• Ancell, B. C., 2012: Examination of analysis and forecast errors of high-resolution assimilation, bias removal, and digital filter initialization with an ensemble Kalman filter. Mon. Wea. Rev., 140, 39924004, doi:10.1175/MWR-D-11-00319.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ancell, B. C., C. F. Mass, and G. J. Hakim, 2011: Evaluation of surface analyses and forecasts with a multiscale ensemble Kalman filter in regions of complex terrain. Mon. Wea. Rev., 139, 20082024, doi:10.1175/2010MWR3612.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ancell, B. C., E. Kashawlic, and J. Schroeder, 2015: Evaluation of wind forecasts and observation impacts from variational and ensemble data assimilation for wind energy applications. Mon. Wea. Rev., 143, 32303245, doi:10.1175/MWR-D-15-0001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bédard, J., W. Yu, Y. Gagnon, and C. Masson, 2013: Development of a geophysic model output statistic module for improving short-term numerical wind predictions over complex sites. Wind Energy, 16, 11311147.

    • Search Google Scholar
    • Export Citation
  • Bédard, J., S. Laroche, and P. Gauthier, 2015: A geo-statistical observation operator for the assimilation of near-surface wind data. Quart. J. Roy. Meteor. Soc., 141, 28572868, doi:10.1002/qj.2569.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., W. R. Moninger, S. R. Sahm, and T. L. Smith, 2007: Mesonet wind quality monitoring allowing assimilation in the RUC and other NCEP models. 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Park City, UT, Amer. Meteor. Soc., P1.33. [Available online at https://ams.confex.com/ams/22WAF18NWP/techprogram/paper_124829.htm.]

  • Benjamin, S. G., B. D. Jamison, W. R. Moninger, S. R. Sahm, B. Schwartz, and T. W. Schlatter, 2010: Relative short-range forecast impact from aircraft, profiler, radiosonde, VAD, GPS-PW, METAR, and mesonet observations via the RUC hourly assimilation cycle. Mon. Wea. Rev., 138, 13191343, doi:10.1175/2009MWR3097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benoit, R., J. Coté, and J. Mailhot, 1989: Inclusion of a TKE boundary layer parameterization in the Canadian regional finite-element model. Mon. Wea. Rev., 117, 17261750, doi:10.1175/1520-0493(1989)117<1726:IOATBL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buehner, M., 2005: Ensemble-derived stationary and flow-dependent background error covariances: Evaluation in a quasi-operational NWP setting. Quart. J. Roy. Meteor. Soc., 131, 10131043, doi:10.1256/qj.04.15.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buehner, M., J. Morneau, and C. Charette, 2013: Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction. Nonlinear Processes Geophys., 20, 669682, doi:10.5194/npg-20-669-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buehner, M., and et al. , 2015: Implementation of deterministic weather forecasting systems based on ensemble-variational data assimilation at Environment Canada. Part I: The global system. Mon. Wea. Rev., 143, 25322559, doi:10.1175/MWR-D-14-00354.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Charron, M., and et al. , 2012: The stratospheric extension of the Canadian global deterministic medium-range weather forecasting system and its impact on tropospheric forecasts. Mon. Wea. Rev., 140, 19241944, doi:10.1175/MWR-D-11-00097.1.

    • Crossref
    • 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, 13731395, doi:10.1175/1520-0493(1998)126<1373:TOCMGE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., 2005: Bias and data assimilation. Quart. J. Roy. Meteor. Soc., 131, 33233343, doi:10.1256/qj.05.137.

  • Dirren, S., R. D. Torn, and G. J. Hakim, 2007: A data assimilation case study using a limited-area ensemble Kalman filter. Mon. Wea. Rev., 135, 14551473, doi:10.1175/MWR3358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, J., M. Xue, and K. Droegemeier, 2011: The analysis and impact of simulated high-resolution surface observations in addition to radar data for convective storms with an ensemble Kalman filter. Meteor. Atmos. Phys., 112, 4161, doi:10.1007/s00703-011-0130-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gauthier, P., M. Buehner, and L. Fillion, 1998: Background-error statistics modelling in a 3D variational data assimilation scheme: Estimation and impact on the analyses. Proc. ECMWF Workshop on the Diagnostics of Assimilation Systems, Reading, United Kingdom, ECMWF, 131–145.

