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  • View in gallery

    DCNet sites in and around the District of Columbia, as of November 2008. Note that several of the sites shown were not operating for long enough for their data to be used here (FedEx Field, Bolling Air Force Base, and U.S. Tower). Map is copyright of Google Earth.

  • View in gallery

    An example of a DCNet tower, showing a sonic anemometer at the top of a roof-mounted 10-m tower, with a standard meteorological instrumentation set mounted slightly below. This particular installation is on the roof of the U.S. Department of Commerce building, in the Federal Triangle area of downtown Washington, D.C.

  • View in gallery

    Plots of the momentum covariance (=) against the square of the wind speed for the AGU location: (a) every fiftieth point in a year-long time sequence (the regression line shown is based on all of the data and not just the subset illustrated) and (b) the subset of AGU data that satisfy the additional constraint that |L| > 1000 m. The regressions are of the form = a + bu2, so that a has the units of meters squared per second squared.

  • View in gallery

    The ratio u*/u as a function of wind direction for the NAS site, overlaid on a depiction of the site derived from copyrighted Google Earth map. Note the proximity of the Department of State complex, to the immediate north. To the south is the sparsely treed National Mall.

  • View in gallery

    As in Fig. 4, but for the HU site.

  • View in gallery

    The directional variation of u*/u for 2008, obtained by combining data (that survive data-quality criteria) from (top) all sites within the District of Columbia and (bottom) the six sites representing the CBD (AGU, DOC, DOE, EMA, NEA, and WMC). On the right-hand ordinate, triangles indicate the overall average and the ±1 standard-error bounds.

  • View in gallery

    Average values (and corresponding standard-error bounds) of u*/u for the two most treed sites—ARB and NAS—obtained as the arithmetic average of all quantifications of u*/u without stability correction. Note the apparent change corresponding (perhaps) to the time of first leafing of the deciduous trees that characterize the surroundings of these sites and the decrease in u*/u at the time of leaf fall in the autumn.

  • View in gallery

    Average diurnal cycles of , presented for each calendar month for the SSG location at a height of 60 m in a suburban residential and light-business area. Data are presented according to calendar month [January–March (JFM), April–June (AMJ), July–September (JAS), and October–December (OND)] with data for the central month of the 3-month period shown as plus signs and the other months (as labeled) being represented by lines.

  • View in gallery

    As in Fig. 8, but for the TSQ location in the business and entertainment area of New York City (several blocks south of Central Park).

  • View in gallery

    A time sequence of nighttime (2200–0500 LT) monthly average covariances (the filled diamonds) for the DOC site in the Federal Triangle area of Washington, D.C., and for the DOE site, about 1.8 km distant, near the National Air and Space Museum. For these presentations, 3-month running means are plotted. The right-hand axes refer to the corresponding air temperatures, plotted as open circles. Note the repeated anticorrelation for the DOC case.

  • View in gallery

    The change of nighttime with outside air temperature for two DCNet datasets (AGU and DOC) and the EML location in New York City. The regression lines drawn are all statistically significant but are constrained to the ranges indicated by the filled circles (in general, <~18°C). Note that the vertical axis for EML is double the scale for the other two sites.

  • View in gallery

    Average diurnal cycles of the sensible heat covariance for the months of (top) January and February and (bottom) July and August, constructed from all reporting stations within the District of Columbia for 2007.

  • View in gallery

    The variation in hourly evaluations of the spatial averages of (left) and (right) u*/u for two months of 2007.

  • View in gallery

    Long-term average values (and ±1 standard-error bounds) of a number of measures of turbulence for all of the sites considered, plus several additional locations. The first block of 12 sites represents the results for Washington sites with nominal 10-m rooftop towers. Sites 15 and 16 represents the two elevated sites in Washington (note that CSPN data have not been used elsewhere in this analysis). Sites 19 and 20 are APH (a reference location located some 60 km south of Washington) and ARB (within the Washington urban area but below the height of the surrounding trees). Sites 23 and 24 are the New York stations—EML and TSQ.

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On the Drag and Heat of Washington, D.C., and New York City

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  • 1 Metcorps, Norris, Tennessee
  • | 2 NOAA/ARL/Atmospheric Turbulence and Diffusion Division, Oak Ridge, Tennessee
  • | 3 NOAA/ARL/Atmospheric Turbulence and Diffusion Division, and Oak Ridge Associated Universities, Oak Ridge, Tennessee
  • | 4 NOAA/Center for Weather and Climate Prediction/Air Resources Laboratory, College Park, Maryland
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Abstract

Data from a network of micrometeorological instruments, mostly mounted 10 m above the roofs of 12 buildings in Washington, D.C., are used to derive average values and spatial differences of the normalized local friction velocity u*/u ≡ ()1/2/u (with u being the wind speed reported at the same height as the covariance is measured, w being the vertical wind component, primes indicating deviations, and the overbar indicating averaging). The analysis is extended through consideration of two additional sites in New York City, New York. The ratio u*/u is found to depend on wind direction for all locations. Averaged values of u*/u appear to be best associated with the standard deviation of local building heights, with little evidence of a dependence on any other of the modern building-morphology indices. Temperature covariance data show a large effect of nearby activities, with the consequences of air-conditioning systems being obvious (especially at night) in some situations. The Washington data show that older buildings, built largely of native limestone, show the greatest effects of air-conditioning systems. The assumption that the nighttime surface boundary layer is stable is likely to be most often incorrect for both Washington and New York City—the sensible heat flux resulting from heating and cooling of building work spaces most often appears to dominate.

