| The land surface skin temperature diurnal cycle (LSTD) is very important for the understanding of surface climate and for evaluating climate models. This variable, however, cannot be obtained globally from polar-orbiting satellites because the satellites usually pass a given area twice per day and because their infrared channels cannot observe the surface when the sky is cloudy.;In order to more optimally use the satellite data, this research is designed, for the first time, to solve the above two problems by advance use of remote sensing techniques and climate modeling. Specifically, this work is divided into two parts. Part one deals with obtaining the skin temperature diurnal cycle for cloud-free cases. We have developed a "cloud-free algorithm" to combine model results with satellite and surface-based observations, thus interpolating satellite twice-daily observations to the diurnal cycle. Part two studies the cloudy cases. The "cloudy-pixel treatment" presented here is a hybrid technique of "neighboring-pixel" and "surface air temperature" approaches. The whole algorithm has been tested against field experiments and climate model CCM3/BATS in global and single column mode simulations. It shows that this proposed algorithm can obtain skin temperature diurnal cycles with an accuracy of 1--2 K at the monthly pixel level. |
