LIMITATIONS OF THE COMPARISONS MODEL VS. OBSERVATIONS ON THE EXAMPLE OF A COST728 MODEL EVALUATION STUDY Ekaterina Batchvarova 1,2 , Sven-Erik Gryning 2 , Markus Quante 3 and Volker Mathias 3 1 National Institute of Meteorology and Hydrology, Sofia, Bulgaria (Ekaterina.Batchvarova@meteo.bg) 2 National Laboratory for Sustainable Energy, RISOE DTU, Denmark (ekba@risoe.dtu.dk) 3 GKSS, Geesthacht, Germany RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
CONTRIBUTING The presented work continues collaboration within COST 728 - A. Aulinger, C. Chemel, G. Geertsema, B. Geyer, H. Jakobs, A. Kerschbaumer, M. Prank, R. San José, H. Schlünzen, J. Struzewska, B. Szintai, R. Wolke are participating the present work through model outputs and discussions in connection with Case 1 inter comparison exercise.
COST Action 728 www.cost728.org Chair Ranjeet S Sokhi, UH, UK Participants from ENHANCING MESO-SALE Austria, Belgium, Bulgaria, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, METEOROLOGICAL Hungary, Italy, Lithuania, Netherlands, Norway, Poland, Portugal, Romania, Spain, Sweden, MODELLING CAPABILITIES Switzerland, Turkey, UK Plus FOR AIR POLLUTION AND JRC (ISPRA) Non-COST: USA, Canada, Russia, Macao DISPERSION APPLICATIONS International cooperation: NOAA, USEPA, WMO RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
Challenges for knowledge in meteorology for air pollution and other applications • Structure of COST728, Topics addressed Requirements from society evolve • Science is advancing in different directions WG1 - Meteorological parametrization/applications (Maria Athanassiadou, UK Met Office, Sven-Erik • Higher order of complexity in models Gryning, Risoe DTU) • Larger run times WG2 - Integrated systems of MetM-CTM, interfaces, module unification, strategy • Large amount of input and output data (Alexander Baklanov, DMI) • Can require larger computing platforms WG3 - Mesoscale models for air pollution and • dispersion applications Users of complex modeling systems are (Mikhail Sofiev, FMI) less familiar with all approaches and models incorporated WG4 - Development of evaluation tools and methodologies • Evaluation of models is very complex task (Heinke Schluenzen, University of Hamburg) • Measurement techniques develop, become more sophisticated and the issues of data interpretation, calibration, missing data treatment, etc are to be discussed
Model Evaluation Methods Comparison with Model measurements intercomparison eg. Statistical eg. Common tests metrics, graphics Model Evaluation Process evaluation Sensitivity analysis eg. PBL , cloud eg. response to schemes changes Operational evaluation Eg. Regulation, Policy
Evaluation of models vs observations 1) Modeled vs observed CASE 1 – Winter/spring 2003 PM – stagnant concentrations at conditions surface CASE 2 – Spring 2006 2) Modeled vs observed Forest fires (Russia) – LRT concentrations at 5 levels (ENSEMBLE) CASE 2 – Summer 2006 – 3) Modeled vs observed PM/O3 meteorology at surface Others and 5 levels Summer 2003 Fires Portugal, Po Valley 4) Modeled vs observed profiles of mean values and fluxes – masts, RSs, WPs
COST728 CASE STUDY 1 February – March 2003 Feb-April 2003 PM episode over ermany Source: Schaap et al 2008 1) Modeled vs observed concentrations at surface RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
SE England, June 2001 1) Modeled vs observed concentrations at surface WRF-Chem July 2002 Source: Grell et al 2005 MM5-CMAQ June 2001 Source: Yu et al 2008 -CMAQ June 2001 Source: Yu et al 2008 RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
Typical Emissions Majority of AQ systems of models Meteorology under predict PM concentrations near the ground Large scatter in Models use different parameterizations of turbulence modeled- and mixing and parameterizations reflect ideal observed conditions concentrations Models predict and use different Atmospheric scatter plots Boundary-Layer height. How is this related to Large differences observations? The ABL height is a parameter defined in between models different way in the fields of temperature, humidity, wind, aerosol. The different measuring techniques correspond also to diverse definitions. Therefore the discussions within COST728 concluded that modeled and measured profiles of meteorological parameters are to be firstly compared rather than ABL height.