Corresponding author address: Bruce Hicks, Metcorps, P.O. Box 1510, Norris, TN 37828. E-mail: hicks.metcorps@gmail.com

Abstract

Data from a network of micrometeorological instruments, mostly mounted 10 m above the roofs of 12 buildings in Washington, D.C., are used to derive average values and spatial differences of the normalized local friction velocity u*/u ≡ ()1/2/u (with u being the wind speed reported at the same height as the covariance is measured, w being the vertical wind component, primes indicating deviations, and the overbar indicating averaging). The analysis is extended through consideration of two additional sites in New York City, New York. The ratio u*/u is found to depend on wind direction for all locations. Averaged values of u*/u appear to be best associated with the standard deviation of local building heights, with little evidence of a dependence on any other of the modern building-morphology indices. Temperature covariance data show a large effect of nearby activities, with the consequences of air-conditioning systems being obvious (especially at night) in some situations. The Washington data show that older buildings, built largely of native limestone, show the greatest effects of air-conditioning systems. The assumption that the nighttime surface boundary layer is stable is likely to be most often incorrect for both Washington and New York City—the sensible heat flux resulting from heating and cooling of building work spaces most often appears to dominate.

Corresponding author address: Bruce Hicks, Metcorps, P.O. Box 1510, Norris, TN 37828. E-mail: hicks.metcorps@gmail.com

1. Introduction

“DCNet” is a research program initiated in the aftermath of the terrorist attack of 11 September 2001, when it was recognized that the ability to describe or forecast dispersion in urban areas was poor despite recent short-term but intensive experimental activity related to the meteorological behavior of cities and the dispersion of chemicals within them. The number of numerical models available and the different ways in which they accessed data were widely seen as evidence of unacceptable disarray, aggravated by the endemic lack of relevant real-time data and of systems to access such data should they exist (see OFCM 2002). Logistic difficulties in erecting tall towers compound the problems encountered. To step past this constraint, rooftop towers have been contemplated, regardless of the fact that interpretation of their data could be a problem because they would necessarily violate some widely accepted micrometeorological constraints. One of the goals of the DCNet program considered here is to learn how to make use of such rooftop data. As recently as 2008, the Government Accounting Office reported (in response to a congressional request) that “[e]valuations and field testing have shown an unpredictable range of uncertainty in urban dispersion models’ analyses” (GAO 2008, p. 44). To obtain the best model results, “ready availability of building-top winds is essential” (GAO 2008, p. 47).

Somewhat in anticipation of later examinations of urban dispersion capabilities, such as that led by the Government Accounting Office (GAO 2008), DCNet was initiated in 2002 with a small network of micrometeorological towers set up on the roofs of selected buildings across the national capital region of Washington, D.C., and extending into neighboring areas. In subsequent years, the network expanded as awareness of the magnitude and consequences of spatial variability in urban areas grew. DCNet was designed to be a long-term study, in contrast to the short-term intensive studies conducted in the last few decades—in the United States, there have been major short-term studies in, for example, Salt Lake City, Utah; Oklahoma City, Oklahoma; and New York City, New York (see Allwine et al. 2002; Allwine and Flaherty 2007; Hanna et al. 2003, 2007). These intensive studies have revealed the complexity of the urban environment in considerable detail. For example, it is now appreciated that details of buildings and street orientation can be controlling factors in the movement of pollutants, and methods to address such matters as the distribution and shape of upwind buildings have been proposed (e.g., Grimmond and Oke 1999; Theurer 1999; Burian et al. 2008; Ching et al. 2009). These complexities lead to a suite of difficulties complicating the provision of forecasts relevant to where most people are actually exposed—at the lowest level of the urban boundary layer and well below the tops of buildings.

Figure 1 shows the network of DCNet sites as of the end of 2010. DCNet employs three-dimensional sonic-anemometer systems at a nominal height of 10 m above large buildings (e.g., Fig. 2) and located to minimize possible effects of roof edges and nearby structures. Data from the sonic anemometers are accessed at 10 Hz by local data-acquisition systems that compute all averages, variances, and covariances over 15-min periods. Every 15 min, computed results are transmitted via cellular modem to a central archive at the Atmospheric Turbulence and Diffusion Division of the National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory (ARL) in Oak Ridge, Tennessee. All archived data are available, upon prior arrangement, via the Internet. There are no supporting wind measurements at different heights, as would be needed to quantify the roughness length z0 and the displacement height d directly.

Fig. 1.
Fig. 1.

DCNet sites in and around the District of Columbia, as of November 2008. Note that several of the sites shown were not operating for long enough for their data to be used here (FedEx Field, Bolling Air Force Base, and U.S. Tower). Map is copyright of Google Earth.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

Fig. 2.
Fig. 2.

