{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:03:53Z","timestamp":1787029433143,"version":"3.56.0"},"reference-count":106,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T00:00:00Z","timestamp":1647561600000},"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":"<jats:p>This article describes the development of a machine learning (ML)-based algorithm for snowfall retrieval (Snow retrievaL ALgorithm fOr gpM\u2013Cross Track, SLALOM-CT), exploiting ATMS radiometer measurements and using the CloudSat CPR snowfall products as references. During a preliminary analysis, different ML techniques (tree-based algorithms, shallow and convolutional neural networks\u2014NNs) were intercompared. A large dataset (three years) of coincident observations from CPR and ATMS was used for training and testing the different techniques. The SLALOM-CT algorithm is based on four independent modules for the detection of snowfall and supercooled droplets, and for the estimation of snow water path and snowfall rate. Each module was designed by choosing the best-performing ML approach through model selection and optimization. While a convolutional NN was the most accurate for the snowfall detection module, a shallow NN was selected for all other modules. SLALOM-CT showed a high degree of consistency with CPR. Moreover, the results were almost independent of the background surface categorization and the observation angle. The reliability of the SLALOM-CT estimates was also highlighted by the good results obtained from a direct comparison with a reference algorithm (GPROF).<\/jats:p>","DOI":"10.3390\/rs14061467","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"1467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Machine Learning Snowfall Retrieval Algorithm for ATMS"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7059-1043","authenticated-orcid":false,"given":"Paolo","family":"San\u00f2","sequence":"first","affiliation":[{"name":"National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniele","family":"Casella","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Camplani","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Rome, Italy"},{"name":"Geodesy and Geomatics Division, Department of Civil, Constructional and Environmental Engineering (DICEA), Sapienza University of Rome, 00184 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4541-8374","authenticated-orcid":false,"given":"Leo Pio","family":"D\u2019Adderio","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5170-7087","authenticated-orcid":false,"given":"Giulia","family":"Panegrossi","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"D19102","DOI":"10.1029\/2005JD006773","article-title":"Snow characterization at a global scale passive microwave satelite observations","volume":"111","author":"Cordisco","year":"2006","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1175\/JAMC-D-18-0124.1","article-title":"Satellite Estimation of Falling Snow: A Global Precipitation Measurement (GPM) Core Observatory Perspective","volume":"58","author":"Kulie","year":"2019","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, G. (2020). Radar Snowfall Measurement. Advances in Global Change Research, Springer.","DOI":"10.1007\/978-3-030-24568-9_16"},{"key":"ref_4","first-page":"4300913","article-title":"Passive Microwave Signatures and Retrieval of High-Latitude Snowfall Over Open Oceans and Sea Ice: Insights from Coincidences of GPM and CloudSat Satellites","volume":"60","author":"Vahedizade","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4468","DOI":"10.1002\/2015JD024546","article-title":"Status of high-latitude precipitation estimates from observations and reanalyses","volume":"121","author":"Behrangi","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9512","DOI":"10.1002\/2015GL065497","article-title":"Importance of snow to global precipitation","volume":"42","author":"Field","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13987","DOI":"10.1029\/96JD03090","article-title":"Precipitation characteristics in Greenland-Iceland-Norwegian Seas determined by using satellite microwave data","volume":"102","author":"Liu","year":"1997","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"145","DOI":"10.3390\/rs3010145","article-title":"Detection and Measurement of Snowfall from Space","volume":"3","author":"Levizzani","year":"2011","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1175\/BAMS-D-14-00283.1","article-title":"So, How Much of the Earth\u2019s Surface Is Covered by Rain Gauges?","volume":"98","author":"Kidd","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8689","DOI":"10.1175\/JCLI-D-18-0163.1","article-title":"Using GRACE to Estitmate Snowfall Accumulation and Assess Gauge Undercatch Corrections in High Latitudes","volume":"31","author":"Behrangi","year":"2018","journal-title":"J. Clim."