Abstract
Compared with traditional microwave humidity sounding capabilities at 183 GHz, new channels at 118 GHz have been mounted on the second generation of the Microwave Humidity Sounder (MWHS-2) onboard the Chinese FY-3C and FY-3D polar orbit meteorological satellites, which helps to perform moisture sounding. In this study, as the all-sky approach can manage non-linear and non-Gaussian behavior in cloud- and precipitation-affected satellite radiances, the MWHS-2 radiances in all-sky conditions were first assimilated in the Yinhe four-dimensional variational data assimilation (YH4DVAR) system. The data quality from MWHS-2 was evaluated based on observation minus background statistics. It is found that the MWHS-2 data of both FY-3C and FY-3D are of good quality in general. Six months of MWHS-2 radiances in all-sky conditions were then assimilated in the YH4DVAR system. Based on the forecast scores and observation fits, we conclude that the all-sky assimilation of the MWHS-2 at 118- and 183-GHz channels on FY-3C/D is beneficial to the analysis and forecast fields of the temperature and humidity, and the impact on the forecast skill scores is neutral to positive. Additionally, we compared the impacts of assimilating the 118-GHz channels and the equivalent Advanced Microwave Sounding Unit-A (AMSUA) channels on global forecast accuracy in the absence of other satellite observations. Overall, the impact of the 118-GHz channels on the forecast accuracy is not as large as that for the equivalent AMSUA channels. Nevertheless, all-sky radiance assimilation of MWHS-2 in the YH4DVAR system has indeed benefited from the 118-GHz channels.
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References
Baordo, F., and A. J. Geer, 2015: All-sky Assimilation of SSMIS Humidity Sounding Channels over Land Within the ECMWF System. EUMETSAT/ECMWF Fellowship Programme Research Report No. 38, EUMETSAT/ECMWF, Shinfield Park, Reading, 26 pp.
Bauer, P., A. J. Geer, P. Lopez, et al., 2010: Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation. Quart. J. Roy. Meteor. Soc., 136, 1868–1885, doi: https://doi.org/10.1002/qj.659.
Bechtold, P., N. Semane, P. Lopez, et al., 2014: Representing equilibrium and nonequilibrium convection in large-scale models. J. Atmos. Sci., 71, 734–753, doi: https://doi.org/10.1175/JAS-D-13-0163.1.
Bédard, J., and M. Buehner, 2020: A practical assimilation approach to extract smaller-scale information from observations with spatially correlated errors: An idealized study. Quart. J. Roy. Meteor. Soc., 146, 468–482, doi: https://doi.org/10.1002/qj.3687.
Bennartz, R., A. Thoss, A. Dybbroe, et al., 2002: Precipitation analysis using the Advanced Microwave Sounding Unit in support of nowcasting applications. Meteor. Appl., 9, 177–189, doi: https://doi.org/10.1017/S1350482702002037.
Bonavita, M., L. Isaksen, and E. Hólm, 2012: On the use of EDA background error variances in the ECMWF 4D-Var. Quart. J. Roy. Meteor. Soc., 138, 1540–1559, doi: https://doi.org/10.1002/qj.1899.
Bormann, N., D. Duncan, S. English, et al., 2021: Growing operational use of FY-3 data in the ECMWF system. Adv. Atmos. Sci., 38, 1285–1298, doi: https://doi.org/10.1007/s00376-020-0207-3.
Candy, B., and S. Migliorini, 2021: The assimilation of microwave humidity sounder observations in all-sky conditions. Quart. J. Roy. Meteor. Soc., 147, 3049–3066, doi: https://doi.org/10.1002/qj.4115.
Carminati, F., and S. Migliorini, 2021: All-sky data assimilation of MWTS-2 and MWHS-2 in the Met Office global NWP system. Adv. Atmos. Sci., 38, 1682–1694, doi: https://doi.org/10.1007/s00376-021-1071-5.
Carminati, F., N. Atkinson, B. Candy, et al., 2021: Insights into the microwave instruments onboard the Fengyun 3D satellite: Data quality and assimilation in the Met Office NWP system. Adv. Atmos. Sci., 38, 1379–1396, doi: https://doi.org/10.1007/s00376-020-0010-1.