An example of a DCNet tower, showing a sonic anemometer at the top of a roof-mounted 10-m tower, with a standard meteorological instrumentation set mounted slightly below. This particular installation is on the roof of the U.S. Department of Commerce building, in the Federal Triangle area of downtown Washington, D.C.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

Several considerations led to the selection of Washington, D.C., as a focal area:

  1. the area was the site of an extensive study of dispersion in the 1980s, lasting more than 1 year (Draxler 1987a,b),

  2. the downtown area is well defined, with building heights not exceeding about 27 m (as required by the Washington Buildings Act of 1910), and

  3. the U.S. capital region is a proven target of terrorists, and results of the DCNet program might therefore be rapidly migrated to operational applications.

In recognition of the importance of the central business district of New York City, two additional sites were deployed, operating as remote members of the DCNet array: one in the immediate vicinity of Times Square (TSQ) and the other adjacent to the Houston subway station near the southern tip of Manhattan Island (EML).

The analysis presented here is intended to introduce the DCNet database to the urban-research community. The analyses here focus on variations of surface roughness with space and season and on sensible heat fluxes at night. In the first part of the analysis to follow, the emphasis will be on the normalized local friction velocity u*/u = ()1/2/u, where u is the wind speed at the height of measurement of the covariance. In the second part of the analysis, examination of the sensible heat exchange will make direct use of the temperature T and covariance evaluations yielded by the same sonic-anemometer systems.

2. Background considerations

Analysis of the data considered here must be done cautiously, because DCNet stations violate standard micrometeorological practices regarding fetch uniformity and height above obstacles. Regardless of such problems, the Monin–Obukhov construct L appears to provide a reasonable basis for analysis.

Measurements of covariances can be made at any location. The challenges are to relate these covariances to eddy fluxes, to identify the upstream areas of relevance (the footprint), and to quantify the corresponding surface properties in a way that improves understanding and permits extrapolation to other situations. The region of influence for the turbulence measurement footprint is expected to be site specific (e.g., Schmid 2002; Leclerc et al. 2003). In the current case, the daytime footprint related to sensible heat is likely to be different from that for the momentum flux, because the former is controlled by buoyancy whereas the latter is not. In both cases, however, the greater the height of measurement is, the more likely it is to derive a spatially representative upwind value.

Over conventional micrometeorological sites (characterized by spatial homogeneity and time stationarity), the roughness length is derived from measurements of wind profiles and the momentum flux by using the neutral relationship
e1
where u is the wind speed, u* is the local friction velocity, and z is the height of measurement above the ground. Here, the von Kármán constant k is taken to be 0.4. In numerical models, it is common to take d and z0 to be constants that characterize a grid cell. Because of subgrid-scale complexity, it is not immediately clear how to derive appropriate gridcell averages of eddy fluxes for an urban area, regardless of the size of the grid. This is not only because the constraints of spatial homogeneity and time stationarity are usually violated but also because obtaining a “representative” set of measurements is demanding. The variable that is most affected by surface structures is the covariance (because surface obstructions strongly influence both u′ and w′). The usual experimental approach is to employ a strategically located tall tower, with wind speed gradients measured and with sonic anemometry high enough to minimize the influences of specific surface features. Any tall-tower installation is constrained by the “rules of thumb” generated in past micrometeorological field studies, however. These legacy guidelines require that measurements should be made sufficiently above surface obstructions that the influence of individual roughness elements is minimized but not so high as to be outside the surface “constant flux” layer (extending to 7%–10% of the relevant mixed layer). The definition of “sufficient” varies among researchers, and actual illustrative data for an urban area are largely lacking. These guidelines have been refined by detailed modeling [e.g., Raupach (1989) for the case of a densely vegetated surface], supporting the conclusion that an urban situation constitutes a demanding application for both experimentation and simulation.

In this regard, note that TSQ in New York City and the Silver Spring, Maryland, (SSG) site in the Washington suburbs are the main violators of the above guidelines among the sites now considered. Data from an additional elevated installation (CSPN) at a height of 87 m in the suburbs of Washington are presently excluded.

3. Data selection and analysis

Table 1 lists details of the DCNet stations considered here. In the following discussion related to surface roughness, all available data collected during calendar year of 2008 have been used. Later, in considering heat exchange, data obtained over several additional years will be used. To eliminate questionable data, the following constraints have been imposed.

  1. For each 15-min period, complete data records are required.

  2. To ensure optimal anemometer performance, cases for which the average wind speed is less than 1 m s−1 have been excluded. (This exclusion is based on field experience with the devices of concern. All in situ anemometers suffer from performance defects at low wind speeds. Experienced field experimenters will have different criteria to apply that are based on their own tests of the instrumentation that they actually use).

  3. Periods for which rainfall was reported are excluded.

  4. After coordinate rotation, the average angle of attack of the wind relative to the horizontal must be less than 10°.

Table 1.

Site details of DCNet installations (in Washington, D.C., and its surroundings). The variable Zh is the height above ground level. All towers are 10 m tall. CoV(1) is the coefficient of variation of the property u*/u, derived from raw data. CoV(2) is derived using data after coordinate rotation. Pertinent details of two additional locations (in New York City) are included.

Table 1.

