Skip to main content
SpringerLink
Log in

Impact of FY-3D MWRI Radiance Assimilation in GRAPES 4DVar on Forecasts of Typhoon Shanshan

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

Abstract

In this study, Fengyun-3D (FY-3D) MicroWave Radiation Imager (MWRI) radiance data were directly assimilated into the Global/Regional Assimilation and PrEdiction System (GRAPES) four-dimensional variational (4DVar) system. Quality control procedures were developed for MWRI applications by using algorithms from similar microwave instruments. Compared with the FY-3C MWRI, the bias of FY-3D MWRI observations did not show a clear node-dependent difference from the numerical weather prediction background simulation. A conventional bias correction approach can therefore be used to remove systematic biases before the assimilation of data. After assimilating the MWRI radiance data into GRAPES, the geopotential height and humidity analysis fields were improved relative to the control experiment. There was a positive impact on the location of the subtropical high, which led to improvements in forecasts of the track of Typhoon Shanshan.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Alishouse, J. C., S. A. Snyder, J. Vongsathorn, et al., 1990: Determination of oceanic total precipitable water from the SSM/I. IEEE Trans. Geosci. Remote Sens., 28, 811–816, doi: https://doi.org/10.1109/36.58967.

    Google Scholar 

  • Arakawa, A., and W. H. Schubert, 1974: Interaction of a cumulus cloud ensemble with the large-scale environment, Part I. J. Atmos. Sci., 31, 674–701, doi: https://doi.org/10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2.

    Google Scholar 

  • Bao, Y. S., F. Mao, J. Z. Min, et al., 2014: Retrieval of bare soil moisture from FY-3B/MWRI data. Remote Sens. Land Resour., 26, 131–137, doi: https://doi.org/10.6046/gtzyyg.2014.04.21. (in Chinese)

    Google Scholar 

  • Bettenhausen, M. H., C. K. Smith, R. M. Bevilacqua, et al., 2006: A nonlinear optimization algorithm for WindSat wind vector retrievals. IEEE Trans. Geosci. Remote Sens., 44, 597–610, doi: https://doi.org/10.1109/TGRS.2005.862504.

    Google Scholar 

  • Bouttier, F., and P. Courtier, 2002: Data assimilation concepts and methods March 1999. Proceedings of ECMWF Meteorological Training Course Lecture Series, ECMWF, Bracknell, 1–58

    Google Scholar 

  • Chen, D. H., J. S. Xue, X. S. Yang, et al., 2008: New generation of multi-scale NWP system (GRAPES): General scientific design. Chinese Sci. Bull., 53, 3433–3445, doi: https://doi.org/10.1007/s11434-008-0494-z.

    Google Scholar 

  • Chen, H., and Y. Q. Jin, 2012: In-orbit intercalibration of FY-3B/MWRI and applications for monitoring drought and flooding. J. Remote Sens., 16, 1024–1034, doi: https://doi.org/10.11834/jrs.20121299. (in Chinese)

    Google Scholar 

  • Chen, L. S., 1979: On the causal analysis of typhoon tracks which turning direction westward suddenly over the sea area near the eastern China. Chinese J. Atmos. Sci., 3, 289–298, doi: https://doi.org/10.3878/j.issn.1006-9895.1979.03.11. (in Chinese)

    Google Scholar 

  • Chen, X. M., Q. J. Liu, and J. C. Zhang, 2007: A numerical simulation study on microphysical structure and cloud seeding in cloud system of Qilian Mountain Region. Meteor. Mon., 33, 33–43, doi: https://doi.org/10.9969/j.issn.1000-0526.2007.07.004. (in Chinese)

    Google Scholar 

  • Connor, L. N., and P. S. Chang, 2000: Ocean surface wind retrievals using the TRMM microwave imager. IEEE Trans. Geosci. Remote Sens., 38, 2009–2016, doi: https://doi.org/10.1109/36.851782.

    Google Scholar 

  • Dai, Y. J., X. B. Zeng, R. E. Dickinson, et al., 2003: The common land model. Bull. Amer. Meteor. Soc., 84, 1013–1024, doi: https://doi.org/10.1175/BAMS-84-8-1013.

