Empirical models for estimating daily surface water vapour pressure, air temperature, and humidity using MODIS and spatiotemporal variables. Applications to peninsular Spain

In this article, we present empirical models for estimating daily surface water vapour pressure (e(0)), air temperature (T-a), and relative humidity (RH) over cloud-free land areas in peninsular Spain using Moderate Resolution Imaging Spectroradiometer (MODIS) and spatiotemporal variables. The models were obtained and validated using daily mean, maximum, and minimum e(0), T-a, and RH data (year 2010) from 331 ground-level meteorological stations and the diurnal Terra-MODIS data in peninsular Spain, but the methodology can easily be extrapolated and used to obtain algorithms for other regions around the world. The best e(0) models are based on total precipitable water (W) estimations obtained by MOD05 or IMAPP WVNIR products and the spatiotemporal variables of longitude (), distance to the coast (d(coast)), and Julian day (JD). Other models based on Sobrino's W algorithm or on Recondo's e(0) algorithm for Asturias (in northern Spain) were also tested. The best T-a models are based on land surface temperature (LST) obtained by the MOD11 LST or IMAPP LST products and on other remote-sensing variables, such as W and the normalized difference vegetation index (NDVI), and the spatiotemporal variables , JD, and height (h). Models based on Sobrino's LST algorithms were also tested. RH can be derived directly from e(0) and T-a or from models similar to those used to obtain e(0) and T-a. Models based on the NASA standard products MOD05 and MOD11 LST are slightly better than those based on IMAPP products, but the advantage of IMAPP products for our purposes is that they can be generated in almost real time from the data obtained by the MODIS antenna at the University of Oviedo. IMAPP models obtain the following: R-2=0.83-0.79-0.70 and RSE=1.62-1.59-1.76 hPa for e(0mean), e(0max), and e(0min); and R-2=0.91-0.91-0.80 and RSE=1.96-2.25-3.00K for T-mean, T-max, and T-min. Worse results are obtained for RH: R-2=0.49-0.39 and RSE=7.21-9.75% for RHmin and RHmean, with no correlation found for RHmax. Model validations yield R-2 and RSE values similar to those obtained in the models, with an RMSD=1.86-1.99-2.21 hPa for e(0mean), e(0max), and e(0min); an RMSD=2.05-2.40-2.95K for T-mean, T-max, and T-min; and RMSD=8-11% for RHmin and RHmean. The bias is small in all cases:<0.2 hPa for e(0), 0.1K for T-a, and|1|% for RH. From the results of this article, we propose substituting the traditionally used RH variable with the e(0) variable to be used as meteorological variable in environmental risk models such as, for example, fire risk models.