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Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Extreme weather and probabilistic forecast
approaches
Petra Friederichs
Sabrina Bentzien, Andreas Hense
Meteorological Institute, University of Bonn
Statistical Methods for Meteorology and Climate Change
Montreal, 12 – 14 January 2011
P.Friederichs Extreme weather 1 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Outline
Extreme weather and probabilistic prediction
I Atmospheric scales and mesoscale atmospheric dynamics
I High-impact weather
I Ensemble prediction systems
Ensemble post-processing
I Post-processing: Extract and calibrate information
I Verification
Results
P.Friederichs Extreme weather 2 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
What are extemes?
I Mathematically:
Defined as block maxima or excedances of large thresholds.
Events that lie in the tails of a distribution
I Perseption:
Rare, exceptional, ”large” and high impact
I Problems:
I 95% quantile of daily precipitation: ≈ 10− 15mm/d
I ≈ 2 years of data – only few extremes events for verification
P.Friederichs Extreme weather 3 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Mesoscale ExtremesPredictabilityCOSMO-DE Ensemble Prediction System
Mesoscale Weather Prediction
I Strong and disastrous impact of many weather extremes calls
for reliable forecasts
I ”Although forecasters have traditionally viewed weather
prediction as deterministic, a cultural change towards
probabilistic forecasting is in progress.” (T N Palmer, 2002)
I Weather extremes do not come ”Out of the Blue”
I Numerical weather forecast models provide reliable forecasts
of the atmospheric circulation prone to generate extremes
I Combination of dynamical and statistical analysis methods
P.Friederichs Extreme weather 4 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Mesoscale ExtremesPredictabilityCOSMO-DE Ensemble Prediction System
Atmospheric scales and mesoscale dynamics
I Different scales exhibit different
dominant force balances,
different wave dynamics
I Mesoscale on horizontal scales
2km – 2000km
I Complex force balances
Steinhorst, Promet 35, 2010
P.Friederichs Extreme weather 5 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Mesoscale ExtremesPredictabilityCOSMO-DE Ensemble Prediction System
Mesoscale weather extremes
I Heavy thunderstorms on July 14, 2010
I Strong horizontal gradients
I Strong vertical mixing
I Embedded in larger scale squall line –
embedded in synoptic situation
P.Friederichs Extreme weather 6 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Mesoscale ExtremesPredictabilityCOSMO-DE Ensemble Prediction System
Connection of Extremes on Different Scales
I Large vertical gradients of
entropy
I Convective instability
I Deep convection lead to
extremal vertical velocities
I Heavy precipitation and
hailstones grow within this
vertical circulation
Koeln 04.07.1994 RR=11,723mm
Zeit in min
Hoe
he in
km
0.2
0.2
0.4
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2
0 10 20 30 40 50 60 70 80 90
0
1
2
3
4
5
6
7
8
9
10
0
10
20
30
40mm/h
g/kg
0
1
2
3
4
S. Bentzien (2009)
P.Friederichs Extreme weather 7 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Mesoscale ExtremesPredictabilityCOSMO-DE Ensemble Prediction System
Predictability
I Inherent limit of predictability
I Fastes error growth at smallest
scales
I Predictability strongly depends
on flow regime
I Moist convection is primary
source of forecast-error growth
I Mesoscale forecasts are issued
for ≤18h-24h
E N Lorenz, Tellus 21, 19 (1969)
P.Friederichs Extreme weather 8 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Mesoscale ExtremesPredictabilityCOSMO-DE Ensemble Prediction System
COSMO-DE Ensemble Prediction System (EPS)
