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

Institut für Physik der Atmosphäre 2 Missions (I) Understanding the climate and how it is affected by aviation

Institut für Physik der Atmosphäre 3 Parameterization of Lightning Activity and NO x Motivation: Natural production and distribution of trace gases is poorly known as compared to air traffic and ground sources.  In particular nitrogen oxides (NO X ) from lightning (LNO X ) 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 NO x parameterization based on convection parameterization (Pinty)

Institut für Physik der Atmosphäre 4 Parameterization of Lightning Activity and NO x ObservationsAircraft Aircraft: Trace gas µ-physics Aircraft: Trace gas µ-physics Lightning Aircraft: Trace gas µ-physics Lightning Radar Satellite Surface obs.

Institut für Physik der Atmosphäre 5 Parameterization of Lightning Activity and NO x Parameterization Lightning cell f cell =f cell (  i ) LNO X

Institut für Physik der Atmosphäre 6 Parameterization of Lightning Activity and NO x Simulation Total Condensed WaterLightning NO X J.-P. Chaboureau et al. for TROCCINOX-2, 2005

Institut für Physik der Atmosphäre 7 Challenges:  Modeled storm represents observations (radar, satellite) Cut-off bei 16 km Radar: TROCCINOX 04 Feb Parameterization of Lightning Activity and NO x

Institut für Physik der Atmosphäre 8 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 NO x

Institut für Physik der Atmosphäre 9 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. NO X ) from aircraft Falcon: ~ 7 anvil crossingsGeophysica: ~ 2 anvil dives Parameterization of Lightning Activity and NO x

Institut für Physik der Atmosphäre 10 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. NO X ) 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 NO x

Institut für Physik der Atmosphäre 11 Parameterization of Lightning Activity and NO x Institut für Physik der Atmosphäre/ Laboratoire d’Aérologie Simulation Tropics (s. Brazil)Mid-latitude (s. Germany)

Institut für Physik der Atmosphäre 12 Missions (II) Understanding the weather and how it affects aviation

Institut für Physik der Atmosphäre 13 Cross section along glideslope LM forecasting domainMM5 forecasting domain 1MM5 forecasting domain 2 Airport area3D view of storm crossing airport Forecasting for airports: model chain with nesting PI: Arnold Tafferner

Institut für Physik der Atmosphäre 14 Ensemble forecasts ranked by image matching Cluster 1 Rank: 9 Meteosat 7 IR, 9 July 2002 LM det Rank: 5 Cluster 3 Rank: 10Cluster 4 Rank: 1 COSMO-LEPS ensemble of 10 LM forecasts driven by clusters from ECMWF EPS PI: Christian Keil

Institut für Physik der Atmosphäre 15 PI: Andreas Dörnbrack Wind and divergence (1/s) 29 January UT Ellrod CAT Index ETI = VWS  [ DEF +CVG ] High-resolution weather simulations predict areas of Clear-Air Turbulence (CAT)

Institut für Physik der Atmosphäre 16 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. SynPolRad Synthetic Polarimetric Radar Evaluating precipitation forecasts using polarimetric radar PI: Monika Pfeifer

Institut für Physik der Atmosphäre 17 Mixing in Cumulus Clouds Very high resolution simulations (<50m) show that to resolve a cumulus cloud, need to resolve the dominant eddy scale (set by the buoyancy and stratification). Vertical velocityRendering PI: George Craig

Institut für Physik der Atmosphäre 18 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