1 / 30

QUEST

QUEST. Quantitative Evaluation of Regional Precipitation Forecasts Using Multi-Dimensional Remote Sensing Observations. Quantitative evaluation of regional precipitation forecasts using multi-dimensional remote sensing observations. Partnership

yoshi
Télécharger la présentation

QUEST

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. QUEST Quantitative Evaluation of Regional Precipitation Forecasts Using Multi-Dimensional Remote Sensing Observations Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  2. Quantitative evaluation of regional precipitation forecasts using multi-dimensional remote sensing observations • Partnership • Susanne Crewell, Thorsten Reinhardt, University of Cologne (IGM) • Jürgen Fischer, Anja Hünerbein, FU Berlin (FUB) • George Craig, Martin Hagen, Monika Pfeifer, (DLR) • Michael Baldauf, Deutscher Wetterdienst (DWD) • Nicole van Lipzig, Ingo Meirold-Mautner, Katholieke Universiteit Leuven (KUL), Belgium (QUEST-B) • Contributes to PQP Goals • Identification of physical and chemical processes responsible for the deficiencies in quantitative precipitation forecast • Determination and use of the potentials of existing and new data and process descriptions to improve quantitative precipitation forecast Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  3. Quantitative evaluation of regional precipitation forecasts using multi-dimensional remote sensing observations (QUEST) satellite Observations MSG ~ 5km; 15min • Cloud Mask • Cloud top pressure MODIS ~ 1km; 1day • Cloud Mask • Optical thickness Radar IPT / Micro-wave GPS Ceilometer DWD radar composite; 1km; 5min • Rain rate • RANIE combined radar and gauge analysis Polarimetric radar (DLR) 17 stations;Germany; 1min; ranges up to 4km • Cloud base height • Cloud cover (<4km) 1D vertical; Lindenberg (and Cabauw) • temperature profile • humidity profile • LWC ~ 147 stations; Germany; 15min • IWV Surface: rain gauges Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  4. QUEST: Strategy Observations - multi-frequency radiances- polarimetric radar quantities - ground-based and space-borne observations Retrieval Forward Operator - water vapour- cloud properties - precipitation - SynPolRad (polari. radar) - SynSat (MSG, MODIS) - SynSatMic (AMSU, SSM/I) Schröder et al. [2006] Weather Forecasts - three-dimensional description of the forecasted atmospheric state- focus on Lokal-Modell Kürzestfrist (LMK) Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  5. Long-Term Evaluation Lokal-Modell Kürzestfrist • test suites • GOP duration 2007 • benefits of high resolution modelling Case Studies (ongoing) Tool development • SynPolRad • SynSat (-Mic) • MSG µ-phys. retrievals • verification measures • .. Identification of systematic model deficits Model Improvement (new) • Conditional verification • regionalization • diurnal cycle • weather situation dep. • cloud microphysics • land surface • turbulence Model Sensitivity Runs Hypothesis formulation "What are the crucial variables/processes to observe and to improve?" Cross correlation of different variables "How important is physical consistency?" QUEST: Approach comparison tools test of hypotheses case study selection for process studies Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  6. Case studies versus long-term evaluation • Detailed analysis • Automated analysis • Formulation of hypothesis • High significance • Low significance • Difficult to identify physical mechanism Case Studies Long-Term Evaluation • Sensitivity runs feasible /physical explanation • Objective selection of cases • Subjectively