Evaluation of Microphysics Schemes in Tropical Cyclones Using Polarimetric Radar Observations: Convective Precipitation in an Outer Rainband

Dan Wu Shanghai Typhoon Institute, Shanghai, China
Key Laboratory of Mesoscale Severe Weather/MOE and School of Atmospheric Sciences, Nanjing University, Nanjing, China
State Key Laboratory of Severe Weather and Joint Center for Atmospheric Radar Research of CMA/NJU, Beijing, China
Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Dan Wu in
Current site
Google Scholar
PubMed
Close
,
Fuqing Zhang Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Fuqing Zhang in
Current site
Google Scholar
PubMed
Close
,
Xiaomin Chen Hurricane Research Division, NOAA/AOML, Miami, Florida

Search for other papers by Xiaomin Chen in
Current site
Google Scholar
PubMed
Close
,
Alexander Ryzhkov Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma

Search for other papers by Alexander Ryzhkov in
Current site
Google Scholar
PubMed
Close
,
Kun Zhao Key Laboratory of Mesoscale Severe Weather/MOE and School of Atmospheric Sciences, Nanjing University, Nanjing, China
State Key Laboratory of Severe Weather and Joint Center for Atmospheric Radar Research of CMA/NJU, Beijing, China

Search for other papers by Kun Zhao in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0002-6022-6226
,
Matthew R. Kumjian Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Matthew R. Kumjian in
Current site
Google Scholar
PubMed
Close
,
Xingchao Chen Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Xingchao Chen in
Current site
Google Scholar
PubMed
Close
, and
Pak-Wai Chan Hong Kong Observatory, Kowloon, Hong Kong, China

Search for other papers by Pak-Wai Chan in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Cloud microphysics significantly impact tropical cyclone precipitation. A prior polarimetric radar observational study by the authors revealed the ice-phase microphysical processes as the dominant microphysics mechanisms responsible for the heavy precipitation in the outer rainband of Typhoon Nida (2016). To assess the model performance regarding microphysics, three double-moment microphysics schemes (i.e., Thompson, Morrison, and WDM6) are evaluated by performing a set of simulations of the same case. While these simulations capture the outer rainband’s general structure, microphysics in the outer rainbands are strikingly different from the observations. This discrepancy is primarily attributed to different microphysics parameterizations in these schemes, rather than the differences in large-scale environments due to cloud–environment interactions. An interesting finding in this study is that the surface rain rate or liquid water content is inversely proportional to the simulated mean raindrop sizes. The mass-weighted raindrop diameters are overestimated in the Morrison and Thompson schemes and underestimated in the WDM6 scheme, while the former two schemes produce lower liquid water content than WDM6. Compared with the observed ice water content based on a new polarimetric radar retrieval method, the ice water content above the environmental 0°C level in all simulations is highly underestimated, especially at heights above 12 km MSL where large concentrations of small ice particles are typically prevalent. This finding suggests that the improper treatment of ice-phase processes is potentially an important error source in these microphysics schemes. Another error source identified in the WDM6 scheme is overactive warm-rain processes that produce excessive concentrations of smaller raindrops.

Zhang: Deceased.

© 2021 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: Dr. Kun Zhao, zhaokun@nju.edu.cn

