Articles | Volume 25, issue 10
https://doi.org/10.5194/nhess-25-3905-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-3905-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Indirect assimilation of radar reflectivity data with an adaptive hydrometer retrieval scheme for severe short-term weather forecasts
Lixin Song
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
Feifei Shen
CORRESPONDING AUTHOR
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
Key Laboratory of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
China Meteorological Administration Tornado Key Laboratory, Guangzhou, China
Zhixin He
Anhui Meteorological Observatory, Hefei 230000, China
Lu Yang
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
Dongmei Xu
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
Aiqing Shu
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
Jiajun Chen
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
Related authors
No articles found.
Feifei Shen, Aiqing Shu, Hong Li, Dongmei Xu, and Jinzhong Min
Nat. Hazards Earth Syst. Sci., 21, 1569–1582, https://doi.org/10.5194/nhess-21-1569-2021, https://doi.org/10.5194/nhess-21-1569-2021, 2021
Short summary
Short summary
The Advanced Himawari Imager (AHI) on Himawari-8 can continuously monitor high-impact weather events with high frequency in space and time. The assimilation of AHI radiance data was implemented with the three-dimensional variational data assimilation system of the Weather Research and Forecasting Model for the analysis and prediction of Typhoon Soudelor (2015) in the Pacific typhoon season.
Cited articles
Bick, T., Simmer, C., Trömel, S., Wapler, K., Hendricks Franssen, H. J., Stephan, K., Blahak, U., Schraff, C., Reich, H., Zeng, Y., and Potthast, R.: Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the convective scale, Q. J. Roy. Meteor. Soc., 142, 1490–1504, https://doi.org/10.1002/qj.2751, 2016.
Borderies, M., Caumont, O., Delanoë, J., Ducrocq, V., Fourrié, N., and Marquet, P.: Impact of airborne cloud radar reflectivity data assimilation on kilometre-scale numerical weather prediction analyses and forecasts of heavy precipitation events, Nat. Hazards Earth Syst. Sci., 19, 907–926, https://doi.org/10.5194/nhess-19-907-2019, 2019.
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Chen, H., Chen, Y., Gao, J., Sun, T., and Carlin, J. T.: A radar reflectivity data assimilation method based on background-dependent hydrometeor retrieval: An observing system simulation experiment, Atmos. Res., 243, 105022, https://doi.org/10.1016/j.atmosres.2020.105022, 2020.
Chen, H., Gao, J., Wang, Y., Chen, Y., Sun, T., Carlin, J., and Zheng, Y.: Radar reflectivity data assimilation method based on background-dependent hydrometeor retrieval: Comparison with direct assimilation for real cases, Q. J. Roy. Meteor. Soc., 147, 2409–2428, https://doi.org/10.1002/qj.4031, 2021.
Chen, J., Xu, D., Shu, A., and Song, L.: The Impact of Radar Radial Velocity Data Assimilation Using WRF-3DVAR System with Different Background Error Length Scales on the Forecast of Super Typhoon Lekima (2019), Remote Sens., 15, 2592, https://doi.org/10.3390/rs15102592, 2023.
China Meteorological Administration: Near-real-time product data set of land data assimilation system (CLDAS-V2.0) of China Meteorological Bureau, Near-real-time product data set of land data assimilation system (CLDAS-V2.0) of China Meteorological Bureau [data set], http://data.cma.cn/dataService/cdcindex/datacode/NAFP_CLDAS2.0_NRT/show_value/normal.html (last access: 22 September 2025), 2017.
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.
Dawson, D. T., Xue, M., Milbrandt, J. A., and Shapiro, A.: Sensitivity of real-data simulations of the 3 May 1999 Oklahoma City tornadic supercell and associated tornadoes to multimoment microphysics, Part I: Storm-and tornado-scale numerical forecasts, Mon. Weather Rev., 143, 2241–2265, https://doi.org/10.1175/MWR-D-14-00279.1, 2015.
Dowell, D. C., Wicker, L. J., and Snyder, C.: Ensemble Kalman filter assimilation of radar observations of the 8 May 2003 Oklahoma City supercell: Influences of reflectivity observations on storm-scale analyses, Mon. Weather Rev., 139, 272–294, https://doi.org/10.1175/2010MWR3438.1, 2011.
Dudhia, J.: Numerical study of convection observed during the winter monsoon experiment using a mesoscale, two-dimensional model, J. Atmos. Sci., 46, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2, 1989.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res.-Oceans, 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994.
