Federal Department of Home Affairs FDHA
Federal Office of Meteorology and Climatology MeteoSwiss
WG4: interpretation and
applications
overview
Pierre Eckert
MeteoSwiss, Geneva
Topics
• Sochi Olympic games  PP CORSO
• FIELDEXTRA  presentation by JM Bettems
• Postprocessing
•
•
•
•
CORSO Kalman filter
COSMO-MOS
CAT diagnostics
Use of chekclist
• Guidelines
• Plans
COSMO General meeting ¦ Lugano, September 2012
Pierre.Eckert[at]meteoswiss.ch
2
Priority project CORSO
• Task 1: implementation of high resolution
model
• Task 2: postprocessing and usability
• Task 3: development of EPS
COSMO General meeting ¦ Lugano, September 2012
Pierre.Eckert[at]meteoswiss.ch
3
Priority project CORSO
• Task 1: implementation of high resolution
model
• Task 2: postprocessing and usability
• Task 3: development of EPS
COSMO General meeting ¦ Lugano, September 2012
Pierre.Eckert[at]meteoswiss.ch
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I.Rozinkina, S.Cheshin, M.Shatunova, I. Ruzanova
Hydrometeorological Research Center of Russia
The temperature T observed at the station at time t is represented as

 2 
 2 
  Xp2 n sin tn

Tt  Xp0    Xp2 n1 cos tn
n 1 
 Tp 
 Tp 
Np
where Tp is the window width used for expanding the temperature forecasts (4-7 days)
and Np is the number of harmonics used (Tp multiplied by (1, 2, or 3)),
The difference D between the observed temperature and the averaged forecast at time t
is represented as

 2 
 2 
Dt  Xd0    Xd2 n1 cos tn
  Xd2 n sin tn

 Td 
 Td 
n 1 
Nd
where Td is the window width used for expanding D (1 day) and Nd is the number of
harmonicas used (1, 2, or 3),
The forecast at the time t is calculated using the formula
COSMO General Meeting 2012, Lugano, September, 10-13
Tt  Dt
Corrected 2m temperature for Tp=7 days, Td=1 day, and various Np and Nd
The 2m temperature forecast at Krasnaya Polyana station was corrected over February,
2012 by applying the described method,
The errors in the initial forecasts:
average deviation:
2,86 K
root-mean-square deviation: 3,89 K
For Np=7 and Nd=1, the errors of the corrected forecasts:
average deviation:
0,18 K
root-mean-square deviation: 2,55 K
For Np=14 and Nd=2, the errors of the corrected forecasts:
average deviation:
0,40 K
root-mean-square deviation: 2,3 K
For Np=21 and Nd=3, the errors of the corrected forecasts:
average deviation:
0,39 K
root-mean-square deviation: 2,19 K
• observed data
• T2forecasts
• revised T2 forecast
COSMO General Meeting 2012, Lugano, September, 10-13
Federal Department of Home Affairs FDHA
Federal Office of Meteorology and Climatology MeteoSwiss
Local forecasts with
COSMO-MOS
Concept, Performance and Implementation
ECAC & EMS, September, 14th 2010
COSMO-GM, Lugano, 10.09.2012
Vanessa Stauch
Objectives of statistical PP
 To complement the NWP forecasts with the information in observations
 To reduce systematic NWP forecast errors, e.g. due to simplified (small
scale) processes, incorrect (smoothed) local forcing, …
 To calibrate (ensemble) forecasts such that they are reliable and sharp
 To derive forecasts for variables that are not predicted by the NWP model
taken from Wilks 2005
COSMO-MOS | COSMO-GM, 10.09.2012
Vanessa Stauch
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Dilemma
global MOS
Length of
training
period ~
“MOSMIX”
+ insensitive to model error „MOSMIX“: multiple
linear regression based
changes
COSMO- simple error model, little
MOS
discrimination
MOS
complexity
+ sampling of many cases,Updateable
good
MOS
discrimination → long lead times,
rare events
» correction mainly of the
systematic
errorupdate
online
“KF”
“UMOS”
- inert when model error changes
on global NWP models
“UMOS”: ‘updateable’
MOS of Canadians (and
Austrians), weighting of
model versions
“KF”: Kalman Filter
based online update of
systematic error
correction
» reduction of the mean error and
Temporal flexibility (e.g.
its variability
change of model version)
COSMO-MOS | COSMO-GM, 10.09.2012
Vanessa Stauch
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Implemented statistical approaches
 Multiple linear regression with stepwise forward model
selection
 Logistic regression (returns probability of exceedance for
one threshold q)
 Extended logistic regression (Wilks, 2009, returns entire
probability distribution of forecast)
n
 pq 
ln
 b0  bi  x i  gq
1  pq
i1
COSMO-MOS | COSMO-GM, 10.09.2012
Vanessa Stauch