I COSMO-DE: 2.8 km grid spacing,
convection resolving NWP model
I Operational forecasts 0-21 hours –
high-impact weather by DWD
I EPS with 20 (40) members
I Uncertainty due to initial conditions,
boundary conditions, and model
parameterisation errors
I First EPS with convection resolving
limited area NWP model
COSMO-EU
COSMO-DE
GME
Initial State Boundary Model
S. Theis, DWD (2010)
P.Friederichs Extreme weather 9 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Censored Quantile RegressionMixture GLMExtrem Value TheoryMixture GLM including Extremes
Probabilistic forecasting: Maximize sharpness of
the predictive distribution subject to calibration
from Hamill (2006)
Calibration:
Raw ensemble data need adjustmend: biased and underdispersive
Gneiting et al. (2005)
Sharpness: Information of forecasts
P.Friederichs Extreme weather 10 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Censored Quantile RegressionMixture GLMExtrem Value TheoryMixture GLM including Extremes
Conditional quantile function
Semi-parametric
I A-priori probability τ , estimate
conditional quantile F−1Y |X(τ |x) = βT
τ x
via (linear) quantile regression
Parametric
I A-priori assumption about parametric
distribtion FY |X(y |x) = G (y ; Θ(x))
Estimate parameter function Θ(x)
Generalized linear model (GLM)
P.Friederichs Extreme weather 11 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Censored Quantile RegressionMixture GLMExtrem Value TheoryMixture GLM including Extremes
Quantile regression
QZQR(τ |X) = βT
τ X, βTτ = (β0, . . . , βK )
βτ = arg minβτ
n∑i=1
ρτ
(yi − βT
τ xi
)Censored quantile regression
QZQR(τ |X) = max(0,βT
τ X), βTτ = (β0, . . . , βK )
βτ = arg minβτ
n∑i=1
ρτ
(yi −max(0,βT
τ xi ))
with ρτ (u) = τu for u ≥ 0 and ρτ (u) = (τ − 1)u for u < 0
P.Friederichs Extreme weather 12 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Censored Quantile RegressionMixture GLMExtrem Value TheoryMixture GLM including Extremes
Censoring
Equivariance with respect to non-decreasing function h(·)Qh(Y )(τ) = h(QY (τ))
Hidden process Y ∗ observed through censored variable Y
Y = h(Y ∗) = max[0,Y ∗]
QY ∗QR
(τ |X) = βTτ X
0.1 0.2 0.3 0.4 0.5
−20
020
4060
meanmedian
QYQR(τ |X) = max(0,βT
τ X)
0.1 0.2 0.3 0.4 0.5
−20
020
4060
80 median
P.Friederichs Extreme weather 13 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Censored Quantile RegressionMixture GLMExtrem Value TheoryMixture GLM including Extremes
Generalized linear model – Mixture model
I Probability of precipitation (Y ≥ 0.1mm) – Logistic regression
Pr(Y ≥ 0.1 | x) = π(x)
I Distribution of precipitation – Gamma GLM
F (Y | x,Y ≥ 0.1) = GΓ(Y ; Θ(x))
Conditional mixture model for precipitation
FYmix(y | x) = (1− π) + π GΓ(y ; Θ(x)) Iy≥0.1
P.Friederichs Extreme weather 14 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Censored Quantile RegressionMixture GLMExtrem Value TheoryMixture GLM including Extremes
Conditional mixture model
Parametric – ’normal’
I A-priori assumption about parametric
distribtion for ’normal’ part
FY |X(y |x) = G (y ; Θ(x))
Estimate parameter function Θ(x)
Generalized linear model (GLM)
Parametric: ’extreme’
I Above threshold/quantile: parametric
distribtion FY |X(y |x) = G (y ; Θ(x)) is
of the familily of max-stable
distributions.
P.Friederichs Extreme weather 15 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Censored Quantile RegressionMixture GLMExtrem Value TheoryMixture GLM including Extremes
Extreme value theory ”Going beyond the range of the data”
I Limit theorem for sample maxima
→ asymptotic distribution for extremes
I Condition of max-stability (de Haan, 1984)
→ maxima follow a generalized extreme value
distribution
I Garantees universal behavior of extremes
→ enables extrapolation!
