{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T06:00:17Z","timestamp":1775455217768,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T00:00:00Z","timestamp":1666396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Startup Foundation for Introducing Talent of NUIST"},{"name":"the Innovation Programm for applied Research and Development (IAFE) of Deutscher Wetterdienst"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Radar data assimilation has been operational at the Deutscher Wetterdienst for several years and is essential for generating accurate precipitation forecasts. The current work attempts to further enhance the radar data assimilation by improving the latent heat nudging (LHN) scheme and by reducing the observation error (OE) caused by the representation error of the efficient modular volume radar operator (EMVORADO). First of all, a series of hindcasts for a one-month convective period over Germany are performed. Compared with radar reflectivity and satellite observations, it is found that the LHN scheme that implicitly adjusts temperature performs better, and the beam broadening effect and the choice of the scattering schemes in EMVORADO are important. Moreover, the Mie scheme with the new parameterization to reduce the brightband effect not only proves to be the best in hindcasts but also that it results in the smallest standard deviations and the shortest horizontal correlation length scales of the OE in data assimilation experiments.<\/jats:p>","DOI":"10.3390\/rs14215295","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"5295","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Evaluating Latent-Heat-Nudging Schemes and Radar forward Operator Settings for a Convective Summer Period over Germany Using the ICON-KENDA System"],"prefix":"10.3390","volume":"14","author":[{"given":"Yuefei","family":"Zeng","sequence":"first","affiliation":[{"name":"Key Laboratory of Meteorological Disaster of Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"given":"Yuxuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Jiangsu Meteorological Observatory, Jiangsu Meteorological Bureau, Nanjing 210008, China"}]},{"given":"Alberto","family":"de Lozar","sequence":"additional","affiliation":[{"name":"Deutscher Wetterdienst, 63067 Offenbach, Germany"}]},{"given":"Klaus","family":"Stephan","sequence":"additional","affiliation":[{"name":"Deutscher Wetterdienst, 63067 Offenbach, Germany"}]},{"given":"Leonhard","family":"Scheck","sequence":"additional","affiliation":[{"name":"Deutscher Wetterdienst, 63067 Offenbach, Germany"},{"name":"Hans Ertel Centre for Weather Research, Ludwig-Maximilians-Universit\u00e4t (LMU) M\u00fcnchen, 80333 Munich, Germany"}]},{"given":"Kobra","family":"Khosravianghadikolaei","sequence":"additional","affiliation":[{"name":"Deutscher Wetterdienst, 63067 Offenbach, Germany"}]},{"given":"Ulrich","family":"Blahak","sequence":"additional","affiliation":[{"name":"Deutscher Wetterdienst, 63067 Offenbach, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1002\/qj.2748","article-title":"Kilometre-Scale Ensemble Data Assimilation for the COSMO Model (KENDA)","volume":"142","author":"Schraff","year":"2016","journal-title":"Q. 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