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In many hyper-arid ecosystems, such as the Atacama Desert, fog is the most important fresh water source. To study biological and geological processes in such water-limited regions, knowledge about the spatio-temporal distribution and variability of fog presence is necessary. In this study, in-situ measurements provided by a network of climate stations equipped, inter alia, with leaf wetness sensors are utilized to create a reference fog data set which enables the validation of satellite-based fog retrieval methods. Further, a new satellite-based fog detection approach is introduced which uses brightness temperatures measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) as input for a neural network. Such a machine learning technique can exploit all spectral information of the satellite data and represent potential non-linear relationships. Compared to a second fog detection approach based on MODIS cloud top height retrievals, the neural network reaches a higher detection skill (Heidke skill score of 0.56 compared to 0.49). A suitable representation of temporal variability on subseasonal time scales is provided with correlations mostly greater than 0.7 between fog occurrence time series derived from the neural network and the reference data for individual climate stations, respectively. Furthermore, a suitable spatial representativity of the neural network approach to expand the application to the whole region is indicated. Three-year averages of fog frequencies reveal similar spatial patterns for the austral winter season for both approaches. However, differences are found for the summer and potential reasons are discussed.

Towards a climatology of fog frequency in the Atacama Desert via multi-spectral satellite data and machine learning techniques