{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:39:46Z","timestamp":1783183186679,"version":"3.54.6"},"reference-count":40,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,29]],"date-time":"2019-09-29T00:00:00Z","timestamp":1569715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFC1502102"],"award-info":[{"award-number":["2017YFC1502102"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Nature Science Foundation of China","award":["41675098"],"award-info":[{"award-number":["41675098"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Stratiform and convective rain types are associated with different cloud physical processes, vertical structures, thermodynamic influences and precipitation types. Distinguishing convective and stratiform systems is beneficial to meteorology research and weather forecasting. However, there is no clear boundary between stratiform and convective precipitation. In this study, a machine learning algorithm, K-nearest neighbor (KNN), is used to classify precipitation types. Six Doppler radar (WSR-98D\/SA) data sets from Jiangsu, Guangzhou and Anhui Provinces in China were used as training and classification samples, and the 2A23 product of the Tropical Precipitation Measurement Mission (TRMM) was used to obtain the training labels and evaluate the classification performance. Classifying precipitation types using KNN requires three steps. First, features are selected from the radar data by comparing the range of each variable for different precipitation types. Second, the same unclassified samples are classified with different k values to choose the best-performing k. Finally, the unclassified samples are put into the KNN algorithm with the best k to classify precipitation types, and the classification performance is evaluated. Three types of cases, squall line, embedded convective and stratiform cases, are classified by KNN. The KNN method can accurately classify the location and area of stratiform and convective systems. For stratiform classifications, KNN has a 95% probability of detection, 8% false alarm rate, and 87% cumulative success index; for convective classifications, KNN yields a 78% probability of detection, a 13% false alarm rate, and a 69% cumulative success index. These results imply that KNN can correctly classify almost all stratiform precipitation and most convective precipitation types. This result suggests that KNN has great potential in classifying precipitation types.<\/jats:p>","DOI":"10.3390\/rs11192277","type":"journal-article","created":{"date-parts":[[2019,9,30]],"date-time":"2019-09-30T05:58:33Z","timestamp":1569823113000},"page":"2277","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Convective\/Stratiform Precipitation Classification Using Ground-Based Doppler Radar Data Based on the K-Nearest Neighbor Algorithm"],"prefix":"10.3390","volume":"11","author":[{"given":"Zhida","family":"Yang","sequence":"first","affiliation":[{"name":"Research and Development Center of Earth System Model (RDCM), College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"Research and Development Center of Earth System Model (RDCM), College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7813-6616","authenticated-orcid":false,"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"Research and Development Center of Earth System Model (RDCM), College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,29]]},"reference":[{"key":"ref_1","unstructured":"Houze, R.A. (1993). Cloud Dynamics, Academic Press."},{"key":"ref_2","first-page":"287","article-title":"The heat balance of the equatorial trough zone, revisited","volume":"52","author":"Riehl","year":"1979","journal-title":"Beitr. Phys. Atmos."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1175\/JHM-D-13-040.1","article-title":"Correction of radar qpe errors associated with low and partially observed brightband layers","volume":"14","author":"Qi","year":"2013","journal-title":"J. Hydrometeorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1175\/JTECH-D-16-0146.1","article-title":"Rain Evaporation rate estimates from dual-wavelength lidar measurements and intercomparison against a model analytical solution","volume":"34","author":"Lolli","year":"2017","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.1175\/1520-0450(2001)040<2129:AISFCS>2.0.CO;2","article-title":"An improved scheme for convective\/stratiform echo classification using radar reflectivity","volume":"39","author":"Biggerstaff","year":"2000","journal-title":"J. Appl. Meteorol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1175\/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2","article-title":"Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets","volume":"30","author":"Yanai","year":"1973","journal-title":"J. Atmos. