Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7435-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-7435-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Nov 2021
Research article |
| 30 Nov 2021
| 30 Nov 2021
Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry
Xingou Xu
The CAS Key Laboratory of Microwave Remote Sensing, National Space
Science Center, Chinese Academy of Sciences, Beijing, 100190, China
Satellite Observations, Royal Netherlands Meteorology Institute, De Bilt, 3730 AE, the
Netherlands
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In this research, the definition of Quality Control indicators for scatterometers are reviewed, then with SIC and IBC products, their abilities of in ice screening are extensively investigated. To decimating between the rain effect, collocated rain information is also obtained for analysing. This research for the first time addresses the effective information for sea ice from sources other than the directly observed NRCS alone.
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Ocean surface can be characterized by the one-dimensional spectra models. Among them, the Goda and Elfouhaily spectra are well applied for remote sensing simulations. However, they do not consider well the sea state factor. In this research, based on them, we apply the spectra measured from the Surface Waves Investigation and Monitoring instrument and establish a Combined spectrum. Validation indicates the established spectrum are closer to the SWIM described sea surface with varied sea states.
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Revised manuscript accepted for AMT
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Ocean surface can be characterized by the one-dimensional spectra models. Among them, the Goda and Elfouhaily spectra are well applied for remote sensing simulations. However, they do not consider well the sea state factor. In this research, based on them, we apply the spectra measured from the Surface Waves Investigation and Monitoring instrument and establish a Combined spectrum. Validation indicates the established spectrum are closer to the SWIM described sea surface with varied sea states.
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Haichen Zuo, Charlotte Bay Hasager, Ioanna Karagali, Ad Stoffelen, Gert-Jan Marseille, and Jos de Kloe
Atmos. Meas. Tech., 15, 4107–4124, https://doi.org/10.5194/amt-15-4107-2022, https://doi.org/10.5194/amt-15-4107-2022, 2022
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The Aeolus satellite was launched in 2018 for global wind profile measurement. After successful operation, the error characteristics of Aeolus wind products have not yet been studied over Australia. To complement earlier validation studies, we evaluated the Aeolus Level-2B11 wind product over Australia with ground-based wind profiling radar measurements and numerical weather prediction model equivalents. The results show that the Aeolus can detect winds with sufficient accuracy over Australia.
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
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Publication in AMT not foreseen
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Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is the comparison of wind speed on a large scale between the Aeolus, ERA5 and RS , shedding important light on the data application of Aeolus wind products.
Jianping Guo, Jian Zhang, Kun Yang, Hong Liao, Shaodong Zhang, Kaiming Huang, Yanmin Lv, Jia Shao, Tao Yu, Bing Tong, Jian Li, Tianning Su, Steve H. L. Yim, Ad Stoffelen, Panmao Zhai, and Xiaofeng Xu
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The planetary boundary layer (PBL) is the lowest part of the troposphere, and boundary layer height (BLH) is the depth of the PBL and is of critical importance to the dispersion of air pollution. The study presents the first near-global BLH climatology by using high-resolution (5-10 m) radiosonde measurements. The variations in BLH exhibit large spatial and temporal dependence, with a peak at 17:00 local solar time. The most promising reanalysis product is ERA-5 in terms of modeling BLH.
Jianping Guo, Boming Liu, Wei Gong, Lijuan Shi, Yong Zhang, Yingying Ma, Jian Zhang, Tianmeng Chen, Kaixu Bai, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 2945–2958, https://doi.org/10.5194/acp-21-2945-2021, https://doi.org/10.5194/acp-21-2945-2021, 2021
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Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China have thus far not been evaluated by in situ comparison. This work is expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future research and applications.
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-41, https://doi.org/10.5194/acp-2021-41, 2021
Revised manuscript not accepted
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future researches and applications.
Cited articles
Bony, S., Stevens, B., Frierson, D., Jakob, C., Kageyama, M., Pincus, R.,
Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., and Sobel, A. H.: Clouds,
circulation and climate sensitivity, Nat. Geosci., 8, 261–268,
https://doi.org/10.1038/ngeo2398, 2015.
