Evaluation of NU-WRF Rainfall Forecasts for IFloodS

Di Wu Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, and Science Systems and Applications, Inc., Lanham, Maryland

Search for other papers by Di Wu in
Current site
Google Scholar
PubMed
Close
,
Christa Peters-Lidard Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Christa Peters-Lidard in
Current site
Google Scholar
PubMed
Close
,
Wei-Kuo Tao Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Wei-Kuo Tao in
Current site
Google Scholar
PubMed
Close
, and
Walter Petersen Code 610.W, NASA Goddard Space Flight Center Wallops Flight Facility, Wallops Island, Virginia

Search for other papers by Walter Petersen in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The Iowa Flood Studies (IFloodS) campaign was conducted in eastern Iowa as a pre-GPM-launch campaign from 1 May to 15 June 2013. During the campaign period, real-time forecasts were conducted utilizing the NASA-Unified Weather Research and Forecasting (NU-WRF) Model to support the daily weather briefing. In this study, two sets of the NU-WRF rainfall forecasts are conducted with different soil initializations, one from the spatially interpolated North American Mesoscale Forecast System (NAM) and the other produced by the Land Information System (LIS) using daily analysis of bias-corrected stage IV data. Both forecasts are then compared with NAM, stage IV, and Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation (QPE) to understand the impact of land surface initialization on the predicted precipitation. In general, both NU-WRF runs are able to reproduce individual peaks of precipitation at the right time. NU-WRF is also able to replicate a better rainfall spatial distribution compared with NAM. Further sensitivity tests show that the high-resolution runs (1 and 3 km) are able to better capture the precipitation event compared to its coarser-resolution counterpart (9 km). Finally, the two sets of NU-WRF simulations produce very close rainfall characteristics in bias, spatial and temporal correlation scores, and probability density function. The land surface initialization does not show a significant impact on short-term rainfall forecast, which is largely because of high soil moisture during the field campaign period.

Corresponding author address: Di Wu, Code 612, NASA Goddard Space Flight Center, 8800 Greenbelt Rd., Greenbelt, MD 20771. E-mail: di.wu@nasa.gov

This article is included in the IFloodS 2013: A Field Campaign to Support the NASA-JAXA Global Precipitation Measurement Mission Special Collection.

Abstract

The Iowa Flood Studies (IFloodS) campaign was conducted in eastern Iowa as a pre-GPM-launch campaign from 1 May to 15 June 2013. During the campaign period, real-time forecasts were conducted utilizing the NASA-Unified Weather Research and Forecasting (NU-WRF) Model to support the daily weather briefing. In this study, two sets of the NU-WRF rainfall forecasts are conducted with different soil initializations, one from the spatially interpolated North American Mesoscale Forecast System (NAM) and the other produced by the Land Information System (LIS) using daily analysis of bias-corrected stage IV data. Both forecasts are then compared with NAM, stage IV, and Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation (QPE) to understand the impact of land surface initialization on the predicted precipitation. In general, both NU-WRF runs are able to reproduce individual peaks of precipitation at the right time. NU-WRF is also able to replicate a better rainfall spatial distribution compared with NAM. Further sensitivity tests show that the high-resolution runs (1 and 3 km) are able to better capture the precipitation event compared to its coarser-resolution counterpart (9 km). Finally, the two sets of NU-WRF simulations produce very close rainfall characteristics in bias, spatial and temporal correlation scores, and probability density function. The land surface initialization does not show a significant impact on short-term rainfall forecast, which is largely because of high soil moisture during the field campaign period.

Corresponding author address: Di Wu, Code 612, NASA Goddard Space Flight Center, 8800 Greenbelt Rd., Greenbelt, MD 20771. E-mail: di.wu@nasa.gov

This article is included in the IFloodS 2013: A Field Campaign to Support the NASA-JAXA Global Precipitation Measurement Mission Special Collection.

