• Anderson, J., , T. Hoar, , K. Raeder, , H. Liu, , N. Collins, , R. Torn, , and A. Arellano, 2009: The data assimilation research testbed: A community data assimilation facility. Bull. Amer. Meteor. Soc., 90, 12831296.

    • Search Google Scholar
    • Export Citation
  • Barker, D. M., , W. Huang, , R.-R. Guo, , A. J. Bourgeois, , and Q. N. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897914.

    • Search Google Scholar
    • Export Citation
  • Bender, M. A., , I. Ginis, , R. Tuleya, , B. Thomas, , and T. Marchok, 2007: The operational GFDL coupled hurricane–ocean prediction system and a summary of its performance. Mon. Wea. Rev., 135, 39653989.

    • Search Google Scholar
    • Export Citation
  • Berg, R. J., , and L. A. Avila, 2011: Atlantic hurricane season of 2009. Mon. Wea. Rev., 139, 10491069.

  • Bleck, R., 2002: An oceanic general circulation model framed in hybrid isopycnic–Cartesian coordinates. Ocean Modell., 4, 5588.

  • Braun, S. A., , and L. Wu, 2007: A numerical study of Hurricane Erin (2001). Part II: Shear and the organization of eyewall vertical motion. Mon. Wea. Rev., 135, 11791194.

    • Search Google Scholar
    • Export Citation
  • Chen, S. S., , J. F. Price, , W. Zhao, , M. A. Donelan, , and E. J. Walsh, 2007: The CBLAST-Hurricane Program and the next generation fully coupled atmosphere–wave–ocean models for hurricane research and prediction. Bull. Amer. Meteor. Soc., 88, 311317.

    • Search Google Scholar
    • Export Citation
  • Chen, Y., , and C. Snyder, 2007: Assimilating vortex position with an ensemble Kalman filter. Mon. Wea. Rev., 135, 18281845.

  • Chou, K. H., , and C. C. Wu, 2008: Typhoon initialization in a mesoscale model—Combination of the bogused vortex and dropwindsonde data in DOTSTAR. Mon. Wea. Rev., 136, 865879.

    • Search Google Scholar
    • Export Citation
  • Chou, M.-D., , and M. J. Suarez, 1994: An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo. 104606, Vol. 3, 85 pp.

  • Davis, C. A., and Coauthors, 2008: Prediction of landfalling hurricanes with the Advanced Hurricane WRF Model. Mon. Wea. Rev., 136, 19902005.

    • Search Google Scholar
    • Export Citation
  • Davis, C. A., , W. Wang, , J. Dudhia, , and R. Torn, 2010: Does increased horizontal resolution improve hurricane wind forecasts? Wea. Forecasting, 25, 18261841.

    • Search Google Scholar
    • Export Citation
  • DeMaria, M., 1996: The effect of vertical shear on tropical cyclone intensity change. J. Atmos. Sci., 53, 20762087.

  • DeMaria, M., 2009: A simplified dynamical system for tropical cyclone intensity prediction. Mon. Wea. Rev., 137, 6882.

  • Done, J., , C. A. Davis, , and M. Weisman, 2004: The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecasting (WRF) model. Atmos. Sci. Lett., 5, 110117.

    • Search Google Scholar
    • Export Citation
  • Donelan, M. A., , B. K. Haus, , N. Reul, , W. J. Plant, , M. Stiassnie, , H. C. Graber, , O. B. Brown, , and E. S. Saltzman, 2004: On the limiting aerodynamic roughness of the ocean in very strong winds. Geophys. Res. Lett., 31, L18306, doi:10.1029/2004GL019460.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1996: A multi-layer soil temperature model for MM5. Preprints, Sixth PSU/NCAR Mesoscale Model Users’ Workshop, Boulder, CO, NCAR, 49–50. [Available online at www.mmm.ucar.edu/mm5/lsm/soil.pdf.]

  • Dudhia, J., and Coauthors, 2008: Prediction of Atlantic tropical cyclones with the Advanced Hurricane WRF (AHW) Model. Preprints, 28th Conf. on Hurricanes and Tropical Meteorology, Orlando, FL, Amer. Meteor. Soc., 18A.2. [Available online at http://ams.confex.com/ams/pdfpapers/138004.pdf.]

