• Baker, N. L., and R. Daley, 2000: Observation and background adjoint sensitivity in the adaptive observation-targeting problem. Quart. J. Roy. Meteor. Soc., 126 , 14311454.

    • Search Google Scholar
    • Export Citation
  • Bergot, T., 1999: Adaptive observations during FASTEX: A systematic survey of upstream flights. Quart. J. Roy. Meteor. Soc., 125 , 32713298.

    • Search Google Scholar
    • Export Citation
  • Bergot, T., G. Hello, A. Joly, and S. Malardel, 1999: Adaptive observations: A feasibility study. Mon. Wea. Rev., 127 , 743765.

  • Bishop, C. H., and Z. Toth, 1999: Ensemble transformation and adaptive observations. J. Atmos. Sci., 56 , 17481765.

  • 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 , 420436.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., 1995: Optimal perturbation time evolution and sensitivity of ensemble prediction to perturbation amplitude. Quart. J. Roy. Meteor. Soc., 121 , 17051738.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., and A. Montani, 1999: Targeted observations using singular vectors. J. Atmos. Sci., 56 , 29652985.

  • Buizza, R., T. Petroliagis, T. N. Palmer, J. Barkmeijer, M. Hamrud, A. Hollingsworth, A. Simmons, and N. Wedi, 1998: Impact of model resolution and ensemble size on the performance of an ensemble prediction system. Quart. J. Roy. Meteor. Soc., 124 , 19351960.

    • Search Google Scholar
    • Export Citation
  • Evensen, G., 1994: Sequential data assimilation with a non-linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 ((C5),) 1014310162.

    • Search Google Scholar
    • Export Citation
  • Gelaro, R., R. H. Langland, G. D. Rohaly, and T. E. Rosmond, 1999: An assessment of the singular vector approach to targeted observations using the FASTEX data set. Quart. J. Roy. Meteor. Soc., 125 , 32993328.

    • Search Google Scholar
    • Export Citation
  • Gelaro, R., C. A. Reynolds, R. H. Langland, and G. D. Rohaly, 2000: A predictability study using geostationary satellite wind observations during NORPEX. Mon. Wea. Rev., 128 , 37893807.

    • Search Google Scholar
    • Export Citation
  • Gilmour, I., L. A. Smith, and R. Buizza, 2001: Linear regime duration: Is 24 hours a long time in synoptic weather forecasting? J. Atmos. Sci., 58 , 35253539.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., and C. Snyder, 2000: A hybrid ensemble Kalman filter–3D variational analysis scheme. Mon. Wea. Rev., 128 , 29052919.

  • Houtekamer, P. L., and J. Derome, 1994: Prediction experiments with two-member ensembles. Mon. Wea. Rev., 122 , 21792191.

  • Houtekamer, P. L., and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126 , 796811.

    • Search Google Scholar
    • Export Citation
  • Langland, R. H., and G. D. Rohaly, 1996: Adjoint-based targeting of observations for FASTEX cyclones. Reprints, Seventh Conf. on Mesoscale Processes, Reading, United Kingdom, Amer. Meteor. Soc., 369–371.

    • Search Google Scholar
    • Export Citation
  • Langland, R. H., and Coauthors. 1999: The North Pacific Experiment (NORPEX-98): Targeted observations for improved North American weather forecasts. Bull. Amer. Meteor. Soc., 80 , 13631384.

    • Search Google Scholar
    • Export Citation
  • Majumdar, S. J., C. H. Bishop, B. J. Etherton, I. Szunyogh, and Z. Toth, 2001: Can an ensemble transform Kalman filter predict the reduction in forecast-error variance produced by targeted observations? Quart. J. Roy. Meteor. Soc., 127 , 28032820.

    • Search Google Scholar
    • Export Citation
  • Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF ensemble prediction system: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122 , 73120.

    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., R. Gelaro, J. Barkmeijer, and R. Buizza, 1998: Singular vectors, metrics, and adaptive observations. J. Atmos. Sci., 55 , 633653.

    • Search Google Scholar
    • Export Citation
  • Rabier, F., H. Jarvinen, E. Klinker, J-F. Mahfouf, and A. Simmons, 2000: The ECMWF operational implementation of four-dimensional variational assimilation I: Experimental results with simplified physics. Quart. J. Roy. Meteor. Soc., 126 , 11431170.

    • Search Google Scholar
    • Export Citation
  • Szunyogh, I., Z. Toth, K. A. Emanuel, C. H. Bishop, C. Snyder, R. E. Morss, J. S. Woolen, and T. P. Marchok, 1999a: Ensemble-based targeting experiments during FASTEX: The effect of dropsonde data from the Lear jet. Quart. J. Roy. Meteor. Soc., 125 , 31893217.

    • Search Google Scholar
    • Export Citation
  • Szunyogh, I., Z. Toth, S. J. Majumdar, R. E. Morss, C. H. Bishop, and S. J. Lord, 1999b: Ensemble-based targeted observations during NORPEX. Preprints, Third Symp. on Integrated Observing Systems, Dallas, TX, Amer. Meteor. Soc., 74–77.

