• Alpert, J. C., and V. K. Kumar, 2007: Radial wind super-obs from the WSR-88D radars in the NCEP operational assimilation system. Mon. Wea. Rev., 135, 10901109, https://doi.org/10.1175/MWR3324.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barker, D. M., W. Huang, Y. R. Guo, and Q. N. Xiao, 2004: A three-dimensional (3DVAR) data assimilation system for use with MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897914, https://doi.org/10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bick, T., and Coauthors, 2016: Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the convective scale. Quart. J. Roy. Meteor. Soc., 142, 14901504, https://doi.org/10.1002/qj.2751.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., X. Liang, and H. Ma, 2017: Application of IVAP-based observation operator in radar radial velocity assimilation: The case of Typhoon Fitow. Mon. Wea. Rev., 145, 41874203, https://doi.org/10.1175/MWR-D-17-0002.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • CMA, 2014: China Climate Impact Assessment. China Meteorological Press.

  • Daley, R., 1993: Atmospheric Data Analysis. Cambridge University Press, 457 pp.

  • Dong, J., and M. Xue, 2013: Assimilation of radial velocity and reflectivity data from coastal WSR-88D radars using an ensemble Kalman filter for the analysis and forecast of landfalling Hurricane Ike (2008). Quart. J. Roy. Meteor. Soc., 139, 467487, https://doi.org/10.1002/qj.1970.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duan, Y., and Coauthors, 2019: Landfalling Tropical Cyclone Research Project (LTCRP) in China. Bull. Amer. Meteor. Soc., 100, ES447ES472, https://doi.org/10.1175/BAMS-D-18-0241.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 30773107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gustafsson, N., and Coauthors, 2018: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres. Quart. J. Roy. Meteor. Soc., 144, 12181256, https://doi.org/10.1002/qj.3179.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hara, T., and Coauthors, 2013: The operational convection-permitting regional model at JMA. Res. Act. Atmos. Oceanic Model., 43, 05050506, https://www.wcrp-climate.org/WGNE/BlueBook/2013/individual-articles/05_Hara_Tabito_wgne_2013_lfm.pdf.