    Google Scholar 

  • Dou, F. L., D. W. An, and J. R. Li, 2014: Sea surface wind speed retrieval based on FY-3B Microwave Imager. Remote Sens. Technol. Appl., 29, 984–992. (in Chinese)

    Google Scholar 

  • Feng, C. C., and H. Zhao, 2015: Identification of radio-frequency interference signal from FY-3B microwave radiation imager over ocean. J. Remote Sens., 19, 465–475, doi: https://doi.org/10.11834/jrs.20154056. (in Chinese)

    Google Scholar 

  • Ferraro, R. R., F. Z. Weng, N. C. Grody, et al., 1996: An eight-year (1987–1994) time series of rainfall, clouds, water vapor, snow cover, and sea ice derived from SSM/I measurements. Bull. Amer. Meteor. Soc., 77, 891–906, doi: https://doi.org/10.1175/1520-0477(1996)077<0891:AEYTSO>2.0.CO;2.

    Google Scholar 

  • Gaiser, P. W., K. M. St Germain, E. M. Twarog, et al., 2004: The WindSat spaceborne Polarimetric microwave radiometer: Sensor description and early orbit performance. IEEE Trans. Geosci. Remote Sens., 42, 2347–2361, doi: https://doi.org/10.1109/TGRS.2004.836867.

    Google Scholar 

  • Geer, A. J., K. Lonitz, P. Weston, et al., 2018: All-sky satellite data assimilation at operational weather forecasting centres. Quart. J. Roy. Meteor. Soc., 144, 1191–1217, doi: https://doi.org/10.1002/qj.3202.

    Google Scholar 

  • Giorgi, F., Y. Huang, K. Nishizawa, et al., 1999: A seasonal cycle simulation over eastern Asia and its sensitivity to radiative transfer and surface processes. J. Geophys. Res. Atmos., 104, 6403–6423, doi: https://doi.org/10.1029/1998JD200052.

    Google Scholar 

  • Grody, N. C., and R. R. Ferraro, 1992: A comparison of passive microwave rainfall retrieval methods. Proceeding of the 6th Conference on Meteorology and Oceanography, American Meteorological Society, Atlanta, 60–65.

    Google Scholar 

  • Guo, L., H. Sheng, J. Wang, et al., 2017: Retrieving near sea surface air temperature by AMSR2 radiometer. Adv. Mar. Sci., 35, 124–130, doi: https://doi.org/10.3969/j.issn.1671-6647.2017.01.013. (in Chinese)

    Google Scholar 

  • Han, W., and N. Bormann, 2016: Constrained adaptive bias correction for satellite radiance assimilation in the ECMWF 4D-Var system. Technical Memorandum No. 783, ECMWF, Shinfield Park, Reading, 26 pp.

    Google Scholar 

  • Hargens, U., C. Simmer, and E. Ruprecht, 1992: Remote sensing of cloud liquid water during ICE’89. Proceedings of Specialist Meeting on Microwave Radiometry and Remote Sensing Applications, IEEE, Boulder, Colorado, 27–31.

    Google Scholar 

  • Harris, B. A., and G. Kelly, 2001: A satellite radiance-bias correction scheme for data assimilation. Quart. J. Roy. Meteor. Soc., 127, 1453–1468, doi: https://doi.org/10.1002/qj.49712757418.

    Google Scholar 

  • Hollinger, J. P., J. L. Peirce, and G. A. Poe, 1990: SSM/I instrument evaluation. IEEE Trans. Geosci. Remote Sens., 28, 781–790, doi: https://doi.org/10.1109/36.58964.

    Google Scholar 

  • Hong, S. Y., and H. L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322–2339, doi: https://doi.org/10.1175/1520-0493(1966)124<2322:NBLVDI>2.0.CO;2.

    Google Scholar 

  • Huang, W., Y. L. Hao, J. Wang, et al., 2013: Brightness temperature data comparison and evaluation of FY-3B microwave radiation imager with AMSR-E. Period. Ocean Univ. China, 43, 99–111, doi: https://doi.org/10.16441/j.ckki.hdxb.2013.11.015. (in Chinese)

    Google Scholar 

  • JAXA, 2013: GCOM-W1 “SHIZUKU” Data Users Handbook, Japan Aerospace Exploration Agency. Tsukuba, Japan, 125 pp. Available online at https://gportal.jaxa.jp/gpr/assets/mng_up-load/GCOM-W/GCOM-W1_SHIZUKU_Data_Users_Hand-book_EN.pdf. Accessed on 19 August 2020.

    Google Scholar 

  • Kawanishi, T., T. Sezai, Y. Ito, et al., 2003: The advanced microwave scanning radiometer for the Earth observing system (AMSR-E), NASDA’S contribution to the EOS for global energy and water cycle studies. IEEE Trans. Geosci. Remote Sens., 41, 184–194, doi: https://doi.org/10.1109/TGRS.2002.808331.