In praxis: often not enough data to reach asymptotic limit
P.Friederichs Extreme weather 16 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Censored Quantile RegressionMixture GLMExtrem Value TheoryMixture GLM including Extremes
Extreme value distribution
Generalized extreme value distribution (GEV)
Gξ(y) =
exp(−(1 + ξ y−µσ )−1/ξ)+, ξ 6= 0
exp(− exp(− y−µσ )), ξ = 0
,
Gumbel (..., shape=0.0)
PD
F
−2 0 2 4 6
0.0
0.1
0.2
0.3
0.4
Frechet (..., shape=0.6)
PD
F
−2 0 2 4 6
0.0
0.1
0.2
0.3
0.4
Weibull (..., shape=−0.3)
PD
F−2 0 2 4 6
0.0
0.1
0.2
0.3
0.4
P.Friederichs Extreme weather 17 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Censored Quantile RegressionMixture GLMExtrem Value TheoryMixture GLM including Extremes
Generalized Pareto distribution
Threshold excesses Z = Y − u follow a GPD
Prob(Z ≤ y − u) =
1−
(1 + ξ y−u
σu
)−1/ξ, ξ 6= 0
1− exp(− y−u
σu
), ξ = 0
,
for y − u > 0 and (1 + ξ y−uσu
) > 0
P.Friederichs Extreme weather 18 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Censored Quantile RegressionMixture GLMExtrem Value TheoryMixture GLM including Extremes
Mixture GLM including Extremes
I Conditional mixture model for precipitation
FYmix(y | x) = (1− π) + π GΓ(y ; Θ(x)) Iy≥0.1
I GPD for ’extreme’ precipitation – above uτ = F−1Y (τ | x)
F (Y | x,Y > uτ ) = GGPD(Y − uτ ;σ(x))
Conditional mixture model for precipitation with extremes
FYGPD(y | x) = (1− π) + π GΓ(y ; Θ(x)) IY≥0.1,Y≤uτ
+ (1− τ)GGPD(Y − uτ ;σ(x) IY>uτ
P.Friederichs Extreme weather 19 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Proper Scoring RulesForecasts in terms of quanitles
Prediction and Verification
I Model parameter training
I Verification on independent data
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Jul.08 Okt.08 Jan.09 Apr.09 Jul.09 Okt.09 Jan.10 Apr.10
n.train20,30,..,90 Tage
30T.
P.Friederichs Extreme weather 20 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Proper Scoring RulesForecasts in terms of quanitles
Forecast verification by means of scores
I Cost functions or distance between forecast and data
I Utility measure in a Bayesian context
A score is proper iff
Ey∼Q [S(P, y)] ≥ Ey∼Q [S(Q, y)] ∀ P 6= Q
S(P, y): score function
Q forecasters best guess
Ey∼Q [S(., y)] expectation of S(., y) over y ∼ Q
P.Friederichs Extreme weather 21 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Proper Scoring RulesForecasts in terms of quanitles
Verification: Goodness-of-fit criterion
QVS(τ) = min{β∈Rq}
∑i
ρτ (yi−βT xi ) QVSref (τ) = min{β0∈R}
∑i
ρτ (yi−β0)
Quantile verification skill score QVSS(τ) = 1− QVS(τ)
QVSref (τ)
Log-likelihood ratio test: asymmetric Laplacian regression
fτ (u) =τ(1− τ)
σLexp(−ρτ (u)/σL).
proportional to log(QVS(τ)/QVSref (τ))
P.Friederichs Extreme weather 22 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Ensemble Post-ProcessingVerificationExampleConclusions and Challenges
SKeleton EPS Interim Solution – Neighborhood method
I ’Time-lagged’ ensemble of
COSMO-DE
I Initialized every 3 h,
forecasts for 21 h
I 1 July 2008 – 30 April 2010
0 3 6 9 12 15 18 21 Vorhersagestunde
I First guess probability (fgp)
I First guess 0.9-quantile
(fgq9)
I 4 COSMO-DE forecasts and
5× 5 neighborhoodlagged average ensemble
forecast (LAF)
• Umgebung
• 4x9 member
• 4x25 member
• ...
