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Tropospheric 3D Winds Measurement Based on Cross-Platform Infrared Hyperspectral Observation |
YANG Tian-hang 1, 2, 3, GU Ming-jian1, 2*, HU Xiu-qing4, WU Chun-qiang4, QI Cheng-li4,SHAO Chun-yuan1, 2 |
1. Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China
2. Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
4. Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, National Satellite Meteorological Center, Beijing 100081, China |
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Abstract Precise wind field data is essential for improving the accuracy of the numerical weather forecast, and tropospheric winds are not satisfied with the requirements as one of the key measurement objectives for improving weather forecasts. Although meteorological satellite-based imager derived winds by tracking the motion of characteristic targets in continuous cloud field images is an effective observation method that has improved numerical weather prediction forecasts on both regional and global scales, an error still exists in the ambiguity of the vector height assignment. Satellite-based infrared hyperspectral sounder has the capability of atmospheric vertical detection of temperature and humidity profiles, which can provide more accurate vector height assignment of wind field by analyzing atmospheric motion vectors among multiple vertical layers, improving the ambiguity of the vector height assignment. We proposed a method of tropospheric 3D winds measurement on cross-platform polar meteorological satellite-based infrared hyperspectral sounders of FY-3D/HIRAS and NOAA-20/CrIS, collocated vapor channel images through nadir overpass observations of both instruments, derived wind field by calculating the motion of dense optical flow field, combined ERA-Interim reanalysis data to verify the mean absolute deviation(MAE) and root mean square error(RMSE) of wind speed and the MAE of wind direction after quality control. The vertical wind fields of 200, 300, 400, 600, 650 and 1 000 hPa are calculated through observations of HIRAS and CrIS at 00:00, 06:00 and 12:00 UTC on February 20, 2019, the results show that, the trend of the variation of wind speed range is consistent with ERA-Interim reanalysis data, the wind speed range decreases as the height decreases, the upper layers are more sensitive to wind speeds above 20 m·s-1, while wind speeds measured near the surface are concentrated within 10 m·s-1. The MAE of wind speed is mostly less than 3 m·s-1 while the maximum value is less than 4 m·s-1, the RMSE of wind speed is mostly less than 3.5 m·s-1 while the maximum value is less than 4.5 m·s-1, the MAE of wind direction is mostly less than 30° while the maximum value is less than 40°. The wind field error mainly comes from the observation deviation of radiation data due to different instrument parameters, along with the positioning deviation of data image re-projection due to different spatial resolution.
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Received: 2020-03-30
Accepted: 2020-07-14
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Corresponding Authors:
GU Ming-jian
E-mail: gumingj@sina.com
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[1] Baker W E, Atlas R, Cardinali C, et al. Bulletin of the American Meteorological Society, 2014, 95(4): 543.
[2] Pagano T S, Abesamis C, Andrade A, et al. Proc. SPIE, 2018, 10769: 1076906.
[3] Velden C, Dengel G, Dengel R, et al. Determination of Wind Vectors by Tracking Features on Sequential Moisture Analyses Derived From Hyperspectral IR Satellite Soundings. 13th AMS Conf. on Satellite Meteror. and Ocean., Amer. Meteor. Soc., 2004.
[4] Maschhoff K R, Polizotti J J, Aumann H H, et al. Mistic Winds: A Microsatellite Constellation Approach to High-Resolution Observations of the Atmosphere Using Infrared Sounding and 3D Winds Measurements. Proc. SPIE, 2016, 9978: 997804.
[5] QI Cheng-li, GU Ming-jian, HU Xiu-qing, et al(漆成莉, 顾明剑, 胡秀清, 等). Advances in Meteorological Science and Technology(气象科技进展), 2016, 6(1): 88.
[6] Wang L, Chen Y. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(7): 2024.
[7] Chen Y, Han Y, Weng F. IEEE Transactions on Geoscience and Remote Sensing, 2016, 55(2): 1147.
[8] Wu C Q, Qi C L, Hu X Q, et al. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(6): 3965.
[9] Glumb R, Lapsley M, Luce S, et al. HyperCube: a Hyperspectral CubeSat Constellation for Measurements of 3D Winds. Proc. SPIE, 2016, 9978: 997805.
[10] Yang J, Zhang P, Lu N, et al. International Journal of Digital Earth, 2012, 5(3): 251.
[11] QI Cheng-li, ZHOU Fang, WU Chun-qiang, et al(漆成莉, 周 方, 吴春强, 等). Optics and Precision Engineering(光学精密工程), 2019, 27(4): 747.
[12] Han Y, Revercomb H, Cromp M, et al. Journal of Geophysical Research: Atmospheres, 2013, 118(22): 12734.
[13] YANG Tian-hang, HU Xiu-qing, XU Han-lie, et al(杨天杭, 胡秀清, 徐寒列, 等). Acta Optica Sinica(光学学报), 2019, 39(11): 377.
[14] Santek D, Nebuda S, Stettner D. Remote Sensing, 2019, 11(22): 2597.
[15] Saunders R, Hocking J, Turner E, et al. Geoscientific Model Development, 2018, 11(7): 2717.
[16] Zhu L, Bao Y, Petropoulos G P, et al. Remote Sensing, 2020, 12(3): 435.
[17] Farnebäck G. Two-Frame Motion Estimation Based on Polynomial Expansion. Scandinavian Conference on Image Analysis. Springer, Berlin, Heidelberg, 2003. 363. |
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