Downscaling of air humidity based on thermal inertia

Air humidity is an important input variable to estimate terrestrial evapotranspiration based on satellite remote sensing. The air humidity obtained by the observations from surface meteorological stations is limited in their spatial and temporal representation. The validated GDAS (Global Data Assimilation System) dataset can provide the simulated data every 3 hours [1]; and the FY-2C (the first operational geostationary meteorological satellite FengYun-2C from China) can provide the data of land surface temperature hourly. Moreover, they are of great value in downscaling the air humidity. Air humidity (2m height above the ground level) both from thermometer screen to high ground is driven by the advection, the turbulence and the radiation processes [2]. As Zent et al. [3] put it in 1993, thermal inertia can be used to study the changes of air humidity. North China was chosen as the study area. Using the GDAS forcing dataset, the MODIS, FY-2C and ground observations, a new method is proposed to downscale the near surface air humidity based on thermal inertia. Finally, in order to evaluate the performance of our method, Inverse Distance Weighted (IDW) interpolation, SPLINE interpolation, together with the ground measurements were used to compare with our method.