[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

Developing the science product algorithm testbed for Chinese next-generation geostationary meteorological satellites: Fengyun-4 series

  • Article
  • Published:
Journal of Meteorological Research Aims and scope Submit manuscript

Abstract

Fengyun-4A (FY-4A), the first of the Chinese next-generation geostationary meteorological satellites, launched in 2016, offers several advances over the FY-2: more spectral bands, faster imaging, and infrared hyperspectral measurements. To support the major objective of developing the prototypes of FY-4 science algorithms, two science product algorithm testbeds for imagers and sounders have been developed by the scientists in the FY-4 Algorithm Working Group (AWG). Both testbeds, written in FORTRAN and C programming languages for Linux or UNIX systems, have been tested successfully by using Intel/g compilers. Some important FY-4 science products, including cloud mask, cloud properties, and temperature profiles, have been retrieved successfully through using a proxy imager, Himawari-8/Advanced Himawari Imager (AHI), and sounder data, obtained from the Atmospheric InfraRed Sounder, thus demonstrating their robustness. In addition, in early 2016, the FY-4 AWG was developed based on the imager testbed—a near real-time processing system for Himawari-8/AHI data for use by Chinese weather forecasters. Consequently, robust and flexible science product algorithm testbeds have provided essential and productive tools for popularizing FY-4 data and developing substantial improvements in FY-4 products.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Ackerman, S. A., R. E. Holz, R. Frey, et al., 2008: Cloud detection with MODIS. Part II: Validation. J. Atmos. Ocean. Technol., 25, 1073–1086, doi: 10.1175/2007JTECHA1053.1.

    Article  Google Scholar 

  • Ackerman, S., R. Frey, K. Strabala, et al., 2010: Discriminating Clear-Sky from Cloud with MODIS—Algorithm Theoretical Basis Document (MOD35) Version 6.1, Tech. rep., MODIS Cloud Mask Team, Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin, 117 pp. Available online at https://modis-atmos.gsfc.nasa.gov/_docs/MOD35_ ATBD_Collection6.pdf (accessed on 12 May 2017).

    Google Scholar 

  • Cao, D. J., F. X. Huang, and X. S. Qie, 2014: Development and evaluation of detection algorithm for FY-4 geostationary lightning imager (GLI) measurement. Proceedings of XV International Conference on Atmospheric Electricity, Norman, Oklahoma, U.S.A.

    Google Scholar 

  • Comiso, J. C., D. J. Cavalieri, and T. Markus, 2003: Sea ice concentration, ice temperature, and snow depth using AMSR-E data. IEEE Trans. Geosci. Remote Sens., 41, 243–252, doi: 10.1109/TGRS.2002.808317.

    Article  Google Scholar 

  • Dee, D. P., S. M. Uppala, A. J. Simmons, et al., 2011: The ERAInterim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc,. 137, 553–597, doi: 10.1002/qj.v137.656.

    Article  Google Scholar 

  • Eyre, J. R., and H. M. Woolf, 1988: Transmittance of atmospheric gases in the microwave region: A fast model. Appl. Opt., 27, 3244–3249, doi: 10.1364/AO.27.003244.

    Article  Google Scholar 

  • Goodman, S. J., R. J. Blakeslee, W. J. Koshak, et al., 2013: The GOES-R geostationary lightning mapper (GLM). Atmos. Res., 125–126, 34–39, doi: 10.1016/j.atmosres.2013.01.006.

    Article  Google Scholar 

  • Greenwald, T. J., R. B. Pierce, T. Schaack, et al., 2016: Real-time simulation of the GOES-R ABI for user readiness and product evaluation. Bull. Amer. Meteor. Soc,. 97, 245–261, doi: 10.1175/BAMS-D-14-00007.1.

    Article  Google Scholar 

  • Heidinger, A. K., A. T. Evan, M. J. Foster, et al., 2012: A naive Bayesian cloud-detection scheme derived from CALIPSO and applied within PATMOS-x. J. Appl. Meteor. Climate, 51, 1129–1144, doi: 10.1175/JAMC-D-11-02.1.

    Article  Google Scholar 

  • Hu, X. Q., N. Xu, F. Z. Weng, et al., 2013: Long-term monitoring and correction of FY-2 infrared channel calibration using AIRS and IASI. IEEE Trans. Geosci. Remote Sens., 51, 5008–5018, doi: 10.1109/TGRS.2013.2275871.

    Article  Google Scholar 

  • Kalnay, E., M. Kanamitsu, R. Kistler, et al., 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437–47, 1 doi: 10.1175/1520-0477(1996)077<0437: TNYRP>2.0.CO;2.

    Article  Google Scholar 

  • Kanamitsu, M., 1989: Description of the NMC global data assimilation and forecast system. Wea. Forecasting, 4, 335–342, doi: 10.1175/1520-0434(1989)004<0335:DOTNGD>2.0.CO;2.

    Article  Google Scholar 

  • Li, J., W. W. Wolf, W. P. Menzel, et al., 2000: Global soundings of the atmosphere from ATOVS measurements: The algorithm and validation. J. Appl. Meteor., 39, 1248–1268, doi: 10.1175/1520-0450(2000)039<1248:GSOTAF>2.0.CO;2.

    Article  Google Scholar 

  • Liang, S. L., 2003: A direct algorithm for estimating land surface broadband albedos from MODIS imagery. IEEE Trans. Geosci. Remote Sens,. 41, 136–145, doi: 10.1109/TGRS. 2002.807751.

