Impact of Assimilation of the Tropical Cyclone Strong Winds Observed by Synthetic Aperture Radar on Analyses and Forecasts

Yasutaka Ikuta aMeteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan

Search for other papers by Yasutaka Ikuta in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0003-2532-8208
and
Udai Shimada aMeteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan

Search for other papers by Udai Shimada in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A few high-wind observations have been obtained from satellites over the ocean around tropical cyclones (TCs), but the impact of data assimilation of such observations over the sea on forecasting has not been clear. The spaceborne synthetic aperture radar (SAR) provides high-resolution and wide-area ocean surface wind speed data around the center of a TC. In this study, the impact of data assimilation of the ocean surface wind speed of SAR (OWSAR) on regional model forecasts was investigated. The assimilated data were estimated from SAR on board Sentinel-1 and RADARSAT-2. The bias of OWSAR depends on wind speed, the observation error variance depends on wind speed and incidence angle, and the spatial observation error correlation depends on the incidence angle. The observed OWSAR is screened using the variational quality control method with the Huber norm. In the case of Typhoon Hagibis (2019), OWSAR assimilation modified the TC low-level inflow, which also modified the TC upper-level outflow. The propagation of this OWSAR assimilation effect from the surface to the upper troposphere was given by a four-dimensional variational method that searches for the optimal solution within strong constraints on the time evolution of the forecast model. Statistical validation confirmed that errors in the TC intensity forecast decreased over lead times of 15 h, but this was not statistically significant. The validation using wind profiler observations showed that OWSAR assimilation significantly improved the accuracy of wind speed predictions from the middle to the upper level of the troposphere.

Significance Statement

The purpose of this study was to demonstrate the impact of the assimilation of ocean surface wind speed by synthetic aperture radar (SAR) on regional model predictions. In the case of tropical cyclones, ocean surface wind speed assimilation modified inflows in the lower layer and outflows in the upper layer. The results indicate that the SAR assimilation improves the accuracy of wind speed forecasts in the middle to upper troposphere.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yasutaka Ikuta, yasutaka.ikuta@mri-jma.go.jp

Abstract

A few high-wind observations have been obtained from satellites over the ocean around tropical cyclones (TCs), but the impact of data assimilation of such observations over the sea on forecasting has not been clear. The spaceborne synthetic aperture radar (SAR) provides high-resolution and wide-area ocean surface wind speed data around the center of a TC. In this study, the impact of data assimilation of the ocean surface wind speed of SAR (OWSAR) on regional model forecasts was investigated. The assimilated data were estimated from SAR on board Sentinel-1 and RADARSAT-2. The bias of OWSAR depends on wind speed, the observation error variance depends on wind speed and incidence angle, and the spatial observation error correlation depends on the incidence angle. The observed OWSAR is screened using the variational quality control method with the Huber norm. In the case of Typhoon Hagibis (2019), OWSAR assimilation modified the TC low-level inflow, which also modified the TC upper-level outflow. The propagation of this OWSAR assimilation effect from the surface to the upper troposphere was given by a four-dimensional variational method that searches for the optimal solution within strong constraints on the time evolution of the forecast model. Statistical validation confirmed that errors in the TC intensity forecast decreased over lead times of 15 h, but this was not statistically significant. The validation using wind profiler observations showed that OWSAR assimilation significantly improved the accuracy of wind speed predictions from the middle to the upper level of the troposphere.

Significance Statement

The purpose of this study was to demonstrate the impact of the assimilation of ocean surface wind speed by synthetic aperture radar (SAR) on regional model predictions. In the case of tropical cyclones, ocean surface wind speed assimilation modified inflows in the lower layer and outflows in the upper layer. The results indicate that the SAR assimilation improves the accuracy of wind speed forecasts in the middle to upper troposphere.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yasutaka Ikuta, yasutaka.ikuta@mri-jma.go.jp
Save
  • Anderson, E., and H. Järvinen, 1999: Variational quality control. Quart. J. Roy. Meteor. Soc., 125, 697722, https://doi.org/10.1002/qj.49712555416.

