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Greenhouse Gas Observing Network Data Set
Download: Data Folder, Data Set Description

Abstract: Design an observing network to monitor emissions of a greenhouse gas (GHG) in California given time series of synthetic observations and tracers from weather model simulations.

Data Set Characteristics:  

Multivariate, Time-Series

Number of Instances:

2921

Area:

Physical

Attribute Characteristics:

Real

Number of Attributes:

5232

Date Donated

2015-04-16

Associated Tasks:

Regression

Missing Values?

N/A

Number of Web Hits:

19810


Source:

D. Lucas (ddlucas .at. alum.mit.edu), Lawrence Livermore National Laboratory.

This data was created under work funded by the National Institute of Standards and Technology and Laboratory Directed Research and Development projects at the Lawrence Livermore National Laboratory. The work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, and is released under UCRL number LLNL-MISC-668913.


Data Set Information:

This data set contains time series of greenhouse gas (GHG) concentrations at 2921 grid cells in California created using simulations of the Weather Research and Forecast model with Chemistry (WRF-Chem). Each grid cell covers an area of 12 km by 12 km, and there is one data file per grid cell. Each file contains 16 time series of GHG concentrations. The data points in the time series are spaced 6 hours apart (4 samples per day) over the period May 10 – July 31, 2010. The first 15 rows are time series of GHG tracers released from 14 distinct spatial regions in California and one outside of California. The last row corresponds to the time series of “synthetic GHG observations” generated with EDGAR emissions of HFC-134a scaled by a factor 0.7 and with noise added.

Using this data, the goals are to (1) use inverse methods to determine the optimal values of the weights in the weighted sum of 15 tracers that best matches the synthetic observations, (2) and use optimization methods to determine the best locations to observe GHGs to constrain the inversion. We used a Bayesian method for (1) and genetic algorithms for (2).

Further details about the data and methods are given in the publication 'Designing optimal greenhouse gas observing networks that consider performance and cost,' Geoscientific Instrumentation Methods and Data Systems.


Attribute Information:

Each file in the data set is labeled ghg.gid.siteWXYZ.dat, where WXYZ is an integer location ID described in our manuscript.

At each location,
Rows 1-15: GHG concentrations of tracers emitted from regions 1-15
Row 16: GHG concentrations of synthetic observations
Columns 1-327: GHG concentrations every 6 hours from May 10 – July 31, 2010.

All GHG concentrations are in units of parts per trillion.


Relevant Papers:

Lucas, D. D., Yver Kwok, C., Cameron-Smith, P., Graven, H., Bergmann, D., Guilderson, T. P Weiss, R., and Keeling, R.: 'Designing optimal greenhouse gas observing networks that consider performance and cost,' Geoscientific Instrumentation Methods and Data Systems, (2015).

[[Web Link]]



Citation Request:

Please cite our final paper in Geoscientific Instrumentation Methods and Data Systems.


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