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The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes
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The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes
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The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes
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Journal Article
The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes
2024
Overview
The High lAtitude sNow Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS) is a new machine-learning (ML)-based snowfall retrieval algorithm for Advanced Technology Microwave Sounder (ATMS) observations that has been developed specifically to detect and quantify high-latitude snowfall events that often form in cold, dry environments and produce light snowfall rates. ATMS and the future European MetOp-SG Microwave Sounder offer good high-latitude coverage and sufficient microwave channel diversity (23 to 190 GHz), which allows surface radiometric properties to be dynamically characterized and the non-linear and sometimes subtle passive microwave response to falling snow to be detected. HANDEL-ATMS is based on a combined active–passive microwave observational dataset in the training phase, where each ATMS multichannel observation is associated with coincident (in time and space) CloudSat Cloud Profiling Radar (CPR) vertical snow profiles and surface snowfall rates. The main novelty of the approach is the radiometric characterization of the background surface (including snow-covered land and sea ice) at the time of the overpass to derive the multichannel surface emissivities and clear-sky contribution to be used in the snowfall retrieval process. The snowfall retrieval is based on four different artificial neural networks (ANNs) for snow water path (SWP) and surface snowfall rate (SSR) detection and estimate. HANDEL-ATMS shows very good detection capabilities, POD = 0.83, FAR = 0.18, and HSS = 0.68, for the SSR detection module. Estimation error statistics show a good agreement with CPR snowfall products for SSR >10-2 mm h−1 (RMSE = 0.08 mm h−1, bias = 0.02 mm h−1). The analysis of the results for an independent CPR dataset and of selected snowfall events is evidence of the unique capability of HANDEL-ATMS to detect and estimate SWP and SSR also in the presence of extremely cold and dry environmental conditions typical of high latitudes.
