{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T11:04:49Z","timestamp":1767611089150,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T00:00:00Z","timestamp":1667865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["8202021","42005119"],"award-info":[{"award-number":["8202021","42005119"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["8202021","42005119"],"award-info":[{"award-number":["8202021","42005119"]}],"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>Atmospheric motion vectors (AMVs) derived from images of the geostationary satellite, Fengyun-4A (FY-4A), can provide high-spatiotemporal-resolution wind observations in the atmospheric middle and upper levels. To explore the potential benefits of these data for the numerical forecasting of severe weather events, the characteristics of FY-4A AMVs in different channels were analyzed and three groups of assimilation experiments were conducted in this study. The impacts of FY-4A AMVs on the forecasts of the rainstorm that occurred in Henan province in China on 20 July 2021, were investigated based on the Weather Research and Forecasting (WRF) model. The results show that FY-4A AMVs with a higher quality indicator (QI) exhibited a lower error characteristic at the cost of a reduced sample size. The assimilation of FY-4A AMVs reduced the error of the upper-level wind fields in 24 h forecasts. A positive impact could also be obtained for 10 m wind in 24 h forecasts, with an improvement of up to 9.74% for the mean bias and 3.0% for the root-mean-square error due to the inclusion of FY-4A AMVs with a QI &gt; 70. Assimilating the AMVs with a QI &gt; 80, there was an overall positive impact on the CSI score skills of 6 h accumulated precipitation above 1.0 mm in the 24 h forecast. A significant improvement could be found in the forecasting of heavy rainfall above 25.0 mm after 6 h of the forecast. The spatial distribution of the 24 h accumulated heavy rainfall zone was closer to the observations with the assimilation of the FY-4A AMVs. The adjustment of the initial wind fields resulting from the FY-4A AMVs brought a clear benefit to the quantitative precipitation forecasting skills in the event of the Henan 7.20 rainstorm; however, the AMV data assimilation still had difficulty in capturing the hourly maximum rainfall and intensity well.<\/jats:p>","DOI":"10.3390\/rs14225637","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T10:49:51Z","timestamp":1667904591000},"page":"5637","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Impacts of FY-4A Atmospheric Motion Vectors on the Henan 7.20 Rainstorm Forecast in 2021"],"prefix":"10.3390","volume":"14","author":[{"given":"Yanhui","family":"Xie","sequence":"first","affiliation":[{"name":"Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China"}]},{"given":"Min","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China"}]},{"given":"Shuting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6163-2912","authenticated-orcid":false,"given":"Jiancheng","family":"Shi","sequence":"additional","affiliation":[{"name":"National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Ruixia","family":"Liu","sequence":"additional","affiliation":[{"name":"China Meteorological Administration (CMA) Earth System Modeling and Prediction Centre (CEMC), Beijing 100081, China"},{"name":"State Key Laboratory of Severe Weather, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1175\/1520-0450(1993)032<1559:ACOSTT>2.0.CO;2","article-title":"A comparison of several techniques to assign heights to cloud tracers","volume":"32","author":"Nieman","year":"1993","journal-title":"J. Appl. Meteorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1175\/1520-0477(1997)078<0173:UTWDFG>2.0.CO;2","article-title":"Upper-tropospheric winds derived from geostationary satellite water vapor observations","volume":"78","author":"Velden","year":"1997","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1002\/qj.2925","article-title":"Diagnosing atmospheric motion vector observation errors for an operational high-resolution data assimilation system","volume":"143","author":"Cordoba","year":"2017","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_4","unstructured":"Bhatia, R.C., Khanna, P.N., and Prasad, S. (1996). Improvements in Automated Cloud Motion Vectors (CMWs) Derivation Scheme Using INSAT VHRR Data. Proc. Third Int. Winds Workshop, EUMETSAT."},{"key":"ref_5","unstructured":"Holmlund, K. (March, January 28). The Atmospheric Motion Vector Retrieval Scheme for Meteosat Second Generation. Proceedings of the Fifth International Winds Workshop, Lorne, Australia."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1175\/1520-0450(1993)032<1206:OCMWFM>2.0.CO;2","article-title":"Operational cloud-motion winds from Meteosat infrared images","volume":"32","author":"Schmetz","year":"1993","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_7","first-page":"89","article-title":"The real time generation and application of cloud drift winds in the Australian region","volume":"42","author":"Pescod","year":"1993","journal-title":"Aust. Meteorol. