• Aksoy, A., Zhang F. , and Nielsen-Gammon J. W. , 2006: Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model. Mon. Wea. Rev., 134, 29512970.

    • Search Google Scholar
    • Export Citation
  • Alpert, P., and Sholokhman T. , 2011: Factor Separation in the Atmosphere: Applications and Future Prospects. Cambridge University Press, 274 pp.

  • Ancell, B. C., Mass C. F. , and Hakim G. J. , 2011: Evaluation of surface analyses and forecasts with a multiscale ensemble Kalman filter in regions of complex terrain. Mon. Wea. Rev., 139, 20082024.

    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129, 28842903.

  • Anderson, J. L., 2003: A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131, 634642.

  • Anderson, J. L., Hoar K. R. T. , Collins N. , Torn R. , and Arellano A. F. , 2009: The Data Assimilation Research Testbed: A community data assimilation facility. Bull. Amer. Meteor. Soc., 90, 12831296.

    • Search Google Scholar
    • Export Citation
  • Beljaars, A. C. M., and Holtstag A. A. M. , 1991: Flux parameterization over land surfaces for atmospheric models. J. Appl. Meteor., 30, 327341.

    • 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 description and implementation. Mon. Wea. Rev., 129, 569585.

    • Search Google Scholar
    • Export Citation
  • Crook, N. A., 1996: Sensitivity of moist convection forced by boundary layer processes to low-level thermodynamic fields. Mon. Wea. Rev., 124, 17671785.

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

  • Delle Monache, L., Nipen T. , Liu Y. , Roux G. , and Stull R. , 2011: Kalman filter and analog schemes to postprocess numerical weather predictions. Mon. Wea. Rev., 139, 35543570.

    • Search Google Scholar
    • Export Citation
  • Desroziers, G., Berre L. , Chapnik B. , and Poli P. , 2005: Diagnosis of observation, background and analysis-error statistics in observation space. Quart. J. Roy. Meteor. Soc., 131, 33853396.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Efron, B., and Tibshirani R. J. , 1993: An Introduction to the Bootstrap. Chapman and Hall, 436 pp.

  • Ek, M. B., Mitchell K. E. , Lin Y. , 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
  • Ghan, S. J., Leung L. R. , and McCaa J. , 1999: A comparison of three different modeling strategies for evaluating cloud and radiation parameterizations. Mon. Wea. Rev., 127, 19671984.

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

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

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

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., and Pan H.-L. , 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 23222339.

    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1996: The surface layer in the NCEP Eta Model. Preprints, 11th Conf. on Numerical Weather Prediction, Norfolk, VA, Amer. Meteor. Soc., 354–355.

  • Jolliffe, I. T., and Stephenson D. B. , 2003: Forecast Verification: A Practitioner’s Guide in Atmospheric Science. Wiley, 240 pp.

  • Kumar, N., and Russell A. G. , 1996: Comparing prognostic and diagnostic meteorological fields and their impacts on photochemical air quality modeling. Atmos. Environ., 12, 19892010.

    • Search Google Scholar
    • Export Citation
  • Leidner, S. M., Stauffer D. R. , and Seaman N. L. , 2001: Improving short-term numerical weather prediction in the California coastal zone by dynamic initialization of the marine boundary layer. Mon. Wea. Rev., 129, 275293.

    • Search Google Scholar
    • Export Citation
  • Martin, W. J., and Xue M. , 2006: Sensitivity analysis of convection of the 24 May 2002 IHOP case using very large ensembles. Mon. Wea. Rev., 134, 192207.

    • Search Google Scholar
    • Export Citation
  • Mason, I., 1982: A model for assessment of weather forecasts. Aust. Meteor. Mag., 30, 291303.

  • Mason, S. J., and Graham N. E. , 1999: Conditional probabilities, relative operating characteristics, and relative operative levels. Wea. Forecasting, 14, 713725.

    • Search Google Scholar
    • Export Citation
  • Mauritsen, T., Svensson G. , Zilitinkevich S. S. , Esau I. , Enger L. , and Grisogono B. , 2007: A total turbulent energy closure model for neutrally and stably stratified atmospheric boundary layers. J. Atmos. Sci., 64, 41134126.

    • Search Google Scholar
    • Export Citation
  • McCaul, E. W., Jr., and Cohen C. , 2002: The impact on simulated storm structure and intensity of variations in the mixed layer and moist layer depths. Mon. Wea. Rev., 130, 17221748.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., Toubman S. J. , Brown P. D. , Iacono M. J. , and Clough S. A. , 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J. Geophys. Res., 102 (D14), 16 66316 682.

    • Search Google Scholar
    • Export Citation
  • Murphy, A. H., 1973: A new vector partition of the probability score. J. Appl. Meteor., 12, 595600.

  • Noh, Y., Cheon W. G. , Hong S. Y. , and Raasch S. , 2003: Improvement of the k-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteor., 107, 401427.

    • Search Google Scholar
    • Export Citation
  • Rémy, S., and Bergot T. , 2010: Ensemble Kalman filter data assimilation in a 1D numerical model used for fog forecasting. Mon. Wea. Rev., 138, 17921810.

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

    • Search Google Scholar
    • Export Citation
  • Shafran, P. C., Seaman N. L. , and Gayno G. A. , 2000: Evaluation of numerical predictions of boundary layer structure during the Lake Michigan Ozone Study. J. Appl. Meteor., 39, 412426.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., Klemp J. B. , Dudhia J. , Gill D. O. , Barker D. M. , Wang W. , and Powers J. G. , 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Rep. NCAR/TN-468+STR, 88 pp.

