Dynamical Precipitation Downscaling for Hydrologic Applications Using WRF 4D-Var Data Assimilation: Implications for GPM Era

Liao-Fan Lin School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia

Search for other papers by Liao-Fan Lin in
Current site
Google Scholar
PubMed
Close
,
Ardeshir M. Ebtehaj School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia

Search for other papers by Ardeshir M. Ebtehaj in
Current site
Google Scholar
PubMed
Close
,
Rafael L. Bras School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia

Search for other papers by Rafael L. Bras in
Current site
Google Scholar
PubMed
Close
,
Alejandro N. Flores Department of Geosciences, Boise State University, Boise, Idaho

Search for other papers by Alejandro N. Flores in
Current site
Google Scholar
PubMed
Close
, and
Jingfeng Wang School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia

Search for other papers by Jingfeng Wang in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.

Corresponding author address: Liao-Fan Lin, School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Dr., Atlanta, GA 30332-0355. E-mail: liaofan.lin@gatech.edu

Abstract

The objective of this study is to develop a framework for dynamically downscaling spaceborne precipitation products using the Weather Research and Forecasting (WRF) Model with four-dimensional variational data assimilation (4D-Var). Numerical experiments have been conducted to 1) understand the sensitivity of precipitation downscaling through point-scale precipitation data assimilation and 2) investigate the impact of seasonality and associated changes in precipitation-generating mechanisms on the quality of spatiotemporal downscaling of precipitation. The point-scale experiment suggests that assimilating precipitation can significantly affect the precipitation analysis, forecast, and downscaling. Because of occasional overestimation or underestimation of small-scale summertime precipitation extremes, the numerical experiments presented here demonstrate that the wintertime assimilation produces downscaled precipitation estimates that are in closer agreement with the reference National Centers for Environmental Prediction stage IV dataset than similar summertime experiments. This study concludes that the WRF 4D-Var system is able to effectively downscale a 6-h precipitation product with a spatial resolution of 20 km to hourly precipitation with a spatial resolution of less than 10 km in grid spacing—relevant to finescale hydrologic applications for the era of the Global Precipitation Measurement mission.

Corresponding author address: Liao-Fan Lin, School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Dr., Atlanta, GA 30332-0355. E-mail: liaofan.lin@gatech.edu
Save
  • Barker, D., Huang W. , Guo Y.-R. , Bourgeois A. J. , and Xiao N. , 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897914, doi:10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Barker, D., and Coauthors, 2012: The Weather Research and Forecasting (WRF) Model’s community variational/ensemble data assimilation system: WRFDA. Bull. Amer. Meteor. Soc., 93, 831843, doi:10.1175/BAMS-D-11-00167.1.

    • Search Google Scholar
    • Export Citation
  • Bauer, P., Lopez P. , Salmond D. , Benedetti A. , Saarinen S. , and Bonazzola M. , 2006a: Implementation of 1D+4D-Var assimilation of precipitation-affected microwave radiances at ECMWF. I 1D-Var. Quart. J. Roy. Meteor. Soc., 132, 22772306, doi:10.1256/qj.05.189.

    • Search Google Scholar
    • Export Citation
  • Bauer, P., Lopez P. , Salmond D. , Benedetti A. , Saarinen S. , and Bonazzola M. , 2006b: Implementation of 1D+4D-Var assimilation of precipitation-affected microwave radiances at ECMWF. II 4D-Var. Quart. J. Roy. Meteor. Soc., 132, 23072332, doi:10.1256/qj.06.07.

    • Search Google Scholar
    • Export Citation
  • Bauer, P., Geer A. J. , Lopez P. , and Salmond D. , 2010: Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation. Quart. J. Roy. Meteor. Soc., 136, 18681885, doi:10.1002/qj.659.

    • Search Google Scholar
    • Export Citation
  • Bauer, P., Ohring G. , Kummerow C. , and Auligne T. , 2011a: Assimilating satellite observations of clouds and precipitation into NWP models. Bull. Amer. Meteor. Soc., 92 (Suppl.), ES25ES28, doi:10.1175/2011BAMS3182.1.

    • Search Google Scholar
    • Export Citation
  • Bauer, P., and Coauthors, 2011b: Satellite cloud and precipitation assimilation at operational NWP centres. Quart. J. Roy. Meteor. Soc., 137, 19341951, doi:10.1002/qj.905.

    • Search Google Scholar
    • Export Citation
  • Case, J. L., Kumar S. V. , Srikishen J. , and Jedlovec G. J. , 2011: Improving numerical weather predictions of summertime precipitation over the southeastern United States through a high-resolution initialization of the surface state. Wea. Forecasting, 26, 785807, doi:10.1175/2011WAF2222455.1.

