The Canopy Horizontal Array Turbulence Study

Patton, Edward G.; Horst, Thomas; Sullivan, Peter P.; Lenschow, Donald H.; Oncley, S.; Brown, William O.; Burns, Sean P.; Guenther, Alex; Held, Andreas; Karl, T.; Mayor, Satyajit; Rizzo, L. V.; Spuler, Scott M.; Sun, Jielun; Turnipseed, A.; Allwine, E.; Edburg, S. L.; Lamb, Brian; Avissar, Roni; Calhoun, Ronald; Kleissl, Jan; Massman, W. J.; U, Kyaw Tha Paw; Weil, Jeffrey

doi:10.1175/2010bams2614.1

Cited by 117 publications

(69 citation statements)

References 51 publications

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“…However, the main source of the problem appears to be with T s not w because w T tc , which uses the same CSAT w , produces reasonable heat fluxes (e.g., predominantly negative at night). Also, similar highfrequency noise in CSAT S T (not shown here) has been observed on a 30-m tower during high winds in the CHATS field project (Patton et al, 2011). This suggests the problem is not specific to the NWT tower.…”

Section: Atmossupporting

confidence: 76%

Using sonic anemometer temperature to measure sensible heat flux in strong winds

Burns

Horst

Jacobsen³

et al. 2012

Atmos. Meas. Tech.

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Abstract. Sonic anemometers simultaneously measure the turbulent fluctuations of vertical wind (w ) and sonic temperature (T s ), and are commonly used to measure sensible heat flux (H ). Our study examines 30-min heat fluxes measured with a Campbell Scientific CSAT3 sonic anemometer above a subalpine forest. We compared H calculated with T s to H calculated with a co-located thermocouple and found that, for horizontal wind speed (U ) less than 8 m s −1 , the agreement was around ± 30 W m −2 . However, for U >≈ 8 m s −1 , the CSAT H had a generally positive deviation from H calculated with the thermocouple, reaching a maximum difference of ≈ 250 W m −2 at U ≈ 18 m s −1 . With version 4 of the CSAT firmware, we found significant underestimation of the speed of sound and thus T s in high winds (due to a delayed detection of the sonic pulse), which resulted in the large CSAT heat flux errors. Although this T s error is qualitatively similar to the well-known fundamental correction for the crosswind component, it is quantitatively different and directly related to the firmware estimation of the pulse arrival time. For a CSAT running version 3 of the firmware, there does not appear to be a significant underestimation of T s ; however, a T s error similar to that of version 4 may occur if the CSAT is sufficiently out of calibration. An empirical correction to the CSAT heat flux that is consistent with our conceptual understanding of the T s error is presented. Within a broader context, the surface energy balance is used to evaluate the heat flux measurements, and the usefulness of sideby-side instrument comparisons is discussed.

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Section: Atmossupporting

confidence: 76%

Using sonic anemometer temperature to measure sensible heat flux in strong winds

Burns

Horst

Jacobsen³

et al. 2012

Atmos. Meas. Tech.

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“…Though estimates of the momentum dispersive flux are reportedly small in the upper canopy (e.g., Poggi et al 2004), these results depend on canopy structure (Moltchanov et al 2011) and need to be verified for sensible heat. Though some of the issues related to small-scale and horizontal transport have been investigated over open snowfields (Parlange et al 2007;Mott et al 2013) and within canopies (e.g., Serafimovich et al 2011;Patton et al 2011;Thomas 2011), we are not aware of any study examining them within a snow-covered forest.…”

Section: B Subcanopy Sensible Heat Flux Measurementsmentioning

confidence: 99%

Snow Temperature Changes within a Seasonal Snowpack and Their Relationship to Turbulent Fluxes of Sensible and Latent Heat

