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

Abstract

In this article, we present empirical models for estimating daily surface water vapour pressure (e₀), 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 e₀, 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 e₀ models are based on total precipitable water (W) estimations obtained by MOD05 or IMAPP WVNIR products and the spatiotemporal variables of longitude (λ), distance to the coast (d_coast), and Julian day (JD). Other models based on Sobrino’s W algorithm or on Recondo’s e₀ 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 λ, JD, and height (h). Models based on Sobrino’s LST algorithms were also tested. RH can be derived directly from e₀ and T_a or from models similar to those used to obtain e₀ 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: R² = 0.83-0.79-0.70 and RSE = 1.62-1.59-1.76 hPa for e_0mean, e_0max, and e_0min; and R² = 0.91-0.91-0.80 and RSE = 1.96-2.25-3.00 K for T_mean, T_max, and T_min. Worse results are obtained for RH: R² = 0.49-0.39 and RSE = 7.21-9.75% for RH_min and RH_mean, with no correlation found for RH_max. Model validations yield R² 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 = 2.05-2.40-2.95 K for T_mean, T_max, and T_min; and RMSD = 8-11% for RH_min and RH_mean. The bias is small in all cases: <0.2 hPa for e₀, ≤0.1 K for T_a, and ≤ |1|% for RH. From the results of this article, we propose substituting the traditionally used RH variable with the e₀ variable to be used as meteorological variable in environmental risk models such as, for example, fire risk models.

Acknowledgements

We wish to thank NASA for providing the MOD05 and MOD11 products used in this study and the Space Science and Engineering Center (SSEC at University of Wisconsin–Madison) and NASA for the IMAPP software, all free of charge. In addition, we wish to thank the Spanish Ministry of Science and Innovation for funding the FireGlobe project (FIREGLOBE project: Ref. CGL2008-01083).