乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:16:24Z","timestamp":1740154584164,"version":"3.37.3"},"reference-count":76,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"This work presents an algorithm based on a neural network (NN) for cloud detection to detect clouds and their thermodynamic phase using spectral observations from spaceborne microwave radiometers. A standalone cloud detection algorithm over the ocean and land has been developed to distinguish clear sky versus ice and liquid clouds from microwave sounder (MWS) observations. The MWS instrument\u2014scheduled to be onboard the first satellite of the Eumetsat Polar System Second-Generation (EPS-SG) series, MetOp-SG A1\u2014has a direct inheritance from advanced microwave sounding unit A (AMSU-A) and the microwave humidity sounder (MHS) microwave instruments. Real observations from the MWS sensor are not currently available as its launch is foreseen in 2024. Thus, a simulated dataset of atmospheric states and associated MWS synthetic observations have been produced through radiative transfer calculations with ERA5 real atmospheric profiles and surface conditions. The developed algorithm has been validated using spectral observations from the AMSU-A and MHS sounders. While ERA5 atmospheric profiles serve as references for the model development and its validation, observations from AVHRR cloud mask products provide references for the AMSU-A\/MHS model evaluation. The results clearly show the NN algorithm\u2019s high skills to detect clear, ice and liquid cloud conditions against a benchmark. In terms of overall accuracy, the NN model features 92% (88%) on the ocean and 87% (85%) on land, for the MWS (AMSU-A\/MHS)-simulated dataset, respectively.<\/jats:p>","DOI":"10.3390\/rs15071798","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T10:15:15Z","timestamp":1679998515000},"page":"1798","source":"Crossref","is-referenced-by-count":4,"title":["A Cloud Detection Neural Network Approach for the Next Generation Microwave Sounder Aboard EPS MetOp-SG A1"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2588-6082","authenticated-orcid":false,"given":"Salvatore","family":"Larosa","sequence":"first","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis, National Research Council (IMAA\/CNR), 85100 Potenza, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5962-223X","authenticated-orcid":false,"given":"Domenico","family":"Cimini","sequence":"additional","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis, National Research Council (IMAA\/CNR), 85100 Potenza, Italy"},{"name":"Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS), University of L\u2019Aquila, 67100 L\u2019Aquila, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6814-193X","authenticated-orcid":false,"given":"Donatello","family":"Gallucci","sequence":"additional","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis, National Research Council (IMAA\/CNR), 85100 Potenza, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9892-0582","authenticated-orcid":false,"given":"Francesco","family":"Di Paola","sequence":"additional","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis, National Research Council (IMAA\/CNR), 85100 Potenza, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2630-8727","authenticated-orcid":false,"given":"Saverio Teodosio","family":"Nilo","sequence":"additional","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis, National Research Council (IMAA\/CNR), 85100 Potenza, Italy"}]},{"given":"Elisabetta","family":"Ricciardelli","sequence":"additional","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis, National Research Council (IMAA\/CNR), 85100 Potenza, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6304-4655","authenticated-orcid":false,"given":"Ermann","family":"Ripepi","sequence":"additional","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis, National Research Council (IMAA\/CNR), 85100 Potenza, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0544-073X","authenticated-orcid":false,"given":"Filomena","family":"Romano","sequence":"additional","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis, National Research Council (IMAA\/CNR), 85100 Potenza, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112971","DOI":"10.1016\/j.rse.2022.112971","article-title":"Machine learning-based retrieval of day and night cloud macrophysical parameters over East Asia using Himawari-8 data","volume":"273","author":"Yang","year":"2022","journal-title":"Remote Sens. 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