Abstract

Cloud microphysics significantly impact tropical cyclone precipitation. A prior polarimetric radar observational study by the authors revealed the ice-phase microphysical processes as the dominant microphysics mechanisms responsible for the heavy precipitation in the outer rainband of Typhoon Nida (2016). To assess the model performance regarding microphysics, three double-moment microphysics schemes (i.e., Thompson, Morrison, and WDM6) are evaluated by performing a set of simulations of the same case. While these simulations capture the outer rainband’s general structure, microphysics in the outer rainbands are strikingly different from the observations. This discrepancy is primarily attributed to different microphysics parameterizations in these schemes, rather than the differences in large-scale environments due to cloud–environment interactions. An interesting finding in this study is that the surface rain rate or liquid water content is inversely proportional to the simulated mean raindrop sizes. The mass-weighted raindrop diameters are overestimated in the Morrison and Thompson schemes and underestimated in the WDM6 scheme, while the former two schemes produce lower liquid water content than WDM6. Compared with the observed ice water content based on a new polarimetric radar retrieval method, the ice water content above the environmental 0°C level in all simulations is highly underestimated, especially at heights above 12 km MSL where large concentrations of small ice particles are typically prevalent. This finding suggests that the improper treatment of ice-phase processes is potentially an important error source in these microphysics schemes. Another error source identified in the WDM6 scheme is overactive warm-rain processes that produce excessive concentrations of smaller raindrops.

Zhang: Deceased.

© 2021 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: Dr. Kun Zhao, zhaokun@nju.edu.cn
Save
  • Black, R. A., and J. Hallett, 1986: Observations of the distribution of ice in hurricanes. J. Atmos. Sci., 43, 802822, https://doi.org/10.1175/1520-0469(1986)043<0802:OOTDOI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, B. R., M. M. Bell, and A. J. Frambach, 2016: Validation of simulated hurricane drop size distributions using polarimetric radar. Geophys. Res. Lett., 43, 910917, https://doi.org/10.1002/2015GL067278.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, X., Y. Wang, K. Zhao, and D. Wu, 2017: A numerical study on rapid intensification of Typhoon Vicente (2012) in the South China Sea. Part I: Verification of simulation, storm-scale evolution, and environmental contribution. Mon. Wea. Rev., 145, 877898, https://doi.org/10.1175/MWR-D-16-0147.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., M. Mainelli, L. K. Shay, J. A. Knaff, and J. Kaplan, 2005: Further improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS). Wea. Forecasting, 20, 531543, https://doi.org/10.1175/WAF862.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Didlake, A. C., Jr., and M. R. Kumjian, 2017: Examining polarimetric radar observations of bulk microphysical structures and their relation to vortex kinematics in Hurricane Arthur (2014). Mon. Wea. Rev., 145, 45214541, https://doi.org/10.1175/MWR-D-17-0035.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Didlake, A. C., Jr., and M. R. Kumjian, 2018: Examining storm asymmetries in Hurricane Irma (2017) using polarimetric radar observations. Geophys. Res. Lett., 45, 13 51313 522, https://doi.org/10.1029/2018GL080739.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doviak, R. J., and D. S. Zrnić, 1993: Doppler Radar & Weather Observations. 2nd ed. Academic Press, 562 pp.

  • Duan, Y., and J. Liu, 2010: TC precipitation (QPE/QPF) and related inland flood modeling. Seventh Int. Workshop on Tropical Cyclones (IWTC-VII), La Réunion, France, World Meteorological Organization, 79 pp., https://library.wmo.int/doc_num.php?explnum_id=9780.

  • 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
  • Feng, Y.-C., and M. M. Bell, 2019: Microphysical characteristics of an asymmetric eyewall in major Hurricane Harvey (2017). Geophys. Res. Lett., 46, 461471, https://doi.org/10.1029/2018GL080770.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Field, P. R., and Coauthors, 2017: Secondary ice production: Current state of the science and recommendations for the future. Ice Formation and Evolution in Clouds and Precipitation: Measurement and Modeling Challenges, Meteor. Monogr., No. 58, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0014.1.

    • Crossref
    • Export Citation
  • Franklin, J. L., C. J. McAdie, and M. B. Lawrence, 2003: Trends in track forecasting for tropical cyclones threatening the United States, 1970–2001. Bull. Amer. Meteor. Soc., 84, 11971204, https://doi.org/10.1175/BAMS-84-9-1197.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harrington, J. Y., K. Sulia, and H. Morrison, 2013: A method for adaptive habit prediction in bulk microphysical models. Part I: Theoretical development. J. Atmos. Sci., 70, 349364, https://doi.org/10.1175/JAS-D-12-040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, https://doi.org/10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 2010: Clouds in tropical cyclones. Mon. Wea. Rev., 138, 293344, https://doi.org/10.1175/2009MWR2989.1.

