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Flux measurements of nitrogen oxides (NOx) were made over London using airborne eddy covariance from a low-flying aircraft. Seven low-altitude flights were conducted over Greater London, performing multiple overpasses across the city during eight days in July 2014. NOx fluxes across the Greater London region (GLR) exhibited high heterogeneity and strong diurnal variability, with central areas responsible for the highest emission rates (20–30 mg m−2 h−1). Other high-emission areas included the M25 orbital motorway. The complexity of London's emission characteristics makes it challenging to pinpoint single emissions sources definitively using airborne measurements. Multiple sources, including road transport and residential, commercial and industrial combustion sources, are all likely to contribute to measured fluxes. Measured flux estimates were compared to scaled National Atmospheric Emissions Inventory (NAEI) estimates, accounting for monthly, daily and hourly variability. Significant differences were found between the flux-driven emissions and the NAEI estimates across Greater London, with measured values up to 2 times higher in Central London than those predicted by the inventory. To overcome the limitations of using the national inventory to contextualise measured fluxes, we used physics-guided flux data fusion to train environmental response functions (ERFs) between measured flux and environmental drivers (meteorological and surface). The aim was to generate time-of-day emission surfaces using calculated ERF relationships for the entire GLR; 98 % spatial coverage was achieved across the GLR at 400 m2 spatial resolution. All flight leg projections showed substantial heterogeneity across the domain, with high emissions emanating from Central London and major road infrastructure. The diurnal emission structure of the GLR was also investigated, through ERF, with the morning rush hour distinguished from lower emissions during the early afternoon. Overall, the integration of airborne fluxes with an ERF-driven strategy enabled the first independent generation of surface NOx emissions, at high resolution using an eddy-covariance approach, for an entire city region.

During March–June 2017 emissions of nitrogen oxides were measured via eddy covariance at the British Telecom Tower in central London, UK. Through the use of a footprint model the expected emissions were simulated from the spatially resolved National Atmospheric Emissions Inventory for 2017 and compared with the measured emissions. These simulated emissions were shown to underestimate measured emissions during the daytime by a factor of 1.48, but they agreed well overnight. Furthermore, underestimations were spatially mapped, and the areas around the measurement site responsible for differences in measured and simulated emissions were inferred. It was observed that areas of higher traffic, such as major roads near national rail stations, showed the greatest underestimation by the simulated emissions. These discrepancies are partially attributed to a combination of the inventory not fully capturing traffic conditions in central London and both the spatial and temporal resolution of the inventory not fully describing the high heterogeneity of the urban centre. Understanding of this underestimation may be further improved with longer measurement time series to better understand temporal variation and improved temporal scaling factors to better simulate sub-annual emissions.

Eddy-covariance fluxes serve as an essential benchmark for Earth system models and remote sensing data. However, two challenges prevent model-data intercomparisons from fully utilizing eddy-covariance fluxes. The first challenge stems from the differing and variable spatial representativeness of the eddy-covariance measurements, or footprint bias and transience. The second originates from the phenomenon of a non-closed energy balance using eddy-covariance measurements, hypothesized to result from unaccounted mesoscale flows or under-sampling of hot spots by flux towers, among others. Previous studies have suggested that these two problems can be mitigated by either building multiple towers or by applying space–time rectification approaches, such as the environmental response function (ERF) approach. Here we ask: (1) How many eddy-flux towers do we need to sufficiently rectify location bias, close the energy budget, and sample the regional domain? (2) Can an advanced space–time rectification approach reduce the tower density, while still adequately sampling the regional flux domain? Furthermore, (3) How accurately can the ERF approach retrieve the surface-flux variation? To answer these questions, we used data from a large-eddy simulation of atmospheric flows above a heterogeneous surface as captured by an ensemble of virtual tower measurements. We calculated eddy-covariance fluxes by spatial and spatio-temporal methods. The spatial eddy-covariance method captured 89% of the prescribed total surface energy flux with about one tower per 15 km2, while the spatio-temporal method required only one tower per 40 km2 to capture 95% of surface energy. To capture 97% of energy, applying the ERF approach further reduced the required tower density to one tower per 200 km2, as a result of space–time rectification and incorporating mesoscale flows. This approach also enabled retrieving the surface spatial variation of the sensible heat flux. The results provide a reference for informing and designing future observation systems based on flux tower clusters, and scale-aware data products.

