{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T21:00:40Z","timestamp":1774472440860,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T00:00:00Z","timestamp":1659916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"the Korea government (MSIT)","doi-asserted-by":"publisher","award":["2021R1A4A1032646"],"award-info":[{"award-number":["2021R1A4A1032646"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"the Korea government (MSIT)","doi-asserted-by":"publisher","award":["NRF-2021R1A6A3A13042215"],"award-info":[{"award-number":["NRF-2021R1A6A3A13042215"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"the Korea government (MSIT)","doi-asserted-by":"publisher","award":["KMI2022-00310"],"award-info":[{"award-number":["KMI2022-00310"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"the Ministry of Education","doi-asserted-by":"publisher","award":["2021R1A4A1032646"],"award-info":[{"award-number":["2021R1A4A1032646"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"the Ministry of Education","doi-asserted-by":"publisher","award":["NRF-2021R1A6A3A13042215"],"award-info":[{"award-number":["NRF-2021R1A6A3A13042215"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"the Ministry of Education","doi-asserted-by":"publisher","award":["KMI2022-00310"],"award-info":[{"award-number":["KMI2022-00310"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003629","name":"the Korea Meteorological Administration Research and Development Program","doi-asserted-by":"publisher","award":["2021R1A4A1032646"],"award-info":[{"award-number":["2021R1A4A1032646"]}],"id":[{"id":"10.13039\/501100003629","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003629","name":"the Korea Meteorological Administration Research and Development Program","doi-asserted-by":"publisher","award":["NRF-2021R1A6A3A13042215"],"award-info":[{"award-number":["NRF-2021R1A6A3A13042215"]}],"id":[{"id":"10.13039\/501100003629","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003629","name":"the Korea Meteorological Administration Research and Development Program","doi-asserted-by":"publisher","award":["KMI2022-00310"],"award-info":[{"award-number":["KMI2022-00310"]}],"id":[{"id":"10.13039\/501100003629","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>An accurate classification of the precipitation type is important for forecasters, particularly in the winter season. We explored the capability of three supervised machine learning (ML) methods (decision tree, random forest, and support vector machine) to determine ground precipitation types (no precipitation, rain, mixed, and snow) for winter precipitation. We provided information on the particle characteristics within a radar sampling volume and the environmental condition to the ML model with the simultaneous use of polarimetric radar variables and thermodynamic variables. The ML algorithms were optimized using predictor selection and hyperparameter tuning in order to maximize the computational efficiency and accuracy. The random forest (RF) had the highest skill scores in all precipitation types and outperformed the operational scheme. The spatial distribution of the precipitation type from the RF model showed a good agreement with the surface observation. As a result, RF is recommended for the real-time precipitation type classification due to its easy implementation, computational efficiency, and satisfactory accuracy. In addition to the validation, this study confirmed the strong dependence of precipitation type on wet-bulb temperature and a 1000\u2013850 hPa layer thickness. The results also suggested that the base heights of the radar echo are useful in discriminating non-precipitating area.<\/jats:p>","DOI":"10.3390\/rs14153820","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"3820","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Classification of Precipitation Types Based on Machine Learning Using Dual-Polarization Radar Measurements and Thermodynamic Fields"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1182-6751","authenticated-orcid":false,"given":"Kyuhee","family":"Shin","sequence":"first","affiliation":[{"name":"Department of Atmospheric Sciences, Kyungpook National University, Daegu 41566, Korea"},{"name":"Center for Atmospheric REmote Sensing (CARE), Kyungpook National University, Daegu 41566, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8684-7277","authenticated-orcid":false,"given":"Kwonil","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Atmospheric Sciences, Kyungpook National University, Daegu 41566, Korea"},{"name":"Center for Atmospheric REmote Sensing (CARE), Kyungpook National University, Daegu 41566, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1385-4924","authenticated-orcid":false,"given":"Joon Jin","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Statistical Science, Baylor University, Waco, TX 76798, USA"}]},{"given":"GyuWon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Atmospheric Sciences, Kyungpook National University, Daegu 41566, Korea"},{"name":"Center for Atmospheric REmote Sensing (CARE), Kyungpook National University, Daegu 41566, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"185","DOI":"10.3354\/cr015185","article-title":"Relationships between Road Slipperiness, Traffic Accident Risk and Winter Road Maintenance Activity","volume":"15","author":"Norrman","year":"2000","journal-title":"Clim. