1 / 5

Verification and Metrics (CAWCR)

Verification and Metrics (CAWCR). Purpose – beyond forecast products. Through the application of appropriate verification methods we: Define the skill of predicting Australian climate on intra-seasonal timescales (forecast products)

summer-sanford
Download Presentation

Verification and Metrics (CAWCR)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Verification and Metrics (CAWCR)

  2. Purpose – beyond forecast products • Through the application of appropriate verification methods we: • Define the skill of predicting Australian climate on intra-seasonal timescales (forecast products) • Examine the impact of initialisation; ensemble generation and model development on forecast skill • Diagnose/understand mechanisms and sources of predictability • Monitor in real-time

  3. Variables - Precipitation (ensemble mean anomaly; tercile catagories) - Tmax (ensemble mean anomaly; tercile catagories) - Tmin (ensemble mean anomaly; tercile catagories) - SST (ensemble mean anomaly) - MJO RMM indices - Monsoon onset - Other key indices (SAM, blocking) Stratification - Season (DJF; MAM; JJA; SON) - According to state of ENSO/IOD/SAM/MJO (degree of stratification possible depends on sample size) Lead time and averaging period - Daily (usually done for prediction of indices) - Fortnight 1 - Fortnight 2 - Month 1 Reference forecast - Persistence of observed - Climatology Leave-one-year-out cross validation (verification of hindcasts) Comparing systems with different ensemble sizes

  4. Probabilistic scores - Reliability (attributes ) diagram (including frequency histogram) Dichotomous (yes/no) forecasts Conditioned on the fc: given the fc, what was the outcome Reliability, resolution, sharpness Aggregation over region/seasons Need fairly large sample (enough cases in each bin) Choice of bin width - ROC curve Dichotomous (yes/no) forecasts Conditioned on obs: given the outcome, what was the fc Resolution (discrimination) Aggregation over region/seasons - ROC area Grid-point basis and showing significance: determined using the Mann–Whitney U statistic (Mason and Graham, 2002; Wilks, 2006). - Brier Skill Score (w.r.t. climatology) Debiased BSS (bias correction for small ensemble size) BBSd - based on weighting of indiv models for MME (not done this yet) Decomposition of BS: BSrel and BSres components “Deterministic” scores - Correlation of ensemble mean (usually use temporal rather than spatial) - RMSEensemble mean - Spread of ensemble

  5. Examples: Rainfall over Australia (Sep/Oct/Nov, fortnight 2) Brier skill score ROC curve Reliability diagram ROC area ROC A BSres Correlation BSrel

More Related