1. Météo-France / CNRM – T. Bergot 1) Introduction 2) The methodology of the inter-comparison 3) Phase 1 : cases study Inter-comparison of numerical models of fog and low clouds : a proposal Inter-comparison : Why? Paris-CDG fog field experiment Input Output Evaluation Schedule 4) Phase 2 : forecast quality over a long period To be discussed precisely at the end of phase 1

2. inter-comparison : why? 1) The goal : link with COST722 objectives 2) The data NOT to create a competition between the different participants! Learn about the value of different existing physical parameterisations Improve our understanding of the sensitivity to different physical parameterisations Hope : lead to some improvement in parameterisations Investigate the potential (limitation?) of the different types of forecast methods Fog field experiment at Paris CDG Performed by Météo-France/CNRM Available following a convention between Météo-France/CNRM and the participants

3. Meteorological tower of 30m : T / Hu% Ground measurements : T / W inside the soil (between ground and –50cm) short- and long-wave radiations Airport terminal 1: T / H% Radiation fluxes Since December 2002 Paris CDG fog field experiment

4. The data 12 visibility measurements /6min 4 ceiling measurements / 6min The distribution is characteristic to events dominated by radiation processes frequency of dense fogs (visi < 600m) / hours 2 winter seasons Every 30min

5. The data Dense fog : visibility <600m A strong variability of events is observed during the 2 studied winter seasons frequency of events / months Low clouds : ceiling <600ft Low Visibility Procedures (LVP): visibility <600m and/or ceiling <600ft

6. inter-comparison : the methodology 1) Phase 1 : cases study 2) Phase 2 : evaluation of the forecast quality Goal : exhibit model deficiencies or weaknesses due to imperfect representation of physical processes Focus on specific cases well defined and observed : radiation fog and low clouds (formation, evolution and dissipation) Lead to improvements in the physical parameterisations themselves? Goal : investigate the potential and limitation of forecast performed by numerical models over a long period in order to get representative results in statistical sense

7. Phase 1 : methodology Objective : Exhibit deficiencies due to imperfect representation of physical processes involved in the formation and evolution of fog and low clouds The study of simulated boundary layer at local scale using high- quality observational data + effect on fog/low clouds simulations Tools : Focus on vertical processes 1D model Same initial conditions No meso-scale flow (no advection + no vertical velocity)

8. Phase 1 : numerical models used France : 1D COBEL-ISBA : COBEL : http://www.rap.ucar.edu.staff/tardif/COBELhttp://www.rap.ucar.edu.staff/tardif/COBEL ISBA : http://www.cnrm.meteo.fr/mc2/index.htmhttp://www.cnrm.meteo.fr/mc2/index.htm Spain : 1D HIRLAM Germany : 1D PAFOG Switzerland : 1D COBEL-OSU Denmark : ? U.K. : ?

9. Fine mesh vertical grid  First level : 0.5m  20 levels below 200m (Bergot 1993 ; Bergot and Guedalia 1994 ;Guedalia and Bergot, 1994) Physical parameterizations  High resolution radiation scheme (232 spectral intervals)  Turbulence scheme : turbulent kinetic energy (TKE) http://www.rap.ucar.edu/staff/tardif/COBEL

10. Phase 1 : input data Initial vertical atmospheric profiles All participants will used the same initial conditions given by Météo-France/CNRM issued from the Paris CDG fog field experiment (send on CD to participants) T, q, ql, U, V, other? Between 0 and 5500m ? Step? Initial soil profiles T, Wl, Wi, other? Soil/vegetation characteristic Profile between ground and 2m in depth? Spatial heterogeneities : every 3h? Geostrophic wind Cloud cover (low, medium, high)

11. Assimilation at local scale 1) Initialisation of dry atmosphere Methodology : variational assimilation 1D-Var Data : local observations, operational 3D NWP forecast 2) Initialisation of fog / low clouds 3) Initialisation of soil parameters Define the depth of the cloudy area (minimization of errors on the radiation fluxes divergence) Correction of the atmospheric profiles below and inside the cloudy area (dry / moist mixed area) Soil temperature and moisture have a strong influence on the surface cooling (energy budget at the surface : spin-up problem!) Offline version of the ISBA model, driven by observed atmospheric forcing

12. Phase 1 : output data Frequency : 30min? – duration : up to 12h? Microphysics : visibility at 2m, ceiling, height of cloud/fog Vertical profiles : T, Q, Ql – levels? Radiation : short- and long-wave at 2m and 45m, other? Turbulent exchanges : TKE? Turbulent fluxes? Mixing length? Soil – vegetation – atmosphere exchanges : H, LE, other? Send on CD to other participants

13. Phase 1 : evaluation  Comparison for a given validity (e.g. 06UTC) and a given lead time (e.g. +6h)  Comparison between the output of participants + comparison between observations  More efficient if performed centrally, but all participants should be associated in the evaluation processes Evaluation of microphysical parameters : formation, evolution and dissipation (fog, low cloud and LVP conditions -visibility < 600m and/or ceiling < 200ft) Evaluation of boundary layer processes : profiles? evolution of specific parameters? Evaluation of radiation processes : short-wave and long-wave? profile? evolution? Turbulent exchanges : TKE? turbulent fluxes? profiles? evolutions? Soil – vegetation – atmosphere exchanges : H, LE? evolution?

14. Phase 1 : schedule Participants : before October 2004 Description of the numerical model : before October 2004 Input data (convention between participants and Météo- France/CNRM + distribution on CD) : before the end of 2004 Collection of output on CD : before April 2005 First analysis of results : mid 2005

15. Inter-comparison : Phase 2  To be discussed precisely at the end of phase 1  should be completed in collaboration with WG3 - task 1 “Determine how to evaluate the potential of existing methods” Goal : learn about the quality of the different numerical models (1D, 3D, …) used for fog and low clouds forecasting in a statistical sense. Input : observations from CDG fog field experiment Output : visibility, ceiling, LVP, T-Hu at 2m, wind at 10m, short- wave and long-wave radiation at ground Evaluation : Statistical verification – ROC curves, scatter-plots, statistical scores (bias, RMS, other)

16. !! END !!

17. Mesoscale terms : ALADIN Advections Geostrophic wind clouds Turbulent processes (stable cases) Radiative processes (IR+vis) Microphysical processes (condensation-evaporation, sedimentation) Exchanges between soil, vegetation and atmosphere ISBA COBEL

18. Initialization / forcing (every 3h) Observations ISBA offline COBEL/ISBA Local fog forecasting formation formation visibility / vertical thickness visibility / vertical thickness clearance clearance Adjustment requirements / forecast guess Mesoscale NWP model (3D)

19. Guess = previous COBEL-ISBA forecast (3h) Altitude « observations » = 3D NWP Aladin forecast Surface observations = local data (30m tower, 2m obs.) 2002-2003 Winter Bias = 0.0°C Std. Dev. = 0.3°C Temperature at 1m (observation) Temperature at 1m (CI Cobel-Isba) 1D-Var : T / q surface boundary layer Temperature at 1m (initial conditions)

20. cloud = mixed layer (moist variables) Assimilation of radiation fluxes at 2m and 45m IR fluxes when low clouds are detected Low clouds from Aladin bias=-41.9W/m2 low clouds from local assimilation bias=-1.0W/m2 3D operational NWP models are not able to give realistic forecasts (occurrence) of low clouds! Initialisation of low clouds