Urban micrometeorological flux observations and surface characterization State of art observational techniques and use of data in urban modeling"

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1 Urban micrometeorological flux observations and surface characterization State of art observational techniques and use of data in urban modeling" Sue Grimmond 1,2 1 Atmospheric Science Program, Geography, Indiana University 2 From January 1, 2006 Environmental Monitoring & Modelling Group, Geography, Acknowledgements: NSF King s College, University of London, UK

2 Scope Modelling Land surface schemes (LSS) Applicable for Meso-scale or larger scale models Other models Observations Input data for models Output data for models Analysis for understanding processes Surface characterization Parameters that are to be used in the LSS with observations

3 Examples HPDM (Hanna & Chang 1992) LUMPS (Grimmond & Oke 2002) UKMO-MOSES (Best 1998, 2005) SM-2U (Dupont et al. 2003) Meteo-France: TEB (Masson 2000) / ISBA Otte et al. (2004) Kusaka et al. (1999) Harman et al. (2004a,b) Brown and Williams (1998) Martilli et al. (2002) Urban Land Surface Schemes Many developed recently Expect this to continue Range in their: level of complexity Slab Single layer Multi layer incorporation of urban features heat partitioning 3-D urban surface data requirements computational demands

4 Evaluation of LSS with observations Considerations What are the required forcing data? (i.e. the inputs) What data are passed to the MM 1? (i.e. the outputs) Are there other variables calculated that can be evaluated? What parameters need to be assigned? Where are the model outputs representative of? Time step of model Offline vs dynamic link Spatial scale of model Obviously, answers are not the same for all LSS/models 1 This could be to any model. It does not need to be a Meso-scale Model (MM).

5 What are the required forcing data? Some that are reasonably universal Incoming solar (shortwave) radiation Incoming longwave radiation Air temperature Humidity (some measure) Wind (some measure) Important these are both collected also Commonly collected What height?

6 What data are passed to the MM? (i.e. LSS outputs) Fluxes Net all-wave radiation Outgoing longwave radiation Outgoing short wave radiation Turbulent Sensible heat flux Latent heat flux Momentum

7 Are there other variables calculated that can be evaluated? Storage heat flux Soil heat flux (a component of net storage heat flux) Anthropogenic heat flux Surface temperatures By facet? By orientation Wind profile Within canopy/canyon

8 Where are the LSS outputs representative of? What height above the surface? LSS model Masson et al. 2002: JAM

9 Masson (2000) BLM MM grid Martilli et al. (2002) BLM LSS grid Oke (2004) WMO

10 How does the model treat the surface? Integrated response Each component of the surface (impervious, vegetated) separately [Tiles] Grimmond & Oke 2002: JAM Best 2005: BLM e.g. LUMPS e.g. TEB/ISBA Masson et al JAM

11 Scales Oke, 1997,2005

12 Local Scale Flux Measurements Measurements need to be > 2 x height of the roughness elements Grimmond and Oke 2002: JAM Christien & Vogt (2004) IJoC Field Observations of Q H Kastner-Klein & Rotach 2004: BLM Wind tunnel observations

13 Impact of the Height of Observations u* L1 Ratio L1/L2 L2 L1/L2 observations U - lowest 2.22 z H D - lowest 1.83 z H Q H Carbon Dioxide each level Grimmond et al. 2004: JGR Grimmond et al JGR

14 Voogt and Grimmond 2000: JAM Tower Turbulence Source Areas Function of: z s /L σ V /u* z s /z 0m wind direction Footprint models (numerous, for example) Schmid (1994, 1997), Kljun et al. (2004) Horst & Weil (1992), Rannik et al. (2000) Oklahoma City

15 Fetch David Karnosky Tower Wind direction Tower Area influencing measurements Spatial consistency at the local scale to minimize Q A More towers (etc) to determine Q A

16 Radiation Flux Source Areas Offerle, Grimmond, Oke 2003: JAM

17 What do the instruments see of the surface? Radiation source area isopleths Turbulence footprint isopleths Turbulence and radiation instruments Remote sensor FOV Oke (2004) WMO IOM81 Soux et al. 2004: BLM

18 To Investigate Surface Controls use Geographic Information Systems (GIS) Database: Land Cover Marseille, France example: Building Fraction Heightsof plan area tile roofs 100 x 100 m grid Lemonsu, Grimmond, Masson (2004) JAM

19 Characteristics associated with observations: Lodz, Poland (Offerle et al. JAM 2005 in press)

20 Definition of the Surface Complex Characterization requires recognition of methods used Voogt and Oke (1997) JAM

21 Characterization of the Surface (2005) Grimmond (2005) TAC

22 Christien and Vogt (2004) IJoC

23 Surface characterization: Lodz, Poland Offerle et al. (2005) JAM in press

24 Urban Climate Zones (Oke/WMO, 2004)

25 Observations in North America Site Code LU Bld Imp. UM TR GR WT Period Mexico City, D.F. Me93 D Dec 93 Vancouver, B.C. Vl92 LI Aug 92 Chicago, IL C95u Sub Jun/Aug 95 Chicago, IL C Tucson, AZ T90u Sub June 90 Miami, FL Mi95 Sub May/Jun 95 San Gabriel, LA, CA Sg94 Sub July 94 Vancouver, B.C. Vs92 Sub Jul/Sept 92 Sacramento, CA S91u Sub Aug 91 Arcadia, LA, CA A94 Sub July 94 Arcadia, LA, CA A93 Sub July/Aug 93 Arcadia, Los Angeles Increasing fraction of vegetation LU: Landuse Downtown (D) Mexico City Light Industrial Vancouver Residential (Sub) Los Angeles (Arcadia & San Gabriel) Sacramento Vancouver Tucson Chicago Miami Grimmond and Oke, 2002: JAM

