Battlefield Meteorology Including Urban Areas - Research Needs And Current Capabilities

Transcription

1 Battlefield Meteorology Including Urban Areas - Research Needs And Current Capabilities R.A. Pielke Sr. and Major T.E. Nobis August 4 th, 2005 VTC Seminar with ARL Cooperative Institute for Research in the Atmosphere Colorado State University, Fort Collins, Colorado Available at:

2 Battlefield Scales Are Mesoscale Nonhydrostatic pressure effects MAY be important The winds are not necessarily in gradient wind balance above the planetary boundary layer 2

3 Reviewed papers include Pielke, R.A., 1985: The use of mesoscale numerical models to assess wind distribution and boundary layer structure in complex terrain. Bound.-Layer Meteor., 31, Pielke, R.A., G. Kallos, and M. Segal, 1989: Horizontal resolution needs for adequate lower tropospheric profiling involved with atmospheric systems forced by horizontal gradients in surface heating. J. Atmos. Oceanic Tech., 6, Avissar, R., M.D. Moran, R.A. Pielke, G. Wu, and R.N. Meroney, 1990: Operating ranges of mesoscale numerical models and meteorological wind tunnels for the simulation of sea and land breezes. Bound.-Layer Meteor., Special Anniversary Issue, Golden Jubilee, 50, Nicholls, M., R. Pielke, and R. Meroney, 1993: Large eddy simulation of microburst winds flowing around a building. J. Wind Eng. and Indus. Aerodyn., 46 & 47, Pielke, R.A., and M.E. Nicholls, 1997: Use of meteorological models in computational wind engineering. J. Wind Eng. Indus. Aerodyn., 67 & 68, Pielke, R.A., J. Eastman, L.D. Grasso, J. Knowles, M. Nicholls, R.L. Walko, and X. Zeng, 1995: Atmospheric vortices. In: Fluid Vortices, S. Green, Editor, Kluwer Academic Publishers, The Netherlands, Pielke, R.A., 1984: Mesoscale meteorological modeling. 1st Edition Academic Press, New York, N.Y., 612 pp. Pielke, R.A., Sr., 2002: Mesoscale meteorological modeling. 2nd Edition, Academic Press, San Diego, CA, 676 pp. 3

4 Surface Forcing Is Always Important On The Battlefield Scale 4

5 Example Of Battlefield Landscapes 5

6 6

7 7

8 Quadrant One: 1. Central Baghdad 2. Saddam International Airport 3. Fallujah: CW and rocket engines 4. Al Doura Quarter: BW and missile parts 8. Al Tajiyat: Missiles and nuclear engineering 9. Rashidiya: Nuclear production 10. Tarmiya: Uranium enrichment center 11. Fudaliyah /Al Safa: Biological warfare 15. Tigris River 19. Abu Ghraib Presidential Compound Quadrant Two: 5. Al Furat: Uranium enrichment 6. Jurf Al Sakhar: Key CBW weapons 7. Salman Pak: BW labs 12. Yousefiya / Al Qa Qaa : Missiles 13. Balad Al Shuhada: Long-range missiles 14. Tuwaitha: Nuclear weapons 16. Euphrates River Quadrant Three: 17. An Numaniyah Air Base 18. Al Kut Air Base 8

9 Types Of Battlefield Meteorological Flows Terrain-induced mesoscale flows Sea and land breezes over flat terrain Vegetation and snow breezes Mountain-valley winds Forced airflow over and around rough terrain Urban circulations Lake effect Synoptically-induced mesoscale flows Convective bands embedded in stratiform cloud systems Squall lines Tropical cyclones Frontal Circulations 9

10 Examples Of Mesoscale Observations And Model Simulations 10

11 From: Segal, M., J.H. Cramer, R.A. Pielke, J.R. Garratt, and P. Hildebrand, 1991: Observational evaluation of the snow-breeze. Mon. Wea. Rev., 119,

12 From: Segal, M., J.H. Cramer, R.A. Pielke, J.R. Garratt, and P. Hildebrand, 1991: Observational evaluation of the snow-breeze. Mon. Wea. Rev., 119,

13 From: Segal, M., J.H. Cramer, R.A. Pielke, J.R. Garratt, and P. Hildebrand, 1991: Observational evaluation of the snow-breeze. Mon. Wea. Rev., 119,

14 From: Segal, M., R. Avissar, M.C. McCumber, and R.A. Pielke, 1988: Evaluation of vegetation effects on the generation and modification of mesoscale circulations. J. Atmos. Sci., 45,

