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Diagnostic verification and extremes: 1 st Breakout. Discussed the need for toolkit to build beyond current capabilities (e.g., NCEP) Identified (and began to address) 3 major questions:

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diagnostic verification and extremes 1 st breakout
Diagnostic verification and extremes: 1st Breakout
  • Discussed the need for toolkit to build beyond current capabilities (e.g., NCEP)
  • Identified (and began to address) 3 major questions:
    • How should confidence intervals and hypothesis tests be computed, especially when there are spatial and temporal correlations?
    • What methods should be included for evaluating extremes?
    • What diagnostic approaches should be included initially; in a 2nd tier; in a 3rd tier
confidence intervals and hypothesis tests
Confidence intervals and hypothesis tests
  • Need to appropriately take into account autocorrelations
    • Reduce sample by eliminating cases
    • Block re-sampling (Candille results indicate spatial correlation may have more impact than temporal, at least for upper air)
  • Identify situations when parametric approaches are “ok”
    • Bootstrapping approaches are computer-intensive and require lots of data storage
      • May not always be practical in operational settings
methods for evaluation of extremes
Methods for evaluation of extremes
  • Need to distinguish (in our minds) between extremes and high impact weather
  • User should define thresholds for extremes
    • May be based on quantiles of sample distribution
    • Could use extreme value theory to help with this (e.g., return level methods)
  • Extreme dependency score is appropriate in many cases
  • Also compute standard scores: Yule’s Q; odds ratio; ORSS; ETS, etc.
diagnostic methods
Diagnostic methods
  • Goal: Identify different tiers of methods/capabilities that will be implemented over time, starting with Tier 1 in 1st release
  • Initial discussion: Stratification
  • Friday discussion: Specific methods
stratification
Stratification
  • Tier 1:

Based on meta-data, including time-of-day, season, location, etc.

  • Tier 2:

Based on other information from the model, such as temperature, wind direction, etc.

  • Tier 3:

Based on feature such as location or strength of jet core; cyclone track; etc.