Abstract
Following the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.
摘要
从上世纪90年代起,卫星资料同化得到快速发展,气象卫星和数值模式的结合改变了科学家研究地球的方式。随着数值天气预报模型和地球系统模型的发展,气象卫星将在未来的地球科学研究中扮演更加重要的角色。从1988年第一颗风云气象卫星FY-1A成功发射开始,风云气象卫星作为空基观测的重要组成部分,以其开放的数据政策和高质量的数据产品,为地球科学研究做出了持续的贡献。目前,新一代极轨气象卫星风云三号和新一代静止轨道气象卫星风云四号的地球观测能力得到大幅提升,产品质量也可以与著名的MODIS卫星相媲美。风云气象卫星产品已广泛应用于天气预报、气候和气候变化研究以及环境灾害监测。本文系统回顾了风云气象卫星搭载的地球观测仪器及其生成的一级数据和二级产品,列举了森林火点、闪电、植被指数、气溶胶土壤湿度以及降水等地球物理参数产品的特性和精度验证结果。同时,多种数据共享方式得以建成和运行,以帮助用户更好地运用风云气象卫星产品。新一代风云气象卫星数据共享平台将基于公有云技术提供更加快速的数据服务。
Article PDF
References
Bindlish, R., T. Jackson, R. J. Sun, M. Cosh, S. Yueh, and S. Dinardo, 2009: Combined passive and active microwave observations of soil moisture during CLASIC. IEEE Geoscience and Remote Sensing Letters, 6(4), 644–648, https://doi.org/10.1109/LGRS.2009.2028441.
Boccippio, D. J., S. J. Goodman, and S. Heckman, 2000: Regional differences in tropical lightning distributions. J. Appl. Meteor., 39, 2231–2248, https://doi.org/10.1175/1520-0450(2001)040<2231:RDITLD>2.0.CO;2.
Cao, D. J., X. S. Qie, S. Duan, Y. J. Xuan, and D. F. Wang, 2012: Lightning discharge process based on short-baseline lightning VHF radiation source locating system. Acta Physica Sinica, 61, 069202, https://doi.org/10.7498/aps.61.069202. (in Chinese with English abstract)
Cao, D. J., F. Lu, X. H. Zhang, and Z. Q. Zhang, 2018: The FY-4A lightning mapper imager applications on convention monitoring. Satellite Application, 2018(11), 18–23.
Carey, L. D., and S. A. Rutledge, 1996: A multiparameter radar case study of the microphysical and kinematic evolution of a lightning producing storm. Meteor. Atmos. Phys., 59, 33–64, https://doi.org/10.1007/BF01032000.
Cecil, D. E., S. J. Goodman, D. J. Boccippio, E. J. Zipser, and S. W. Nesbitt, 2005: Three years of TRMM precipitation features. Part I: Radar, radiometric, and lightning characteristics. Mon. Wea. Rev., 133, 543–566, https://doi.org/10.1175/MWR-2876.1.
Cecil, D. J., D. E. Buechler, and R. J. Blakeslee, 2014: Gridded lightning climatology from TRMM-LIS and OTD: Dataset description. Atmospheric Research, 155–166, 404–414, https://doi.org/10.1016/j.atmosres.2012.06.028.
Chen S., 2008: GEO-Information Science, Higher Education Press, 531pp.
Christian, H. J., and Coauthors, 2003: Global frequency and distribution of lightning as observed from space by the Optical Transient Detector. J. Geophys. Res., 108(D1), ACL4-1–ACL4-15, https://doi.org/10.1029/2002JD002347.
DeMaria, M., R. T. DeMaria, J. A. Knaff, and D. Molenar, 2012: Tropical cyclone lightning and rapid intensity change. Mon. Wea. Rev., 140, 1828–1842, https://doi.org/10.1175/MWR-D-11-00236.1.
Du, J. Y., 2012: A method to improve satellite soil moisture retrievals based on Fourier analysis. Geophys. Res. Lett., 39, L15404, https://doi.org/10.1029/2012GL052435.
Entekhabi, D., and Coauthors, 2010: The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5), 704–716, https://doi.org/10.1109/JPROC.2010.2043918.
Fang, Z. Y., and D. Y. Qin, 2006: A review of satellite observed heavy rainfall cloud clusters. Journal of Applied Meteorological Science, 17(5), 583–593, https://doi.org/10.3969/j.issn.1001-7313.2006.05.008. (in Chinese with English abstract)
Florence, R., S. J. English, and R. Engelen, 2018: Satellite data assimilation at ECMWF. Proc. 98th American Meteorological Society Annual Meeting, htps://arns.confex.com/arns/98Annual/webprogram/Paper327333.html.
Han, X. Z., J. Yang, S. H. Tang, and Y. Han, 2020: Vegetation products derived from Fengyun-3D medium resolution spectral imager-II. Journal of Meteorological Research, 34(4), 775–785, https://doi.org/10.1007/s13351-020-0027-5.
