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Estimation of marine winds in and around typhoons using multi-platform satellite observations: Application to Typhoon Soulik (2018)

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Abstract

Estimating horizontal winds in and around typhoons is important for improved monitoring and prediction of typhoons and mitigating their damages. Here, we present a new algorithm for estimating typhoon winds using multiple satellite observations and its application to Typhoon Soulik (2018). Four kinds of satellite remote sensing data, along with their relationship to typhoon intensity, derived statistically from hundreds of historical typhoon cases, were merged into the final product of typhoon wind (MT wind): 1) geostationary-satellite-based infrared images (IR wind), 2) passive microwave sounder (MW wind), 3) feature-tracked atmospheric motion vectors, and 4) scatterometer-based sea surface winds (SSWs). The algorithm was applied to two cases (A and B) of Typhoon Soulik and validated against SSWs independently retrieved from active microwave synthetic aperture radar (SAR) and microwave radiometer (AMSR2) images, and vertical profiles of wind speed derived from reanalyzed data and dropsonde observations. For Case A (open ocean), the algorithm estimated the realistic maximum wind, radius of maximum wind, and radius of 15 m/s, which could not be estimated using the reanalysis data, demonstrating reasonable and practical estimates. However, for Case B (when the typhoon rapidly weakened just before making landfall in the Korean Peninsula), the algorithm significantly overestimated the parameters, primarily due to the overestimation of typhoon intensity. Our study highlights that realistic typhoon winds can be monitored continuously in real-time using multiple satellite observations, particularly when typhoon intensity is reasonably well predicted, providing timely analysis results and products of operational importance.

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References

  • Bessho K, DeMaria M, Knaff J A (2006). Tropical cyclone wind retrievals from the advanced microwave sounding unit: Application to surface wind analysis. J Appl Meteorol Climatol, 45(3): 399–415

    Article  Google Scholar 

  • Choi Y-S, Ho C H, Ahn M H, Kim Y M (2007). An exploratory study of cloud remote sensing capabilities of the Communication, Ocean and Meteorological Satellite (COMS) imagery. Int J Remote Sens, 28(21): 4715–4732

    Article  Google Scholar 

  • Choi Y-S, Ho C H, Ahn M H, Kim Y M (2014). Remote sensing of cloud properties from the Communications, Oceanography and Meteorology Satellite (COMS) Imagery

  • Chou K H, Wu C, Lin S Z (2013). Assessment of the ASCAT wind error characteristics by global dropwindsonde observations. J Geophys Res D Atmospheres, 118(16): 9011–9021

    Article  Google Scholar 

  • CLS (2019). Sentinel-1 Level 1 Detailed Algorithm Definition, Document Number: SEN-TN-52-7445, Issue 2.2

  • Demuth J L, DeMaria M, Knaff J A, Vonder Haar T H (2004). Evaluation of advanced microwave sounding unit tropical-cyclone intensity and size estimation algorithms. J Appl Meteorol, 43(2): 282–296

    Article  Google Scholar 

  • Demuth J L, DeMaria M, Knaff J A (2006). Improvement of advanced microwave sounding unit tropical cyclone intensity and size estimation algorithms. J Appl Meteorol Climatol, 45(11): 1573–1581

    Article  Google Scholar 

  • EUMETSAT (2019). ASCAT Wind Product User Manual, version 1.16. 1–23

  • Figa-Saldaña J, Wilson J J W, Attema E, Gelsthorpe R, Drinkwater M R, Stoffelen A (2002). The advanced scatterometer (ASCAT) on the meteorological operational (MetOp) platform: a follow on for European wind scatterometers. Can J Rem Sens, 28(3): 404–412

    Article  Google Scholar 

  • Huang L, Liu B, Li X, Zhang Z, Yu W (2017). Technical evaluation of Sentinel-1 IW mode cross-pol radar backscattering from the ocean surface in moderate wind condition. Remote Sens, 9(8): 854

