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ScanLag: High-throughput Quantification of Colony Growth and Lag Time
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The Weighted-Average Lagged Ensemble.

T DelSole1,2, L Trenary1,2, M K Tippett3,4

  • 1Department of Atmospheric, Ocean, and Earth Sciences George Mason University Fairfax VA USA.

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Summary
This summary is machine-generated.

Optimal weighting of lagged ensemble forecasts can improve skill. This study reveals conditions where weights become negative or non-monotonic, particularly with rapid error growth and high error correlation.

Keywords:
lagged ensembleweather forecast

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Area of Science:

  • Meteorology and Climate Science
  • Data Assimilation and Ensemble Forecasting

Background:

  • Lagged ensembles combine forecasts from the same model initialized at different times.
  • Weighted-average lagged ensembles optimize forecast skill by assigning weights.
  • Optimal weights typically decay monotonically with lead time for uncorrelated errors.

Purpose of the Study:

  • To investigate conditions leading to non-standard optimal weights in weighted-average lagged ensembles.
  • To analyze scenarios where optimal weights become negative or depend non-monotonically on lead time.

Main Methods:

  • Development of analytic examples to study optimal weight behavior.
  • Examination of the relationship between error characteristics and weight behavior.
  • Analysis of mean square error constancy across forecast lead times.

Main Results:

  • Negative weights are probable when forecast errors grow rapidly and exhibit high cross-lead-time correlation.
  • Non-monotonic weights are likely when mean square error remains constant across the ensemble's lead time range.
  • An extreme case demonstrates optimal weights concentrating only at the shortest and longest lead times.

Conclusions:

  • The behavior of optimal weights in lagged ensembles is sensitive to error dynamics.
  • Understanding these conditions is crucial for accurate ensemble forecast interpretation and application.
  • Non-standard weight behavior can arise even in bias-corrected, unbiased weighted ensembles.