Gao, J. and Stensrud, D. J.: Assimilation of reflectivity data in a convective-scale, cycled 3DVAR framework with hydrometeor classification, J. Atmos. Sci., 69, 1054–1065, https://doi.org/10.1175/JAS-D-11-0162.1, 2012.
Gao, J., Xue, M., Brewster, K., and Droegemeier, K. K.: A three-dimensional variational data analysis method with recursive filter for Doppler radars, J. Atmos. Ocean. Tech., 21, 457–469, https://doi.org/10.1175/1520-0426(2004)021<0457:ATVDAM>2.0.CO;2, 2004.
Gustafsson, N., Janjić, T., Schraff, C., Leuenberger, D., Weissmann, M., Reich, H., Brousseau, P., Montmerle, T., Wattrelot, E., Bučánek, A., Mile, M., Hamdi, R., Lindskog, M., Barkmeijer, J., Dahlbom, M., Macpherson, B., Ballard, S., Inverarity, G., Carley, J., Alexander, C., Dowell, D., Liu, S., Ikuta, Y., and Fujita, T.: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres, Q. J. Roy. Meteor. Soc., 144, 1218–1256, https://doi.org/10.1002/qj.3179, 2017.
Hong, S. Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
Hu, M., Xue, M., and Brewster, K.: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part I: Cloud analysis and its impact, Mon. Weather Rev., 134, 675–698, https://doi.org/10.1175/MWR3092.1, 2006.
Huang, L., Xu, D., Li, H., Jiang, L., and Shu, A.: Assimilating FY-3D MWHS2 Radiance Data to Predict Typhoon Muifa Based on Different Initial Background Conditions and Fast Radiative Transfer Models, Remote Sens., 15, 3220, https://doi.org/10.3390/rs15133220, 2023.
Kain, J. S., Xue, M., Coniglio, M. C., Weiss, S. J., Kong, F., Jensen, T. L., Brown, B. G., Gao, J., Brewster, K., Thomas, K. W., Wang, Y., Schwartz, C S., and Levit, J. J.: Assessing advances in the assimilation of radar data and other mesoscale observations within a collaborative forecasting-research environment, Weather Forecast., 25, 1510–1521, https://doi.org/10.1175/2010WAF2222405.1, 2010.
Kong, R., Xue, M., and Liu, C.: Development of a hybrid En3DVar data assimilation system and comparisons with 3DVar and EnKF for radar data assimilation with observing system simulation experiments, Mon. Weather Rev., 146, 175–198, https://doi.org/10.1175/MWR-D-17-0164.1, 2018.
Kong, R., Xue, M., Liu, C., and Jung, Y. : Comparisons of hybrid En3DVar with 3DVar and EnKF for radar data assimilation: Tests with the 10 May 2010 Oklahoma tornado outbreak, Mon. Weather Rev., 149, 21–40, https://doi.org/10.1175/MWR-D-20-0053.1, 2020.
Li, X., Ming, J., Wang, Y., Zhao, K., and Xue, M.: Assimilation of T-TREC-retrieved wind data with WRF 3DVAR for the short-term forecasting of typhoon Meranti (2010) near landfall, J. Geophys. Res.-Atmos., 118, 10–361, https://doi.org/10.1002/jgrd.50815, 2013.
Li, X., Zeng, M., Wang, Y., Wang, W., Wu, H., and Mei, H.: Evaluation of two momentum control variable schemes and their impact on the variational assimilation of radar wind data: Case study of a squall line, Adv. Atmos. Sci., 33, 1143–1157, https://doi.org/10.1007/s00376-016-5255-3, 2016.
Lindskog, M., Salonen, K., Järvinen, H., and Michelson, D. B.: Doppler radar wind data assimilation with HIRLAM 3DVAR, Mon. Weather Rev., 132, 1081–1092, https://doi.org/10.1175/1520-0493(2004)132<1081:DRWDAW>2.0.CO;2, 2004.
Lilly, D. K.: Numerical prediction of thunderstorms-Has its time come?, Q. J. Roy. Meteor. Soc., 116, 779–798, https://doi.org/10.1002/qj.49711649402, 1990.
Liu, C., Xue, M., and Kong, R.: Direct assimilation of radar reflectivity data using 3DVAR: Treatment of hydrometeor background errors and OSSE tests, Mon. Weather Rev., 147, 17–29, https://doi.org/10.1175/MWR-D-18-0033.1, 2019.