The National Ecological Observatory Network (NEON) is a multidecadal and continental-scale observatory with sites across the United States. Having entered its operational phase in 2018, NEON data products, software, and services become available to facilitate research on the impacts of climate change, land-use change, and invasive species. An essential component of NEON are its 47 tower sites, where eddy-covariance (EC) sensors are operated to determine the surface–atmosphere exchange of momentum, heat, water, and CO2. EC tower networks such as AmeriFlux, the Integrated Carbon Observation System (ICOS), and NEON are vital for providing the distributed observations to address interactions at the soil–vegetation–atmosphere interface. NEON represents the largest single-provider EC network globally, with standardized observations and data processing explicitly designed for intersite comparability and analysis of feedbacks across multiple spatial and temporal scales. Furthermore, EC is tightly integrated with soil, meteorology, atmospheric chemistry, isotope, phenology, and rich contextual observations such as airborne remote sensing and in situ sampling bouts. Here, we present an overview of NEON’s observational design, field operation, and data processing that yield community resources for the study of surface–atmosphere interactions. Near-real-time data products become available from the NEON Data Portal, and EC and meteorological data are ingested into AmeriFlux and FLUXNET globally harmonized data releases. Open-source software for reproducible, extensible, and portable data analysis includes the eddy4R family of R packages underlying the EC data product generation. These resources strive to integrate with existing infrastructures and networks, to suggest novel systemic solutions, and to synergize ongoing research efforts across science communities.

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.

The observing system design of multi-disciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increase in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel approach to observing system simulation experiments that aids surface-atmosphere synthesis at the interface of meso- and microscale meteorology. We used this approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19).During pre-field simulation experiments, we considered the placement of 20 eddy-covariance flux towers, operations for 72 hours of low-altitude flux aircraft measurements, and integration of various remote sensing data products. High-resolution Large Eddy Simulations generated a super-sample of virtual ground, airborne, and satellite observations to explore two specific design hypotheses. We then analyzed these virtual observations through Environmental Response Functions to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals.We demonstrate how this novel approach doubled CHEESEHEAD19’s ability to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its extensibility, the approach lends itself to optimize observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection and multi-species applications, among other use cases.

The observing system design of multi-disciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator...

The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.

The observing system design of multidisciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increases in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel numerical simulation–environmental response function (NS–ERF) approach to observing system simulation experiments that aids surface–atmosphere synthesis at the interface of mesoscale and microscale meteorology. In a case study we demonstrate application of the NS–ERF approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19). During CHEESEHEAD19 pre-field simulation experiments, we considered the placement of 20 eddy covariance flux towers, operations for 72 h of low-altitude flux aircraft measurements, and integration of various remote sensing data products. A 2 h high-resolution large eddy simulation created a cloud-free virtual atmosphere for surface and meteorological conditions characteristic of the field campaign domain and period. To explore two specific design hypotheses we super-sampled this virtual atmosphere as observed by 13 different yet simultaneous observing system designs consisting of virtual ground, airborne, and satellite observations. We then analyzed these virtual observations through ERFs to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals. We demonstrate how the novel NS–ERF approach doubled CHEESEHEAD19's potential to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its modular extensibility, NS–ERF lends itself to optimizing observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection, and multi-species applications, among other use cases.

NOx (the sum of NO + NO2) is emitted during most combustion processes. NO2 is a well-known air pollutant detrimental to human health, critical in the formation of tropospheric ozone and its concentration is regulated in many cities. London is a megacity which often finds itself in breach of these air quality regulations. Emission inventories are used in air quality forecast models to predict current and future air pollution levels and to guide abatement strategy. The National Atmospheric Emissions Inventory (NAEI) has been shown to underestimate NOx emission in London (Lee et al. 2012, Vaughan et al. 2016). Top down measurements allow assessment of emissions help understand the difference between measurement and model.During March – June 2017 NOx emissions were measured using the eddy covariance method sampling from a height of 180 m at the British Telecom (BT) tower in Central London. In July of 2017 measurements of NOx by the UK’s Facility for Airborne Atmospheric Measurement (FAAM) were made as a part of the Effect of Megacities on the Transport and Transformation of Pollutants on the Regional to Global Scales (EMeRGe). A mass balance approach (after O’Shea et al. 2014 and Pitt et al. 2019) has been applied to these measurements producing a measurement of bulk emission of NOx from Greater London and surrounding areas.Through comparison of these measurements with the NAEI we present an exploration of NOx emission from London and assess how this is captured in the emissions inventory. Lee et al., Environmental Science & Technology, 2015, 49, 1025-1034Vaughan et al., Faraday Discussions, 2016, 189, 455-472O’Shea et al., J. Geophys. Res. Atmos., 2014, 119, 4940–4952Pitt et al., Atmos. Chem. Phys., 2019, 19, 8931-8945