Chang, C.-C. and Lin, C.-J.: LIBSVM: A library for support vector machines,
ACM Trans. Intell. Syst. Technol., 2, 27, https://doi.org/10.1145/1961189.1961199,
2011.
Chelton, D. B., Schlax, M. G., Samelson, R. M., Farrar, J. T., Molemaker, M.
J., McWilliams, J. C., and Gula, J.: Prospects for future satellite
estimation of small-scale variability of ocean surface velocity and
vorticity, Prog. Oceanogr., 173, 256–350,
https://doi.org/10.1016/j.pocean.2018.10.012, 2019.
Cornford, D., Nabney, I. T., and Bishop, C. M.: Neural network-based wind
vector retrieval from satellite scatterometer data, Neural Comput.
Appl., 8, 206–217, https://doi.org/10.1007/s005210050023, 1999.
Courtier, P., Thépaut, J. N., and Hollingsworth, A.: A strategy for
operational implementation of 4D-Var, using an incremental approach,
Q. J. Roy. Meteor. Soc., 120, 1367–1387,
https://doi.org/10.1002/qj.49712051912, 1994.
Descombes, G., Auligné, T., Vandenberghe, F., Barker, D. M., and Barré, J.: Generalized background error covariance matrix model (GEN_BE v2.0), Geosci. Model Dev., 8, 669–696, https://doi.org/10.5194/gmd-8-669-2015, 2015.
Draper, D. W. and Long, D. G.: Simultaneous wind and rain retrieval using
SeaWinds data, IEEE T. Geosci. Remote, 42,
1411–1423, https://doi.org/10.1109/tgrs.2004.830169, 2004.
Du, Y., Dong, X., Jiang, X., Zhang, Y., Zhu, D., Sun, Q., Wang, Z., Niu, X.,
Chen, W., and Zhu, C.: Ocean Surface Current multiscale Observation Mission
(OSCOM): Simultaneous measurement of ocean surface current, vector wind, and
temperature, Prog. Oceanogr., 193, 102531,
https://doi.org/10.1016/j.pocean.2021.102531, 2021.
EUMETSAT: Wind products, EUMETSAT [data set], available at: https://osi-saf.eumetsat.int/products/wind-products, last access: 19 November 2021.
Gill, A. E.: Atmosphere-Ocean Dynamic, in: International Geophysics Series, volume 30, Academic Press, San Diego, California, USA, 1982.
Huffman, G., Bolvin, D., Braithwaite, D., Hsu, K., Joyce, R.,
Kidd, C., Nelkin, E., Sorooshian, S., Tan, J., and Xie, P.: NASA global precipitation measurement
(GPM) integrated multi-satellitE retrievals for GPM (IMERG) version
5.2, NASA's Precipitation Process. Center [data set], available at: https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_ATBD_V5.pdf (last access: 17 November 2021), 2018.
Japan Aerospace Exploration Agency (JAXA): JAXA Himawari Monitor, JAXA [data set], available at: https://www.eorc.jaxa.jp/ptree/index.html, last access: 19 November 2021.
Japan Meteorological Agency: Himawari-8/9 Himawari Standard Data User's Guide, JMA Tech, available at: http://www.data.jma.go.jp/mscweb/en/himawari89/space_segment/hsd_sample/HS_D_users_guide_en_v12.pdf (last access: 17 November 2021), 2015.
King, G. P., Portabella, M., Lin, W., and Stoffelen, A.: Correlating extremes in wind and stress divergence with extremes in rain over the Tropical Atlantic, KNMI Sci. Rep., OSI_AVS_15_02, available at: http://digital.csic.es/bitstream/10261/158566/1/King_et_al_2017.pdf (last access: 10 November 2021), 2017.
KNMI: Wind products, KNMI [data set], available at:
https://scatterometer.knmi.nl/archived_prod/, last access:
19 November 2021.
Kumar, A., Ramsankaran, R., Brocca, L., and Muñoz-Arriola, F.: A simple
machine learning approach to model real-time streamflow using satellite
inputs: Demonstration in a data scarce catchment, J. Hydrol., 595,
126046, https://doi.org/10.1016/j.jhydrol.2021.126046, 2021.