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lolli, S., D\u2019Adderio, L., Campbell, J., Sicard, M., Welton, E., Binci, A., Rea, A., Tokay, A., Comer\u00f3n, A., and Barragan, R. (2018). Vertically resolved precipitation intensity retrieved through a synergy between the ground-based NASA MPLNET lidar network measurements, surface disdrometer datasets and an analytical model solution. Remote Sens., 10.","DOI":"10.20944\/preprints201805.0266.v1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1175\/JHM-D-15-0188.1","article-title":"A real-time automated quality control of hourly rain gauge data based on multiple sensors in mrms system","volume":"17","author":"Qi","year":"2016","journal-title":"J Hydrometeorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1175\/BAMS-D-14-00174.1","article-title":"Multi-radar multi-sensor (mrms) quantitative precipitation estimation: Initial operating capabilities","volume":"97","author":"Zhang","year":"2016","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.1029\/JD095iD03p02161","article-title":"The estimation of convective rainfall by area integrals: 2. The Height-Area Rainfall Threshold (HART) method","volume":"95","author":"Rosenfeld","year":"1990","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1175\/JHM-D-13-0131.1","article-title":"Improving wsr-88d radar qpe for orographic precipitation using profiler observations","volume":"15","author":"Qi","year":"2013","journal-title":"J Hydrometeorol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1175\/1520-0450(1972)011<0926:AOTSOP>2.0.CO;2","article-title":"Analysis of the structure of precipitation patterns in New England","volume":"11","author":"Austin","year":"1972","journal-title":"J. Appl. Meteorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1175\/1520-0469(1973)030<1112:ACSOVT>2.0.CO;2","article-title":"A Climatological study of vertical transports by cumulus-scale convection","volume":"30","author":"Houze","year":"1973","journal-title":"J. Atmos. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1175\/1520-0469(1984)041<0933:DASOWM>2.0.CO;2","article-title":"Development and structure of winter monsoon cloud clusters on 10 December 1978","volume":"41","author":"Churchill","year":"1984","journal-title":"J. Atmos. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1978","DOI":"10.1175\/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2","article-title":"Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data","volume":"34","author":"Steiner","year":"1995","journal-title":"J. Appl. Meteorol."},{"key":"ref_16","unstructured":"DeMott, C.A., Cifelli, R., and Rutledge, S.A. (1995, January 9\u201313). An improved method for partitioning radar data into convective and stratiform components. Proceedings of the 27th Conference on Radar Meteorology, Vail, CO, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1175\/1520-0469(2003)060<0354:RSDIDC>2.0.CO;2","article-title":"Raindrop size distribution in different climatic regimes from disdrometer and dual-polarized radar analysis","volume":"60","author":"Bringi","year":"2003","journal-title":"J. Atmos. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1017\/S1350482704001409","article-title":"A convective\/stratiform precipitation classification algorithm for volume scanning weather radar observations","volume":"11","author":"Anagnostou","year":"2004","journal-title":"Meteorol. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"11","DOI":"10.5194\/adgeo-16-11-2008","article-title":"Precipitation classification at mid-latitudes in terms of drop size distribution parameters","volume":"16","author":"Caracciolo","year":"2008","journal-title":"Adv. Geosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1175\/2010JHM1201.1","article-title":"A real-time algorithm for the correction of brightband effects in radar-derived qpe","volume":"11","author":"Zhang","year":"2010","journal-title":"J Hydrometeorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.1002\/qj.2095","article-title":"A real-time automated convective and stratiform precipitation segregation algorithm in native radar coordinates","volume":"139","author":"Qi","year":"2013","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3627","DOI":"10.1002\/jgrd.50364","article-title":"Vpr correction of bright band effects in radar qpes using polarimetric radar observations","volume":"118","author":"Qi","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1175\/JHM-D-12-0165.1","article-title":"Correction of radar qpe errors for nonuniform vprs in mesoscale convective systems using trmm