Comparisons and quality control of wind observations in a mountainous city using wind profile radar and the Aeolus satellite
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Published:2024-01-12
Issue:1
Volume:17
Page:167-179
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ISSN:1867-8548
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Container-title:Atmospheric Measurement Techniques
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language:en
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Short-container-title:Atmos. Meas. Tech.
Author:
Lu Hua, Xie MinORCID, Zhao Wei, Liu Bojun, Wang Tijian, Zhuang BingliangORCID
Abstract
Abstract. Observations of the vertical wind profile in Chongqing, a typical mountainous city in China, are important, but they are sparse and have low resolution. To obtain more wind profile data, this study matched the Aeolus track with ground-based wind observation sites in Chongqing in 2021. Based on the obtained results, verification and quality control studies were conducted on the wind observations of a wind profile radar (WPR) with radiosonde (RS) data, and a comparison of the Aeolus Mie-cloudy and Rayleigh-clear wind products (Aeolus winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data and winds measured in clear-air conditions from Rayleigh-collected data) with WPR data was then performed. The conclusions can be summarized as follows: (1) a clear correlation between the wind observations of WPR and RS was found, with a correlation coefficient (R) of 0.71. Their root mean square deviation increased with height but decreased at heights between 3 and 4 km. (2) After quality control using Gaussian filtering (GF) and empirical orthogonal function construction (EOFc; G=87.23 %) of the WPR data, the R between the WPR and RS reached 0.83 and 0.95, respectively. The vertical distribution showed that GF could better retain the characteristics of WPR wind observations but with limited improvement in decreasing deviations, whereas EOFc performed better in decreasing deviations but considerably modified the original characteristics of the wind field, especially regarding intensive vertical wind shear in strong convective weather processes. (3) In terms of the differences between the Aeolus and WPR data, 56.0 % and 67.8 % deviations were observed within ±5 m s−1 for Rayleigh-clear and Mie-cloudy winds (Aeolus winds measured in cloudy and aerosol-rich atmospheric conditions from Mie-channel-collected data and winds measured in clear-air conditions from Rayleigh-collected data) vs WPR winds, respectively. Vertically, large mean differences of both Rayleigh-clean and Mie-cloudy winds versus WPR winds appeared below 1.5 km, which is attributed to the prevailing quiet and small winds within the boundary layer in Chongqing; in this case the movement of molecules and aerosols is mostly affected by irregular turbulence. Additionally, large mean differences at a height range between 4 and 8 km for Mie-cloudy versus WPR winds may be related to the high content of cloud liquid water in the middle troposphere of Chongqing. (4) The differences in both Rayleigh-clear and Mie-cloudy versus WPR winds had changed. Deviations of 58.9 % and 59.6 % were concentrated within ±5 m s−1 for Rayleigh-clear versus WPR winds with GF and EOFc quality control, respectively. In contrast, 69.1 % and 70.2 % of deviations appeared within ±5 m s−1 for Rayleigh-clear versus WPR and EOFc WPR winds, respectively. These results shed light on the comprehensive applications of multi-source wind profile data in mountainous cities or areas with sparse ground-based wind observations.
Funder
National Natural Science Foundation of China Natural Science Foundation of Chongqing Municipality Sichuan Province Science and Technology Support Program Key Laboratory of Heavy Rain and Drought-Flood Disasters in Plateau and Basins of Sichuan Province Natural Science Foundation of Jiangsu Province Nanjing Normal University
Publisher
Copernicus GmbH
Subject
Atmospheric Science
Reference47 articles.
1. Baker, W. E., Atlas, R., Cardinali, C., Clement, A., Emmitt, G. D., Gentry, B. M., Hardesty, R. M., Källén, E., Kavaya, M. J., Langland, R., Ma, Z., Masutani, M., McCarty, W., Pierce, R. B., Pu, Z., Riishojgaard, L. P., Ryan, J., Tucker, S., Weissmann, M., and Yoe, J. G.: Lidar-Measured Wind Profiles: The Missing Link in the Global Observing System, B. Am. Meteorol. Soc., 95, 543–564, https://doi.org/10.1175/BAMS-D-12-00164.1, 2014. 2. Baker, W. E., Emmitt, G. D., Robertson, F. R., Atlas, R., Molinari, J. E., Bowdle, D. A., Paegle, J. N., Hardesty, R. M., Menzies, R. T., Krishnamurti, T. N., Brown, R. A., Post, M. J., Anderson, J. R., Lorenc, A. C., and McElroy, J. L.: Lidar-measured winds from space: A key component for weather and climate prediction, B. Am. Meteorol. Soc., 76, 869–888, 1995. 3. Barre, H. M. J. P., Duesmann, B., and Kerr, Y. H.: SMOS: the mission and the system, IEEE T. Geosci. Remote Sens., 46, 587–593, 2008. 4. Belmonte Rivas, M. and Stoffelen, A.: Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT, Ocean Sci., 15, 831–852, https://doi.org/10.5194/os-15-831-2019, 2019. 5. Benjamin, S. G., Schwartz, B. E., Szoke, E. J., and Koch, S. E.: The Value of Wind Profiler Data in U.S. Weather Forecasting, B. Am. Meteorol. Soc. 85, 1871–1886, 2004.
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