15 From Pielke Sr., R.A., 2001: Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev. Geophys., 39,

16 Pielke Sr., R.A., 2001: Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev. Geophys., 39,

17 Pielke Sr., R.A., 2001: Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev. Geophys., 39,

18 Pielke Sr., R.A., 2001: Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev. Geophys., 39,

19 From Pielke Sr., R.A., 2001: Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev. Geophys., 39,

20 Pielke, R.A., 1974: A three-dimensional numerical model of the sea breezes over south Florida. Mon. Wea. Rev., 102,

21 Pielke, R.A., 1974: A three-dimensional numerical model of the sea breezes over south Florida. Mon. Wea. Rev., 102,

22 Pielke, R.A., 1974: A three-dimensional numerical model of the sea breezes over south Florida. Mon. Wea. Rev., 102,

23 Pielke, R.A., 1974: A three-dimensional numerical model of the sea breezes over south Florida. Mon. Wea. Rev., 102,

24 Pielke, R.A., 1974: A three-dimensional numerical model of the sea breezes over south Florida. Mon. Wea. Rev., 102,

25 Marshall, C.H. Jr., R.A. Pielke Sr., L.T. Steyaert, and D.A. Willard, 2004: The impact of anthropogenic land-cover change on the Florida peninsula sea breezes and warm season sensible weather. Mon. Wea. Rev., 132,

26 Marshall, C.H. Jr., R.A. Pielke Sr., L.T. Steyaert, and D.A. Willard, 2004: The impact of anthropogenic land-cover change on the Florida peninsula sea breezes and warm season sensible weather. Mon. Wea. Rev., 132,

27 Marshall, C.H. Jr., R.A. Pielke Sr., L.T. Steyaert, and D.A. Willard, 2004: The impact of anthropogenic land-cover change on the Florida peninsula sea breezes and warm season sensible weather. Mon. Wea. Rev., 132,

28 Limitations Of Battlefield Models And Observations 28

29 29

30 All Parameterizations are 1-D Column Models Stable Clouds and Precipitation Deep Cumulus Clouds And Precipitation Surface Interfacial Fluxes Pressure Gradient Force Advection Gravity Longwave Radiation/ Shortwave Radiation Subgrid-Scale Mixing Parameterizations: green boxes Dynamic Core: red box 30

31 All Parameterizations Have Tunable Coefficients and Functions 31

32 32

33 All Boundary-Layer Parameterizations Are Tuned From Golden Day Data 33

34 34

35 Boundary-Layer Data For The Parameterizations Are Developed For Horizontally Homogeneous (Including Flat) Landscape, And For Near-Steady Or Slowly Changing Atmospheric Conditions 35

36 36

37 37

38 Location of Field Campaigns Hays, Kansas Wangara, Australia 38

39 Basic Terms Are Ignored In The Model Equations 39

40 40

41 with 41

42 42

43 43

44 44

45 Models Provide Grid Volume Information. Observations Provide Point, Line, Area, or Volume-Average Information. Thus a Mismatch Exists Between Models And Observations. 45

46 46

47 Meteorological monitoring aircraft from NCAR. 47

48 New Research Directions To Reduce These Limitations 48

49 Decomposition Of Advection And Pressure Gradient Force Into Linear And Nonlinear Components Permits a more accurate representation of advection and the pressure gradient force Giovanni Leoncini s Research 49

50 Diagnosis Of The Importance Of Nonhydrostatic Pressure Effects Permits an evaluation when remote sensing can be used to diagnose the pressure field from an instantaneous snapshot of the temperature field Giovanni Leoncini s Research 50

51 Replacement Of Existing Model Parameterizations With Look-up-tables (Luts) (Multi-investigator Research) Pielke Sr., R.A., T. Matsui, G. Leoncini, T. Nobis, U. Nair, E. Lu, J. Eastman, S. Kumar, C. Peters-Lidard, Y. Tian, and R. Walko, 2005: A new paradigm for parameterizations in numerical weather prediction and other atmospheric models. National Wea. Digest, submitted. 51

52 Combined Use Of Battlefield Numerical Modeling And Battlefield Wind Tunnel Modeling An excellent recent report on urban models is in Tunick (2005; Critical Assessment of Selected Urban Microclimate Model Frameworks. ARL-MR-619). 52

53 Avissar, R., M.D. Moran, R.A. Pielke, G. Wu, and R.N. Meroney, 1990: Operating ranges of mesoscale numerical models and meteorological wind tunnels for the simulation of sea and land breezes. Bound.-Layer Meteor., Special Anniversary Issue, Golden Jubilee, 50,

54 Avissar, R., M.D. Moran, R.A. Pielke, G. Wu, and R.N. Meroney, 1990: Operating ranges of mesoscale numerical models and meteorological wind tunnels for the simulation of sea and land breezes. Bound.-Layer Meteor., Special Anniversary Issue, Golden Jubilee, 50, (Basis for proposed collaboration between David Neff, Ken Eis, and R. Pielke Sr.) 54

