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Observation & simulation of urban-effects on climate, weather, and air quality

Observation & simulation of urban-effects on climate, weather, and air quality. Bob Bornstein Dept. of Meteorology, SJSU Haider Tahabbb, Altostratus, Inc. pblmodel@hotmail.com presented at NCAR 8 August 2008. Acknowledgements. Ex-students: R. Balmori S. Kasaksch E. Weinroth Data

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Observation & simulation of urban-effects on climate, weather, and air quality

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  1. Observation & simulation of urban-effects on climate, weather, and air quality Bob Bornstein Dept. of Meteorology, SJSU Haider Tahabbb, Altostratus, Inc. pblmodel@hotmail.com presented at NCAR 8 August 2008

  2. Acknowledgements • Ex-students: • R. Balmori • S. Kasaksch • E. Weinroth • Data • S. Burian, J. Ching • TCEQ, USFS • D. Byun • Urbanization of • A. Martilli • S. Dupont • Funds: NSF, USAID, DHS

  3. OVERVIEW • > URBAN MESO-MET MODELS • FORMULATION • APPLICATIONS • Houston • NYC • Sacramento • > FUTURE EFFORTS

  4. GOOD MESO-MET MODELING MUST CORRECTLY REPRODUCE: • UPPER-LEVEL Syn/GC FORCING FIRST: pressure (“the” GC/Syn driver)  Syn/GC winds • TOPOGRAPHY NEXT: min horiz grid-spacing flow-channeling • MESO SFC-CONDITIONS LAST: temp (“the” meso-driver) & roughness  meso-winds

  5. Mid-east Obs vs. MM5: 2 m temp(Kasakech; USAID) First 2 days show GC/Syn trend not in MM5, as MM5-runs had no analysisnudging Obs Run 1 Run 4: Reduced Seep-soil T July 29 August 1 August 2 obs MM5:Run 4 July 31 Aug 1 Aug2 Standard-MM5 summer night-time min-T, But lower input deep-soil temp  better 2-m T results  better winds  better O3

  6. Recent Meso-met Model Urbanization • > Need to urbanize momentum, thermo , & TKE • surface & SfcBL diagnostic-Eqs. • PBL prognostic-Eqs. • > Start: veg-canopy model (Yamada 1982) • > Veg-param replacedwith GIS/RS urban-param/data • Brown and Williams (1998) • Masson (2000) • Martilli et al. (2001) in TVM/URBMET • Dupont, Ching, et al. (2003) in EPA/MM5 • Taha et al. (2005, 08), Balmori et al. (2006) in uMM5: detailed input urban-parameters as f(x,y)

  7. Within Gayno-Seaman PBL/TKE scheme From EPA uMM5: Mason + Martilli (by Dupont)

  8. _________ ______ 3 new terms in each prog equation  Advanced urbanization scheme from Masson (2000)

  9. New GIS/RS inputs for uMM5 as f (x, y, z) • land use (38 categories) • roughness elements • anthropogenic heat as f (t) • vegetation and building heights • paved-surface fractions • drag-force coefficients for buildings & vegetation • building H to W, wall-plan, & impervious-area ratios • building frontal, plan, & rooftop area densities • wall and roof: ε, cρ, α, etc. • vegetation: canopies, root zones, stomatal resistances

  10. Martilli/EPFL q2-results Urbanization day & nite on same line  stability effects not important Non-urban: urban Urban-model values > rooftop max > match obs

  11. uMM5 for Houston: Balmori (2006) Goal: Accurate urban/rural temps & winds for Aug 2000 O3 episode via • uMM5 • Houston LU/LC & urban morphology parameters • TexAQS2000 field-study data • USFS urban-reforestation scenarios  UHI & O3 changes

  12. i) H H j) 23 H L 21 16 18 19 14 UTC 15 17 At 2300 UTC & summary of N-max ----

  13. uMM5 Simulation period: 22-26 August 2000 • Model configuration • 5 domains: 108, 36, 12, 4, 1 km • (x, y) grid points: (43x53, 55x55, 100x100, 136x151, 133x141 • full-s levels: 29 in D 1-4 & 49 in D-5; lowest ½ s-level=7 m • 2-way feedback in D 1-4 • Parameterizations/physics options > Grell cumulus (D 1-2) > ETA or MRF PBL (D 1-4) > Gayno-Seaman PBL (D-5) > Simple ice moisture, > urbanization module NOAH LSM > RRTM radiative cooling • Inputs > NNRP Reanalysis fields, ADP obs data > Burian morphology from LIDAR building-data in D-5 > LU/LC modifications (from Byun)

  14. Domain 4 (3 PM) : cyclone off-Houston only on O3-day (25th)  Episode day L L

  15. Urbanized Domain 5: near-sfc 3-PM V, 4-days Hot Cool • Episode day Cold-L

  16. 1 km uMM5 Houston UHI: 8 PM, 21 Aug Left:MM5UHI = 2.0 K ; Right: uMM5 UHI = 3.5 K

