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Papale, D.

Publications and source records attributed to Papale, D..

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Effects of the Gill-Solent WindMaster-Pro “w-boost” firmware bug on eddy covariance fluxes and some simple recovery strategies

In late 2015 and early 2016, work done by the AmeriFlux Management Project Technical Team (amerilfux.lbl.gov) helped to uncover an issue with Gill WindMaster and WindMaster Pro sonic anemometers used by many researchers for eddy covariance flux measurements. Gill has addressed this issue and has since sent out a notice that the vertical wind speed component (a critical piece of all eddy covariance fluxes) was being erroneously computed and reported. The problem (known as the “w-boost” bug) ended in positive (upward) wind speeds being under-reported by 16.6% and negative (downward) wind speeds being under-reported by 28.9%. This has the possibility to cause similar under estimates in fluxes derived from measurements using these instruments. Additionally, the bug affects corrections for angle of attack as derived by Nakai and Shimoyama, rendering them invalid. Although the manufacturer has offered a firmware upgrade for existing instruments that will fix this issue, many existing data sets have been affected by it and are currently in use by the scientific community. To address the issue of affected data, currently in use, we analyzed multi-year and short-term data sets from a variety of ecosystems to assess methods of correcting existing flux data. We found that simple multiplicative correction factors (~1.18) may be used to remove most of the “w-boost” bias from fluxes in existing data sets that do not include angle of attack corrections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Filling the gaps in meteorological continuous data measured at FLUXNET sites with ERA-Interim reanalysis

In this study, exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 registered sites, and up to 250 of them share data (free fair-use data set). Many modelling groups use the FLUXNET data set for evaluating ecosystem models' performance, but this requires uninterrupted time series for the meteorological variables used as input. Because original in situ data often contain gaps, from very short (few hours) up to relatively long (some months) ones, we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-Interim) and a high temporal resolution spanning from 1989 to today. These data are, however, not measured at site level, and for this reason a method to downscale and correct the ERA-Interim data is needed. We apply this method to the level 4 data (L4) from the La Thuile collection, freely available after registration under a fair-use policy. The performance of the developed method varies across sites and is also function of the meteorological variable. On average over all sites, applying the bias correction method to the ERA-Interim data reduced the mismatch with the in situ data by 10 to 36 %, depending on the meteorological variable considered. In comparison to the internal variability of the in situ data, the root mean square error (RMSE) between the in situ data and the unbiased ERA-I (ERA-Interim) data remains relatively large (on average over all sites, from 27 to 76 % of the standard deviation of in situ data, depending on the meteorological variable considered). The performance of the method remains poor for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations.

58 GEOSCIENCES↗
DOE OSTI2012

Database Maintenance, Data Sharing Policy, Collaboration

Scientific questions of today are now more global than ever before. The answers to these questions are buried within multiple disciplines and across a diverse range of scientists and institutions. The expanse and complexity of data required by researchers often exceed the means of a single scientist. Data sharing in the form of its distributed collection and analysis is increasingly common. Collective research now takes place in what may be called 'collaboratories' or in 'centers without walls' (Clery 2006). Creating effective artifacts, which enable scientists to collaborate on data analyses, continues to be a significant challenge for today's science activities. It is rare that providing a file system abstraction on distributed data enables acceleration of scientific discoveries. By explicitly identifying and addressing the different requirements for data contributors, data curators, and data consumers, we can create a data management architecture which enables the creation of datasets that evolve over time with growing and changing data, data annotations, participants, and use rules. This involves also a crucial contribution by the teams and people collecting the data, that in addition to carefully acquire and process the measurements and to be ready to share their measurements within the scientific community, need to follow general rules that help to make their data well documented and safely stored and to maximize visibility to their works and sites. In this chapter, we provide examples of the types of functions and capabilities typically provided within the data management systems, focusing in particular on databases structures and characteristics, data practices, and data user services. Finally, the importance and advantages of collective efforts like data sharing for synthesis activities and the relative data policy options are discussed and analyzed.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

The role of trace gas flux networks in biogeosciences

Vast networks of meteorological sensors ring the globe, providing continuous measurements of an array of atmospheric state variables such as temperature, humidity, rainfall, and the concentration of carbon dioxide [New etal., 1999; Tans etal., 1996]. These measurements provide input to weather and climate models and are key to detecting trends in climate, greenhouse gases, and air pollution. Yet to understand how and why these atmospheric state variables vary in time and space, biogeoscientists need to know where, when, and at what rates important gases are flowing between the land and the atmosphere. Tracking trace gas fluxes provides information on plant or microbial metabolism and climate-ecosystem interactions. The existence of trace gas flux networks is a relatively new phenomenon, dating back to research in 1984. The first gas flux measurement networks were regional in scope and were designed to track pollutant gases such as sulfur dioxide, ozone, nitric acid, and nitrogen dioxide. Atmospheric observations and model simulations were used to infer the depositional rates of these hazardous chemicals [Fowler etal., 2009; Meyers etal., 1991]. In the late 1990s, two additional trace gas flux measurement networks emerged. One, the United States Trace Gas Network (TRAGNET), was a short-lived effort that measured trace gas emissions from the soil and plants with chambers distributed throughout the country [Ojima etal., 2000]. The other, FLUXNET, was an international endeavor that brought many regional networks together to measure the fluxes of carbon dioxide, water vapor, and sensible heat exchange with the eddy covariance technique [Baldocchi etal., 2001]. FLUXNET, which remains active today, currently includes more than 400 tower sites, dispersed across most of the world's climatic zones and biomes, with sites in North and South America, Europe, Asia, Africa, and Australia. More recently, several specialized networks have emerged, including networks dedicated to urban areas (Urban Fluxnet), nitrogen compounds in Europe (NitroEurope), and methane (MethaneNet). Technical Aspects of Flux Networks Eddy covariance flux measurements are the preferred method by which biogeoscientists measure trace gas exchange between ecosystems and the atmosphere [Baldocchi, 2003].

54 ENVIRONMENTAL SCIENCES↗