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

20
EMS LJUBLJANA, 2006 Mathias D. Müller 1 , C. Schmutz 2 , E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction Institute of Meteorology, Climatology & Remote Sensing rsity of Basel, Switzerland [email protected] eteoblue.ch 2) MeteoSwiss

Upload: harvey-norris

Post on 17-Jan-2018

217 views

Category:

Documents


0 download

DESCRIPTION

Initial conditions Initialization: - observations of temperature & humidity - 3D model data: aLMo, NMM-22, NMM-4, NMM-2 Data assimilation

TRANSCRIPT

Page 1: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

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, [email protected]

www.meteoblue.ch

2) MeteoSwiss

Page 2: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

1D fog modeling (COBEL-NOAH and PAFOG)

Radiation land surface model

Turbulence microphysics

+ initial (IC) and boundary conditions (BC)

Page 3: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

Initial conditions

Initialization:

- observations of temperature & humidity

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

ata

assi

mila

tion

Page 4: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

Boundary conditionsBoundary conditions:

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

- Clouds

- Advection of temperature & humidity

Valley fog

3D

t

Page 5: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

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

Page 6: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

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

Page 7: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

Initialization – Data assimilation (example)

28 Nov 2004Zürich Airport

21 hour forecastof NMM-2

Page 8: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

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

Page 9: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

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

Page 10: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

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?

Page 11: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

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

Page 12: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

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

Page 13: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

- 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

Page 14: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

• 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

Page 15: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

3D simulations even more promising

Model

satellite

Page 16: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of
Page 17: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of
Page 18: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

Assimilation – R für Radiosonde in Payerne

Page 19: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

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

Page 20: EMS LJUBLJANA, 2006 Mathias D. Müller 1, C. Schmutz 2, E. Parlow 3 An ensemble assimilation and forecast system for 1D fog prediction 1,3) Institute of

NMC-Method (use 3D models):

Assimilation – Error covariance Matrix