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

InputOutputEvaluationSchedule

4) Phase 2 : forecast quality over a long periodTo be discussed precisely at the end of phase 1

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 parameterisationsImprove our understanding of the sensitivity to different physical parameterisationsHope : lead to some improvement in parameterisationsInvestigate the potential (limitation?) of the different types of forecast methods

Fog field experiment at Paris CDG Performed by Météo-France/CNRMAvailable following a convention between Météo-France/CNRM and the participants

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

The data

12 visibility measurements /6min4 ceiling measurements / 6min

The distribution is characteristic to events dominated by radiation processes

frequency of dense fogs (visi < 600m) / hours

2 winter seasonsEvery 30min

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/orceiling <600ft

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 processesFocus 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

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 modelSame initial conditionsNo meso-scale flow (no advection + no vertical velocity)

Phase 1 : numerical models used

France : 1D COBEL-ISBA : COBEL : http://www.rap.ucar.edu.staff/tardif/COBELISBA : http://www.cnrm.meteo.fr/mc2/index.htm

Spain : 1D HIRLAM

Germany : 1D PAFOG

Switzerland : 1D COBEL-OSU

Denmark : ?

U.K. : ?

Fine mesh vertical gridFine mesh vertical gridFirst level : 0.5m

20 levels below 200m

(Bergot 1993 ; Bergot and Guedalia 1994 ;Guedalia and Bergot, 1994)

Physical parameterizationsPhysical parameterizationsHigh resolution radiation scheme (232 spectral intervals)Turbulence scheme : turbulent kinetic energy (TKE)

http://www.rap.ucar.edu/staff/tardif/COBEL

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 characteristicProfile between ground and 2m in depth?

Spatial heterogeneities : every 3h?

Geostrophic windCloud cover (low, medium, high)

Assimilation at local scale

1) Initialisation of dry atmosphereMethodology : variational assimilation 1D-VarData : 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

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

Phase 1 : evaluationComparison for a given validity (e.g. 06UTC) and a given lead time (e.g. +6h)Comparison between the output of participants + comparison between observationsMore 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?

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

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 experimentOutput : visibility, ceiling, LVP, T-Hu at 2m, wind at 10m, short-wave and long-wave radiation at groundEvaluation : Statistical verification – ROC curves, scatter-plots, statistical scores (bias, RMS, other)

!! END !!

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 atmosphereISBA

COBEL

Initialization / forcing Initialization / forcing (every 3h)(every 3h)

ObservationsObservations ISBA offlineISBA offline

COBEL/ISBACOBEL/ISBA

Local fog forecastingLocal fog forecastingformationformationvisibility / vertical thickness visibility / vertical thickness clearanceclearance

Adjustment Adjustment requirements / forecastrequirements / forecast

guess

Mesoscale NWP Mesoscale NWP model (3D)model (3D)

Guess = previous COBEL-ISBA forecast (3h)Altitude « observations » = 3D NWP Aladin forecastSurface observations = local data (30m tower, 2m obs.)

2002-2003 WinterBias = 0.0°CStd. Dev. = 0.3°C

Temperature at 1m (observation)

Tem

pera

ture

at 1

m (

CI

Cob

el-I

sba)

1D-Var : T / q surface boundary layer

Temperature at 1m(initial conditions)

cloud = mixed layer (moist variables)Assimilation of radiation fluxes at 2m and 45m

IR fluxes when low clouds are detectedLow clouds from Aladin

bias=-41.9W/m2

low clouds from local assimilationbias=-1.0W/m2

3D operational NWP models are not able to give realistic forecasts (occurrence) of low clouds!

Initialisation of low clouds