Surface-Atmosphere Exchange Group

About the lab

Our Surface-Atmosphere Exchange (SAE) Group is part of NEON's Terrestrial Instruments Department headquartered in Boulder, Colorado, USA.

We guide the operation of NEON's SAE assets (see our QA/QC book project), develop novel, scale-aware data products, adaptive algorithms and usability tools (see our eddy4R project), and actively contribute to network science.

Join our annual workshop at the AGU Fall Meeting to get your ideas on our development backlog; participate on our external advisory group to steer each quarter's developments and pre-review new resources; join our two-weekly implementation cycles, e.g. to produce NEON data products with your latest algorithm; or discuss community-collaborative proposal ideas.

Featured projects (3)

Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors The living biosphere interacts with atmospheric processes at a multitude of scales. Understanding these processes requires integration of multiple observations for comparison to theories embedded in atmospheric models. But, all observations mismatch the scale of all models. Therefore, spatial and temporal scaling of surface fluxes is fundamental to how we evaluate theories on what happens within the sub-grid of atmospheric models and how those feed back onto larger scale dynamics. The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD) is an intensive field-campaign designed specifically to address long-standing puzzles regarding the role of atmospheric boundary-layer responses to scales of spatial heterogeneity in surface-atmosphere heat and water exchanges. The high-density observing network is coupled to LES and machine-learning scaling-experiments to better understand sub-mesoscale responses and improve numerical weather and climate prediction formulations of sub-grid processes. This project will advance spatiotemporal scaling methods for heterogeneous land surface properties and fluxes and theories on the scales at which the lower atmosphere responds to surface heterogeneity. CHEESEHEAD aims to provide a level of observation density and instrumentation reliability never previously achieved to test and develop hypotheses on spatial heterogeneity and atmosphere feedbacks. The proposed experiment generates knowledge that advances the science of surface flux measurement and modeling, relevant to many scientific applications such as numerical weather prediction, climate change, energy resources, and computational fluid dynamics. We intend to train next generation land- atmosphere graduate and undergraduate students. Field support outreach and teacher training is included via middle, high school, and undergraduate student involvement at nearby schools and colleges in coordination with the GLOBE program, Northland College, and local school districts. The database of observations and models will be made immediately available to the community and public for general use for further scientific advancement.
eddy4R is a family of open-source packages for eddy-covariance raw data processing, analyses and modeling in the R Language for Statistical Computing ( Among others NEON uses eddy4R-Docker to operationally generate surface-atmosphere exchange data products from 47 sites across the United States that can be freely downloaded ( Data processing software is oftentimes limited in either portability, extensibility, reproducibility or combinations thereof. The eddy4R-Docker project overcomes this limitation by packaging eddy4R alongside its computational environment and all dependencies into a Docker image ( The eddy4R-Docker project solicits community input which is continuously integrated on the basis of "Development and Systems Operations" principles. Get started here: .
The project collaborators are working on a book chapter on quality assurance and control in atmospheric measurements. Atmospheric measurements are not exclusively of interest for meteorologists and intended for weather forecasting and climate research. In fact, atmospheric measurements are of crucial importance for all disciplines of environmental research and ecology. The planned book chapter will describe helpful practices and techniques for a comprehensive quality assurance programme designed to minimize problems and quantify quality along the entire data generation chain.

Featured research (18)

Nature‐based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem‐scale carbon fluxes, e.g., by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state‐of‐the‐art remote sensing products and land‐surface models are not yet being widely used to inform NbCS policy making or implementation. As a result, there is a critical mismatch between the point‐ and tree‐ scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policy making. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem‐scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre‐existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.
Quality assurance and control is fundamental to ensuring the scientific usefulness of atmospheric measurements. Quality is relevant to all stages of data generation, from site selection and system design to all physical components of a measurement system (including their calibration, operation, and maintenance) as well as data handling and processing. This chapter describes useful practices and techniques for a comprehensive quality management program that is designed to minimize problems and quantify quality along the entire data generation chain.Widely applicable methods of post-field data quality control for instrumented (in-situ), visual, and remotely sensed observations are presented. The chapter concludes with example applications of QA/QC in measurement networks, and a discussion of common data problems. Finally, future developments in quality assurance and quality control are presented.
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.
Surface‐atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub‐grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub‐kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not continuously available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES‐16 and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station [ECOSTRESS]) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to independent observations from a network of 20 micrometeorological towers and piloted aircrafts in addition to Landsat‐based LST retrieval and drone‐based LST observed at one tower site. The downscaled 50‐m hourly LST showed good relationships with tower (r² = 0.79, RMSE = 3.5 K) and airborne (r² = 0.75, RMSE = 2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio‐temporal variation compared to a geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hot and cold spots across the landscape as evidenced by independent drone LST, with significant reduction in RMSE by 1.3 K. These results demonstrate a simple pathway for multi‐sensor retrieval of high space and time resolution LST.
The AmeriFlux Science Steering Committee has formed a new working group focused on enhancing the usefulness of AmeriFlux data for Natural Climate Solutions (NCS). NCS are strategies to manage or preserve ecosystem services to reduce greenhouse gas (GHG) emissions or enhance their sequestration. We love to hear your ideas about the most effective and creative ways to engage with you on this important topic, and our community call for nominations has already received an enthusiastic response. This breakout will serve to align group purpose, vision and mission, to determine concrete future activities, appoint topical leads and agree on a process for group rapport. Envisioned activities include but are not limited to: – Creating an AmeriFlux-NCS list-server. – Convening NCS-themed sessions at conferences and meetings (ESA, AGU, EGU, AMS, etc). – Creating a community bibliography of NSC-themed research that uses or has conceptual links to AmeriFlux data. – Creating a living calendar of scientific seminars, meetings, and other virtual or in-person events focused on NCS. – Creating a journal special issue on flux community contributions to NCS quantification.

Lab head

Stefan Metzger
  • Terrestrial Instruments
About Stefan Metzger
  • More about Stefan here:

Members (6)

Hongyan Luo
  • National Ecological Observatory Network
David J Durden
  • National Ecological Observatory Network
Christopher Florian
  • National Ecological Observatory Network
Natchaya Pingintha-Durden
  • National Ecological Observatory Network
Ke Xu
  • University of Michigan
Kevin Styers
  • National Ecological Observatory Network