Editorial Type:
Article
Article Type:
Research Article

Radar Data Assimilation in the Canadian High-Resolution Ensemble Kalman Filter System: Performance and Verification with Real Summer Cases

Weiguang Chang Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Québec, Canada

Search for other papers by Weiguang Chang in
Current site
Google Scholar
PubMed Close
,
Kao-Shen Chung Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, and Data Assimilation and Satellite Meteorology Section, Dorval, Québec, Canada

Search for other papers by Kao-Shen Chung in
Current site
Google Scholar
PubMed Close
,
Luc Fillion Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, and Data Assimilation and Satellite Meteorology Section, Dorval, Québec, Canada

Search for other papers by Luc Fillion in
Current site
Google Scholar
PubMed Close
, and
Seung-Jong Baek Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, and Data Assimilation and Satellite Meteorology Section, Dorval, Québec, Canada

Search for other papers by Seung-Jong Baek in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jun 2014
DOI:
https://doi.org/10.1175/MWR-D-13-00291.1
Page(s):
2118–2138
Restricted access

Abstract

An 80-member high-resolution ensemble Kalman filter (HREnKF) is implemented for assimilating radar observations with the Canadian Meteorological Center’s (CMC’s) Global Environmental Multiscale Limited-Area Model (GEM-LAM). This system covers the Montréal, Canada, region and assimilates radar data from the McGill Radar Observatory with 4-km data thinning. The GEM-LAM operates in fully nonhydrostatic mode with 58 hybrid vertical levels and 1-km horizontal grid spacing. As a first step toward full radar data assimilation, only radial velocities are directly assimilated in this study. The HREnKF is applied on three 2011 summer cases having different precipitation structures: squall-line structure, isolated small-scale structures, and widespread stratiform precipitation. The short-term (<2 h) accuracy of the HREnKF analyses and forecasts is examined.

In HREnKF, the ensemble spread is sufficient to cover the estimated error from innovations and lead to filter convergence. It results in part from a realistic initiation of HREnKF data assimilation cycle by using a Canadian regional EnKF system (itself coupled to a global EnKF) working at meso- and synoptic scales. The filter convergence is confirmed by the HREnKF background fields gradually approaching to radar observations as the assimilation cycling proceeds. At each analysis step, it is clearly shown that unobserved variables are significantly modified through HREnKF cross correlation of errors from the ensemble. Radar reflectivity observations are used to verify the improvements in analyses and short-term forecasts achievable by assimilating only radial velocities. Further developments of the analysis system are discussed.

Corresponding author address: Mr. Weiguang Chang, Dept. of Atmospheric and Oceanic Sciences, McGill University, Montréal, QC H3A 2K6, Canada. E-mail: weiguang.chang@mail.mcgill.ca

Abstract

An 80-member high-resolution ensemble Kalman filter (HREnKF) is implemented for assimilating radar observations with the Canadian Meteorological Center’s (CMC’s) Global Environmental Multiscale Limited-Area Model (GEM-LAM). This system covers the Montréal, Canada, region and assimilates radar data from the McGill Radar Observatory with 4-km data thinning. The GEM-LAM operates in fully nonhydrostatic mode with 58 hybrid vertical levels and 1-km horizontal grid spacing. As a first step toward full radar data assimilation, only radial velocities are directly assimilated in this study. The HREnKF is applied on three 2011 summer cases having different precipitation structures: squall-line structure, isolated small-scale structures, and widespread stratiform precipitation. The short-term (<2 h) accuracy of the HREnKF analyses and forecasts is examined.

In HREnKF, the ensemble spread is sufficient to cover the estimated error from innovations and lead to filter convergence. It results in part from a realistic initiation of HREnKF data assimilation cycle by using a Canadian regional EnKF system (itself coupled to a global EnKF) working at meso- and synoptic scales. The filter convergence is confirmed by the HREnKF background fields gradually approaching to radar observations as the assimilation cycling proceeds. At each analysis step, it is clearly shown that unobserved variables are significantly modified through HREnKF cross correlation of errors from the ensemble. Radar reflectivity observations are used to verify the improvements in analyses and short-term forecasts achievable by assimilating only radial velocities. Further developments of the analysis system are discussed.