    • Search Google Scholar
    • Export Citation
  • Honda, T., and Coauthors, 2018: Assimilating all-sky Himawari-8 satellite infrared radiances: A case of Typhoon Soudelor (2015). Mon. Wea. Rev., 146, 213229, https://doi.org/10.1175/MWR-D-16-0357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103120, https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjić, T., and Coauthors, 2018: On the representation error in data assimilation. Quart. J. Roy. Meteor. Soc., 144, 12571278, https://doi.org/10.1002/qj.3130.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, P., and M. V. Shukla, 2019: Assimilating insat-3D thermal infrared window imager observation with the particle filter: A case study for Vardah cyclone. J. Geophys. Res. Atmos., 124, 18971911, https://doi.org/10.1029/2018JD028827.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, Y., C. D. Kummerow, and M. Zupanski, 2018: Impacts of assimilating vertical velocity, latent heating, or hydrometeor water contents retrieved from a single reflectivity data set. J. Geophys. Res. Atmos., 123, 16731693, https://doi.org/10.1002/2017JD027637.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., and Z. Pu, 2014: Numerical simulations of the genesis of Typhoon Nuri (2008): Sensitivity to initial conditions and implications for the roles of intense convection and moisture conditions. Wea. Forecasting, 29, 14021424, https://doi.org/10.1175/WAF-D-14-00003.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., Z. Pu, J. Sun, and W.-C. Lee, 2014: Impacts of 4DVAR assimilation of airborne Doppler radar observations on numerical simulations of the genesis of Typhoon Nuri (2008). J. Appl. Meteor. Climatol., 53, 23252343, https://doi.org/10.1175/JAMC-D-14-0046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lippi, D. E., J. R. Carley, and D. T. Kleist, 2019: Improvements to the assimilation of Doppler radial winds for convection-permitting forecasts of a heavy rain event. Mon. Wea. Rev., 147, 36093632, https://doi.org/10.1175/MWR-D-18-0411.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, J., T. Feng, J. Li, Z. Cai, X. Xu, L. Li, and J. Li, 2019: Impact of assimilating Himawari-8-derived layered precipitable water with varying cumulus and microphysics parameterization schemes on the simulation of Typhoon Hato. J. Geophys. Res. Atmos., 124, 30503071, https://doi.org/10.1029/2018JD029364.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meng, Z., and F. Zhang, 2007: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II: Imperfect model experiments. Mon. Wea. Rev., 135, 14031423, https://doi.org/10.1175/MWR3352.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meng, Z., and F. Zhang, 2008a: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV: Comparison with 3DVAR in a month-long experiment. Mon. Wea. Rev., 136, 36713682, https://doi.org/10.1175/2008MWR2270.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meng, Z., and F. Zhang, 2008b: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III: Comparison with 3DVAR in a real-data case study. Mon. Wea. Rev., 136, 522540, https://doi.org/10.1175/2007MWR2106.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montmerle, T., and C. Faccani, 2009: Mesoscale assimilation of radial velocities from Doppler radars in a preoperational framework. Mon. Wea. Rev., 137, 19391953, https://doi.org/10.1175/2008MWR2725.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noh, Y., W. G. Cheon, S. Y. Hong, and S. Raasch, 2003: Improvement of the k-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteor., 107, 401427, https://doi.org/10.1023/A:1022146015946.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pu, Z., X. Li, and J. Sun, 2009: Impact of airborne Doppler radar data assimilation on the numerical simulation of intensity changes of Hurricane Dennis near a landfall. J. Atmos. Sci., 66, 33513365, https://doi.org/10.1175/2009JAS3121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, R. F., and Coauthors, 2013: NOAA’s hurricane intensity forecasting experiment: A progress report. Bull. Amer. Meteor. Soc., 94, 859882, https://doi.org/10.1175/BAMS-D-12-00089.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salonen, K., H. Järvinen, G. Haase, S. Niemelä, and R. Eresmaa, 2009: Doppler radar radial winds in HIRLAM. Part II: Optimizing the super-observation processing. Tellus, 61A, 288295, https://doi.org/10.1111/j.1600-0870.2008.00381.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shao, H., and Coauthors, 2016: Bridging research to operations transitions: Status and plans of community GSI. Bull. Amer. Meteor. Soc., 97, 14271440, https://doi.org/10.1175/BAMS-D-13-00245.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shen, Y., P. Zhao, Y. Pan, and J. Yu, 2014: A high spatiotemporal gauge-satellite merged precipitation analysis over China. J. Geophys. Res. Atmos., 119, 30633075, https://doi.org/10.1002/2013JD020686.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simonin, D., S. P. Ballard, and Z. Li, 2014: 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. Quart. J. Roy. Meteor. Soc., 140, 22982314, https://doi.org/10.1002/qj.2298.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waller, J. A., E. Bauernschubert, S. L. Dance, N. K. Nichols, R. Potthast, and D. Simonin, 2019: Observation error statistics for Doppler radar radial wind superobservations assimilated into the DWD COSMO-KENDA system. Mon. Wea. Rev., 147, 33513364, https://doi.org/10.1175/MWR-D-19-0104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., J. Sun, S. Fan, and X.-Y. Huang, 2013a: Indirect assimilation of radar reflectivity with WRF 3D-VAR and its impact on prediction of four summertime convective events. J. Appl. Meteor. Climatol., 52, 889902, https://doi.org/10.1175/JAMC-D-12-0120.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., J. Sun, X. Zhang, X.-Y. Huang, and T. Auligné, 2013b: Radar data assimilation with WRF 4D-VAR. Part I: System development and preliminary testing. Mon. Wea. Rev., 141, 22242244, https://doi.org/10.1175/MWR-D-12-00168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weng, Y., and F. Zhang, 2012: Assimilating airborne Doppler radar observations with an ensemble Kalman filter for convection-permitting hurricane initialization and prediction: Katrina (2005). Mon. Wea. Rev., 140, 841859, https://doi.org/10.1175/2011MWR3602.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weng, Y., M. Zhang, and F. Zhang, 2011: Advanced data assimilation for cloud-resolving hurricane initialization and prediction. Comput. Sci. Eng., 13, 4049, https://doi.org/10.1109/MCSE.2011.18.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiao, Q., and Coauthors, 2008: Doppler radar data assimilation in KMA’s operational forecasting. Bull. Amer. Meteor. Soc., 89, 3944, https://doi.org/10.1175/BAMS-89-1-39.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiao, Y., Y. Wan, and Z. Wang, 2016: Quality control of dual PRF velocity data for Doppler weather radars (in Chinese). Plateau Meteor., 35, 11121122.