  • Gauthier, P., M. Tanguay, S. Laroche, S. Pellerin, and J. Morneau, 2007: Extension of 3DVAR to 4DVAR: Implementation of 4DVAR at the Meteorological Service of Canada. Mon. Wea. Rev., 135, 23392354, doi:10.1175/MWR3394.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hacker, J. P., and C. Snyder, 2005: Ensemble Kalman filter assimilation of fixed screen-height observations in a parameterized PBL. Mon. Wea. Rev., 133, 32603275, doi:10.1175/MWR3022.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hacker, J. P., and D. Rostkier-Edelstein, 2007: PBL state estimation with surface observations, a column model, and an ensemble filter. Mon. Wea. Rev., 135, 29582972, doi:10.1175/MWR3443.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hacker, J. P., J. L. Anderson, and M. Pagowski, 2007: Improved vertical covariance estimates for ensemble-filter assimilation of near-surface observations. Mon. Wea. Rev., 135, 10211036, doi:10.1175/MWR3333.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., X. Deng, H. L. Mitchell, S. J. Baek, and N. Gagnon, 2014: Higher resolution in an operational ensemble Kalman filter. Mon. Wea. Rev., 142, 11431162, doi:10.1175/MWR-D-13-00138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ingleby, B., 2015: Global assimilation of air temperature, humidity, wind and pressure from surface stations. Quart. J. Roy. Meteor. Soc., 141, 504517, doi:10.1002/qj.2372.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laroche, S., P. Gauthier, J. St-James, and J. Morneau, 1999: Implementation of a 3D variational data assimilation system at the Canadian Meteorological Centre. Part II: The regional analysis. Atmos.–Ocean, 37, 281307, doi:10.1080/07055900.1999.9649630.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lott, F., and M. J. Miller, 1997: A new subgrid-scale orographic drag parametrization: Its formulation and testing. Quart. J. Roy. Meteor. Soc., 123, 101127, doi:10.1002/qj.49712353704.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mailhot, J., and R. Benoit, 1982: A finite-element model of the atmospheric boundary layer suitable for use with numerical weather prediction models. J. Atmos. Sci., 39, 22492266, doi:10.1175/1520-0469(1982)039<2249:AFEMOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parrish, D. F., and J. C. Derber, 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pu, Z., H. Zhang, and J. Anderson, 2013: Ensemble Kalman filter assimilation of near-surface observations over complex terrain: Comparison with 3DVAR for short-range forecasts. Tellus, 65A, 19620, doi:10.3402/tellusa.v65i0.19620.

    • Search Google Scholar
    • Export Citation
  • Rodwell, M. J., and T. N. Palmer, 2007: Using numerical weather prediction to assess climate models. Quart. J. Roy. Meteor. Soc., 133, 129146, doi:10.1002/qj.23.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rostkier-Edelstein, D., and J. P. Hacker, 2010: The roles of surface-observation ensemble assimilation and model complexity for nowcasting of PBL profiles: A factor separation analysis. Wea. Forecasting, 25, 16701690, doi:10.1175/2010WAF2222435.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zack, J., E. J. Natenberg, S. Young, G. V. Knowe, K. Waight, J. Manobianco, and C. Kamath, 2010: Application of ensemble sensitivity analysis to observation targeting for short-term wind speed forecasting in the Washington–Oregon region. Lawrence Livermore National Laboratory Tech. Rep. LLNL-TR-458086, 65 pp. [Available online at http://computation.llnl.gov/projects/starsapphire-data-driven-modeling-analysis/LLNL-TR-458086.pdf.]

    • Crossref
    • Export Citation
  • Zack, J., E. J. Natenberg, G. V. Knowe, K. Waight, J. Manobianco, D. Hanley, and C. Kamath, 2011: Observing system simulation experiments (OSSEs) for the Mid-Columbia basin. Lawrence Livermore National Laboratory Tech. Rep. LLNL-TR-499162, 17 pp. [Available online at https://e-reports-ext.llnl.gov/pdf/515298.pdf.]