    Google Scholar 

  • Kazumori, M., Q. H. Liu, R. Treadon, et al., 2008: Impact study of AMSR-E radiances in the NCEP global data assimilation system. Mon. Wea. Rev., 136, 541–559, doi: https://doi.org/10.1175/2007MWR2147.1.

    Google Scholar 

  • Kazumori, M., A. J. Geer, and S. J. English, 2014: Effects of all-sky assimilation of GCOM-W1/AMSR2 radiances in the ECMWF system. Technical Memo 732, ECMWF, Reading, 1–34.

    Google Scholar 

  • Krasnopolsky, V. M., L. C. Breaker, and W. H. Gemmill, 1995: A neural network as a nonlinear transfer function model for retrieving surface wind speeds from the special sensor microwave imager. J. Geophys. Res. Oceans, 100, 11,033–11,045, doi: https://doi.org/10.1029/95JC00857.

    Google Scholar 

  • Kummerow, C., J. Simpson, O. Thiele, et al., 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor., 39, 1965–1982, doi: https://doi.org/10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2.

    Google Scholar 

  • Kuria, D., and T. Koike, 2011: Convective cloud discrimination using multi-frequency microwave signatures of the AMSR-E sensor: Evaluation over the Tibetan Plateau. Int. J. Remote Sens., 32, 3451–3460, doi: https://doi.org/10.1080/01431161003749451.

    Google Scholar 

  • Lawrence, H., F. Carminati, W. Bell, et al., 2017: An Evaluation of FY-3C MWRI and Assessment of the Long-term Quality of FY-3C MWHS-2 at ECMWF and the Met Office. ECMWF Technical Memoranda 798, ECMWF, doi: https://doi.org/10.21957/lhuph6fb3.

  • Lee, D.-K., and M.-S. Suh, 2000: Ten-year East Asian summer monsoon simulation using a regional climate model (RegCM2). J. Geophys. Res. Atmos., 105, 29565–29577, doi: https://doi.org/10.1029/2000JD900438.

    Google Scholar 

  • Li, L., E. G. Njoku, E. Im, et al., 2004: A preliminary survey of radio-frequency interference over the US in Aqua AMSR-E data. IEEE Trans. Geosci. Remote Sens., 42, 380–390, doi: https://doi.org/10.1109/TGRS.2003.817195.

    Google Scholar 

  • Li, X. Q., H. Yang, R. You, et al., 2012: Remote sensing Typhoon Songda’s rainfall structure based on Microwave Radiation Imager of FY-3B satellite. Chinese J. Geophys., 55, 2843–2853. (in Chinese)

    Google Scholar 

  • Liu, K., Q. Y. Chen, and J. Sun, 2015: Modification of cumulus convection and planetary boundary layer schemes in the GRAPES global model. J. Meteor. Res., 29, 806–822, doi: https://doi.org/10.1007/s13351-015-5043-5.

    Google Scholar 

  • Liu, Q. J., Z. J. Hu, and X. J. Zhou, 2003: Explicit cloud schemes of HLAFS and simulation of heavy rainfall and clouds. Part I: Explicit cloud schemes. J. Appl. Meteor. Sci., 14, 60–67, doi: https://doi.org/10.3969/j.issn.1001-7313.2003.z1.008. (in Chinese)

    Google Scholar 

  • Liu, Z. Q., and F. Rabier, 2002: The interaction between model resolution, observation resolution and observation density in data assimilation: A one-dimensional study. Quart. J. Roy. Meteor. Soc., 128, 1367–1386, doi: https://doi.org/10.1256/003590002320373337.

    Google Scholar 

  • Liu, Z. Q., F. Y. Zhang, X. B. Wu, et al., 2007: A regional ATOVS radiance-bias correction scheme for rediance assimilation. Acta Meteor. Sinica, 65, 113–123, doi: https://doi.org/10.3321/j.issn:0577-6619.2007.01.011. (in Chinese)

    Google Scholar 

  • Liu, Z. Q., C. S. Schwartz, C. Snyder, et al., 2012: Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area Ensemble Kalman Filter. Mon. Wea. Rev., 140, 4017–4034, doi: https://doi.org/10.1175/MWR-D-12-00083.1.

