| Summary: | The air temperature near the Earth’s surface is one of the most important meteorological and climatological parameters. Yet, accurate and timely readings are not available in significant parts of the world. The development and first validation of a methodology for the estimation of the near-surface air temperature (NSAT) is presented here. Machine learning and satellite products are at the core of the developed model. Land Surface Analysis Satellite Application Facility (LSA SAF) products related to Earth’s surface radiation, temperature and humidity budgets, albedo and land cover, along with static topography parameters and weather station measurements, are used in the analysis. A series of experiments showed that the Random Forest regression with 20 selected satellite and topography predictors was the optimum selection for the estimation of the NSAT. The mean absolute error (MAE) of the NSAT estimation model was 0.96 °C, while the mean biased error (MBE) was −0.01 °C and the R<sup>2</sup> was 0.976. Limited seasonality was present in the efficiency of the model, while an increase in errors was noted during the first morning and afternoon hours. The topography influence in the model efficiency was rather limited. Cloud-free conditions were associated to only marginally smaller errors, supporting the applicability of the model under both cloud-free and cloudy conditions.
|
|---|
