Improving Simulations of Warm Rain in a Bulk Microphysics Scheme

Robert Conrick aDepartment of Atmospheric Sciences, University of Washington, Seattle, Washington

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Clifford F. Mass aDepartment of Atmospheric Sciences, University of Washington, Seattle, Washington

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Lynn McMurdie aDepartment of Atmospheric Sciences, University of Washington, Seattle, Washington

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Abstract

Current bulk microphysical parameterization schemes underpredict precipitation intensities and drop size distributions (DSDs) during warm rain periods, particularly upwind of coastal terrain. To help address this deficiency, this study introduces a set of modifications, called RCON, to the liquid-phase (warm rain) parameterization currently used in the Thompson–Eidhammer microphysical parameterization scheme. RCON introduces several model modifications, motivated by evaluating simulations from a bin scheme, which together result in more accurate precipitation simulations during periods of warm rain. Among the most significant changes are 1) the use of a wider cloud water DSD of lognormal shape instead of the gamma DSD used by the Thompson–Eidhammer parameterization and 2) enhancement of the cloud-to-rain autoconversion parameterization. Evaluation of RCON is performed for two warm rain events and an extended period during the Olympic Mountains Experiment (OLYMPEX) field campaign of winter 2015/16. We show that RCON modifications produce more realistic precipitation distributions and rain DSDs than the default Thompson–Eidhammer configuration. For the multimonth OLYMPEX period, we show that rain rates, rainwater mixing ratios, and raindrop number concentrations were increased relative to the Thompson–Eidhammer microphysical parameterization, while concurrently decreasing raindrop diameters in liquid-phase clouds. These changes are consistent with an increase in simulated warm rain. Finally, real-time evaluation of the scheme from August 2021 to August 2022 demonstrated improved precipitation prediction over coastal areas of the Pacific Northwest.

Significance Statement

Although the accurate simulation of warm rain is critical to forecasting the hydrology of coastal areas and windward slopes, many warm rain parameterizations underpredict precipitation in these locations. This study introduces and evaluates modifications to the Thompson–Eidhammer microphysics parameterization scheme that significantly improve the accuracy of rainfall prediction in those regions.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the The Olympic Mountains Experiment (OLYMPEX) Special Collection.

Corresponding author: Robert Conrick, robert.conrick@gmail.com

Abstract

Current bulk microphysical parameterization schemes underpredict precipitation intensities and drop size distributions (DSDs) during warm rain periods, particularly upwind of coastal terrain. To help address this deficiency, this study introduces a set of modifications, called RCON, to the liquid-phase (warm rain) parameterization currently used in the Thompson–Eidhammer microphysical parameterization scheme. RCON introduces several model modifications, motivated by evaluating simulations from a bin scheme, which together result in more accurate precipitation simulations during periods of warm rain. Among the most significant changes are 1) the use of a wider cloud water DSD of lognormal shape instead of the gamma DSD used by the Thompson–Eidhammer parameterization and 2) enhancement of the cloud-to-rain autoconversion parameterization. Evaluation of RCON is performed for two warm rain events and an extended period during the Olympic Mountains Experiment (OLYMPEX) field campaign of winter 2015/16. We show that RCON modifications produce more realistic precipitation distributions and rain DSDs than the default Thompson–Eidhammer configuration. For the multimonth OLYMPEX period, we show that rain rates, rainwater mixing ratios, and raindrop number concentrations were increased relative to the Thompson–Eidhammer microphysical parameterization, while concurrently decreasing raindrop diameters in liquid-phase clouds. These changes are consistent with an increase in simulated warm rain. Finally, real-time evaluation of the scheme from August 2021 to August 2022 demonstrated improved precipitation prediction over coastal areas of the Pacific Northwest.

Significance Statement

Although the accurate simulation of warm rain is critical to forecasting the hydrology of coastal areas and windward slopes, many warm rain parameterizations underpredict precipitation in these locations. This study introduces and evaluates modifications to the Thompson–Eidhammer microphysics parameterization scheme that significantly improve the accuracy of rainfall prediction in those regions.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the The Olympic Mountains Experiment (OLYMPEX) Special Collection.

