Retrieval of Temperature and Water Vapor vertical profile from ATMS Measurements with Random Forests technique

The Advanced Technology Microwave Sounder (ATMS) is a cross-track scanning microwave (MW) radiometer currently flying on the Suomi National Polar-orbiting Partnership (SNPP) satellite mission, that provides passive observations in the oxygen absorption band at 50÷58 GHz, in the water vapor absorption bands at 22 GHz and 183 GHz, as well as in some window frequencies, useful to retrieve Temperature (T) and Water Vapor (WV) atmospheric vertical profiles. Using spatial and temporal coincidences between ATMS observations and two different datasets of vertical T and WV, a global training dataset for machine learning purpose was built for the whole 2016. For each ATMS Brightness Temperatures (BT) acquisition, 32 levels of T (between 10 and 1000 hPa), and 23 levels of WV (between 200 and 1000 hPa), are used to train an algorithm based on Random Forests (RF) regression technique. A single RF was trained for each level and atmospheric variable, using the evaluation on the Out of Bag (OOB) error to optimize the number of random selection of the input variables at each node splitting step (hereinafter "Ntry"), the number of trees in each forest ("Ntrees") and the minimum leaf size parameter("Nleaf"), to avoid overfitting problem and obtain an accurate retrieval. Considering that the sounding below the precipitation level becomes unreliable, the precipitation-affected observations were removed from the training dataset by means of a pre-screening test based on BT.