  • Huang, H., G. Zhang, K. Zhao, and S. E. Giangrande, 2017: A hybrid method to estimate specific differential phase and rainfall with linear programming and physics constraints. IEEE Trans. Geosci. Remote Sens., 55, 96111, https://doi.org/10.1109/TGRS.2016.2596295.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jung, Y., M. Xue, and G. Zhang, 2010: Simulations of polarimetric radar signatures of a supercell storm using a two-moment bulk microphysics scheme. J. Appl. Meteor. Climatol., 49, 146163, https://doi.org/10.1175/2009JAMC2178.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 24, Amer. Meteor. Soc., 165170.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalina, E. A., and Coauthors, 2017: The ice water paths of small and large ice species in Hurricanes Arthur (2014) and Irene (2011). J. Appl. Meteor. Climatol., 56, 13831404, https://doi.org/10.1175/JAMC-D-16-0300.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., 2013: Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables. J. Oper. Meteor., 1, 226242, https://doi.org/10.15191/nwajom.2013.0119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and A. V. Ryzhkov, 2010: The impact of evaporation on polarimetric characteristics of rain: Theoretical model and practical implications. J. Appl. Meteor. Climatol., 49, 12471267, https://doi.org/10.1175/2010JAMC2243.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and O. P. Prat, 2014: The impact of raindrop collisional processes on the polarimetric radar variables. J. Atmos. Sci., 71, 30523067, https://doi.org/10.1175/JAS-D-13-0357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and K. A. Lombardo, 2017: Insights into the evolving microphysical and kinematic structure of Northeastern U.S. winter storms from dual-polarization Doppler radar. Mon. Wea. Rev., 145, 10331061, https://doi.org/10.1175/MWR-D-15-0451.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., A. P. Khain, N. Benmoshe, E. Ilotoviz, A. V. Ryzhkov, and V. T. J. Phillips, 2014: The anatomy and physics of ZDR columns: Investigating a polarimetric radar signature with a spectral bin microphysical model. J. Appl. Meteor. Climatol., 53, 18201843, https://doi.org/10.1175/JAMC-D-13-0354.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., C. P. Martinkus, O. P. Prat, S. Collis, M. van Lier-Walqui, and H. C. Morrison, 2019: A moment-based polarimetric radar forward operator for rain microphysics. J. Appl. Meteor. Climatol., 58, 113130, https://doi.org/10.1175/JAMC-D-18-0121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 15871612, https://doi.org/10.1175/2009MWR2968.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • May, P. T., J. D. Kepert, and T. D. Keenan, 2008: Polarimetric radar observations of the persistently asymmetric structure of Tropical Cyclone Ingrid. Mon. Wea. Rev., 136, 616630, https://doi.org/10.1175/2007MWR2077.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., and R. A. Black, 2004: Observations of particle size and phase in tropical cyclones: Implications for mesoscale modeling of microphysical processes. J. Atmos. Sci., 61, 422439, https://doi.org/10.1175/1520-0469(2004)061<0422:OOPSAP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., H. Zhang, G. Heymsfield, J. B. Halverson, R. Hood, J. Dudhia, and F. Marks Jr., 2006: Factors affecting the evolution of Hurricane Erin (2001) and the distributions of hydrometeors: Role of microphysical processes. J. Atmos. Sci., 63, 127150, https://doi.org/10.1175/JAS3590.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., B. F. Jewett, M. S. Gilmore, S. W. Nesbitt, and T.-L. Hsieh, 2012: Vertical velocity and microphysical distributions related to rapid intensification in a simulation of Hurricane Dennis (2005). J. Atmos. Sci., 69, 35153534, https://doi.org/10.1175/JAS-D-12-016.1.