  • Dyer, A. J., , and B. B. Hicks, 1970: Flux-gradient relationships in the constant flux layer. Quart. J. Roy. Meteor. Soc., 96, 715721.

  • Fierro, A. O., , R. F. Rogers, , F. D. Marks, , and D. S. Nolan, 2009: The impact of horizontal grid spacing on the microphysical and kinematic structures of strong tropical cyclones simulated with the WRF-ARW model. Mon. Wea. Rev., 137, 37173743.

    • Search Google Scholar
    • Export Citation
  • Frank, W. M., , and E. A. Ritchie, 2001: Effects of vertical wind shear on the intensity and structure of numerically simulated hurricanes. Mon. Wea. Rev., 129, 22492269.

    • Search Google Scholar
    • Export Citation
  • Gaspari, G., , and S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions. Quart. J. Roy. Meteor. Soc., 125, 723757.

    • Search Google Scholar
    • Export Citation
  • Environmental Modeling Center, 2003: The GFS atmospheric model. Tech. Rep., NCEP Office Note 442, Global Climate and Weather Modeling Branch, 14 pp. [Available online at http://www.emc.ncep.noaa.gov/officenotes/newernotes/on442.pdf.]

  • Gopalakrishnan, S. G., , Q. Liu, , T. Marchok, , D. Sheinin, , N. Surgi, , R. Tuleya, , R. Yablonsky, , and X. Zhang, 2010: Hurricane Weather Research and Forecasting (HWRF) Model scientific documentation. Tech. Rep., NOAA/NCAR/Development Tech Center, 80 pp. [Available online at http://www.dtcenter.org/HurrWRF/users/docs/scientific_documents/HWRF_final_2-2_cm.pdf.]

  • Hamill, T. M., , J. S. Whitaker, , M. F. Fiorino, , and S. G. Benjamin, 2011: Global ensemble predictions of 2009’s tropical cyclones initialized with an ensemble Kalman filter. Mon. Wea. Rev., 139, 668688.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., , and H.-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 23222239.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., , J. Dudhia, , and S.-H. Chen, 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
  • Houtekamer, P. L., , H. L. Mitchell, , G. Pellerin, , M. Buehner, , M. Charron, , L. Spacek, , and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133, 604620.

    • Search Google Scholar
    • Export Citation
  • Hsiao, L.-F., , C.-S. Liou, , T.-C. Yeh, , Y.-R. Guo, , D.-S. Chen, , K.-N. Huang, , C.-T. Terng, , and J.-H. Chen, 2010: A vortex relocation scheme for tropical cyclone initialization in Advanced Research WRF. Mon. Wea. Rev., 138, 32983315.

    • Search Google Scholar
    • Export Citation
  • Jones, S. C., 1995: The evolution of vorticies in vertical shear. I: Initially barotropic vorticies. Quart. J. Roy. Meteor. Soc., 121, 821851.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., , and J. M. Fritsch, 1990: A one-dimensional entraining detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 27842802.

    • Search Google Scholar
    • Export Citation
  • Kamineni, R., , T. N. Krishnamurti, , S. Pattnaik, , E. V. Browell, , S. Ismail, , and R. A. Ferrare, 2006: Impact of CAMEX-4 datasets for hurricane forecasts using a global model. J. Atmos. Sci., 63, 151174.

    • Search Google Scholar
    • Export Citation
  • Klinker, E., , and P. D. Sardeshmukh, 1992: The diagnosis of mechanical dissipation in the atmosphere from large-scale balance requirements. J. Atmos. Sci., 49, 608627.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., , M. DeMaria, , C. R. Sampson, , and J. M. Gross, 2003: Statistical, 5-day tropical cyclone intensity forecasts derived from climatology and persistence. Wea. Forecasting,18, 80–92.

  • Kurihara, Y., , M. A. Bender, , and R. J. Ross, 1993: An initialization scheme of hurricane models by vortex specification. Mon. Wea. Rev., 121, 20302045.

    • Search Google Scholar
    • Export Citation
  • Liu, Q., , T. Marchok, , H.-L. Pan, , M. Bender, , and S. Lord, 2000: Improvements in hurricane initialization and forecasting at NCEP with global and regional (GFDL) models. NOAA Tech. Procedures Bull. 472, 7 pp. [Available online at http://www.nws.noaa.gov/om/tpb/472.pdf.]

