THE BLACK FOREST STORM OF 15 JULY 2007: NUMERICAL SIMULATION AND SENSITIVITY STUDIES Evelyne Richard 1, Jean-Pierre Chaboureau 1 Cyrille Flamant 2 and.

Slides:



Advertisements
Similar presentations
Institut für Physik der Atmosphäre POLDIRAD and LINET observations during COPS Martin Hagen and Hartmut Höller DLR Oberpfaffenhofen.
Advertisements

Ewan OConnor, Robin Hogan, Anthony Illingworth Drizzle comparisons.
C. Flamant (1), C. Champollion (1,2), S. Bastin (1) and E. Richard (3) (1) Institut Pierre-Simon Laplace, UPMC/CNRS/UVSQ (2) Géosciences Montpellier UM2/CNRS,
Meso-NH model 40 users laboratories
The Persistence and Dissipation of Lake Michigan-Crossing Mesoscale Convective Systems Nicholas D. Metz* and Lance F. Bosart # * Department of Geoscience,
7 th COPS Workshop Collège Doctoral Européen Strasbourg, France, October 2008 The impact of convergence zones on the initiation of deep convection.
5/18/2015 Prediction of the 10 July 2004 Beijing Flood with a High- Resolution NWP model Ying-Hwa Kuo 1 and Yingchun Wang 2 1. National Center for Atmospheric.
Aspects of 6 June 2007: A Null “Moderate Risk” of Severe Weather Jonathan Kurtz Department of Geosciences University of Nebraska at Lincoln NOAA/NWS Omaha/Valley,
Danielle M. Kozlowski NASA USRP Intern. Background Motivation Forecasting convective weather is a challenge for operational forecasters Current numerical.
The primary initiation of deep convection from boundary-layer convection during CSIP School of Earth and Environment NATIONAL CENTRE FOR ATMOSPHERIC SCIENCE.
COPS - FRANCE Proposal submitted to ANR White call – March 2006.
Institut für Physik der Atmosphäre WG4: Data Assimilation and Predictability George C. Craig DLR-Institut für Physik der Atmosphäre.
This is the footer WRF : “Great North Run” and COPS Ralph Burton, NCAS (Leeds) Alan Gadian, NCAS (Leeds) Alison Coals, NCAS (Leeds)
COPS - FRANCE Proposal submitted to ANR, LEFE/INSU & TOSCA/CNES.
The Effect of the Terrain on Monsoon Convection in the Himalayan Region Socorro Medina 1, Robert Houze 1, Anil Kumar 2,3 and Dev Niyogi 3 Conference on.
The Effect of the Terrain on Monsoon Convection in the Himalayan Region Socorro Medina 1, Robert Houze 1, Anil Kumar 2,3 and Dev Niyogi 3 Cloud and Precipitation.
This is the footer Modelling of deep convective clouds and orographic triggering of convection during the COPS experiment Ralph Burton,
A Survey of Wyoming King Air and Cloud Radar Observations in the Cumulus Photogrammetric In-Situ and Doppler Observations (CuPIDO) experiment J. Cory Demko.
January 29-30, 2013 simulated composite reflectivity (dBZ).January 29-30, 2013 simulated surface equivalent potential temperature (K) and winds (m/s).
Impact of the 4D-Var Assimilation of Airborne Doppler Radar Data on Numerical Simulations of the Genesis of Typhoon Nuri (2008) Zhan Li and Zhaoxia Pu.
Operationnal use of high resolution model AROME image source: Sander Tijm, KNMI.
Roll or Arcus Cloud Supercell Thunderstorms.
Campaign data for parameterization tests: Examples from MAP‘99 VERTIKATOR’02, AWIATOR‘03 Hans Volkert, Thorsten Fehr, Christoph Kiemle, Oliver Reitebuch,
Ensemble Numerical Prediction of the 4 May 2007 Greensburg, Kansas Tornadic Supercell using EnKF Radar Data Assimilation Dr. Daniel T. Dawson II NRC Postdoc,
Evaluation of simulated precipitation fields of some MAP events: sensitivity experiments and model intercomparison ( 1) LA CNRS/UPS, Toulouse, France (2)
Meso-γ 3D-Var Assimilation of Surface measurements : Impact on short-range high-resolution simulations Geneviève Jaubert, Ludovic Auger, Nathalie Colombon,
High-resolution modelling in mountainous areas: MAP results Evelyne Richard Laboratoire d’Aérologie CNRS / Univ. Paul Sabatier Toulouse, France.
Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss.
The IOP6 (24 September 2012) heavy precipitation event over Southern France: observational and model analysis Lagouvardos, K. (1), Kotroni, V. (1), Bousquet.
WMO workshop, Hamburg, July, 2004 Some aspects of the STERAO case study simulated by Méso-NH by Jean-Pierre PINTY, Céline MARI Christelle BARTHE and Jean-Pierre.
High resolution simulation of August 1 AMMA case: impact of soil moisture initial state on the PBL dynamics and comparison with observations. S. Bastin.