Corresponding author address: Mr. Weiguang Chang, Dept. of Atmospheric and Oceanic Sciences, McGill University, Montréal, QC H3A 2K6, Canada. E-mail: weiguang.chang@mail.mcgill.ca
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Aksoy, A., D. C. Dowell, and C. Snyder, 2009: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I: Storm-scale analyses. Mon. Wea. Rev., 137, 1805–1824, doi:10.1175/2008MWR2691.1.

    • Search Google Scholar
    • Export Citation

  • Aksoy, A., D. C. Dowell, and C. Snyder, 2010: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part II: Short-range ensemble forecasts. Mon. Wea. Rev., 138, 1273–1292, doi:10.1175/2009MWR3086.1.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 2884–2903, doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., 2007: An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus, 59A, 210–224, doi:10.1111/j.1600-0870.2006.00216.x.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., and S. L. Anderson, 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127, 2741–2758, doi:10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 1283–1296, doi:10.1175/2009BAMS2618.1.

    • Search Google Scholar
    • Export Citation

  • Bishop, C. H., B. J. Etherton, and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129, 420–436, doi:10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Caron, J.-F., 2013: Mismatching perturbations at the lateral boundaries in limited-area ensemble forecasting: A case study. Mon. Wea. Rev., 141, 356–374, doi:10.1175/MWR-D-12-00051.1.

    • Search Google Scholar
    • Export Citation

  • Cho, Y.-H., G. W. Lee, K.-E. Kim, and I. Zawadzki, 2006: Identification and removal of ground echoes and anomalous propagation using the characteristics of radar echoes. J. Atmos. Oceanic Technol., 23, 1206–1222, doi:10.1175/JTECH1913.1.

    • Search Google Scholar
    • Export Citation

  • Chung, K.-S., I. Zawadzki, M. K. Yau, and L. Fillion, 2009: Short-term forecasting of a midlatitude convective storm by the assimilation of single-Doppler radar observations. Mon. Wea. Rev., 137, 4115–4135, doi:10.1175/2009MWR2731.1.

    • Search Google Scholar
    • Export Citation

  • Chung, K.-S., W. Chang, L. Fillion, and M. Tanguay, 2013: Examination of situation-dependent background error covariances at the convective scale in the context of the ensemble Kalman filter. Mon. Wea. Rev., 141, 3369–3387, doi:10.1175/MWR-D-12-00353.1.

    • Search Google Scholar
    • Export Citation

  • Côté, J., S. Gravel, A. Méthot, A. Patoine, M. Roch, and A. Staniforth, 1998: The operational CMC-MRB Global Environmental Multiscale (GEM) Model. Part I: Design considerations and formulation. Mon. Wea. Rev., 126, 1373–1395, doi:10.1175/1520-0493(1998)126<1373:TOCMGE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Doviak, R. J., and D. S. Zrnic, 1984: Doppler Radar and Weather Observations. Academic Press, 458 pp.

  • Dowell, D. C., and L. J. Wicker, 2009: Additive noise for storm-scale ensemble data assimilation. J. Atmos. Oceanic Technol., 26, 911–927, doi:10.1175/2008JTECHA1156.1.

    • Search Google Scholar
    • Export Citation

  • Dowell, D. C., F. Zhang, L. J. Wicker, C. Snyder, and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132, 1982–2005, doi:10.1175/1520-0493(2004)132<1982:WATRIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dowell, D. C., L. J. Wicker, and C. Snyder, 2011: Ensemble Kalman filter assimilation of radar observations of the 8 May 2003 Oklahoma City supercell: Influences of reflectivity observations on storm-scale analyses. Mon. Wea. Rev., 139, 272–294, doi:10.1175/2010MWR3438.1.

    • Search Google Scholar
    • Export Citation

  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99, 10 143–10 162, doi:10.1029/94JC00572.

    • Search Google Scholar
    • Export Citation

  • Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343–367, doi:10.1007/s10236-003-0036-9.

    • Search Google Scholar
    • Export Citation

  • Fabry, F., and A. Kilambi, 2011: The devil is in the details: Preparing radar information for its proper assimilation. 35th Conf. on Radar Meteorology, Pittsburgh, PA, Amer. Meteor. Soc., 19A.6. [Available online at https://ams.confex.com/ams/35Radar/webprogram/Paper191698.html.]

