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Abstract: Deposition of trace gases, such as sulfur dioxide (SO2), can affect plant and soil chemistry in different ecosystems. Measurements over a complex ecosystem, like a forest clearing, are necessary to determine more accurate deposition rates that can be used to improve parameterizations and models. The flux-gradient technique was used to determine SO2 fluxes over grass in a clearing at Duke Forest, North Carolina, USA on 25 June – 2 July 2008. The mean flux was −0.037 ± 0.024 μg m−2 s−1. Dew on the canopy enhanced the uptake of SO2, which increased deposition rates. Deposition velocities (Vd ) fluctuated greatly with a mean of 1.00 ± 0.48 cm s−1. The large variation in Vd was not fully captured by estimates determined from a multilayer model (MLM) and a big-leaf model (BLM). Mean deposition velocities derived from the MLM and BLM were 1.25 ± 0.21 cm s−1 and 0.63 ± 0.12 cm s−1, respectively. The model estimations of Vd in this study were probably affected by uncertainties associated with canopy resistance, particularly with stomatal and non-stomatal processes. [Copyright &y& Elsevier]

Surface-layer parameterizations for heat, mass, momentum, and turbulence exchange are a critical component of the land surface models (LSMs) used in weather prediction and climate models. Although formulations derived from Monin–Obukhov similarity theory (MOST) have long been used, bulk Richardson (Rib) parameterizations have recently been suggested as a MOST alternative but have been evaluated over a limited number of land-cover and climate types. Examining the parameterizations' applicability over other regions, particularly drylands that cover approximately 41% of terrestrial land surfaces, is a critical step toward implementing the parameterizations into LSMs. One year (1 January–31 December 2018) of eddy covariance measurements from a 10-m tower in southeastern Arizona and a 200-m tower in western Texas were used to determine how well the Rib parameterizations for friction velocity ( u * ), sensible heat flux (H), and turbulent kinetic energy (TKE) compare against MOST-derived parameterizations of these quantities. Independent of stability, wind speed regime, and season, the Rib u * and TKE parameterizations performed better than the MOST parameterizations, whereas MOST better represented H. Observations from the 200-m tower indicated that the parameterizations' performance degraded as a function of height above ground. Overall, the Rib parameterizations revealed promising results, confirming better performance than traditional MOST relationships for kinematic (i.e., u * ) and turbulence (i.e., TKE) quantities, although caution is needed when applying the RibH parameterizations to drylands. These findings represent an important milestone for the applicability of Rib parameterizations, given the large fraction of Earth's surface covered by drylands. Significance Statement: Weather forecasting models rely upon complex mathematical relationships to predict temperature, wind, and moisture. Monin–Obukhov similarity theory (MOST) has long been used to forecast these quantities near the land surface, even though MOST's limitations are well known in the scientific community. Researchers have suggested an alternative to MOST called the bulk Richardson (Rib) approach. To allow for the Rib approach to be used in weather forecasting models, the approach needs to be tested over different land-cover and climate types. In this study, we applied the Rib approach to dry areas of the United States and found that the approach better represented turbulence variables than MOST relationships. These findings are an important step toward using Rib relationships in weather forecasting models. [ABSTRACT FROM AUTHOR]

We present unique observations of a total solar eclipse from a small unmanned aircraft system (sUAS) platform that was operated during the 21 August 2017 North American solar eclipse. The observations were collected near Ten Mile, Tennessee, where eclipse totality lasted 2 min 38 s. A 2-m micrometeorological tripod was erected on-site to measure surface and air temperature, near-surface water vapour, incoming and outgoing shortwave and longwave radiative fluxes, and turbulent fluxes. The sUAS platform and micrometeorological tripod observations indicate significant cooling below a height of 50 m above ground level (a.g.l.) during and shortly after totality. Near-surface temperatures do not return to pre-eclipse values until about 60 min following totality. Above about 50 m a.g.l., smaller temperature changes are observed during the eclipse, as the duration of the eclipse has less influence on deeper boundary-layer turbulence. Additionally, the sensible heat flux becomes slightly negative around totality, and the turbulence kinetic energy and vertical velocity variance concurrently decrease. The evolution of the near-surface meteorological fields are investigated in more detail using a large-eddy simulation (LES) model. The simulations generally reproduce the observations well, in terms of the timing and magnitude of changes in temperature, moisture and sensible and latent heat fluxes. However, the LES model slightly underestimates the diurnal range and decrease in temperature during the eclipse while overestimating the sensible heat fluxes. [ABSTRACT FROM AUTHOR]