  • Michalakes, J., , J. Dudhia, , D. Gill, , T. Henderson, , J. Klemp, , W. Skamarock, , and W. Wang, 2005: The Weather Reserach and Forecast Model: Software architecture and performance. Proceedings of the 11th ECMWF Workshop on High Performance Computing in Meteorology, G. Mozdzynski, Ed., World Scientific, 156–168. [Available online at http://wrf-model.org/wrfadmin/docs/ecmwf_2004.pdf.]

  • Mlawer, E. J., , S. J. Taubman, , P. D. Brown, , M. J. Iacono, , and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 (D14), 16 66316 682.

    • Search Google Scholar
    • Export Citation
  • Paulson, C. A., 1970: The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J. Appl. Meteor., 9, 857861.

    • Search Google Scholar
    • Export Citation
  • Phillips, T. J., and Coauthors, 2004: Evaluating parameterizations in general circulation models: Climate simulation meets weather prediction. Bull. Amer. Meteor. Soc., 85, 19031915.

    • Search Google Scholar
    • Export Citation
  • Pollard, R. T., , P. B. Rhines, , and R. O. R. Y. Thompson, 1973: The deepening of the wind-mixed layer. Geophys. Fluid Dyn., 3, 381404.

  • Qu, X.,, and J. Heming, 2002: The impact of dropsonde data on forecasts of Hurricane Debby by the Meteorological Office Unified Model. Adv. Atmos. Sci., 19, 10291044.

    • Search Google Scholar
    • Export Citation
  • Rappaport, E. N., and Coauthors, 2009: Advances and challenges at the National Hurricane Center. Wea. Forecasting, 24, 395419.

  • Rosenfeld, D., , M. Clavner, , and R. Nirel, 2011: Pollution and dust aerosols modulating tropical cyclones intensities. Atmos. Res., 102, 6676, doi:10.1016/j.atmosres.2011.06.006.

    • Search Google Scholar
    • Export Citation
  • Saffir, H. S., 1973: Hurricane wind and storm surge. Mil. Eng., 423, 45.

  • Simpson, R. H., 1974: The hurricane disaster potential scale. Weatherwise, 27, 169186.

  • Skamarock, W. C., and Coauthors, 2008: A description of the advanced research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 125 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.]

  • Torn, R. D., 2010: Performance of a mesoscale ensemble Kalman filter (EnKF) during the NOAA High-Resolution Hurricane Test. Mon. Wea. Rev., 138, 43754392.

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., , and G. J. Hakim, 2009: Ensemble data assimilation applied to RAINEX observations of Hurricane Katrina (2005). Mon. Wea. Rev., 137, 28172829.

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and C. Snyder, 2012: Uncertainty of tropical cyclone best-track information. Wea. Forecasting,27, 715–729.

  • Torn, R. D., , and C. A. Davis, 2012: The influence of shallow convection on tropical cyclone track forecasts. Mon. Wea. Rev., 140, 21882197.

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., , G. J. Hakim, , and C. Snyder, 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev., 134, 24902502.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., and Coauthors, 2005: Recent innovations in deriving tropospheric winds from meteorological satellites. Bull. Amer. Meteor. Soc., 86, 205223.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., , and C.-C. Wu, 2004: Current understanding of tropical cyclone structure and intensity changes—A review. Meteor. Atmos. Phys., 87, 257278.

    • Search Google Scholar
    • Export Citation
  • Webb, E. K., 1970: Profile relationships: The log-linear range, and extension to strong stability. Quart. J. Roy. Meteor. Soc., 96, 6790.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1997: Resampling hypothesis tests for autocorrelated fields. J. Climate, 10, 6582.

  • Wong, S., , A. E. Dessler, , N. M. Mahowald, , P. Yang, , and Q. Feng, 2009: Maintenance of lower tropospheric temperature inversion in the Saharan air layer by dust and dry anomaly. J. Climate, 22, 51495162.

    • Search Google Scholar
    • Export Citation
  • Wu, C. C., , G. Y. Lien, , J. H. Chen, , and F. Zhang, 2010: Assimilation of tropical cyclone track and structure based on the ensemble Kalman filter (EnKF). J. Atmos. Sci., 67, 38063822.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., , Z. Weng, , Z. Meng, , J. A. Sippel, , and C. H. Bishop, 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Dopper radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 21052125.