Meteorological measurements at sites with tall masts and ABL profile measurements – non-routine data Hamburg • 320 meter mast: wind speed, wind direction, temperature, sensible heat flux, momentum flux at 10, 50, 110, 175 and 250 m (5 levels) Cabauw • 200 meter mast: wind speed, direction and temperature at 2,10, 40, 80, 140 and 200 m) • Wind profiler data up to 5 km • Radiosoundings at 0 and 12 UTC Lindenberg • 99 meter mast over grassland: wind speed, wind direction and temperature at 40 and 98 m • 28 meter mast over forest: wind speed, wind direction and temperature at 28 meters above the forest) • Wind profiler data up to 5 km • Radiosoundings at 0, 6, 12 and 18 UTC
Radiosonde measurements Large differences RS vs models within the entire BL Cabauw, 24 Feb 2003 GKSS-MM5-10 UTC Models smooth the GKSS-MM5 11 UTC GKSS-MM5 12 UTC meteorological fields in GKSS-MM5 13 UTC 3500 GKSS-MM5 14 UTC space and time 3000 RS 12 UTC 2500 UPM-MM5 10 UTC UPM-MM5 11 UTC Height [m] 2000 UPM-MM5 12 UTC UPM-MM5 13 UTC 1500 UPM-MM5 14 UTC 1000 500 0 260 265 270 275 280 285 Temperature [K]
Wind profilers 915 MHz 1290 MHz 482 / 1290 MHz RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
COST 728: Wind velocity – obs / model 25 PBL profiler Wind velocity at EURAD FUB 20 Lindenberg at about GKSS_MM5 Wind Velocity [ms -1 ] GKSS_CosmoCLM 500 m asl IFT MeteoSwiss_a 15 UH_WRF time series 10 24.02.2003 to 11.03.2003 5 some systematic deviations – phase / amplitude 0 0 50 100 150 200 250 300 350 400 hours after 24/03/2003 0 UTC RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
COST 728: Wind velocity – obs / model 24.02.2003 to 11.03.2003 4 1x10 Wind velocity at 3 1x10 Lindenberg at about 500 m asl 2 1x10 1 1x10 S(f) UU power spectra 0 1x10 PBL profiler EURAD -1 1x10 FUB GKSS_MM5 GKSS_CosmoCLM -2 1x10 IFT Only models with MeteoSwiss_a UH_WRF highest resolution -3 1x10 capture intraday 1000 100 10 1 fluctuations Period [hrs.] RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
wind direction wind velocity Wind profiles – obs /model Observation Lindenberg (a, b) Lindenberg 27.02.2003 00 UTC 6000 COSMO-CLM (c, d) 4000 height [m] PBL-profiler TROP-profiler Radiosonde 00UTC EURAD FMI FUB 2000 GKSS_MM5 GKSS_CosmoCLM UPM-WRF IFT MeteoSwiss_a (e, f) UPM_MM5 UPM_WRF WUT 0 0 4 8 12 16 20 wind velocity [m/s] Meteo Swiss - COSMO (g, h) at the hour entire period (hourly) RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
nearest Radiosonde included RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
Some bulk statistics Average profiles of wind speed at Lindenberg, 24.02.2003 to 11.03.2003; based on hourly data RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
Some bulk statistics Average profiles of Correlation coefficient for wind speed wind speed bias at Lindenberg, 24.02.2003 to 11.03.2003; based on hourly data RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
Some bulk statistics COST 728, test case 1 Average profiles of wind speed Hit Rate wind direction Hit Rate at Lindenberg, 24.02.2003 to 11.03.2003; based on hourly data RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
Concluding remarks Compared to radio sonde data wind profiler observations have the advantage of much higher time resolution (at least hourly data). The RS and WP measurements are representing different volumes, therefore should not expected to be close. Some points can be made on models performance: underestimation of wind speed above PBL by many models and overestimating within the PBL hit rate WS ( 1ms -1 ): 0.2 to 0.4 hit rate WD ( 10°): = 0.2 to 0.6 local circulation systems sufficient model resolution (~6 km) effective resolution is larger than 4 times the grid resolution RISØ DTU, Denmark --- NIMH BAS, Bulgaria --- GKSS, Germany Harmo 13, 1 – 4 June 2010, Paris, France
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