    • Search Google Scholar
    • Export Citation
  • Beljaars, A. C. M., 1995: The parameterization of surface fluxes in large-scale models under free convection. Quart. J. Roy. Meteor. Soc., 121, 255270, https://doi.org/10.1002/qj.49712152203.

    • Search Google Scholar
    • Export Citation
  • Beljaars, A. C. M., and A. A. M. Holtslag, 1991: Flux parameterization over land surfaces for atmospheric models. J. Appl. Meteor., 30, 327341, https://doi.org/10.1175/1520-0450(1991)030<0327:FPOLSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Cameron, J., and W. Bell, 2018: The testing and implementation of Variational Bias Correction (VarBC) in the Met Office global NWP system. Weather Science Tech. Rep. 631, Met Office, 22 pp., https://library.metoffice.gov.uk/Portal/Default/en-GB/RecordView/Index/633663.

  • Charnock, H., 1955: Wind stress on a water surface. Quart. J. Roy. Meteor. Soc., 81, 639640, https://doi.org/10.1002/qj.49708135027.

  • Clarizia, M. P., and C. S. Ruf, 2016: Wind speed retrieval algorithm for the Cyclone Global Navigation Satellite System (CYGNSS) mission. IEEE Trans. Geosci. Remote Sens., 54, 44194432, https://doi.org/10.1109/TGRS.2016.2541343.

    • Search Google Scholar
    • Export Citation
  • Combot, C., A. Mouche, J. Knaff, Y. Zhao, Y. Zhao, L. Vinour, Y. Quilfen, and B. Chapron, 2020: Extensive high-resolution Synthetic Aperture Radar (SAR) data analysis of tropical cyclones: Comparisons with SFMR flights and best track. Mon. Wea. Rev., 148, 45454563, https://doi.org/10.1175/MWR-D-20-0005.1.

    • Search Google Scholar
    • Export Citation
  • Cotton, J., P. Francis, J. Heming, M. Forsythe, N. Reul, and C. Donlon, 2018: Assimilation of SMOS L-band wind speeds: Impact on Met Office global NWP and tropical cyclone predictions. Quart. J. Roy. Meteor. Soc., 144, 614629, https://doi.org/10.1002/qj.3237.

    • Search Google Scholar
    • Export Citation
  • Courtier, P., J.-N. Thépaut, and A. Hollingsworth, 1994: A strategy for operational implementation of 4D-Var, using an incremental approach. Quart. J. Roy. Meteor. Soc., 120, 13671387, https://doi.org/10.1002/qj.49712051912.

    • Search Google Scholar
    • Export Citation
  • Cui, Z., Z. Pu, V. Tallapragada, R. Atlas, and C. S. Ruf, 2019: A preliminary impact study of CYGNSS ocean surface wind speeds on numerical simulations of hurricanes. Geophys. Res. Lett., 46, 29842992, https://doi.org/10.1029/2019GL082236.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., 2004: Variational bias correction of radiance data in the ECMWF system. ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP, Shinfield Park, United Kingdom, ECMWF, 97112.

    • Search Google Scholar
    • Export Citation
  • Desroziers, G., L. Berre, B. Chapnik, and P. Poli, 2005: Diagnosis of observation, background and analysis-error statistics in observation space. Quart. J. Roy. Meteor. Soc., 131, 33853396, https://doi.org/10.1256/qj.05.108.

    • Search Google Scholar
    • Export Citation
  • ESA, 2012: Sentinel-1 Product Definition. Tech. Doc. S1-RS-MDA-52-7440, 129 pp., https://sentinel.esa.int/documents/247904/1877131/Sentinel-1-Product-Definition.

  • ESA, 2021: SMOS wind products description document. ESA, 29 pp., https://earth.esa.int/eogateway/documents/20142/37627/SMOS-Wind-Products-Description.pdf.

