs) over the global oceans are estimated using bulk formula. LHFs are computed using wind speed (U) from the Special Sensor Microwave Imager (SSM/I), sea surface temperature (SST) from the Advanced Very High Resolution Radiometer (AVHRR), and near-surface specific humidity. Near-surface specific humidity (Qa) is estimated from SSM/I-observed precipitable water (W) and AVHRR-observed SST using a genetic algorithm (GA) approach. The GA-retrieved monthly mean Qa has an accuracy of 0.80 ± 0.32 g kg?1 as compared with surface marine observations based on the Comprehensive Ocean?Atmosphere Data Set (COADS). The GA approach improves upon the surface specific humidity retrieval based on regression, the EOF approach, and is comparable to the artificial neural network technique. The satellite-derived LHFs are compared with globally distributed surface marine observations to monthly averages of 1° ? 1° latitude?longitude bins, during 1988?93. When GA-retrieved Qa is used in the computation of satellite-derived latent heat fluxes (LHFGA) the global mean rmse, bias, and correlation are 22 ± 8 W m?2, 5 W m?2, and 0.85, respectively, for monthly mean latent heat fluxes. The rmses in LHF are larger when Qa is retrieved using regression and EOF approaches.