  17. C C C C UHI-Induced Convergence: obs vs. uMM5 OBSERVED uMM5

  18. Base-case (current) • veg-cover (0.1’s) • urban min (red) • rural max (green) min Modeled changes of veg-cover (0.01’s) > Urban-reforestation (green) > Rural-deforestation (purple) max increase

  19. Run 12 (urban-max reforestation) minus Run 10 (base case): near-sfc ∆T at 4 PMreforested central urban-area cools &surrounding deforested rural-areas warm warmer cooler warmer

  20. RURAL URBAN Max-impact of –0.9 K of a 3.5 K Noon-UHI, of which 1.5 K was from uMM5 DUHI(t): Base-case minus Runs 15-18 • UHI = Temp inUrban-Box minus Temp inRural-Box • Runs 15-18: urban re-forestationscenarios • DUHI = Run-17 UHI minus Run-13 UHI  • max effect, green line • Reduced UHI  lower max-O3 (not shown)  • EPA emission-reduction credits $ saved

  21. NYC DHS Urban Dispersion Study: Emergency Response

  22. NYC/UDS MSG & MIDTOWN DHS/STRA From: J. Allwine

  23. uMM5 for NYC DHS MSG UDS Goal: Accurate urban/rural temps & winds for 9-15 March‘05 tracer releases via • uMM5 • NYC LU/LC & urban morphology parameters from S. Burian • DHS MSG UDS field-study data • met • tracer (not used as of yet)

  24. NYC uMM5 DHS UDS MSG: 9-15 March ‘05 Model configuration 4 domains: 36, 12, 4, 1 km (x, y) grid points: (110x85, 91x91, 91x91, 33x33) full-s levels: 29 in D 1-3 & 48 in D-4; lowest ½ s-level=7 m 2-way feedback in D 1-3 Parameterizations/physics options > Grell cumulus (D 1-2) > ETA or MRF PBL (D 1-4) > Gayno-Seaman PBL (D-5) > Simple ice moisture, > urbanization module NOAH LSM > RRTM radiative cooling Inputs > NNRP Reanalysis fields, ADP obs data > Burian morphology from LIDAR building-data in D-5 > LU/LC modifications (from Byun)

  25. NWS 700 hPa 3/10/05: 00 & 12 UTC 00 UTC = 19 EST on 3/9/05 High speed zonal flow from Low N of NYC 12 UTC = 07 EST on 3/10/05

  26. MM5 moved Low away too fast 1 km uMM5 Domain

  27. 1 km uMM5 Domain

  28. Summary of uMM5 MSG flow field • Low level high speed (& thus weak UHI) roughness-induced deceleration  convergence  upward motion • Upper level (“return flow”) compensating down motion  acceleration  divergence

  29. 1 km uMM5 Speed (flag = 5 m/s) & T (K)09 EST, 3/10/05, 4 levels Weak UHI

  30. 1 km uMM5 Speed (flag = 5 m/s): 10 EST, 3/10/05, 4 levels SLOW FAST

  31. 1 km uMM5 Speed (flag = 5 m/s) & Con/Div (1/s)11 EST, 3/10/05, 4 levels CON DIV

  32. 1 km uMM5 Speed (flag = 5 m/s) & w (m/s)11 EST, 3/10/05, 4 levels UP Down

  33. Urban Ocean-Atmosphere Observatory (UOAO) by Jorge E. González1, Mark Arend1, Fred Moshary1 Alan F. Blumberg2 Stuart Gaffin3, Cynthia Rosenzweig3 Dave Robinson4 Brian Colle5 Robert D. Bornstein6,1 1City College of New York (CCNY) 2Stevens 3NASA Goddard Institute for Space Studies (GISS) 4Rutgers University 5State University of NY (SUNY) at Stonybrook 6San José State University (SJSU) Presented to 3rd Annual Interagency Workshop, NYC 15 July 2008

  34. CCNY Met-Net: roof top sites, sodars, lidar

  35. NYC Heat Burden: Past, Present, & Projected (Columbia University & GISS) GW GW = Global Warming UHI = urban heat island ~7oC / 13oF GW UHI UHI UHI 2oC 1900 2000 2080 7 days above 90oF 14 days above 90oF 3-4 days above 95oF Most of Summer above 90oF 17-50 days above 95oF

  36. Modeling and applications of urbanized MM5 (uMM5) for Houston, Sacramento, and SoCAB by Haider Taha Altostratus Inc. ALTOSTRATUS 36

  37. uMM5 updates (1 of 3) • Nested grids – two-way feedback • Drag coefficients – vegetation and buildings / shape-dependent • Multiple directions FAD-related wind and TKE computations • Multiple directions FAD / TAD directional grid-cell zo computations • Canyon orientation / urban radiation (and air flow, see next item) • Microscale model nest and feedback in grids of interest (e.g., high-rise or pollution/dispersion application) • Species-specific spatially-varying vegetation albedo • Spatiotemporally-varying indoor air temperature, as function of building type, season, and heating/cooling loads • Watering schedules in evapotranspiration calculations ALTOSTRATUS 37

