Abstract
Accurate estimations of typhoon-level winds are highly desired over the western Pacific Ocean. A wind speed retrieval algorithm is used to retrieve the wind speeds within Super Typhoon Nepartak (2016) using 6.9- and 10.7-GHz brightness temperatures from the Japanese Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor on board the Global Change Observation Mission-Water 1 (GCOM-W1) satellite. The results show that the retrieved wind speeds clearly represent the intensification process of Super Typhoon Nepartak. A good agreement is found between the retrieved wind speeds and the Soil Moisture Active Passive wind speed product. The mean bias is 0.51 m/s, and the root-mean-square difference is 1.93 m/s between them. The retrieved maximum wind speeds are 59.6 m/s at 04:45 UTC on July 6 and 71.3 m/s at 16:58 UTC on July 6. The two results demonstrate good agreement with the results reported by the China Meteorological Administration and the Joint Typhoon Warning Center. In addition, Feng-Yun 2G (FY-2G) satellite infrared images, Feng-Yun 3C (FY-3C) microwave atmospheric sounder data, and AMSR2 brightness temperature images are also used to describe the development and structure of Super Typhoon Nepartak.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Goodberlet M A, Swift C T, Wilkerson J C (1990). Ocean surface wind speed measurements of the Special Sensor Microwave/Imager (SSM/ I). IEEE Trans Geosci Remote Sens, 28(5): 823–828
He J Y, Zhang S W, Wang Z Z (2015). Advanced microwave atmospheric sounder (AMAS) channel specifications and T/V calibration results on FY-3C Satellite. IEEE Trans Geosci Remote Sens, 53(1): 481–493
Krasnopolsky V M, Breaker L C, Gemmill W H (1995). A neural network as a nonlinear transfer function model for retrieving surface wind speeds from the special sensor microwave imager. J Geophys Res Oceans, 100(C6): 11033–11045
Meissner T, Ricciardulli L, Wentz F J (2016). Remote Sensing Systems SMAP daily Sea Surface Winds Speeds on 0.25 deg grid, Version 00.1 (BETA). Remote Sensing Systems, Santa Rosa, CA
Meissner T, Wentz F J (2009). Wind-vector retrievals under rain with passive satellite microwave radiometers. IEEE Trans Geosci Remote Sens, 47(9): 3065–3083
Quilfen Y, Prigent C, Chapron B, Mouche A A, Houti N (2007). The potential of QuikSCAT and WindSat observations for the estimation of sea surface wind vector under severe weather conditions. J Geophys Res Oceans, 112: C09023
Reul N, Tenerelli J, Chapron B, Chapron B, Vandemark D, Quilfen Y, Kerr Y (2012). SMOS satellite L-band radiometer: a new capability for ocean surface remote sensing in hurricanes. Journal of Geophysical Research: Oceans, 117: C02006
Roy C, Kovordányi R (2012). Tropical cyclone track forecasting techniques—A review. Atmos Res, 104–105: 40–69
Shibata A (2006). A wind speed retrieval algorithm by combining 6 and 10 GHz data from advanced microwave scanning radiometer: wind speed inside hurricanes. J Oceanogr, 62(3): 351–359
Uhlhorn E W, Black P G (2003). Verification of remotely sensed sea surface winds in hurricanes. J Atmos Ocean Technol, 20(1): 99–116
Uhlhorn E W, Black P G, Franklin J L, Goodberlet M, Carswell J, Goldstein A S (2007). Hurricane surface wind measurements from an operational stepped frequency microwave radiometer. Mon Weather Rev, 135(9): 3070–3085
Wentz F J (1997). A well-calibrated ocean algorithm for special sensor microwave/imager. J Geophys Res Oceans, 102(C4): 8703–8718
Wentz F J, Meissner T (2000). AMSR Ocean Algorithm, Algorithm Theoretical Basis Document. Remote Sensing Systems, 2000
Yan B H, Weng F Z (2008). Applications of AMSR-E measurements for tropical cyclone predictions part I: retrieval of sea surface temperature and wind speed. Adv Atmos Sci, 25(2): 227–245
Yueh S H (2008). Directional signals in WindSat observations of hurricane ocean winds. IEEE Trans Geosci Remote Sens, 46(1): 130–136
Yueh S H, Wilson W J, Dinardo S J, Hsiao S V (2006). Polarimetric microwave wind radiometer model function and retrieval testing for WindSat. IEEE Trans Geosci Remote Sens, 44(3): 584–596
Zabolotskikh E V, Mitnik L M, Chapron B (2014). GCOM-W1 AMSR2 and MetOp-A ASCAT wind speeds for the extratropical cyclones over the North Atlantic. Remote Sens Environ, 147: 89–98
Zabolotskikh E V, Mitnik L M, Reul N, Chapron B (2015). New possibilities for geophysical parameter retrievals opened by GCOMW1 AMSR2. IEEE J Sel Top Appl Earth Obs Remote Sens, 8(9): 4248–4261
Zhang L, Wang Z Z, Shi H Q, Long Z Y, Du H D (2016a). Chaos particle swarm optimization combined with circular median filtering for geophysical parameters retrieval fromWindSat. J Ocean Univ China, 15(4): 593–605
Zhang L, Yin X, Shi H, Wang Z Z (2016b). Hurricane wind speed estimation using WindSat 6 and 10 GHz brightness temperatures. Remote Sens, 8(9): 721
Acknowledgements
This work was funded by the National Natural Science Foundation of China (Grant No. 61501433). The authors would like to thank the National Snow and Ice Data Center and the Japan Aerospace Exploration Agency for providing the AMSR2 brightness temperature data. The authors would like to thank the Hurricane Research Division for providing the SFMR data. SMAP data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science funding. The authors declare that they have no conflict of interests regarding the publication of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, L., Yin, X., Shi, H. et al. Estimation of wind speeds inside Super Typhoon Nepartak from AMSR2 low-frequency brightness temperatures. Front. Earth Sci. 13, 124–131 (2019). https://doi.org/10.1007/s11707-018-0698-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11707-018-0698-8