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Probabilities from COSMO-2 derived with the neighborhood method

Probabilities from COSMO-2 derived with the neighborhood method. Pirmin Kaufmann, MeteoSwiss. COSMO General Meeting 18 – 21 September 2007 Athens, Greece. Neighborhood method. Probabilistic forecast from deterministic model

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Probabilities from COSMO-2 derived with the neighborhood method

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  1. Probabilities from COSMO-2 derived with the neighborhood method Pirmin Kaufmann, MeteoSwiss COSMO General Meeting 18 – 21 September 2007 Athens, Greece

  2. Neighborhood method • Probabilistic forecast from deterministic model • Represents probability related to local-scale spatial and temporal model uncertainty and predictability • Important especially on small scales (e.g. thunderstorms) • Method does not represent uncertainty in synoptic forcing (complement to, not substitute for EPS)

  3. Original shape of the neighborhood • Ellipsoidal • Original shape of Theis et al. (2005) • Spatial radius decreases with increasing distance in time: Space-time dependency t y x

  4. Shape of the neighborhood • Cylindrical • Assuming spatial and temporal uncertainty are independent • True in weak ambient winds, not true for strong ambient winds • Equal weights for all grid poins t y x

  5. Linearly fading weights Large, medium, small neighborhood • Problem: Circles around singular high model values • Idea: Smooth edges • Introduce linear fading of weights (similar to relaxation) • Adds sponge layer around cylindrical neighborhood

  6. Cases from „Windbank“ Simulations1995 – 1999 Threshold 35 mm / 12 h

  7. 1995-07-12 (Case A) Temporal radius obs rt=1 rt=3 rt=6

  8. 1995-07-12 (Case A) Spatial radius obs rxy=5 rxy=10 rxy=15

  9. 1995-09-13 (Case E) Temporal radius obs rt=1 rt=3 rt=6

  10. 1995-09-13 (Case E) Spatial radius obs rxy=5 rxy=10 rxy=15

  11. 1999-10-25 (Case L)Temporal radius obs rt=1 rt=3 rt=6

  12. 1999-10-25 (Case L)Spatial radius obs rxy=5 rxy=10 rxy=15

  13. Recent case: 8/9 August 2007 Flood

  14. Objective probabilistic verification • Verification needs to be done, but can it be done? • High density rain gauge network: Only 24 h resolution • Network with 10 min rainfall data: Only coarse spatial resolution • Optimal radius: Quality measure (e.g. BSS) increases with increasing radius, „an optimal neighborhood size cannot be found at all“ (experience of S. Theis; Theis 2005) • Result for 7 km COSMO resolution -> still valid at 2 km ? • Sparseness of data: Extreme precipitation • occurs only in few cases • is often limited in spatial extent

  15. Neighborhood method • Main influence is spatial radius rxy and fading zone rf. • Only minor changes with temporal radius rt. • Consequence of accumulated precipitation values • Should we treat the time dimension differently altogether? -> Maximum over time • Current settings at MeteoSwiss (precipitation): • COSMO-2: rxy=15, rf=15, rt=3 • COSMO-7: rxy= 5, rf= 5, rt=3 • Need of high resolution data for verification: Resolution of rain gauges insufficient, either spatial or temporal THE END

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