    Article  Google Scholar 

  • IFREMER-CERSAT (1996). Off-line wind scatterometer ERS products: user manual, Technical Report C2-MUT-W-01-1F, Version 2.0, IFREMER-CERSAT, BP 70, 29280 PLOUZANE, France

  • Kim S, Ou M L (2013). Retrieval of mesoscale atmospheric motion vectors using COMS images at KMA/NIMR. In: International Geoscience and Remote Sensing Symposium. Melbourne: IEEE, 558–561

    Google Scholar 

  • Knaff J A, Demaria M, Molenar D A, Sampson C R, Seybold M G (2011). An automated, objective, multiple-satellite-platform tropical cyclone surface wind analysis. J Appl Meteorol Climatol, 50(10): 2149–2166

    Article  Google Scholar 

  • Knaff J A, Longmore S P, DeMaria R T, Molenar D A (2015). Improved tropical-cyclone flight-level wind estimates using routine infrared satellite reconnaissance. J Appl Meteorol Climatol, 54(2): 463–478

    Article  Google Scholar 

  • Kunitsugu M (2012). Tropical cyclone information provided by the RSMC Tokyo-Typhoon Center. Trop Cyclone Res Rev, 1: 51–59

    Google Scholar 

  • Lajoie F, Walsh K (2008). A technique to determine the radius of maximum wind of a tropical cyclone. Weather Forecast, 23(5): 1007–1015

    Article  Google Scholar 

  • Mears C A, Smith D K, Wentz F J (2001). Comparison of Special Sensor Microwave Imager and buoy-measured wind speeds from 1987 to 1997. J Geophys Res Oceans, 106(C6): 11719–11729

    Article  Google Scholar 

  • Moncrieff M W, Waliser D E, Miller M J, Shapiro M A, Asrar G R, Caughey J (2012). Multiscale convective organization and the YOTC virtual global field campaign. Bull Am Meteorol Soc, 93(8): 1171–1187

    Article  Google Scholar 

  • Moon W M, Staples G, Kim D, Park S E, Park K A (2010). RADARSAT-2 and coastal applications: surface wind, waterline, and intertidal flat roughness. Proc IEEE, 98(5): 800–815

    Article  Google Scholar 

  • Mueller K J, DeMaria M, Knaff J, Kossin J P, Vonder Haar T H (2006). Objective estimation of tropical cyclone wind structure from infrared satellite data. Weather Forecast, 21(6): 990–1005

    Article  Google Scholar 

  • Nam S, Park K A (2018). Status and prospects of marine wind observations from geostationary and polar-orbiting satellites for tropical cyclone studies. Journal of the Korean Earth Science Society, 39(4): 305–316

    Article  Google Scholar 

  • Olauson J (2018). ERA5: the new champion of wind power modelling? Renew Energy, 126: 322–331

    Article  Google Scholar 

  • Park J H, Yeo D E, Lee K J, Lee H, Lee S W, Noh S, Kim S, Shin J, Choi Y, Nam S (2019). Rapid decay of slowly moving Typhoon Soulik (2018) due to interactions with the strongly stratified northern East China Sea. Geophys Res Lett, 46(24): 14595–14603

    Article  Google Scholar 

  • Park M S, Kim M, Lee M I, Im J, Park S (2016). Detection of tropical cyclone genesis via quantitative satellite ocean surface wind pattern and intensity analyses using decision trees. Remote Sens Environ, 183: 205–214

    Article  Google Scholar 

  • Portabella M, Stoffelen A, Verhoef A, Verspeek J (2012a). A new method for improving scatterometer wind quality control. IEEE Geosci Remote Sens Lett, 9(4): 579–583

    Article  Google Scholar 

  • Portabella M, Stoffelen A, Lin W, Turiel A, Verhoef A, Verspeek J, Ballabrera-Poy J (2012b). Rain effects on ASCAT-retrieved winds: toward an improved quality control. IEEE Trans Geosci Remote Sens, 50(7): 2495–2506

