Sampling Error in Aircraft Flux Measurements Based on a High-Resolution Large Eddy Simulation of the Marine Boundary Layer

A high-resolution (1.25 m) LES simulation of the nocturnal cloud-topped marine boundary layer is used to evaluate random error as a function of continuous track length L for virtual aircraft measurements of turbulent fluxes of sensible heat, latent heat, and horizontal momentum. Results are compared with the theoretically derived formula of Lenschow and Stankov (1986). In support of these comparisons, we also evaluate and document the relevant integral length scales and correlations and show that for heights up to approximately 100 m (z / zi = 0.12), the length scales are accurately predicted by empirical expressions of the form If = Azb. The Lenschow and Stankov expression is found to be remarkably accurate at predicting the random error for shorter flight tracks, but our empirically determined errors decay more rapidly with L than the L−1/2 relationship predicted from theory. Consistent with earlier findings, required track lengths to obtain useful precision increase sharply with altitude.

eddy4R: A community-extensible processing, analysis and modeling framework for eddy-covariance data based on R, Git, Docker and HDF5

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.

An objective prior error quantification for regional atmospheric inverse applications

Assigning proper prior uncertainties for inverse modeling of CO2 is of high importance, both to regularize the otherwise ill-constrained inverse problem, and to quantitatively characterize the magnitude and structure of the error between prior and "true" flux. We use surface fluxes derived from three biosphere models VPRM, ORCHIDEE, and 5PM, and compare them against daily averaged fluxes from 53 Eddy Covariance sites across Europe for the year 2007, and against repeated aircraft flux measurements encompassing spatial transects. In addition we create synthetic observations to substitute observed by modeled fluxes to explore the potential to infer prior uncertainties from model-model residuals. To ensure the realism of the synthetic data analysis, a random measurement noise was added to the tower fluxes which were used as reference. The temporal autocorrelation time for tower model-data residuals was found to be around 35 days for both VPRM and ORCHIDEE, but significantly different for the 5PM model with 76 days. This difference is caused by a few sites with large model-data bias. The spatial correlation of the model-data residuals for all models was found to be very short, up to few tens of km. Long spatial correlation lengths up to several hundreds of km were determined when synthetic data were used. Results from repeated aircraft transects in south-western France, are consistent with those obtained from the tower sites in terms of spatial autocorrelation (35 km on average) while temporal autocorrelation is markedly lower (13 days). Our findings suggest that the different prior models have a common temporal error structure. Separating the analysis of the statistics for the model data residuals by seasons did not result in any significant differences of the spatial correlation lengths.

Tower and Aircraft Eddy Covariance Measurements of Water Vapor, Energy, and Carbon Dioxide Fluxes during SMACEX

A network of eddy covariance (EC) and micrometeorological flux (METFLUX) stations over corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] canopies was established as part of the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) in central Iowa during the summer of 2002 to measure fluxes of heat, water vapor, and carbon dioxide (CO2) during the growing season. Additionally, EC measurements of water vapor and CO2 fluxes from an aircraft platform complemented the tower-based measurements. Sensible heat, water vapor, and CO2 fluxes showed the greatest spatial and temporal variability during the early crop growth stage. Differences in all of the energy balance components were detectable between corn and soybean as well as within similar crops throughout the study period. Tower network–averaged fluxes of sensible heat, water vapor, and CO2 were observed to be in good agreement with area-averaged aircraft flux measurements.