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Thorsten Fehr et al. 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. Missions (I).

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Thorsten Fehr et al. Institut für Physik der Atmosphäre

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  1. 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. Missions (I) • Understanding the climate • and how it is affected by aviation

  3. 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. Aircraft • Aircraft: • Trace gas • µ-physics • Aircraft: • Trace gas • µ-physics • Lightning • Aircraft: • Trace gas • µ-physics • Lightning • Radar • Satellite • Surface obs. Parameterization of Lightning Activity and NOx Observations

  5. Parameterization of Lightning Activity and NOx Parameterization LNOX Lightning fcell =fcell(i)

  6. Parameterization of Lightning Activity and NOx Simulation Total Condensed Water Lightning NOX J.-P. Chaboureau et al. for TROCCINOX-2, 2005

  7. Radar: TROCCINOX 04 Feb. 2005 Cut-off bei 16 km Parameterization of Lightning Activity and NOx • Challenges: • Modeled storm represents observations (radar, satellite)

  8. DLR LINET, 04 Feb 2005: • LF lightning detection network • IC strokes (51.420) • CG strokes (82.462) Parameterization of Lightning Activity and NOx • Challenges: • Modeled storm represents observations (radar, satellite) • Lightning parameterization (explicit electricity or bulk) represents local lightning distribution (VLF/LF, optical) • Location, IC/CG, intensity

  9. Parameterization of Lightning Activity and NOx • 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 Falcon: ~ 7 anvil crossings Geophysica: ~ 2 anvil dives

  10. Parameterization of Lightning Activity and NOx • 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

  11. Parameterization of Lightning Activity and NOx Tropics (s. Brazil) Mid-latitude (s. Germany) Institut für Physik der Atmosphäre/ Laboratoire d’Aérologie Simulation

  12. Missions (II) Understanding the weather and how it affects aviation

  13. Forecasting for airports: model chain with nesting LM forecasting domain MM5 forecasting domain 1 MM5 forecasting domain 2 3D view of storm crossing airport Cross section along glideslope Airport area PI: Arnold Tafferner

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

  15. High-resolution weather simulations predict areas of Clear-Air Turbulence (CAT) Wind and divergence (1/s) 29 January 1998 21 UT Ellrod CAT Index ETI = VWS  [ DEF +CVG ] PI: Andreas Dörnbrack

  16. Evaluating precipitation forecasts using polarimetric radar SynPolRadSynthetic Polarimetric Radar • 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. PI: Monika Pfeifer

  17. 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

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