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Fog prediction in a 3D model with parameterized microphysics

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

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Fog prediction in a 3D model with parameterized microphysics

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

  2. NMM_PAFOG Janjic, Z. I., 2003: A Nonhydrostatic Model Based on a New Approach. Meteorology and Atmospheric Physics, 82, 271-285. Droplet number concentration Liquid water content NMM (Nonhydrostatic Mesoscale Model) dynamical framework PAFOG microphysics Condensation/evaporation in the lowest 1500 m is replaced by PAFOG

  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 Supersat. where S is the Supersaturation 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)

  5. Boundary conditions for dNc HEIGHT PAFOG TOP 1000 m σc 1000m PAFOG TOP

  6. Nesting GFS NMM-22 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!) NMM_PAFOG NMM-4 NMM-2 15 UTC

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

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

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

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

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

  12. qc at 5m height (01:00 MEZ) PAFOG STANDARD

  13. qc at 5m height (06:00 MEZ) PAFOG STANDARD

  14. Cold air pooling (05:00 MEZ)

  15. Cold bias problem Z.Janjic

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

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

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

  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. 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

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

  22. Fog case - Observations CASE 1 CASE 2

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

  24. References 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 Review117, 1006-1015. Bott, A. & Trautmann, T. (2002), PAFOG – a new efficient forecast model of radiation fog and low-level stratiform clouds, Atmospheric Research64, 191-203. 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. 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

  25. References Sakakibara, H. (1979), A scheme for stable numerical computation of the condensation process with large time step, J. Meteorol. Soc. Japan57, 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.

  26. Cost function for variational assimilation (physical space) Write in incremental Form Introduce T and U transform to eliminate B from the cost function (Control variable space)

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

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

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