Chambon, P., A. Geer, N. Bormann, et al., 2020: Overview of the assimilation of microwave imagers and humidity sounders observations within clouds and precipitation. Proceedings of the 4th Workshop on Assimilating Satellite Cloud and Precipitation Observations for NWP, ECMWF, Reading, 56 pp.
Dee, D. P., 2004: Variational Bias Correction of Radiance Data in the ECMWF System. ECMWF, Reading, 16 pp.
Desroziers, G., L. Berre, B. Chapnik, et al., 2005: Diagnosis of observation, background and analysis-error statistics in observation space. Quart. J. Roy. Meteor. Soc., 131, 3385–3396, doi: https://doi.org/10.1256/qj.05.108.
Forbes, R., and A. Tompkins, 2011: An improved representation of cloud and precipitation. ECMWF Newsletter 129, 13–18. Available online at https://www.ecmwf.int/sites/default/files/elibrary/2011/17431-improved-representation-cloud-and-pre-cipitation.pdf. Accessed on 26 September 2022.
Forbes, R., A. Geer, K. Lonitz, et al., 2020: Observation-informed model development for cloud and precipitation. Proceedings of the 4th Workshop on Assimilating Satellite Cloud and Precipitation Observations for NWP, ECMWF, Reading, 31 pp.
Geer, A. J., and P. Bauer, 2011: Observation errors in all-sky data assimilation. Quart. J. Roy. Meteor. Soc., 137, 2024–2037, doi: https://doi.org/10.1002/qj.830.
Geer, A. J., F. Baordo, N. Bormann, et al., 2017: The growing impact of satellite observations sensitive to humidity, cloud and precipitation. Quart. J. Roy. Meteor. Soc., 143, 3189–3206, doi: https://doi.org/10.1002/qj.3172.
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.
Han, Y., P. van Delst, Q. H. Liu, et al., 2006: Community Radiative Transfer Model (CRTM)-Version 1. NOAA Technical Report NESDIS 122, U.S. Department of Commerce, National Oceanic and Atmospheric Administration, Washington, 33 pp.
Karbou, F., C. Prigent, L. Eymard, et al., 2005: Microwave land emissivity calculations using AMSU measurements. IEEE Trans. Geosci. Remote Sens., 43, 948–959, doi: https://doi.org/10.1109/TGRS.2004.837503.
Kazumori, M., 2019: Assimilation experiments of microwave and infrared radiance data in JMA global numrical weather prediction system. Proceedings of 2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Yokohama, 4738–4740, doi: https://doi.org/10.1109/IGARSS.2019.8898781.
Lawrence, H., N. Bormann, A. J. Geer, et al., 2018: Evaluation and assimilation of the microwave sounder MWHS-2 onboard FY-3C in the ECMWF numerical weather prediction system. IEEE Trans. Geosci. Remote Sens., 56, 3333–3349, doi: https://doi.org/10.1109/TGRS.2018.2798292.
Li, J., Z. K. Qin, and G. Q. Liu, 2016: A new generation of Chinese FY-3C microwave sounding measurements and the initial assessments of its observations. Int. J. Remote Sens., 37, 4035–4058, doi: https://doi.org/10.1080/01431161.2016.1207260.
Li, Y., K. Y. Chen, and Z. P. Xian, 2021: Evaluation of all-Sky assimilation of FY-3C/MWHS- 2 on Mei-yu precipitation forecasts over the Yangtze-Huaihe river basin. Adv. Atmos. Sci., 38, 1397–1414, doi: https://doi.org/10.1007/s00376-021-0401-y.
Liu, B. N., W. M. Zhang, X. Q. Cao, et al., 2016: Investigations and experiments of variances filtering technology in the ensemble data assimilation. Chinese J. Geophys., 59, 33–42, doi: https://doi.org/10.1002/cjg2.20211.
