Current Micrometeorological Flux Methodologies with Applications in Agriculture

Meyers, Tilden P.; Baldocchi, Dennis

doi:10.2134/agronmonogr47.c16

Cited by 23 publications

(18 citation statements)

References 51 publications

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“…In the 1950s and 1960s, ozone dry deposition was typically measured using gradient methods during short campaigns (e.g., Galbally, 1971). By the 1970s, the eddy covariance (EC) approach-the preferred approach for measuring turbulent fluxes Meyers & Baldocchi, 2005)-became possible with fast ozone analyzers deployed on masts and towers (e.g., Wesely et al, 1978) and aircraft (e.g., Lenschow et al, 1980). Growing recognition of the importance of biogeochemical cycles led to workshops in the late 1970s and 1980s recommending research priorities for fluxes of ozone, carbon dioxide, and other constituents (Georgii, 1989;Hicks et al, 1980;Hosker & Lindberg, 1982;.…”

Section: History Of Measurements and Survey Of Current Data Setsmentioning

confidence: 99%

Dry Deposition of Ozone Over Land: Processes, Measurement, and Modeling

Clifton

Fiore

Massman

et al. 2020

Reviews of Geophysics

115

133

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Dry deposition of ozone is an important sink of ozone in near‐surface air. When dry deposition occurs through plant stomata, ozone can injure the plant, altering water and carbon cycling and reducing crop yields. Quantifying both stomatal and nonstomatal uptake accurately is relevant for understanding ozone's impact on human health as an air pollutant and on climate as a potent short‐lived greenhouse gas and primary control on the removal of several reactive greenhouse gases and air pollutants. Robust ozone dry deposition estimates require knowledge of the relative importance of individual deposition pathways, but spatiotemporal variability in nonstomatal deposition is poorly understood. Here we integrate understanding of ozone deposition processes by synthesizing research from fields such as atmospheric chemistry, ecology, and meteorology. We critically review methods for measurements and modeling, highlighting the empiricism that underpins modeling and thus the interpretation of observations. Our unprecedented synthesis of knowledge on deposition pathways, particularly soil and leaf cuticles, reveals process understanding not yet included in widely used models. If coordinated with short‐term field intensives, laboratory studies, and mechanistic modeling, measurements from a few long‐term sites would bridge the molecular to ecosystem scales necessary to establish the relative importance of individual deposition pathways and the extent to which they vary in space and time. Our recommended approaches seek to close knowledge gaps that currently limit quantifying the impact of ozone dry deposition on air quality, ecosystems, and climate.

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Section: History Of Measurements and Survey Of Current Data Setsmentioning

confidence: 99%

Dry Deposition of Ozone Over Land: Processes, Measurement, and Modeling

Clifton

Fiore

Massman

et al. 2020

Reviews of Geophysics

115

133

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“…[36] The response variable in this Bayesian investigation is the sensible heat flux observed at each of the eddy covariance towers. A number of recent studies [e.g., Foken, 2008;Foken et al, 2012;Hollinger and Richardson, 2005;Mauder et al, 2008;Meyers and Baldocchi, 2005;Richardson et al, 2012] have highlighted the uncertainties in eddy covariance estimations of turbulent heat fluxes. In addition to standard data quality controls (e.g., coordinate rotation and density correction) that need to be performed on the high-frequency eddy covariance measurements, there are issues related to inadequacy of fetch, heterogeneity of the footprint, improper averaging times, and noncapture of large eddies that add to the uncertainties in the eddy covariance estimates [Allen et al, 2011].…”

Section: Prior Uncertainty Analysis Of Response Variablementioning

confidence: 99%

A Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model prediction

Ershadi

McCabe

Evans

et al. 2013

Water Resources Research

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[1] The influence of uncertainty in land surface temperature, air temperature, and wind speed on the estimation of sensible heat flux is analyzed using a Bayesian inference technique applied to the Surface Energy Balance System (SEBS) model. The Bayesian approach allows for an explicit quantification of the uncertainties in input variables: a source of error generally ignored in surface heat flux estimation. An application using field measurements from the Soil Moisture Experiment 2002 is presented. The spatial variability of selected input meteorological variables in a multitower site is used to formulate the prior estimates for the sampling uncertainties, and the likelihood function is formulated assuming Gaussian errors in the SEBS model. Land surface temperature, air temperature, and wind speed were estimated by sampling their posterior distribution using a Markov chain Monte Carlo algorithm. Results verify that Bayesian-inferred air temperature and wind speed were generally consistent with those observed at the towers, suggesting that local observations of these variables were spatially representative. Uncertainties in the land surface temperature appear to have the strongest effect on the estimated sensible heat flux, with Bayesianinferred values differing by up to 65 C from the observed data. These differences suggest that the footprint of the in situ measured land surface temperature is not representative of the larger-scale variability. As such, these measurements should be used with caution in the calculation of surface heat fluxes and highlight the importance of capturing the spatial variability in the land surface temperature: particularly, for remote sensing retrieval algorithms that use this variable for flux estimation.

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“…Several types of micrometeorological methods have been applied to NH 3 measurements. Meyers and Baldocchi (2005, this volume) review a broad range of micrometeorological methods for trace gases, many of which have been used for NH 3 . They include:…”

Section: Micrometeorological Methodsmentioning

confidence: 99%

“…where bars denote average across a period and primes, the fluctuations about the means (Denmead, 1983). Meyers and Baldocchi (2005, this volume) describe the assumptions underlying eddy covariance. Fast response sensors are required (and a number of sensors for NH 3 have been developed) but fluxes will be underestimated if the sensor responds too slowly, so to keep the necessary correction small (Hicks, 1972), sensor deployment should be no less than two response lengths above the measurement surface [see LeClerc and Thurtell (1990) for a discussion of fetch/height ratios].…”

Section: Micrometeorological Methodsmentioning

confidence: 99%

Ammonia: Measurement Issues

Harper¹

2015

Agronomy Monographs

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Historically, there has been a lack of reliable, fast-responding, and sensitive sensors for the measurement of NH 3 . There are several sensors becoming available and this review will not try to characterize available sensors because technologies are rapidly changing. There are, however, several classes of sensors for NH 3 measurement. Gas-WashingThis technique has been used as a standard for many years. Much of the early work on atmospheric transport was accomplished, by necessity, using this 348 HARPER Fig. 15-3. Ammonia flux density variation compared with windspeed and lagoon water temperature over a swine lagoon (after Harper et al., 2001(after Harper et al., , 2004a.

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Current Micrometeorological Flux Methodologies with Applications in Agriculture

Cited by 23 publications

References 51 publications

Dry Deposition of Ozone Over Land: Processes, Measurement, and Modeling

Dry Deposition of Ozone Over Land: Processes, Measurement, and Modeling

A Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model prediction

Ammonia: Measurement Issues

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