Fog prediction in a 3D model with parameterized microphysics

Mathias D. Müller1, Matthieu Masbou2, Andreas Bott2, Zavisa I. Janjic3

1) Institute of Meteorology Climatology & Remote SensingUniversity of Basel, Switzerland

2) Meteorological Institue, University of Bonn

3) NOAA/NCEP

WSN-05 TOULOUSE, Sept. 2005

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NMM (Nonhydrostatic Mesoscale Model) dynamical framework

PAFOG microphysics

NMM_PAFOG

Droplet number concentration

Liquid water content

Condensation/evaporation in the lowest 1500 m is replaced by PAFOG

Janjic, Z. I., 2003: A Nonhydrostatic Model Based on a New Approach. Meteorology and Atmospheric Physics, 82, 271-285.

PAFOG microphysics

Detailed condensation/evaporation (parameterized Köhler [Sakakibara 1979, Chaumerilac et. al. 1987])

Evolving droplet population (prognostic mean diameter)

Droplet size dependent sedimentation

Positive definite advection scheme (Bott 1989)

PAFOG microphysics

Assumption on the droplet size distribution : Log-normal function

D droplet Diameter

Dc,0 mean value of D

σc Standart deviation of the given droplet size distribution (σc=0.2)

where S is the Supersaturation

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Boundary conditions for dNc

1000m

PAFOG TOP

PAFOG TOP

1000 m

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HEIGHT

GFS

NMM-22

NMM-4 NMM-2 15 UTC

Nesting

NMM_PAFOG

NMM_PAFOGGRID: 50 x 50 x 45 (+11 soil layers)dx: 1 kmdt: 2s (dynamics) / 10s (physics)CPU: 40 min/24hr on 9 Pentium-4

(very efficient!)

19:00 MEZ (3 hr forecast)

PAFOG

STANDARD

27 Nov 2004

DROPLET NUMBER CONCENTRATIONLIQUID WATER CONTENT

22:00 MEZ (6 hr forecast)

STANDARD

PAFOG

27 Nov 2004

DROPLET NUMBER CONCENTRATIONLIQUID WATER CONTENT

02:00 MEZ (10 hr forecast)

STANDARD

PAFOG

28 Nov 2004

Accurate sedimentation in PAFOGdue to dNc computation.

DROPLET NUMBER CONCENTRATIONLIQUID WATER CONTENT

08:00 MEZ (16 hr forecast)

PAFOG

STANDARD

28 Nov 2004

DROPLET NUMBER CONCENTRATIONLIQUID WATER CONTENT

10:00 MEZ (18 hr forecast)

STANDARD

PAFOG

28 Nov 2004

DROPLET NUMBER CONCENTRATIONLIQUID WATER CONTENT

qc at 5m height (01:00 MEZ)

PAFOG STANDARD

qc at 5m height (06:00 MEZ)

PAFOG STANDARD

Cold air pooling (05:00 MEZ)

Cold bias problem

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3D-Model runs

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3D - Forecast time

1D Ensemble prediction system

www.meteoblue.ch

1D-models

With assimilation – CASE 1 15:00

27-28 Nov 2004 observed fog

28-29 Nov 2004

With assimilation – CASE 2 15:00

Conclusions

3D model with detailed microphysics

Promising first results

Computationally very efficient and feasible in todays operationalframework

More cases and ‘verification’ needed

Solves advection problem of 1D approach

GRID of NMM_PAFOG

50 x 50 x 45

27 layers in the lowest 1000 m

11 soil layers

Thickness(cm):0.50.75 1.21.82.74.06.0103060100

Advection statistics

1 December 2004 – 30 April 2005, all forecasthours and levels

Deviation often stronger than signal

Fog case - Observations

CASE 1 CASE 2

Assimilation example

28 Nov 2004Zürich Kloten Airport

21 hour forecastof NMM-2

Chaumerliac, N., Richard, E. & Pinty, J.-P. (1987), Sulfur scavenging in a mesoscale model with quasi-spectral microphysic : Two dimensional results for continental and maritime clouds, J. Geophys. Res. 92, 3114- 3126.

Berry, E.X & Pranger, M. P. (1974), Equation for calculating the terminal velocities of water drops, J. Appl. Meteor. 13, 108-113.

Bott, A. (1989), A positive definite advection schemme obtained by nonlinear renormalization of the advective fluxes, Monthly Weather Review 117, 1006-1015.

Bott, A. & Trautmann, T. (2002), PAFOG – a new efficient forecast model of radiation fog and low-level stratiform clouds, Atmospheric Research 64, 191-203.

References

Janjic, Z. I., 2003: A Nonhydrostatic Model Based on a New Approach. Meteorology and Atmospheric Physics, 82, 271-285.

Janjic, Z. I., J. P. Gerrity, Jr. and S. Nickovic, 2001: An Alternative Approach to Nonhydrostatic Modeling.  Monthly Weather Review, 129, 1164-1178

Sakakibara, H. (1979), A scheme for stable numerical computation of the condensation process with large time step, J. Meteorol. Soc. Japan 57, 349-353.

Twomey, S. (1959), The nuclei of natural cloud formation. Part ii : The supersaturation in natural clouds and the variation of cloud droplet concentration, Geophys. Pura Appl. 43, 243-249.

References

Write in incremental Form

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

(physical space)

(Control variable space)

Cost function for variational assimilation

Error covariance matrix

NMC-Method (use 3D models):

NMC estimates of B (winter season)

NMM-4 1400 UTC

large model and time dependence