chosen cases • Tool development Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  7. Deutscher Wetterdienst‘s Lokal-Modell Kürzestfrist (“COSMO-LMK”) • Mesh size: x = 2.8 km • direct simulation of deep convection • convection parameterization for shallow part only • assimilation of radar data by latent heat nudging method • timestep T=25 s • 421 x 461 x 50 gridpoints, lowest model level in 10 m above surface • Centre of domain: 10 °E, 50 °N • Forecast time: 21 h, started every 3 h • Boundary conditions from Lokal-Modell Europa (“COSMO-LME”) with x = 7 km Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  8. Casestudy 26-08-2004 (M. Baldauf DWD) Accumulated precipitation over 24 hr Radar LM LMK 5.2 mm/day 3.9 mm/day 3.4 mm/day mm/day Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  9. General Observation Period (GOP)Year 2007 Central activity of QUEST in second phase of PP. http://gop.meteo.uni-koeln.de Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  10. GOP Organization and Performance The General Observation Period─ January to December 2007 ─ encompasses COPS in time and space • gather as many data about the atmospheric state as possible within an area covering Germany and it neighboring states. • to provide information of all kinds of precipitation types • to identify systematic model deficits • to select case studies for specific problems • to relate the COPS results to a broader perspective (longer time series and larger spatial domain) http://gop.meteo.uni-koeln.de Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  11. GOP General Observation Period 2007 • Partnership • Karl Bumke (IFM-Geomar) Disdrometer observations (WP 3) • Susanne Crewell (IGM) Overall GOP organisation • Galina Dick (GFZ) GPS observations (WP 5) • Jürgen Fischer (FUB) Satellite observations (WP 7) • Martin Hagen (DLR) GOP weather radar data (WP 2) • Thomas Hauf (UHan) Lightning networks (WP 6) • Christian Koziar (Thomas Hanisch) (DWD) Access to DWD observations (all WPs) • Armin Mathes (Univ. Bonn) Coordination/QC rain gauges (WP 1) • Mario Mech (IGM) GOP data management • Gerhard Peters (UHH) Micro Rain Radar (WP 3) • Matthias Wiegner (LMU) EARLINET Observations (WP 4) • and many more • + DKRZ (Claudia Wunram, Hannes Thiermann) • + COPS Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  12. GOP Ingredients: Precipitation Drop Size Distribution Rain gauge Weather Radar • WP-GOP-1 Rain gauges; DWD precip analyses (RANIE, REGNIE) • WP-GOP-2 Weather Radar • WP-GOP-3 Drop Size Distribution DSD several hundred independent observations by DWD, water authorities, environmentalagencies etc DWD analyses: RANIE, REGNIE DWDradar network and research radars, 3D volume scans, PI, RY, QY, RADOLAN vertical structure at about 15 locations with Micro Rain Radar (MRR) Continuous precipitation observation with high temporal resolution Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  13. MPI_3/IG1 MPI_2 UHH_1/IG3 Zingst Helgoland MPI UHH_2 MPI_1/IG2 DWD_1 Lindenberg UBO_2 UBO_1 LAMP Wien IMK MPI_4/IG4 DWD_2 UKO LAMP DLR Lichtenau GOP-3 Micro Rain Radar MRR-2 Optical Distrometer ODM470_1 Optical Distrometer FD12P Optical Distrometer PARSIVEL Distrometer JOSS/WALDVOGEL Scanning X-Band Radar (LAWR) DLR Inst. Phys. Atmos., Oberpfaffenhofen DWD_1 R. Assmann Obs., Lindenberg DWD_2 Met.. Obs. Hohenpeissenberg IG_1-4 IfM Geomar, Kiel IMK Inst. Met. Klim., Karlsruhe LAMP Laboratoire de Météorologie Physique MPI_1-4 MPI Hamburg UBO_1-2 Uni Bonn UHH_1-2 Uni Hamburg UKÖ Uni Köln Wien Uni Wien Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln Gerhard Peters

