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Verification techniques for high resolution NWP precipitation forecasts
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  1. Verification techniques for high resolution NWP precipitation forecasts Emiel van der Plas ( KeesKok Maurice Schmeits

  2. Introduction NWP has come a long way… It was: Then it became Hirlam: Now it is Harmonie It should be GALES (or so) It looks better… But how is it better? Does it perform better? That remains to be seen… 2

  3. Representation: “double penalty” Forecast localised phenomena: False alarm + Miss = double penalty Station (gauge) data: Forecast vs Radar data: When we take point-by-point errors (ME/RMSE): 3

  4. This talk HARP: Hirlam Aladin R-based verification Packages Tools for spatial, ensemble verification Based on R FSS, SAL, … Relies on eg SpatialVX package (NCAR) Generalized MOS approach Comparison high vs low resolution Hirlam (11 km, hydrostatic) Harmonie (2.5 km, non-hydrostatic, w/ & w/o Mode-S) ECMWF (T1279, deterministic) Lead times: +003, +006, +009, +012 Accumulated precipitation vs (Dutch) radar, synop 4

  5. Neo-classical: neighborhood methods, FSS Options: FSS, ISS, SAL, … Fraction Skill Score (fuzzy verification) (Roberts & Lean, 2008) Straightforward interpretation ‘Resolves’ double penalty But ‘smoothes’ away resolution that may contain information! ( Vstorm D t ) == upscaling Baserate , FSS  forecast observation 5

  6. FSS results: Differences are sometimes subtle: 1x1 3x3 7

  7. FSS: more results Higher resolutions: higher thresholds? DMO! 8

  8. How would a trained meteorologist look at direct model output? Model Output Statistics Learn for each model, location, … separately! 9 9/15

  9. Model Output Statistics Construct a set of predictors (per model, station, starting and lead time): For now: use precipitation only Use various ‘areas of influence’: 25,50,75,100 km DMO, coverage, max(DMO) within area, distance to forecasted precipitation, … Apply logistic regression Forward stepwise selection, backward deletion Probability of threshold exceedance! Verify probabilities based on DMO, coefficients of selected predictors Training data: day 1-20, `independent’ data: day 21 – 28/31 10

  10. Model (predictor) selection Based on AIC (Akaike Information Criterion) Take the predictor with highest AIC in training set (day 1 - 20) Test on independent set (day 21 – 28/31) Sqrt(max)_100 More predictors != more skill Sqrt(tot_100) distext_100 exp2int_100 11

  11. Model comparison (April – October 2012) Hirlam, Harmonie (based on Hirlam) ECMWF 12UTC+003 12UTC+006 12UTC+009 13

  12. Discussion, to do MOS method: Stratification per station, season, … More data necessary, reforecasting under way Representation error: take (small) radar area Use ELR, conditional probabilities for higher thresholds Extend to wind, fog/visibility, MSG/cloud products, etc FSS: Use OPERA data 15/15

  13. Conclusion/Discussion Comparison between NWP’s of different resolution is, well, fuzzy Realism != Score Fraction Skill Score yields numbers, but sometimes hard to draw conclusions MOS method: Resolution/model independent Takes into account what we know Doubles (potentially) as predictive guide Thank you for your attention! 16/15

  14. Binary predictand yi (here: precip > q) Probability: logistic: Joint likelihood: L2 penalisation (using R: stepPLR by Mee Young Park and Trevor Hastie, 2008): minimise Use threshold (sqrt(q)) as predictor: complete distribution function (Wilks, 2009) Few cases, many potential predictors: pool stations, max 5 terms Extended Logistic Regression (ELR) 17 17/15