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1175\/1520-0477(1997)078<1121:FACDWI>2.0.CO;2","article-title":"Fully automated cloud-drift winds in NESDIS operations","volume":"78","author":"Nieman","year":"1997","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1175\/1520-0434(1994)009<0361:AOSFGC>2.0.CO;2","article-title":"An Operational System for Generating Cloud Drift Winds in the Australian Region and Their Impact on Numerical Weather Prediction","volume":"9","author":"Pescod","year":"1994","journal-title":"Weather Forecast."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1175\/JAM2264.1","article-title":"Application of satellite-derived atmospheric motion vectors for estimating mesoscale flows","volume":"44","author":"Bedka","year":"2005","journal-title":"J. Appl. Meteorol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1175\/MWR3163.1","article-title":"The impact of satellite-derived atmospheric motion vectors on mesoscale forecasts over Hawaii","volume":"134","author":"Cherubini","year":"2006","journal-title":"Mon. Weather Rev."},{"key":"ref_12","first-page":"359","article-title":"The contribution of locally generated MTSat-1R atmospheric motion vectors to operational meteorology in the Australian region","volume":"57","author":"Seecamp","year":"2008","journal-title":"Aust. Meteorol. Mag."},{"key":"ref_13","unstructured":"Bormann, N., Salonen, K., Peubey, C., Mcnally, T., and Lupu, C. (2012, January 20\u201324). An Overview of the Status of the Operational Assimilation of AMVS at ECMWF. Proceedings of the 11th International Winds Workshop, Auckland, New Zealand."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1175\/WAF-D-16-0061.1","article-title":"Effect of enhanced satellite-derived atmospheric motion vectors on numerical weather prediction in East Asia using an adjoint-based observation impact method","volume":"32","author":"Kim","year":"2017","journal-title":"Weather Forecast."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1175\/MWR-D-16-0229.1","article-title":"Assimilation of high-resolution satellite-derived atmospheric motion vectors: Impact on HWRF forecasts of tropical cyclone track and intensity","volume":"145","author":"Velden","year":"2017","journal-title":"Mon. Weather Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1175\/1520-0493(1999)127<0971:UAIOSA>2.0.CO;2","article-title":"Use and impact of satellite atmospheric motion winds on ECMWF analyses and forecasts","volume":"127","author":"Tomassini","year":"1999","journal-title":"Mon. Weather Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1175\/1520-0477(1999)080<1363:TNPENT>2.0.CO;2","article-title":"NORPEX-98: Targeted observations for improved North American weather forecasts","volume":"80","author":"Langland","year":"1999","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3331","DOI":"10.1175\/MWR-D-12-00232.1","article-title":"The impact of MetOp and other satellite data within the Met Office Global NWP system using an adjoint-based sensitivity method","volume":"141","author":"Joo","year":"2013","journal-title":"Mon. Weather Rev."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lewis, W.E., Velden, C.S., and Stettner, D. (2020). Strategies for assimilating high-density atmospheric motion vectors into a regional tropical cyclone forecast model (HWRF). Atmosphere, 11.","DOI":"10.3390\/atmos11060673"},{"key":"ref_20","first-page":"1","article-title":"Impact of Assimilating High-Resolution Atmospheric Motion Vectors on Convective Scale Short-Term Forecasts: 2. Assimilation Experiments of GOES-16 Satellite Derived Winds","volume":"13","author":"Zhao","year":"2021","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4009","DOI":"10.1175\/2010MWR3393.1","article-title":"The THORPEX Observation Impact Intercomparison Experiment","volume":"138","author":"Gelaro","year":"2010","journal-title":"Mon. Weather Rev."},{"key":"ref_22","unstructured":"Daley, R. (1993). Atmospheric Data Analysis, Cambridge University Press."},{"key":"ref_23","first-page":"706","article-title":"The Spatial Structure of Observation Errors in Atmospheric Motion Vectors from Geostationary Satellite Data","volume":"131","author":"Bormann","year":"2003","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_24","first-page":"123","article-title":"Error characterisation of atmospheric motion vectors","volume":"53","author":"Marshall","year":"2004","journal-title":"Aust. Meteorol. Mag."},{"key":"ref_25","first-page":"31","article-title":"Atmospheric Motion Vectors from model simulations. Part I: Methods and characterisation as single-level estimates of wind","volume":"677","author":"Bormann","year":"2012","journal-title":"ECMWF Tech. Memo."