  14. GOP Ingredients: Auxillary Information AMF GPS Lidar • WP-GOP-4 Lidar (aerosol, cloud base, mixing layer height) • WP-GOP-5 GPS water vapour column • WP-GOP-6 Lightning networks • WP-GOP-7 Satellite observations (cloud properties, water vapor, aerosol) • WP-GOP-8 Meteorological stations EARLINET stations (4), about 100 lidar ceilometer stations in Germany DWD: ca 147 stations in LMK area, ca 200 in LME area + GPS COPS + Switzerland European and national networks VLF and VHF MSG, MODIS, MERIS, AMSU, CLOUDSAT, CALIPSO ARM Mobile Facility (AMF), Lindenberg, diverse universities and research institutes Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  15. GOP-7: Satellites • MSG:- cloud mask • cloud top pressure (+temperature?), • optical depth • IR brightness temperature • MODIS:- cloud mask • - cloud optical thickness τ- liquid water path LWP- effective radius reff - geometric cloud thickness H- IWV • - aerosol? • MERIS:- cloud mask • cloud optical thickness τ • cloud top pressure (+temperature?) Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  16. Evaluation Areas • Northsea • Baltic Sea • Alps • North western German lowland • North eastern German lowland • Low mountain ranges • COPS area • Countries (D, B, A, CH, NL, F) • River catchments (in Germany) LMK domain Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  17. GOP - First order model evaluation • Diurnal comparisons / plots, processed near real-time (“Quicklooks”) • Radiosoundings: Plots for each sounding in Germany and neigbouring countries- Stüve diagramm together with corresponding +12h LMK forecast- differences of temperature, specific humidity and wind speed forecasts (+0,+3,+6,+9,+12,+15,+18,+21 h) at each model level • GPS, Ceilometer: Daily colour coded maps of BIAS/RMSE of cloud base height (ceilometer) and IWV (GPS); LMK vs. observation • Monthly comparisons / plots. • Radiosoundings:Bias and RMSE profiles for temperature, humidity and wind for all stations • Ceilometer / GPS: Monthly time series of Bias/RMSE for each station or region (depending on number of stations within regions) • Ceilometer / GPS: Monthly analysis of mean diurnal cycle and comparison to differnet model runs (lagged ensemble) Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  18. Example for GPS Quicklook Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  19. Example for Radiosonding Quicklooks Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  20. Task: Archiving model output Total LMK output too large for permanent storage! => Therefore: Extracting model output relevant for model evaluation : 3 types of data extraction: 1.) statistics over (sub-) areas, timeseries at stations (1-d in time) 2.) column output at individual gridpoints (2-d in height and time) 3.) field output (3-d in x,y,t) Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  21. 1d output (time series) -- statistics of individual quantities (precip, wind, …) in (sub-)domains for direct evaluation & classification für weather-type dependent evaluation -- time series of near-surface variables Werte at Synop stations -- time series of integrated water vapour (IWV) at GPS stations -- time series of cloud base height at ceilometer stations -- time series of precipitation at precipitation stations Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  22. 2d output (column output) All available variables at certain gridpoints with vertically sounding instruments: -- Radiosonding stations -- Micro rain radars (ca 15) -- ARM Mobile Facility -- Earlinet stations -- Cloudnet stations -- COPS Supersites -- Meteorological Observatories Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  23. Field Output for comparison with area covering instruments (radar, satellite) -- brightness temperature of synthetic MSG channels -- radar reflectivity in 850 hPa, max. radar reflectivity in column -- Integrated condensate (TQC,TQR,TQS,TQG) -- height of cloud top and cloud base -- cloud cover (CLCT, CLCL, CLCM, CLCH) -- optical thickness -- precipitation (R, S, G), rates and sums -- radiation balances -- CAPE -- HZERO; 850-hPA temperature, wind; 500-hPa geopotential -- albedo, ground temperature For AMSU: all prognostic variables at overpass times Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  24. Examples of LTE (I): cloud parameters Cloud base Ceilometer Cloud top MSG Cloud cover MSG Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  25. Example of LTE (II): cloud cover Meteosat Second Generation comparison: July 2004 Lindenberg Cabauw AMF run 00UTC run 12UTC Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  26. Example of LTE (III): cloud cover MSG data – Cloud top pressure Daily cycle Daily cycle of RSME OBSmodel (run started at 00UTC) • Overestimation of cloud top height by model • Model simulates realistically no great variation throughout a day. Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  27. Summary LMK LTE LE • Precipitation underestimated by 20% • (problem addressed in the maintime by several model changes) • Daily cycle not well forecast • Boundary layer too thin and too wet • IWV generally well predicted • IWV Bias of -0.85 kg/m2 for runs started at 12 UTC • Clouds too thick • Cloud cover in good agreement with MSG Case studies to look into more detail in the problems Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  28. Case study example: pol.radar Reflectivity By Monika Pfeifer, DLR LMK, 2 comp. Rain, snow Observation LMK, Thompson, Rain, snow, graupel LMK, 3 comp. Rain, snow, graupel Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  29. Case study example: pol.radar Hydrometeor Classification By Monika Pfeifer, DLR LMK : 2 comp. Rain, snow Observation LMK: Thompson Rain, snow, graupel LMK :3 comp. Rain, snow, graupel Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

  30. Summary QUEST Goals • Optimization and refinement of existing evaluation tools • Identification of systematic errors in precipitation and cloud fields forecasts • Exploitation of the complementary information of the different remote sensing observations; model consistency; cross-correlation of model performance for different variables • Evaluate model using long-term observations collected during the GOP • Provide an independent test bed for model improvements • Improve LMK performance by changes in the treatment of cloud microphysics, turbulence, land surface,… (motivated by results of model evaluation) Thorsten Reinhardt, Institut für Geophysik und Meteorologie, Universität zu Köln

More Related