66 research outputs found
Basic and extensible post-processing of eddy covariance flux data with REddyProc
With the eddy covariance (EC) technique, net fluxes of carbon dioxide
(CO2) and other trace gases as well as water and energy fluxes can be
measured at the ecosystem level. These flux measurements are a main source
for understanding biosphere–atmosphere interactions and feedbacks through
cross-site analysis, model–data integration, and upscaling. The raw fluxes
measured with the EC technique require extensive and laborious data
processing. While there are standard
tools1 available in an open-source environment for
processing high-frequency (10 or 20 Hz) data into half-hourly
quality-checked fluxes, there is a need for more usable and extensible tools
for the subsequent post-processing steps. We tackled this need by developing
the REddyProc package in the cross-platform language R that provides
standard CO2-focused post-processing routines for reading
(half-)hourly data from different formats, estimating the u*
threshold, as well as gap-filling, flux-partitioning, and visualizing the
results. In addition to basic processing, the functions are extensible
and allow easier integration in extended analysis than current tools. New
features include cross-year processing and a better treatment of
uncertainties. A comparison of REddyProc routines with other
state-of-the-art tools resulted in no significant differences in monthly and
annual fluxes across sites. Lower uncertainty estimates of both u* and
resulting gap-filled fluxes by 50 % with the presented tool were achieved
by an improved treatment of seasons during the bootstrap analysis. Higher
estimates of uncertainty in daytime partitioning (about twice as high)
resulted from a better accounting for the uncertainty in estimates of
temperature sensitivity of respiration. The provided routines can be easily
installed, configured, and used. Hence, the eddy covariance community will
benefit from the REddyProc package, allowing easier integration of
standard post-processing with extended analysis.
1http://fluxnet.fluxdata.org/2017/10/10/toolbox-a-rolling-list-of-softwarepackages-for-flux-related-data-processing/,
last access: 17 August 2018</p
Long-term evolution and coupling of the boundary layers in the Stratus Deck Regions of the eastern Pacific (STRATUS)
A surface mooring was deployed in the eastern tropical Pacific west of northern Chile from the R/V Melville as part of the Eastern Pacific Investigation of Climate (EPIC). EPIC is a CLIVAR study with the goal of investigating links between sea surface temperature variability in the eastern tropical Pacific and climate over the American continents. Important to that goal is an understanding of the role of clouds in the eastern Pacific in modulating atmosphere-ocean coupling. The mooring was deployed near 20°S 85°W, at a location near the western edge of the stratocumulus cloud deck found west of Peru and Chile. This deployment started a three-year occupation of that site by a WHOI surface mooring in order to collect accurate time series of surface forcing and upper ocean variability. The surface mooring was deployed by the Upper Ocean Processes Group of the Woods Hole Oceanographic Institution (WHOI). In collaboration with investigators from the University of Concepcion, Concepcion, Chile, an XBT section was made on the way out to the mooring from Arica, Chile, and an XBT and CTD section was made on the way into Arica.
The buoy was equipped with meteorological instrumentation, including two Improved METeorological (IMET) systems. The mooring also carried Vector Measuring Current Meters, single-temperature recorders, and conductivity and temperature recorders located in the upper meters of the mooring line. In addition to the instrumentation noted above, a variety of other instruments, including an acoustic current meter, an acoustic doppler current profiler, a bio-optical instrument package, and an acoustic rain guage, were deployed.
This report describes, in a general manner, the work that took place and the data collected during the Cook 2 cruise aboard the R/V Melville. The surface mooring deployed during this cruise will be recovered and re-deployed after approximately 12 months and again after 24 months, with a final recovery planned for 36 months after the first setting. Details of the mooring design and preliminary data from the XBT and CTD sections are included.Funding was provided by the National Oceanic and Atmospheric Administration
under grant number NA96GP0429
Viral Load, Clinical Disease Severity and Cellular Immune Responses in Primary Varicella Zoster Virus Infection in Sri Lanka
BACKGROUND: In Sri Lanka, varicella zoster virus (VZV) is typically acquired during adulthood with significant associated disease morbidity and mortality. T cells are believed to be important in the control of VZV replication and in the prevention of reactivation. The relationship between viral load, disease severity and cellular immune responses in primary VZV infection has not been well studied. METHODOLOGY: We used IFNgamma ELISpot assays and MHC class II tetramers based on VZV gE and IE63 epitopes, together with quantitative real time PCR assays to compare the frequency and phenotype of specific T cells with virological and clinical outcomes in 34 adult Sri Lankan individuals with primary VZV infection. PRINCIPAL FINDINGS: Viral loads were found to be significantly higher in patients with moderate to severe infection compared to those with mild infection (p<0.001) and were significantly higher in those over 25 years of age (P<0.01). A significant inverse correlation was seen between the viral loads and the ex vivo IFNgamma ELISpot responses of patients (P<0.001, r = -0.85). VZV-specific CD4+ T cells expressed markers of intermediate differentiation and activation. CONCLUSIONS: Overall, these data show that increased clinical severity in Sri Lankan adults with primary VZV infection associates with higher viral load and reduced viral specific T cell responses
Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)
lincRNAs act in the circuitry controlling pluripotency and differentiation
Although thousands of large intergenic non-coding RNAs (lincRNAs) have been identified in mammals, few have been functionally characterized, leading to debate about their biological role. To address this, we performed loss-of-function studies on most lincRNAs expressed in mouse embryonic stem (ES) cells and characterized the effects on gene expression. Here we show that knockdown of lincRNAs has major consequences on gene expression patterns, comparable to knockdown of well-known ES cell regulators. Notably, lincRNAs primarily affect gene expression in trans. Knockdown of dozens of lincRNAs causes either exit from the pluripotent state or upregulation of lineage commitment programs. We integrate lincRNAs into the molecular circuitry of ES cells and show that lincRNA genes are regulated by key transcription factors and that lincRNA transcripts bind to multiple chromatin regulatory proteins to affect shared gene expression programs. Together, the results demonstrate that lincRNAs have key roles in the circuitry controlling ES cell state.Broad InstituteHarvard UniversityNational Human Genome Research Institute (U.S.)Merkin Family Foundation for Stem Cell Researc
Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe
Impact of cross-section uncertainties on supernova neutrino spectral parameter fitting in the Deep Underground Neutrino Experiment
A primary goal of the upcoming Deep Underground Neutrino Experiment (DUNE) is
to measure the MeV neutrinos produced by a Galactic
core-collapse supernova if one should occur during the lifetime of the
experiment. The liquid-argon-based detectors planned for DUNE are expected to
be uniquely sensitive to the component of the supernova flux, enabling
a wide variety of physics and astrophysics measurements. A key requirement for
a correct interpretation of these measurements is a good understanding of the
energy-dependent total cross section for charged-current
absorption on argon. In the context of a simulated extraction of
supernova spectral parameters from a toy analysis, we investigate the
impact of modeling uncertainties on DUNE's supernova neutrino
physics sensitivity for the first time. We find that the currently large
theoretical uncertainties on must be substantially reduced
before the flux parameters can be extracted reliably: in the absence of
external constraints, a measurement of the integrated neutrino luminosity with
less than 10\% bias with DUNE requires to be known to about 5%.
The neutrino spectral shape parameters can be known to better than 10% for a
20% uncertainty on the cross-section scale, although they will be sensitive to
uncertainties on the shape of . A direct measurement of
low-energy -argon scattering would be invaluable for improving the
theoretical precision to the needed level.Comment: 25 pages, 21 figure
Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions
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