Airborne turbulence measurement gives a spatial distribution of air-surface fluxes that networks of fixed surface sites typically cannot capture. Much work has improved the accuracy of such measurements and the estimation of the uncertainty peculiar to streams of turbulence data measured from the air. A particularly significant challenge and opportunity is to distinguish fluxes from different surface types, especially those occurring in patches smaller than the necessary averaging length. The flux fragment method (FFM), a conditional-sampling variant of eddy covariance in the space-time domain, was presented in 2008. It was shown capable of segregating the mean flux density (CO2, H2O, sensible heat) in maize from that in soybeans over the patchwork farmlands of Illinois. This was, however, an ideal surface for the method, and the random-error estimate used a relatively rudimentary bootstrap resampling. The present paper describes an upgraded random-error estimate that accounts for the serial correlation of the time/space series and the heterogeneity of the signal. Results are presented from the Alaskan tundra. Though recognized as important, systematic error estimates are not covered in this paper. Some discussion is offered on the relation of the FFM to other approaches similarly motivated, particularly those using wavelets. Successful measurement of the variation of air-surface exchange over heterogeneous surfaces has value for developing and improving process models relating surface flux to remotely sensible quantities, such as the vegetative land-cover type and its condition. [ABSTRACT FROM AUTHOR]

In Kochendorfer et al. (Boundary-Layer Meteorol 145:383-398, , hereafter K2012) the vertical wind speed $$(w)$$ measured by a non-orthogonal three-dimensional sonic anemometer was shown to be underestimated by $$\approx $$12 %. Turbulent statistics and eddy-covariance fluxes estimated using $$w$$ were also affected by this underestimate in $$w$$. Methodologies used in K2012 are clarified here in response to Mauder's comment. In addition, further analysis of the K2012 study is presented to help address questions raised in the comment. Specific responses are accompanied with examples of time series, calculated correlation coefficients, and additional explanation of the K2012 methods and assumptions. The discussion and analysis included in the comment and in this response do not affect the validity of the methods or conclusions presented in K2012. [ABSTRACT FROM AUTHOR]

Sonic anemometers are capable of measuring the wind speed in all three dimensions at high frequencies (10-50 Hz), and are relied upon to estimate eddy-covariance-based fluxes of mass and energy over a wide variety of surfaces and ecosystems. In this study, wind-velocity measurement errors from a three-dimensional sonic anemometer with a non-orthogonal transducer orientation were estimated for over 100 combinations of angle-of-attack and wind direction using a novel technique to measure the true angle-of-attack and wind speed within the turbulent atmospheric surface layer. Corrections to the vertical wind speed varied from −5 to 37% for all angles-of-attack and wind directions examined. When applied to eddy-covariance data from three NOAA flux sites, the wind-velocity corrections increased the magnitude of CO fluxes, sensible heat fluxes, and latent heat fluxes by ≈11%, with the actual magnitude of flux corrections dependent upon sonic anemometer, surface type, and scalar. A sonic anemometer that uses vertically aligned transducers to measure the vertical wind speed was also tested at four angles-of-attack, and corrections to the vertical wind speed measured using this anemometer were within ±1% of zero. Sensible heat fluxes over a forest canopy measured using this anemometer were 15% greater than sensible heat fluxes measured using a sonic anemometer with a non-orthogonal transducer orientation. These results indicate that sensors with a non-orthogonal transducer orientation, which includes the majority of the research-grade three-dimensional sonic anemometers currently in use, should be redesigned to minimize sine errors by measuring the vertical wind speed using one pair of vertically aligned transducers. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key variable in the determination of land surface energy exchange processes from local to global scales. Accurate ground measurements of LST are necessary for a number of applications including validation of satellite LST products or improvement of both climate and numerical weather prediction models. With the objective of assessing the quality of in situ measurements of LST and to evaluate the quantitative uncertainties in the ground-based LST measurements, intensive field experiments were conducted at NOAA's Air Resources Laboratory (ARL)'s Atmospheric Turbulence and Diffusion Division (ATDD) in Oak Ridge, Tennessee, USA, from October 2015 to January 2016. The results of the comparison of LSTs retrieved by three narrow angle broadband infrared temperature sensors (IRT), hemispherical longwave radiation (LWR) measurements by pyrgeometers, forward looking infrared camera with direct LSTs by multiple thermocouples (TC), and near surface air temperature (AT) are presented here. The brightness temperature (BT) measurements by the IRTs agreed well with a bias of <0.23 °C, and root mean square error (RMSE) of <0.36 °C. The daytime LST(TC) and LST(IRT) showed better agreement (bias = 0.26 °C and RMSE = 0.67 °C) than with LST(LWR) (bias > 1.1 and RMSE > 1.46 °C). In contrast, the difference between nighttime LSTs by IRTs, TCs, and LWR were <0.47 °C, whereas nighttime AT explained >81% of the variance in LST(IRT) with a bias of 2.64 °C and RMSE of 3.6 °C. To evaluate the annual and seasonal differences in LST(IRT), LST(LWR) and AT, the analysis was extended to four grassland sites in the USA. For the annual dataset of LST, the bias between LST (IRT) and LST (LWR) was <0.7 °C, except at the semiarid grassland (1.5 °C), whereas the absolute bias between AT and LST at the four sites were <2 °C. The monthly difference between LST (IRT) and LST (LWR) (or AT) reached up to 2 °C (5 °C), whereas half-hourly differences between LSTs and AT were several degrees in magnitude depending on the site characteristics, time of the day and the season. [ABSTRACT FROM AUTHOR]