{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T14:34:48Z","timestamp":1775486088010,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":["2022YFC3004002"],"award-info":[{"award-number":["2022YFC3004002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["GYHY201506022"],"award-info":[{"award-number":["GYHY201506022"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Fund for Scientific Research (Meteorology) in the Public Interest of China","award":["2022YFC3004002"],"award-info":[{"award-number":["2022YFC3004002"]}]},{"name":"Special Fund for Scientific Research (Meteorology) in the Public Interest of China","award":["GYHY201506022"],"award-info":[{"award-number":["GYHY201506022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Prior to assimilating hyperspectral infrared data on the FengYun (FY) satellite in the numerical weather prediction (NWP) system, it is necessary to identify the quality and bias characteristics of these data, especially as China\u2019s first early-morning-orbit satellite data. Using the numerical model CMA-GFS (China Meteorological Administration Global Forecast System) and the observation of FY-3E HIRAS-II (Hyperspectral Infrared Atmospheric Sounder-II), the differences between observed and simulated brightness temperatures (O-Bs) are comprehensively analyzed, with a focus on evaluating the long-wave (LW) temperature sounding channels of HIRAS-II observation in the clear sky. The results show that the O-Bs in the LW channels are between \u00b11.0 K, except for the CO2 absorption line peak at 667 cm\u22121. Only a tiny variation in O-Bs, with relative consistency, could be observed during the day, the line of dawn and dusk, and night. The difference in the standard deviations of O-Bs in the three cases is less than 0.1 K. The O-Bs of two typical channels (channels 14 and 47) in the stratosphere have disturbances at individual times, whereas the O-Bs are much more stable in time series in the tropospheric channels. The O-Bs in different channels show the characteristics of changing with the latitude, but the bias and standard deviations of O-Bs during the ascending and descending stages are not much different, except for the bias of channel 47 in low latitude. The optimal ranking of Fields of View (FOVs) in assimilation is derived from a priori analysis of O-Bs. The results demonstrate that FOV4 and FOV5 are the best in a Field of Regard (FOR) compared to all LW channels of HIRAS-II in constructions of their O-Bs and magnitude of O-B standard deviations, and they can be used as the preferred FOVs for assimilation. While the O-Bs in FOV1 and FOV2 are slightly larger, the O-Bs\u2019 characteristics also meet the assimilation requirements and can be used as assimilation FOVs in HIRAS-II LW channels after FOV4 and FOV5 lose their efficacy.<\/jats:p>","DOI":"10.3390\/rs15235525","type":"journal-article","created":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T11:54:48Z","timestamp":1701086088000},"page":"5525","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The Evaluation of FY-3E Hyperspectral Infrared Atmospheric Sounder-II Long-Wave Temperature Sounding Channels"],"prefix":"10.3390","volume":"15","author":[{"given":"Jing","family":"Huang","sequence":"first","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China"},{"name":"State Key Laboratory of Severe Weather, China Meteorological Administration, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8729-9356","authenticated-orcid":false,"given":"Gang","family":"Ma","sequence":"additional","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China"},{"name":"State Key Laboratory of Severe Weather, China Meteorological Administration, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guiqing","family":"Liu","sequence":"additional","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China"},{"name":"State Key Laboratory of Severe Weather, China Meteorological Administration, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Li","sequence":"additional","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China"},{"name":"State Key Laboratory of Severe Weather, China Meteorological Administration, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Zhang","sequence":"additional","affiliation":[{"name":"CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China"},{"name":"State Key Laboratory of Severe Weather, China Meteorological Administration, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1002\/qj.4228","article-title":"Assimilation of satellite data in numerical weather prediction. 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