Li, L., Im, E., Connor, L. N., and Chang, P. S.: Retrieving ocean surface
wind speed from the TRMM precipitation radar measurements, IEEE T. Geosci. Remote, 42, 1271–1282, https://doi.org/10.1109/TGRS.2004.828924,
2004.
Lin, W. and Portabella, M.: Toward an improved wind quality control for
RapidScat, IEEE T. Geosci. Remote, 55,
3922–3930, https://doi.org/10.1109/TGRS.2017.2683720, 2017.
Linwood Jones, W., Black, P., Boggs, D., Bracalente, E., Brown, R., Dome, G., Ernst, J., Halberstam, I., Overland, J., Peteherych, S., Pierson, W., Wentz, F., Woiceshyn, P., and Wurtele, M.: Seasat Scatterometer: Results of the Gulf of Alaska Workshop, Science, 204, 1413–1415, https://doi.org/10.1126/science.204.4400.1413, 1979.
Liu, C.-Y., Aryastana, P., Liu, G.-R., and Huang, W.-R.: Assessment of
satellite precipitation product estimates over Bali Island, Atmos.
Res., 244, 105032, https://doi.org/10.1016/j.atmosres.2020.105032, 2020.
Majumdar, S. J., Sun, J., Golding, B., Joe, P., Dudhia, J., Caumont, O.,
Chandra Gouda, K., Steinle, P., Vincendon, B., and Wang, J.: Multiscale
Forecasting of High-Impact Weather: Current Status and Future Challenges,
B. Am. Meteorol. Soc., 102, E635–E659,
https://doi.org/10.1175/BAMS-D-20-0111.1, 2021.
NASA: Precipitation Data Directory, NASA [data set], available at: https://gpm.nasa.gov/data/directory, last access: 19 November 2021.
Owen, M. P. and Long, D. G.: M-ary Bayes estimator selection for QuikSCAT
simultaneous wind and rain retrieval, IEEE T. Geosci.
Remote, 49, 4431–4444, https://doi.org/10.1109/TGRS.2011.2143721, 2011.
Parrish, D. F. and Derber, J. C.: The National Meteorological Center's
spectral statistical-interpolation analysis system, Mon. Weather Rev.,
120, 1747–1763, https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2, 1992.
Portabella, M.: Wind field retrieval from satellite radar systems, PhD, Astron. Meteorol. Dept., Universitat de Barcelona Barcelona, Spain, available at: https://cdn.knmi.nl/system/data_center_publications/files/000/067/780/original/phd_thesis.pdf?1495620892 (last access: 19 November 2021), 2002.
Portabella, M. and Stoffelen, A.: Characterization of residual information
for SeaWinds quality control, IEEE T. Geosci. Remote, 40, 2747–2759, https://doi.org/10.1109/TGRS.2002.807750, 2002.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., and
Carvalhais, N.: Deep learning and process understanding for data-driven
Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019.
Smola, A. J. and Schölkopf, B.: A tutorial on support vector regression, Stat. Comput., 14, 199–222, https://doi.org/10.1023/b:stco.0000035301.49549.88, 2004.
Stiles, B. W. and Dunbar, R. S.: A neural network technique for improving
the accuracy of scatterometer winds in rainy conditions, IEEE T.
Geosci. Remote, 48, 3114–3122, https://doi.org/10.1109/TGRS.2010.2049362,
2010.
Stoffelen, A. and Anderson, D.: Scatterometer data interpretation:
Measurement space and inversion, J. Atmos. Ocean.
Tech., 14, 1298–1313, https://doi.org/10.1175/1520-0426(1997)014<1298:SDIMSA>2.0.CO;2, 1997.
Stoffelen, A. and Vogelzang, J.: Wind bias correction guide, EUMETSAT,
Darmstadt, Germany, 2018.
Stoffelen, A., Kumar, R., Zou, J., Karaev, V., Chang, P. S., and Rodriguez, E.: Ocean Surface Vector Wind Observations, in: Remote Sensing of the Asian Seas, edited by: Barale, V. and Gade, M., Springer International Publishing, Cham, 429–447, https://doi.org/10.1007/978-3-319-94067-0_24, 2019.