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1007\/s13143-019-00161-6","article-title":"Comparative Analysis of Snowfall Accumulation and Gauge Undercatch Correction Factors from Diverse Data Sets: In Situ, Satellite, and Reanalysis","volume":"56","author":"Panahi","year":"2019","journal-title":"Asia-Pac. J. Atmos. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1918","DOI":"10.1002\/2014JD022427","article-title":"A snowfall detection algorithm over land utilizing high-frequency passive microwave measurements-Application to ATMS","volume":"120","author":"Kongoli","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1002\/qj.3270","article-title":"A hybrid snowfall detection method from satellite passive microwave measurements and global forecast weather models","volume":"144","author":"Kongoli","year":"2018","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kongoli, C., Pellegrino, P., Ferraro, R., Grody, N.C., and Meng, H. (2003). A new snowfall detection algorithm over land using measurements from the Advanced Microwave Sounding Unit (AMSU). Geophys. Res. Lett., 30.","DOI":"10.1029\/2003GL017177"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rysman, J.-F., Panegrossi, G., San\u00f2, P., Marra, A.C., Dietrich, S., Milani, L., and Kulie, M.S. (2018). SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager. Remote Sens., 10.","DOI":"10.3390\/rs10081278"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1175\/BAMS-D-13-00262.1","article-title":"Global Precipitation Measurement Cold Season Precipitation Experiment (GCPEX): For Measurement\u2019s Sake, Let It Snow","volume":"96","author":"Hudak","year":"2015","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4177","DOI":"10.1109\/TGRS.2012.2227763","article-title":"Detection Thresholds of Falling Snow From Satellite-Borne Active and Passive Sensors","volume":"51","author":"Johnson","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"D22216","DOI":"10.1029\/2005JD006826","article-title":"Development of a snowfall retrieval algorithm at high microwave frequencies","volume":"111","author":"Noh","year":"2006","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1634","DOI":"10.1175\/2007JAMC1728.1","article-title":"Precipitating Snow Retrievals from Combined Airborne Cloud Radar and Millimeter-Wave Radiometer Observations","volume":"47","author":"Grecu","year":"2008","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.atmosres.2012.10.011","article-title":"Evaluation of precipitation detection over various surfaces from passive microwave imagers and sounders","volume":"131","author":"Munchak","year":"2013","journal-title":"Atmos. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1002\/jgrd.50172","article-title":"Detecting snowfall over land by satellite high-frequency microwave observations: The lack of scattering signature and a statistical approach","volume":"118","author":"Liu","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6154","DOI":"10.1002\/2017GL073451","article-title":"Microwave retrievals of terrestrial precipitation over snow-covered surfaces: A lesson from the GPM satellite","volume":"44","author":"Ebtehaj","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1175\/JHM-D-16-0190.1","article-title":"Quantifying the Snowfall Detection Performance of the GPM Microwave Imager Channels over Land","volume":"18","author":"You","year":"2017","journal-title":"J. Hydrometeorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3471","DOI":"10.1175\/2010JAS3520.1","article-title":"Uncertainties in Microwave Properties of Frozen Precipitation: Implications for Remote Sensing and Data Assimilation","volume":"67","author":"Kulie","year":"2010","journal-title":"J. Atmos. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Skofronick-Jackson, G., and Johnson, B.T. (2011). Surface and atmospheric contributions to passive microwave brightness temperatures for falling snow events. J. Geophys. Res. Earth Surf., 116.","DOI":"10.1029\/2010JD014438"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.5194\/amt-8-1913-2015","article-title":"On the microwave optical properties of randomly oriented ice hydrometeors","volume":"8","author":"Eriksson","year":"2015","journal-title":"Atmos. Meas. Tech."