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.

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