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1175\/1520-0477(2001)082<0033:CTWSIF>2.3.CO;2","article-title":"Cloud tracking with satellite imagery: From the pioneering work of Ted Fujita to the present","volume":"82","author":"Menzel","year":"2001","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1175\/2008JAMC1957.1","article-title":"Identifying the uncertainty in determining satellite-derived atmospheric motion vector height attribution","volume":"48","author":"Velden","year":"2008","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1175\/1520-0450(1982)021<0384:DOSTCC>2.0.CO;2","article-title":"Determination of semi-transparent cirrus cloud temperatures from infrared radiances: Application to Meteosat","volume":"21","author":"Szejwach","year":"1982","journal-title":"J. Appl. Meteorol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1175\/1520-0450(1983)022<0377:ICMWVA>2.0.CO;2","article-title":"Improved cloud motion wind vector and altitude assignment using VAS","volume":"22","author":"Menzel","year":"1983","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1175\/1520-0450(2002)041<0253:TVECOG>2.0.CO;2","article-title":"The vertical error characteristics of GOES-derived winds: Description and experiments with Numerical Weather Prediction","volume":"41","author":"Rao","year":"2002","journal-title":"J. Appl. Meteorol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1007\/s13351-012-0506-4","article-title":"A study on height reassignment for the AMV products of the FY-2C satellite","volume":"26","author":"Yang","year":"2012","journal-title":"Acta Meteorol. Sin."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1175\/1520-0434(1998)013<1093:TUOSPO>2.0.CO;2","article-title":"The utilization of statistical properties of satellite-derived atmospheric motion vectors to derive quality indicators","volume":"13","author":"Holmlund","year":"1998","journal-title":"Weather Forecast."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2392","DOI":"10.1175\/1520-0493(2001)129<2392:IOANCM>2.0.CO;2","article-title":"Impact of a new cloud motion wind product from Meteosat on NWP analyses","volume":"129","author":"Rohn","year":"2001","journal-title":"Mon. Weather Rev."},{"key":"ref_34","first-page":"1","article-title":"Impact of rapid-scan-based dynamical information from GOES-16 on HWRF hurricane forecasts","volume":"125","author":"Li","year":"2020","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1071\/ES17003","article-title":"Himawari-8 atmospheric motion vectors\u2014Operational generation and assimilation","volume":"67","author":"Marshall","year":"2017","journal-title":"J. South. Hemisph. Earth Syst. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Borde, R., Carranza, M., Hautecoeur, O., and Barbieux, K. (2019). Winds of change for future operational AMV at EUMETSAT. Remote Sens., 11.","DOI":"10.3390\/rs11182111"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1175\/BAMS-D-16-0065.1","article-title":"Introducing the new generation of Chinese geostationary weather satellites\u2014FengYun 4","volume":"98","author":"Yang","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1007\/s00376-020-0080-0","article-title":"Characteristics of fengyun-4a satellite atmospheric motion vectors and their impacts on data assimilation","volume":"37","author":"Chen","year":"2020","journal-title":"Adv. Atmos. Sci."},{"key":"ref_39","first-page":"458","article-title":"The evaluation of FY-4A AMVs in GRAPES_RAFS","volume":"45","author":"Wan","year":"2019","journal-title":"Meteorol. Mon."},{"key":"ref_40","first-page":"1366","article-title":"Observational analysis of the dynamic, thermal, and water vapor characteristics of the \u201c7.20\u201d extreme rainstorm event in Henan province, 2021","volume":"45","author":"Ran","year":"2021","journal-title":"Chin. J. Atmos. Sci."},{"key":"ref_41","unstructured":"Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.Y., Wang, W., and Powers, J.G. (2008). A Description of the Advanced Research WRF Version 3, National Center for Atmospheric Research. NCAR Tech. Note NCAR\/TN-475+STR."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"181","DOI":"10.2151\/jmsj1965.75.1B_181","article-title":"Unified notation for data assimilation: Operational, sequential and variational","volume":"75","author":"Ide","year":"1997","journal-title":"J. Met. Soc. Jpn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1002\/met.1433","article-title":"Assessment of a new quality control technique in the retrieval of atmospheric motion vectors","volume":"22","author":"Deb","year":"2013","journal-title":"Meteorol. Appl."},{"key":"ref_44","first-page":"102","article-title":"The method and application of automatic quali ty control for real time data from Automatic Weather Stations","volume":"33","author":"Wang","year":"2007","journal-title":"Meteorol. Mon."},{"key":"ref_45","first-page":"1265","article-title":"Development of three-step quality control system of real-time observation data from AWS in China","volume":"41","author":"Ren","year":"2015","journal-title":"Meteorol. Mon."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1175\/1520-0434(1990)005<0570:TCSIAA>2.0.CO;2","article-title":"The critical success index as an indicator of warning skill","volume":"5","author":"Schaffer","year":"1990","journal-title":"Weather Forecast."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5637\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:12:31Z","timestamp":1760145151000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5637"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,8]]},"references-count":46,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225637"],"URL":"https:\/\/doi.org\/10.3390\/rs14225637","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,11,8]]}}}