EMS   LJUBLJANA, 2006
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EMS LJUBLJANA, 2006. An ensemble assimilation and forecast system for 1D fog prediction. Mathias D. Müller 1 , C. Schmutz 2 , E. Parlow 3. 1,3) Institute of Meteorology, Climatology & Remote Sensing University of Basel, Switzerland [email protected] www.meteoblue.ch.

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EMS LJUBLJANA, 2006

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Ems ljubljana 2006

EMS LJUBLJANA, 2006

An ensemble assimilation and forecast system for 1D fog prediction

Mathias D. Müller1, C. Schmutz2, E. Parlow3

1,3) Institute of Meteorology, Climatology & Remote Sensing

University of Basel, Switzerland

[email protected]

www.meteoblue.ch

2) MeteoSwiss


Ems ljubljana 2006

1D fog modeling(COBEL-NOAH and PAFOG)

Radiationland surface model

Turbulencemicrophysics

+ initial (IC) and boundary conditions (BC)


Ems ljubljana 2006

Initial conditions

  • Initialization:

  • observations of

  • temperature & humidity

  • 3D model data:

  • aLMo, NMM-22, NMM-4, NMM-2

Data assimilation


Ems ljubljana 2006

Boundary conditions

t

3D

Valley fog

Boundary conditions:

From 3D models: aLMo, NMM-22, NMM-4, NMM-2

- Clouds

- Advection of temperature & humidity


Ems ljubljana 2006

Initialization – Data assimilation

error:

„the magic“

background

analysis

observation

20

21.5

22

Temperatur

B and R determine the relative importance

analysis (x)

observation (y)

background (xb)

15 16 17 18 19 20 21 22 23 24 25 26 27 28

Temperature (°C)


Ems ljubljana 2006

Assimilation - B for 3 different 3D models (Winter)

NMM-22 00 UTC

NMM-4 1400 UTC

NMM-4 00 UTC

aLMo 00 UTC

large model and time dependence


Ems ljubljana 2006

Initialization – Data assimilation (example)

21 hour forecast

of NMM-2

28 Nov 2004

Zürich Airport


Ems ljubljana 2006

The ensemble forecast system

Obser -

vations

3D-Model runs

1D-models

aLMo

NMM-2

Fog forecast period

B-matrices

PAFOG

variational assimilation

COBEL-NOAH

post-processing

NMM-22

NMM-4

www.meteoblue.ch

3D - Forecast time

Different IC and BC


Ems ljubljana 2006

Ensemble Forecast - Example

16

14

12

10

8

6

4

2 m rel. Hum. (%)

2 m Temperature (°C)

100

90

80

70

60

50

HEIGHT (m)

fog

INITIALIZED:

14 OCTOBER 2005 1500 UTC


Ems ljubljana 2006

Verification of the 1D ensemble forecast - ROC

1

10

40

60

HIT RATE

no skill

60

60

10

fog:

0

1

FALSE ALARM RATE

Fog – yes/no?

ROC

Fog (observation) = visibility < 1000 m

Fog (model) = liquid water content > threshold has probability x


Ems ljubljana 2006

Verification of the 1D ensemble forecast - ROC

Importance of Advection

Sensitivity to humidity assimilation

03-11 UTC from 1 November 2004 until 30 April 2005


Ems ljubljana 2006

Hourly advection estimates (different 3D models)

warm

cool

humid

dry

advection of cooler and drier air

03-11 UTC from 1 November 2004 until 30 April 2005


Ems ljubljana 2006

Verification of the 1D ensemble forecast - ROC

15:00 UTC

18:00 UTC

- Initialisierungszeitpunkt

- Multimodel

21:00 UTC

00:00 UTC

PAFOG

MODEL-ENSEMBLE

COBEL-NOAH


Ems ljubljana 2006

Conclusions

  • 1D ensemble forecast has the potential to improve fog prediction at Zürich airport:

  • Advection (of cooler and drier air) is very important

  • Humidity assimilation with large uncertainty → more observations, humidity ensemble

  • COST-722

  • MeteoSwiss

1D

Thanks


Ems ljubljana 2006

3D simulations even more promising

satellite

Model


Ems ljubljana 2006

Assimilation – R für Radiosonde in Payerne


Ems ljubljana 2006

Assimilation – inkrementelle cost function

(physical space)

Write in incremental Form

Introduce T and U transform to

eliminate B from the cost function

(Control variable space)


Ems ljubljana 2006

Assimilation – Error covariance Matrix

NMC-Method (use 3D models):


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