The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes
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 t...
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Copernicus Publications
2024
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Online Access: | https://doi.org/10.5194/amt-17-2195-2024 https://doaj.org/article/e7ca15669fdb4abca56f2d0562f668c0 |
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ftdoajarticles:oai:doaj.org/article:e7ca15669fdb4abca56f2d0562f668c0 2024-09-15T18:35:38+00:00 The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes A. Camplani D. Casella P. Sanò G. Panegrossi 2024-04-01T00:00:00Z https://doi.org/10.5194/amt-17-2195-2024 https://doaj.org/article/e7ca15669fdb4abca56f2d0562f668c0 EN eng Copernicus Publications https://amt.copernicus.org/articles/17/2195/2024/amt-17-2195-2024.pdf https://doaj.org/toc/1867-1381 https://doaj.org/toc/1867-8548 doi:10.5194/amt-17-2195-2024 1867-1381 1867-8548 https://doaj.org/article/e7ca15669fdb4abca56f2d0562f668c0 Atmospheric Measurement Techniques, Vol 17, Pp 2195-2217 (2024) Environmental engineering TA170-171 Earthwork. Foundations TA715-787 article 2024 ftdoajarticles https://doi.org/10.5194/amt-17-2195-2024 2024-08-05T17:49:34Z 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 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="35pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="804d40643d3e7d9ed710a4fd4e2f7042"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-17-2195-2024-ie00001.svg" width="35pt" height="13pt" src="amt-17-2195-2024-ie00001.png"/></svg:svg> mm h −1 (RMSE = 0.08 mm h −1 , bias = 0.02 mm h ... Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Atmospheric Measurement Techniques 17 7 2195 2217 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Environmental engineering TA170-171 Earthwork. Foundations TA715-787 |
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Environmental engineering TA170-171 Earthwork. Foundations TA715-787 A. Camplani D. Casella P. Sanò G. Panegrossi The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes |
topic_facet |
Environmental engineering TA170-171 Earthwork. Foundations TA715-787 |
description |
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 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="35pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="804d40643d3e7d9ed710a4fd4e2f7042"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-17-2195-2024-ie00001.svg" width="35pt" height="13pt" src="amt-17-2195-2024-ie00001.png"/></svg:svg> mm h −1 (RMSE = 0.08 mm h −1 , bias = 0.02 mm h ... |
format |
Article in Journal/Newspaper |
author |
A. Camplani D. Casella P. Sanò G. Panegrossi |
author_facet |
A. Camplani D. Casella P. Sanò G. Panegrossi |
author_sort |
A. Camplani |
title |
The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes |
title_short |
The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes |
title_full |
The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes |
title_fullStr |
The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes |
title_full_unstemmed |
The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes |
title_sort |
high latitude snowfall detection and estimation algorithm for atms (handel-atms): a new algorithm for snowfall retrieval at high latitudes |
publisher |
Copernicus Publications |
publishDate |
2024 |
url |
https://doi.org/10.5194/amt-17-2195-2024 https://doaj.org/article/e7ca15669fdb4abca56f2d0562f668c0 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Atmospheric Measurement Techniques, Vol 17, Pp 2195-2217 (2024) |
op_relation |
https://amt.copernicus.org/articles/17/2195/2024/amt-17-2195-2024.pdf https://doaj.org/toc/1867-1381 https://doaj.org/toc/1867-8548 doi:10.5194/amt-17-2195-2024 1867-1381 1867-8548 https://doaj.org/article/e7ca15669fdb4abca56f2d0562f668c0 |
op_doi |
https://doi.org/10.5194/amt-17-2195-2024 |
container_title |
Atmospheric Measurement Techniques |
container_volume |
17 |
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7 |
container_start_page |
2195 |
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2217 |
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1810478818551398400 |