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5194\/hess-21-1-2017","article-title":"Rain or Snow: Hydrologic Processes, Observations, Prediction, and Research Needs","volume":"21","author":"Harpold","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"D06113","DOI":"10.1029\/2007JD008548","article-title":"The SAFRAN-ISBA-MODCOU Hydrometeorological Model Applied over France","volume":"113","author":"Habets","year":"2008","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.atmosres.2018.02.002","article-title":"Discriminating the Precipitation Phase Based on Different Temperature Thresholds in the Songhua River Basin, China","volume":"205","author":"Zhong","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.jhydrol.2014.03.038","article-title":"The Dependence of Precipitation Types on Surface Elevation and Meteorological Conditions and Its Parameterization","volume":"513","author":"Ding","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1175\/JHM-D-14-0211.1","article-title":"A Parameterization of the Probability of Snow\u2013Rain Transition","volume":"16","author":"Sims","year":"2015","journal-title":"J. Hydrometeorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1002\/qj.3240","article-title":"On Distinguishing Snowfall from Rainfall Using Near-Surface Atmospheric Information: Comparative Analysis, Uncertainties and Hydrologic Importance","volume":"144","author":"Behrangi","year":"2018","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13825","DOI":"10.1029\/2019GL085722","article-title":"A Wet-Bulb Temperature-Based Rain-Snow Partitioning Scheme Improves Snowpack Prediction Over the Drier Western United States","volume":"46","author":"Wang","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","unstructured":"Rogers, R.R., and Yau, M.K. (1996). A Short Course in Cloud Physics, Elsevier. [3rd ed.]."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1175\/2010JAMC2321.1","article-title":"On the Dependence of Winter Precipitation Types on Temperature, Precipitation Rate, and Associated Features","volume":"49","author":"Stewart","year":"2010","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1175\/JHM-D-13-073.1","article-title":"Sensitivity of Precipitation Phase over the Swiss Alps to Different Meteorological Variables","volume":"15","author":"Froidurot","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1175\/1520-0434(1991)006<0456:TOUOOA>2.0.CO;2","article-title":"The Objective Use of Observed and Forecast Thickness Values to Predict Precipitation Type in North Carolina","volume":"6","author":"Keeter","year":"1991","journal-title":"Weather Forecast."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1175\/1520-0434(1992)007<0683:SVRLBT>2.0.CO;2","article-title":"Snow versus Rain: Looking beyond the \u201cMagic\u201d Numbers","volume":"7","author":"Heppner","year":"1992","journal-title":"Weather Forecast."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1175\/1520-0434(2000)015<0583:AMTDPT>2.0.CO;2","article-title":"A Method to Determine Precipitation Types","volume":"15","author":"Bourgouin","year":"2000","journal-title":"Weather Forecast."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"151","DOI":"10.14191\/Atmos.2014.24.2.151","article-title":"A Method for the Discrimination of Precipitation Type Using Thickness and Improved Matsuo\u2019s Scheme over South Korea","volume":"24","author":"Lee","year":"2014","journal-title":"Atmosphere"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"462","DOI":"10.2151\/jmsj1965.59.4_462","article-title":"Relationship between Types of Precipitation on the Ground and Surface Meteorological Elements","volume":"59","author":"Matsuo","year":"1981","journal-title":"J. Meteorol. Soc. Jpn. Ser. II"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1175\/WAF-D-14-00007.1","article-title":"Sources of Uncertainty in Precipitation-Type Forecasting","volume":"29","author":"Reeves","year":"2014","journal-title":"Weather Forecast."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bringi, V.N., and Chandrasekar, V. (2001). Polarimetric Doppler Weather Radar: Principles and Applications, Cambridge University Press.","DOI":"10.1017\/CBO9780511541094"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"243","DOI":"10.15191\/nwajom.2013.0120","article-title":"Principles and Applications of Dual-Polarization Weather Radar. Part II: Warm and Cold Season Applications","volume":"1","author":"Kumjian","year":"2013","journal-title":"J. Oper. Meteorol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1175\/2008WAF2222205.1","article-title":"The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS","volume":"24","author":"Park","year":"2009","journal-title":"Weather Forecast."