Corresponding author: Robert Conrick, robert.conrick@gmail.com

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  • Abel, S. J., and I. A. Boutle, 2012: An improved representation of the raindrop size distribution for single moment microphysics schemes. Quart. J. Roy. Meteor. Soc., 138, 21512162, https://doi.org/10.1002/qj.1949.

    • Search Google Scholar
    • Export Citation
  • Ahmed, T., H.‐G. Jin, and J.‐J. Baik, 2020: A physically based raindrop–cloud droplet accretion parametrization for use in bulk microphysics schemes. Quart. J. Roy. Meteor. Soc., 146, 33683383, https://doi.org/10.1002/qj.3850.

    • Search Google Scholar
    • Export Citation
  • Barros, A. P., P. Shrestha, S. Chavez, and Y. Duan, 2018: Modeling aerosol–cloud–precipitation interactions in mountainous regions: Challenges in the representation of indirect microphysical effects with impacts at subregional scales. Rainfall— Extremes, Distribution, and Properties, J. Abbot and A. Hammond, Eds., IntechOpen, https://doi.org/10.5772/intechopen.80025.

  • Berry, E. X., and R. L. Reinhardt, 1974: An analysis of cloud drop growth by collection. Part IV: A new parameterization. J. Atmos. Sci., 31, 21272135, https://doi.org/10.1175/1520-0469(1974)031<2127:AAOCDG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Brandes, E. A., G. Zhang, and J. Vivekanandan, 2004: Comparison of polarimetric radar drop size distribution retrieval algorithms. J. Atmos. Oceanic Technol., 21, 584598, https://doi.org/10.1175/1520-0426(2004)021<0584:COPRDS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., G.-J. Huang, V. Chandrasekar, and E. Gorgucci, 2002: A methodology for estimating the parameters of a gamma raindrop size distribution model from polarimetric radar data: Application to a squall-line event from the TRMM/Brazil campaign. J. Atmos. Oceanic Technol., 19, 633645, https://doi.org/10.1175/1520-0426(2002)019<0633:AMFETP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Cao, Q., G. Zhang, E. Brandes, T. Schuur, A. Ryzhkov, and K. Ikeda, 2008: Analysis of video disdrometer and polarimetric radar data to characterize rain microphysics in Oklahoma. J. Appl. Meteor. Climatol., 47, 22382255, https://doi.org/10.1175/2008JAMC1732.1.

    • Search Google Scholar
    • Export Citation
  • Colle, B. A., and C. F. Mass, 2000: The 5–9 February 1996 flooding event over the Pacific Northwest: Sensitivity studies and evaluation of the MM5 precipitation forecasts. Mon. Wea. Rev., 128, 593617, https://doi.org/10.1175/1520-0493(2000)128<0593:TFFEOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Colle, B. A., K. J. Westrick, and C. F. Mass, 1999: Evaluation of MM5 and Eta-10 precipitation forecasts over the Pacific Northwest during the cool season. Wea. Forecasting, 14, 137154, https://doi.org/10.1175/1520-0434(1999)014<0137:EOMAEP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Conrick, R., and C. F. Mass, 2019a: Evaluating simulated microphysics during OLYMPEX using GPM satellite observations. J. Atmos. Sci., 76, 10931105, https://doi.org/10.1175/JAS-D-18-0271.1.

    • Search Google Scholar
    • Export Citation
  • Conrick, R., and C. F. Mass, 2019b: An evaluation of simulated precipitation characteristics during OLYMPEX. J. Hydrometeor., 20, 11471164, https://doi.org/10.1175/JHM-D-18-0144.1.

    • Search Google Scholar
    • Export Citation
  • Darby, L. S., A. B. White, D. J. Gottas, and T. Coleman, 2019: An evaluation of integrated water vapor, wind, and precipitation forecasts using water vapor flux observations in the western United States. Wea. Forecasting, 34, 18671888, https://doi.org/10.1175/WAF-D-18-0159.1.

