The Figure.tar.gz contains a directory for each WRF ensemble run. In these directories are *.csv files for each meteorology variable examined. These are comma delimited text files that contain statistics for each observation site. Also provided is an R script that reads these files (user would need to change directory pointers) and computes the variability of error and bias of the ensemble at each site and plots these for reproduction of figure 3. This dataset is not publicly accessible because: 30Mb tar, 15 Mb tar.gz. It can be accessed through the following means: On the EPA HPC system sol archive: /asm/grc/JGR_ENSEMBLE_ScienceHub/figure3.tar. Format: tar.gz file of text files that contain the surface meteorology statistics that were used to created Figure 3. Also included is a R script that will allow anyone interested to re-generate the figure. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

Excel file containing diurnal ensemble statistics of 2-m temperature, 2-m mixing ratio and 10-m wind speed. This Excel file contains figures for Figure 2 in the paper and worksheets containing all statistics for the 14 members of the ensemble and a base simulation. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

Excel file containing diurnal ensemble statistics of 2-m temperature, 2-m mixing ratio and 10-m wind speed. This Excel file contains figures for Figure 2 in the paper and worksheets containing all statistics for the 14 members of the ensemble and a base simulation. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

R script: ensemble_rrf_sigma_vs_mean_play.R Data: ensemble_mean_sigma_rrf_allgrids_epismax_new_13runs.csv Plot: boxplot_ensemble_rrf_sigma_vs_mean_nowater_new_13runs_epimax.pdf. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

R script: ensemble_rrf_sigma_vs_mean_play.R Data: ensemble_mean_sigma_rrf_allgrids_epismax_new_13runs.csv Plot: boxplot_ensemble_rrf_sigma_vs_mean_nowater_new_13runs_epimax.pdf. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

This is an R statistics package script that allows the reproduction of Figure 5. The script includes the links to large NetCDF files that the figures access for O3, CO, wind speed, radiation and PBL height. It pulls the timeseries for each variable at a number of cities (lat-lon specified). This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

NetCDF file of the SREF standard deviation of wind speed and direction that was used to inject variability in the FDDA input. variable U_NDG_OLD contains standard deviation of wind speed (m/s) variable V_NDG_OLD contains the standard deviation of wind direction (deg). This dataset is not publicly accessible because: This is a netcdf file that is 3.9Gb. It can be accessed through the following means: On the HPC system sol (2016). In the asm archive here: /asm/grc/JGR_ENSEMBLE_ScienceHub/figure1.nc. Format: Figure 1 data. This is the variability of wind speed and direction of the four dimensional data assimilation inputs. The variability includes the 14 members of the ensemble. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

Two files provided. The ENS.tar file contains text data files (*.csv) used to create Figure 7 and Figure 8. The Figure7.txt is an R script that reads these files and generates the plots for various cities including the four published in Figure 7 and Figure 8. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

Software Model simulations were conducted using WRF version 3.8.1 (available at https://github.com/NCAR/WRFV3) and CMAQ version 5.2.1 (available at https://github.com/USEPA/CMAQ). The meteorological and concentration fields created using these models are too large to archive on ScienceHub, approximately 1 TB, and are archived on EPA’s high performance computing archival system (ASM) at /asm/MOD3APP/pcc/02.NOAH.v.CLM.v.PX/. Figures Figures 1 – 6 and Figure 8: Created using the NCAR Command Language (NCL) scripts (https://www.ncl.ucar.edu/get_started.shtml). NCLD code can be downloaded from the NCAR website (https://www.ncl.ucar.edu/Download/) at no cost. The data used for these figures are archived on EPA’s ASM system and are available upon request. Figures 7, 8b-c, 8e-f, 8h-i, and 9 were created using the AMET utility developed by U.S. EPA/ORD. AMET can be freely downloaded and used at https://github.com/USEPA/AMET. The modeled data paired in space and time provided in this archive can be used to recreate these figures. The data contained in the compressed zip files are organized in comma delimited files with descriptive headers or space delimited files that match tabular data in the manuscript. The data dictionary provides additional information about the files and their contents. This dataset is associated with the following publication: Campbell, P., J. Bash, and T. Spero. Updates to the Noah Land Surface Model in WRF‐CMAQ to Improve Simulated Meteorology, Air Quality, and Deposition. Journal of Advances in Modeling Earth Systems. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(1): 231-256, (2019).

NetCDF files of PBL height (m), Shortwave Radiation, 10 m wind speed from WRF and Ozone from CMAQ. The data is the standard deviation of these variables for each hour of the 4 day simulation. Figure 4 is only one of the time periods: June 8, 2100 UTC. The NetCDF files have a time stamp (Times) that can be used to find this time in order to reproduce the Figure 4. Also included is a data dictionary that describes the domain and all other attributes of the model simulation. This dataset is not publicly accessible because: The file is 202Mb binary NetCDF file that is too large. It can be accessed through the following means: Archived on the US EPA HPC Sol computer system:/asm/grc/JGR_ENSEMBLE_ScienceHub/Figure4.tar.gz. Format: Tar.gz file that contains NetCDF files required to reproduce Figure 4. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).