Assessing the Impacts of Different WRF Precipitation Physics in Hurricane Simulations

Nasrin Nasrollahi Center for Hydrometeorology and Remote Sensing, University of California, Irvine, Irvine, California

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Amir AghaKouchak Center for Hydrometeorology and Remote Sensing, University of California, Irvine, Irvine, California

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Jialun Li Center for Hydrometeorology and Remote Sensing, University of California, Irvine, Irvine, California

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Xiaogang Gao Center for Hydrometeorology and Remote Sensing, University of California, Irvine, Irvine, California

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Kuolin Hsu Center for Hydrometeorology and Remote Sensing, University of California, Irvine, Irvine, California

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Soroosh Sorooshian Center for Hydrometeorology and Remote Sensing, University of California, Irvine, Irvine, California

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Abstract

Numerical weather prediction models play a major role in weather forecasting, especially in cases of extreme events. The Weather Research and Forecasting Model (WRF), among others, is extensively used for both research and practical applications. Previous studies have highlighted the sensitivity of this model to microphysics and cumulus schemes. This study investigated the performance of the WRF in forecasting precipitation, hurricane track, and landfall time using various microphysics and cumulus schemes. A total of 20 combinations of microphysics and cumulus schemes were used, and the model outputs were validated against ground-based observations. While the choice of microphysics and cumulus schemes can significantly impact model output, it is not the case that any single combination can be considered “ideal” for modeling all characteristics of a hurricane, including precipitation amount, areal extent, hurricane track, and the time of landfall. For example, the model’s ability to simulate precipitation (with the least total bias) is best achieved using Betts–Miller–Janjić (BMJ) cumulus parameterization in combination with the WRF single-moment five-class microphysics scheme (WSM5). It was determined that the WSM5–BMJ, WSM3 (the three-class version of the WSM scheme)–BMJ, and Ferrier microphysics in combination with the Grell–Devenyi cumulus scheme were the best combinations for simulation of the landfall time. However, the hurricane track was best estimated using the Lin et al. and Kessler microphysics options with BMJ cumulus parameterization. Contrary to previous studies, these results indicated that the use of cumulus schemes improves model outputs when the grid size is smaller than 10 km. However, it was found that many of the differences between parameterization schemes may be well within the uncertainty of the measurements.

Corresponding author address: Nasrin Nasrollahi, Center for Hydrometeorology and Remote Sensing, Dept. of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA 92617. E-mail: nasrin.n@uci.edu

Abstract

Numerical weather prediction models play a major role in weather forecasting, especially in cases of extreme events. The Weather Research and Forecasting Model (WRF), among others, is extensively used for both research and practical applications. Previous studies have highlighted the sensitivity of this model to microphysics and cumulus schemes. This study investigated the performance of the WRF in forecasting precipitation, hurricane track, and landfall time using various microphysics and cumulus schemes. A total of 20 combinations of microphysics and cumulus schemes were used, and the model outputs were validated against ground-based observations. While the choice of microphysics and cumulus schemes can significantly impact model output, it is not the case that any single combination can be considered “ideal” for modeling all characteristics of a hurricane, including precipitation amount, areal extent, hurricane track, and the time of landfall. For example, the model’s ability to simulate precipitation (with the least total bias) is best achieved using Betts–Miller–Janjić (BMJ) cumulus parameterization in combination with the WRF single-moment five-class microphysics scheme (WSM5). It was determined that the WSM5–BMJ, WSM3 (the three-class version of the WSM scheme)–BMJ, and Ferrier microphysics in combination with the Grell–Devenyi cumulus scheme were the best combinations for simulation of the landfall time. However, the hurricane track was best estimated using the Lin et al. and Kessler microphysics options with BMJ cumulus parameterization. Contrary to previous studies, these results indicated that the use of cumulus schemes improves model outputs when the grid size is smaller than 10 km. However, it was found that many of the differences between parameterization schemes may be well within the uncertainty of the measurements.

Corresponding author address: Nasrin Nasrollahi, Center for Hydrometeorology and Remote Sensing, Dept. of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA 92617. E-mail: nasrin.n@uci.edu
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  • AghaKouchak, A., Bárdossy A. , and Habib E. , 2010a: Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula. Adv. Water Resour., 33, 624634.