    Google Scholar 

  • Ma, Z. S., Q. J. Liu, C. F. Zhao, et al., 2018: Application and evaluation of an explicit prognostic cloud-cover scheme in GRAPES global forecast system. J. Adv. Model. Earth Syst., 10, 652–667, doi: https://doi.org/10.1002/2017MS001234.

    Google Scholar 

  • Madrid, C. R., 1978: The Nimbus 7 User’s Guide. NAS5-23740, NASA Goddard Space Flight Center, Greenbelt.

    Google Scholar 

  • Moncet, J.-L., P. Liang, J. F. Galantowicz, et al., 2011: Land surface microwave emissivities derived from AMSR-E and MODIS measurements with advanced quality control. J. Geophys. Res. Atmos., 116, D16104, doi: https://doi.org/10.1029/2010JD015429.

    Google Scholar 

  • Morcrette, J.-J., H. W. Barker, J. N. S. Cole, et al., 2008: Impact of a new radiation package, McRad, in the ECMWF integrated forecasting system. Mon. Wea. Rev., 136, 4773–4798, doi: https://doi.org/10.1175/2008MWR2363.1.

    Google Scholar 

  • Nielsen-Englyst, P., J. L. Hoyer, L. T. Pedersen, et al., 2018: Optimal estimation of sea surface temperature from AMSR-E. Remote Sens., 10, 229, doi: https://doi.org/10.3390/rs10020229.

    Google Scholar 

  • Oki, T., K. Imaoka, and M. Kachi, 2010: AMSR instruments on GCOM-W1/2: Concepts and applications. Proceedings of 2010 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Honolulu, HI, 1363–1366, doi: https://doi.org/10.1109/IGARSS.2010.5650001.

    Google Scholar 

  • Pan, H.-L., and W. S. Wu, 1995: Implementing a mass flux convective parameterization package for the NMC medium-range forecast model. NMC Office Note 409, NMC, Washington, DC, 1–40.

    Google Scholar 

  • Peng, L. C., W. B. Li, and H. Z. Liu, 2011: Estimation of the soil moisture using FY-3A/MWRI data over semiarid areas. Acta Sci. Nat. Univ. Pekin., 47, 797–804, doi: https://doi.org/10.13209/j.0479-8023.2011.111. (in Chinese)

    Google Scholar 

  • Pincus, R., H. W. Barker, and J.-J. Morcrette, 2003: A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. J. Geophys. Res. Atmos., 108, 4376, doi: https://doi.org/10.1029/2002JD003322.

    Google Scholar 

  • Spreen, G., L. Kaleschke, and G. Heygster, 2008: Sea ice remote sensing using AMSR-E 89-GHz channels. J. Geophys. Res. Oceans, 113, C02S03, doi: https://doi.org/10.1029/2005JC003384.

    Google Scholar 

  • Su, J., G. H. Hao, X. X. Ye, et al., 2013: The experiment and validation of sea ice concentration AMSR-E retrieval algorithm in polar region. J. Remote Sens., 17, 495–513, doi: https://doi.org/10.11834/jrs.20132043. (in Chinese)

    Google Scholar 

  • Sun, L. E., J. Wang, T. W. Cui, et al., 2012: Statistical retrieval algorithms of the sea surface temperature (SST) and wind speed (SSW) for FY-3B Microwave Radiometer Imager (MWRI). J. Remote Sens., 16, 1262–1271, doi: https://doi.org/10.11834/jrs.20121323. (in Chinese)

    Google Scholar 

  • Sun, N. H., and F. Z. Weng, 2008: Evaluation of special sensor microwave imager/sounder (SSMIS) environmental data records. IEEE Trans. Geosci. Remote Sens., 46, 1006–1016, doi: https://doi.org/10.1109/TGRS.2008.917368.

    Google Scholar 

  • Tang, F., and X. L. Zou, 2017: Liquid water path retrieval using the lowest frequency channels of FengYun-3C microwave radiation imager (MWRI). J. Meteor. Res., 31, 1109–1122, doi: https://doi.org/10.1007/s13351-017-7012-7.

    Google Scholar 

  • Tang, F., and X. L. Zou, 2018: Diurnal variation of liquid water path derived from two polar-orbiting FengYun-3 MicroWave Radiation Imagers. Geophys. Res. Lett., 45, 6281–6288, doi: https://doi.org/10.1029/2018GL077857.

    Google Scholar 

  • Tiedtke, M., 1993: Representation of clouds in large-scale models. Mon. Wea. Rev., 221, 3040–3061, doi: https://doi.org/10.1175/15200-0493(1993)121<3040:ROCILS>2.0.CO;2.