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bennartz, R., and Bauer, P. (2003). Sensitivity of microwave radiances at 85-183 GHz to precipitating ice particles. Radio Sci., 38.","DOI":"10.1029\/2002RS002626"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1256\/qj.05.164","article-title":"Passive microwave radiometer channel selection based on cloud and precipitation information content","volume":"132","author":"Bauer","year":"2006","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Edel, L., Rysman, J.-F., Claud, C., Palerme, C., and Genthon, C. (2019). Potential of Passive Microwave around 183 GHz for Snowfall Detection in the Arctic. Remote Sens., 11.","DOI":"10.3390\/rs11192200"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Panegrossi, G., Rysman, J.-F., Casella, D., Marra, A.C., San\u00f2, P., and Kulie, M.S. (2017). CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities. Remote Sens., 9.","DOI":"10.3390\/rs9121263"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kongoli, C., Meng, H., Dong, J., and Ferraro, R. (2020). Ground-based Assessment of Snowfall Detection over Land Using Polarimetric High Frequency Microwave Measurements. Remote Sens., 12.","DOI":"10.3390\/rs12203441"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1016\/j.jhydrol.2016.07.047","article-title":"Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA\/NSSL Multi-Radar Multi-Sensor System","volume":"541","author":"Chen","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1175\/JHM-D-15-0123.1","article-title":"A Shallow Cumuliform Snowfall Census Using Spaceborne Radar","volume":"17","author":"Kulie","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kulie, M.S., Milani, L., Wood, N.B., and L\u2019Ecuyer, T.S. (2020). Global Snowfall Detection and Measurement. Advances in Global Change Research, Springer.","DOI":"10.1007\/978-3-030-35798-6_12"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hamada, A., Iguchi, T., and Takayabu, Y.N. (2020). Snowfall Detection by Spaceborne Radars. Advances in Global Change Research, Springer.","DOI":"10.1007\/978-3-030-35798-6_13"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.atmosres.2017.06.018","article-title":"Evaluation of the GPM-DPR snowfall detection capability: Comparison with CloudSat-CPR","volume":"197","author":"Casella","year":"2017","journal-title":"Atmos. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e2020EA001357","DOI":"10.1029\/2020EA001357","article-title":"Comparative Assessment of Snowfall Retrieval from Microwave Humidity Sounders Using Machine Learning Methods","volume":"7","author":"Adhikari","year":"2020","journal-title":"Earth Space Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1175\/JHM-D-18-0021.1","article-title":"A Prognostic Nested k-Nearest Approach for Microwave Precipitation Phase Detection over Snow Cover","volume":"20","author":"Takbiri","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5194\/amt-9-9-2016","article-title":"The microwave properties of simulated melting precipitation particles: Sensitivity to initial melting","volume":"9","author":"Johnson","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.atmosres.2012.06.008","article-title":"Liquid water in snowing clouds: Implications for satellite remote sensing of snowfall","volume":"131","author":"Wang","year":"2012","journal-title":"Atmos. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2718","DOI":"10.1109\/36.803419","article-title":"A neural-network approach to radiometric sensing of land-surface parameters","volume":"37","author":"Liou","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"14887","DOI":"10.1029\/2001JD900085","article-title":"A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations","volume":"106","author":"Aires","year":"2001","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"E1016","DOI":"10.1175\/BAMS-D-20-0031.1","article-title":"Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences","volume":"102","author":"Boukabara","year":"2021","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_44","first-page":"299","article-title":"Neural Network Applications in High-Resolution Atmospheric Remote Sensing","volume":"15","author":"Blackwell","year":"2005","journal-title":"Linc. Lab. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/TGRS.2007.908302","article-title":"Global Millimeter-Wave Precipitation Retrievals Trained With a Cloud-Resolving Numerical Weather Prediction Model, Part I: Retrieval Design","volume":"46","author":"Surussavadee","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.atmosres.2011.09.003","article-title":"Artificial neural network based microwave precipitation estimation using scattering index and polarization corrected temperature","volume":"102","author":"Mahesh","year":"2011","journal-title":"Atmos. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5441","DOI":"10.5194\/amt-9-5441-2016","article-title":"The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: Description and verification study over Europe and Africa using GPM and TRMM spaceborne radars","volume":"9","author":"Panegrossi","year":"2016","journal-title":"Atmos. Meas. Tech."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"San\u00f2, P., Panegrossi, G., Casella, D., Marra, A.C., D\u2019Adderio, L.P., Rysman, J.F., and Dietrich, S. (2018). The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer. Remote Sens., 10.","DOI":"10.3390\/rs10071122"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., and Aryal, J. (2019). Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Prakash, N., Manconi, A., and Loew, S. (2020). Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-11876"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"ES473","DOI":"10.1175\/BAMS-D-18-0324.1","article-title":"Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges","volume":"100","author":"Boukabara","year":"2019","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.rse.2003.12.002","article-title":"Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM\/I data","volume":"90","author":"Tedesco","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s00521-009-0320-9","article-title":"Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran","volume":"19","author":"Tabari","year":"2009","journal-title":"Neural Comput. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"13593","DOI":"10.1029\/2019GL084576","article-title":"Retrieving Surface Snowfall with the GPM Microwave Imager: A New Module for the SLALOM Algorithm","volume":"46","author":"Rysman","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Tsai, Y.-L.S., Dietz, A., Oppelt, N., and Kuenzer, C. (2019). Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique. Remote Sens., 11.","DOI":"10.3390\/rs11080895"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.1175\/JTECH-D-19-0055.1","article-title":"Method for Classification of Snowflakes Based on Images by a Multi-Angle Snowflake Camera Using Convolutional Neural Networks","volume":"36","author":"Hicks","year":"2019","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1175\/WAF1000.1","article-title":"Real-Time Forecasting of Snowfall Using a Neural Network","volume":"22","author":"Roebber","year":"2007","journal-title":"Weather Forecast."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, Y., Cheng, X., and Hu, Y. (2019). Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11232864"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3985","DOI":"10.1109\/JSTARS.2017.2713485","article-title":"The Cloud Dynamics and Radiation Database Algorithm for AMSR2: Exploitation of the GPM Observational Dataset for Operational Applications","volume":"10","author":"Casella","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1175\/1520-0469(1998)055<1644:UOCMMF>2.0.CO;2","article-title":"Use of Cloud Model Microphysics for Passive Microwave-Based Precipitation Retrieval: Significance of Consistency between Model and Measurement Manifolds","volume":"55","author":"Panegrossi","year":"1998","journal-title":"J. Atmos. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/TGRS.2005.844726","article-title":"Bayesian algorithm for microwave-based precipitation retrieval: Description and application to TMI measurements over ocean","volume":"43","author":"Tassa","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1175\/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2","article-title":"The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors","volume":"40","author":"Kummerow","year":"2001","journal-title":"J. Appl. Meteorol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4650","DOI":"10.1109\/TGRS.2013.2258161","article-title":"Transitioning From CRD to CDRD in Bayesian Retrieval of Rainfall from Satellite Passive Microwave Measurements: Part 2. Overcoming Database Profile Selection Ambiguity by Consideration of Meteorological Control on Microphysics","volume":"51","author":"Casella","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4119","DOI":"10.1109\/TGRS.2012.2227332","article-title":"Transitioning from CRD to CDRD in bayesian retrieval of rainfall from satellite passive microwave measurements: Part 1. Algorithm description and testing","volume":"51","author":"Sano","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","unstructured":"San\u00f2, P., Casella, D., Panegrossi, G., Marra, A.C., Petracca, M., and Dietrich, S. (2015, January 21\u201325). The Passive Microwave Neural Network Precipitation Retrieval (PNPR) for the Cross-track Scanning ATMS Radiometer. Proceedings of the 2015 EUMETSAT Meteorological Satellite Conference, Toulouse, France."