Burns

Molotch

Williams

et al. 2014

Journal of Hydrometeorology

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Snowpack temperatures from a subalpine forest below Niwot Ridge, Colorado, are examined with respect to atmospheric conditions and the 30-min above-canopy and subcanopy eddy covariance fluxes of sensible Q h and latent Q e heat. In the lower snowpack, daily snow temperature changes greater than 18C day 21 occurred about 1-2 times in late winter and early spring, which resulted in transitions to and from an isothermal snowpack. Though air temperature was a primary control on snowpack temperature, rapid snowpack warm-up events were sometimes preceded by strong downslope winds that kept the nighttime air (and canopy) temperature above freezing, thus increasing sensible heat and longwave radiative transfer from the canopy to the snowpack. There was an indication that water vapor condensation on the snow surface intensified the snowpack warm-up. In late winter, subcanopy Q h was typically between 210 and 10 W m 22 and rarely had a magnitude larger than 20 W m 22 . The direction of subcanopy Q h was closely related to the canopy temperature and only weakly dependent on the time of day. The daytime subcanopy Q h monthly frequency distribution was near normal, whereas the nighttime distribution was more peaked near zero with a large positive skewness. In contrast, above-canopy Q h was larger in magnitude (100-400 W m 22 ) and primarily warmed the forest-surface at night and cooled it during the day. Around midday, decoupling of subcanopy and above-canopy air led to an apparent cooling of the snow surface by sensible heat. Sources of uncertainty in the subcanopy eddy covariance flux measurements are suggested. Implications of the observed snowpack temperature changes for future climates are discussed.

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“…Two clear 24-h periods (June 8/9 and May 28/29) with identical mean air temperature (19.3 • C) and similar air temperature spread between daily maximum and minimum (19.8 • C vs. 18.7 • C) were chosen to merge the above-and below-canopy TIR data. Air temperature and relative humidity profiles, as well as H, G, and LE values, were obtained from the Canopy Horizontal Array Turbulence Study (CHATS, Patton et al 2008), a set-up with aspirated temperature and humidity probes, and eddy-covariance flux (EC) measurements at 10 heights on a 30-m tower located in the orchard 116 m north of the TIR camera. In this study we use LE and H from the EC sensors at z = 15 m, and T air and specific humidity w from the aspirated thermocouples and humidity sensors at 1.5, 3, 4.5, 6, 7.5 and 9 m. G was estimated from a REBS HFT-3 soil heat-flux plate at 0.05-m depth where heat storage in the soil between the soil heat flux plate and the ground surface was obtained through a Hukseflux TP01 thermal property probe and soil temperature measurements at 0.05 m depth.…”

Section: The Chats Experimentsmentioning

confidence: 99%

Estimation of Biomass Heat Storage Using Thermal Infrared Imagery: Application to a Walnut Orchard

Garai

Kleissl

Smith

2010

Boundary-Layer Meteorol

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A new method to estimate tree biomass heat storage from thermal infrared (TIR) imaging of biomass surface temperature is presented. TIR images of the canopy are classified into trunk, branches, and leaves. The one-dimensional heat equation in cylindrical coordinates is forced with trunk and branch surface temperatures to simulate the temperature distribution and heat storage in tree trunks and branches. Assuming uniform leaf temperatures, heat storage in leaves is computed from the surface temperature of the leaves separately for the sunlit upper and shaded lower canopy. The sum of trunk, branches, leaf, and air heat storage gives the canopy heat storage. Measurements in a walnut orchard near Davis, California, in early June 2007 showed that biomass heat storage was of the same order as air heat storage and about 1% of daytime and 9% of nighttime net radiation.

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The Canopy Horizontal Array Turbulence Study

Cited by 117 publications

References 51 publications

Using sonic anemometer temperature to measure sensible heat flux in strong winds

Using sonic anemometer temperature to measure sensible heat flux in strong winds

Snow Temperature Changes within a Seasonal Snowpack and Their Relationship to Turbulent Fluxes of Sensible and Latent Heat

Estimation of Biomass Heat Storage Using Thermal Infrared Imagery: Application to a Walnut Orchard

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