  38. uMM5 updates, cont’d • Modifications to input generation techniques, processing, and data ingestion in model • Non-LULC-based input: remote-sensing, externally processed data, surveys, location-specific • Alternative UCP / morphology generation approach (using earth-PRO data) • Adaptation for UHI studies; sets of surface modification scenarios 38 ALTOSTRATUS

  39. uMM5 updates, cont’d • Surface physical properties of roofs, walls, pavements, etc. (i.e., material, construction type, age, albedo, emissivity, etc.) • Surface types (i.e., flat roofs, sloped roofs, geometrical features, green/garden roofs, parking structures) • Canyon orientation (e.g., gridded 15º binned canyon lengths) • Vegetation-specific information: LAI (function of season), geometry, albedo, age, evergreen/deciduous, potential evapotranspiration, proximity to buildings • 4-D anthropogenic heat flux (LULC-independent), source location (3-D) • 4-D latent heat flux / water vapor sources, e.g., cooling towers ALTOSTRATUS 39

  40. Extrapolation to non-data regions: Vertical profiles of building and vegetation canopies Plan-area density Top-area density Frontal-area density e.g., per-LULC vertical profile averages in Downtown Sacramento (representative of that area only). Red: commercial, Brown: mixed, Light blue: industrial/commercial, Blue: residential, Yellow: industrial Building PAD profiles as basis for extrapolation into non-UCP regions of Greater Sacramento area. PAD then used in computing other parameters, e.g., FAD, TAD, h2w, w2p, mean building height, and SVF Plan-area density ALTOSTRATUS Taha, H. 2008c, Atmospheric Environment 40

  41. for Sacramento, 1 August 2000 Sacramento nighttime heat island Meso-urban modeling; fine-resolution meteorological features Sacramento morning cool island Taha, H. 2008c, Atmospheric Environment ALTOSTRATUS 41

  42. Downtown Sacramento Fine-resolution photochemical simulations Taha, H. 2008c, Atmospheric Environment Sacramento 1-km uMM5 domain, 1300 PDT, 31 July 2000 ALTOSTRATUS 42

  43. DT (surface) PAD (m2/m3) e.g., impacts from UHI mitiga-tion: Sacramento Domain 5 DT (air) Change in sfc temp (top left) from increased urban surface albedo, compared to building PAD function at 1m AGL (top right). Air temp change at a randomly selected location (bottom left). ALTOSTRATUS 43

  44. Potential air-quality improvements from UHI control Taha, H. 2008c, Atmospheric Environment August 1st, simulated ozone at a location in Sacramento (top of graph) and changes resulting from UHI control (bottom of graph) Top: Simulated daily max 8-hour average ozone in Sacramento (at Folsom / Natoma monitor). Bottom: reduction (%) in daily max as RRF from UHI control.n

  45. Performance of uMM5 (base case) Houston Observed and simulated air temperature at sampling height for selected stations (subset from 26 monitors). bold line=observed, thin line=uMM5 C010 Texas City (open) C053 (urban residential) C034 Galveston (open) C603 sub-urban industrial KGLS Scholes Field (open) C607 (urban industrial) Near-shore stations: Note absence of characteristic diurnal signal Urban stations: Locations are relatively removed from shore & exhibits diurnal pattern ALTOSTRATUS Taha, H. 2008a, Boundary-Layer Meteorology

  46. Overall Lessons • > Models can’t assumed to be > perfect > black boxes • > Need good large-scale forcing-model fields • > If obs not available, OK to make reasonable educated estimates, e.g., for rural > deep-soil temp > soil moisture • > Need datafor comparisons with simulated-fields • > Need good urban > morphological data > urbanization schemes • > Need better rural-SfcBLparameterizations

  47. FUTURE WORK • uWRF • Martilli-Taha-Chen urbanization • SST (x,y,t) from J. Pullen • S. Zilitinkevich, et al. • SfcBL stability-functions (convective to wave-q2) • zoh • Sea-sfc zo • D. Steyn diagnostic hi(x,y) scheme • PBL-turbulence of: S. Zilitinkevich, F. Freedman, B. Galperin, L. Mahrt

  48. FUTURE WORK (cont.) • > Applications • Linkage (1- & 2-way) BC (x,y,t) for CFD & rapid-ER canyon-models for NYC • UHI and heat-stress trends under climate-change conditions & Qf(x,y,z,t) (with D. Sailor for Portland) • Urban thunderstorms (with NSF): initiation & splitting • urban Wx-forecasts (with NWS): stat & uWRF • Participation in EU MEGAPOLI urbanization project • With J. Gonzales: Silicon V. NSF Center of Excellence (SCU); NYC UOAO (CCNY), San Juan UHI (UPR); & UHI impacts on Calif. coastal cooling with uRAMS (SCU) re O3 (with CARB), energy with CCEC), ag. (Wine Board)

  49. Thanks for listening!Time for discussion/questions

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