Ceilometers are devices for measuring and recording the height of clouds using laser based LiDAR technologies. They also can measure the height of planetary boundary layer (PBL), which is the lowest layer in the atmosphere directly influenced by the Earth’s surface. This dataset consists of retrievals from an automated planetary boundary layer (PBL) algorithm. This algorithm is proposed as a common cross-platform method for use with commercially available ceilometers. For additional assistance in access and interpreting the data please contact the listed authors. This dataset is associated with the following publication: Szykman, J., D. Williams, V. Caicedo, R. Delgado, T. Knepp , K. Cavender, and B. Lefer. An automated common algorithm for planetary boundary layer retrievals using aerosol lidars in support of the U.S. EPA Photochemical Monitoring Assessment Program. Journal of Atmospheric and Oceanic Technology. American Meteorological Society, Boston, MA, USA, 1-51, (2020).

Human activities such as agricultural fertilization and fossil fuel combustion have introduced a massive amount of anthropogenic nitrogen (N) in reactive forms to the environment. As agricultural fertilization is the single largest anthropogenic N source, an integrated approach to understand the interactions among agriculture, atmosphere, and hydrology is essential in examining human-altered N cycling. We have developed an integrated modeling system with agriculture EPIC, atmosphere WRF/CMAQ, and hydrology SWAT. This integrated system is useful tool for scientists and policy-makers to answer many questions on cycling of water, carbon, and nutrients for sustaining the food production while protecting the environment. This dataset is associated with the following publication: Ran, L., Y. Yuan, E. Cooter, V. Benson, J. Pleim, R. Wang, and J. Williams. An Integrated Agriculture, Atmosphere, and Hydrology Modeling System for Ecosystem Assessments. Journal of Advances in Modeling Earth Systems. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(12): 4645-4668, (2019).

This work is the first of a two‐part study that aims to develop a computationally efficient bias correction framework to improve surface PM2.5 forecasts in the United States. Here, an ensemble‐based Kalman filter (KF) technique is developed primarily for nonrural areas with approximately 500 surface observation sites for PM2.5 and applied to three (GEOS‐Chem, WRF‐Chem, and WRF‐CMAQ) chemical transport model (CTM) hindcast outputs for June 2012. While all CTMs underestimate daily surface PM2.5 mass concentration by 20–50%, KF correction is effective for improving each CTM forecast. Subsequently, two ensemble methods are formulated: (1) the arithmetic mean ensemble (AME) that equally weights each model and (2) the optimized ensemble (OPE) that calculates the individual model weights by minimizing the least‐square errors. While the OPE shows superior performance than the AME, the combination of either the AME or the OPE with a KF performs better than the OPE alone, indicating the effectiveness of the KF technique. Overall, the combination of a KF with the OPE shows the best results. Lastly, the Successive Correction Method (SCM) was applied to spread the bias correction from model grids with surface PM2.5 observations to the grids lacking ground observations by using a radius of influence of 125 km derived from surface observations, which further improves the forecast of surface PM2.5 at the national scale. Our findings provide the foundation for the second part of this study that uses satellite‐based aerosol optical depth (AOD) products to further improve the forecast of surface PM2.5 in rural areas by performing statistical analysis of model output. This dataset is associated with the following publication: Spero, T., B. Murphy, H. Huanxin Zhang1,2, Jun Wang1,2, Lorena Castro García1,2, Cui Ge, J. Wang, L. Castro García, C. Ge, and T. Plessel. Improving surface PM2.5 forecasts in the U.S. using an ensemble of chemical transport model outputs, part I: bias correction with surface observations in non-rural areas. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 125(14): e2019JD032293, (2020).

This is a NetCDF file in ioapi format that contains the probability that ozone is above the 8 hr max O3 standard for the four days of the simulation. This dataset is not publicly accessible because: The file size is a large binary NetCDF file of 56Mb. It can be accessed through the following means: File is located on US EPA's HPC system sol file archive: /asm/grc/JGR_ENSEMBLE_ScienceHub/Figure9.nc. Format: NetCDF file that contains the O3 gridded data to reproduce Figure 9. 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).

Fortran/NCARgraphics program to compute and plot RRF mean and variability:map_rrf_variability_13runs_epimax.f Ioapi files needed by Fortran/NCARGraphics code: CMAQ.CONC.SREF.June2011.New.13runs.o3_8hrdm CMAQ.CONC.SREF.June2011.N50V25.New.13runs.o3_8hrdm GRIDCRO2D_060607 Plotting routines map_rrf_mean_sigma_ne_13runs_epimax.ps map_rrf_mean_sigma_ne_13runs_epimax.ncgm. 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 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).