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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.

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Presentation on theme: "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."— Presentation transcript:

1 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 2) Meteorological Institue, University of Bonn 3) NOAA/NCEP WSN-05 TOULOUSE, Sept. 2005

2 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.

3 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)

4 PAFOG microphysics Assumption on the droplet size distribution : Log-normal function D droplet Diameter D c,0 mean value of D σ c Standart deviation of the given droplet size distribution (σ c =0.2) where S is the Supersaturation Supersat.

5 Boundary conditions for dN c 1000m PAFOG TOP 1000 m σcσc HEIGHT

6 GFS NMM-22 NMM-4 NMM-2 15 UTC Nesting NMM_PAFOG GRID: 50 x 50 x 45 (+11 soil layers) dx:1 km dt: 2s (dynamics) / 10s (physics) CPU:40 min/24hr on 9 Pentium-4 (very efficient!)

7 19:00 MEZ (3 hr forecast) PAFOG STANDARD 27 Nov 2004 DROPLET NUMBER CONCENTRATION LIQUID WATER CONTENT

8 22:00 MEZ (6 hr forecast) STANDARD PAFOG 27 Nov 2004 DROPLET NUMBER CONCENTRATION LIQUID WATER CONTENT

9 02:00 MEZ (10 hr forecast) STANDARD PAFOG 28 Nov 2004 Accurate sedimentation in PAFOG due to dN c computation. DROPLET NUMBER CONCENTRATION LIQUID WATER CONTENT

10 08:00 MEZ (16 hr forecast) PAFOG STANDARD 28 Nov 2004 DROPLET NUMBER CONCENTRATION LIQUID WATER CONTENT

11 10:00 MEZ (18 hr forecast) STANDARD PAFOG 28 Nov 2004 DROPLET NUMBER CONCENTRATION LIQUID WATER CONTENT

12 q c at 5m height (01:00 MEZ) PAFOG STANDARD

13 q c at 5m height (06:00 MEZ) PAFOG STANDARD

14 Cold air pooling (05:00 MEZ)

15 Cold bias problem Z.Janjic

16 variational assimilation B-matrices COBEL-NOAH PAFOG Obser - vations 3D-Model runs post-processing Fog forecast period NMM-4 NMM-2 NMM-22 aLMo 3D - Forecast time 1D Ensemble prediction system www.meteoblue.ch 1D-models

17 With assimilation – CASE 1 15:00 27-28 Nov 2004 observed fog

18 28-29 Nov 2004 With assimilation – CASE 2 15:00

19 Conclusions 3D model with detailed microphysics Promising first results Computationally very efficient and feasible in todays operational framework More cases and ‘verification’ needed Solves advection problem of 1D approach

20

21 GRID of NMM_PAFOG 50 x 50 x 45 27 layers in the lowest 1000 m 11 soil layers Thickness(cm): 0.5 0.75 1.2 1.8 2.7 4.0 6.0 10 30 60 100

22 Advection statistics 1 December 2004 – 30 April 2005, all forecasthours and levels Deviation often stronger than signal

23 Fog case - Observations CASE 1CASE 2

24 Assimilation example 28 Nov 2004 Zürich Kloten Airport 21 hour forecast of NMM-2

25 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

26 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

27 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

28 Error covariance matrix NMC-Method (use 3D models):

29 NMC estimates of B (winter season) NMM-4 1400 UTC large model and time dependence

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