    Article  Google Scholar 

  • Ricciardulli L, Wentz F (2014). Integrating the ASCAT observations into a climate data record of ocean vector winds. In: European Geosciences Union General Assembly Conference

  • Sohn E H, Chung S R, Park J S (2012). Current status of COMS AMV in NMSC/KMA. In Proc. of (16–20)

  • Velden C S, Hayden C M, Nieman S J, Menzel W P, Wanzong S, Goerss J S (1997). Upper-tropospheric winds derived from geostationary satellite water vapor observations. Bull Am Meteorol Soc, 78(2): 173–195

    Article  Google Scholar 

  • Waliser D E, Moncrieff M W, Burridge D, Fink A H, Gochis D, Goswami B N, Guan B, Harr P, Heming J, Hsu H H, Jakob C, Janiga M, Johnson R, Jones S, Knippertz P, Marengo J, Nguyen H, Pope M, Serra Y, Thorncroft C, Wheeler M, Wood R, Yuter S (2012). The “year” of tropical convection (May 2008–April 2010): climate variability and weather highlights. Bull Am Meteorol Soc, 93(8): 1189–1218

    Article  Google Scholar 

  • Wentz F J (1997). A well-calibrated ocean algorithm for special sensor microwave/imager. J Geophys Res Oceans, 102(C4): 8703–8718

    Article  Google Scholar 

  • Zhang G, Li X, Perrie W, Hwang P A, Zhang B, Yang X (2017). A hurricane wind speed retrieval model for C-band RADARSAT-2 cross-polarization ScanSAR images. IEEE Trans Geosci Remote Sens, 55(8): 4766–4774

    Article  Google Scholar 

  • Zhang G, Li X, Perrie W, Zhang B, Wang L (2016). Rain effects on the hurricane observations over the ocean by C-band Synthetic Aperture Radar. J Geophys Res Oceans, 121(1): 14–26

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank four anonymous reviewers for valuable comments and useful suggestions, and Editage for English language editing, and give special thanks to John Knaff, MyeongHee Han, Minho Kwon, Il-Ju Moon, and Chu-Yong Chung for advice and discussions on developing the algorithm. The best track data sets were obtained from the Regional Specialized Meteorological Center of the Japan Meteorological Agency (available at Japan Meteorological Agency website). ECMWF-YOTC and ERA5 data are provided by ECMWF (available at ECMWF website) and Copernicus (available at Copernicus website). The COMS CTT and AMV data were provided by the National Meteorological Satellite Center, Korea Meteorological Administration (available at Korea Meteorological Administration website). NOAA15 AMSU-a data were provided by the Global Hydrology Resource Center (GHRC), one of NASA’s DAACs (available at NASA website). MetOp-A and-B ASCAT data were provided by the JPL/NASA (available at JPL/NASA OPeNDAP website). This work was supported by the ‘Development of Typhoon and Ocean Applications’ project, funded by ETRI, which is a subproject of the ‘Development of Geostationary Meteorological Satellite Ground Segment (NMSC-2019-01)’ program funded by NMSC of KMA. This research was a part of the project titled “Construction of Ocean Research Station and their Application Studies” funded by the Ministry of Oceans and Fisheries in republic of Korea.

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Authors and Affiliations

  1. School of Earth and Environmental Sciences, College of Natural Sciences, Seoul National University, Seoul, 08826, Republic of Korea

    Seung-Woo Lee, Sung Hyun Nam & Duk-Jin Kim

  2. Research Institution of Oceanography, College of Natural Sciences, Seoul National University, Seoul, 08826, Republic of Korea

    Sung Hyun Nam

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  1. Seung-Woo Lee
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  2. Sung Hyun Nam
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Correspondence to Sung Hyun Nam.

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Lee, SW., Nam, S.H. & Kim, DJ. Estimation of marine winds in and around typhoons using multi-platform satellite observations: Application to Typhoon Soulik (2018). Front. Earth Sci. 16, 175–189 (2022). https://doi.org/10.1007/s11707-020-0849-6

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