  • Eyre, J. R., W. Bell, J. Cotton, S. J. English, M. Forsythe, S. B. Healy, and E. G. Pavelin, 2022: Assimilation of satellite data in numerical weather prediction. Part II: Recent years. Quart. J. Roy. Meteor. Soc., 148, 521556, https://doi.org/10.1002/qj.4228.

    • Search Google Scholar
    • Export Citation
  • Heming, J. T., 2016: Met Office Unified Model tropical cyclone performance following major changes to the initialization scheme and a model upgrade. Wea. Forecasting, 31, 14331449, https://doi.org/10.1175/WAF-D-16-0040.1.

    • Search Google Scholar
    • Export Citation
  • Hoffman, R. N., 2018: The effect of thinning and superobservations in a simple one-dimensional data analysis with mischaracterized error. Mon. Wea. Rev., 146, 11811195, https://doi.org/10.1175/MWR-D-17-0363.1.

    • Search Google Scholar
    • Export Citation
  • Huber, P. J., 1972: The 1972 Wald lecture robust statistics: A review. Ann. Math. Stat., 43, 10411067, https://doi.org/10.1214/aoms/1177692459.

    • Search Google Scholar
    • Export Citation
  • Ide, K., P. Courtier, M. Ghil, and A. C. Lorenc, 1997: Unified notation for data assimilation: Operational, sequential and variational. J. Meteor. Soc. Japan, 75, 181189.

    • Search Google Scholar
    • Export Citation
  • Ikuta, Y., M. Satoh, M. Sawada, H. Kusabiraki, and T. Kubota, 2021a: Improvement of the cloud microphysics scheme of the mesoscale model at the Japan Meteorological Agency using spaceborne radar and microwave imager of the global precipitation measurement as reference. Mon. Wea. Rev., 149, 38033819, https://doi.org/10.1175/MWR-D-21-0066.1.

    • Search Google Scholar
    • Export Citation
  • Ikuta, Y., T. Fujita, Y. Ota, and Y. Honda, 2021b: Variational data assimilation system for operational regional models at Japan Meteorological Agency. J. Meteor. Soc. Japan, 99, 15631592, https://doi.org/10.2151/jmsj.2021-076.

    • Search Google Scholar
    • Export Citation
  • Ikuta, Y., H. Seko, and Y. Shoji, 2022: Assimilation of shipborne precipitable water vapour by Global Navigation Satellite Systems for extreme precipitation events. Quart. J. Roy. Meteor. Soc., 148, 5775, https://doi.org/10.1002/qj.4192.

    • Search Google Scholar
    • Export Citation
  • Ikuta, Y., M. Sawada, and M. Satoh, 2023: Determining the impact of boundary layer schemes on the secondary circulation of Typhoon Faxai using radar observations in the gray zone. J. Atmos. Sci., 80, 961981, https://doi.org/10.1175/JAS-D-22-0169.1.

    • Search Google Scholar
    • Export Citation
  • Ishida, J., K. Aranami, K. Kawano, K. Matsubayashi, Y. Kitamura, and C. Muroi, 2022: ASUCA: The JMA operational non-hydrostatic model. J. Meteor. Soc. Japan, 100, 825846, https://doi.org/10.2151/jmsj.2022-043.

    • Search Google Scholar
    • Export Citation
  • Isoguchi, O., T. Tadono, M. Ohki, U. Shimada, M. Yamaguchi, M. Hayashi, and W. Yanase, 2021: Hurricane ocean surface wind retrieval from ALOS-2 PALSAR-2 cross-polarized measurements. IEEE Int. Geoscience and Remote Sensing Symp. IGARSS, Brussels, Belgium, Institute of Electrical and Electronics Engineers, 7291–7294, https://doi.org/10.1109/IGARSS47720.2021.9554411.

  • Isoguchi, O., T. Tadono, M. Ohki, U. Shimada, M. Yamaguchi, M. Hayashi, and W. Yanase, 2022: Hurricane ocean surface winds retrieval by ALOS-2/PALSAR-2 and comparison with Sentinel-1 products. ESA Living Planet Symp., Bonn, Germany, ESA, https://lps22.eu/.

