DOI: 10.5281/zenodo.3241466
title: Geostatistical inverse modeling with large atmospheric data: data files for a case study from OCO-2

description: The files in this data repository provide the inputs required to run an inverse modeling case study. This case study will estimate CO2 fluxes across North America for July 2015 using synthetic observations that have been created to resemble observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite.

This data repository is specifically linked to a GitHub code repository (http://doi.org/10.5281/zenodo.3241524 or https://github.com/greenhousegaslab/geostatistical_inverse_modeling). That GitHub repository provides scripts for constructing a geostatistical inverse model that will estimate greenhouse gas fluxes or air pollution emissions using atmospheric observations. The GitHub repository includes a case study that can be run out-of-the-box; the case study provides users an opportunity to test out and explore the inverse modeling code. All of the input data files for that case study are provided for download here.

Here is a brief explanation of the different files included in this data repository, but refer to the linked GitHub repository for greater details. All of these files are in a ".mat" file that can be read into Matlab using the load function or can be read into R using the R.matlab package.



H.tar.gz: This tar file contains the H matrices or sensitivity matrices required by the inverse model. These inputs were generated using the Stochastic Time-Inverted Lagrangian Transport (STILT) model as part of NOAA's CarbonTracker-Lagrange program (https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/). The H matrix is too large to store in a single file. We have therefore split up the matrix into 328 different files (all contained within H.tar.gz). Each file contains a vertical strip of the H matrix that corresponds to a different time period of fluxes to be estimated as part of the inverse model.

Z.mat: This file contains the synthetic OCO-2 observations used in the case study. 

areas_us.mat: This file lists the area of each model grid box used in the case study in units of meters2. This file only includes grid box area for model grid boxes that fall within the continental United States. We estimate CO2 fluxes across terrestrial North America on a 1 degree latitude by 1 degree longitude grid as part of the case study. Each of these model grid boxes will have a different area, depending upon the latitude of that model grid box. 

distmat.mat: This file contains a matrix that lists the distance (in kilometers) between the center of each model grid box used in the case study. 

land_mask.mat: We only estimate CO2 fluxes for terrestrial regions of North America as part of the case study. This land mask is used to convert the fluxes estimated by the inverse model to a latitude-longitude grid that can then be plotted.

H_all_OCO2.mat: This file contains the H matrices summed across differnt time periods. I.e., this file is the sum of all the individual H files contained within H.tar.gz.

Xvar.tar.gz: This file contains different environmental variables from ERA5 meteorology that have been reformatted to match the H footprint matrices. These different variables can be used as predictors of CO2 fluxes in an inverse model. The different variables included in this file are as follows:



Xvar_e.mat                     Evaporation

Xvar_msdwswrf.mat      Mean surface downward short-wave radiation flux

Xvar_q.mat                     Specific humidity

Xvar_stl1.mat                 Soil temperature level 1

Xvar_stl3.mat                Soil temperature level 3

Xvar_swvl1.mat             Volumetric soil water layer 1

Xvar_t2m.mat               2 metre temperature

Xvar_tp.mat                  Total precipitation

Xvar_mer.mat               Mean evaporation rate

Xvar_pev.mat               Potential evaporation

Xvar_r.mat                   Relative humidity

Xvar_swvl3.mat          Volumetric soil water layer 1

Xvar_tcc.mat              Total cloud cover
description: This work is funded by NASA ROSES grant no. 80NSSC18K0976 and partially supported by NSF grant no. DMS-1720398.