Holmes, T. R. H., R. A. M. De Jeu, M. Owe, and A. J. Dolman, 2009: Land surface temperature from Ka band (37 GHz) passive microwave observations. J. Geophys. Res., 114, D04113, https://doi.org/10.1029/2008JD010257.
Jackson, T. J., D. M. Le Vine, A. Y. Hsu, A. Oldak, P. J. Starks, C. T. Swift, J. D. Isham, and M. Haken, 1999: Soil moisture mapping at regional scales using microwave radiometry: The Southern Great Plains Hydrology Experiment. IEEE Trans. Geosci. Remote Sens., 37(5), 2136–2151, https://doi.org/10.1109/36.789610.
Jackson, T. J., M. H. Cosh, R. Bindlish, P. J. Starks, D. D. Bosch, M. Seyfried, M. S. Moran, and J. Y. Du, 2010: Validation of advanced microwave scanning radiometer soil moisture products. IEEE Trans. Geosci. Remote Sens., 48(12), 4256–4272, https://doi.org/10.1109/TGRS.2010.2051035.
Li, Y.-J., W. Zheng, J. Chen, and C. Liu, 2017: Fire monitoring and application based on meteorological satellite. Aerospace Shanghai, 44(4), 62–72, https://doi.org/10.13288/j.cnki.1006-1630.2017.04.008. (in Chinese with English abstract)
Liu, C., Y. J. Li, C. H. Zhao, H. Yan, and H. M. Zhao, 2004: The method of evaluating sub-pixel size and temperature of fire spot in AVHRR data. Journal of Applied Meteorological Science, 15(3), 273–280, https://doi.org/10.3969/j.issn.100-7313.2004.03.003. (in Chinese with English abstract)
Liu, Q., J. Y. Du, J. C. Shi, and L. M. Jiang, 2013: Analysis of spatial distribution and multi-year trend of the remotely sensed soil moisture on the Tibetan Plateau. Science China Earth Sciences, 56(12), 2173–2185, https://doi.org/10.1007/s11430-013-4700-8.
Lu, N. M., and R. Z. Wu, 1997: Strong convective cloud characteristics derived from satellite cloud pictuer. Quarterly Journal of Applied Meteorology, 4(3), 269–275. (in Chinese with English abstract)
Matson, M., and S. R. Schneider, 1984: Fire detection using the NOAA-Series satellite. NOAA Tech. Rep. Noaa: 19318, NESDIS.
Min M., and Coauthors, 2017: Developing the science product algorithm testbed for Chinese next-generation geostationary meteorological satellites: Fengyun-4 series. Journal of Meteorological Research, 31(4), 708–719, https://doi.org/10.1007/s13351-017-6161-z.
Min M., J. Li, F. Wang, Z. J. Liu, and W. P. Menzel, 2020: Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms. Remote Sens. Environ., 239, 111616, https://doi.org/10.1016/j.rse.2019.111616.
Mo, T., B. J. Choudhury, T. J. Schmugge, J. R. Wang, and T. J. Jackson, 1982: A model for microwave emission from vegetation-covered fields. J. Geophys. Res., 87(C13), 1229–1237, https://doi.org/10.1029/JC087iC13p11229.
Qin, D. Y., Z. Y. Fang, and J. X. Jiang, 2005: The relationship between tropical water vapor plume and heavy rainfall during 20–25 July 2002. Acta Meteorologica Sinica, 63(4), 493–503, https://doi.org/10.3321/j.issn:0577-6619.2005.04.011. (in Chinese with English abstract)
Ren, S. L., W. Zhao, D. J. Cao, and R. X. Liu, 2020: Application of FY-4A daytime convective storm and lightning products in analyzing severe thunderstorm weather in North China. Journal of Marine Meteorology, 40(1), 33–46, https://doi.org/10.19513/j.cnki.issn2096-3599.2020.01.004. (in Chinese with English abstract)
Shi, J., L. Jiang, L. Zhang, K. S. Chen, J. P. Wigneron, A. Chanzy, and T. J. Jackson, 2006: Physically based estimation of bare-surface soil moisture with the passive radiometers. IEEE Trans. Geosci. Remote Sens., 44(11), 3145–3153, https://doi.org/10.1109/TGRS.2006.876706.
Sun, R. J., Y. P. Zhang, S. L. Wu, H. Yang, and J. Y. Du, 2014: The FY-3B/MWRI soil moisture product and its application in drought monitoring. Proc. 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, Canada, IEEE, 3296–3298, https://doi.org/10.1109/IGARSS.2014.6947184.
Wang, J. J., C. Liu, B. Yao, M. Min, H. Letu, Y. Yin, and Y. L. Yung, 2019: A multilayer cloud detection algorithm for the Suomi-NPP Visible Infrared Imager Radiometer Suite (VIIRS). Remote Sens. Environ., 227, 1–11, https://doi.org/10.1016/j.rse.2019.02.024.