Stoffelen, A., Rivas, M. B., and Verspeek, J.: Cone Metrics for C and Ku-Band Scatterometers, in: IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11–16 July 2021, 1627–1629, https://doi.org/10.1109/igarss47720.2021.9554778, 2021.
Stoffelen, A. C. M.: Scatterometry, PhD, Utrecht University, Utrecht, the Netherlands, available at: https://dspace.library.uu.nl/bitstream/handle/1874/636/full.pdf (last access: 19 November 2021), 1998.
Thiria, S., Mejia, C., Badran, F., and Crepon, M.: A neural network approach
for modeling nonlinear transfer functions: Application for wind retrieval
from spaceborne scatterometer data, J. Geophys. Res.-Oceans,
98, 22827–22841, https://doi.org/10.1029/93JC01815, 1993.
Vapnik, V.: Statistical learning theory 624, Wiley, New York, 2 pp., 1998.
Vogelzang, J.: Two dimensional variational ambiguity removal (2DVAR), KNMI Tech. Note NWP SAF NWPSAF-KN-TR-004, available at: https://cdn.knmi.nl/system/data_center_publications/files/000/067/778/original/two_dimensional_variational_ambiguity_removal_v1.2.pdf?1495620892 (last access: 15 November 2021), 2007.
Vogelzang, J. and Stoffelen, A.: NWP model error structure functions
obtained from scatterometer winds, IEEE T. Geosci.
Remote, 50, 2525–2533, https://doi.org/10.1109/TGRS.2011.2168407, 2011.
Vogelzang, J. and Stoffelen, A.: Improvements in Ku-band scatterometer wind
ambiguity removal using ASCAT-based empirical background error correlations,
Q. J. Roy. Meteor. Soc., 144, 2245–2259, https://doi.org/10.1002/qj.3349, 2018.
Vogelzang, J., Stoffelen, A., Verhoef, A., and Figa-Saldaña, J.: On the
quality of high-resolution scatterometer winds, J. Geophys.
Res.-Oceans, 116, C10033, https://doi.org/10.1029/2010JC006640, 2011.
Wolters, E. L. A., van den Hurk, B. J. J. M., and Roebeling, R. A.: Evaluation of rainfall retrievals from SEVIRI reflectances over West Africa using TRMM-PR and CMORPH, Hydrol. Earth Syst. Sci., 15, 437–451, https://doi.org/10.5194/hess-15-437-2011, 2011.
Xu, X. and Stoffelen, A.: Improved rain screening for ku-band wind
scatterometry, IEEE T. Geosci. Remote, 58,
2494–2503, https://doi.org/10.1109/TGRS.2019.2951726, 2020.
Xu, X. and Stoffelen, A.: A Further Evaluation of the Quality Indicator Joss for Ku-Band Wind Scatterometry in Tropical Regions, in: IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11–16 July 2021, 7299–7302, https://doi.org/10.1109/igarss47720.2021.9553442, 2021.
Xu, X., Stoffelen, A., and Meirink, J. F.: Comparison of ocean surface rain
rates from the global precipitation mission and the Meteosat
second-generation satellite for wind scatterometer quality control, IEEE
J. Sel. Top. Appl.,
13, 2173–2182, https://doi.org/10.1109/JSTARS.2020.2995178, 2020a.
Xu, X., Stoffelen, A., Lin, W., and Dong, X.: Rain False-Alarm-Rate
Reduction for CSCAT, IEEE Geosci. Remote S., 1–5,
https://doi.org/10.1109/LGRS.2020.3039622, 2020b.
Short summary
The support vector machine can effectively represent the increasing effect of rain affecting wind speeds. This research provides a correction of deviations that are skew- to Gaussian-like features caused by rain in Ku-band scatterometer wind. It demonstrates the effectiveness of a machine learning method when used based on elaborate analysis of the model establishment and result validation procedures. The corrected winds provide information previously lacking, which is vital for nowcasting.
The support vector machine can effectively represent the increasing effect of rain affecting...