The last criterion eliminates occasions that are egregiously affected by upwind obstructions and the resulting flow distortion. Every site of the DCNet program has unique characteristics that require consideration. For example, the NAS site is located on the roof of the National Academy of Science, on Constitution Avenue near the Vietnam Memorial. To the north is the complex of the U.S. Department of State. To the south is the grassland of the National Mall. To the east and west are buildings and gardens similar to the surroundings of the National Academy itself. It is clear that some consequences of upwind obstacles must be expected. As a first-order correction for the consequences of streamline deformation, this analysis will make use of coordinate rotation (see, e.g., Wesely et al. 1972). Such coordinate rotation is commonly applied to correct for sensor-induced (and other) deformations of local streamlines away from the true reference plane, across which there is no net exchange of dry air, or to derive orthogonal wind components from measurements made using a different coordinate system. Table 1 lists evaluations of coefficients of variation (CoVs, defined as the ratios of standard deviations to average values) of u*/u, computed for near-neutral conditions (|L| > 1000 m) that satisfy the data-quality constraints given above. Two values of the CoV are given, one using raw data [CoV(1)] and the other using data after coordinate rotation [CoV(2)]. A lower value of the CoV means that the standard deviation in the derived friction velocity is reduced, even after allowance for the dependence of u* on the wind speed. The coefficients of variation show that coordinate rotation does indeed improve the dataset, with the resulting values of the CoV being reduced for all sites and by a substantial amount for some. The wind analysis here will make use of rotated datasets, arranged to yield stress evaluations along the vector wind with no average crosswind or vertical components.

Figure 3 shows the results of a first-cut inspection of the data, using results obtained at the American Geophysical Union (AGU) site. For the present, the low wind speed criterion is relaxed; all data are used for Fig. 3a, which shows how the covariance = varies with the square of the longitudinal wind velocity u2. A more conventional plot might be of u* against u; however, the present intent is to look at the entire dataset with recognition that site imperfections (and other factors) will give rise to positive and nonphysical values of and that analysis should not discount these measurements. Because of the large number of measurements, Fig. 3a presents every fiftieth record of the overall time sequence, without any additional sorting. It is the high wind speed data shown in Fig. 3a that are most indicative of neutral conditions, and these data are widely scattered. Figure 3b shows how the near-neutral subset of the AGU dataset behaves. For this purpose, only those data for which the sensible heat flux is near zero have been selected (|| < 0.001 K m s−1). All such data also satisfying the other constraints above are shown. The scatter is reduced considerably. The lines drawn are the results of a regression analysis yielding the properties shown in the diagrams. Even though R2 is improved by the data sorting of near-neutral runs, the slopes of the two lines are nearly identical. Furthermore, the slope of a regression constrained to pass through the origin is −0.0117 ± 0.0030, as compared with values of −0.0122 for the near-neutral data of Fig. 3b and −0.0118 for the data of Fig. 3a, with similar standard errors. These values are determinations of the drag coefficient Cd prevailing at the times of measurement. The corresponding average friction coefficient Cf (=u*/u) is 0.11. {Note that the drag coefficient used here is as appears in recent micrometeorological convention [Cd = (u*/u)2], whereas elsewhere the drag coefficient is defined with a multiplicative coefficient of 0.5.}

Fig. 3.
Fig. 3.

Plots of the momentum covariance (=) against the square of the wind speed for the AGU location: (a) every fiftieth point in a year-long time sequence (the regression line shown is based on all of the data and not just the subset illustrated) and (b) the subset of AGU data that satisfy the additional constraint that |L| > 1000 m. The regressions are of the form = a + bu2, so that a has the units of meters squared per second squared.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

Analyses like that illustrated by Fig. 3 have no allowance for any variation with wind direction. Moreover, they ignore the known criterion by which stability is measured (Z/L, where Z is the height above the relevant zero plane displacement d). In the lack of an objective mechanism to define d, data have been selected to satisfy the criteria above but with the near-neutral constraint being that |L| > 1000 m. Figure 4 shows the resulting dependence of u*/u on wind direction, overlaid on a depiction of the location—in this case the NAS site. The effects of the large structure to the north (the U.S. Department of State building) are immediately obvious. With winds from the north, few sets of observations survive the data-selection criteria. Most sites suffer from similar directional favoritism.

Fig. 4.
Fig. 4.

The ratio u*/u as a function of wind direction for the NAS site, overlaid on a depiction of the site derived from copyrighted Google Earth map. Note the proximity of the Department of State complex, to the immediate north. To the south is the sparsely treed National Mall.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

Figure 5 is a similar illustration, for the installation at Howard University (HU). In this case there is also a considerable variability with wind direction. Close inspection reveals some variation in u*/u that could correspond to street channeling, with somewhat lower values when the mean wind is aligned with the local streets. There is no apparent effect attributable to the presence of a lake to the northeast, about 0.5 km distant. To the northeast is also the main administration building, a large structure located on a knoll. The region due east of the measurement location is a student mall, largely grassed. This corresponds to the minimum in u*/u seen in the diagram.

Fig. 5.
Fig. 5.

