Abstract
Abstract. Remotely sensed brightness temperatures from passive observations in the microwave (MW) range are used to retrieve various geophysical parameters, e.g. near-surface temperature. Cloud contamination, although less of an issue at MW than at visible to infrared wavelengths, may adversely affect retrieval quality, particularly in the presence of strong cloud formation (convective towers) or precipitation. To limit errors associated with cloud contamination, we present an index derived from stand-alone MW brightness temperature observations, which measure the probability of residual cloud contamination. The method uses a statistical neural network model trained with the Global Precipitation Microwave Imager (GMI) observations and a cloud classification from Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). This index is available over land and ocean and is developed for multiple frequency ranges to be applicable to successive generations of MW imagers. The index confidence increases with the number of available frequencies and performs better over the ocean, as expected. In all cases, even for the more challenging radiometric signatures over land, the model reaches an accuracy of ≥70 % in detecting contaminated observations. Finally an application of this index is shown that eliminates grid cells unsuitable for land surface temperature estimation.
Highlights
Visible–infrared satellite imagers provide excellent information about land surface characterization
Adding more information by using the channels more sensitive to ice content leads to a better detection of cloud contamination, we show here that it is possible to filter out cloud-contaminated measurements even above land with a restricted number of channels
Passive microwave observations from satellites are less sensitive to clouds than visible–infrared measurements and can provide an almost “all weather” land surface characterization
Summary
Visible–infrared (vis–IR) satellite imagers provide excellent information about land surface characterization. Passive microwave observations from satellites can partly fill this gap: they are much less sensitive to clouds and can provide valuable estimates of surface properties, despite their coarser spatial and temporal resolutions. Land surface temperature can be retrieved from IR observations for ∼ 60 % of the locations with a spatial resolution of 1 km twice a day from polar orbiters (Prata et al, 1995) and with a spatial resolution of 2 km every 15 min from geostationary satellites Long time series of land surface temperature estimations with passive microwave observations are under construction, using different generations of passive microwave satellite instruments to be used in synergy with IR estimates Long time series of land surface temperature estimations with passive microwave observations are under construction, using different generations of passive microwave satellite instruments to be used in synergy with IR estimates (e.g. Prigent et al, 2016; Jiménez et al, 2017)
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