Save
  • Arakawa, A., 2004: The cumulus parameterization problem: Past, present, and future. J. Climate, 17, 2493–2525, doi:10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Baldauf, M., Seifert A. , Förstner J. , Majewski D. , Raschendorfer M. , and Reinhardt T. , 2011: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Mon. Wea. Rev., 139, 3887–3905, doi:10.1175/MWR-D-10-05013.1.

    • Search Google Scholar
    • Export Citation
  • Bélair, S., and Mailhot J. , 2001: Impact of horizontal resolution on the numerical simulation of a midlatitude squall line: Implicit versus explicit condensation. Mon. Wea. Rev., 129, 2362–2376, doi:10.1175/1520-0493(2001)129<2362:IOHROT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Betts, A. K., 1986: A new convective adjustment scheme. Part I: Observational and theoretical basis. Quart. J. Roy. Meteor. Soc., 112, 677–691, doi:10.1002/qj.49711247307.

    • Search Google Scholar
    • Export Citation
  • Betts, A. K., and Miller M. J. , 1986: A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets. Quart. J. Roy. Meteor. Soc., 112, 693–709, doi:10.1002/qj.49711247308.

    • Search Google Scholar
    • Export Citation
  • Carbone, R. E., Tuttle J. D. , Ahijevych D. A. , and Trier S. B. , 2002: Inferences of predictability associated with warm season precipitation episodes. J. Atmos. Sci., 59, 2033–2056, doi:10.1175/1520-0469(2002)059<2033:IOPAWW>2.0.CO;2.

    • 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, 785–807, doi:10.1175/2011WAF2222455.1.

    • Search Google Scholar
    • Export Citation
  • Chin, M., Rood R. B. , Lin S.-J. , Muller J. F. , and Thomspon A. M. , 2000: Atmospheric sulfur cycle in the global model GOCART: Model description and global properties. J. Geophys. Res., 105, 24 671–24 687, doi:10.1029/2000JD900384.

    • Search Google Scholar
    • Export Citation
  • Chou, M.-D., and Suarez M. J. , 1999: A solar radiation parameterization for atmospheric studies. Tech. Memo. NASA/TM-1999-104606, Vol. 15, 38 pp. [Available online at http://gmao.gsfc.nasa.gov/pubs/docs/Chou136.pdf.]

  • Cosgrove, B. A., and Coauthors, 2003a: Land surface model spin-up behavior in the North American Land Data Assimilation System (NLDAS). J. Geophys. Res., 108, 8845, doi:10.1029/2002JD003316.

    • Search Google Scholar
    • Export Citation
  • Crago, R., 1996: Conservation and variability of the evaporative fraction during the daytime. J. Hydrol., 180, 173–194, doi:10.1016/0022-1694(95)02903-6.

    • Search Google Scholar
    • Export Citation
  • Crago, R., and Brutsaert W. , 1996: Daytime evaporation and the self-preservation of the evaporative fraction and the Bowen ratio. J. Hydrol., 178, 241–255, doi:10.1016/0022-1694(95)02803-X.

    • Search Google Scholar
    • Export Citation
  • Cunha, L. K., Smith J. A. , Krajewski W. F. , Baeck M. L. , and Seo B.-C. , 2015: NEXRAD NWS polarimetric precipitation product evaluation for IFloodS. J. Hydrometeor., 16, 1676–1699, doi:10.1175/JHM-D-14-0148.1.

  • Cuo, L., Pagano T. C. , and Wang Q. J. , 2011: A review of quantitative precipitation forecasts and their use in short- to medium-range streamflow forecasting. J. Hydrometeor., 12, 713–729, doi:10.1175/2011JHM1347.1.

    • Search Google Scholar
    • Export Citation
  • Done, J., Davis C. A. , and Weisman M. , 2004: The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecasting (WRF) Model. Atmos. Sci. Lett., 5, 110–117, doi:10.1002/asl.72.

  • Ebert, E. E., and McBride J. L. , 2000: Verification of precipitation in weather systems: Determination of systematic errors. J. Hydrol., 239, 179–202, doi:10.1016/S0022-1694(00)00343-7.