    Google Scholar 

  • Wang, J. C., H. J. Lu, W. Han, et al., 2017: Improvements and performances of the operational GRAPES_GFS 3DVar system. J. Appl. Meteor. Sci., 28, 11–24, doi: https://doi.org/10.11898/1001-7313.20170102. (in Chinese)

    Google Scholar 

  • Weng, F. Z., N. C. Grody, R. Ferraro, et al., 1997: Cloud liquid water climatology from the special sensor microwave/imager. J. Climate, 10, 1086–1098, doi: https://doi.org/10.1175/1520-0442(1997)010<1086:clwcft>2.0.co;2.

    Google Scholar 

  • Wu, Q., L. Yang, and H. Yang, 2012: Image quality evaluation of MWRI from FY-3B satellite. Remote Sens. Technol. Appl., 1, 542–548, doi: https://doi.org/10.11873/j.issn.1004-0323.2012.4.542. (in Chinese)

    Google Scholar 

  • Wu, Y., and F. Z. Weng, 2011: Detection and correction of AM-SR-E radio-frequency interference. Acta Meteor. Sinica, 25, 669–681, doi: https://doi.org/10.1007/s13351-011-0510-0.

    Google Scholar 

  • Xie, X. X., S. L. Wu, H. X. Xu, et al., 2019: Ascending-descending bias correction of microwave radiation imager on board FengYun-3C. IEEE Trans. Geosci. Remote Sens., 57, 3126–3134, doi: https://doi.org/10.1109/TGRS.2018.2881094.

    Google Scholar 

  • Xue, J. S., and D. H. Chen, 2008: Scientific Design and Application of Numerical Prediction System GRAPES. Science Press, Beijing, 383 pp. (in Chinese)

    Google Scholar 

  • Xue, J. S., S. Y. Zhuang, G. F. Zhu, et al., 2008: Scientific design and preliminary results of three-dimensional variational data assimilation system of GRAPES. Chinese Sci. Bull., 53, 3446–3457, doi: https://doi.org/10.1007/s11434-008-0416-0.

    Google Scholar 

  • Yang, C., Z. Q. Liu, J. Bresch, et al., 2016: AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system. Tellus A, 38, 30917, doi: https://doi.org/10.3402/tellusa.v68.30917.

    Google Scholar 

  • Yang, C., J. Z. Min, and Z. Q. Liu, 2017: The impact of AMSR2 radiance data assimilation on the analysis and forecast of Typhoon Son-Tinh. Chinese J. Atmos. Sci., 41, 372–384, doi: https://doi.org/10.3878/j.issn.1006-9895.1608.16127. (in Chinese)

    Google Scholar 

  • Yang, H., X. Q. Li, R. You, et al., 2013: Environmental data records from FengYun-3B microwave radiation imager. Adv. Meteor. Sci. Technol., 3, 136–143. (in Chinese)

    Google Scholar 

  • Yin, H. G., Q. Wu, S. Y. Gu, et al., 2016: Analysis of rainfall measurement power in the FY-3(03) rain measurement satellite. Adv. Meteor. Sci. Technol., 3, 55–61. (in Chinese)

    Google Scholar 

  • Yu, Z. W., J. W. Liu, J. P. Huang, et al., 2017: Assimilation experiment of AMSR2 microwave imaging data and its influence on typhoon forecasting. Meteor. Hydrol. Mar. Instrum., 34, 1–8. (in Chinese)

    Google Scholar 

  • Yu, Z. W., J. W. Liu, Z. Zhong, et al., 2018: Assimilation experiment of AMSR2 microwave imaging data under cloudy and rainy condition and its application on the forecast of a typhoon process. J. Meteor. Sci., 38, 203–211. (in Chinese)

    Google Scholar 

  • Zhang, L., Y. Z. Liu, Y. Liu, et al., 2019a: The operational global four-dimensional variational data assimilation system at the China Meteorological Administration. Quart. J. Roy. Meteor. Soc., 145, 1882–1896, doi: https://doi.org/10.1002/qj.3533.

