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COPS ( Convective and Orographically- induced Precipitation Study)

COPS ( Convective and Orographically- induced Precipitation Study) Goal: Advance the quality of forecasts of orographically-induced convective precipitation by 4D observations and modeling of its life cycle. Volker Wulfmeyer Institute of Physics and Meteorology (IPM )

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COPS ( Convective and Orographically- induced Precipitation Study)

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  1. COPS (Convectiveand Orographically-inducedPrecipitation Study) Goal:Advancethequalityofforecastsoforographically-inducedconvectiveprecipitationby 4D observationsandmodelingofitslifecycle Volker Wulfmeyer Institute ofPhysicsandMeteorology(IPM) University of Hohenheim, Stuttgart, Germany andthe COPS International Science SteeringCommittee • Research strategy • Recenthighlights • Research desiderates • Recommendations Wulfmeyer et al., Bull. Amer. Meteor. Soc. 89, 1477-1486, 2008, DOI:10.1175/2008BAMS2367.1. Brandnew: Rotach et al., Bull. Amer. Meteor. Soc. 90, 1321-1336, 2009, DOI:10.1175/2009BAMS2776.1.

  2. COPS TreasureChest Area-wideandsynergeticobservationofprocesschain Integrated verificationofprocesschain Impact studiesanddataassimilationmethodology Predictive skill of probabilistic QPF (PrQPF), predictability of convection and precipitation

  3. Area-wide, synergetic observation of process chain • Achievements: • European-wide JDC data set • Soil moisture and flux analyses • Detailed observations of ABL, clouds, and precipitation • Various process studies ongoing Soil moisture intercomparison with DWD GME model at site Simonswald, courtesy Christian Barthlott, KIT. Frequency distribution of IPT-LWC with height at AMF site, April-December 2007. Courtesy Kerstin Ebell et al., B1.

  4. IPM Water-vapor Differential Absorption Lidar Measurement During IOP11b, July 26, 2007 10 s, 60 m 1 s, 15 m 10 s, 150 m RHI scan New performance! Applicationof remote sensingdatais a keychallengeofthisworkshop.

  5. Area-wide, synergetic observation of process chain • Challenges and strategy: • Few integrations of different data sets but 3D analyses missing, e.g., by 3DVAR. • Emerging picture: Severe problems of models to simulate dynamics and humidity distribution on meso- scale, • but relative importance of deficiencies in process understanding with respect to PrQPF, e.g., role of aerosol-cloud- precipitation microphysics, unknown. IOP 14a, August 6, 2007, 16 UTC COSMO2 output Thorough analyses of process chain based on integrated data sets. Data overlay and COSMO2 output, Aoshima et al., A2.

  6. good bad Integrated verification of process chain • Achievements: • D-PHASE model verification (precipitation only) over Switzerland • Superior performance of convection-permitting models demonstrated Difference 90 58 33 20 7 = JJA 2007, Verification versus Swiss Radar Composite, 3-hourly acc. , rain events only Spatial scale (km) COSMO-2 (2.2km) COSMO-7 (7km) - Threshold (mm/3h) Fraction skill score COSMO-7 better COSMO-2 better Threshold (mm/3h) Threshold (mm/3h) Courtesy TanjaWeusthoff, Marco Arpagaus, MeteoSwiss

  7. Integrated verification of process chain • Challenges and strategy: • D-PHASE ensemble available but data gaps • First results over COPS domain (see A3, C7, Dorninger et al.), however, strong inhomogeneity of European surface data • Incorporation of new ensemble members, e.g., DWD reanalyses (B4) T2M corr. T2M rms Agreement essential on: - data sources and variables, - verification approach and skill scores, - temporal resolution, e.g., diurnal cycle, - spatial resolution and domains, - deterministic models and ensembles. AROME temperature corr. and rms during July 2007

  8. Data assimilation methodology and impact studies • Achievements, challenges, and strategy: • 3DVAR (Arome, Meteo France) and 3DVAR/4DVAR (WRF, IPM) with GPS and radar data • Nudging (DWD reanalyses, ARPA SIMC) • Strong impact of the assimilation of humidity, Doppler wind, and radar reflectivity data • but no comparison of data assimilation methodology (e.g., VAR versus EnKF), • so far data assimilation efforts not coordinated. Control 3DVAR Data assimilation testbed required, as suggested by WWRP WG MWFR and JSC. IOP9c, improvement of QPF by WRF 3DVAR using synop, sat, and GPS ZTD, Bauer et al., A3

  9. Predictive skill of PrQPF and predictability • Achievements, challenges, and strategy: • Analyses of ensemble simulations (Barthlott et al., Keil et al., Hanley et al. C5) • First results of D-PHASE models (A3) • Calibration of multi-model ensembles • Comparison of predictive skill in dependence on forcing conditions, boundaries, model physics, and initial conditions • Predictability Pr(F, B, I, P, PDF) AROME

  10. Recommendations The WWRP Integrated Mesoscale Research Environment (IMRE) according to the new WWRP Strategic Plan 2009-2017 • Integrated data sets and verification tools • Joint multi-boundary, multi-model ensemble forecast system with different data assimilation techniques • Platform to link CSIP and COPS research activities (QPF in different regions)

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