Peng, J., J. P. Wu, W. M. Zhang, et al., 2019: A modified nonhydrostatic moist global spectral dynamical core using a dry-mass vertical coordinate. Quart. J. Roy. Meteor. Soc., 145, 2477–2490, doi: https://doi.org/10.1002/qj.3574.
Saunders, R., J. Hocking, E. Turner, et al., 2018: An update on the RTTOV fast radiative transfer model (currently at version 12). Geosci. Model Dev., 11, 2717–2737, doi: https://doi.org/10.5194/gmd-11-2717-2018.
Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779–1800, doi: https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.
Tiedtke, M., 1993: Representation of clouds in large-scale models. Mon. Wea. Rev., 128, 3040–3061, doi: https://doi.org/10.1175/1520-0493(1993)12173040:ROCILS>2.0.CO;2.
Tompkins, A. M., and M. Janiskova, 2004: A cloud scheme for data assimilation: Description and initial tests. Quart. J. Roy. Meteor. Soc., 130, 2495–2517, doi: https://doi.org/10.1256/qj.03.162.
Trémolet, Y., 2004: Diagnostics of linear and incremental approximations in 4D-Var. Quart. J. Roy. Meteor. Soc., 130, 2233–2251, doi: https://doi.org/10.1256/qj.03.33.
Wang, Z. Z., J. Y. Li, J. Y. He, et al., 2019: Performance analysis of microwave humidity and temperature sounder onboard the FY-3D satellite from prelaunch multiangle calibration data in thermal/vacuum test. IEEE Trans. Geosci. Remote Sens., 57, 1664–1683, doi: https://doi.org/10.1109/TGRS.2018.2868324.
Weng, F. Z., 2007: Advances in radiative transfer modeling in support of satellite data assimilation. J. Atmos. Sci., 64, 3799–3807, doi: https://doi.org/10.1175/2007JAS2112.1.
Xian, Z. P., K. Y. Chen, and J. Zhu, 2019: All-sky assimilation of the MWHS-2 observations and evaluation the impacts on the analyses and forecasts of binary typhoons. J. Geophys. Res. Atmos., 124, 6359–6378, doi: https://doi.org/10.1029/2018JD029658.
Xing, X., B. N. Liu, W. M. Zhang, et al., 2020: The impact of length-scale variation when diagnosing the standard deviations of background error in a 4D-Var system and filtering method investigation. Adv. Meteor., 2020, 8885607, doi: https://doi.org/10.1155/2020/8885607.
Zhang, W. M., X. Q. Cao, and J. Q. Song, 2012: Design and implementation of four-dimensional variational data assimilation system constrained by the global spectral model. Acta Phys. Sinica, 61, 249202, doi: https://doi.org/10.7498/aps.61.249202. (in Chinese)
Zhu, M. B., W. M. Zhang, X. Q. Cao, et al., 2014: Impact of GNSS radio occultation bending angle data assimilation in YH4DVAR system. Chinese Phys. B, 23, 069202, doi: https://doi.org/10.1088/1674-1056/23/6/069202.
Zhu, Y. Q., E. Liu, R. Mahajan, et al., 2016: All-sky microwave radiance assimilation in NCEP’s GSI analysis system. Mon. Wea. Rev., 144, 4709–4735, doi: https://doi.org/10.1175/MWR-D-15-0445.1.
Zhu, Y. Q., G. Gayno, R. J. Purser, et al., 2019: Expansion of the all-sky radiance assimilation to ATMS at NCEP. Mon. Wea. Rev, 147, 2603–2620, doi: https://doi.org/10.1175/MWR-D-18-0228.1.
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The authors thank the anonymous reviewers for their constructive comments and suggestions.
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Supported by the National Key Research and Development Program of China (2018YFC1506704) and National Natural Science Foundation of China (41705007).
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Ma, S., Zhang, W., Cao, X. et al. Assimilation of All-Sky Radiance from the FY-3 MWHS-2 with the Yinhe 4D-Var System. J Meteorol Res 36, 750–766 (2022). https://doi.org/10.1007/s13351-022-1208-1
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DOI: https://doi.org/10.1007/s13351-022-1208-1