Wu, X. D., Q. Xiao, J. G. Wen, D. Q. You, and A. Hueni, 2019: Advances in quantitative remote sensing product validation: Overview and current status. Earth-Science Reviews, 196, 102875, https://doi.org/10.1016/j.earscirev.2019.102875.
Xian D., J. M. Qian, Z. Xu, Y. Gao, and L. W. Liu, 2012: Classification of Meteorological Satellite Data (QX/T 158-2012). China Meteorological Press, 6 pp. (in Chinese)
Xian D., X. Fang, X. Jia, and C. Ying, 2020a: The FY-4 satellite weather application platform and its applications. Satellite Application(2), 20–24. (in Chinese)
Xian, D., P. Zhang, M. Fang, C. Liu, and X. Jia, 2020b: The first Fengyun satellite international user conference. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-020-2011-5.
Xu J. M., Yang J., Zhang Z. Q., and Sun A. L., 2010: Chinese Meteorological Satellite, Achievements and Applications. Meteorological Monthly, 36(7), 94–100, https://doi.org/10.7519/j.issn.1000-0526.2010.07.016.
Xu, W. X., S. A. Rutledge, and W. J. Zhang, 2017: Relationships between total lightning, deep convection, and tropical cyclone intensity change. J. Geophys. Res., 122, 7047–7063, https://doi.org/10.1002/2017JD027072.
Yang, J., 2012: Meteorological Satellite and Applications. China Meteorological Press, 770–775. (in Chinese)
Yang, J., D. Xian, and S. H. Tang, 2018: Latest progress and applications of the Fengyun meteorological satellite program. Satellite Application(11), 8–14, https://doi.org/10.3969/j.issn.1674-9030.2018.11.005. (in Chinese)
Yang, J., Z. Q. Zhang, C. Y. Wei, F. Lu, and Q. Guo, 2017: Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bull. Amer. Meteor. Soc., 34(8), 1637–1658, https://doi.org/10.1175/BAMS-D-16-0065.1.
Yang, L., Hu, X., Wang, H., He, X., Liu, P., Xu, N., Yang, Z., Zhang, P., 2020. Preliminary test of quantitative capability in aerosol retrieval over land from MERSI-II onboard Fengyun-3D. National Remote Sensing Bulletin, Published Online, https://doi.org/10.11834/jrs.20200286.
Yang, Z. D., and Coauthors, 2019: Capability of Fengyun-3D satellite in earth system observation. Journal of Meteorological Research, 33(6), 1113–1130, https://doi.org/10.1007/s13351-019-9063-4.
Zhang, P., and Coauthors, 2019: Latest progress of the Chinese meteorological satellite program and core data processing technologies. Adv. Atmos. Sci., 36(9), 1027–1045, https://doi.org/10.1007/s00376-019-8215-x.
Zhang, P., and Coauthors, 2009: General introduction on payloads, ground segment and data application of Fengyun 3A. Front. Earth Sci. China., 3, 367–373, https://doi.org/10.1007/s11707-009-0036-2.
Zhang, P., and Coauthors, 2019a: General Comparison of FY-4A/AGRI with other GEO/LEO instruments and its potential and challenges in non-meteorological applications. Frontiers in Earth Science, 6, 224, https://doi.org/10.3389/feart.2018.00224.
Zhang, P., L. Chen, D. Xian, Z. Xu, and M. Guan, 2020a: Update on Fengyun meteorological satellite program and development. Chinese Journal of Space Science, 40 (5), 884–897, https://doi.org/10.11728/cjss2020.05.884.
Zhang, X. Y., and Coauthors, 2020b: The development and application of satellite remote sensing for atmospheric compositions in China. Atmospheric Research, 245, 105056, https://doi.org/10.1016/j.atmosres.2020.105056.
Zheng, W., J. Chen, S. H. Tang, X. Q. Hu, and C. Liu, 2020: Fire monitoring based on FY-3D/MERSI-II far-infrared data. Journal of Infrared and Millimeter Waves, 39, 120–127, https://doi.org/10.11972/j.issn.1001-9014.2020.01.016. (in Chinese with English abstract)
Acknowledgements
This work was supported by the National Key Research and Development Program of China (2018YFB0504900, 2018YFB0504905). We thank the editor and reviewers for their constructive suggestions and comments.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Xian, D., Zhang, P., Gao, L. et al. Fengyun Meteorological Satellite Products for Earth System Science Applications. Adv. Atmos. Sci. 38, 1267–1284 (2021). https://doi.org/10.1007/s00376-021-0425-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00376-021-0425-3
Key words
- Fengyun meteorological satellite
- sensor-dependent level 1 product
- inversion algorithm-dependent level 2 product
- product validation