Lopez, P.: Direct 4D-Var assimilation of NCEP stage IV radar and gauge precipitation data at ECMWF, Mon. Weather Rev., 139, 2098–2116, https://doi.org/10.1175/2010MWR3565.1, 2011.
Mlawer, E., Taubman, S., Brown, P., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive, updated daily, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D65D8PWK, 2015.
Navon, I. M.: Data assimilation for numerical weather prediction: a review. Data assimilation for atmospheric, Oceanic and hydrologic applications, Springer, 21–65, https://doi.org/10.1007/978-3-540-71056-1_2, 2009.
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.
Schenkman, A. D., Xue, M., Shapiro, A., Brewster, K., and Gao, J.: The analysis and prediction of the 8-9 May 2007 Oklahoma tornadic mesoscale convective system by assimilating WSR-88D and CASA radar data using 3DVAR, Mon. Weather Rev., 139, 224–246, https://doi.org/10.1175/2010MWR3336.1, 2011.
Shen, F., Min, J., and Xu, D.: Assimilation of radar radial velocity data with the WRF Hybrid ETKF–3DVAR system for the prediction of Hurricane Ike (2008), Atmos. Res., 169, 127–138, https://doi.org/10.1016/j.atmosres.2015.09.019, 2016.
Shen, F., Xu, D., and Min, J.: Effect of momentum control variables on assimilating radar observations for the analysis and forecast for Typhoon Chanthu (2010), Atmos. Res., 234, 104771, https://doi.org/10.1016/j.atmosres.2019.104622, 2019.
Shen, F., Xu, D., Li, H., and Liu, R.: Impact of radar data assimilation on a squall line over the Yangtze–Huaihe River Basin with a radar reflectivity operator accounting for ice-phase hydrometeors, Meteorol. Appl., 28, e1967, https://doi.org/10.1002/met.1967, 2020a.
Shen, F., Xu, D., Min, J., Chu, Z., and Li, X.: Assimilation of radar radial velocity data with the WRF hybrid 4DEnVar system for the prediction of hurricane Ike (2008), Atmos. Res., 234, 104771, https://doi.org/10.1016/j.atmosres.2019.104771, 2020b.
Shen, F., Min, J., Li, H., Xu, D., Shu, A., Zhai, D., Guo, Y., and Song, L.: Applications of radar data assimilation with hydrometeor control variables within the WRFDA on the prediction of landfalling hurricane IKE (2008), Atmosphere, 12, 853, https://doi.org/10.3390/atmos12070853, 2021.
Shen, F., Song, L., Li, H., He, Z., and Xu, D.: Effects of different momentum control variables in radar data assimilation on the analysis and forecast of strong convective systems under the background of northeast cold vortex, Atmos. Res., 280, 106415, https://doi.org/10.1016/j.atmosres.2022.106415, 2022.
Shen, F., Shu, A., Min, J., Wu, Z., Wang, Y., Xu, D., Chen, J., and Wan, S.: Assimilation of dual-pol radar KDP observations with the GSI ensemble Kalman filter for the analysis and prediction of a squall line, J. Geophys. Res.-Atmos., 130, e2024JD041933, https://doi.org/10.1029/2024JD041933, 2025a.
Shen, F., Wan, S., Li, H., Luo, J., He, Z., Fei, H., Song, L., Sun, Q., Xu, D., and Chen, J.: Data assimilation of weather radar reflectivity with a blending hydrometer retrieval scheme for two convective storms in East China, Atmos. Res., 321, 108110, https://doi.org/10.1016/j.atmosres.2025.108110, 2025b.
Simonin, D., Ballard, S. P., and Li, Z.: Doppler radar radial wind assimilation using an hourly cycling 3D-Var with a 1.5 km resolution version of the Met Office Unified Model for nowcasting, Q. J. Roy. Meteor. Soc., 140, 2298–2314, https://doi.org/10.1002/qj.2298, 2014.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A Description of the Advanced Research WRF Version 4, NCAR Tech. Note NCAR/TN-556+STR [code], https://doi.org/10.5065/1dfh-6p97 (last access: 22 September 2025), 2019.
Sun, J. and Crook, N. A.: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments, J. Atmos. Sci., 54, 1642–1661, https://doi.org/10.1175/1520-0469(1997)054<1642:DAMRFD>2.0.CO;2, 1997.