    Google Scholar 

  • Zhang, M., H. Qiu, X. Fang, et al., 2015: Study on the multivariate statistical estimation of tropical cyclone intensity using FY-3 MWRI brightness temperature data. J. Trop. Meteor., 31, 87–94, doi: https://doi.org/10.16032/j.issn.1004-4965.2015.01.010. (in Chinese)

    Google Scholar 

  • Zhang, M., Q. F. Lu, S. Y. Gu, et al., 2019b: Analysis and correction of the difference between the ascending and descending orbits of the FY-3C microwave imager. J. Remote Sens., 23, 841–849, doi: https://doi.org/10.11834/jrs.20198235. (in Chinese)

    Google Scholar 

  • Zhang, S. J., L. S. Chen, and X. D. Xu, 2005: The diagnoses and numerical simulation on the unusual track of Helen (9505). Chinese J. Atmos. Sci., 29, 937–946, doi: https://doi.org/10.3878/j.issn.1006-9895.2005.06.09. (in Chinese)

    Google Scholar 

  • Zhao, Y. L., 2013: Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model. Chinese J. Oceanol. Limn., 31, 210–218, doi: https://doi.org/10.1077/s00343-013-2079-1.

    Google Scholar 

  • Zhao, Y. L., and M. X. He, 2013: A simplified forward model of WindSat for sea surface wind vector retrieving. Prriod. Ocean Univ. China, 33, 98–105, doi: https://doi.org/10.16441/j.ckki.hdxb.2013.12.016. (in Chinese)

    Google Scholar 

  • Zhou, Y. Q., and J. H. Yu, 2015: Circulation characteristics of track variation anomaly of tropical cyclone in the northwestern Pacific. J. Meteor. Sci., 35, 720–727. (in Chinese)

    Google Scholar 

  • Zhou, Z. H., X. L. Zou, and Z. K. Qin, 2017: Detection and analysis of television frequency interference from an FY-3C microwave radiation imager. J. Remote Sens., 21, 689–701, doi: https://doi.org/10.11834/jrs.20176364.

    Google Scholar 

  • Zhu, E. Z., L. Zhang, H. Q. Shi, et al., 2016: Accuracy of WindSat sea surface temperature: Comparison of buoy data from 2004 to 2013. J. Remote Sens., 20, 315–327, doi: https://doi.org/10.11834/jrs.20165093. (in Chinese)

    Google Scholar 

  • Zou, X. L., 2012: Introduction to microwave imager radiance observations from polar-orbiting meteorological satellites. Adv. Meteor. Sci. Technol., 2, 45–50. (in Chinese)

    Google Scholar 

  • Zou, X. L., J. Zhao, F. Z. Weng, et al., 2012: Detection of radio-frequency interference signal over land from FY-3B Microwave Radiation Imager (MWRI). IEEE Trans. Geosci. Remote Sens., 50, 4994–5003, doi: https://doi.org/10.1109/TGRS.2012.2191792.

    Google Scholar 

  • Zou, X. L., J. Zhao, F. Z. Weng, et al., 2013: Detection of radio-frequency interference signal over land from FY-3B Microwave Radiation Imager (MWRI). Adv. Meteor. Sci. Technol., 3, 144–153. (in Chinese)

    Google Scholar 

Download references

Acknowledgments

We acknowledge Dr Hao Chen from the Jiangsu Meteorological Bureau for providing the FY-3D/MWRI coefficient in RTTOV. We acknowledge Professor Fuzhong Weng for providing beneficial suggestions and editing the paper. We acknowledge Dr Jin Zhang for providing the code to calculate the typhoon track from the output data of GRAPES_GFS. We also acknowledge helpful discussions with Dr Shengli Wu and PhD student Ruoying Yin.

Author information

Authors and Affiliations

  1. Numerical Weather Prediction Center, China Meteorological Administration, Beijing, 100081, China

    Hongyi Xiao, Wei Han, Hao Wang, Jincheng Wang & Guiqing Liu

  2. National Meteorological Center, China Meteorological Administration, Beijing, 100081, China

    Hongyi Xiao, Wei Han, Hao Wang, Jincheng Wang & Guiqing Liu

  3. Tai’an City Meteorological Bureau, Shandong Meteorological Bureau, Tai’an, 271000, China

    Changshan Xu

Authors
  1. Hongyi Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jincheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guiqing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Changshan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Han.

Additional information

Supported by the National Natural Science Foundation of China (41675108), National Key Research and Development Program (2018YFC1506700), and Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0105).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, H., Han, W., Wang, H. et al. Impact of FY-3D MWRI Radiance Assimilation in GRAPES 4DVar on Forecasts of Typhoon Shanshan. J Meteorol Res 34, 836–850 (2020). https://doi.org/10.1007/s13351-020-9122-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13351-020-9122-x

Key words

Search

Navigation