In 1990, the Committee on Earth and the Environmental Sciences reported that the understanding of the effects of clouds in climate and hydrologic systems was the highest priority of the U. S. Global Change Research Program. In recognition of this, hemispheric sky imagers were added at SURFRAD stations in 1999. Measurements of cloud fraction and cloud opacity, in conjunction with surface radiation budget measurements and atmospheric fluxes at some sites make SURFRAD data ideal for studies of the effects of clouds on the surface radiation budget, and for validation of the surface physics formulations of weather, hydrology, and climate models. Data from SURFRAD have been used to validate surface irradiance estimates from NASAs EOS satellites, satellite-based retrievals of surface erythemal UV radiation, the national UV index, real-time NESDIS products, and NCEPs operational models. SURFRAD data have also been used for carbon sequestration studies, as validation for radiative transfer codes, for basic research and instruction at universities, and many other applications.
Stations of the SURFRAD network are located in diverse climate regions of the U.S. This is important because it is likely that changing climate patterns will be manifested as extraordinary changes in regional climate characteristics. With five years of high quality surface radiation budget measurements, preliminary assessments of the surface radiation climatologies at the stations are feasible. For example, it may be possible to quantitatively infer regional climatological effects that result from anomalous weather patterns, such as those brought about by El Nino and La Nina, both of which fortuitously coincided with the SURFRAD deployment. Surface radiation climatologies for each station, as well as quantitative insights derived from these analyses will be presented. Novel software that can extract cloud effect information from solar radiation measurements alone has been adapted and successfully applied to SURFRAD data. These results will be compiled over the same intervals as the climatological averages and will be used to corroborate inferences from the climatological analyses.