Sun, J., Xue, M., Wilson, J. W., Zawadzki, I., Ballard, S. P., Onvlee-Hooimeyer, J., Joe, P., Barker, D. M., Li, W. P., Golding, B., Xu, M., and Pinto, J.: Use of NWP for nowcasting convective precipitation: Recent progress and challenges, B. Am. Meteor. Soc., 95, 409–426, https://doi.org/10.1175/BAMS-D-11-00263.1, 2014.
Tong, C. C., Jung, Y., Xue, M., and Liu, C.: Direct assimilation of radar data with ensemble Kalman filter and hybrid ensemble-variational method in the National Weather Service operational data assimilation system GSI for the stand-alone regional FV3 model at a convection-allowing resolution, Geophys. Res. Lett., 47, e2020GL090179, https://doi.org/10.1029/2020GL090179, 2020.
Tong, M. and Xue, M.: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments, Mon. Weather Rev., 133, 1789–1807, https://doi.org/10.1175/MWR2898.1, 2005.
Wan, S., Shen, F., Chen, J., Liu, L., Dong, D., and He, Z.: Evaluation of Two Momentum Control Variable Schemes in Radar Data Assimilation and Their Impact on the Analysis and Forecast of a Snowfall Case in Central and Eastern China, Atmosphere, 15, 342, https://doi.org/10.3390/atmos15030342, 2024.
Wang, H., Sun, J., Fan, S., and Huang, X.: Indirect assimilation of radar reflectivity with WRF 3D-Var and its impact on prediction of four summertime convective events, J. Appl. Meteor. Clim., 52, 889–902, https://doi.org/10.1175/JAMC-D-12-0120.1, 2013a.
Wang, H., Sun, J., Zhang, X., Huang, X. Y., and Auligné, T.: Radar data assimilation with WRF 4D-Var. Part I: System development and preliminary testing, Mon. Weather Rev., 141, 2224–2244, https://doi.org/10.1175/MWR-D-12-00168.1, 2013b.
Xiao, Q., Kuo, Y. H., Sun, J., Lee, W. C., Lim, E., Guo, Y. R., and Barker, D. M.: Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case, J. Appl. Meteor., 44, 768–788, https://doi.org/10.1175/JAM2248.1, 2005.
Xiao, Q., Kuo, Y. H., Sun, J., Lee, W. C., Barker, D. M., and Lim, E.: An approach of radar reflectivity data assimilation and its assessment with the inland QPF of Typhoon Rusa (2002) at landfall, J. Appl. Meteor. Clim., 46, 14–22, https://doi.org/10.1175/JAM2439.1, 2007.
Xu, D., Shen, F., and Min, J.: Effect of adding hydrometeor mixing ratios control variables on assimilating radar observations for the analysis and forecast of a typhoon, Atmosphere, 10, 415, https://doi.org/10.3390/atmos10070415, 2019.
Xu, D., Shu, A., Li, H., Shen, F., Li, Q., and Su, H.: Effects of Assimilating Clear-Sky FY-3D MWHS2 Radiance on the Numerical Simulation of Tropical Storm Ampil, Remote Sens., 13, 2873, https://doi.org/10.3390/rs13152873, 2021.
Xu, D., Yang, G., Wu, Z., Shen, F., Li, H., and Zhai, D.: Evaluate radar data assimilation in two momentum control variables and the effect on the forecast of southwest China vortex precipitation, Remote Sens., 14, 3460, https://doi.org/10.3390/rs14143460, 2022.
Zhao, K., Li, X., Xue, M., Jou, B. J. D., and Lee, W. C.: Short-term forecasting through intermittent assimilation of data from Taiwan and mainland China coastal radars for Typhoon Meranti (2010) at landfall, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2011JD017109, 2012.
Short summary
When retrieving hydrometeors from reflectivity, there are two methods to allocate hydrometeor types: temperature-based and background hydrometer-dependent schemes. The temperature-based method divides hydrometeor proportions based on the background temperature, while the other scheme calculates average weights of each hydrometeor in various reflectivity intervals from background fields. The blending scheme adaptively combines these methods and is found to improve precipitation forecast accuracy.
When retrieving hydrometeors from reflectivity, there are two methods to allocate hydrometeor...
Altmetrics
Final-revised paper
Preprint