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Quantitative precipitation forecasts in the Alps – first results from the Forecast Demonstration Project MAP D-PHASE. Felix Ament, Marco Arpagaus, and Mathias W Rotach MeteoSwiss. COSMO-2. Radar. Lyss. (Lyss-Hochwasser, 29.8.2007). Verification – rules of the game. Models.

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slide1

Quantitative precipitation forecasts in the Alps – first results from the Forecast Demonstration Project MAP D-PHASE

Felix Ament, Marco Arpagaus, and Mathias W RotachMeteoSwiss

COSMO-2

Radar

Lyss

(Lyss-Hochwasser, 29.8.2007)

verification rules of the game
Verification – rules of the game

Models

apply warnlevels

domain averages

Most recent forecast, but starting not before +03h

hourly accumulations

Alert time series

RR time series

OBS

  • 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: 3h)
observational data
Observational data

RADARtime series

Swiss Radar composite

  • Warn regions averages
  • Hourly accumulations
  • 3 Radar stations
  • 5 min scans accumulated to hourly estimates
  • 1km resolution
  • Spatial average
  • Multiplicative correction to achieve match of daily accumulations

RADAR_CALtime series

Gridded raingauge data

  • Spatial average
  • Warn regions averages
  • Hourly accumulations
  • Daily sums equivalent to gridded gauge data
  • Statistical interpolation + elevation correction
  • Daily accumulations
verification of precipitation amount radar cal reference
Verification of precipitation amount(RADAR_CAL reference)

resolved conv.

global model

param. conv.

RADAR

Whole Switzerland,summer 2007,relative BIAS

Single target region,summer 2007,relative BIAS

Single target region,3 hourly resolution,correlation

fuzzy verification
Fuzzy Verification

Verification on coarser scales than model scale: “Do not require a point wise match!“

fuzzy verification cosmo 2 cosmo 7

good

bad

Fuzzy Verification COSMO-2 – COSMO-7

JJA 2007, Verification against Swiss Radar Composite, 3 hourly accumulations

-

90

58

33

20

7

=

Upscaling

Spatial scale (km)

Difference

COSMO-2 (2.2km)

COSMO-7 (7km)

-

90

58

33

20

7

=

Fraction skill score

Spatial scale (km)

Threshold (mm/3h)

Threshold (mm/3h)

Threshold (mm/3h)

COSMO-7 better

COSMO-2 better

fuzzy verification cosmo de cosmo eu

good

bad

Fuzzy Verification COSMO-DE – COSMO-EU

JJA 2007, Verification against Swiss Radar Composite, 3 hourly accumulations

-

90

58

33

20

7

=

Upscaling

Spatial scale (km)

Difference

COSMO-DE (2.8km)

COSMO-EU (7km)

-

90

58

33

20

7

=

Fraction skill score

Spatial scale (km)

Threshold (mm/3h)

Threshold (mm/3h)

Threshold (mm/3h)

COSMO-EU better

COSMO-DE better

alerts level yellow 3h intervals
Alerts – level „yellow“, 3h intervals

(Alert level yellow = return frequency of 6 times per year)

and

but

concept of relative value
Concept of “Relative Value”

Economic point of view:

Event

Precautions causes Costs

Having no protection results in Losses

Precaution

Relative Value

-

0.0

useless

0.25

Total Cost

0.5

0.75

+

useful

1.0

noforecast

realforecast

perfectforecast

relative value alert level yellow
Relative value – Alert level „yellow“

(03h, 06h and 12h accumulations, cut-off +03h)

all models

param. conv.

+

useful

resolved conv.

RADAR

global model

-

useless

insensitive …

sensitive …

… against false alarms

calibration versus ensemble
Calibration versus Ensemble

D-PHASE poor- men\'s ensemble

Issue an alert, if a certain fraction of all models gives a warning

Simple calibration of COSMO-2

Multiply COSMO-2 precipitation forecasts by a factor of

10%

20%

30%

40%

50%

60%

70%

80%

90%

2.0

1.25

1.0

0.8

0.5

COSMO-2

conclusions
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!
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