observations","volume":"14","author":"Qi","year":"2013","journal-title":"J. Hydrometeorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1002\/jgrd.50214","article-title":"Classification of convective\/stratiform echoes in radar reflectivity observations using a fuzzy logic algorithm","volume":"118","author":"Yang","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2457489","DOI":"10.1155\/2016\/2457489","article-title":"Radar-derived quantitative precipitation estimation based on precipitation classification","volume":"2016","author":"Yang","year":"2016","journal-title":"Adv. Meteorol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1175\/1520-0450(1988)027<0030:ASITTE>2.0.CO;2","article-title":"A satellite infrared technique to estimate tropical convective and stratiform rainfall","volume":"27","author":"Adler","year":"1988","journal-title":"J. Appl. Meteorol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1175\/1520-0450(1984)023<0280:TCHRRF>2.0.CO;2","article-title":"Thunderstorm cloud height-rainfall rate relations for use with satellite rainfall estimation techniques","volume":"23","author":"Adler","year":"1984","journal-title":"J. Clim. Appl. Meteorol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"37","DOI":"10.2151\/jmsj1965.68.1_37","article-title":"Convective and stratiform components of a winter monsoon cloud cluster determined from geosynchronous infrared satellite data","volume":"68","author":"Goldenberg","year":"1990","journal-title":"J. Meteorol. Soc. Japan Ser II"},{"key":"ref_29","unstructured":"Waka, J., Iguchi, T., Kumagai, H., and Okamoto, K. (1997, January 3\u20138). Rain type classification algorithm for TRMM precipitation radar. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium Proceedings, Remote Sensing-A Scientific Vision for Sustainable Development, Singapore."},{"key":"ref_30","unstructured":"Zhou, Z. (2016). Machine Learning, 1st ed, Tsinghua University Press."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fix, E., and Hodge, J.L. (1951). Discriminatory Analysis-Nonparametric Discrimination: Consistency Properties, California University Berkeley.","DOI":"10.1037\/e471672008-001"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1175\/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2","article-title":"A technique for maximizing details in numerical weather map analysis","volume":"3","author":"Barnes","year":"1964","journal-title":"J. Appl. Meteorol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1175\/1520-0442(2002)015<0983:DVOTRR>2.0.CO;2","article-title":"Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information","volume":"15","author":"Sorooshian","year":"2002","journal-title":"J. Clim."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1175\/1520-0477(2001)082<0659:IGAASF>2.3.CO;2","article-title":"Improving global analysis and short-range forecast using rainfall and moisture observations derived from TRMM and SSM\/I passive microwave sensors","volume":"660","author":"Hou","year":"2001","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1175\/1520-0450(2001)040<1381:RSOPOT>2.0.CO;2","article-title":"Remote Sensing of Precipitation on the Tibetan Plateau Using the TRMM Microwave Imager","volume":"40","author":"Yao","year":"2001","journal-title":"J. Appl. Meteorol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","article-title":"ML-KNN: A lazy learning approach to multi-label learning","volume":"40","author":"Zhang","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gao, J., Tang, G., and Hong, Y. (2017). Similarities and improvements of GPM Dual-frequency Precipitation Radar (DPR) upon TRMM Precipitation Radar (PR) in global precipitation rate estimation, type classification and vertical profiling. Remote Sens., 9.","DOI":"10.3390\/rs9111142"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1175\/1520-0493(1972)100<0548:VILWNA>2.3.CO;2","article-title":"Vertically integrated liquid water\u2014A new analysis tool","volume":"100","author":"Greene","year":"1972","journal-title":"Mon. Weather Rev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/TSMC.1985.6313426","article-title":"A fuzzy k-nearest neighbor algorithm","volume":"4","author":"Keller","year":"1985","journal-title":"IEEE Trans. Syst. Man Cybern."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2277\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:25:57Z","timestamp":1760189157000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2277"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,29]]},"references-count":40,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11192277"],"URL":"https:\/\/doi.org\/10.3390\/rs11192277","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,29]]}}}