55 Battlefield Meteorology Urban Focus Major T.E. Nobis

56 Motivation Current mesoscale forecast models (RAMS, MM5, etc.) have attempted to account for the influence of the land surface through the creation of land surface models (e.g. LEAF2, NOAH) However, the handling of urban landscapes is still a crude affair Employ a slab approach with no urban geometry Contain no direct anthropogenic effects such as industry, vehicles, and building climate control. Basically incapable of generating proper UHI s and thus their effects 56

57 Motivation Continued Civilian, Military, and Homeland Defense personnel are relying on boundary layer information from mesoscale models to make informed decisions in urban areas. Military: TAWS (Decision aid aimed at enhancing the performance of smart weapons), night vision goggles, and even sound propagation prediction Homeland Defense: Use boundary layer information from the MM5 to drive the JEM dispersion model. 57

58 Research Interest Could an urban parameterization improve the performance of urban boundary layer forecast What are the challenges / limitations of this approach for operations 58

59 Urban Energy Balance (Oke, 1988) Q* + Q F = Q H + Q E + Q S + Q A Q* = Q(1-α) + LW -LW 59

60 Anthropogenic Input Q F : The Anthropogenic Heat Term Building Temperature Regulation Internal-external temperature difference Insulation quality: R-factor / Window ratio Combustion Engines Important in stagnate traffic for individual street canyons Metabolic Upshot: Ignored or set to a climatological value of W/M 2 Most questionable for high latitude cities in winter 60

61 Net Radiation Q*: Net Radiation Albedo, Geometry, and Pollution compete for overall effect on Q* Albedo: Generally lower in Urban Q* Geometry: Reduces effective albedo, traps LW Q* Pollution: Reduces SW, enhances LW Q* 61

62 Storage Q S : Storage term Key difference between rural and urban landscapes Urban areas generally have much higher storage Mainly controlled by construction materials and geometry Highly variable between urban areas 62

63 Latent Heat Q E : Turbulent Latent Heat Flux Humans seek to control this term (indirectly) Water Planning Irrigation (particularly in sub-urban areas) Other influences Impervious surfaces (Little or no system memory) % vegetation produces a large impact (Avissar, 1996) Natural water sources Rivers, Ocean Result: Highly variable in time and space 63

64 Urban Parameterization Use a parameterization that can capture some of these effects Parameterization necessary to capture urban variation and non-linear interactions with other flows Balance between complexity and needs of operations 64

65 What I Have Done RAMS: Version 4.3 with LEAF2 and Chris Castro s KF scheme Town Energy Balance (TEB): Urban Parameterization scheme dated May 2002 Follow-on to Chris Rozoff s work 65

66 Town Energy Balance (TEB) The TEB model is an urban parameterization scheme developed by Valery Masson of the National Center of Meteorological Research in Toulouse, France Has been coupled to RAMS before by Chris Rozoff TEB treats each grid cell as a giant urban canyon, using morphology information along with meteorological input from the parent model to process the urban energy balance. It then provides surface flux information back to the parent model. It is the urban equivalent of the big leaf concept in vegetation modeling. 66

67 TEB Continued Some features: Urban canyon geometry with separate energy balances for walls roads and roofs Inputs for traffic and industry SH and LH fluxes Model evolved climate control in buildings allowing for interaction with outside atmosphere. Detractor: Semi-slab approach 67

68 Simulations Simulations centered over Washington DC in Jun 84: the golden day 7-8 Jan 84: double frontal passage 7 Nov 84: classic fall Canadian High Why?: METREX Year long field campaign collected data on all days Dataset breaks with the golden days paradigm 68

69 Intentions Use 26 Jun to test sensitivities Land surface resolution, moisture, Baltimore Morphology full, plateau Radiation full, filtered Initialization time Simulate all three days in operational setting to test the value of TEB addition Set-up model in operational mode 69

70 Model Set-up Set-up model in operational mode 3 grids centered over Wash DC 80/20/5 km 33/33/36 vertical layers First vertical level 23 meters on grid 3 70

71 Special Data Land Surface 30 Sec data (RAMS) 30 Meter LULC data Morphology Created my own 1km morphology over a 36 X 32 km area which encompasses the entire beltway Data Sources for my Morphology: 1:24,000 USGS Topo series from the DC area. Dates on these varied from High Altitude Photography: Apr 2, 1981 and Mar 26, 1983 Robert Camerson s Above Washington DC 71

72 Land Surface/ Morphology 72

73 Results Reagan Obs vs Model Temp (f) obs TEB No TEB Time (Z) 73

74 So Far. Addition of TEB seems a positive step in generation of urban effects under operational conditions Sensitivity runs are proving that consideration must be made, especially with land-surface resolution 74