  • Ek, M. B., Mitchell K. E. , Lin Y. , Rogers E. , Grunmann P. , Koren V. , Gayno G. , and Tarpley J. D. , 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Fritsch, J. M., and Carbone R. E. , 2004: Improving quantitative precipitation forecasts in the warm season: A USWRP research and development strategy. Bull. Amer. Meteor. Soc., 85, 955–965, doi:10.1175/BAMS-85-7-955.

  • Gallus, W. A., Jr., 1999: Eta simulations of three extreme rainfall events: Impact of resolution and choice of convective scheme. Wea. Forecasting, 14, 405–426, doi:10.1175/1520-0434(1999)014<0405:ESOTEP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gallus, W. A., Jr., and Segal M. , 2000: Sensitivity of forecast rainfall in a Texas convective system to soil moisture and convective scheme. Wea. Forecasting, 15, 509–526, doi:10.1175/1520-0434(2000)015<0509:SOFRIA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gochis, D. J., Shuttleworth W. J. , and Yang Z.-L. , 2002: Sensitivity of the modeled North American monsoon regional climate to convective parameterization. Mon. Wea. Rev., 130, 1282–1298, doi:10.1175/1520-0493(2002)130<1282:SOTMNA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev., 121, 764–787, doi:10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2.

  • Grell, G. A., and Dévényi D. , 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 1693, doi:10.1029/2002GL015311.

    • Search Google Scholar
    • Export Citation
  • Iowa–Cedar Watershed Interagency Coordination Team, 2015: Iowa Cedar Watershed. Accessed 10 November 2015. [Available online at http://iowacedarbasin.org/.]

  • Iowa Flood Center, 2013: IFC-2013 progress report. Accessed 10 November 2015. [Available online at http://iowafloodcenter.org/wordpress/wp-content/uploads/2011/09/IFC-2013ProgressReport.pdf.]

  • Kalb, M. W., 1987: The role of convective parameterization in the simulation of a Gulf Coast precipitation system. Mon. Wea. Rev., 115, 214–234, doi:10.1175/1520-0493(1987)115<0214:TROCPI>2.0.CO;2.

  • Koster, R. D., Suarez M. J. , Higgins R. W. , and Van den Dool H. M. , 2003: Observational evidence that soil moisture variations affect precipitation. Geophys. Res. Lett., 30, 1241, doi:10.1029/2002GL016571.

  • Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927–945, doi:10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.

  • Jankov, I., Gallus W. A. Jr., Segal M. , Shaw B. , and Koch S. E. , 2005: The impact of different WRF Model physical parameterizations and their interactions on warm season MCS rainfall. Wea. Forecasting, 20, 1048–1060, doi:10.1175/WAF888.1.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and Coauthors, 2008: Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Wea. Forecasting, 23, 931–952, doi:10.1175/WAF2007106.1.

    • Search Google Scholar
    • Export Citation
  • Kumar, S. V., and Coauthors, 2006: Land Information System: An interoperable framework for high resolution land surface modeling. Environ. Modell. Software, 21, 1402–1415, doi:10.1016/j.envsoft.2005.07.004.

    • Search Google Scholar
    • Export Citation
  • Lang, S. E., Tao W.-K. , Cifelli R. , Olson W. , Halverson J. , Rutledge S. , and Simpson J. , 2007: Improving simulations of convective system from TRMM LBA: Easterly and westerly regimes. J. Atmos. Sci., 64, 1141–1164, doi:10.1175/JAS3879.1.

    • Search Google Scholar
    • Export Citation
  • Lang, S. E., Tao W.-K. , Zeng X. , and Li Y. , 2011: Reducing the biases in simulated radar reflectivities from a bulk microphysics scheme: Tropical convective systems. J. Atmos. Sci., 68, 2306–2320, doi:10.1175/JAS-D-10-05000.1.

