scholarly journals Supplementary material to "A robust data cleaning procedure for eddy covariance flux measurements"

2019 ◽  
Author(s):  
Domenico Vitale ◽  
Gerardo Fratini ◽  
Massimo Bilancia ◽  
Giacomo Nicolini ◽  
Simone Sabbatini ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Data Cleaning ◽  
Cleaning Procedure ◽  
Flux Measurements ◽  
Supplementary Material

scholarly journals A robust data cleaning procedure for eddy covariance flux measurements

2020 ◽  
Vol 17 (6) ◽  
pp. 1367-1391
Author(s):  
Domenico Vitale ◽  
Gerardo Fratini ◽  
Massimo Bilancia ◽  
Giacomo Nicolini ◽  
Simone Sabbatini ◽  
...  
Keyword(s):  
Systematic Error ◽  
Eddy Covariance ◽  
Outlier Detection ◽  
Stochastic Dynamics ◽  
Data Cleaning ◽  
Observation System ◽  
Correct Identification ◽  
Cleaning Procedure ◽  
Flux Data ◽  
The Impact

Abstract. The sources of systematic error responsible for introducing significant biases in the eddy covariance (EC) flux computation are manifold, and their correct identification is made difficult by the lack of reference values, by the complex stochastic dynamics, and by the high level of noise characterizing raw data. This work contributes to overcoming such challenges by introducing an innovative strategy for EC data cleaning. The proposed strategy includes a set of tests aimed at detecting the presence of specific sources of systematic error, as well as an outlier detection procedure aimed at identifying aberrant flux values. Results from tests and outlier detection are integrated in such a way as to leave a large degree of flexibility in the choice of tests and of test threshold values, ensuring scalability of the whole process. The selection of best performing tests was carried out by means of Monte Carlo experiments, whereas the impact on real data was evaluated on data distributed by the Integrated Carbon Observation System (ICOS) research infrastructure. Results evidenced that the proposed procedure leads to an effective cleaning of EC flux data, avoiding the use of subjective criteria in the decision rule that specifies whether to retain or reject flux data of dubious quality. We expect that the proposed data cleaning procedure can serve as a basis towards a unified quality control strategy for EC datasets, in particular in centralized data processing pipelines where the use of robust and automated routines ensuring results reproducibility constitutes an essential prerequisite.

scholarly journals A robust data cleaning procedure for eddy covariance flux measurements

2019 ◽  
Author(s):  
Domenico Vitale ◽  
Gerardo Fratini ◽  
Massimo Bilancia ◽  
Giacomo Nicolini ◽  
Simone Sabbatini ◽  
...  
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Eddy Covariance ◽  
Outlier Detection ◽  
Data Cleaning ◽  
False Negative ◽  
Error Rates ◽  
Quality Data ◽  
Cleaning Procedure ◽  
Flux Data

Abstract. Integration of long-term eddy covariance (EC) flux datasets over regional and global scales requires high degree of comparability of flux data measured at different stations, which entails not only similar-performing instrumentation and their appropriate deployment, but also standardized and reproducible data processing and quality control (QC) procedures. This work focuses on the latter topic and, in particular, on the development of a robust data cleaning procedure. The proposed strategy includes a set of tests aimed at detecting the presence of specific sources of systematic error in the data, as well as an outlier detection procedure aimed at identifying aberrant flux values. Results from tests and outlier detection are integrated in such a way as to leave a large degree of flexibility in the choice of tests and of test threshold values without losing in efficacy and, at the same time, to avoid the use of subjective criteria in the decision rule that specifies whether to retain or reject flux data of dubious quality. Tests development was rooted on advanced time series analysis techniques that consider the stochastic properties of both raw, high-frequency EC data and of flux time series, such as complex dynamics, high persistence and possible presence of stochastic trends. The performance of each proposed test is evaluated by means of Monte Carlo simulations on synthetic datasets, whereas their impact on observed times series was evaluated on a selection of EC datasets distributed by the ICOS research infrastructure. Simulation results evidenced that the proposed tests have a better performance compared to alternative existing QC routines, showing lower false positive and false negative error rates. The application of the proposed tests on real datasets led to an effective cleaning of EC flux data retaining the maximum number of good quality data. Although there is still room for improvement, in particular with the development of new QC tests, we think that the proposed data cleaning procedure can serve as a basis towards a unified QC strategy for EC datasets which i) includes only completely data-driven routines and is therefore suitable for automatic and centralized data processing pipelines, ii) guarantees results reproducibility and iii) is flexible and scalable to accommodate new and additional tests that makes the approach also suitable for other greenhouse gases.

scholarly journals Eddy covariance flux measurements confirm extreme CH<sub>4</sub> emissions from a Swiss hydropower reservoir and resolve their short-term variability

2011 ◽  
Vol 8 (9) ◽  
pp. 2815-2831 ◽  
Author(s):  
W. Eugster ◽  
T. DelSontro ◽  
S. Sobek
Keyword(s):  
Greenhouse Gas ◽  
Eddy Covariance ◽  
Land Surface ◽  
Lake Level ◽  
Driving Forces ◽  
Open Water ◽  
Short Term ◽  
Flux Measurements ◽  
Floating Chamber ◽  
Hydropower Reservoir