    • Crossref
    • Export Citation
  • Zadra, A., M. Roch, S. Laroche, and M. Charron, 2003: The subgrid scale orographic blocking parametrization of the GEM model. Atmos.–Ocean, 41, 155170, doi:10.3137/ao.410204.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zadra, A., and et al. , 2014: Improvements to the Global Deterministic Prediction System (GDPS) (from version 2.2.2 to 3.0.0), and related changes to the Regional Deterministic Prediction System (RDPS) (from version 3.0.0 to 3.1.0). Tech. Note, Canadian Meteorological Centre, 88 pp. [Available online at http://collaboration.cmc.ec.gc.ca/cmc/CMOI/product_guide/docs/lib/op_systems/doc_opchanges/technote_gdps300_20130213_e.pdf.]

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Near-Surface Wind Observation Impact on Forecasts: Temporal Propagation of the Analysis Increment

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  • 1 Centre pour l’étude et la simulation du climat à l’échelle régionale (ESCER), Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montréal, and Data Assimilation and Satellite Meteorology Section, Environment and Climate Change Canada, Dorval, Québec, Canada
  • | 2 Data Assimilation and Satellite Meteorology Section, Environment and Climate Change Canada, Dorval, Québec, Canada
  • | 3 Centre pour l’étude et la simulation du climat à l’échelle régionale (ESCER), Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montréal, Québec, Canada
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Abstract

This study examines the assimilation of near-surface wind observations over land to improve wind nowcasting and short-term tropospheric forecasts. A new geostatistical operator based on geophysical model output statistics (GMOS) is compared with a bilinear interpolation scheme (Bilin). The multivariate impact on forecasts and the temporal evolution of the analysis increments produced are examined as well as the influence of background error covariances on different components of the prediction system. Results show that Bilin significantly degrades surface and upper-air fields when assimilating only wind data from 4942 SYNOP stations. GMOS on the other hand produces smaller increments that are in better agreement with the model state. It leads to better short-term near-surface wind forecasts and does not deteriorate the upper-air forecasts. The information persists longer in the system with GMOS, although the local improvements do not propagate beyond 6-h lead time. Initial model tendencies indicate that the mass field is not significantly altered when using static error covariances and the boundary layer parameterizations damp the poorly balanced increment locally. Conversely, most of the analysis increment is propagated when using flow-dependent error statistics. It results in better balanced wind and mass fields and provides a more persistent impact on the forecasts. Forecast accuracy results from observing system experiments (assimilating SYNOP winds with all observations used operationally) are generally neutral. Nevertheless, forecasts and analyses from GMOS are more self-consistent than those from both Bilin and a control experiment (not assimilating near-surface winds over land) and the information from the observations persists up to 12-h lead time.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Joël Bédard, bedard.joel@gmail.com

Abstract

This study examines the assimilation of near-surface wind observations over land to improve wind nowcasting and short-term tropospheric forecasts. A new geostatistical operator based on geophysical model output statistics (GMOS) is compared with a bilinear interpolation scheme (Bilin). The multivariate impact on forecasts and the temporal evolution of the analysis increments produced are examined as well as the influence of background error covariances on different components of the prediction system. Results show that Bilin significantly degrades surface and upper-air fields when assimilating only wind data from 4942 SYNOP stations. GMOS on the other hand produces smaller increments that are in better agreement with the model state. It leads to better short-term near-surface wind forecasts and does not deteriorate the upper-air forecasts. The information persists longer in the system with GMOS, although the local improvements do not propagate beyond 6-h lead time. Initial model tendencies indicate that the mass field is not significantly altered when using static error covariances and the boundary layer parameterizations damp the poorly balanced increment locally. Conversely, most of the analysis increment is propagated when using flow-dependent error statistics. It results in better balanced wind and mass fields and provides a more persistent impact on the forecasts. Forecast accuracy results from observing system experiments (assimilating SYNOP winds with all observations used operationally) are generally neutral. Nevertheless, forecasts and analyses from GMOS are more self-consistent than those from both Bilin and a control experiment (not assimilating near-surface winds over land) and the information from the observations persists up to 12-h lead time.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Joël Bédard, bedard.joel@gmail.com
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