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Ensemble Prediction Systems and Probabilistic Forecasting
Richard (Rick) JonesSWFDP Training Workshop on Severe Weather Forecasting
Bujumbura, Burundi, Nov 11-16 , 2013Slides courtesy: UKMO Paul Davies and Chris
Tubbs
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Computer models
Forecaster
Customer/Public
Atmosphere
Scientists
The forecaster misled me
The NWP misled me
Errorneous observations misled the NWP
The atmosphere is ”chaotic”
””The Blame Game” The Blame Game” or ”The Passing of The Buck”or ”The Passing of The Buck”
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Understanding chaos
Numerical Weather Prediction
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The Atmosphere
Model System
Data Assimilation
ForecastAnalysisError
Forecast distribution
DiagnosticTools and Products
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Quantifying uncertainty with ensembles
time
Forecast uncertainty
Climatology
Initial Condition Uncertainty
X
Deterministic Forecast
Analysis
CHAOS
April 2013 Anders Persson 6
-12 0 +12 +24 +36 +48 +60 +72 +84 h
ψ
Exchanging the “accurate” forecast with a more “honest” one
Dangerous threshold
Today’s NWP forecast
Today’s EPS forecast
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ψ
Correction for systematic errors
-12 0 +12 +24 +36 +48 +60 +72 +84 h
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ψ
●●
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● ●
●●●
●
●
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obs ●● ●
The final ensemble forecast – with verification
70% 50%
-12 0 +12 +24 +36 +48 +60 +72 +84 h
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Ψ
Probability
Investigations show that “jumpiness” correlates badly to the accuracy of the last forecast
NWP today
NWP yesterdayNWP the day
before yesterday
We might form an opinion by looking at the last NWPs from the same model, so called “lagged” forecasts
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Medium range forecasting…with deterministic and EPS information
Latest three NWP
Latest EPS
Most common case with good agreement between EPS
spread and NWP “jumpiness”
Most common case
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Rather poor agreement between larger EPS spread and small NWP
“jumpiness”.
The analysis system has obviously managed to avoid possible
problems because the NWP is not very “jumpy”
Should the forecasters be more certain than the EPS indicates?
Rather common case
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Rather poor agreement between small EPS spread
and large NWP “jumpiness”.
The perturbations have not been quite able to cover the
analysis uncertainties
Should the forecasters be more uncertain than the EPS indicates?
Not uncommon case
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Poor agreement between the main directions of the EPS
and the NWP
This puts the forecasters in a very difficult situation and there is not enough experience or investigations about this situation
Rare case
Best choice: create a “super ensemble”
See TIGGE
The Effect of Chaos
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Tiny errors in our analysis of the current state of the atmosphere lead to large errors in the forecast – these are both equally valid 4-day forecasts.
• We can usually forecast the general pattern of the weather up to about 3 days ahead.
• Chaos then becomes a major factor
• Fine details (eg rainfall) have shorter predictability
Ensembles• In an ensemble forecast we run the model many times from slightly different initial conditions• This provides a range of likely forecast solutions which allows forecasters to:
– assess possible outcomes;
– estimate risks – gauge confidence.
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Reminder on scales and predictability
Temporal Resolution
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Hail shaft
Thunderstorm
Front
Tropical Cyclone
Space Scale
Lifetime
Predictability?
10mins 1 hr 12hrs 3 days
30mins 3 hrs 36hrs 9 days
1000km
100km
10km
1km
MCS
Short-range Ensembles
ECMWF EPS has transformed the way we do Medium-Range Forecasting
• Uncertainty also in short-range:– Rapid Cyclogenesis often poorly forecast deterministically– Uncertainty of sub-synoptic systems (eg thunderstorms)– Many customers most interested in short-range
• Assess ability to estimate uncertainty in local weather– QPF– Cloud Ceiling, Fog – Winds etc
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Initial conditions perturbations
• Perturbations centred around 4D-Var analysis• Transforms calculated using same set of
observations as used in 4D-Var (including all satellite obs) within +/- 3 hours of data time
• Ensemble uses 12 hour cycle (data assimilation uses 6 hour cycle)
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Initial conditions perturbationsDifferences with ECWMF Singular Vectors:
- It focuses on errors growing during the assimilation period, not growing period:
- Suitable for Short-range!