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.1175\/JAMC-D-12-0236.1","article-title":"A New Fuzzy Logic Hydrometeor Classification Scheme Applied to the French X-, C-, and S-Band Polarimetric Radars","volume":"52","author":"Boumahmoud","year":"2013","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2162","DOI":"10.1175\/JAMC-D-12-0275.1","article-title":"A Robust C-Band Hydrometeor Identification Algorithm and Application to a Long-Term Polarimetric Radar Dataset","volume":"52","author":"Dolan","year":"2013","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/TGRS.2007.906476","article-title":"Supervised Classification and Estimation of Hydrometeors from C-Band Dual-Polarized Radars: A Bayesian Approach","volume":"46","author":"Marzano","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhao, K., Zhang, G., Chen, G., Huang, H., and Chen, H. (2019). A Bayesian Hydrometeor Classification Algorithm for C-Band Polarimetric Radar. Remote Sens., 11.","DOI":"10.3390\/rs11161884"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1175\/1520-0477(1999)080<0381:CMRUSB>2.0.CO;2","article-title":"Cloud Microphysics Retrieval Using S-Band Dual-Polarization Radar Measurements","volume":"80","author":"Vivekanandan","year":"1999","journal-title":"Bull. Am. Meteor. Soc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1175\/1520-0426(2000)017<0140:COHBOP>2.0.CO;2","article-title":"Classification of Hydrometeors Based on Polarimetric Radar Measurements: Development of Fuzzy Logic and Neuro-Fuzzy Systems, and In Situ Verification","volume":"17","author":"Liu","year":"2000","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1175\/1520-0450(2000)039<1341:BHCAQU>2.0.CO;2","article-title":"Bulk Hydrometeor Classification and Quantification Using Polarimetric Radar Data: Synthesis of Relations","volume":"39","author":"Straka","year":"2000","journal-title":"J. Appl. Meteor."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1002\/asl.89","article-title":"Correction of the Bright Band Using Dual-Polarisation Radar","volume":"6","author":"Cluckie","year":"2005","journal-title":"Atmos. Sci. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1175\/JAMC-D-11-091.1","article-title":"Classification of Precipitation Types during Transitional Winter Weather Using the RUC Model and Polarimetric Radar Retrievals","volume":"51","author":"Schuur","year":"2012","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1175\/WAF-D-20-0232.1","article-title":"Hymec: Surface Precipitation Type Estimation at the German Weather Service","volume":"36","author":"Steinert","year":"2021","journal-title":"Weather Forecast."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2073","DOI":"10.1175\/BAMS-D-16-0123.1","article-title":"Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather","volume":"98","author":"McGovern","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1175\/WAF-D-19-0170.1","article-title":"Classifying Convective Storms Using Machine Learning","volume":"35","author":"Jergensen","year":"2020","journal-title":"Weather Forecast."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shin, K., Song, J.J., Bang, W., and Lee, G. (2021). Quantitative Precipitation Estimates Using Machine Learning Approaches with Operational Dual-Polarization Radar Data. Remote Sens., 13.","DOI":"10.3390\/rs13040694"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104928","DOI":"10.1016\/j.atmosres.2020.104928","article-title":"An Improved Forecast of Precipitation Type Using Correlation-Based Feature Selection and Multinomial Logistic Regression","volume":"240","author":"Moon","year":"2020","journal-title":"Atmos. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Seo, B.C. (2020). A Data-Driven Approach for Winter Precipitation Classification Using Weather Radar and NWP Data. Atmosphere, 11.","DOI":"10.3390\/atmos11070701"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s00376-020-0165-9","article-title":"Determination of Surface Precipitation Type Based on the Data Fusion Approach","volume":"38","author":"Kolendowicz","year":"2021","journal-title":"Adv. Atmos. Sci."},{"key":"ref_37","first-page":"19","article-title":"Improving Observations of Precipitation Type at the Surface: A 5-Year Verification of a Radar-Derived Product from the United Kingdom\u2019s Met Office","volume":"22","author":"Pickering","year":"2021","journal-title":"J. Hydrometeorol."},{"key":"ref_38","unstructured":"(2022, July 28). Korea Meteorological Administration Manual of Surface Weather Observation. Available online: https:\/\/book.kma.go.kr\/viewer\/MediaViewer.ax?cid=33393&rid=5&moi=5241."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lee, J.E., Jung, S.H., and Kwon, S. (2020). Characteristics of the Bright Band Based on Quasi-Vertical Profiles of Polarimetric Observations from an s-Band Weather Radar Network. Remote Sens., 12.","DOI":"10.3390\/rs12244061"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lee, J.-E., Kwon, S., and Jung, S.-H. (2021). Real-Time Calibration and Monitoring of Radar Reflectivity on Nationwide Dual-Polarization Weather Radar Network. Remote Sens., 13.","DOI":"10.3390\/rs13152936"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Oh, Y.A., Kim, H.L., and Suk, M.K. (2020). Clutter Elimination Algorithm for Non-Precipitation Echo of Radar Data Considering Meteorological and Observational Properties in Polarimetric Measurements. Remote Sens., 12.","DOI":"10.3390\/rs12223790"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1007\/s00024-019-02288-z","article-title":"Visibility Data Assimilation and Prediction Using an Observation Network in South Korea","volume":"177","author":"Kim","year":"2020","journal-title":"Pure Appl. Geophys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.1175\/JAMC-D-11-0143.1","article-title":"Wet-Bulb Temperature from Relative Humidity and Air Temperature","volume":"50","author":"Stull","year":"2011","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_44","unstructured":"May, R.M., Arms, S.C., Marsh, P., Bruning, E., Leeman, J.R., Goebbert, K., Thielen, J.E., Bruick, Z.S., and Camron, M.D. (2022). MetPy: A Python Package for Meteorological Data. Unidata."