    • 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, 1666316682, http://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mohr, C. G., L. Jay Miller, R. L. Vaughan, and H. W. Frank, 1986: The merger of mesoscale datasets into a common Cartesian format for efficient and systematic analyses. J. Atmos. Oceanic Technol., 3, 143161, https://doi.org/10.1175/1520-0426(1986)003<0143:TMOMDI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 9911007, https://doi.org/10.1175/2008MWR2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oue, M., P. Kollias, A. Shapiro, A. Tatarevic, and T. Matsui, 2019: Investigation of observational error sources in multi-Doppler-radar three-dimensional variational vertical air motion retrievals. Atmos. Meas. Tech., 12, 19992018, https://doi.org/10.5194/amt-12-1999-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oye, R., C. Mueller, and S. Smith, 1995: Software for radar translation, visualization, editing, and interpolation. Preprints, 27th Conf. on Radar Meteorology, Vail, CO, Amer. Meteor. Soc., 359–361.

  • Ray, P. S., K. K. Wagner, K. W. Johnson, J. J. Stephens, W. C. Bumgarner, and E. A. Mueller, 1978: Triple-Doppler observations of a convective storm. J. Appl. Meteor., 17, 12011212, https://doi.org/10.1175/1520-0450(1978)017<1201:TDOOAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A., P. Bukovcic, A. Murphy, P. Zhang, and G. McFarquhar, 2018: Ice microphysical retrievals using polarimetric radar data. 10th European Conf. on Radar in Meteorology and Hydrology, Netherlands, KNMI, 40, https://projects.knmi.nl/erad2018/ERAD2018_extended_abstract_040.pdf.

  • Ryzhkov, A. V., J. Snyder, J. T. Carlin, A. Khain, and M. Pinsky, 2020: What polarimetric weather radars offer to cloud modelers: Forward radar operators and microphysical/thermodynamic retrievals. Atmosphere, 11, 362, https://doi.org/10.3390/atmos11040362.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schrom, R. S., M. R. Kumjian, and Y. Lu, 2015: Polarimetric radar signatures of dendritic growth zones within Colorado winter storms. J. Appl. Meteor. Climatol., 54, 23652388, https://doi.org/10.1175/JAMC-D-15-0004.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seliga, T. A., and V. N. Bringi, 1976: Potential use of radar differential reflectivity measurements at orthogonal polarizations for measuring precipitation. J. Appl. Meteor., 15, 6976, https://doi.org/10.1175/1520-0450(1976)015<0069:PUORDR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Steiner, M., R. A. Houze Jr., and S. E. Yuter, 1995: Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data. J. Appl. Meteor., 34, 19782007, https://doi.org/10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, https://doi.org/10.1175/2008MWR2387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Lier-Walqui, M., and Coauthors, 2016: On polarimetric radar signatures of deep convection for model evaluation: Columns of specific differential phase observed during MC3E. Mon. Wea. Rev., 144, 737758, https://doi.org/10.1175/MWR-D-15-0100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, M., K. Zhao, M. Xue, G. Zhang, S. Liu, L. Wen, and G. Chen, 2016: Precipitation microphysics characteristics of a Typhoon Matmo (2014) rainband after landfall over Eastern China based on polarimetric radar observations. J. Geophys. Res. Atmos., 121, 12 41512 433, https://doi.org/10.1002/2016JD025307.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, D., and Coauthors, 2018: Kinematics and microphysics of convection in the outer rainband of Typhoon Nida (2016) revealed by polarimetric radar. Mon. Wea. Rev., 146, 21472159, https://doi.org/10.1175/MWR-D-17-0320.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., 2016: Weather Radar Polarimetry. CRC Press, 304 pp.

All Time Past Year Past 30 Days
Abstract Views 647 0 0
Full Text Views 1332 392 38
PDF Downloads 1193 323 27