As in Fig. 4, but for the HU site.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

The top panel of Fig. 6 shows the results obtained if all near-neutral data collected within the District of Columbia are averaged, according to wind direction. The bottom panel presents averages for a subset of the D.C. sites—six stations within the central business district (CBD) of Washington (AGU, DOC, DOE, NEA, NAS, and WMC). The overall mean u*/u for the larger dataset (Fig. 6, top) is 0.143, and for the CBD it is 0.151. Averages and ±1 standard-error bounds are indicated along the right-hand ordinates of the two panels. Directional variation remains similar, regardless of the CBD siting constraint. There appears to be little basis for relating variations of the average results to street orientations: the street grid of Washington is largely aligned with the north–south/east–west Cartesian system, although with an overlay of angled boulevards. Both panels of Fig. 6 indicate, for example, that winds from the south yield a minimum value of u*/u, whereas winds from the opposite direction yield a maximum.

Fig. 6.
Fig. 6.

The directional variation of u*/u for 2008, obtained by combining data (that survive data-quality criteria) from (top) all sites within the District of Columbia and (bottom) the six sites representing the CBD (AGU, DOC, DOE, EMA, NEA, and WMC). On the right-hand ordinate, triangles indicate the overall average and the ±1 standard-error bounds.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

For the CBD dataset of Fig. 6, the average height of deployment of sensors is about 35 m above ground level. No assumption is made here about the zero plane displacement. This matter is complicated since some of the buildings are sufficiently large that they probably constitute the “relevant fetch” in their own right. For example, it is not clear whether the DOC observations relate to a height of measurement best taken to be 10 m above the local rooftop or 40 m above the level of the surrounding streets.

In Table 2, values are given of the mean value of u*/u (computed geometrically, i.e., by averaging the logarithms of the raw data) for each location of the Washington DCNet, using only those observations that satisfy the criteria presented above (including the constraint |L| > 1000 m) and presented as averages in 30° wind direction sectors centered on 30°, 60°, and so on. (Similar data were already summarized in Table 1, where they constituted support for the use of coordinate rotation. The data of Table 1 were not constrained to near neutral.) Two of the sites identified in Table 1 are excluded from Table 2: ARB is a subcanopy forest site (the U.S. National Arboretum in Washington), and APH is a reference site well outside the D.C. area (at Fort A. P. Hill in Virginia). Some of the data given in Table 2 could be interpreted as indicating a possible influence of local street orientations (e.g., AGU, HU, and WTOP, all of which show a slight minimum in u*/u for winds from the east, along the adjacent street). This dependence is not seen at all sites.

Table 2.

Estimates of the normalized local friction velocity u*/u, for the sites used in the present analysis (as in Table 1, but eliminating ARB, APH, and the two sites in New York City), as a function of wind direction. Averages are over the complete year of 2008. Data are confined to near neutrality and are sorted by wind direction in 30° intervals centered on the values listed. The entries in parentheses indicate the numbers of near-neutral runs surviving all of the data-quality checks discussed here.

Table 2.

Burian et al. (2008) present tabulations of building characteristics for a number of cities, including the central business districts of Washington, D.C., and New York City. Their data provide the opportunity to examine the influence of the surroundings on eddy fluxes as are reported here. To this end, Table 3 gives a summary of the surroundings of the six sites within the CBD of Washington. The properties listed are derived from the National Building Statistics Database, version 2 (Burian et al. 2008). Also listed are the overall average values of u*/u, quantified as the grand averages of the values given in Table 2. The overall average value of the normalized friction velocity is 0.159 (cf. 0.151 quoted above as the corresponding geometric mean) for the central Washington area. The value obtained for the EML site in New York City should not be considered as representative of the New York metropolis, since the site in question is a substantial distance from the area of the major buildings. The TSQ location, which is within the area of the tallest structures, yields data that are too scattered to be interpreted with confidence.

Table 3.

Summaries of normalized local friction velocities (u*/u) and surrounding building morphologies for six sites within the CBD of Washington, D.C. The u*/u values are derived by averaging the sector averages in Table 2. Other quantities are from Burian et al. (2008), for the 250 m × 250 m area containing each site. Here, λP is the building plan-area fraction, Ht is the mean building height, σ(Ht) is the standard deviation of the building height, λB is the building ratio of surface to plan area, CAR is the complete aspect ratio (see Voogt and Oke 1997), and Ht/W is the ratio of building height to width. Also listed are the corresponding data relevant for the EML site in New York City.

Table 3.

For the Washington CBD sites identified in Table 3, the most significant association is between u*/u and the reported standard deviation of the local building height, with correlation coefficient squared R2 = 0.77 (statistically significant at the 95% level). Further examination, using data on the upwind building plan-area fraction [λP, also derived from the listings of Burian et al. (2008)] has proved to be inconclusive. For some sites, statistically significant relationships (at the 90%–95% confidence level) between u*/u and λP can be found, but not for all (or even for a majority) of the locations. The outcome of such examinations of the data depends on how the upwind area of influence is determined—for example, the width of the sector over which average building morphologies are estimated and the radius of the sector.