Land surface heterogeneity influences patterns of sensible and latent heat flux, which in turn affect processes in the atmospheric boundary layer. However, gridded atmospheric models often fail to incorporate the influence of land surface heterogeneity due to differences between the temporal and spatial scales of models compared to the local, sub-grid processes. Improving models requires the scaling of surface flux measurements; a process made difficult by the fact that surface measurements usually find an imbalance in the energy budget.The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD19) was an observational experiment designed to investigate how the atmospheric boundary layer responds to scales of spatial heterogeneity in surface-atmosphere heat and water exchanges. The campaign was conducted from June – October 2019, measuring surface energy fluxes over a heterogeneous forest ecosystem as fluxes transitioned from latent heat-dominated summer through sensible heat-dominated fall. Observations were made by ground, airborne, and satellite platforms within the 10 x 10 km study region, which was chosen to match the scale of a typical model grid cell. The spatial distribution of energy fluxes was observed by an array of 20 eddy covariance towers and a low-flying aircraft. Mesoscale atmospheric properties were measured by a suite of LiDAR and sounding instruments, measuring winds, water vapor, temperature, and boundary layer development. Plant phenology was measured in-situ and mapped remotely using hyperspectral imaging.The dense set of multi-scale observations of land-atmosphere exchange collected during the CHEESEHEAD field campaign permits combining the spatial and temporal distribution of energy fluxes with mesoscale surface and atmospheric properties. This provides an unprecedented data foundation to evaluate theoretical explanations of energy balance non-closure, as well as to evaluate methods for scaling surface energy fluxes for improved model-data comparison. Here we show how fluxes calculated using a spatial eddy covariance technique across the 20-tower network compare to those of standard temporal eddy covariance fluxes in order to characterize of the spatial representativeness of single tower eddy covariance measurements. Additionally, we show how spatial EC fluxes can be used to better understand the energy balance over heterogeneous ecosystems.

The National Ecological Observatory Network (NEON) is a continental-scale observatory with sites across the US collecting standardized ecological observations that will operate for multiple decades. To maximize the utility of NEON data, we envision edge computing systems that gather, calibrate, aggregate, and ingest measurements in an integrated fashion. Edge systems will employ machine learning methods to cross-calibrate, gap-fill and provision data in near-real time to the NEON Data Portal and to High Performance Computing (HPC) systems, running ensembles of Earth system models (ESMs) that assimilate the data. For the first time gridded EC data products and response functions promise to offset pervasive observational biases through evaluating, benchmarking, optimizing parameters, and training new machine learning parameterizations within ESMs all at the same model-grid scale. Leveraging open-source software for EC data analysis, we are already building software infrastructure for integration of near-real time data streams into the International Land Model Benchmarking (ILAMB) package for use by the wider research community. We will present a perspective on the design and integration of end-to-end infrastructure for data acquisition, edge computing, HPC simulation, analysis, and validation, where Artificial Intelligence (AI) approaches are used throughout the distributed workflow to improve accuracy and computational performance.

Technological advances have allowed in situ monitoring of soil water content in an automated manner. These advances, along with an increase in large-scale networks monitoring soil water content, stress the need for a robust calibration framework that ensures that soil water content measurements are accurate and reliable. We have developed an approach to make consistent and comparable soil water content sensor calibrations across a continental-scale network in a production framework that incorporates a thorough accounting of uncertainties. More than 150 soil blocks of varying characteristics from 33 locations across the United States were used to generate soil-specific calibration coefficients for a capacitance sensor. We found that the manufacturer’s nominal calibration coefficients poorly fit the data for nearly all soil types. This resulted in negative (91% of samples) and positive (5% of samples) biases and a mean root mean square error (RMSE) of 0.123 cm cm (1σ) relative to reference standard measurements. We derived soil-specific coefficients, and when used with the manufacturer’s nominal function, the biases were corrected and the mean RMSE dropped to ±0.017 cm cm (±1σ). A logistic calibration function further reduced the mean RMSE to ±0.016 cm cm (±1σ) and increased the range of soil moistures to which the calibration applied by 18% compared with the manufacturer’s function. However, the uncertainty of the reference standard was notable (±0.022 cm cm), and when propagated in quadrature with RMSE estimates, the combined uncertainty of the calibrated volumetric soil water content values increased to ±0.028 cm cm regardless of the calibration function used.