    • Search Google Scholar
    • Export Citation
  • Xue, M., K. K. Droegemeier, and V. Wong, 2000: The Advanced Regional Prediction System (ARPS)—A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification. Meteor. Atmos. Phys., 75, 161193, https://doi.org/10.1007/s007030070003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, M., and Coauthors, 2001: The Advanced Regional Prediction System (ARPS)—A multi-scale nonhydrostatic atmospheric simulation and prediction tool. Part II: Model physics and applications. Meteor. Atmos. Phys., 76, 143165, https://doi.org/10.1007/s007030170027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, M., D. Wang, J. Gao, K. Brewster, and K. K. Droegemeier, 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82, 139170, https://doi.org/10.1007/s00703-001-0595-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ye, M., J. Wu, C. Wang, and X. He, 2019: Historical and future changes in asset value and GOP in areas exposed to tropical cyclones in China. Wea. Climate Soc., 11, 307319, https://doi.org/10.1175/WCAS-D-18-0053.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yue, J., and Z. Meng, 2017: Impact of assimilating Taiwan’s coastal radar radial velocity on forecasting Typhoon Morakot (2009) in southeastern China using a WRF-based ENKF. Sci. China Earth Sci., 60, 315327, https://doi.org/10.1007/s11430-015-0259-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yue, J., Z. Meng, C.-K. Yu, and L.-W. Cheng, 2017: Impact of coastal radar observability on the forecast of the track and rainfall of Typhoon Morakot (2009) using WRF-based ensemble Kalman filter data assimilation. Adv. Atmos. Sci., 34, 6678, https://doi.org/10.1007/s00376-016-6028-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., and Y. Weng, 2015: Predicting hurricane intensity and associated hazards: A five-year real-time forecast experiment with assimilation of airborne Doppler radar observations. Bull. Amer. Meteor. Soc., 96, 2533, https://doi.org/10.1175/BAMS-D-13-00231.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., C. Snyder, and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132, 12381253, https://doi.org/10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., Z. Meng, and A. Aksoy, 2006: Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part I: Perfect model experiments. Mon. Wea. Rev., 134, 722736, https://doi.org/10.1175/MWR3101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., Y. Weng, J. A. Sippel, Z. Meng, and C. H. Bishop, 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 21052125, https://doi.org/10.1175/2009MWR2645.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., Y. Weng, J. F. Gamache, and F. D. Marks, 2011: Performance of convection-permitting hurricane initialization and prediction during 2008-2010 with ensemble data assimilation of inner-core airborne Doppler radar observations. Geophys. Res. Lett., 38, L15810, https://doi.org/10.1029/2011GL048469.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., M. Minamide, R. G. Nystrom, X. Chen, S.-J. Lin, and L. M. Harris, 2019: Improving Harvey forecasts with next-generation weather satellites: Advanced hurricane analysis and prediction with assimilation of GOES-R all-sky radiances. Bull. Amer. Meteor. Soc., 100, 12171222, https://doi.org/10.1175/BAMS-D-18-0149.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, H., and Z. Pu, 2014: Influence of assimilating surface observations on numerical prediction of landfalls of Hurricane Katrina (2005) with an ensemble Kalman filter. Mon. Wea. Rev., 142, 29152934, https://doi.org/10.1175/MWR-D-14-00014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, S., and Z. Pu, 2019: Numerical simulation of rapid weakening of Hurricane Joaquin with assimilation of high-definition sounding system dropsondes during the tropical cyclone intensity experiment: Comparison of three- and four-dimensional ensemble–variational data assimilation. Wea. Forecasting, 34, 521538, https://doi.org/10.1175/WAF-D-18-0151.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, L., and Coauthors, 2016: Prediction and predictability of high-impact western Pacific landfalling Tropical Cyclone Vicente (2012) through convection-permitting ensemble assimilation of Doppler radar velocity. Mon. Wea. Rev., 144, 2143, https://doi.org/10.1175/MWR-D-14-00403.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, X., and J. Zhu, 2004: New generation weather radar network in China (in Chinese). Mater. Sci. Technol., 32, 255258.