The matter of upwind “footprints” has been the subject of substantial research, and many detailed models have been developed accordingly (see Schmid 2002). These models typically relate to good experimental sites and are often based on the findings of wind-tunnel studies. The case presented here differs, and hence the models developed elsewhere are seen as indicative rather than explanatory. In this case, the local exposure of the sensors could be a dominant factor obscuring the role of the upwind landscape, and additional uncertainty could result from the role of the pervasive atmospheric instability. (In this regard, discussion of the sensible heat flux regimes is in a later part of this paper.) It is concluded that attribution of surface-roughness properties to upwind configurations of buildings is a task that will require extensive study, as is well evident in the quantity of related literature already available. The conclusion found here regarding the relative significance of the standard deviation of local building heights (among other properties) is in agreement with the results of large-eddy simulations reported by Kanda et al. (2013), however. For a summary of other related findings, the reader is referred to Britter and Hanna (2003); there are many later examples, but there are insufficient DCNet data to explore this aspect in detail.

Inspection of the data reveals some patterns that appear to be indicative of seasonal changes in the surface [of a kind similar to those reported by Gallo et al. (1993)]. Figure 7 presents evidence of some seasonality for two locations: ARB, where the instrumentation is mounted 10 m above ground level in a clearing largely surrounded by trees, and NAS, also largely surrounded by trees except to the north, for which direction there are few surviving data because of the presence of the large State Department structure (see Fig. 4). The lines shown are drawn by eye as an interpretation of the observations that correlates well with the leafing of the surrounding deciduous trees in the early spring and the fall of leaves in the autumn. The differences apparent in the two diagrams are small but are statistically significant. For NAS, the two lines as drawn correspond to u*/u = 0.1854 ± 0.0029 (sample size N = 137) and 0.1615 ± 0.0017 (N = 107) for the leafed and leafless periods, respectively. The corresponding results for ARB are 0.1695 ± 0.0014 (N = 923) and 0.1455 ± 0.0015 (N = 1053). No other site yields results as clear cut as the cases illustrated in Fig. 7, but no other site is as affected by trees. The association of the change in surface roughness with the leafing of trees is appealing but cannot be proved with these data.

Fig. 7.
Fig. 7.

Average values (and corresponding standard-error bounds) of u*/u for the two most treed sites—ARB and NAS—obtained as the arithmetic average of all quantifications of u*/u without stability correction. Note the apparent change corresponding (perhaps) to the time of first leafing of the deciduous trees that characterize the surroundings of these sites and the decrease in u*/u at the time of leaf fall in the autumn.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

The two sites in New York City yield (as expected) measurements that are considerably more scattered than for Washington; the building height constraint imposed in Washington is absent in New York City. As has already been emphasized, the TSQ site is too high to warrant application of conventional micrometeorological methods. On the other hand, the EML dataset pertains to a location that is surrounded by structures of similar height, much as for the sites of the Washington CBD. The EML data yield a best estimate of the spatial average u*/u = 0.113.

4. Heat fluxes

The heat island of Washington is a well-accepted feature of the area, having been the focus of research for many decades (e.g., Woollum 1964; Kim 1992) and having recently been examined using data from rooftop sensors (Hicks et al. 2010). The heat island is the result of many factors, all associated with the activities of the human population. In the case of New York City, the heat-island issue has also been the subject of considerable research (e.g., Bornstein 1968; Kirkpatrick and Shulman 1987; Gaffin et al. 2008), culminating in studies of possible mitigation strategies (Rosenzweig et al. 2006). From the perspective of dispersion modeling, Hanna et al. (2011) summarize the variation of sensible heat fluxes across various urban areas.

In the analysis of roughness presented above, only 1 year of data was used so as to minimize variability introduced by year-to-year changes in buildings and their surroundings. In the following, a longer period of available data has been used, starting in 2004 and ending in 2010. Figure 8 shows monthly average diurnal cycles of local for the SSG location in the northern suburbs of Washington, D.C. Data are separated into the four calendar seasons (winter is January, February and March and so on), as described in the caption. Figure 9 parallels Fig. 8, but for the TSQ location in New York City. The New York City data differ from their Washington counterparts. For the elevated TSQ station (125 m), the average midwinter nighttime value of is in the range from 0.10 to 0.15 K m s−1, substantially greater than the maximum average value for the Washington area. Moreover, for TSQ there are few months for which the average nighttime is negative. The EML location (in lower Manhattan, near the former site of the World Trade Center) displays a winter maximum similar to that for DOC, but once again there is no month with a negative average value of . This is similar to what has been reported elsewhere. For example, Grimmond and Oke (2002) derive an anthropogenically imposed sensible heat flux of about 100 W m−2 (≈0.08 K m s−1) for the downtown area of Nagoya, Japan. Moreover, the result is supported by the results of recent modeling studies (e.g., Giovannini et al. 2013; Pigeon et al. 2008).

Fig. 8.
Fig. 8.

Average diurnal cycles of , presented for each calendar month for the SSG location at a height of 60 m in a suburban residential and light-business area. Data are presented according to calendar month [January–March (JFM), April–June (AMJ), July–September (JAS), and October–December (OND)] with data for the central month of the 3-month period shown as plus signs and the other months (as labeled) being represented by lines.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

Fig. 9.
Fig. 9.

As in Fig. 8, but for the TSQ location in the business and entertainment area of New York City (several blocks south of Central Park).

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

Two notable features of Fig. 9 are 1) that the entire average diurnal cycles are elevated relative to their SSG counterparts and 2) that the early morning peak in seen in Fig. 8 is also seen in Fig. 9 for the same time period as for SSG. Figures 8 and 9 display features that are common among many locations:

  • For the Washington sites, nighttime sensible heat fluxes are typically close to zero; the negative average heat fluxes expected on the basis of classical micrometeorology are observed for only some months and some sites. For many locations, however, the average heat fluxes remain strongly positive throughout the entire diurnal cycle.