Experiences with 0-36 h Explicit Convective Forecasting with the WRF-ARW Model Morris Weisman (Wei Wang, Chris Davis) NCAR/MMM WSN05 September 8, 2005.
IHOP Workshop, Toulouse, June 14-18, 2004 MODELING OF A BORE OBSERVED ON JUNE 04 DURING IHOP 2002 Mariusz Pagowski NOAA Research - Forecast Systems Laboratory.
Comparison of Evaporation and Cold Pool Development between Single- Moment (SM) and Multi-moment (MM) Bulk Microphysics Schemes In Idealized Simulations.
An ensemble study of HyMeX IOP6 and IOP7a Alan Hally (1,2), Evelyne Richard (1), Véronique Ducrocq (2) (1)LA, University of Toulouse, France (2)CNRM, Météo-France,
Dual-Aircraft Investigation of the inner Core of Hurricane Norbert. Part Ⅲ : Water Budget Gamache, J. F., R. A. Houze, Jr., and F. D. Marks, Jr., 1993:
LIDAR OBSERVATIONS CONSTRAINT FOR CIRRUS MODELISATION IN Large Eddy Simulations O. Thouron, V. Giraud (LOA - Lille) H. Chepfer, V. Noël(LMD - Palaiseau)
RICO Modeling Studies Group interests RICO data in support of studies.
Toulouse IHOP meeting 15 June 2004 Water vapour variability within the growing convective boundary layer of 14 June 2002 with large eddy simulations and.
Objective Evaluation of the Meso-NH Simulations during Hibiscus-Troccinox-Troccibras I.Forecasts for the TROCCINOX campaign II.Three case studies: 13 Feb,
Three real case simulations by Meso-NH validated against satellite observations J.-P. Chaboureau and J.-P. Pinty Laboratoire d’Aérologie, Toulouse 1.Elbe.
Preliminary LES simulations with Méso-NH to investigate water vapor variability during IHOP_2002 F. Couvreux F. Guichard, V.
Moist processes involved in IOP13 and IOP16. Fanny DUFFOURG Olivier NUISSIER Christine LAC CNRM-GAME / Météo-France & CNRS HyMeX ST-WV meeting, Toulouse,
Orographic Precipitation in Potentially Unstable Alpine Storms: MAP IOPs 2b, 3, and 5 Socorro Medina and Robert A. Houze.
A regional-model « climatology » of vertical mass and water-vapour transport for the Hibiscus-Troccinox-Troccibras campaign 2004 F. Gheusi, J.-P. Cammas,
Tammy M. Weckwerth Various Features Influencing Convection Initiation on 12 June 2002 during IHOP_2002* Tammy M. Weckwerth (NCAR) WWRP Symposium on Nowcasting.
Numerical investigation of the multi-scale processes inducing convection initiation for the 12 June 2002 IHOP case study Preliminary study: testing the.
ARPS( Advanced Regional Prediction System ) Version Center for Analysis and Prediction of Storms (CAPS), Oklahoma University tridimensional compressible.
LAM activities in Austria in 2003 Yong WANG ZAMG, AUSTRIA 25th EWGLAM and 10th SRNWP meetings, Lisbon,
COSMO model simulations for COPS IOP 8b, 15 July 2007 Jörg Trentmann, Björn Brötz, Heini Wernli Institute for Atmospheric Physics, Johannes Gutenberg-University.
WRF-based rapid updating cycling system of BMB(BJ-RUC) and its performance during the Olympic Games 2008 Min Chen, Shui-yong Fan, Jiqin Zhong Institute.
OKX The OKX sounding at 1200 UTC has 153 J kg -1 CIN extending upwards to 800 hPa and < 500 J kg -1 CAPE. There was 41.8 mm of precipitable water. By 1400.
Mesoscale Assimilation of Rain-Affected Observations Clark Amerault National Research Council Postdoctoral Associate - Naval Research Laboratory, Monterey,
1 James D. Doyle and Clark Amerault Naval Research Laboratory, Monterey, CA James D. Doyle and Clark Amerault Naval Research Laboratory, Monterey, CA Sensitivity.
Xuexing Qiu and Fuqing Dec. 2014
Numerical Weather Forecast Model (governing equations)
Impact of North Atlantic hurricanes on episodes of intense rainfall over the Mediterranean Florian Pantillon1,2 Jean-Pierre Chaboureau1 and Evelyne.
Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part II: Case Study Comparisons with Observations and Other.
WRF model runs of 2 and 3 August
By SANDRA E. YUTER and ROBERT A. HOUZE JR
Simulation of the Arctic Mixed-Phase Clouds
Alan F. Srock and Lance F. Bosart
The May 24 Shamrock cold front
Holger Mahlke, Ulrich Corsmeier, Christoph Kottmeier
Group interests RICO data required
Convective and orographically-induced precipitation study
Rita Roberts and Jim Wilson National Center for Atmospheric Research
Sensitivity to WRF microphysics/ Cu parametrisation
Braun, S. A., 2006: High-Resolution Simulation of Hurricane Bonnie (1998). Part II: Water Budget. J. Atmos. Sci., 63, Gamache, J. F., R. A. Houze.
Group interests RICO data in support of studies
Presentation transcript:

