{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T01:00:45Z","timestamp":1773882045309,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,9,23]],"date-time":"2017-09-23T00:00:00Z","timestamp":1506124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Observations of sea surface wind field are critical for typhoon prediction. The scatterometer observation is one of the most important sources of sea surface winds, which provides both wind speed and wind direction information. However, the spatial resolution of scatterometer wind is low. Synthetic Aperture Radar (SAR) can provide a more detailed wind structure of the tropical cyclone. In addition, the cross-polarization observation of SAR can provide more detailed information of high speed wind (&gt;25 m\u00b7s      \u2212 1     ) than the scatterometer. Nevertheless, due to the narrow swath of SAR, the number of retrieved sea surface wind data used in the data assimilation is limited, and another limitation of SAR wind observation is that it does not provide true wind direction information. In this paper, the joint assimilation of the Advanced Scatterometer (ASCAT) wind and Sentinel-1 SAR wind was investigated. Another limitation in the current operational typhoon prediction is the inefficient quality control (QC) method used in the data assimilation since a large number of high speed wind observations was rejected by the traditional Gaussian distribution QC. We introduce the Huber norm distribution quality control (QC) into the data assimilation successfully. A numerical simulation experiment of typhoon by Lionrock (2016) is conducted to test the proposed method. The experimental results showed that the new quality control scheme not only greatly increases the availability of wind data in the area of the typhoon center, but also improves the typhoon track prediction, as well as the intensity prediction. The joint assimilation of scatterometer and SAR winds does have a positive impact on the typhoon prediction.<\/jats:p>","DOI":"10.3390\/rs9100987","type":"journal-article","created":{"date-parts":[[2017,9,26]],"date-time":"2017-09-26T04:28:01Z","timestamp":1506400081000},"page":"987","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Assimilation of Typhoon Wind Field Retrieved from Scatterometer and SAR Based on the Huber Norm Quality Control"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7287-7261","authenticated-orcid":false,"given":"Boheng","family":"Duan","sequence":"first","affiliation":[{"name":"Academy of Ocean Science and Engineering, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Weimin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Academy of Ocean Science and Engineering, National University of Defense Technology, Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9920-4641","authenticated-orcid":false,"given":"Xiaofeng","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"The Key Laboratory for Earth Observation of Hainan Province, Sanya 572029, China"}]},{"given":"Haijin","family":"Dai","sequence":"additional","affiliation":[{"name":"Academy of Ocean Science and Engineering, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Yi","family":"Yu","sequence":"additional","affiliation":[{"name":"Academy of Ocean Science and Engineering, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3405","DOI":"10.1175\/2011MWR3391.1","article-title":"Assessment of Assimilating ASCAT Surface Wind Retrievals in the NCEP Global Data Assimilation System","volume":"139","author":"Li","year":"2011","journal-title":"Mon. Weather Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1256\/qj.03.110","article-title":"Impact of ERS scatterometer winds in ECMWF\u2019s assimilation system","volume":"130","author":"Isaksen","year":"2004","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2794","DOI":"10.1175\/1520-0493(1991)119<2794:TIOASD>2.0.CO;2","article-title":"The impact of Seasat-A scatterometer data on high-resolution analyses and forecasts: The development of the QEII storm","volume":"119","author":"Stoffelen","year":"1991","journal-title":"Mon. Weather Rev."},{"key":"ref_4","unstructured":"Stoffelen, A., and Beukering, P.V. (2017, January 01). The Impact of Improved Scatterometer Winds on HIRLAM Analyses and Forecasts. Available online: http:\/\/hirlam.org\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1885","DOI":"10.1109\/36.851771","article-title":"ERS scatterometer wind data impact on ECMWF\u2019s tropical cyclone forecasts","volume":"38","author":"Isaksen","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Atlas, R., and Hoffman, R.N. (2000). The Use of Satellite Surface Wind Data to Improve Weather Analysis and Forecasting at the NASA Data Assimilation Office, Elsevier.","DOI":"10.1016\/S0422-9894(00)80005-7"},{"key":"ref_7","unstructured":"Candy, B. (2001). The Assimilation of Ambiguous Scatterometer Winds Using a Variational Technique: Method and Forecast Impact, Met Office, NWP Division."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1175\/1520-0477(2001)082<1965:TEOMWF>2.3.CO;2","article-title":"The effects of marine winds from scatterometer data on weather analysis and forecasting","volume":"82","author":"Atlas","year":"2001","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_9","first-page":"1831","article-title":"The ECMWF implementation of three-dimensional variational assimilation (3D-Var). III: Experimental results","volume":"124","author":"Andersson","year":"1998","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_10","first-page":"627","article-title":"Impact of OSCAT surface wind data on T574L64 assimilation and forecasting system\u2014A study involving tropical cyclone Thane","volume":"104","author":"Prasad","year":"2013","journal-title":"Curr. Sci."},{"key":"ref_11","unstructured":"Hersbach, H. (2010). Assimilation of Scatterometer Data as Equivalent-Neutral Wind, ECMWF Publications."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1175\/1525-7541(2003)4<473:SAOESD>2.0.CO;2","article-title":"Sequential Assimilation of ERS-1 SAR data into a doupled land surface-hydrological model using an extended kalman filter","volume":"4","author":"Francois","year":"2003","journal-title":"J. Hydrometeorol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.5194\/hess-14-1773-2010","article-title":"Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the particle filter: Proof of Concept","volume":"14","author":"Matgen","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Scott, A.K., Ashouri, Z., Buehner, M., Pogson, L., and Carrieres, T. (2015). Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration. Tellus A, 67.","DOI":"10.3402\/tellusa.v67.27218"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.5194\/tc-8-1975-2014","article-title":"1D-Var multilayer assimilation of X-band SAR data into a detailed snowpack model","volume":"8","author":"Phan","year":"2014","journal-title":"Cryosphere"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3859","DOI":"10.1109\/JSTARS.2014.2357685","article-title":"InSAR water vapor data assimilation into mesoscale model MM5: Technique and pilot study","volume":"8","author":"Pichelli","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","first-page":"178","article-title":"Evidence of a topographic signal in surface soil moisture derived from ENVISAT ASAR wide swath data","volume":"45","author":"Mason","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2131","DOI":"10.1109\/TGRS.2002.802474","article-title":"Observation of hurricane-generated ocean swell refraction at the Gulf Stream north wall with the RADARSAT-1 synthetic aperture","volume":"40","author":"Li","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1175\/BAMS-D-11-00211.1","article-title":"Tropical Cyclone Morphology from Spaceborne Synthetic Aperture Radar","volume":"94","author":"Li","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1080\/01431161.2014.916447","article-title":"Typhoon eye extraction with an automatic SAR image segmentation method","volume":"35","author":"Jin","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","first-page":"1","article-title":"The first Sentinel-1 SAR image of a typhoon","volume":"34","author":"Li","year":"2015","journal-title":"Acta Oceanol. Sin."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1029\/2015EO034581","article-title":"A Weather Eye on Coastal Winds","volume":"96","author":"Monaldo","year":"2015","journal-title":"Eos"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2016.03.020","article-title":"Extracting hurricane eye morphology from spaceborne SAR images using morphological analysis","volume":"117","author":"Lee","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/TGRS.2015.2472282","article-title":"Comparison of Typhoon Centers from SAR and IR Images and Those from Best Track Data Sets","volume":"54","author":"Zheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1007\/s11430-013-4633-2","article-title":"Estimation of tropical cyclone parameters and wind fields from SAR images","volume":"56","author":"Zhou","year":"2013","journal-title":"Sci. China Earth Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1175\/BAMS-D-12-00165.1","article-title":"Ocean Wind Speed Climatology from Spaceborne SAR Imagery","volume":"95","author":"Monaldo","year":"2014","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6164","DOI":"10.1002\/2015JC011052","article-title":"Synergistic measurements of ocean winds and waves from SAR","volume":"120","author":"Zhang","year":"2015","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2589","DOI":"10.1109\/JSTARS.2017.2650410","article-title":"Retrieving Hurricane Wind Speed from Dominant Wave Parameters","volume":"10","author":"Hwang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4743","DOI":"10.1109\/TGRS.2011.2159802","article-title":"Comparison of Ocean Surface Winds from ENVISAT ASAR, MetOp ASCAT Scatterometer, Buoy Measurements, and NOGAPS Model","volume":"49","author":"Yang","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1109\/LGRS.2010.2053345","article-title":"Comparison of Ocean-Surface Winds Retrieved from QuikSCAT Scatterometer and Radarsat-1 SAR in Offshore Waters of the U.S. West Coast","volume":"8","author":"Yang","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2638","DOI":"10.1109\/JSTARS.2015.2504324","article-title":"Preliminary Evaluation of Sentinel-1A Wind Speed Retrievals","volume":"9","author":"Monaldo","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1109\/TGRS.2016.2631663","article-title":"A Hurricane Morphology and Sea Surface Wind Vector Estimation Model Based on C-Band Cross-Polarization SAR Imagery","volume":"55","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1002\/qj.49711448012","article-title":"Objective quality control of observationsusing Bayesian methods\u2014Theory, and practical implementation","volume":"114","author":"Lorenc","year":"1988","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_34","unstructured":"Lorenc, C.A. (1984, January 6\u20139). Analysis methods for the quality control of observations. Proceedings of the ECMWF Workshop on the Use and Quality Control of Meteorological Observations for Numerical Weather Prediction, Reading, UK."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1214\/aoms\/1177703732","article-title":"Robust estimates of a location parameter","volume":"35","author":"Huber","year":"1964","journal-title":"Ann. Math. Statist."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1214\/aoms\/1177692459","article-title":"The 1972 Wald Lecture Robust statistics: A review","volume":"43","author":"Huber","year":"1972","journal-title":"Ann. Math. Stat."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1175\/BAMS-D-11-00001.1","article-title":"Cross-Polarized Synthetic Aperture Radar: A New Potential Measurement Technique for Hurricanes","volume":"93","author":"Zhang","year":"2012","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1514","DOI":"10.1002\/qj.2440","article-title":"On the Use of a Huber Norm for Observation Quality Control in the ECMWF 4D-Var","volume":"141","author":"Tavolato","year":"2015","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1002\/qj.49711247414","article-title":"Analysis methods for numerical weather prediction","volume":"112","author":"Lorenc","year":"1986","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1175\/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2","article-title":"A three-dimensional variational data assimilation system for MM5: Implementation and initial results","volume":"132","author":"Barker","year":"2004","journal-title":"Mon. Weather Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1175\/2009MWR2645.1","article-title":"Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter","volume":"137","author":"Zhang","year":"2009","journal-title":"Mon. Weather Rev."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/10\/987\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:45:45Z","timestamp":1760208345000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/10\/987"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,23]]},"references-count":41,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2017,10]]}},"alternative-id":["rs9100987"],"URL":"https:\/\/doi.org\/10.3390\/rs9100987","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,23]]}}}