6
P.Friederichs Extreme weather 23 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Ensemble Post-ProcessingVerificationExampleConclusions and Challenges
12h accumulated precipitation between 12 and 00 UTC
I 83 Station in NRW
I Rain radar composite
6 7 8 9
50.5
51.0
51.5
52.0
52.5
longitude
latitude
0
200
400
600
800
1000
1200
1400
P.Friederichs Extreme weather 24 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Ensemble Post-ProcessingVerificationExampleConclusions and Challenges
Reliability
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Forecast probability, yi
Obs
erve
d re
lativ
e fr
eque
ncy,
o1
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020000040000060000080000010000001200000
020000040000060000080000010000001200000
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0 20 40 60 80
0
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0.9 quantile
quantile forecast
empi
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ntile
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Ensemble QRmix.mod transf.fgq9
0 20 40 60 80
0.0
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0.9 quantile
bins
freq
uenc
y
P.Friederichs Extreme weather 25 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Ensemble Post-ProcessingVerificationExampleConclusions and Challenges
Quantile forecasts - Skill Score
6 7 8 9
50.5
51.0
51.5
52.0
52.5
longitude
latit
ude
0.0
0.1
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6 7 8 9
50.5
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longitude
latit
ude
0.0
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P.Friederichs Extreme weather 26 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Ensemble Post-ProcessingVerificationExampleConclusions and Challenges
Quantile forecasts - Skill Score
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QV
SS
0 10 20 30 40 50 60 70 80QR MIX MIX.t GPD
0.0
0.1
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0.5
0.6
0.7
● QRMIXMIX.tGPD+
+
+++++
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++++++++
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++++++++
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P.Friederichs Extreme weather 27 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Ensemble Post-ProcessingVerificationExampleConclusions and Challenges
Quantile forecasts - Skill Score
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0.99 quantile
QV
SS
0 10 20 30 40 50 60 70 80QR MIX MIX.t GPD
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
● QRMIXMIX.tGPD
++++
+
++
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P.Friederichs Extreme weather 28 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Ensemble Post-ProcessingVerificationExampleConclusions and Challenges
● ● ●
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Kleve
mm
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Jul 05 Jul 15 Jul 25 Aug 04
q0.75
q0.90
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q0.99● station
radarQRMIXMIX.tGPD
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mm
/12h
Jul 05 Jul 15 Jul 25 Aug 04
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q0.90
q0.95
q0.99● station
radarQRMIXMIX.tGPD
P.Friederichs Extreme weather 29 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Ensemble Post-ProcessingVerificationExampleConclusions and Challenges
Lagged ensemble forecasts and radar measurements
6 7 8 9
50.5
51.0
51.5
52.0
52.5
longitude
latit
ude
20
40
60
80
6 7 8 9
50.5
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51.5
52.0
52.5
longitude
latit
ude
20
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50.5
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longitude
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6 7 8 9
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longitude
latit
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latit
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6 7 8 9
50.5
51.0
51.5
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52.5
longitude
latit
ude
20
40
60
80
P.Friederichs Extreme weather 30 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Ensemble Post-ProcessingVerificationExampleConclusions and Challenges
Conclusions
I Weather forcasts provide information that conditions occurence of
extremes
I Linear (non-linear) statistical modeling extracts information
I Extreme value theory provides distributions tailored for extremes
I Parametric method less uncertain than non-parametric method and
non-linear dependecy (shape parameter) is not parsimony
I High-impact weather: insufficient data available for training and for
validation
P.Friederichs Extreme weather 31 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Ensemble Post-ProcessingVerificationExampleConclusions and Challenges
Challenges
I Improve physical understanding of generation processes of extremes
I Application to multi-variable and spatio-temporal predictions
I Combine spatial statistics with model post-processing
(Berrocal et al., 2007)
I Develop methods for multivariate post-processing
I Develop novel ensemble methods tailored to extremes
(Bayesian model averaging)
I Verification tailored to extremes
I Verification for probabilistic multivariate and spatial forecasts
P.Friederichs Extreme weather 32 / 33
Mesoscale Weather PredictionEnsemble Post-Processing
Prediction and VerificationResults
Ensemble Post-ProcessingVerificationExampleConclusions and Challenges
Reference
I Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman, 2005: Calibrated
probabilistic forecasting using ensemble model output statistics and minimum
CRPS estimation. Monthly Weather Review, 133, 1098-1118.
I C. Gebhardt, S.E. Theis, M. Paulat, and Z. Ben Bouallegue, 2010: Uncertainties
in COSMO-DE precipitation forecasts introduced by model perturbations and
variation of lateral boundaries. Atmos. Res. (in press).
I Friederichs, P., 2010: Statistical downscaling of extreme precipitation using
extreme value theory. Extremes 13, 109-132.
Special thanks to:
Susanne Theis, Martin Gober, Deutscher Wetterdienst, Offenbach
Thank You for Your Attention!
P.Friederichs Extreme weather 33 / 33