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- Slides: 12
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology Meteo. Swiss Quantitative precipitation forecasts in the Alps – first results from the Forecast Demonstration Project MAP D-PHASE Felix Ament, Marco Arpagaus, and Mathias W Rotach Meteo. Swiss COSMO-2 Radar Lyss (Lyss-Hochwasser, 29. 8. 2007)
Verification – rules of the game Models apply warnlevels domain averages Most recent forecast, but starting not before +03 h hourly accumulations OBS RR time series 6 times a year Twice a year • Period: Summer 2007 (June, July and August) • Spatial resolution: 18 target regions in Switzerland • Temporal resolution: 3 hour intervals • Forecast range: Use most recent forecast, but ignore a certain cut-off time at the beginning of each forecast (default cut-off: 3 h) First results of D-PHASE | 6 th COPS Workshop, 27 -29 February 2008, Hohenheim Felix. Ament@meteoswiss. ch Every 10 years Alert time series 2
Observational data RADAR time series Swiss Radar composite • Warn regions averages • 3 Radar stations • 5 min scans accumulated to hourly estimates • Hourly accumulations Spatial average Multiplicative correction to achieve match of daily accumulations • 1 km resolution Gridded rain gauge data Spatial average RADAR_CAL time series • Statistical interpolation + elevation correction • Warn regions averages • Daily accumulations • Daily sums equivalent to gridded gauge data First results of D-PHASE | 6 th COPS Workshop, 27 -29 February 2008, Hohenheim Felix. Ament@meteoswiss. ch • Hourly accumulations 3
Verification of precipitation amount (RADAR_CAL reference) resolved conv. global model param. conv. RADAR Whole Switzerland, summer 2007, relative BIAS Single target region, 3 hourly resolution, correlation First results of D-PHASE | 6 th COPS Workshop, 27 -29 February 2008, Hohenheim Felix. Ament@meteoswiss. ch 4
Fuzzy Verification on coarser scales than model scale: “Do not require a point wise match!“ Method Raw Data Fuzzyfication Score Example result Average X Upscaling X X X Equitable threat score x X X X x Fractional coverage Fraction Skill Score (Roberts and Lean, 2005) X X X x X X X Skill score with reference to worst forecast x First results of D-PHASE | 6 th COPS Workshop, 27 -29 February 2008, Hohenheim Felix. Ament@meteoswiss. ch 5
Fuzzy Verification COSMO-2 – COSMO-7 90 Fraction skill score COSMO-2 (2. 2 km) = 20 7 Difference COSMO-7 (7 km) - 33 90 = 58 33 20 7 Threshold (mm/3 h) bad Threshold (mm/3 h) good COSMO-7 better First results of D-PHASE | 6 th COPS Workshop, 27 -29 February 2008, Hohenheim Felix. Ament@meteoswiss. ch COSMO-2 better 6 Spatial scale (km) Upscaling - 58 Spatial scale (km) JJA 2007, Verification against Swiss Radar Composite, 3 hourly accumulations
Fuzzy Verification COSMO-DE – COSMO-EU 90 Fraction skill score COSMO-DE (2. 8 km) = 20 7 Difference COSMO-EU (7 km) - 33 90 = 58 33 20 Spatial scale (km) Upscaling - 58 Spatial scale (km) JJA 2007, Verification against Swiss Radar Composite, 3 hourly accumulations 7 Threshold (mm/3 h) bad Threshold (mm/3 h) good COSMO-EU better First results of D-PHASE | 6 th COPS Workshop, 27 -29 February 2008, Hohenheim Felix. Ament@meteoswiss. ch COSMO-DE better 7
Alerts – level „yellow“, 3 h intervals (Alert level yellow = return frequency of 6 times per year) Relative frequency of an alert (frequency bias) and but Probability to detect an event (probability of detection) Probability to issue a false alarm (false alarm ratio) First results of D-PHASE | 6 th COPS Workshop, 27 -29 February 2008, Hohenheim Felix. Ament@meteoswiss. ch 8
Concept of “Relative Value” Economic point of view: Having no protection results in Losses Precautions causes Costs Event Yes No Yes C C No L 0 Relative Value Total Cost 0. 0 - useless 0. 25 0. 75 1. 0 + useful no real perfect forecast First results of D-PHASE | 6 th COPS Workshop, 27 -29 February 2008, Hohenheim Felix. Ament@meteoswiss. ch 9
Relative value – Alert level „yellow“ (03 h, 06 h and 12 h accumulations, cut-off +03 h) + useful param. all models conv. resolved conv. RADAR global model - useless insensitive … … against false alarms First results of D-PHASE | 6 th COPS Workshop, 27 -29 February 2008, Hohenheim Felix. Ament@meteoswiss. ch 10
Calibration versus Ensemble Simple calibration of COSMO-2 D-PHASE poormen's ensemble Multiply COSMO-2 precipitation forecasts by a factor of Issue an alert, if a certain fraction of all models gives a warning 2. 0 1. 25 1. 0 COSMO-2 0. 8 0. 5 First results of D-PHASE | 6 th COPS Workshop, 27 -29 February 2008, Hohenheim Felix. Ament@meteoswiss. ch 10% 20% 30% 40% 50% 60% 70% 80% 90% 11
Conclusions • Observational uncertainties (QPE) are not significantly smaller than forecast errors (QPF) – at least for extremes! • High resolution models resolving deep convection tend to perform better than models with parameterized convection. This applies for all international models. • Probabilistic forecasts are useful for customers. However, the method of choice is still unclear: Simple static recalibration and an uncalibrated ensemble forecasting system perform equally! First results of D-PHASE | 6 th COPS Workshop, 27 -29 February 2008, Hohenheim Felix. Ament@meteoswiss. ch 12
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