    • Search Google Scholar
    • Export Citation
  • AghaKouchak, A., Bárdossy A. , and Habib E. , 2010b: Copula-based uncertainty modeling: Application to multi-sensor precipitation estimates. Hydrol. Processes, 24, 21112124.

    • Search Google Scholar
    • Export Citation
  • AghaKouchak, A., Habib E. , and Bárdossy A. , 2010c: Modeling radar rainfall estimation uncertainties: Random error model. J. Hydrol. Eng., 15, 265274.

    • Search Google Scholar
    • Export Citation
  • AghaKouchak, A., Nasrollahi N. , Li J. , Imam B. , and Sorooshian S. , 2011: Geometrical characterization of precipitation patterns. J. Hydrometeor., 12, 274285.

    • Search Google Scholar
    • Export Citation
  • Arnaud, P., Bouvier C. , Cisner L. , and Dominguez R. , 2002: Influence of rainfall spatial variability on flood prediction. J. Hydrol., 260, 216230.

    • Search Google Scholar
    • Export Citation
  • Chen, F., and Dudhia J. , 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569585.

    • Search Google Scholar
    • Export Citation
  • Ciach, G., Krajewski W. , and Villarini G. , 2007: Product-error-driven uncertainty model for probabilistic quantitative precipitation estimation with NEXRAD data. J. Hydrometeor., 8, 13251347.

    • Search Google Scholar
    • Export Citation
  • Corradini, C., and Singh V. , 1985: Effect of spatial variability of effective rainfall on direct runoff by geomorphologic approach. J. Hydrol., 81, 2743.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Ferrier, B. S., 1994: A double-moment multiple-phase four-class bulk ice scheme. Part I: Description. J. Atmos. Sci., 51, 249280.

  • Fiener, P., and Auerswald K. , 2009: Spatial variability of rainfall on a sub-kilometre scale. Earth Surf. Processes Landforms, 34, 848859.

    • Search Google Scholar
    • Export Citation
  • Fovell, R., 2006: Impact of microphysics on hurricane track and intensity forecasts. Seventh WRF Users’ Workshop, Boulder, CO, National Center of Atmospheric Research, 3.2. [Available online at http://www.mmm.ucar.edu/wrf/users/workshops/WS2006/abstracts/Session03/3_2_Fovell.pdf.]

  • Fovell, R., Corbosiero K. , and Kuo H.-C. , 2010: Influence of cloud-radiative feedback on tropical cyclone motion symmetric contributions. Preprints, 29th Conf. on Hurricanes and Tropical Meteorology, Tucson, AZ, Amer. Meteor. Soc., 13C.5. [Available online at http://ams.confex.com/ams/pdfpapers/168859.pdf.]

  • Gallus, W., Jr., 1999: Eta simulations of three extreme rainfall events: Impacts of resolution and choice of convective scheme. Wea. Forecasting, 14, 405426.

    • Search Google Scholar
    • Export Citation
  • Goodrich, D., Faures J. , Woolhiser D. , Lane L. , and Sorooshian S. , 1995: Measurement and analysis of small-scale convective storm rainfall variability. J. Hydrol., 173, 283308.

    • Search Google Scholar
    • Export Citation
  • Grell, G., and Devenyi D. , 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 16931696.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., and Dudhia J. , 2003: Testing of a new non-local boundary layer vertical diffusion scheme in numerical weather prediction applications. Preprints, 20th Conf. on Weather Analysis and Forecasting/16th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 17.3. [Available online at http://ams.confex.com/ams/pdfpapers/72744.pdf.]

  • Hong, S.-Y., Dudhia J. , and Chen S.-H. , 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103120.

    • Search Google Scholar
    • Export Citation
  • Hong, Y., Hsu K. , Moradkhani H. , and Sorooshian S. , 2006: Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response. Water Resour. Res., 42, W08421, doi:10.1029/2005WR004398.

    • Search Google Scholar
    • Export Citation
  • Janjić, Z., 1994: The step-mountain eta coordinate model: Further development of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927945.

    • Search Google Scholar
    • Export Citation
  • Jankov, I., Schultz P. , Anderson C. , and Koch S. , 2007: The impact of different physical parameterizations and their interactions on cold season QPF in the American River basin. J. Hydrometeor., 8, 11411151.

