{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T21:19:52Z","timestamp":1774127992904,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,22]],"date-time":"2019-10-22T00:00:00Z","timestamp":1571702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Project","award":["2018YFC1506904"],"award-info":[{"award-number":["2018YFC1506904"]}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["41875027"],"award-info":[{"award-number":["41875027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["4166144039"],"award-info":[{"award-number":["4166144039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province","award":["SCSF201804"],"award-info":[{"award-number":["SCSF201804"]}]},{"name":"Research Program for Key Laboratory of Meteorology and Ecological Environment of Hebei Province","award":["Z201603Z"],"award-info":[{"award-number":["Z201603Z"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automated and accurate radar dealiasing algorithms are very important for their assimilation into operational numerical weather forecasting models. A radar radial velocity dealiasing algorithm aimed at radar data assimilation is introduced and assessed using from several S-band and C-band radar observations under the severe weather conditions of hurricanes, typhoons, and deep continental convection in this paper. This dealiasing algorithm, named automated dealiasing for data assimilation (ADDA), is a further development of the dealiasing algorithm named the China radar network (CINRAD) improved dealiasing algorithm (CIDA), originally developed for China\u2019s CINRAD (China Next Generation Weather Radar) radar network. The improved scheme contains five modules employed to remove noisy data, select the suitable first radial, preserve the convective regions, execute multipass dealiasing in both azimuthal and radial directions and conduct the final local dealiasing with an error check. This new dealiasing algorithm was applied to two hurricane cases, two typhoon cases, and three intense-convection cases that were observed from the CINRAD of China, Taiwan\u2018s radar network, and NEXRAD (Next Generation Weather Radar) of the U.S. with a continuous period of more than 12 h for each case. The dealiasing results demonstrated that ADDA performed better than CIDA for all selected cases. This algorithm not only produced a high success rate for the S-band radar, but also a reasonable performance for the C-band radar.<\/jats:p>","DOI":"10.3390\/rs11202457","type":"journal-article","created":{"date-parts":[[2019,10,23]],"date-time":"2019-10-23T11:46:59Z","timestamp":1571831219000},"page":"2457","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Radar Radial Velocity Dealiasing Algorithm for Radar Data Assimilation and its Evaluation with Observations from Multiple Radar Networks"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2179-5119","authenticated-orcid":false,"given":"Guangxin","family":"He","sequence":"first","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/ Joint International Research Laboratory of Climate and Environment Change (ILCEC)\/ Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science &amp; Technology, Nanjing 210044, China"},{"name":"Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province, Hainan Provincial Meteorological Administration, Haikou 571800, China"},{"name":"National Center for Atmospheric Research, Boulder, CO 80304, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juanzhen","family":"Sun","sequence":"additional","affiliation":[{"name":"National Center for Atmospheric Research, Boulder, CO 80304, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuming","family":"Ying","sequence":"additional","affiliation":[{"name":"National Center for Atmospheric Research, Boulder, CO 80304, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lejian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Meteorological Observation center of CMA, China Meteorological Administration, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1175\/MWR2887.1","article-title":"Initialization and numerical forecasting of a supercell storm observed during STEPS","volume":"133","author":"Sun","year":"2005","journal-title":"Mon. 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