Transcript Slide 1
Progress on an urban surface energy balance model comparison study Sue Grimmond, Martin Best, Janet Barlow King's College London, UK Met Office, University of Reading With (people participating so far): J-J Baik (Korea), M Best (UK), M Bruse (Germany), I Calmet (France), A Dandou (Greece), K Fortuniak (Poland), R Hamdi (Belgium), M Kanda (Japan), H Kondo (Japan), S Krayenhoff (Canada), S-B Limor (Israel), A Martilli (Spain), V Masson (France), K Oleson (USA), A Porson (UK), U Sievers (Germany), H Thompson (UK) Acknowledge: UK Met Office, Vasilis Pappas (KCL), Rob Mullen (KCL) COST-728 Exeter meeting, 3-4 May 2007 Variety of Applications for Urban Energy Balance Models For example: Meso-scale modelling Global climate modelling Air quality View factor determinations Heat island studies Upper boundary conditions for other models Weather forecasting Energy assessments Emergency response Meso-scale This Study Suite of different models Range of complexity Range of applications Range of data needs Range of computer needs Common: all run offline Urban Vegetation Water Computational Requirements Available models Too expensive to run? Globally more applicable? Parameters difficult to get? Number of Parameters Past Model Evaluations TEB Vancouver: Masson et al. 2002 Mexico City: Masson et al. 2002 Marseille: Lemonsu et al. 2004 MOSES Vancouver: Best et al. 2006 Mexico City: Best et al. 2006 Lodz: Offerle 2003 LUMPS Lodz: Offerle 2003 CLMU Vancouver: Oleson et al. 2007 Mexico City: Oleson et al. 2007 Distinct Features of Comparison Models run offline Following the methodology used by PILPS Project for Intercomparison of Land-Surface Parameterization Schemes Henderson-Sellers et al. (1993) Common Forcing Data Set All fluxes evaluated Canyon variables: Temperature, Wind speed Increasing levels of information provided Forcing data only Easily obtained urban morphology Urban fabric properties Evaluation data (back calculate parameters) Key Questions What are the main physical processes controlling the urban energy balance which need to be resolved? How complex does a model need to be in order to produce a realistic simulation of urban surface fluxes and temperatures? Which input parameter information is required by an urban model to perform realistically? Are we measuring the correct variables at the correct scales for model evaluation? Current Status Determine level of interest and identify participants Inventory of models • Collect details of all participating models Set up project infrastructure How to distribute data Website / Email www.kcl.ac.uk/ip/suegrimmond/model_comparison.htm Staged distribution of data Data formats Sample forcing dataset preparation Sent out to people Waiting for data runs to come back Obtain suitable observational datasets • Comprehensive set of observations • Ideally dataset unused previously for model testing Immediate Next steps Analysis for suitability of observational dataset • Ensure quality of observational datasets • Ensure dataset fulfils requirements of comparison • Identify limitations of experiment from observational dataset Funding • Waiting to hear from NERC • Other alternatives • Met Office has funded the initial stages Multi-step model runs • Different levels of input data are released to the modellers • At each stage more information is released about the morphology and physical properties of the site • enables determination of model parameters with more accuracy Multi-step model runs Simulation of the components of the surface energy balance (net radiation, storage, sensible and latent heat fluxes) for the location(s) of the evaluation dataset Four stages Different levels of input data are released to the modellers At each stage more information is released about the morphology and physical properties of the site enables determination of model parameters with more accuracy Staged approach to establish the required accuracy for each model parameter by comparing the quality of the simulation at each stage. Multi-step model runs Forcing data only: Add urban morphology: •Models run with no prior knowledge of the urban surface •i.e. model default values for all parameters •only the main forcing data supplied, e.g. winds, temperature, solar radiation. •Morphological information provided, e.g. building density, mean building height, vegetation fraction. •More easily obtained data sets Add urban fabric properties: •Urban building materials information would be given •e.g. thermal properties, albedo •information specific to each city/site not known in general on a global basis. •Reliance on these types of data makes a scheme difficult to use for global applications Add evaluation data: •Evaluation dataset released • optimisation of model parameters for best fit to observations •Optimised parameters returned as well as the standard outputs. •requested limit parameter values between observational limits •encouraged to undertake analysis of their results if the optimal solution required unrealistic parameter values. Process-oriented statistical analysis Statistical analysis of the model performance relative to the observations Analysis to assess urban climatological phenomena explicitly Many of the models also predict variables beyond the SEB terms •flux by flux •hour by hour evaluation as well as central tendency of the mean •assessment will be done at a series of time-scales (hourly, daily, monthly, annual etc.) to determine any biases within the model performance. •statistics will consist of a range of metrics • mean, standard deviation, probability distribution function, linear regression, root mean square error (systematic, unsystematic), index of agreement, mean absolute error, mean bias error, correlation coefficient, coefficient of determination, etc. •e.g. •positive sensible heat flux at night •storage heat flux magnitude and timing •latent heat flux - often neglected term • e.g. air temperature, surface temperature • Evaluate performance Urban Energy Balance Models participating so far CODE BEP02 BEP0X CLMU CTTC ENVI LUMPS MCBM Authors Martilli Martilli Oleson et al Limor & Hoffman Bruse Grimmond & Oke Kondo, Hiroaki Contact Person Alberto Martilli Heather Thompson Keith Oleson S-B Limor Michael Bruse Sue Grimmond Hiroaki Kondo Version used older version Linked to METRAS v.1.0 Country Spain UK USA Israel Germany UK/USA Japan MM5u Dandou & Tombrou Aggeliki Dandou, Maria Tombrou MM5V3-6-1 Greece MOSES1T MOSES2T MUKLIMO SM2U SRUM M. Best M. Best Siewers, Uwe Dupont & Mestayer Porson, Harman, M. Best M. Best U. Sievers Isabelle Calmet A. Porson One tile version Two tile version Thermodynamic UK UK Germany France UK SUMM Kanda, T.Kawai, R Moriwaki Masson, Valery Masson, Valery Clark, Best, Belcher Manabu Kanda, Toru Kawai, Ryo Moriwaki TEB Valery Masson TEB07 Rafiq Hamdi Krayenhoff & Voogt TUF2d Scott Krayenhoff Krayenhoff & Voogt TUF3d Scott Krayenhoff Krayenhoff & Voogt TUFopt Scott Krayenhoff TVM_BEP05 Martilli, Alberto Rafiq Hamdi ULEB Fortuniak, Krzysztof K. Fortuniak VUCM Lee, S-H & Park, S-U Jong-Jin Baik Green CTTC model Under development Coupled with 1D-vegetation model Japan Single-layer last version 2-d version 3-d version Optimized 3-d ver last version France Belgium Canada Canada Canada Belgium Poland Korea Multiple versions CODE BEP02 BEP0X CLMU CTTC ENVI LUMPS MCBM Authors Martilli Martilli Oleson et al Limor & Hoffman Bruse Grimmond & Oke Kondo, Hiroaki Contact Person Alberto Martilli Heather Thompson Keith Oleson S-B Limor Michael Bruse Sue Grimmond Hiroaki Kondo Version used older version Linked to METRAS v.1.0 Country Spain UK USA Israel Germany UK/USA Japan MM5u Dandou & Tombrou Aggeliki Dandou, Maria Tombrou MM5V3-6-1 Greece MOSES1T MOSES2T MUKLIMO SM2U SRUM M. Best M. Best Siewers, Uwe Dupont & Mestayer Porson, Harman, M. Best M. Best U. Sievers Isabelle Calmet A. Porson One tile version Two tile version Thermodynamic UK UK Germany France UK SUMM Kanda, T.Kawai, R Moriwaki Masson, Valery Masson, Valery Clark, Best, Belcher Manabu Kanda, Toru Kawai, Ryo Moriwaki TEB Valery Masson TEB07 Rafiq Hamdi Krayenhoff & Voogt TUF2d Scott Krayenhoff Krayenhoff & Voogt TUF3d Scott Krayenhoff Krayenhoff & Voogt TUFopt Scott Krayenhoff TVM_BEP05 Martilli, Alberto Rafiq Hamdi ULEB Fortuniak, Krzysztof K. Fortuniak VUCM Lee, S-H & Park, S-U Jong-Jin Baik Green CTTC model Under development Coupled with 1D-vegetation model Japan Single-layer last version 2-d version 3-d version Optimized 3-d ver last version France Belgium Canada Canada Canada Belgium Poland Korea Methods used to model outgoing shortwave radiation CODE # reflections MUKLIMO TEB TEB07 BEP02 SRUM CLMU TVM_BEP05 infinite infinite multiple multiple multiple multiple BEP0X TUF3d TUF2d TUFopt VUCM MCBM MOSES2T MOSES1T SM2U MM5u ENVI CTTC ULEB multiple multiple (min 2) multiple (min 2) multiple (min 2) three two one one one one one one one albedo canyon, roof canyon, roof canyon bulk/effective by facet canyon patches /facet patches /facet patches /facet by facet canyon, roof bulk