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1 October 26, 2009 8 th COPS Workshop, Cambridge, UK COPS (Convective and Orographically-induced Precipitation Study) Goal: Advance the quality of forecasts.

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Presentation on theme: "1 October 26, 2009 8 th COPS Workshop, Cambridge, UK COPS (Convective and Orographically-induced Precipitation Study) Goal: Advance the quality of forecasts."— Presentation transcript:

1 1 October 26, th COPS Workshop, Cambridge, UK 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 Wulfmeyer et al., Bull. Amer. Meteor. Soc. 89, , 2008, DOI: /2008BAMS Volker Wulfmeyer Institute of Physics and Meteorology (IPM) University of Hohenheim, Stuttgart, Germany and the COPS International Science Steering Committee  Research strategy  Recent highlights  Research desiderates  Recommendations Brandnew: Rotach et al., Bull. Amer. Meteor. Soc. 90, , 2009, DOI: /2009BAMS

2 2 October 26, th COPS Workshop, Cambridge, UK COPS Treasure Chest Area-wide and synergetic observation of process chain Impact studies and data assimilation methodology Integrated verification of process chain Predictive skill of probabilistic QPF (PrQPF), predictability of convection and precipitation

3 3 October 26, th COPS Workshop, Cambridge, UK Achievements:  European-wide JDC data set  Soil moisture and flux analyses  Detailed observations of ABL, clouds, and precipitation  Various process studies ongoing Area-wide, synergetic observation of process chain 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 Courtesy Kerstin Ebell et al., B1.

4 4 October 26, th COPS Workshop, Cambridge, UK 10 s, 150 m IPM Water-vapor Differential Absorption Lidar Measurement During IOP11b, July 26, 2007 Application of remote sensing data is a key challenge of this workshop. 10 s, 60 m 1 s, 15 m New performance! RHI scan

5 5 October 26, th COPS Workshop, Cambridge, UK 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. Area-wide, synergetic observation of process chain Data overlay and COSMO2 output, Aoshima et al., A2. IOP 14a, August 6, 2007, 16 UTC COSMO2 output Thorough analyses of process chain based on integrated data sets.

6 6 October 26, th COPS Workshop, Cambridge, UK Achievements:  D-PHASE model verification (precipitation only) over Switzerland  Superior performance of convection-permitting models demonstrated good bad Threshold (mm/3h) Fraction skill score - COSMO-7 (7km)COSMO-2 (2.2km) Threshold (mm/3h) Difference Spatial scale (km) = COSMO-7 better COSMO-2 better JJA 2007, Verification versus Swiss Radar Composite, 3-hourly acc., rain events only Courtesy Tanja Weusthoff, Marco Arpagaus, MeteoSwiss Integrated verification of process chain

7 7 October 26, th COPS Workshop, Cambridge, UK 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) Integrated verification of process chain 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 T2M corr. T2M rms

8 8 October 26, th COPS Workshop, Cambridge, UK 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. Data assimilation methodology and impact studies 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 3DVAR Control

9 9 October 26, th COPS Workshop, Cambridge, UK 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) Predictive skill of PrQPF and predictability AROME

10 10 October 26, th COPS Workshop, Cambridge, UK Recommendations  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) The WWRP Integrated Mesoscale Research Environment (IMRE) according to the new WWRP Strategic Plan


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