Mapping regional air temperature fields using satellite-derived surface skin temperatures

Screen air temperature is an important climatological variable and accurate mapping of its spatial and temporal distribution is of great interest for various scientific disciplines. The low spatial density of meteorological stations, however, results in relatively large errors during data interpolation and makes it difficult to retrieve the spatial pattern of the temperature field. Errors of the order of 1 to 3 K are mentioned in the literature. The current study investigates the possibilities of mapping and monitoring the spatial distribution of daily maximum air temperatures with the help of time series of NOAA-AVHRR images. The study has been performed for the Mediterranean region of Andalusia in southern Spain. Data analysis included 31 meteorological stations and 148 AVHRR images from the year 1992. Regression analysis between the daily maximum air temperature (Tmax) and the mean surface skin temperature (Ts) retrieved for 11 km2image windows centred over each station, suggests that Tmaxis strongly linked to Tsin the given environment (mean R2=0·823) and that for individual stations Tmaxcan be retrieved from Tswith a mean error of about 2 K. The spatial representativity of the station measurements as well as the influence of altitude and land use on the results are discussed. Finally, the possibilities of retrieving the spatial pattern of Tmaxhave been evaluated through a cross-validation approach. In this analysis Tmaxhas been predicted for each station and for all days of available image data based on a regression model retrieved from all other stations. Again the results indicate that we are able to reproduce the daily distribution of maximum air temperatures with a mean error of the order of 2 to 2·5 K, using satellite-retrieved surface skin temperatures. In addition, the method allows for the detection of stations with a low spatial representativity or a pronounced measurement bias. Future research will aim at the inclusion of further physiographic data, the grouping of stations according to site-specific characteristics and an analysis according to seasons.