Institut für
Physik der Atmosphäre
Institut für
Physik der Atmosphäre
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
2
Institut für
Physik der Atmosphäre
Missions (I)
Understanding the climate
and how it is affected by aviation
3
Institut für
Physik der Atmosphäre
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)
4
Institut für
Physik der Atmosphäre
Parameterization of Lightning Activity and NOx
Observations
AircraftAircraft:Trace gasµ-physics
Aircraft:Trace gasµ-physics
Lightning
Aircraft:Trace gasµ-physics
Lightning
Radar
Satellite
Surface obs.
5
Institut für
Physik der Atmosphäre
Parameterization of Lightning Activity and NOx
Parameterization
Lightning
fcell =fcellcell(i)
LNOX
6
Institut für
Physik der Atmosphäre
Parameterization of Lightning Activity and NOx
Simulation
Total Condensed Water Lightning NOX
J.-P. Chaboureau et al. for TROCCINOX-2, 2005
7
Institut für
Physik der Atmosphäre
Challenges: Modeled storm represents observations (radar, satellite)
Cut-off bei 16 km
Radar: TROCCINOX 04 Feb. 2005
Parameterization of Lightning Activity and NOx
8
Institut für
Physik der Atmosphäre
Challenges: Modeled storm represents observations (radar, satellite)
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)
Parameterization of Lightning Activity and NOx
9
Institut für
Physik der Atmosphäre
Challenges: Modeled storm represents observations (radar, satellite)
Lightning parameterization (explicit electricity or bulk) represents local lightning distribution (VLF/LF, optical)
Location, IC/CG, intensity
Very limited set of observations (trace gases, e.g. NOX) from aircraftFalcon: ~ 7 anvil crossings Geophysica: ~ 2 anvil dives
Parameterization of Lightning Activity and NOx
10
Institut für
Physik der Atmosphäre
Challenges: Modeled storm represents observations (radar, satellite)
Lightning parameterization (explicit electricity or bulk) represents local lightning distribution (VLF/LF, optical)
Location, IC/CG, intensity
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
Parameterization of Lightning Activity and NOx
11
Institut für
Physik der Atmosphäre
Parameterization of Lightning Activity and NOx
Institut für Physik der Atmosphäre/Laboratoire d’AérologieSimulation
Tropics (s. Brazil) Mid-latitude (s. Germany)
12
Institut für
Physik der Atmosphäre
Missions (II)
Understanding the weather
and how it affects aviation
13
Institut für
Physik der Atmosphäre
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
14
Institut für
Physik der Atmosphäre
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
15
Institut für
Physik der Atmosphäre
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)
16
Institut für
Physik der Atmosphäre
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
18
Institut für
Physik der Atmosphäre
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