Météo-France / CNRM – T. Bergot 1) Introduction 2) The methodology of the inter-comparison 3)...
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Transcript of Météo-France / CNRM – T. Bergot 1) Introduction 2) The methodology of the inter-comparison 3)...
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