Large differences in instrumentation, site setup, data format, and operating system stymie the adoption of a universal computational environment for processing and analyzing eddy-covariance (EC) data. This results in limited software applicability and extensibility in addition to often substantial inconsistencies in flux estimates. Addressing these concerns, this paper presents the systematic development of portable, reproducible, and extensible EC software achieved by adopting a development and systems operation (DevOps) approach. This software development model is used for the creation of the eddy4R family of EC code packages in the open-source R language for statistical computing. These packages are community developed, iterated via the Git distributed version control system, and wrapped into a portable and reproducible Docker filesystem that is independent of the underlying host operating system. The HDF5 hierarchical data format then provides a streamlined mechanism for highly compressed and fully self-documented data ingest and output. The usefulness of the DevOps approach was evaluated for three test applications. First, the resultant EC processing software was used to analyze standard flux tower data from the first EC instruments installed at a National Ecological Observatory (NEON) field site. Second, through an aircraft test application, we demonstrate the modular extensibility of eddy4R to analyze EC data from other platforms. Third, an intercomparison with commercial-grade software showed excellent agreement (R2 = 1.0 for CO2 flux). In conjunction with this study, a Docker image containing the first two eddy4R packages and an executable example workflow, as well as first NEON EC data products are released publicly. We conclude by describing the work remaining to arrive at the automated generation of science-grade EC fluxes and benefits to the science community at large. This software development model is applicable beyond EC and more generally builds the capacity to deploy complex algorithms developed by scientists in an efficient and scalable manner. In addition, modularity permits meeting project milestones while retaining extensibility with time.

In the widely-used eddy-covariance (EC) technique, it is often assumed that the air storage term, i.e. the change of below-turbulence-sensor scalar abundance, is negligible or comprises a small part of net surface-atmosphere exchange (NSAE). Previous studies have demonstrated that this assumption is often violated where non-turbulent processes prevail, and thus it is important to measure and calculate air storage in flux measurements. However, the implementation of air storage measurement and calculation is not ubiquitous as EC standard turbulent flux. In most cases, air storage is not a standard data product or even neglected in EC flux tower measurements. In other cases, air storage term is calculated simply using only the measurements at the tower top. This gap between the ideal initiative and actual implementation motivates us to derive and release one of the first community resources to facilitate the consistent measurement and calculation of EC air storage across sites. These resources include (i) the standardized air storage term measurement setup design at National Ecological Observatory Network (NEON) sites; (ii) the development and public release of the eddy4R.stor open-source air storage R-package; (iii) the derivation and public release of storage term data products, measured and calculated consistently across 47 NEON sites; and (iv) exploration the scientific usefulness of these resources through example use cases, specifically the exploration of the bias of the air storage term when different measurement level intensity used and exploration of the air storage term pattern. We expect the consistent air storage measurement and calculation can better serve the overall purpose of the EC technique to provide more reliable measurement of NSAE for the community. This can further benefit the community accurate depiction of the sub-daily to diurnal cycle of surface fluxes in doing carbon cycle flux partitioning, land modeling, and studying ecosystem response to weather extremes.

This study presents the systematic development of an open-source, flexible and modular eddy-covariance (EC) data processing framework. This is achieved through adopting a Development and Systems Operation (DevOps) philosophy, building on the eddy4R family of EC code packages in the R Language for Statistical Computing as foundation. These packages are community-developed via the GitHub distributed version control system and wrapped into a portable and reproducible Docker filesystem that is independent of the underlying host operating system. The HDF5 hierarchical data format then provides a streamlined mechanism for highly compressed and fully self-documented data ingest and output. This framework is applicable beyond EC, and more generally builds the capacity to deploy complex algorithms developed by scientists in an efficient and scalable manner. In addition, modularity permits meeting project milestones while retaining extensibility with time. The efficiency and consistency of this framework is demonstrated in the form of three application examples. These include tower EC data from first instruments installed at a National Ecological Observatory (NEON) field site, aircraft flux measurements in combination with remote sensing data, as well as a software intercomparison. In conjunction with this study, the first two eddy4R packages and simple NEON EC data products are released publicly. While this proof-of-concept represents a significant advance, substantial work remains to arrive at the automated framework needed for the streaming generation of science-grade EC fluxes.