THE BLACK FOREST STORM OF 15 JULY 2007: NUMERICAL SIMULATION AND SENSITIVITY STUDIES Evelyne Richard 1, Jean-Pierre Chaboureau 1 Cyrille Flamant 2 and Cédric Champollion 3 1. Laboratoire d’Aérologie, CNRS/UPS Toulouse, France 2. LATMOS/IPSL, CRNS/UPMC, Paris, France 3. Géosciences, Montpellier, France

COPS IOP8b – 15 July 2007 (Courtesy of DLR) > 60 dBz 12 km

Radar reflectivity : Time evolution of the 10 dBZ contour Montancy radar Simulation (ECMWF) 12:15 –13:00 13:15 – 14:00 14:15 – 15:00 15:15 – 16:00

Radar reflectivity : Time evolution of the 10 dBZ contour Montancy radar Simulation (ARPEGE) 12:15 –13:00 13:15 – 14:00 14:15 – 15:00 15:15 – 16:00

Motivations and outlines The real time Meso-NH forecast of this event was surprisingly good … but ■ Is this good forecast obtained for the good reasons? ■ What makes the ARPEGE based forecast different?  CAPE / CIN  Surface conditions  Sensitivity tests

CAPE MESO-NH / ECMWFMESO-NH ARPEGE 11:00 UTC 14:00 UTC

CIN MESO-NH / ECMWFMESO-NH / ARPEGE 11:00 UTC 14:00 UTC

Karlruhe Meiztreizheim Achern Hornisgrinde Heselbach Burnhaupt TIME 6 KM 0 KM Water vapor mixing ratio (g/kg) OBS. ECMWF ARPEGE

Karlruhe Meiztreizheim Achern Hornisgrinde Heselbach Burnhaupt TIME 6 KM 0 KM OBS. ECMWF ARPEGE Potential temperature (K)

Surface moisture analysis : 00UTC ECMWF ARPEGE

Sensitivity tests ■ ECMWF surface moisture reduced by 20% ■ ECMWF surface moisture reduced by 50%  Thermodynamical conditions  Convection

Karlruhe Meiztreizheim Achern Hornisgrinde Heselbach Burnhaupt TIME 6 KM 0 KM OBS. ECMWF ARPEGE Potential temperature (K)

Water vapor mixing 13 UTC Control Observation 20% reduction

Water vapor mixing 13 UTC Observation 20% reduction 50% reduction

Cloud top height (color) + Instant precipitation 15 UTC Control 20% reduction 50% reduction Convection is strongly controlled by initial moisture conditions

Summary / Conclusion ■ Convection is well captured by the ECMWF- based Meso-NH forecast although the PBL is too cold and too moist. ■ The PBL structure is more correct in the ARPEGE-based MESO-NH forecast but convection is over predicted. ■ There is a large discrepancy between ECMWF and ARPEGE surface moisture analysis ■ The sensitivity tests shows that the convection triggering is strongly controlled by the surface moisture conditions ■ Do we have enough data to validate the surface moisture conditions?

MESO-NH Forecasts  3 domains (32, 8, and 2 km) with 2-way interaction.  30h forecast starting at 00 UTC from ECMWF analysis  Mixed phase microphysics (including hail particles)  No convection parameterization in the 2km-mesh model 16/07 06UTC MNH-ECM 12UTC 12UTC15/07 00UTCMNH-ARP MNH-ECM MNH-ECM Run starting from the ECMWF analysis and forced with ECMWF forecasts MNH-ARP MNH-ARP: Run starting from the ARPEGE analysis and forced with ARPEGE forecasts 16UTC Convection over the Black-Forest

Karlruhe Meiztreizheim Achern Hornisgrinde Heselbach Burnhaupt TIME 6 KM 0 KM COR

Morning Afternoon 1000 m 2000 m Rv mod – 2g/kg Squares : Rv obs ( 2 lidars + Do28)

Water vapor mixing ratio : 13:00 – 13:30 UTC Lidar observations Lidar observations

Water vapor mixing ratio : 13:00 – 13:30 UTC Meso-NH Forecast Lidar observations

Upward motion

Convergence > s -1 +

11 UTC 12 UTC 13 UTC14 UTC

Convergence > s-1 + the surface

Moisture supply  Soundings (3 hourly)  Airborne lidar (Leandre II + DLR Dial)  In situ aircraft data (Dornier 28)  Soundings (3 hourly)  Airborne lidar (Leandre II + DLR Dial)  In situ aircraft data (Dornier 28)

Convergence line in the lee of the Feldberg : Feldberg Radar 13:08 UTC Doppler velocity 900hPa > 7g/kg Meso-NH Forecast 13:00 UTC

Water vapor mixing ratio : 13:00 – 13:30 UTC Meso-NH Forecast Lidar observations

Observation / Simulation METEOSAT TB = 280 K METEOSAT TB = 280 K

Observation / Simulation MONTANCY R = 1dBZ MONTANCY R = 1dBZ METEOSAT TB = 280 K METEOSAT TB = 280 K