ems ljubljana, 2006 mathias d. müller 1, c. schmutz 2, e. parlow 3 an ensemble assimilation and...

EMS LJUBLJANA, 2006

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

An ensemble assimilation and forecast system for 1D fog prediction

1,3) Institute of Meteorology, Climatology & Remote SensingUniversity of Basel, SwitzerlandMathias.mueller@unibas.ch

www.meteoblue.ch

2) MeteoSwiss

1D fog modeling (COBEL-NOAH and PAFOG)

Radiation land surface model

Turbulence microphysics

+ initial (IC) and boundary conditions (BC)

Initial conditions

Initialization:

- observations of temperature & humidity

- 3D model data: aLMo, NMM-22, NMM-4, NMM-2D

ata

assi

mila

tion

Boundary conditionsBoundary conditions:

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

- Clouds

- Advection of temperature & humidity

Valley fog

3D

t

Initialization – Data assimilation

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

Temperature (°C)

analysis (x)

observation (y)background (xb)

error:

„the magic“

Temperatur20 2221.5

observationbackground analysis

B and R determine the relative importance

NMM-4 1400 UTC

large model and time dependence

Assimilation - B for 3 different 3D models (Winter)NMM-22 00 UTC

NMM-4 00 UTC

aLMo 00 UTC

Initialization – Data assimilation (example)

28 Nov 2004Zürich Airport

21 hour forecastof NMM-2

The ensemble forecast system

var

iatio

nal a

ssim

ilatio

n

B-m

atric

es

CO

BE

L-N

OA

H P

AFO

G

Obser -vations

3D-Model runs

post

-pro

cess

ing

Fog

fore

cast

per

iod

NM

M-4

NM

M-2

NM

M-2

2aL

Mo

3D - Forecast time

www.meteoblue.ch

1D-models

Different IC and BC

Ensemble Forecast - Example

fogHEI

GH

T (m

)

2 m Temperature (°C) 2 m rel. Hum. (%)

INITIALIZED:14 OCTOBER 2005 1500 UTC

100

90

80

70

60

50

14

12

16

10

8

6

4

Verification of the 1D ensemble forecast - ROC

FALSE ALARM RATE

HIT

RA

TE

no sk

ill

0

1

1

1040

60

Fog (observation) = visibility < 1000 m

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

ROC

fog: 106060

Fog – yes/no?

Importance of Advection Sensitivity to humidity assimilation

Verification of the 1D ensemble forecast - ROC

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

advection of cooler and drier air

cool warm dry humid

Hourly advection estimates (different 3D models)

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

- Initialisierungszeitpunkt

- Multimodel

PAFOG

MODEL-ENSEMBLECOBEL-NOAH

15:00 UTC 18:00 UTC

21:00 UTC 00:00 UTC

Verification of the 1D ensemble forecast - ROC

• 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

Conclusions

Ensemble Hit Rate False Alarm rate

COBEL-NOAHPAFOG

60 %80 %

30 %45 %1D

Than

ks

3D simulations even more promising

Model

satellite

Assimilation – R für Radiosonde in Payerne

Write in incremental Form

Introduce T and U transform to eliminate B from the cost function

(physical space)

(Control variable space)

Assimilation – inkrementelle cost function

NMC-Method (use 3D models):

Assimilation – Error covariance Matrix