    • Search Google Scholar
    • Export Citation
  • Chambon, P., Zhang S. Q. , Hou A. Y. , Zupanski M. , and Cheung S. , 2014: Assessing the impact of pre-GPM microwave precipitation observations in the Goddard WRF ensemble data assimilation system. Quart. J. Roy. Meteor. Soc., 140, 12191235, doi:10.1002/qj.2215.

    • 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, doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chen, F., Warner T. T. , and Manning K. , 2001: Sensitivity of orographic moist convection to landscape variability: A study of the Buffalo Creek, Colorado, flash flood case of 1996. J. Atmos. Sci., 58, 32043223, doi:10.1175/1520-0469(2001)058<3204:SOOMCT>2.0.CO;2.

    • 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, doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Flores, A. N., Bras R. L. , and Entekhabi D. , 2012: Hydrologic data assimilation with a hillslope-scale resolving model and L-band radar observations: Synthetic experiments with the ensemble Kalman filter. Water Resour. Res., 48, W08509, doi:10.1029/2011WR011500.

    • Search Google Scholar
    • Export Citation
  • Fowler, H. J., Blenkinsop S. , and Tebaldi C. , 2007: Linking climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modelling. Int. J. Climatol., 27, 15471578, doi:10.1002/joc.1556.

    • Search Google Scholar
    • Export Citation
  • Geer, A. J., Bauer P. , and Lopez P. , 2010: Direct 4D-Var assimilation of all-sky radiances. Part II: Assessment. Quart. J. Roy. Meteor. Soc., 136, 18861905, doi:10.1002/qj.681.

    • Search Google Scholar
    • Export Citation
  • Gutmann, E. D., Rasmussen R. M. , Liu C. , Ikeda K. , Gochis D. J. , Clark M. P. , Dudhia J. , and Thompson G. , 2012: A comparison of statistical and dynamical downscaling of winter precipitation over complex terrain. J. Climate, 25, 262281, doi:10.1175/2011JCLI4109.1.

    • Search Google Scholar
    • Export Citation
  • Ha, J.-H., and Lee D.-K. , 2012: Effect of length scale tuning of background error in WRF-3DVAR system on assimilation of high-resolution surface data for heavy rainfall simulation. Adv. Atmos. Sci., 29, 11421158, doi:10.1007/s00376-012-1183-z.

    • Search Google Scholar
    • Export Citation
  • Ha, J.-H., Kim H.-W. , and Lee D.-K. , 2011: Observation and numerical simulations with radar and surface data assimilation for heavy rainfall over central Korea. Adv. Atmos. Sci., 28, 573590, doi:10.1007/s00376-010-0035-y.

    • Search Google Scholar
    • Export Citation
  • Hellstrom, C., Chen D. , Achberger C. , and Raisanen J. , 2001: Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation. Climate Res., 19, 4555, doi:10.3354/cr019045.

    • Search Google Scholar
    • Export Citation
  • 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, doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.

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

    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., Skofronick-Jackson G. , Kummerow C. D. , and Shepherd J. M. , 2008: Global precipitation measurement. Precipitation: Advances in Measurement, Estimation, and Prediction, S. Michaelides, Ed., Springer-Verlag, 131–169.

  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701722, doi:10.1175/BAMS-D-13-00164.1.

    • Search Google Scholar
    • Export Citation
  • Hsiao, L.-F., Chen D.-S. , Kuo Y.-H. , Guo Y.-R. , Yeh T.-C. , Hong J.-S. , Fong C.-T. , and Lee C.-S. , 2012: Application of WRF 3DVAR to operational typhoon prediction in Taiwan: Impact of outer loop and partial cycling approaches. Wea. Forecasting, 27, 12491263, doi:10.1175/WAF-D-11-00131.1.

    • Search Google Scholar
    • Export Citation
  • Huang, X.-Y., and Coauthors, 2009: Four-dimensional variational data assimilation for WRF: Formula and preliminary results. Mon. Wea. Rev., 137, 299314, doi:10.1175/2008MWR2577.1.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and Fritsch J. M. , 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 27842802, doi:10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Koizumi, K., Ishikawa Y. , and Tsuyuki T. , 2005: Assimilation of precipitation data to the JMA mesoscale model with a four-dimensional variational method and its impact on precipitation forecasts. SOLA, 1, 4548, doi:10.2151/sola.2005-013.

    • Search Google Scholar
    • Export Citation
  • Lin, Y., and Mitchell K. E. , 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 https://ams.confex.com/ams/pdfpapers/83847.pdf.]

