{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:43:13Z","timestamp":1764175393810,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T00:00:00Z","timestamp":1675641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFC3004004","2021YFB3900403","42075155"],"award-info":[{"award-number":["2022YFC3004004","2021YFB3900403","42075155"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YFC3004004","2021YFB3900403","42075155"],"award-info":[{"award-number":["2022YFC3004004","2021YFB3900403","42075155"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The second Chinese ocean dynamic environment satellite Haiyang-2B (HY-2B), carrying a scanning microwave radiometer (SMR) to provide information on the ocean and atmosphere, was successfully launched on 25 October 2018. Before the data assimilation, it is necessary to characterize and evaluate the biases of the HY-2B SMR observations. This study is the first to conduct a systematic assessment of the SMR radiance data based on observation minus background simulation (O-B). Three types of numerical weather prediction (NWP) datasets, including ECMWF Reanalysis v5 (ERA5), the analysis fields from the NCEP Global Forecast System (NCEP-GFS), and the analysis fields from the Global Regional Assimilation and Prediction System-Global Forecast System (GRAPES-GFS), were used as input information for RTTOV v12.3 to simulate the SMR\u2019s observed brightness temperature (TB) under clear-sky conditions. Study results showed that the O-B biases and IQR of the SMR for most channels were within \u22122.5\u20130.4 K and smaller than 4 K, respectively. The SMR observations were generally consistent with the RTTOV simulations, even based on the different NWP fields. These results indicate a good prospect for the assimilated application of HY-2B SMR radiance data. However, due to the impact of RFI, the SMR\u2019s descending data for two 10.7 GHz channels showed some significant positive biases larger than 50 K over the seas of the European region. In addition, it seems that the bias characteristics of the SMR\u2019s ascending data were obviously different from those of the descending data. It was also found that the variation trend of scan-position-dependent bias was generally stable for the SMR\u2019s ascending data but fluctuates significantly for the descending data, with a maximum amplitude greater than 0.7 K for some channels.<\/jats:p>","DOI":"10.3390\/rs15040889","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T05:29:05Z","timestamp":1675661345000},"page":"889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Biases\u2019 Characteristics Assessment of the HY-2B Scanning Microwave Radiometer (SMR)\u2019s Observations"],"prefix":"10.3390","volume":"15","author":[{"given":"Zeting","family":"Li","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 & Technology, Nanjing 210044, China"},{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1966-446X","authenticated-orcid":false,"given":"Wei","family":"Han","sequence":"additional","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), Beijing 100081, China"},{"name":"State Key Laboratory of Severe Weather (LaSW), Chinese Academy of Meteorological Sciences, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3363-2779","authenticated-orcid":false,"given":"Haiming","family":"Xu","sequence":"additional","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 & Technology, Nanjing 210044, China"}]},{"given":"Hejun","family":"Xie","sequence":"additional","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9245-7045","authenticated-orcid":false,"given":"Juhong","family":"Zou","sequence":"additional","affiliation":[{"name":"National Satellite Ocean Application Service, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1002\/qj.3654","article-title":"Assimilation of satellite data in numerical weather prediction. 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Data"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/889\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:25:28Z","timestamp":1760120728000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,6]]},"references-count":38,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15040889"],"URL":"https:\/\/doi.org\/10.3390\/rs15040889","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,2,6]]}}}