    • Search Google Scholar
    • Export Citation
  • Kain, J., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181.

  • Kain, J., and Fritsch J. , 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 27842802.

    • Search Google Scholar
    • Export Citation
  • Kain, J., and Fritsch J. , 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170.

    • Search Google Scholar
    • Export Citation
  • Kessler, E., 1969: On the Distribution and Continuity of Water Substance in Atmospheric Circulation. Meteor. Monogr., No. 32, Amer. Meteor. Soc., 84 pp.

  • Krajewski, W., and Smith J. , 2002: Radar hydrology: Rainfall estimation. J. Hydrol., 25, 13871394.

  • Li, X., and Pu Z. , 2009: Sensitivity of numerical simulations of the early rapid intensification of Hurricane Emily to cumulus parameterization schemes in different model horizontal resolutions. J. Meteor. Soc. Japan, 87, 403421.

    • Search Google Scholar
    • Export Citation
  • Lin, Y., and Mitchell K. , 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. Preprints, 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at http://ams.confex.com/ams/pdfpapers/83847.pdf.]

  • Lin, Y.-L., Rarley R. , and Orville H. , 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 10651092.

    • Search Google Scholar
    • Export Citation
  • Lowrey, M., and Yang Z. , 2008: Assessing the capability of a regional-scale weather model to simulate extreme precipitation patterns and flooding in central Texas. Wea. Forecasting, 23, 11021126.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E., Taubman S. , Brown P. , and Iacono M. , 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 (D14), 16 66316 682.

    • Search Google Scholar
    • Export Citation
  • Mölders, N., 2008: Suitability of the Weather Research and Forecasting (WRF) model to predict the June 2005 fire weather for interior Alaska. Wea. Forecasting, 23, 953973.

    • Search Google Scholar
    • Export Citation
  • NHC, 2007: November 2005 Atlantic tropical weather summary. NOAA/National Hurricane Center, 33 pp. [Available online at http://www.nhc.noaa.gov/pdf/TCR-AL182005_Rita.pdf.]

  • Olson, D. A., Junker N. , and Korty B. , 1995: Evaluation of 33 years of quantitative precipitation forecasting. Wea. Forecasting, 10, 498511.

    • Search Google Scholar
    • Export Citation
  • Seed, A., and Srikanthan R. , 1999: A space and time model for design storm rainfall. J. Geophys. Res., 104 (D24), 31 62331 630.

  • Seo, D.-J., Breidenbach, and J. , Miller D. , 1999: Real-time adjustments of mean field and range-dependent biases in WSR-88d rainfall estimation. Preprints, 15th Int. Conf. on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Dallas, TX, Amer. Meteor. Soc., 5.20.

  • Skamarock, W. C., and Weisman M. L. , 2009: The impact of positive-definite moisture transport on NWP precipitation forecasts. Mon. Wea. Rev., 137, 488494.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., Klemp J. , Dudhia J. , Gill D. , Barker D. , Wang W. , and Powers J. , 2007: A description of the advanced research WRF version 2. NCAR Tech. Note NCAR/TN-468+STR, 88 pp.

  • Steiner, M., and Smith J. , 2000: Reflectivity, rain rate, and kinetic energy flux relationships based on raindrop spectra. J. Appl. Meteor., 39, 19231940.

    • Search Google Scholar
    • Export Citation
  • Vie, B., Nuissier O. , and Ducrocq V. , 2011: Cloud-resolving ensemble simulations of Mediterranean heavy precipitation events: Uncertainty on initial condition and lateral boundary condition. Mon. Wea. Rev., 139, 403419.

    • Search Google Scholar
    • Export Citation
  • Wang, W., and Seaman N. , 1997: A comparison study of convective schemes in a mesoscale model. Mon. Wea. Rev., 125, 252278.

  • Wang, X., Zhong Z. , Hu J. , and Yuan H. , 2010: Effect of lateral boundary scheme on the simulation of tropical cyclone track in regional climate model RegCM3. Asia-Pac. J. Atmos. Sci., 46, 221230.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., Odins A. , and Nielsen-Gammon J. , 2006: Mesoscale predictability of an extreme warm-season precipitation event. Wea. Forecasting, 21, 149166.

    • Search Google Scholar
    • Export Citation
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