    • Search Google Scholar
    • Export Citation
  • Duan, Y., M. D. Petters, and A. P. Barros, 2019: Understanding aerosol-cloud interactions through modeling the development of orographic cumulus congestus during IPHEx. Atmos. Chem. Phys., 19, 14131437, https://doi.org/10.5194/acp-19-1413-2019.

    • Search Google Scholar
    • Export Citation
  • Garreaud, R., M. Falvey, and A. Montecinos, 2016: Orographic precipitation in coastal southern Chile: Mean distribution, temporal variability, and linear contribution. J. Hydrometeor., 17, 11851202, https://doi.org/10.1175/JHM-D-15-0170.1.

    • Search Google Scholar
    • Export Citation
  • Garvert, M. F., C. P. Woods, B. A. Colle, C. F. Mass, P. V. Hobbs, M. T. Stoelinga, and J. B. Wolfe, 2005a: The 13–14 December 2001 IMPROVE-2 event. Part II: Comparisons of MM5 model simulations of clouds and precipitation with observations. J. Atmos. Sci., 62, 35203534, https://doi.org/10.1175/JAS3551.1.

    • Search Google Scholar
    • Export Citation
  • Geoffroy, O., A. P. Siebesma, and F. Burnet, 2014: Characteristics of the raindrop distributions in RICO shallow cumulus. Atmos. Chem. Phys., 14, 10 89710 909, https://doi.org/10.5194/acp-14-10897-2014.

    • Search Google Scholar
    • Export Citation
  • Gilmore, M. S., and J. M. Straka, 2008: The Berry and Reinhardt autoconversion parameterization: A digest. J. Appl. Meteor. Climatol., 47, 375396, https://doi.org/10.1175/2007JAMC1573.1.

    • Search Google Scholar
    • Export Citation
  • Grabowski, W. W., H. Morrison, S.-I. Shima, G. C. Abade, P. Dziekan, and H. Pawlowska, 2019: Modeling of cloud microphysics: Can we do better? Bull. Amer. Meteor. Soc., 100, 655672, https://doi.org/10.1175/BAMS-D-18-0005.1.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., and S. R. Freitas, 2014: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 14, 52335250, https://doi.org/10.5194/acp-14-5233-2014.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., and Coauthors, 2017: The Olympic Mountains experiment (OLYMPEX). Bull. Amer. Meteor. Soc., 98, 21672188, https://doi.org/10.1175/BAMS-D-16-0182.1.

    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Search Google Scholar
    • Export Citation
  • Khain, A. P., and Coauthors, 2015: Representation of microphysical processes in cloud-resolving models: Spectral (bin) microphysics versus bulk parameterization. Rev. Geophys., 53, 247322, https://doi.org/10.1002/2014RG000468.

    • Search Google Scholar
    • Export Citation
  • Khairoutdinov, M., and Y. Kogan, 2000: A new cloud physics parameterization in a large-eddy simulation model of marine stratocumulus. Mon. Wea. Rev., 128, 229243, https://doi.org/10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Klemp, J. B., 2011: A terrain-following coordinate with smoothed coordinate surfaces. Mon. Wea. Rev., 139, 21632169, https://doi.org/10.1175/MWR-D-10-05046.1.

    • Search Google Scholar
    • Export Citation
  • Lee, H., and J.-J. Baik, 2017: A physically based autoconversion parameterization. J. Atmos. Sci., 74, 15991616, https://doi.org/10.1175/JAS-D-16-0207.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Lin, Y., and B. A. Colle, 2009: The 4–5 December 2001 IMPROVE-2 event: Observed microphysics and comparisons with the Weather Research and Forecasting Model. Mon. Wea. Rev., 137, 13721392, https://doi.org/10.1175/2008MWR2653.1.

    • Search Google Scholar
    • Export Citation
  • Lin, Y., B. A. Colle, and S. E. Yuter, 2013: Impact of moisture flux and freezing level on simulated orographic precipitation errors over the Pacific Northwest. J. Hydrometeor., 14, 140152, https://doi.org/10.1175/JHM-D-12-019.1.