  • Fillion, L., and S. Bélair, 2004: Tangent linear aspects of the Kain–Fritsch moist convective parameterization scheme. Mon. Wea. Rev., 132, 2477–2494, doi:10.1175/1520-0493(2004)132<2477:TLAOTK>2.0.CO;2.

    • 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, 723–757, doi:10.1002/qj.49712555417.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126, 796–811, doi:10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Houtekamer, P. L., and H. L. Mitchell, 2001: A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Wea. Rev., 129, 123–137, doi:10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2.

    • 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, 604–620, doi:10.1175/MWR-2864.1.

    • 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, 2784–2802, doi:10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Keeler, R. J., and S. M. Ellis, 2000: Observational error covariance matrices for radar data assimilation. Phys. Chem. Earth, Part B Hydrol. Oceans Atmos., 25, 1277–1280, doi:10.1016/S1464-1909(00)00193-3.

    • Search Google Scholar
    • Export Citation

  • Mailhot, J., and Coauthors, 1998: Scientific description of RPN Physics Library—version 3.6. Recherche en Prévision Numérique, 188 pp. [Available online at http://collaboration.cmc.ec.gc.ca/science/rpn/physics/physic98.pdf.]

  • Milbrandt, J. A., and M. K. Yau, 2005: A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the spectral shape parameter. J. Atmos. Sci., 62, 3051–3064, doi:10.1175/JAS3534.1.

    • Search Google Scholar
    • Export Citation

  • Mitchell, H. L., and P. L. Houtekamer, 2009: Ensemble Kalman filter configurations and their performance with the logistic map. Mon. Wea. Rev., 137, 4325–4343, doi:10.1175/2009MWR2823.1.

    • Search Google Scholar
    • Export Citation

  • Mitchell, H. L., P. L. Houtekamer, and G. Pellerin, 2002: Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Wea. Rev., 130, 2791–2808, doi:10.1175/1520-0493(2002)130<2791:ESBAME>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Noilhan, J., and S. Planton, 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536–549, doi:10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Nutter, P., D. Stensrud, and M. Xue, 2004a: Effects of coarsely resolved and temporally interpolated lateral boundary conditions on the dispersion of limited-area ensemble forecasts. Mon. Wea. Rev., 132, 2358–2377, doi:10.1175/1520-0493(2004)132<2358:EOCRAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Nutter, P., M. Xue, and D. Stensrud, 2004b: Application of lateral boundary condition perturbations to help restore dispersion in limited-area ensemble forecasts. Mon. Wea. Rev., 132, 2378–2390, doi:10.1175/1520-0493(2004)132<2378:AOLBCP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Saito, K., H. Seko, M. Kunii, and T. Miyoshi, 2012: Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction. Tellus, 64A, 11594, doi:10.3402/tellusa.v64i0.11594.

    • Search Google Scholar
    • Export Citation

  • Snyder, C., and F. Zhang, 2003: Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131, 1663–1677, doi:10.1175//2555.1.

    • Search Google Scholar
    • Export Citation

  • Sun, J., and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, 1642–1661, doi:10.1175/1520-0469(1997)054<1642:DAMRFD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Sundqvist, H., 1978: A parameterization scheme for non-convective condensation including prediction of cloud water content. Quart. J. Roy. Meteor. Soc., 104, 677–690, doi:10.1002/qj.49710444110.

    • Search Google Scholar
    • Export Citation

  • Tippett, M. K., J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker, 2003: Ensemble square root filters. Mon. Wea. Rev., 131, 1485–1490, doi:10.1175/1520-0493(2003)131<1485:ESRF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Tong, M., and M. Xue, 2005: Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments. Mon. Wea. Rev., 133, 1789–1807, doi:10.1175/MWR2898.1.

    • Search Google Scholar
    • Export Citation

  • Wang, H., J. Sun, X. Zhang, X.-Y. Huang, and T. Auligné, 2013: Radar data assimilation with WRF 4D-Var. Part I: System development and preliminary testing. Mon. Wea. Rev., 141, 2224–2244, doi:10.1175/MWR-D-12-00168.1.

    • Search Google Scholar
    • Export Citation

  • Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913–1924, doi:10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Xu, Q., K. Nai, and L. Wei, 2007: An innovation method for estimating radar radial-velocity observation error and background wind error covariances. Quart. J. Roy. Meteor. Soc., 133, 407–415, doi:10.1002/qj.21.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 481 215 26
PDF Downloads 191 49 10