- Calculated using the same resolution than the forecast
- ETKF includes moist processes
- Running in conjunction with stochastic physics to propagate effect
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Model error: parameterisations
Random parameters
Parameter Scheme min/std/MaxEntrainment rate CONVECTION 2 / 3 / 5
Cape timescale CONVECTION 30 / 30 / 120
RH critical LRG. S. CLOUD 0.6 / 0.8 / 0.9
Cloud to rain (land) LRG. S. CLOUD 1E-4/8E-4/1E-3
Cloud to rain (sea) LRG. S. CLOUD 5E-5/2E-4/5E-4
Ice fall LRG. S. CLOUD 17 / 25.2 / 33
Flux profile param. BOUNDARY L. 5 / 10 / 20
Neutral mixing length BOUNDARY L. 0.05 / 0.15 / 0.5
Gravity wave const. GRAVITY W.D. 1E-4/7E-4/7.5E-4
Froude number GRAVITY W.D. 2 / 2 / 4
QUMP (Murphy et al., 2004)Initial stoch. Phys. Scheme for the UM (Arribas, 2004)
Using probabilities
• Recipients of forecasts & warnings are sensitive to different levels of risk: reflecting cost of mitigation vs expected loss
• An intelligent response to forecasts & warnings depends on risk analysis, requiring knowledge of impact probability
• Use of ensembles to estimate probability at longer lead times is well established in meteorology
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WMO SWFDP Macau 10 April 2013 Anders Persson
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10 20 30 40 50 Ψ
Median 50-50%
25%=12-13 forecastsΨ= 31-37
25%=12-13 forecastsΨ= 23-30
25%=12-13 forecastsΨ= 10-22
25%=12-13 forecastsΨ= 38-50
10 20 30 40 50 Ψ
0% 25% 50% 75% 100%
| | ||| ||| || | | | ||| | | || || ||| |||| || || || | | | | | ||| | || | | | |
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Probability maps
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EC Total ppn prob > 20mm 12z Tue – 12z Wed
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EPSgrammes
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Total cloud cover
6 hourly precipitation
10m wind speed
2m temperature
EPS control
Deterministic
median (50%)
max
min
90%
10%
75%
25%
Bujumbura 06 Nov 12UTC
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The Extreme Forecast Index (EFI)
Extreme forecast index (EFI)
• EFI measures the distance between the EPS cumulative distribution and the model climate distribution
• Takes values from –1 (all members break climate minimum records) and +1 (all beyond model climate records)
• The main idea is to have an index that can be conveniently mapped – removing the effect from different climatologies – to use as an “alarm bell”
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EFI
• Experience suggests that EFI magnitudes of 0.5 - 0.8 (irrespective of sign) can be generally regarded as signifying that "unusual" weather is likely whilst magnitudes above 0.8 usually signify that "very unusual" or extreme weather is likely. Although larger EFI values indicate that an extreme event is more likely, the values do not represent probabilities as such
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0%
EPS distributionClimate
distribution
Clim.mean
Temperature
EPSmean
Advantages with probability density functionsMeans and asymmetric variances are easily spotted
100%
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0%
100%
EPS distributionClimate
distribution
Clim.mean
Temperature EPSmean
Advantages with probability density functionsMeans and asymmetric variances are easily spotted
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EFI ~ 70%
EFI ~ 70%
The EFI generally does not take the probability into account
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EFI ~ -50%
EFI ~ 50%
For temperature the EFI can take values < 0
EFI 2m Temp
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EFI 10m gusts
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EFI wind speed
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EFI precip
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Other products
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Tropical strike probability
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Tropical strike
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Ensemble mean
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Forecast Probability
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UKMO African LAM
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UKMO African LAM
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Ensemble forecasts
• Ensemble forecasts help us
1. To judge the (un)certainty of the weather situation
2. To acquire probability estimates of anomalous events (extreme or high impact)
3. To get the most accurate and least “jumpy” deterministic forecast value
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Questions & Answers