    • Search Google Scholar
    • Export Citation
  • Mansell, E. R., C. L. Ziegler, and E. C. Bruning, 2010: Simulated electrification of a small thunderstorm with two-moment bulk microphysics. J. Atmos. Sci., 67, 171194, https://doi.org/10.1175/2009JAS2965.1.

    • Search Google Scholar
    • Export Citation
  • Massmann, A. K., J. R. Minder, R. D. Garreaud, D. E. Kingsmill, R. A. Valenzuela, A. Montecinos, S. L. Fults, and J. R. Snider, 2017: The Chilean coastal orographic precipitation experiment: Observing the Influence of microphysical rain regimes on coastal orographic precipitation. J. Hydrometeor., 18, 27232743, https://doi.org/10.1175/JHM-D-17-0005.1.

    • Search Google Scholar
    • Export Citation
  • McMurdie, L. A., A. K. Rowe, R. A. Houze Jr., S. R. Brodzik, J. P. Zagrodnik, and T. M. Schuldt, 2018: Terrain-enhanced precipitation processes above the melting layer: Results from OLYMPEX. J. Geophys. Res. Atmos., 123, 12 19412 209, https://doi.org/10.1029/2018JD029161.

    • Search Google Scholar
    • Export Citation
  • Miles, N. L., J. Verlinde, and E. E. Clothiaux, 2000: Cloud droplet size distributions in low-level stratiform clouds. J. Atmos. Sci., 57, 295311, https://doi.org/10.1175/1520-0469(2000)057<0295:CDSDIL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Min, K.-H., S. Choo, D. Lee, and G. Lee, 2015: Evaluation of WRF cloud microphysics schemes using radar observations. Wea. Forecasting, 30, 15711589, https://doi.org/10.1175/WAF-D-14-00095.1.

    • Search Google Scholar
    • Export Citation
  • Minder, J. R., D. R. Durran, G. H. Roe, and A. M. Anders, 2008: The climatology of small-scale orographic precipitation over the Olympic Mountains: Patterns and processes. Quart. J. Roy. Meteor. Soc., 134, 817839, https://doi.org/10.1002/qj.258.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., M. Witte, G. H. Bryan, J. Y. Harrington, and Z. J. Lebo, 2018: Broadening of modeled cloud droplet spectra using bin microphysics in an Eulerian spatial domain. J. Atmos. Sci., 75, 40054030, https://doi.org/10.1175/JAS-D-18-0055.1.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., and Coauthors, 2020a: Confronting the challenge of modeling cloud and precipitation microphysics. J. Adv. Model. Earth Syst., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., M. van Lier-Walqui, M. R. Kumjian, and O. P. Prat, 2020b: A Bayesian approach for statistical-physical bulk parameterization of rain microphysics. Part I: Scheme description. J. Atmos. Sci., 77, 10191041, https://doi.org/10.1175/JAS-D-19-0070.1.

    • Search Google Scholar
    • Export Citation
  • Naeger, A. R., B. A. Colle, N. Zhou, and A. Molthan, 2020: Evaluating warm and cold rain processes in cloud microphysical schemes using OLYMPEX field measurements. Mon. Wea. Rev., 148, 21632190, https://doi.org/10.1175/MWR-D-19-0092.1.

    • Search Google Scholar
    • Export Citation
  • Nickerson, E. C., E. Richard, R. Rosset, and D. R. Smith, 1986: The numerical simulation of clouds, rains and airflow over the Vosges and Black Forest mountains: A meso-β model with parameterized microphysics. Mon. Wea. Rev., 114, 398414, https://doi.org/10.1175/1520-0493(1986)114<0398:TNSOCR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schumacher, V., A. Fernández, F. Justino, and A. Comin, 2020: WRF high resolution dynamical downscaling of precipitation for the Central Andes of Chile and Argentina. Front. Earth Sci., 8, 328, https://doi.org/10.3389/feart.2020.00328.

    • Search Google Scholar
    • Export Citation
  • Seifert, A., and K. D. Beheng, 2001: A double-moment parameterization for simulating autoconversion, accretion and self-collection. Atmos. Res., 5960, 265281, https://doi.org/10.1016/S0169-8095(01)00126-0.

