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



• FIELDEXTRA



PP CORSO 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

4

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

T t



Xp

0 

N n

 

p

1   

Xp

2

n

 1 cos  

tn

2 

Tp

  

Xp

2

n

sin  

tn

2 

Tp

    

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

D t



Xd

0 

N n

 

d

1  

Xd

2

n

 1 cos

tn

2 

Td



Xd

2

n

sin

tn

2 

Td

 

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

T t



D t

COSMO General Meeting 2012, Lugano, September, 10-13

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: average deviation: 2,55 K For Np=14 and Nd=2, the errors of the corrected forecasts: 0,40 K root-mean-square deviation: For Np=21 and Nd=3 2,3 K , the errors of the corrected forecasts: average deviation: root-mean-square deviation: 0,39 K 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, 14 th 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 COSMO-MOS |

COSMO-GM, 10.09.2012

Vanessa Stauch taken from Wilks 2005 9

Dilemma

global MOS “MOSMIX” Length of training period ~ MOS complexity

+

Updateable MOS

-

rare events “UMOS” inert when model error changes

+

insensitive to model error „MOSMIX“: multiple changes linear regression based on global NWP models » correction mainly of the online update “KF” » reduction of the mean error and its variability Temporal flexibility (e.g. change of model version) “UMOS”: ‘updateable’ MOS of Canadians (and Austrians), weighting of model versions “KF”: Kalman Filter based online update of systematic error correction COSMO-MOS |

COSMO-GM, 10.09.2012

Vanessa Stauch 10

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) ln    1        

b

0 

n



i

 1

b i



x i

  COSMO-MOS |

COSMO-GM, 10.09.2012

Vanessa Stauch 11

Data sampling & estimation strategies

12 COSMO-MOS |

COSMO-GM, 10.09.2012

Vanessa Stauch

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 14

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 15

COSMO-MOS: Performance and recommendations

RESULTS WITH EXTENDED LOGISTIC REGRESSION

COSMO-MOS |

COSMO-GM, 10.09.2012

Vanessa Stauch 16

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 17

Overall comparison CRPSS

COSMO-MOS |

COSMO-GM, 10.09.2012

Vanessa Stauch 18

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 19

IAC

ETH

Clear Air Turbulence over Europe: Climatology, Dynamics and Representation in COSMO-7

Masterthesis of Lysiane Mayoraz Supervised by Michael Sprenger and Vanessa Stauch

IAC

ETH



Turbulence indices:

• • •

TI 2 RI (Ellrod & Knapp Index 2)

→ deformation, shearing und divergence

(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 21

IAC

ETH



Turbulence indices:

• • •

TI 2 RI (Ellrod & Knapp Index 2)

→ deformation, shearing und divergence

(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 22

IAC

ETH

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 23

IAC

ETH



Comparison Observations / Model

Results:  All clear detected events are associated with a

long-lasting

event from the model!

large

and

TI 2 EDR RI Detection rate

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 Bias Frequency Mean Value

19% 1 h 2% 1.5 h 17% 17% 660 m 1090 m 9% 59% 91%

15/05/2012 Clear Air Turbulence over Europe / Masterthesis / Lysiane Mayoraz 24

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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