Abstract. Sampling errors in eddy correlation flux measurements arise from the small number of large eddies that dominate the flux during typical sampling periods. Several methods to estimate sampling, or random error in flux measurements, have been published. These methods are compared to a more statistically rigorous method which calculates the variance of a covariance when the two variables in the covariance are autoand cross-correlated. Comparisons are offered between the various methods. Compared to previously published methods, error estimates from this technique were 20 to 25% higher because of the incorporation of additional terms in the estimate of the variance. This new approach is then applied to define the random error component of representative eddy correlation flux measurements of momentum, sensible and latent heat, carbon dioxide, and ozone from five field studies, three over agricultural crops (corn, soybean, and pasture), and two from towers over forests (deciduous and mixed). The mean normalized error for each type of flux measurement over the five studies ranged from 12% for sensible heat flux to 31% for ozone flux. There were not large or significant differences between random errors for fluxes measured over crops versus those measured over forests. The effects of stability, flux magnitude, and wind speed on measurement error are discussed. IntroductionEddy correlation, also known as eddy covariance (EC), measurements of heat, momentum, and trace gas fluxes are frequently the most accurate and reliable way to measure exchange processes between the atmosphere and the land or water surface. In an EC measurement the flux is the covariance of the vertical velocity (w) with the state variable of interest (c); that is, flux is equal to w' c', where c can be a scalar such as temperature, concentration of a gas, etc., or a vector such as horizontal wind velocity, and the prime denotes departure from the mean. The advent of more reliable and less expensive sonic anemometers, fast response instruments for temperature, water vapor, carbon dioxide, and other trace gases, and, not least of all, the ready availability of small, cheap, and powerful computers for data acquisition, has put the equipment to make good EC measurements within the reach of many researchers.Like any other complex measurement, EC measurements can be subject to significant bias and random errors. However, unlike many measurements, there are no straightforward ways Comparison Experiment (ITCE), indicating the need for statistically meaningful samples to achieve repeatable flux profile relationships. Shaw et al. [1983] was one of the first to quantify the infrequent energy-containing eddies that contribute to a flux measurement. Longer sampling periods increase the number of independent samples and may reduce sampling error, but longer sampling times frequently lead to other problems including lack of stationarity in the atmosphere. In field programs carefully designed to avoid the other errors noted by Businger [1986], sampling erro...

Abstract. In this paper, we describe the latest version of the dry deposition inferential model, which is used to estimate the deposition velocities (V a) for SO2, 03, HNO3, and particles with diameters less than 2 •m. The dry deposition networks operated by the National Oceanic and Atmospheric Administration (NOAA) and the Environmental Protection Agency (EPA) use this model to estimate dry deposition on a weekly basis. This model uses a multilayer approach, discretizing the vegetated canopy into 20 layers. The use of canopy radiative transfer and simple wind profile models allows for estimates of stomatal (rs) and leaf boundary layer (%) resistances to be determined at each layer in the plant canopy for both sunlit and shaded leaves. The effect of temperature, water stress, and vapor pressure deficits on the stomatal resistance (rs) have been included. This paper describes the multilayer modeling approach for estimating the dry deposition of SO2, HNO3, and 03 that is currently implemented in the NOAA and EPA national networks. The model is evaluated against 30 min average direct flux measurements recently obtained over corn near Bondville, Illinois, over soybeans near Nashville, Tennessee, and over grass near Sand Mountain, Alabama. 22,645

The emotional and psychological responses of medical students to the human anatomy laboratory at Stanford University School of Medicine were studied over four successive years. Students' reactions to dissection were assessed by interview, questionnaire, recording of laboratory conversations, and analysis of dreams. Data were collected from approximately 175 of the 350 students enrolled in Human Anatomy over this period. Interviews were also conducted with faculty and staff.

Abstract. Fluxes and deposition velocities of 03 and SO2 were measured over both a deciduous and a mixed coniferous-deciduous forest for full growing seasons. Fluxes and deposition velocities of 03 were measured over a coniferous forest for a month. Mean deposition velocities of 0.35 to 0.48 cm/s for 03 and 0.6 to 0.72 cm/s for SO2 were observed during the growing seasons of 1997 and 1998. Weekly averages of 03 deposition velocity ranged from 0.25 cm/s at the beginning and end of the season to 1.25 cm/s in late June. SO2 had a smaller seasonal variation, from 0.75 to 1.5 cm/s between the beginning and peak of the season. Because 03 concentrations are higher, the flux of 03 to forests is considerably greater than the flux of SO2. Daytime deposition velocities are very similar at each site, from 0.75 to 0.79 cm/s for 03, and from 1.01 to 1.04 cm/s for SO2. Diurnal cycles for both gases are discussed, as are the impact of some weather events. The peak time for 03 deposition velocity is in midmorning, while it is near midday for SO2. Surface wetness is usually associated with a small increase in deposition velocity, but for some rain events a major increase was noted. Minimum deposition velocities usually occur at night and increase slowly in the predawn hours before light. Comparisons are made between observations of deposition velocity and predictions made with the Meyers multilayer deposition velocity model. While the model is, on average, unbiased for 03, it tends to underpredict the higher deposition velocity values. The model is slightly biased low (underpredicts) for SO2 deposition velocity. The strengths of the model are noted, as are opportunities for improvement. IntroductionIn addition to their use in local and regional scale air quality models, deposition velocity models are integral to the inferential dry deposition monitoring networks that operate in the United States, Canada, and Europe ]. Because of the high cost and complexity of direct flux measurements, operational dry deposition networks measure concentrations of air pollutants and infer the flux of pollutants to the surface using a modeled deposition velocity (flux equal to deposition velocity times concentration).In an earlier paper [Meyers et al., 1998] we reported on the formulation of the Multilayer Model (MLM) for deposition velocity which is being used in operational dry deposition networks in the United States and the observations from three field studies which were used to evaluate and improve the MLM. Those studies were performed over agricultural fields of pasture, corn, and soybeans. Details of the model and field This work reports on three new field studies which measured fluxes of 03 and SO2 over forests, and the evaluation of the MLM with those data. The studies were conducted over a pine plantation in central North Carolina during the spring of 1996, over a deciduous forest in northwestern Pennsylvania during the growing season of 1997, and over a mixed forest in the Adirondack Mountain region of New York during the growing season...

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