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WSN-05 TOULOUSE, Sept. 2005. Fog prediction in a 3D model with parameterized microphysics. Mathias D. Müller 1 , Matthieu Masbou 2 , Andreas Bott 2 , Zavisa I. Janjic 3. 1) Institute of Meteorology Climatology & Remote Sensing University of Basel, Switzerland - PowerPoint PPT Presentation
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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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Supersat.
Boundary conditions for dNc
1000m
PAFOG TOP
PAFOG TOP
1000 m
σc
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
Z.Janjic
var
iatio
nal a
ssim
ilatio
n
B-m
atric
es
CO
BE
L-N
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PA
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Obser -vations
3D-Model runs
post
-pro
cess
ing
Fo
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ore
cast
per
iod
NM
M-4
NM
M-2
NM
M-2
2aL
Mo
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
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