bulk/effective bulk/town by facet by facet bulk/town CODE Methods used to heat determine Anthropogenic Heat Flux Anthropogenic flux Methods BEP0X MUKLIMO heat fluxes from the interior of the buildings TEB domestic heating computed TEB07 domestic heating computed BEP02 Partially accounted for by imposing a fixed temp at the building interior BEP05 Partially accounted for by imposing a fixed temp at the building interior TUF3d Prescribed bulk value TUF2d Prescribed bulk value TUFopt Prescribed bulk value VUCM Prescribed bulk value SM2U Prescribed CTTC Prescribed per vehicle (for vehicles only) CLMU prescribed traffic fluxes, parameterized waste heat fluxes from heating/ air conditioning MOSES2T not modelled itself but possible to be included for calculation of turbulent fluxes MOSES1T not modelled itself but possible to be included for calculation of turbulent fluxes SRUM not modelled itself but possible to be included for calculation of turbulent fluxes ULEB not modelled itself but possible to be included for calculation of turbulent fluxes MM5u calculated (offline) as a temporal & spatial function of the anthropogenic emissions ENVI from heat transfer ew through walls, no storage term MCBM Modelled by Kikegawa et al. offline CODE Methods to calculate turbulent sensible heat flux CTTC TEB07 calculated by the model From each surface BEP02 From each surface BEP05 From each surface SRUM Resistance network based on Harman et al. (2004) CLMU BEP0X TUF3d TUF2d TUFopt SM2U TEB resistances between canyon surfaces and canyon air based on Rowley (1930), between canyon air and atmosphere depend on stability as in CLM3 Resistances based on Clarke (1985) Resistances based on flat-plate heat transfer coeffs (vertical patches) and based on MO similarity (horiz. patches) Resistance (Guilloteau, 1998 + Zilitinkevich, 1995) Resistance MOSES2T Standard resistance MOSES1T Standard resistance ENVI from turbulence model (wall function) and surface energy balance MM5u Parametric formulation VUCM Parametric formulation MCBM MUKLIMO MO or Jurges ULEB M-O similarity: Louis (1979) modified by Mascart at al. (1995) MO-laws CODE Methods used to calculate Heat Storage Flux CTTC calculated by the model BEP02 BEP0X TEB Diffusion TEB07 diffusion CLMU Diffusion BEP05 Diffusion TUF3d Diffusion TUF2d Diffusion TUFopt Diffusion MOSES2T Diffusion MOSES1T Diffusion VUCM Diffusion SM2U Difference + Diffusion + Force restore MM5u OHM scheme (Grimmond et al., 1991) ENVI soil: 1D model, fully resolved, walls/building system: no storage term ULEB As QG in urban slab (solution of multi layer thermal diffusion equation) MCBM Finite difference MUKLIMO Walls and roofs have a heat capacity What is resolved in the model? CODE Resolved: CANYONS Resolved: Roof Resolved: walls Walls with orientation Walls sunlit/ shaded Road sunlit/ shaded Turbulence within canyon resolved BEP02 Yes Yes Yes Yes Yes No Yes BEP05 Yes Yes Yes Yes Yes No Yes BEP0X No No No No No No No CLMU No Yes Yes No Yes No No CTTC Yes Yes Yes No No No Yes ENVI No Yes Yes No No No Yes MCBM No Yes Yes No Yes Yes Yes MM5u No No No No No No No MOSES1T No No No No No No No MOSES2T Yes Yes No No No No No MUKLIMO No No No No No No Yes SM2U No No No No No No No SRUM Yes Yes No No No No No TEB No Yes Yes No No No No TEB07 No No No No No No No TUF Yes Yes Yes Yes Yes Yes No ULEB No No No No No No No VUCM Yes Yes Yes No Yes Yes No Final Comments Models that are already participating show a wide range of approaches Need to follow up on some details Multiple versions of some individual models are participating Initial trial dataset now available Data back from three groups This is allowing us to iron out issues at both ends People can still participate Encouraged to do so! Contact me: [email protected] Participants will be co-authors in manuscripts etc Waiting to hear if NERC will fund the next parts of this project Within Canyon processes modelled Canyons are resolved Above canyon modelled Canyon top modelled CODE Type of Model BEP02 Multiple layer No No No No BEP05 Multiple layer No No No No BEP0X Multiple layer No No No No CLMU Single layer Yes No Yes Yes CTTC Single layer No No No No Yes Yes Yes Yes ENVI MCBM Multiple layer No No No No MM5u Single layer No No No No MOSES1T Single Layer No No No No MOSES2T Single Layer No No No No Yes Yes No No MUKLIMO SM2U Single Layer No No No No SRUM Single Layer No No No No TEB No No No No TEB07 No No No No TUF No Yes No No BEP05 No No No No ULEB Multiple layer No No No No VUCM Single layer Yes No Yes Yes