Abstract. Greenhouse gas budgets quantified via land-surface eddy covariance (EC) flux sites differ significantly from those obtained via inverse modeling. A possible reason for the discrepancy between methods may be our gap in quantitative knowledge of methane (CH4) fluxes. In this study we carried out EC flux measurements during two intensive campaigns in summer 2008 to quantify methane flux from a hydropower reservoir and link its temporal variability to environmental driving forces: water temperature and pressure changes (atmospheric and due to changes in lake level). Methane fluxes were extremely high and highly variable, but consistently showed gas efflux from the lake when the wind was approaching the EC sensors across the open water, as confirmed by floating chamber flux measurements. The average flux was 3.8 ± 0.4 μg C m−2 s−1 (mean ± SE) with a median of 1.4 μg C m−2 s−1, which is quite high even compared to tropical reservoirs. Floating chamber fluxes from four selected days confirmed such high fluxes with 7.4 ± 1.3 μg C m−2 s−1. Fluxes increased exponentially with increasing temperatures, but were decreasing exponentially with increasing atmospheric and/or lake level pressure. A multiple regression using lake surface temperatures (0.1 m depth), temperature at depth (10 m deep in front of the dam), atmospheric pressure, and lake level was able to explain 35.4% of the overall variance. This best fit included each variable averaged over a 9-h moving window, plus the respective short-term residuals thereof. We estimate that an annual average of 3% of the particulate organic matter (POM) input via the river is sufficient to sustain these large CH4 fluxes. To compensate the global warming potential associated with the CH4 effluxes from this hydropower reservoir a 1.3 to 3.7 times larger terrestrial area with net carbon dioxide uptake is needed if a European-scale compilation of grasslands, croplands and forests is taken as reference. This indicates the potential relevance of temperate reservoirs and lakes in local and regional greenhouse gas budgets.

Eddy covariance flux measurements of momentum, heat and carbon dioxide in Hudson Bay at the onset of sea ice melt 

2021 ◽  
Author(s):  
Richard Sims ◽  
Brian Butterworth ◽  
Tim Papakyriakou ◽  
Mohamed Ahmed ◽  
Brent Else
Keyword(s):  
Carbon Dioxide ◽  
Gas Exchange ◽  
Sea Ice ◽  
Eddy Covariance ◽  
Open Water ◽  
The Arctic ◽  
Gas Transfer ◽  
Hudson Bay ◽  
Flux Measurements ◽  
Ice Fraction

&lt;p&gt;Remoteness and tough conditions have made the Arctic Ocean historically difficult to access; until recently this has resulted in an undersampling of trace gas and gas exchange measurements. The seasonal cycle of sea ice completely transforms the air sea interface and the dynamics of gas exchange. To make estimates of gas exchange in the presence of sea ice, sea ice fraction is frequently used to scale open water gas transfer parametrisations. It remains unclear whether this scaling is appropriate for all sea ice regions. Ship based eddy covariance measurements were made in Hudson Bay during the summer of 2018 from the icebreaker CCGS Amundsen. We will present fluxes of carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;), heat and momentum and will show how they change around the Hudson Bay polynya under varying sea ice conditions. We will explore how these fluxes change with wind speed and sea ice fraction. As freshwater stratification was encountered during the cruise, we will compare our measurements with other recent eddy covariance flux measurements made from icebreakers and also will compare our turbulent CO&lt;sub&gt;2&amp;#160;&lt;/sub&gt;fluxes with bulk fluxes calculated using underway and surface bottle pCO&lt;sub&gt;2&lt;/sub&gt;&amp;#160;data.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Options to correct local turbulent flux measurements for large-scale fluxes using a LES-based approach

2021 ◽  
Author(s):  
Matthias Mauder ◽  
Andreas Ibrom ◽  
Luise Wanner ◽  
Frederik De Roo ◽  
Peter Brugger ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Large Scale ◽  
Heat Fluxes ◽  
Eddy Covariance Method ◽  
Boundary Layer Height ◽  
Secondary Circulations ◽  
Low Pass ◽  
Measurement Height ◽  
Flux Measurements ◽  
Dispersive Fluxes

Abstract. The eddy-covariance method provides the most direct estimates for fluxes between ecosystems and the atmosphere. However, dispersive fluxes can occur in the presence of secondary circulations, which can inherently not be captured by such single-tower measurements. In this study, we present options to correct local flux measurements for such large-scale transport based on a non-local parametric model that has been developed from a set of idealized LES runs for three real-world sites. The test sites DK-Sor, DE-Fen, and DE-Gwg, represent typical conditions in the mid-latitudes with different measurement height, different terrain complexity and different landscape-scale heterogeneity. Different ways to determine the boundary-layer height, which is a necessary input variable for modelling the dispersive fluxes, are applied, either from operational radio-soundings and local in-situ measurements for the flat site or from backscatter-intensity profile obtained from collocated ceilometers for the two sites in complex terrain. The adjusted total fluxes are evaluated by assessing the improvement in energy balance closure and by comparing the resulting latent heat fluxes with evapotranspiration rates from nearby lysimeters. The results show that not only the accuracy of the flux estimates is improved but also the precision, which is indicated by RMSE values that are reduced by approximately 50 %. Nevertheless, it needs to be clear that this method is intended to correct for a bias in eddy-covariance measurements due to the presence of large-scale dispersive fluxes. Other reasons potentially causing a systematic under- or overestimation, such as low-pass filtering effects and missing storage terms, still need to be considered and minimized as much as possible. Moreover, additional transport induced by surface heterogeneities is not considered.

Sign in / Sign up

Export Citation Format

Share Document