    • Search Google Scholar
    • Export Citation
  • Seifert, A., and K. D. Beheng, 2006: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description. Meteor. Atmos. Phys., 92, 4566, https://doi.org/10.1007/s00703-005-0112-4.

    • Search Google Scholar
    • Export Citation
  • Sena, E. T., A. McComiskey, and G. Feingold, 2016: A long-term study of aerosol–cloud interactions and their radiative effect at the southern Great Plains using ground-based measurements. Atmos. Chem. Phys., 16, 11 30111 318, https://doi.org/10.5194/acp-16-11301-2016.

    • Search Google Scholar
    • Export Citation
  • Shpund, J., and Coauthors, 2019: Simulating a mesoscale convective system using WRF with a new spectral bin microphysics. 1: Hail vs graupel. J. Geophys. Res. Atmos., 124, 14 07214 101, https://doi.org/10.1029/2019JD030576.

    • 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.

  • Song, H.-J., and B.-J. Sohn, 2018: An evaluation of WRF microphysics schemes for simulating the warm-type heavy rain over the Korean Peninsula. Asia-Pac. J. Atmos. Sci., 54, 225236, https://doi.org/10.1007/s13143-018-0006-2.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., and T. Eidhammer, 2014: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J. Atmos. Sci., 71, 36363658, https://doi.org/10.1175/JAS-D-13-0305.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Thurai, M., P. Gatlin, V. N. Bringi, W. Petersen, P. Kennedy, B. Notaroš, and L. Carey, 2017: Toward completing the raindrop size spectrum: Case studies involving 2D-video disdrometer, droplet spectrometer, and polarimetric radar measurements. J. Appl. Meteor. Climatol., 56, 877896, https://doi.org/10.1175/JAMC-D-16-0304.1.

    • Search Google Scholar
    • Export Citation
  • Thurai, M., V. Bringi, P. N. Gatlin, W. A. Petersen, and M. T. Wingo, 2019: Measurements and modeling of the full rain drop size distribution. Atmosphere, 10, 39, https://doi.org/10.3390/atmos10010039.

    • Search Google Scholar
    • Export Citation
  • Wang, L.-P., O. Ayala, B. Rosa, and W. W. Grabowski, 2008: Turbulent collision efficiency of heavy particles relevant to cloud droplets. New J. Phys., 10, 075013, https://doi.org/10.1088/1367-2630/10/7/075013.

    • Search Google Scholar
    • Export Citation
  • Zagrodnik, J. P., L. A. McMurdie, and R. A. Houze, Jr., 2018: Stratiform precipitation processes in cyclones passing over a coastal mountain range. J. Atmos. Sci., 75, 9831004, https://doi.org/10.1175/JAS-D-17-0168.1.

    • Search Google Scholar
    • Export Citation
  • Zagrodnik, J. P., L. A. McMurdie, R. A. Houze, Jr., and S. Tanelli, 2019: Vertical structure and microphysical characteristics of frontal systems passing over a three-dimensional coastal mountain range. J. Atmos. Sci., 76, 15211546, https://doi.org/10.1175/JAS-D-18-0279.1.

    • Search Google Scholar
    • Export Citation
  • Zeng, X., and X. Li, 2020: A two-moment bulk parameterization of the drop collection growth in warm clouds. J. Atmos. Sci., 77, 797811, https://doi.org/10.1175/JAS-D-19-0015.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, G., J. Vivekanandan, E. A. Brandes, R. Meneghini, and T. Kozu, 2003: The shape–slope relation in observed gamma raindrop size distributions: Statistical error or useful information? J. Atmos. Oceanic Technol., 20, 11061119, https://doi.org/10.1175/1520-0426(2003)020<1106:TSRIOG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, G., J. Sun, and E. A. Brandes, 2006: Improving parameterization of rain microphysics with disdrometer and radar observations. J. Atmos. Sci., 63, 12731290, https://doi.org/10.1175/JAS3680.1.

    • Search Google Scholar
    • Export Citation
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