    • Search Google Scholar
    • Export Citation
  • Szunyogh, I., Z. Toth, R. E. Morss, S. J. Majumdar, B. J. Etherton, and C. H. Bishop, 2000: The effect of targeted dropsonde observations during the 1999 Winter Storm Reconnaissance Program. Mon. Wea. Rev., 128 , 35203537.

    • Search Google Scholar
    • Export Citation
  • Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125 , 32973319.

  • Toth, Z., I. Szunyogh, S. J. Majumdar, R. E. Morss, B. J. Etherton, C. H. Bishop, and S. J. Lord, 1999: The 1999 Winter Storm Reconnaissance Program. Preprints, 13th Conf. on Numerical Weather Prediction, Denver, CO, Amer. Meteor. Soc., 27–32.

    • Search Google Scholar
    • Export Citation
  • Toth, Z., and Coauthors. 2000: Targeted observations at NCEP: Toward an operational implementation. Preprints, Fourth Symp. on Integrated Observing Systems, Long Beach, CA, Amer. Meteor. Soc., 186–193.

    • Search Google Scholar
    • Export Citation
  • Toth, Z., I. Szunyogh, S. J. Majumdar, C. H. Bishop, and S. J. Lord, 2001: Targeted observations for improving numerical weather forecasts. Preprints, Fifth Symp. on Integrated Observing Systems, Albuquerque, NM, Amer. Meteor. Soc., 72–79.

    • Search Google Scholar
    • Export Citation
  • Wu, W-S., and S-W. Joo, 1996: The change of the observational errors in the NCEP SSI analysis. Preprints 11th Conf. on Numerical Weather Prediction, Norfolk, VA, Amer. Meteor. Soc., 84–85.

    • Search Google Scholar
    • Export Citation
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Adaptive Sampling with the Ensemble Transform Kalman Filter. Part II: Field Program Implementation

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  • 1 Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania
  • | 2 SAIC at Environmental Modeling Center, NCEP, NWS, NOAA, Camp Springs, Maryland
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Abstract

The practical application of the ensemble transform Kalman filter (ET KF), used in recent Winter Storm Reconnaissance (WSR) programs by the National Centers for Environmental Prediction (NCEP), is described. The ET KF assesses the value of targeted observations taken at future times in improving forecasts for preselected critical events. It is based on a serial assimilation framework that makes it an order of magnitude faster than its predecessor, the ensemble transform technique. The speed of the ET KF enabled several different forecast scenarios to be assessed for targeting during recent WSR programs.

Each potential observational network is broken down into idealized routine and adaptive components. The adaptive component represents a predesigned flight track along which GPS dropwindsondes are released. For a large number of flight tracks, the ET KF estimates the forecast error reducing effects of these observations (via the “signal variance”). The track that maximizes the average forecast signal variance within a selected verification region is deemed optimal for targeting. Secondary flight tracks can also be chosen using serial assimilation, by calculating the signal variance for each flight track given that the primary track had already been selected.

For the second consecutive year the ET KF was able to estimate, via a statistical rescaling, the variance of NCEP signal realizations produced by the dropwindsonde data. A monotonic increasing relationship between the ET KF signal variance and the reduction in NCEP forecast error variance due to the targeted observations was then deduced for the operational 2001 WSR program.

Current affiliation: RSMAS/MPO, University of Miami, Miami, Florida

Corresponding author address: Dr. S. J. Majumdar, RSMAS/MPO, University of Miami, 4600 Rickenbacker Cswy., Miami, FL 33149-1098. Email: smajumdar@rsmas.miami.edu

Abstract

The practical application of the ensemble transform Kalman filter (ET KF), used in recent Winter Storm Reconnaissance (WSR) programs by the National Centers for Environmental Prediction (NCEP), is described. The ET KF assesses the value of targeted observations taken at future times in improving forecasts for preselected critical events. It is based on a serial assimilation framework that makes it an order of magnitude faster than its predecessor, the ensemble transform technique. The speed of the ET KF enabled several different forecast scenarios to be assessed for targeting during recent WSR programs.

Each potential observational network is broken down into idealized routine and adaptive components. The adaptive component represents a predesigned flight track along which GPS dropwindsondes are released. For a large number of flight tracks, the ET KF estimates the forecast error reducing effects of these observations (via the “signal variance”). The track that maximizes the average forecast signal variance within a selected verification region is deemed optimal for targeting. Secondary flight tracks can also be chosen using serial assimilation, by calculating the signal variance for each flight track given that the primary track had already been selected.

For the second consecutive year the ET KF was able to estimate, via a statistical rescaling, the variance of NCEP signal realizations produced by the dropwindsonde data. A monotonic increasing relationship between the ET KF signal variance and the reduction in NCEP forecast error variance due to the targeted observations was then deduced for the operational 2001 WSR program.

Current affiliation: RSMAS/MPO, University of Miami, Miami, Florida

Corresponding author address: Dr. S. J. Majumdar, RSMAS/MPO, University of Miami, 4600 Rickenbacker Cswy., Miami, FL 33149-1098. Email: smajumdar@rsmas.miami.edu

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