  • Stein, U., and Alpert P. , 1993: Factor separation in numerical simulations. J. Atmos. Sci., 50, 21072115.

  • Storm, B., Dudhia J. , Basu S. , Swift A. , and Giammanco I. , 2009: Evaluation of the Weather Research and Forecasting Model on forecasting low-level jets: Implications for wind energy. Wind Energy, 12, 8190, doi:10.1002/we.288.

    • Search Google Scholar
    • Export Citation
  • Tong, M., and Xue M. , 2008: Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemble square root Kalman filter. Part I: Sensitivity analysis and parameter identifiability. Mon. Wea. Rev., 136, 16301648.

    • Search Google Scholar
    • Export Citation
  • Troen, I., and Mahrt L. , 1986: A simple model of the atmospheric boundary layer: Sensitivity to surface evaporation. Bound.-Layer Meteor., 37, 129148.

    • Search Google Scholar
    • Export Citation
  • Wagner, R., Antoniou I. , Pedersen S. M. , Courtney M. S. , and Jørgensen H. E. , 2009: The influence of the wind speed profile on wind turbine performance measurements. Wind Energy, 12, 348362, doi:10.1002/we.297.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. Academic Press, 467 pp.

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Impact of Flow Dependence, Column Covariance, and Forecast Model Type on Surface-Observation Assimilation for Probabilistic PBL Profile Nowcasts

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  • 1 Israel Institute for Biological Research, Ness-Ziona, Israel
  • | 2 Naval Postgraduate School, Monterey, California
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Abstract

A probabilistic verification and factor-separation analysis (FSA) elucidate skillful nowcasts of planetary boundary layer (PBL) temperature, moisture, and wind profiles with a single-column model (SCM) and ensemble filter (EF) assimilation of surface observations. Recently, an FSA showed the importance of surface assimilation versus advection and radiation on ensemble-mean skill. That work addressed the necessary complexity of the model and assimilation scheme for improving PBL nowcasts, relative to deterministic-mesoscale predictions. Here, probabilistic ensemble-based SCM forecasts are compared to a simple probabilistic postprocessing scheme termed climatological dressing (CD). CD adjusts a deterministic mesoscale forecast using surface-atmosphere 3D-climatological covariances, a 30-min persistence model, and surface-forecast errors. It also dresses the adjusted profile with an in-sample uncertainty distribution (obtained from archives) scaled by the 30-min forecast error. Superior deterministic skill from SCM/EF results during night when flow-dependent covariances are more accurate than climatological covariances. CD is deterministically more skillful for temperature and moisture profiles during daytime because SCM/PBL parameterization yields biased covariances. SCM/EF is most probabilistically skillful because (a) the EF covariances accommodate large seasonal variability, (b) the 30-min error persistence assumption fails during nighttime, and (c) vertical error covariance estimates from archived forecasts are generally poor estimates of actual error covariances. A probabilistic FSA of the SCM/EF shows the relative importance of surface assimilation, radiation parameterization, and advection during night. Results confirm surface assimilation as the most important factor. A factor can be deterministically beneficial and probabilistically detrimental, or vice versa, depending on its role in reducing mean error or improving sharpness. Assimilation results in notable probabilistic improvement for nowcasts of low-level jet structures.

Corresponding author address: Dorita Rostkier-Edelstein, Israel Institute for Biological Research, P.O. Box 19, Ness-Ziona 74100, Israel. E-mail: doritar@iibr.gov.il

Abstract

A probabilistic verification and factor-separation analysis (FSA) elucidate skillful nowcasts of planetary boundary layer (PBL) temperature, moisture, and wind profiles with a single-column model (SCM) and ensemble filter (EF) assimilation of surface observations. Recently, an FSA showed the importance of surface assimilation versus advection and radiation on ensemble-mean skill. That work addressed the necessary complexity of the model and assimilation scheme for improving PBL nowcasts, relative to deterministic-mesoscale predictions. Here, probabilistic ensemble-based SCM forecasts are compared to a simple probabilistic postprocessing scheme termed climatological dressing (CD). CD adjusts a deterministic mesoscale forecast using surface-atmosphere 3D-climatological covariances, a 30-min persistence model, and surface-forecast errors. It also dresses the adjusted profile with an in-sample uncertainty distribution (obtained from archives) scaled by the 30-min forecast error. Superior deterministic skill from SCM/EF results during night when flow-dependent covariances are more accurate than climatological covariances. CD is deterministically more skillful for temperature and moisture profiles during daytime because SCM/PBL parameterization yields biased covariances. SCM/EF is most probabilistically skillful because (a) the EF covariances accommodate large seasonal variability, (b) the 30-min error persistence assumption fails during nighttime, and (c) vertical error covariance estimates from archived forecasts are generally poor estimates of actual error covariances. A probabilistic FSA of the SCM/EF shows the relative importance of surface assimilation, radiation parameterization, and advection during night. Results confirm surface assimilation as the most important factor. A factor can be deterministically beneficial and probabilistically detrimental, or vice versa, depending on its role in reducing mean error or improving sharpness. Assimilation results in notable probabilistic improvement for nowcasts of low-level jet structures.

Corresponding author address: Dorita Rostkier-Edelstein, Israel Institute for Biological Research, P.O. Box 19, Ness-Ziona 74100, Israel. E-mail: doritar@iibr.gov.il
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