    Article  Google Scholar 

  • Liu, C., P. Yang, P. Minnis, et al., 2014: A two-habit model for the microphysical and optical properties of ice clouds. Atmos. Chem. Phys., 14, 13719–13737, doi: 10.5194/acp-14-13719-2014.

    Article  Google Scholar 

  • Liu, H., and J. Li, 2011: An improvement in forecasting rapid intensification of Typhoon Sinlaku (2008) using clear-sky full spatial resolution advanced IR soundings. J. Appl. Meteor. Climate, 49, 821–827.

    Article  Google Scholar 

  • Liu, Q. H., and S. Boukabara, 2014: Community Radiative Transfer Model (CRTM) applications in supporting the Suomi National Polar-orbiting Partnership (SNPP) mission validation and verification. Remote Sens. Environ., 140, 744–754, doi: 10.1016/j.rse.2013.10.011.

    Article  Google Scholar 

  • Lu, F., X. H. Zhang, and J. M. Xu, 2008: Image navigation for the FY2 geosynchronous meteorological satellite. J. Atmos. Ocean. Technol,. 25, 1149–1165, doi: 10.1175/2007JTECHA964.1.

    Article  Google Scholar 

  • Ma, X. L., T. J. Schmit, and W. A. Smith, 1999: A nonlinear physical retrieval algorithm—Its application to the GOES-8/9 sounder. J. Appl. Meteor., 38, 501–513, doi: 10.1175/1520-0450(1999)038<0501:ANPRAI>2.0.CO;2.

    Article  Google Scholar 

  • Matricardi, M., 2010: A principal component based version of the RTTOV fast radiative transfer model. Quart. J. Roy. Meteor. Soc., 136, 1823–1835, doi: 10.1002/qj.v136:652.

    Article  Google Scholar 

  • Menzel, W. P., R. A. Frey, H. Zhang, et al., 2008: MODIS global cloud-top pressure and amount estimation: Algorithm description and results. J. Appl. Meteor. Climate, 47, 1175–1198, doi: 10.1175/2007JAMC1705.1.

    Article  Google Scholar 

  • Min, M., Y. Zhang, Z. G. Rong, et al., 2014: A method for monitoring the on-orbit performance of a satellite sensor infrared window band using oceanic drifters. Int. J. Remote Sens., 35, 382–400, doi: 10.1080/01431161.2013.871393.

    Article  Google Scholar 

  • Schmetz, J., P. Pili, S. Tjemkes, et al., 2002: An introduction to Meteosat Second Generation (MSG). Bull. Amer. Meteor. Soc., 83, 977–992, doi: 10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2.

    Article  Google Scholar 

  • Schmit, T. J., M. M. Gunshor, W. P. Menzel, et al., 2005: Introducing the next-generation advanced baseline imager on GOESR. Bull. Amer. Meteor. So,c. 86, 1079–1096, doi: 10.1175/BAMS-86-8-1079.

    Article  Google Scholar 

  • Schmit, T. J., J. Li, J. L. Li, et al., 2008: The GOES-R advanced baseline imager and the continuation of current sounder products. J. Appl. Meteor. Climate, 47, 2696–2711, doi: 10.1175/2008JAMC1858.1.

    Article  Google Scholar 

  • Stuhlmann, R., A. Rodriguez, S. Tjemkes, et al., 2005: Plans for EUMETSAT’s Third Generation Meteosat geostationary satellite programme. Adv. Space Res,. 36, 975–981, doi: 10.1016/j.asr.2005.03.091.

    Article  Google Scholar 

  • Susskind, J., C. D. Barnet, and J. M. Blaisdell, 2003: Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Trans. Geosci. Remote Sens., 41, 390–409, doi: 10.1109/TGRS.2002.808236.

    Article  Google Scholar 

  • Wan, Z. M., 2008: New refinements and validation of the MODIS land–surface temperature/emissivity products. Remote Sens. Environ., 112, 59–74, doi: 10.1016/j.rse.2006.06.026.

    Article  Google Scholar 

  • Wang, M. H., and M. D. King, 1997: Correction of Rayleigh scattering effects in cloud optical thickness retrievals. J. Geophys. Res., 102, 25915–25926, doi: 10.1029/97JD02225.

    Article  Google Scholar 

  • Yang, J., P. Zhang, N. M. Lu, et al., 2012: Improvements on global meteorological observations from the current Fengyun 3 satellites and beyond. Int. J. Digit. Earth, 5, 251–265, doi: 10.1080/17538947.2012.658666.

    Article  Google Scholar 

  • Yang, J., Z. Zhang, and C. Wei, 2017: Introducing the new generation of Chinese geostationary weather satellites—Fengyun 4 (FY-4). Bull. Amer. Meteor. Soc., doi: 10.1175/BAMS-D-16-0065.1, in press.

    Google Scholar 

  • Yu, Y. Y., D. Tarpley, J. L. Privette, et al., 2009: Developing algorithm for operational GOES-R land surface temperature product. IEEE Trans. Geosci. Remote Sens., 47, 936–951, doi: 10.1109/TGRS.2008.2006180.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Min Min.

Additional information

Supported by the National Natural Science Foundation (41405035, 41571348, and 41405038) and China Meteorological Administration Special Public Welfare Research Fund (GYHY201406011 and GYHY201506074).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Min, M., Wu, C., Li, C. et al. Developing the science product algorithm testbed for Chinese next-generation geostationary meteorological satellites: Fengyun-4 series. J Meteorol Res 31, 708–719 (2017). https://doi.org/10.1007/s13351-017-6161-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13351-017-6161-z

Key words

Navigation