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1175\/JAMC-D-15-0130.1","article-title":"The Microwave Radiative Properties of Falling Snow Derived from Nonspherical Ice Particle Models. Part I: An Extensive Database of Simulated Pristine Crystals and Aggregate Particles, and Their Scattering Properties","volume":"55","author":"Kuo","year":"2016","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Milani, L., and Wood, N. (2021). Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations. Remote Sens., 13.","DOI":"10.3390\/rs13112041"},{"key":"ref_68","first-page":"1297","article-title":"Cross-validation of active and passive microwave snowfall products over the continental United States","volume":"22","author":"Mroz","year":"2021","journal-title":"J. Hydrometeorol."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Battaglia, A., and Panegrossi, G. (2020). What Can We Learn from the CloudSat Radiometric Mode Observations of Snowfall over the Ice-Free Ocean?. Remote Sens., 12.","DOI":"10.3390\/rs12203285"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/2013JD020448","article-title":"A physical approach for a simultaneous retrieval of sounding, surface, hydrometeor, and cryospheric parameters from SNPP\/ATMS","volume":"118","author":"Boukabara","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"D19112","DOI":"10.1029\/2012JD018144","article-title":"Introduction to Suomi national polar-orbiting partnership advanced technology microwave sounder for numerical weather prediction and tropical cyclone applications","volume":"117","author":"Weng","year":"2012","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"13463","DOI":"10.1002\/2013JD020389","article-title":"Joint Polar Satellite System: The United States next generation civilian polar-orbiting environmental satellite system","volume":"118","author":"Goldberg","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"11558","DOI":"10.1002\/2013JD020405","article-title":"Impacts of assimilation of ATMS data in HWRF on track and intensity forecasts of 2012 four landfall hurricanes","volume":"118","author":"Zou","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"8941","DOI":"10.1002\/2013JD021303","article-title":"Estimating snow microphysical properties using collocated multisensor observations","volume":"119","author":"Wood","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Rodgers, C.D. (2000). Inverse Methods for Atmospheric Sounding: Theory and Practice, Series on Atmospheric, Oceanic and Planetary Physics, World Scientific.","DOI":"10.1142\/9789812813718"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"7962","DOI":"10.1002\/jgrd.50579","article-title":"From CloudSat-CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar-lidar observations","volume":"118","author":"Ceccaldi","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1175\/1520-0450(1994)033<0003:APMTFE>2.0.CO;2","article-title":"A Passive Microwave Technique for Estimating Rainfall and Vertical Structure Information from Space. Part I: Algorithm Description","volume":"33","author":"Kummerow","year":"1994","journal-title":"J. Appl. Meteorol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1175\/JHM-D-15-0051.1","article-title":"Global Precipitation Estimates from Cross-Track Passive Microwave Observations Using a Physically Based Retrieval Scheme","volume":"17","author":"Kidd","year":"2015","journal-title":"J. Hydrometeorol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2265","DOI":"10.1175\/JTECH-D-15-0039.1","article-title":"The Evolution of the Goddard Profiling Algorithm to a Fully Parametric Scheme","volume":"32","author":"Kummerow","year":"2015","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Randel, D.L., Kummerow, C.D., and Ringerud, S. (2020). The Goddard Profiling (GPROF) Precipitation Retrieval Algorithm. Advances in Global Change Research, Springer.","DOI":"10.1007\/978-3-030-24568-9_8"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1017\/S1350482704001173","article-title":"Neural networks in satellite rainfall estimation","volume":"11","author":"Tapiador","year":"1999","journal-title":"Meteorol. Appl."},{"key":"ref_84","first-page":"409","article-title":"Neural networks: A comprehensive foundation by Simon Haykin","volume":"13","author":"Haykin","year":"1999","journal-title":"Knowl. Eng. Rev."