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

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