26 Q E : Observed Spatial and Temporal Patterns Q E (W m -2 ) A93 A94 C95 Me93 Mi95 S91 Sg94 T90 Vl92 Vs C95 A CBD RES IND RES ET (mm h -1 ) Q E (W m -2 ) ET (mm h -1 ) Grimmond and Oke, 1999: IAHS Time LAT (h)

27 Convective Flux Partitioning β Surface Cover: Vegetation Grimmond and Oke 2002: JAM

28 Ensemble Diurnal Fluxes: Lodz, Poland 17 Aug 2 Sept 2002 Offerle et al. (2005) IJoC CBD Q* Q H Q E RES Flux density (W m -2 ) IND Q H RUR Time (h)

29 Controls on flux partitioning: vegetation Within a city: Lodz, Poland Bowen Ratio LUMPS CV (2004) CBD IND RES RUR site mean Log fit Vegetation Coverage of Source Area (fraction of plan area) Offerle et al. (2005) IJoC

30 Spatial variability across a city and the surface characteristics: Basel, Switzerland Christien and Vogt (2004) IJoC

31 Ouagadougou, Burkina Faso: ASTER surface reflectance Jan 2001 S29 Tower Belle Vue RURAL.276 (.023) DOWNTOWN.210 (.020) SECTOR (.024) Albedo RURAL DOWNTOWN SECTOR (.8) 309 (1.6) 312 (.6) Surface Temperature (K) Offerle et al JC in press

32 Methods Storage Heat Flux Measurements Residual SEB Surface Temperature measurements + Model (ESTM, TMS) LSS (e.g. TEB) Net all wave radiation + model (OHM) Components Lodz Offerle et al. (2005) IJoC Marseille Roberts et al. (2005) in prep.

33 Surface temperatures

34 Marseille Measurements Tower (S. Grimmond, B. Offerle, J. Salmond) T, RH, U, V, K, L Q*, Q H, Q E Albedo measurements (S. Roberts, P. Jackson, T. Oke) Surface temperatures of walls, roads and roofs (J. Voogt, S. Roberts) Internal building temperatures (J. Voogt) Canyon temperatures (G. Pigeon) Scintillometry (JP. Lagouarde, M. Irvine)

35 Surface Temperature T s wall (averaged) Model comparison with surface temperature measurements Walls 5 sites (1N,2S,1W,1E) Roads 3 sites Roofs 1 gravel roof 4 tile roofs (1EW,1N,2S) T s road (averaged) Statistical results ( o C) bias (TEB-obs) T s wall = bias (TEB-obs) T s road = bias (TEB-obs) T s roof = T s roof (averaged) Lemonsu, Grimmond, Masson 2004: JAM

36 TEB Modeled vs Measured Energy Fluxes Net radiation (Q*) Sensible heat flux (Q H ) Time (d) Time (d) Latent heat flux (Q E ) Storage heat flux ( Q S ) Lemonsu, Grimmond, Masson, 2004: JAM

37 Averaged Fluxes (21 days): Marseille Scintillometer Path Q* Q H Mistral Days Q E Q S Sea Breeze Days Lemonsu et al JAM

38 Downtown Lodz, Poland (LTM) CBD

39 Long-Term Surface Energy Balance Observations: Seasonal Variability (Lodz, Poland) Offerle et al., 2005: TAC

40 Lodz, Poland: Downtown Data 2002 TEB/ISBA LUMPS Offerle, 2003

41 Final Comments Observations Important to include some of the simple measurements Incoming radiation (short, long- wave) Outgoing radiation (short, long- wave) Locations/Siting of Measurements Care is needed using the data for different applications Need to understand the model and the observation technique Site Characterization Critical for explaining observations (and for model parameters) Need care in definition Surface means many different things Dynamic Urban areas vary in surface morphology, cover and anthropogenic emissions Need to take this into account when we model or describe surface-atmosphere exchanges Need long term flux observations to understand processes/ controls and to evaluate models Very few worldwide compared to other environments (e.g. forests - FLUXNET) Like other environments observations are biased to dry conditions Important implications for understanding of Heat storage, Latent heat flux

Figure 2: Mean diurnal flux ratios (symbols) and box plots (showing interquartile range (IQR) and whiskers for values within 1.5IQR) for four of the

Figure 2: Mean diurnal flux ratios (symbols) and box plots (showing interquartile range (IQR) and whiskers for values within 1.5IQR) for four of the Loridan T & CSB Grimmond (2012) Characterization of energy flux partitioning in urban environments: links with surface seasonal properties Journal of Applied Meteorology and Climatology 51, 219-241 doi:

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