Thursday, 13 February 2003
Retrieval of water vapor over land surfaces from microwave measurements
Satellite derived water vapor analyses have shortcomings with respect to spatial coverage. Infrared instruments have very limited capabilities in cloudy areas. Microwave measurements are skillful in many cloudy conditions, but have had marginal skill over land surfaces. Operational applications of retrievals from microwave data from sensors such as SSM/I and AMSU have been largely confined to ocean areas, where microwave signatures of water vapor conditions are strongest. The difference in retrieval skill between ocean and land has been generally attributed to differences in surface emissivity and its stability over time, but there has not been a thorough understanding of the relative importance of various factors. We show in this paper that the skill with which atmospheric water vapor (and cloud water amounts) can be retrieved over land is dominated by the surface emissivity near 23 GHz in particular and by the precision of any prior knowledge of that emissivity. The emissivity and its stability over time are closely associated with vegetative cover.
Our findings have significant implications for design of microwave sensors and retrieval algorithms. A localized, dynamic database can provide the critical prior knowledge of microwave emissivities. Considering that emissivities can change suddenly, it is essential to test current measurements to see whether emissivities have departed from recent behavior, and to have alternative retrieval options depending on the degree of emissivity change. One effective option is to employ pre-classification of scenes. The pre-classification and retrieval processes each benefit substantially from measurement in horizontal (H) polarization near 23 GHz. No operational sensors have included a 23 GHz H channel. These findings have been applied to the design of the forthcoming Conical-scanning Microwave Imager/Sounder (CMIS), including measurement in V and H polarization near 23 GHz and the use of cross-sensor infrared data to aid in building and maintaining an emissivity database.