    • Search Google Scholar
    • Export Citation
  • Lang, S. E., Tao W.-K. , Chern J.-D. , Wu D. , and Li X. , 2014: Benefits of a fourth ice class in the simulated radar reflectivities of convective systems using a bulk microphysics scheme. J. Atmos. Sci., 71, 3583–3612, doi:10.1175/JAS-D-13-0330.1.

    • Search Google Scholar
    • Export Citation
  • Lean, H. W., Clark P. A. , Dixon M. , Roberts N. M. , Fitch A. , Forbes R. , and Halliwell C. , 2008: Characteristics of high-resolution versions of the Met Office Unified Model for forecasting convection over the United Kingdom. Mon. Wea. Rev., 136, 3408–3424, doi:10.1175/2008MWR2332.1.

  • Lin, Y., and Mitchell K. E. , 2005: The NCEP stage II/IV hourly precipitation analyses: Development and application. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.]

  • Liu, C., Moncrieff M. W. , and Grabowski W. W. , 2001: Hierarchical modelling of tropical convective systems using explicit and parametrized approaches. Quart. J. Roy. Meteor. Soc., 127, 493–515, doi:10.1002/qj.49712757213.

  • Matsui, T., Zeng X. , Tao W.-K. , Masunaga H. , Olson W. S. , and Lang S. , 2009: Evaluation of long-term cloud-resolving model simulations using satellite radiance observations and multi-frequency satellite simulators. J. Atmos. Oceanic Technol., 26, 1261–1274, doi:10.1175/2008JTECHA1168.1.

    • Search Google Scholar
    • Export Citation
  • Mellor, G. L., and Yamada T. , 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851–875, doi:10.1029/RG020i004p00851.

    • Search Google Scholar
    • Export Citation
  • Mesinger, F., 1996: Improvements in quantitative precipitation forecasts with the eta regional model at the National Centers for Environmental Prediction: The 48-km upgrade. Bull. Amer. Meteor. Soc., 77, 2637–2649, doi:10.1175/1520-0477(1996)077<2637:IIQPFW>2.0.CO;2.

  • Molinari, J., and Dudek M. , 1986: Implicit versus explicit convective heating in numerical weather prediction models. Mon. Wea. Rev., 114, 1822–1831, doi:10.1175/1520-0493(1986)114<1822:IVECHI>2.0.CO;2.

  • Molinari, J., and Dudek M. , 1992: Parameterization of convective precipitation in mesoscale numerical models: A critical review. Mon. Wea. Rev., 120, 326–344, doi:10.1175/1520-0493(1992)120<0326:POCPIM>2.0.CO;2.

  • Mukhopadhyay, P., Taraphdar S. , Goswami B. N. , and Krishnakumar K. , 2010: Indian summer monsoon precipitation climatology in a high-resolution regional climate model: Impacts of convective parameterization on systematic biases. Wea. Forecasting, 25, 369–387, doi:10.1175/2009WAF2222320.1.

    • Search Google Scholar
    • Export Citation
  • NCAR, 2012: Changes in Noah LSM versions. Accessed 10 November 2015. [Available online at http://www.ral.ucar.edu/research/land/technology/lsm/noahlsm-v3.4.1/CHANGES.]

  • Nichols, W. E., and Cuenca R. H. , 1993: Evaluation of the evaporative fraction for parameterization of the surface energy balance. Water Resour. Res., 29, 3681–3690, doi:10.1029/93WR01958.

    • Search Google Scholar
    • Export Citation
  • Peters-Lidard, C. D., and Coauthors, 2007: High-performance Earth system modeling with NASA/GSFC’s Land Information System. Innovations Syst. Software Eng., 3, 157–165, doi:10.1007/s11334-007-0028-x.

  • Peters-Lidard, C. D., and Coauthors, 2015: Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales. Environ. Modell. Software, 67, 149–159, doi:10.1016/j.envsoft.2015.01.007.

    • Search Google Scholar
    • Export Citation
  • Roberts, R. D., and Rutledge S. , 2003: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, 562–584, doi:10.1175/1520-0434(2003)018<0562:NSIAGU>2.0.CO;2.