    • Search Google Scholar
    • Export Citation
  • Zhang, F., , Y. Weng, , J. F. Gamache, , and F. D. Marks, 2011: Performance of convective-permitting hurricane initialization and prediction during 2008-2010 with ensemble data assimilation of inner-core airborne Doppler radar observations. Geophys. Res. Lett., 38, L15810, doi:10.1029/2011GL048469.

    • Search Google Scholar
    • Export Citation
  • Zhang, H., , G. M. McFarquhar, , S. M. Saleeby, , and W. R. Cotton, 2007: Impacts of Saharan dust as CCN on the evolution of an idealized tropical cyclone. Geophys. Res. Lett., 34, L14812, doi:10.1029/2007GL029876.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 56 56 4
PDF Downloads 41 41 5

Evaluation of the Advanced Hurricane WRF Data Assimilation System for the 2009 Atlantic Hurricane Season

View More View Less
  • 1 National Center for Atmospheric Research,* Boulder, Colorado
  • | 2 University at Albany, State University of New York, Albany, New York
  • | 3 National Center for Atmospheric Research,* Boulder, Colorado
© Get Permissions
Restricted access

Abstract

Real-time analyses and forecasts using an ensemble Kalman filter (EnKF) and the Advanced Hurricane Weather Research and Forecasting Model (AHW) are evaluated from the 2009 North Atlantic hurricane season. This data assimilation system involved cycling observations that included conventional in situ data, tropical cyclone (TC) position, and minimum SLP and synoptic dropsondes each 6 h using a 96-member ensemble on a 36-km domain for three months. Similar to past studies, observation assimilation systematically reduces the TC position and minimum SLP errors, except for strong TCs, which are characterized by large biases due to grid resolution. At 48 different initialization times, an AHW forecast on 12-, 4-, and 1.33-km grids is produced with initial conditions drawn from a single analysis member. Whereas TC track analyses and forecasts exhibit a pronounced northward bias, intensity forecast errors are similar to (lower than) the NWS Hurricane Weather Research Model (HWRF) and GFDL forecasts for forecast lead times ≤60 h (>60 h), with the largest track errors associated with the weakest systems, such as Tropical Storm (TS) Erika. Several shortcomings of the data assimilation system are addressed through postseason sensitivity tests, including using the maximum 800-hPa circulation to identify the TC position during assimilation and turning off the quality control for the TC minimum SLP observation when the initial intensity is far too weak. In addition, the improved forecast of TS Erika relative to HWRF is shown to be related to having initial conditions that are more representative of a sheared TC and not using a cumulus parameterization.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Steven Cavallo, School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Norman, OK 73072-7307. E-mail: cavallo@ou.edu

Abstract

Real-time analyses and forecasts using an ensemble Kalman filter (EnKF) and the Advanced Hurricane Weather Research and Forecasting Model (AHW) are evaluated from the 2009 North Atlantic hurricane season. This data assimilation system involved cycling observations that included conventional in situ data, tropical cyclone (TC) position, and minimum SLP and synoptic dropsondes each 6 h using a 96-member ensemble on a 36-km domain for three months. Similar to past studies, observation assimilation systematically reduces the TC position and minimum SLP errors, except for strong TCs, which are characterized by large biases due to grid resolution. At 48 different initialization times, an AHW forecast on 12-, 4-, and 1.33-km grids is produced with initial conditions drawn from a single analysis member. Whereas TC track analyses and forecasts exhibit a pronounced northward bias, intensity forecast errors are similar to (lower than) the NWS Hurricane Weather Research Model (HWRF) and GFDL forecasts for forecast lead times ≤60 h (>60 h), with the largest track errors associated with the weakest systems, such as Tropical Storm (TS) Erika. Several shortcomings of the data assimilation system are addressed through postseason sensitivity tests, including using the maximum 800-hPa circulation to identify the TC position during assimilation and turning off the quality control for the TC minimum SLP observation when the initial intensity is far too weak. In addition, the improved forecast of TS Erika relative to HWRF is shown to be related to having initial conditions that are more representative of a sheared TC and not using a cumulus parameterization.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Steven Cavallo, School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Norman, OK 73072-7307. E-mail: cavallo@ou.edu
Save