},{"key":"ref_45","unstructured":"Breiman, L., Friedman, J., Stone, C.J., and Olshen, R.A. (1984). Classification and Regression Trees, Routledge."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.1175\/BAMS-D-18-0195.1","article-title":"Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning","volume":"100","author":"McGovern","year":"2019","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_49","first-page":"18","article-title":"Classification and Regression by Random Forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_50","unstructured":"Therneau, T., Atkinson, B., and Ripley, B. (2022, July 28). Rpart: Recursive Partitioning and Regression Trees. Available online: https:\/\/cran.r-project.org\/web\/packages\/rpart\/."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1175\/2010JAMC2243.1","article-title":"The Impact of Evaporation on Polarimetric Characteristics of Rain: Theoretical Model and Practical Implications","volume":"49","author":"Kumjian","year":"2010","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1175\/JTECH-D-15-0244.1","article-title":"Radar Observation of Evaporation and Implications for Quantitative Precipitation and Cooling Rate Estimation","volume":"33","author":"Xie","year":"2016","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_53","first-page":"1035","article-title":"Polarimetric Observations and Simulations of Sublimating Snow: Implications for Nowcasting","volume":"60","author":"Carlin","year":"2021","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Song, J.J., Innerst, M., Shin, K., Ye, B., Kim, M., Yeom, D., and Lee, G. (2021). Estimation of Precipitation Area Using S-Band Dual-Polarization Radar Measurements. Remote Sens., 13.","DOI":"10.3390\/rs13112039"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1175\/JHM-D-17-0093.1","article-title":"A Real-Time Evaporation Correction Scheme for Radar-Derived Mosaicked Precipitation Estimations","volume":"19","author":"Martinaitis","year":"2018","journal-title":"J. Hydrometeorol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2849","DOI":"10.1175\/JAMC-D-13-073.1","article-title":"Polarimetric Radar Characteristics of Melting Hail. Part I: Theoretical Simulations Using Spectral Microphysical Modeling","volume":"52","author":"Ryzhkov","year":"2013","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2344","DOI":"10.1175\/JAMC-D-14-0050.1","article-title":"Investigations of Backscatter Differential Phase in the Melting Layer","volume":"53","author":"Ryzhkov","year":"2014","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1175\/WAF-D-17-0054.1","article-title":"Polarimetric Radar and Surface-Based Precipitation-Type Observations of Ice Pellet to Freezing Rain Transitions","volume":"32","author":"Tobin","year":"2017","journal-title":"Weather Forecast."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1175\/JAMC-D-16-0044.1","article-title":"Discrimination between Winter Precipitation Types Based on Spectral-Bin Microphysical Modeling","volume":"55","author":"Reeves","year":"2016","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1175\/WAF-D-18-0154.1","article-title":"Long-Duration Freezing Rain Events over North America: Regional Climatology and Thermodynamic Evolution","volume":"34","author":"McCray","year":"2019","journal-title":"Weather Forecast."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1175\/JAMC-D-20-0047.1","article-title":"Applications of Uncrewed Aerial Vehicles (UAVs) in Winter Precipitation-Type Forecasts","volume":"60","author":"Tripp","year":"2021","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1175\/MWR-D-21-0185.1","article-title":"Characteristics of Precipitation Particles and Microphysical Processes during the 11\u201312 January 2020 Ice Pellet Storm in the Montr\u00e9al Area, Qu\u00e9bec, Canada","volume":"150","author":"Lachapelle","year":"2022","journal-title":"Mon. Weather Rev."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.atmosres.2016.02.012","article-title":"Hail Observations and Hailstorm Characteristics in Europe: A Review","volume":"176\u2013177","author":"Punge","year":"2016","journal-title":"Atmos. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e2019RG000665","DOI":"10.1029\/2019RG000665","article-title":"Understanding Hail in the Earth System","volume":"58","author":"Allen","year":"2020","journal-title":"Rev. Geophys."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3820\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:05:38Z","timestamp":1760141138000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3820"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,8]]},"references-count":64,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153820"],"URL":"https:\/\/doi.org\/10.3390\/rs14153820","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,8]]}}}