  • On average, the months of November–March show short-term increases in in the hours immediately before dawn. A possible cause is the time-dependent ramping up of heating systems in the colder months, in advance of the start of the working day.

Detailed consideration of the daytime data would require information on such properties as net radiation, measurements of which are lacking. Consideration of the nighttime data is informative, however. Figure 10 presents a multiyear sequence of monthly averages of nighttime (from 2200 to 0500 LT) measurements for the U.S. Department of Commerce (DOC) site in the Federal Triangle area of central Washington. Also shown are corresponding average air temperatures. The dominant feature of Fig. 10 is the cyclic nature of the nocturnal heat flux, anticorrelated with air temperature. Some sites yield stronger signals of this kind, whereas other sites indicate negligible correlation. For the Washington case, the sites with the strongest negative correlation are the ones located in the older part of the city, where large limestone buildings dominate. These buildings were constructed more than 50 years ago, well before the modern emphasis on the need for energy conservation. In the temperature extremes of winter and summer, there is a greater need for air conditioning of such buildings, the consequences of which appear to be evident in many of the datasets generated by DCNet. The dependence on building construction (among other local characteristics) becomes most evident in a comparison between DOC and DOE. Whereas the DOC data show the consistently highest values of nocturnal , the nearby DOE monitoring station (the more modern Forrestal Building, about 1 km away) yields a comparatively constrained set of values, with smaller excursions that could be attributed to summer cooling and winter heating.

Fig. 10.
Fig. 10.

A time sequence of nighttime (2200–0500 LT) monthly average covariances (the filled diamonds) for the DOC site in the Federal Triangle area of Washington, D.C., and for the DOE site, about 1.8 km distant, near the National Air and Space Museum. For these presentations, 3-month running means are plotted. The right-hand axes refer to the corresponding air temperatures, plotted as open circles. Note the repeated anticorrelation for the DOC case.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

Figure 11 plots the monthly-averaged nighttime values of local against the measured air temperature for a different selection of stations, again intended to illustrate the spread of behaviors. The regression lines shown are based on data points that are emphasized—the filled dots, generally corresponding to temperatures of less than about 18°C. At higher temperatures, there is some evidence of a reversed relationship for some locations, but this is not a common feature.

Fig. 11.
Fig. 11.

The change of nighttime with outside air temperature for two DCNet datasets (AGU and DOC) and the EML location in New York City. The regression lines drawn are all statistically significant but are constrained to the ranges indicated by the filled circles (in general, <~18°C). Note that the vertical axis for EML is double the scale for the other two sites.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

Table 4 gives details of the regressions derived from plots such as are shown in Fig. 11 but for all of the locations considered here. It is obvious that the variation among the datasets is extreme, with some showing results that are compatible with the more-heating-in-colder-weather syndrome and others showing negligible effects of this kind. Earlier, it was proposed that the present considerations might best be considered as a sampling of the Washington area. On this basis, it appears that the nighttime characteristics of the surface boundary layer might best be described using a relationship of the form assumed in consideration of Fig. 11:
e2
with representative values of a (K m s−1) and b (m s−1) derived from the listing presented in Table 4:
eq1
eq2
It is relevant to emphasize that these remarks apply to the relationships among averages and that any particular occasion might well depart from any expectations that are based on the current analysis. Regardless of this consideration, it is clear that these results suggest benefit from comparison with power-consumption data in the vicinities of the sites (see, e.g., Kato and Yamaguchi 2005), but such a study has not yet been attempted.
Table 4.

Details of the relationships between average nighttime air temperature T and average kinematic heat flux Ht (=) for situations likely to impose a need for heating of buildings (T < ~18°C). Here, R is the correlation coefficient and a and b are the linear regression best fits, assuming a dependence of the kind Ht = a + bT.

Table 4.

The variability evident in the sensible heat averages illustrated in Fig. 10 has been emphasized by Gaffin et al. (2008) for the specific case of New York City. They conclude that (for residential areas, at least) these variations correlate with changes in ambient temperature, much as is apparent in datasets presented here.

5. Spatial average fluxes

The site-to-site variability evident in much of the preceding discussion emphasizes that no single DCNet observing system should be expected to yield data that are representative of an area like that of a mesoscale model grid cell. The matter is usually addressed by elevating the instrumentation until the upwind footprint is sufficiently large. In central city locations, tall towers are usually difficult to arrange. Here, the logistical constraints have been addressed by using many smaller towers and by viewing the problem as one of sampling.

Other models with smaller grid cells might find benefit in local data like those analyzed here. In such cases, the need to interpolate between observation points could prove to be a limitation unless the model in question makes full use of surface parameterization schemes [e.g., those proposed by Kanda et al. (2013) and Voogt and Grimmond (2000) for the mechanical and thermal attributes, respectively].