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Improved Prediction of Landfalling Tropical Cyclone in China Based on Assimilation of Radar Radial Winds with New Super-Observation Processing

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  • 1 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing, China
  • 2 College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
  • 3 Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, China Meteorological Administration, Guangzhou, China
  • 4 Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah
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Abstract

This work explores the impact of assimilating radial winds from the Chinese coastal Doppler radar on track, intensity, and quantitative precipitation forecasts (QPF) of landfalling tropical cyclones (TCs) in a numerical weather prediction model, focusing mainly on two aspects: 1) developing a new coastal radar super-observation (SO) processing method, namely, an evenly spaced thinning method (ESTM) that is fit for landfalling TCs, and 2) evaluating the performance of the radar radial wind data assimilation in QPFs of landfalling TCs with multiple TC cases. Compared to a previous method of generating SOs (i.e., the radially spaced thinning method), in which the density of SOs is equal within the radial space of a radar scanning volume, the SOs created by ESTM are almost evenly distributed in the horizontal grids of the model background, resulting in more observations located in the TC inner-core region being involved in SOs. The use of SOs from ESTM leads to more cyclonic wind innovation, and larger analysis increments of height and horizontal wind in the lower level in an ensemble Kalman filter data assimilation experiment with TC Mujigae (2015). Overall, forecasts of a TC’s landfalling position, intensity, and QPF are improved by radar data assimilation for all cases, including Mujigae and the other eight TCs that made landfall on the Chinese mainland in 2017. Specifically, through assimilation, TC landing position error and intensity error are reduced by 33% and 25%, respectively. The mean equitable threat score of extreme rainfall [>80 mm (3 h)−1] forecasts is doubled on average over all cases.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yihong Duan, duanyh@cma.gov.cn

Abstract

This work explores the impact of assimilating radial winds from the Chinese coastal Doppler radar on track, intensity, and quantitative precipitation forecasts (QPF) of landfalling tropical cyclones (TCs) in a numerical weather prediction model, focusing mainly on two aspects: 1) developing a new coastal radar super-observation (SO) processing method, namely, an evenly spaced thinning method (ESTM) that is fit for landfalling TCs, and 2) evaluating the performance of the radar radial wind data assimilation in QPFs of landfalling TCs with multiple TC cases. Compared to a previous method of generating SOs (i.e., the radially spaced thinning method), in which the density of SOs is equal within the radial space of a radar scanning volume, the SOs created by ESTM are almost evenly distributed in the horizontal grids of the model background, resulting in more observations located in the TC inner-core region being involved in SOs. The use of SOs from ESTM leads to more cyclonic wind innovation, and larger analysis increments of height and horizontal wind in the lower level in an ensemble Kalman filter data assimilation experiment with TC Mujigae (2015). Overall, forecasts of a TC’s landfalling position, intensity, and QPF are improved by radar data assimilation for all cases, including Mujigae and the other eight TCs that made landfall on the Chinese mainland in 2017. Specifically, through assimilation, TC landing position error and intensity error are reduced by 33% and 25%, respectively. The mean equitable threat score of extreme rainfall [>80 mm (3 h)−1] forecasts is doubled on average over all cases.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yihong Duan, duanyh@cma.gov.cn
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