  • JMA, 2019: Outline of the operational numerical weather prediction at the Japan Meteorological Agency: Appendix to WMO technical progress report on the global data-processing and forecasting system and numerical weather prediction. Japan Meteorological Agency Tech. Rep., 242 pp., https://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline2019-nwp/index.htm.

  • JMA, 2020: Climate change monitoring report 2019. Japan Meteorological Agency Tech. Rep., 100 pp., https://www.jma.go.jp/jma/en/NMHS/ccmr/ccmr2019.pdf.

  • Kudryavtsev, V., I. Kozlov, B. Chapron, and J. A. Johannessen, 2014: Quad-polarization SAR features of ocean currents. J. Geophys. Res. Oceans, 119, 60466065, https://doi.org/10.1002/2014JC010173.

    • Search Google Scholar
    • Export Citation
  • Mouche, A., and B. Chapron, 2015: Global C-band envisat, RADARSAT-2 and Sentinel-1 SAR measurements in copolarization and cross-polarization. J. Geophys. Res. Oceans, 120, 71957207, https://doi.org/10.1002/2015JC011149.

    • Search Google Scholar
    • Export Citation
  • Mouche, A., B. Chapron, B. Zhang, and R. Husson, 2017: Combined co- and cross-polarized SAR measurements under extreme wind conditions. IEEE Trans. Geosci. Remote Sens., 55, 67466755, https://doi.org/10.1109/TGRS.2017.2732508.

    • Search Google Scholar
    • Export Citation
  • Mouche, A., B. Chapron, J. Knaff, Y. Zhao, B. Zhang, and C. Combot, 2019: Copolarized and cross-polarized SAR measurements for high-resolution description of major hurricane wind structures: Application to Irma category 5 hurricane. J. Geophys. Res. Oceans, 124, 39053922, https://doi.org/10.1029/2019JC015056.

    • Search Google Scholar
    • Export Citation
  • Nagata, K., 2011: Quantitative precipitation estimation and quantitative precipitation forecasting by the Japan Meteorological Agency. RSMC Tokyo Tech. Rev. 13, 14 pp., https://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/techrev/text13-2.pdf.

    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895912, https://doi.org/10.2151/jmsj.87.895.

    • Search Google Scholar
    • Export Citation
  • Nolan, D. S., J. A. Zhang, and E. W. Uhlhorn, 2014: On the limits of estimating the maximum wind speeds in hurricanes. Mon. Wea. Rev., 142, 28142837, https://doi.org/10.1175/MWR-D-13-00337.1.

    • Search Google Scholar
    • Export Citation
  • Powell, M. D., P. J. Vickery, and T. A. Reinhold, 2003: Reduced drag coefficient for high wind speeds in tropical cyclones. Nature, 422, 279283, https://doi.org/10.1038/nature01481.

    • Search Google Scholar
    • Export Citation
  • Tavolato, C., and L. Isaksen, 2015: On the use of a Huber norm for observation quality control in the ECMWF 4D-Var. Quart. J. Roy. Meteor. Soc., 141, 15141527, https://doi.org/10.1002/qj.2440.

    • Search Google Scholar
    • Export Citation
  • Verhoef, A., and A. Stoffelen, 2018: ASCAT wind validation report. OSI SAF Tech. Rep., 12 pp., https://scatterometer.knmi.nl/publications/pdf/ascat_validation.pdf.

  • Waller, J. A., S. L. Dance, and N. K. Nichols, 2016: Theoretical insight into diagnosing observation error correlations using observation-minus-background and observation-minus-analysis statistics. Quart. J. Roy. Meteor. Soc., 142, 418431, https://doi.org/10.1002/qj.2661.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. International Geophysics Series, Vol. 100, Academic Press, 704 pp.

All Time Past Year Past 30 Days
Abstract Views 200 200 81
Full Text Views 96 96 46
PDF Downloads 124 124 54