Figure 12 shows four examples of the diurnal cycle of , spatially averaged from all rooftop sites operational throughout calendar year 2007 and within the District of Columbia (i.e., AGU, DOC, DOE, EMA, HU, NAS, NEA, NRL, RFK, WMC, WTOP, and SSG). Average cycles are shown for the months of January, February, July, and August. Inspection shows that even in the average there is evidence of the wintertime predawn heating evident in the SSG data of Fig. 8 and the TSQ data of Fig. 9 (for the months of November–March).

Fig. 12.
Fig. 12.

Average diurnal cycles of the sensible heat covariance for the months of (top) January and February and (bottom) July and August, constructed from all reporting stations within the District of Columbia for 2007.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

Figure 13 combines spatial average surface roughness information with the sensible heat information discussed immediately above. To construct Fig. 13, the original 15-min data have been averaged into hourly spatial averages. Thus, there are 31 values plotted for each hour (one for each day of the month). Values of the hourly average covariances and wind speeds have been used to determine the hourly values of the normalized local friction velocity u*/u. In this procedure, there is no allowance of (or correction for) the effects of stability. One particularly interesting observation from the resulting evaluations is that there is not a great variation of the scatter with time through the day.

Fig. 13.
Fig. 13.

The variation in hourly evaluations of the spatial averages of (left) and (right) u*/u for two months of 2007.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

So far, only two turbulence properties have been considered: and u*/u. Figure 14 shows how a number of familiar micrometeorological parameters vary with location across the array of stations now considered. The least variable property is the horizontal turbulence shape ratio σ(υ)/σ(u). An assumption that this was about 0.82 would seem a good approximation regardless of the location. The corresponding ratio σ(w)/σ(u) is similarly tightly constrained, at about 0.55. Neither of these varies greatly from conventional micrometeorological expectations. The ratio σ(w)/u* is also compatible with expectations—typically about 1.5. In comparison, the standard deviation of the wind direction σ(θ) appears to vary greatly from location to location, as also do the normalized local friction velocity u*/u and the uw correlation coefficient. The overall conclusion to be drawn is that the wind speed and wind direction are the variables most affected by siting, with the structure of eddies being less influenced. In constructing Fig. 14, analysis has been confined to near-neutral situations (as previously defined), and hence there is minimal confounding influence of stability.

Fig. 14.
Fig. 14.

Long-term average values (and ±1 standard-error bounds) of a number of measures of turbulence for all of the sites considered, plus several additional locations. The first block of 12 sites represents the results for Washington sites with nominal 10-m rooftop towers. Sites 15 and 16 represents the two elevated sites in Washington (note that CSPN data have not been used elsewhere in this analysis). Sites 19 and 20 are APH (a reference location located some 60 km south of Washington) and ARB (within the Washington urban area but below the height of the surrounding trees). Sites 23 and 24 are the New York stations—EML and TSQ.

Citation: Journal of Applied Meteorology and Climatology 53, 6; 10.1175/JAMC-D-13-0154.1

6. Conclusions

As anticipated, the surface roughness characteristics of Washington, D.C., depend on wind direction for all locations, but such directional variations tend to average out as a spatial average is constructed. For the downtown area of Washington, average values of u*/u range from 0.13 to 0.19 (see Table 3), with a best overall estimate of the (spatially averaged) normalized friction velocity of about 0.158. No corresponding result can be derived from the New York dataset, because the only site with low-enough scatter to warrant examination is located in a nonrepresentative area. The data available for Washington and New York City do not permit determination of optimal values of average displacement heights or roughness lengths.

Several of the Washington datasets show signs of “rectification” according to the orientation of adjacent streets. Lower values of u*/u are associated with wind directions along the streets rather than across them.

There is some evidence for a seasonality in the values of u*/u, corresponding to the leafing of deciduous trees in early spring. This small effect is most obvious in the data from the National Academy of Sciences location, on a tree-lined boulevard adjacent to the National Mall and near the Vietnam Memorial.

Discussion of the heat-island effect usually draws attention to the many causative factors, such as the urban changes in albedo and vegetation, but the data in this paper support the conclusion of Makar et al. (2006) that an important factor is the direct generation of heat by building climate controls and other human activity: the older the buildings are, the more striking is this effect. The present expectation that the nocturnal heat-island effect is indeed greater for the more massive structures of New York than for the height-constrained buildings of Washington appears to be in agreement with the results of Hicks et al. (2010), on the basis of Washington and New York rooftop temperature data.

Across Washington, D.C., spatial average nighttime sensible heat fluxes are typically close to zero. For some months, the averages at night remain positive. The months of November–March show short-term increases in the average in the hours immediately before dawn. This increase is possibly due to the time-dependent ramping up of heating systems in winter, in advance of the start of the working day. For some sites (e.g., those closer to the downtown areas) the average heat fluxes remain strongly positive throughout the entire diurnal cycle; the variability with time and space is such that an assumption of always-prevailing instability might be in significant error, however. For some of the sites, especially within the central business areas of Washington, there is a clear relationship between the nighttime sensible heat flux and the outside air temperature (with greater values of corresponding to colder weather), presumably a consequence of air conditioning required to elevate the internal temperature of buildings within the eddy flux footprint. This is most striking for the site above the Department of Commerce building in Washington—an old structure that occupies a complete city block.

Acknowledgments

This work was carried out as a contribution to the NOAA Dispersion Program, with support from the Air Resources Laboratory.

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