  • Liu, Z., Schwartz C. S. , Snyder C. , and Ha S. , 2012: Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area ensemble Kalman filter. Mon. Wea. Rev., 140, 40174034, doi:10.1175/MWR-D-12-00083.1.

    • Search Google Scholar
    • Export Citation
  • Lopez, P., 2011: Direct 4D-Var assimilation of NCEP stage IV radar and gauge precipitation data at ECMWF. Mon. Wea. Rev., 139, 20982116, doi:10.1175/2010MWR3565.1.

    • Search Google Scholar
    • Export Citation
  • Lopez, P., and Bauer P. , 2007: “1D+4DVAR” assimilation of NCEP stage-IV radar and gauge hourly precipitation data at ECMWF. Mon. Wea. Rev., 135, 25062524, doi:10.1175/MWR3409.1.

    • Search Google Scholar
    • Export Citation
  • Margulis, S. A., McLaughlin D. , Entekhabi D. , and Dunne S. , 2002: Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 field experiment. Water Resour. Res., 38, 1299, doi:10.1029/2001WR001114.

    • Search Google Scholar
    • Export Citation
  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360, doi:10.1175/BAMS-87-3-343.

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

    • Search Google Scholar
    • Export Citation
  • Parrish, D. F., and Derber J. C. , 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 17471763, doi:10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Peters-Lidard, C. D., Kumar S. V. , Mocko D. M. , and Tian Y. , 2011: Estimating evapotranspiration with land data assimilation systems. Hydrol. Processes, 25, 39793992, doi:10.1002/hyp.8387.

    • Search Google Scholar
    • Export Citation
  • Routray, A., Mohanty U. C. , Niyogi D. , Rizvi S. R. H. , and Osuri K. K. , 2010: Simulation of heavy rainfall events over Indian monsoon region using WRF-3DVAR data assimilation system. Meteor. Atmos. Phys., 106, 107125, doi:10.1007/s00703-009-0054-3.

    • Search Google Scholar
    • Export Citation
  • Schmidli, J., Goodess C. M. , Frei C. , Haylock M. R. , Hundecha Y. , Ribalaygua J. , and Schmith T. , 2007: Statistical and dynamical downscaling of precipitation: An evaluation and comparison of scenarios for the European Alps. J. Geophys. Res., 112, D04105,doi:10.1029/2005JD007026.

    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., Liu Z. , Chen Y. , and Huang X. , 2012: Impact of assimilating microwave radiances with a limited-area ensemble data assimilation system on forecasts of typhoon Morakot. Wea. Forecasting, 27, 424437, doi:10.1175/WAF-D-11-00033.1.

    • 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., doi:10.5065/D68S4MVH.

  • Tsuyuki, T., 1996a: Variational data assimilation in the tropics using precipitation data. Part I: Column model. Meteor. Atmos. Phys., 60, 87104, doi:10.1007/BF01029787.

    • Search Google Scholar
    • Export Citation
  • Tsuyuki, T., 1996b: Variational data assimilation in the tropics using precipitation data. Part II: 3D model. Mon. Wea. Rev., 124, 25452561, doi:10.1175/1520-0493(1996)124<2545:VDAITT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tsuyuki, T., 1997: Variational data assimilation in the tropics using precipitation data. Part III: Assimilation of SSM/I precipitation rates. Mon. Wea. Rev., 125, 14471464, doi:10.1175/1520-0493(1997)125<1447:VDAITT>2.0.CO;2.

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

    • Search Google Scholar
    • Export Citation
  • Xu, J., and Powell A. M. , 2012: Dynamic downscaling precipitation over South Asia: Impact of radiance data assimilation on the forecasts of the WRF-ARW Model. Atmos. Res., 111, 90113, doi:10.1016/j.atmosres.2012.03.005.

    • Search Google Scholar
    • Export Citation
  • Zhang, S. Q., Zupanski M. , Hou A. Y. , Lin X. , and Cheung S. H. , 2013: Assimilation of precipitation-affected radiances in a cloud-resolving WRF ensemble data assimilation system. Mon. Wea. Rev., 141, 754772, doi:10.1175/MWR-D-12-00055.1.

    • Search Google Scholar
    • Export Citation
  • Zupanski, D., and Mesinger F. , 1995: Four-dimensional variational assimilation of precipitation data. Mon. Wea. Rev., 123, 11121127, doi:10.1175/1520-0493(1995)123<1112:FDVAOP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zupanski, D., Zhang S. Q. , Zupanski M. , Hou A. Y. , and Cheung S. H. , 2011: A prototype WRF-based ensemble data assimilation system for dynamically downscaling satellite precipitation observations. J. Hydrometeor., 12, 118134, doi:10.1175/2010JHM1271.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 748 207 10
PDF Downloads 452 117 7