Thorsten Fehr et al. Institut für Physik der Atmosphäre

Institut für

Physik der Atmosphäre

Institut für


Modelisation a meso-echelle au IPA-DLR : Des eclairs au trafic aérien

Mesoscale Modeling at the IPA-DLR: From lightning to aviation

Thorsten Fehr et al.

Institut für Physik der Atmosphäre

Deutsches Zentrum für Luft- und Raumfahrt, DLR

Oberpfaffenhofen, Allemange

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Missions (I)

Understanding the climate

and how it is affected by aviation

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Parameterization of Lightning Activity and NOx

Motivation:

Natural production and distribution of trace gases is poorly known as compared to air traffic and ground sources.

In particular nitrogen oxides (NOX) from lightning (LNOX) vs. air traffic in the upper troposphere

Model Studies:

Cloud Scale Lightning parameterization based on model µ-physics (Barthe,

Pinty) or cloud scale variables (Price and Rind, Fehr)

Meso Scale/GCM Lightning NOx parameterization based on convection

parameterization (Pinty)

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Observations

AircraftAircraft:Trace gasµ-physics

Aircraft:Trace gasµ-physics

Lightning

Aircraft:Trace gasµ-physics

Lightning

Radar

Satellite

Surface obs.

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Parameterization

Lightning

fcell =fcellcell(i)

LNOX

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Simulation

Total Condensed Water Lightning NOX

J.-P. Chaboureau et al. for TROCCINOX-2, 2005

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Challenges: Modeled storm represents observations (radar, satellite)

Cut-off bei 16 km

Radar: TROCCINOX 04 Feb. 2005


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Lightning parameterization (explicit electricity or bulk) represents local lightning distribution (VLF/LF, optical)

Location, IC/CG, intensity

DLR LINET, 04 Feb 2005:

LF lightning detection network

IC strokes (51.420)

CG strokes (82.462)


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Very limited set of observations (trace gases, e.g. NOX) from aircraftFalcon: ~ 7 anvil crossings Geophysica: ~ 2 anvil dives


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Very limited set of observations (trace gases, e.g. NOX) from aircraft

Where and how to place aircraft observations in the model storm?

Extrapolation to flash, storm, regional or global production rates

Necessary to have a good estimate for the outflow regions A sample of case studies necessary Different climatic location


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Institut für Physik der Atmosphäre/Laboratoire d’AérologieSimulation

Tropics (s. Brazil) Mid-latitude (s. Germany)

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Missions (II)

Understanding the weather

and how it affects aviation

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Cross section along glideslope

LM forecasting domain MM5 forecasting domain 1 MM5 forecasting domain 2

Airport area 3D view of storm crossing airport

Forecasting for airports: model chain with nesting

PI: Arnold Tafferner

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Ensemble forecasts ranked by image matchingCluster 1 Rank: 9

Meteosat 7 IR, 9 July 2002

LM det Rank: 5

Cluster 3 Rank: 10 Cluster 4 Rank: 1

COSMO-LEPS ensemble of 10 LM forecasts driven by

clusters from ECMWF EPS

PI: Christian Keil

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PI: Andreas Dörnbrack

Wind and divergence (1/s) 29 January 1998 21 UT

Ellrod CAT Index

ETI = VWS [ DEF +CVG ]

High-resolution weather simulations predict areas of Clear-Air Turbulence (CAT)

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Translation of model variables (liquid water content) into radar observables (reflectivity)

Verification of precipitation forecasts by polarimetric radar

Improvement of the cloud physical parameterizations of numerical weather prediction models.

SynPolRadSynthetic Polarimetric Radar

Evaluating precipitation forecasts using polarimetric radar

PI: Monika Pfeifer

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High-Resolution Modeling

Challenge

predictability of small-scale weather hazards

Recent Successes

high resolution cloud simulations (EULAG, MM5, LM, LM-K, MesoNH)

wave breaking and Clear air turbulence (CAT) indices

regional ensemble forecasts

Future

probabilistic convection forecasts

climatology and validation of CAT predictions

parameterisation of processes

Documents

Thorsten Fehr et al. Institut für Physik der Atmosphäre