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A Hybrid NWP–Analog Ensemble

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  • 1 The Climate Corporation, Seattle, Washington
  • | 2 National Center for Atmospheric Research, Boulder, Colorado
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Abstract

An analog ensemble (AnEn) is constructed by first matching up the current forecast from a numerical weather prediction (NWP) model with similar past forecasts. The verifying observation from each match is then used as an ensemble member. For at least some applications, the advantages of AnEn over an NWP ensemble (multiple real-time model runs) may include higher efficiency, avoidance of initial condition and model perturbation challenges, and little or no need for postprocessing calibration. While AnEn can capture flow-dependent error growth, it may miss aspects of error growth that can be represented dynamically by the multiple real-time model runs of an NWP ensemble. To combine the strengths of the AnEn and NWP ensemble approaches, a hybrid ensemble (HyEn) is constructed by finding m analogs for each member of a small n-member NWP ensemble, to produce a total of m × n members.

Forecast skill is compared between the AnEn, HyEn, and an NWP ensemble calibrated using logistic regression. The HyEn outperforms the other approaches for probabilistic 2-m temperature forecasts yet underperforms for 10-m wind speed. The mixed results reveal a dependence on the intrinsic skill of the NWP members employed. In this study, the NWP ensemble is underspread for both 2-m temperature and 10-m winds, yet displays some ability to represent flow-dependent error for the former and not the latter. Thus, the HyEn is a promising approach for efficient generation of high-quality probabilistic forecasts, but requires use of a small, and at least partially functional, NWP ensemble.

Corresponding author address: Dr. Tony Eckel, The Climate Corporation, 710 2nd Ave. 1100, Seattle, WA 98104. E-mail: tony.eckel@climate.com

Abstract

An analog ensemble (AnEn) is constructed by first matching up the current forecast from a numerical weather prediction (NWP) model with similar past forecasts. The verifying observation from each match is then used as an ensemble member. For at least some applications, the advantages of AnEn over an NWP ensemble (multiple real-time model runs) may include higher efficiency, avoidance of initial condition and model perturbation challenges, and little or no need for postprocessing calibration. While AnEn can capture flow-dependent error growth, it may miss aspects of error growth that can be represented dynamically by the multiple real-time model runs of an NWP ensemble. To combine the strengths of the AnEn and NWP ensemble approaches, a hybrid ensemble (HyEn) is constructed by finding m analogs for each member of a small n-member NWP ensemble, to produce a total of m × n members.

Forecast skill is compared between the AnEn, HyEn, and an NWP ensemble calibrated using logistic regression. The HyEn outperforms the other approaches for probabilistic 2-m temperature forecasts yet underperforms for 10-m wind speed. The mixed results reveal a dependence on the intrinsic skill of the NWP members employed. In this study, the NWP ensemble is underspread for both 2-m temperature and 10-m winds, yet displays some ability to represent flow-dependent error for the former and not the latter. Thus, the HyEn is a promising approach for efficient generation of high-quality probabilistic forecasts, but requires use of a small, and at least partially functional, NWP ensemble.

Corresponding author address: Dr. Tony Eckel, The Climate Corporation, 710 2nd Ave. 1100, Seattle, WA 98104. E-mail: tony.eckel@climate.com
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