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How much is it going to rain? What is the probability of such an event to happen?

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Improving COSMO-LEPS forecasts of extreme events with reforecastsF. Fundel, A. Walser, M. Liniger, C. Appenzeller

How much is it going to rain?

What is the probability of such an event to happen?

Are there systematic model errors?

Do model errors vary in space, time?

Did the model ever forecast a such an event?

Should a warning be given?

Model

Obs

25. Jun. +-14d

Difference of CDF of observations and COSMO-LEPS 24h total precipitation

10/2003-12/2006

Model too wet, worse in southern Switzerland

“However, the improved skill from calibration using large datasets is equivalent to the skill increases afforded by perhaps 5–10 yr of numerical modeling system development and model resolution increases.” (Wilks and Hamill, Mon. Wea. Rev. 2007)

“Use of reforecasts improved probabilistic precipitation forecasts dramatically, aided the diagnosis of model biases, and provided enough forecast samples to answer some interesting questions about predictability in the forecast model.” (Hamill et. al, BAMS 2006)

“…reforecast data sets may be particularly helpful in the improvement of

probabilistic forecasts of the variables that are most directly relevant to many forecast users…” (Hamill and Whitaker, subm. to Mon. Wea. Rev 2006)

Setup

- Reforecasts over a period of 30 years (1971-2000)
- Deterministic run of COSMO-LEPS (1 member)
(convective scheme = tiedtke)

- ERA40 Reanalysis as Initial/Boundary
- 42h lead time, 12:00 Initial time
- Calculated on hpce at ECMWF
- Archived on Mars at ECMWF (surf (30 parameters),
4 plev (8 parameters); 3h step)

- Post processing at CSCS
Limitations

- Reforecasts with lead time of 42h are used to calibrate forecasts of up to 132h
- Only one convection scheme (COSMO-LEPS uses 2)
- New climatology needed with each model version change
- Building a climatology is slow and costly
- Currently only a monthly subset of the climatology is used for calibration (warning indices need to be interpreted with respect to the actual month)

x Model Climate

Ensemble Forecast

p

F(p) = proportion of EPS members below

the p percentile

F(p)

-1 < EFI > 1

EFI = -1 : All Forecast are below the climatology

EFI = 1 : All Forecast are above the climatology

EFI for 24h total precipitation

05.09.2007 00 UTC – 06.09.2007 00 UTC05.09.2007 06 UTC – 06.09.2007 06 UTC

ECMWF

COSMO-LEPS

0.8???

EFI properties (desired?)

Combines properties of two CDFs in one number

Forecast and climatology spread influence the EFI

Ambiguous interpretation

without further information

EFI for varying forecast mean and standard deviation

constant climatology with mean=0 and =1

Approach:

- fit a distribution function to the model climate
(e.g. Gamma for precipitation)

- find the return levels according to a given
return period

- find the number of forecasts exceeding the
return level of a given return period

Advantages:

- calibrated forecast
- probabilistic forecast
- straight forward to interpret
- return periods are a often related to warning levels (favorably for forecasters)
Limitation:

- Not applicable on extreme (rare) events

Probability of Return Period exceedance PRP

- Dependent on the climatology used to calculate
return levels/periods

- Here, a monthly subset of the climatology is used
(e.g. only data from September 1971-2000)

- PRP1 = Event that happens once per September
- PRP100 = Event that happens in one out of 100 Septembers

twice per September

each Septembers

once in 2 Septembers

once in 6 Septembers

COSMO-PRP1/2

COSMO-PRP1

COSMO-PRP2

COSMO-PRP6

24h total precipitation 04.09.2007 12UTC

VT: 05.09.2007 00UTC – 06.09.2007 00UTC

EFI

COSMO-PRP2

twice per September (15.8 mm/24h)

once per September (21 mm/24h)

once in 3 Septembers (26.3 mm/24h)

once in 6 Septembers (34.8 mm/24h)

Extremal types Theorem:

Maxima of a large number of independent random data of the same distribution function follow the Generalized Extreme Value distribution (GEV)

→0 : Gumbel

> 0 : Frechet

< 0 : Weibull

=position; =scale; =shape

C. Frei, Introduction to EVA

The underlying distribution function of extreme values y=x-u above a threshold u is the Generalized Pareto Distribution (GPD) (a special case of the GEV)

=scale; =shape

C. Frei, Introduction to EVA

Steps towards a GPD based probabilistic forecast of extreme events

- Find an eligible threshold for the detection of extreme events
(97.5% percentile of the climatology)

- Fit the GPD to the found extreme values
- Calculate return levels for chosen return periods
- Find the proportion of forecast members exceeding a return level

GPD fit to extreme values (>97.5 %-ile i.e. top 25) of COSMO-LEPS 24h precipitation (1 grid point only)

and 5%,95% confidence intervals

Return Level [mm/24h]

Return Period [days]

COSMO-PRP2

COSMO-PRP2 (GPD)

COSMO-PRP12 (GPD)

COSMO-PRP60 (GPD)

Difficulties of GPD based warning products

- In case of precipitation very dry regions sometimes do not have enough
days of precipitation (solution: extend reforecasts/mask regions)

- A low number of extreme events increases the uncertainty of the GPD fit
(solution: extend reforecasts)

- Verification of extreme events is difficult due to the low number of
events available.

- Extend the model climate used for calibration
- and extreme value statistics
- Probabilistic verification of the calibrated
- COSMO-LEPS forecast
- Translate model output to real atmospheric values

- A 30-years COSMO-LEPS climatology is about to being
completed

- New probabilistic, calibrated forecasts of extreme events are in quasi operational use
- An objective verification is necessary
- Extreme events might only be verified with case studies
- Forecaster feedback is necessary