  • Rodell, M., Houser P. R. , Berg A. A. , and Famiglietti J. S. , 2005: Evaluation of 10 methods for initializing a land surface model. J. Hydrometeor., 6, 146–155, doi:10.1175/JHM414.1.

    • Search Google Scholar
    • Export Citation
  • Seity, Y., Brousseau P. , Malardel S. , Hello G. , Bénard P. , Bouttier F. , Lac C. , and Masson V. , 2011: The AROME-France convective-scale operational model. Mon. Wea. Rev., 139, 976–991, doi:10.1175/2010MWR3425.1.

  • Seo, B.-C., Cunha L. K. , and Krajewski W. F. , 2013: Uncertainty in radar–rainfall composite and its impact on hydrologic prediction for the eastern Iowa flood of 2008. Water Resour. Res., 49, 2747–2764, doi:10.1002/wrcr.20244.

    • Search Google Scholar
    • Export Citation
  • Tang, L., Tian Y. , and Lin X. , 2014: Validation of precipitation retrievals over land from satellite-based passive microwave sensors. J. Geophys. Res. Atmos., 119, 4546–4567, doi:10.1002/2013JD020933.

    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., and Coauthors, 2003: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys., 82, 97–137.

    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., and Coauthors, 2011: High-resolution numerical simulation of the extreme rainfall associated with Typhoon Morakot. Part I: Comparing the impact of microphysics and PBL parameterizations with observations. Terr. Atmos. Oceanic Sci., 22, 673–696, doi:10.3319/TAO.2011.08.26.01(TM).

  • Tao, W.-K., Wu D. , Lang S. , Chern J.-D. , Peters-Lidard C. , Fridlind A. , and Matsui T. , 2016: High-resolution NU-WRF simulations of a deep convective-precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations. J. Geophys. Res. Atmos., 121, 1278–1305, doi:10.1002/2015JD023986.

  • Wang, W., and Seaman N. L. , 1997: A comparison study of convective schemes in a mesoscale model. Mon. Wea. Rev., 125, 252–278, doi:10.1175/1520-0493(1997)125<0252:ACSOCP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Weiss, S. J., Pyle M. E. , Janjić Z. , Bright D. R. , Kain J. S. , and DiMego G. J. , 2008: The operational High Resolution Window WRF Model runs at NCEP: Advantages of multiple model runs for severe convective weather forecasting. 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., P10.8. [Available online at https://ams.confex.com/ams/24SLS/techprogram/paper_142192.htm.]

  • Weisman, M. L., Skamarock W. C. , and Klemp J. B. , 1997: The resolution dependence of explicitly modeled convective systems. Mon. Wea. Rev., 125, 527–548, doi:10.1175/1520-0493(1997)125<0527:TRDOEM>2.0.CO;2.

  • Weisman, M. L., Davis C. , Wang W. , Manning K. W. , and Klemp J. B. , 2008: Experiences with 0–36-h explicit convective forecasts with the WRF-ARW Model. Wea. Forecasting, 23, 407–437, doi:10.1175/2007WAF2007005.1.

    • Search Google Scholar
    • Export Citation
  • Wu, D., Dong X. , Xi B. , Feng Z. , Kennedy A. , Mullendore G. , Gilmore M. , and Tao W.-K. , 2013: Impacts of microphysical scheme on convective and stratiform characteristics in two high precipitation squall line events. J. Geophys. Res. Atmos., 118, 11 119–11 135, doi:10.1002/jgrd.50798.

  • Zhang, D.-L., Hsie E.-Y. , and Moncrieff M. W. , 1988: A comparison of explicit and implicit predictions of convective and stratiform precipitating weather systems with a meso-β-scale numerical model. Quart. J. Roy. Meteor. Soc., 114, 31–60, doi:10.1002/qj.49711447903.

  • Zhang, J., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-14-00174.1, in press.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 586 250 12
PDF Downloads 223 81 5