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

Seung-Woo LEE , Sung Hyun NAM , Duk-Jin KIM

Front. Earth Sci. ›› 2022, Vol. 16 ›› Issue (1) : 175 -189.

PDF (5621KB)
Front. Earth Sci. ›› 2022, Vol. 16 ›› Issue (1) : 175 -189. DOI: 10.1007/s11707-020-0849-6
RESEARCH ARTICLE
RESEARCH ARTICLE

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

Author information +
History +
PDF (5621KB)

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.

Graphical abstract

Keywords

sea surface wind / multi-platform satellites / Typhoon Soulik (2018)

Cite this article

Download citation ▾
Seung-Woo LEE, Sung Hyun NAM, Duk-Jin KIM. Estimation of marine winds in and around typhoons using multi-platform satellite observations: Application to Typhoon Soulik (2018). Front. Earth Sci., 2022, 16(1): 175-189 DOI:10.1007/s11707-020-0849-6

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

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

[2]

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

[3]

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

[4]

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

[5]

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

[6]

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

[7]

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

[8]

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

[9]

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

[10]

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

[11]

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

[12]

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

[13]

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

[14]

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

[15]

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

[16]

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

[17]

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

[18]

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

[19]

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

[20]

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

[21]

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

[22]

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

[23]

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

[24]

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

[25]

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

[26]

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

[27]

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

[28]

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

[29]

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

[30]

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

[31]

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

[32]

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

[33]

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

RIGHTS & PERMISSIONS

Higher Education Press

AI Summary AI Mindmap
PDF (5621KB)

2463

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/