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Data sampling & estimation strategies
COSMO-MOS | COSMO-GM, 10.09.2012
Vanessa Stauch
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10m wind speed: setup comparison
01.12.2010 – 28.02.2011 COSMO-7
COSMO-MOS | COSMO-GM, 10.09.2012
Vanessa Stauch
13
10m wind speed: setup comparison
01.12.2010 – 28.02.2011 COSMO-2
COSMO-MOS | COSMO-GM, 10.09.2012
Vanessa Stauch
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Summary multiple linear regression
 MOS forecasts reduce forecast error variance and
systematic error
 In comparison to Kalman filter approach, effect on the error
variance much higher
 Comparison COSMO-2 with COSMO-7 shows positive
effect of higher resolved (=better) inputs
 Recommendation for production setup:
• training period: 50 days for temperature, 90 days for
wind speed
• daytime dependent coefficients, all runs.
• update once a day
COSMO-MOS | COSMO-GM, 10.09.2012
Vanessa Stauch
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COSMO-MOS: Performance and recommendations
RESULTS WITH EXTENDED
LOGISTIC REGRESSION
COSMO-MOS | COSMO-GM, 10.09.2012
Vanessa Stauch
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Simulation setup 10m wind gusts
Verification period:
01.09.2010 – 02.11.2010
Hourly wind gust observations from
the Swiss automatic measurement
network (~70 stations used)
Thresholds for estimation:
25, 50, 75 % quantiles
COSMO-2 time lagged ensemble
“eps”: median and std as predictors
“lag”: all members separate predictors
COSMO-MOS | COSMO-GM, 10.09.2012
Vanessa Stauch
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Overall comparison CRPSS
COSMO-MOS | COSMO-GM, 10.09.2012
Vanessa Stauch
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Summary 10m wind gusts
 Extended logistic regression is a suitable statistical model
for deriving PDFs from deterministic model output
 COSMO-2 time lagged ensemble does contain useful
ensemble information for statistical post-processing
 Leadtime dependency of “eps” approach apparent but
might be alleviated with longer runs (→ COSMO NExT?)
 Training periods need to be seasonal → maybe include
more years in order to improve the distributions
COSMO-MOS | COSMO-GM, 10.09.2012
Vanessa Stauch
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IACETH
Clear Air Turbulence over Europe:
Climatology, Dynamics and Representation
in COSMO-7
Masterthesis of Lysiane Mayoraz
Supervised by Michael Sprenger and Vanessa Stauch
IACETH
 Turbulence indices:
• TI2 (Ellrod & Knapp Index 2) → deformation, shearing und divergence
• RI (Gradient Richardson Number) → rate between the static stability and the
vertical windshear. If RI < 1: instable
• EDR (Eddy Dissipation Rate) → rate at which turbulent kinetic energy is
converted into heat
→ Turbulent spot well visible with the three indices calculated from the COSMO-7
forecasts!
→ But signal too low (~ 1'000 m)
15/05/2012
Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz
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IACETH
 Turbulence indices:
• TI2 (Ellrod & Knapp Index 2) → deformation, shearing und divergence
• RI (Gradient Richardson Number) → rate between the static stability and the
vertical windshear. If RI < 1: instable
• EDR (Eddy Dissipation Rate) → rate at which turbulent kinetic energy is
converted into heat
Without extended turbulence
parametrisation
 Extended turbulence parametrisation: brings a significant amelioration
compared to the operational forecasts
15/05/2012
Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz
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IACETH
Observations Data
 Flight Data Monitoring Data from Swiss (year 2011)
 Selection criteria  50 turb. events (out of 100'000 flights)
15/05/2012
Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz
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IACETH
Comparison Observations / Model
 Results:
 All clear detected events are associated with a large and
long-lasting event from the model!
Detection rate
TI2
EDR
RI
86%
86%
77%
No detection by any of the three indices
Detection by all three indices
Detection by at least one of the three indices
Inaccuracies
in the signal
too early
too late
too high
too low
15/05/2012
9%
59%
91%
Bias
Frequency Mean Value
19%
1h
2%
1.5 h
17%
660 m
17%
1090 m
Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz
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Check list «risk of thunderstorms»
COSMO General meeting ¦ Lugano, September 2012
Pierre.Eckert[at]meteoswiss.ch
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Federal Department of Home Affairs FDHA
Federal Office of Meteorology and Climatology MeteoSwiss
Guidelines
http://www.wmo.int/pages/prog/
www/manuals.html
• 2. WHY SHOULD WE USE EPS?
• 3. TYPES OF EPS
• 3.1 Global EPS
• 3.2 Regional EPS
• 3.3 Convective-scale EPS
• 6. USE OF EPS IN DETERMINISTIC FORECASTING
• 6.1 Decision-making from deterministic forecasts
•
•
•
•
7. SCENARIOS
8. FULL PROBABILISTIC FORECASTS
9. POST-PROCESSING
10. USE OF EPS IN PREDICTION OF SEVERE
WEATHER AND ISSUE OF WARNINGS
• 11. SEVERE WEATHER IMPACT MODELLING
• 13. FORECASTER TRAINING
COSMO General meeting ¦ Lugano, September 2012
Pierre.Eckert[at]meteoswiss.ch
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Plans
Aviation
• COSMO-MOS: visibility, ceiling, wind direction
• Improve and operationalise CAT forecasts
• Other applications
First guess into forecast matrix
• «Best» deterministic input temperature, wind, sunshine
duration, precipitation,…
• Estimates for probabilities (compatible with deterministic)
Guidelines
• Strenghts and weaknesses of the various models
• Use of O(1km) models, use of O(2km) EPS
Exchange of experiences and methods
COSMO General meeting ¦ Lugano, September 2012
Pierre.Eckert[at]meteoswiss.ch
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