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13201-013-0079-0","article-title":"Identification of raining clouds using a method based on optical and microphysical cloud properties from Meteosat second generation daytime and nighttime data","volume":"3","author":"Lazri","year":"2013","journal-title":"Appl. Water Sci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"837","DOI":"10.5194\/amt-8-837-2015","article-title":"The Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for AMSU\/MHS observations: Description and application to European case studies","volume":"8","author":"Panegrossi","year":"2015","journal-title":"Atmos. Meas. Tech."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1016\/B978-0-444-64241-7.50369-4","article-title":"Deep Learning Based Soft Sensor and Its Application on a Pyrolysis Reactor for Compositions Predictions of Gas Phase Components","volume":"44","author":"Zhu","year":"2018","journal-title":"Comput. Aided Chem. Eng."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"8612","DOI":"10.1109\/TGRS.2020.2989183","article-title":"Infrared Precipitation Estimation Using Convolutional Neural Network","volume":"58","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Alkhelaiwi, M., Boulila, W., Ahmad, J., Koubaa, A., and Driss, M. (2021). An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13112221"},{"key":"ref_91","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_93","first-page":"1727","article-title":"The Passive microwave Empirical cold Surface Classification Algorithm (PESCA): Application to GMI and ATMS","volume":"22","author":"Camplani","year":"2021","journal-title":"J. Hydrometeorol."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_95","unstructured":"Freund, Y. (2009). A more robust boosting algorithm. arXiv."},{"key":"ref_96","unstructured":"Hastie, T.J., Tibshirani, R., and Friedman, J.H. (2017). The Elements of Statistical Learning, Springer. [2nd ed.]."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Takbiri, Z., Milani, L., Guilloteau, C., and Foufoula-Georgiou, E. (2021). Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land. Remote Sens., 13.","DOI":"10.20944\/preprints202106.0544.v1"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1002\/qj.803","article-title":"A Tool to Estimate Land-Surface Emissivities at Microwave frequencies (TELSEM) for use in numerical weather prediction","volume":"137","author":"Aires","year":"2011","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1175\/2009JAS3146.1","article-title":"Microwave Backscatter and Extinction by Soft Ice Spheres and Complex Snow Aggregates","volume":"67","author":"Petty","year":"2010","journal-title":"J. Atmos. Sci."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/978-3-030-24568-9_15","article-title":"Scattering of Hydrometeors","volume":"67","author":"Kneifel","year":"2020","journal-title":"Adv. Glob. Chang. Res."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"8101","DOI":"10.5194\/acp-19-8101-2019","article-title":"Spatial and temporal variability of snowfall over Greenland from CloudSat observations","volume":"19","author":"Bennartz","year":"2019","journal-title":"Atmos. Chem. Phys."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1109\/LGRS.2018.2875007","article-title":"How Does Ground Clutter Affect CloudSat Snowfall Retrievals Over Ice Sheets?","volume":"16","author":"Palerme","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"55","DOI":"10.2528\/PIER14030405","article-title":"Snowfall Detectability of NASA\u2019s CloudSat: The First Cross-Investigation of Its 2C-Snow-Profile Product and National Multi-Sensor Mosaic QPE (NMQ) Snowfall Data","volume":"148","author":"Cao","year":"2014","journal-title":"Prog. Electromagn. Res."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1175\/JAMC-D-17-0176.1","article-title":"Validation of GMI Snowfall Observations by Using a Combination of Weather Radar and Surface Measurements","volume":"57","author":"Moisseev","year":"2018","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1175\/JTECH-D-20-0064.1","article-title":"Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation","volume":"38","author":"Milani","year":"2021","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/JSTARS.2022.3140768","article-title":"A Snowfall Detection Algorithm for ATMS Over Ocean, Sea Ice, and Coast","volume":"15","author":"You","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:39:03Z","timestamp":1760135943000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,18]]},"references-count":106,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14061467"],"URL":"https:\/\/doi.org\/10.3390\/rs14061467","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,18]]}}}