L1_cation.txt is a text dump of CEC data from EPIC output for the 12 km CMAQ grid for each of 42 crop types CMAQv53_bidi_fix_NH3_290871_scatterplot.csv is data table used for scatter plot of observed vs AMoN NH3 concentrations shown is fig 5 in the paper CMAQv53_bidi_fix_PM25_NH4_494657_spatialplot_diff.csv is the data table used to produce the spatial plot of the biases in modeled ammonium PM2.5 (g m-3) compared to CSN (circles), CASTNet (triangles), and SEARCH (squares) networks averaged over May to September 2016 as shown in Fig6. CMAQv53_bidi_fix_PM25_SO4_886932_spatialplot_diff.csv is the data table used to produce the spatial plot of the biases in modeled sulfate PM2.5 (g m-3) compared to IMPROVE (circles), CSN (triangles), CASTNET (squares), and SEARCH (diamonds) networks averaged over May to September 2016. This dataset is associated with the following publication: Pleim, J., L. Ran, K. Appel, M. Shephard, and K. Cady-Pereira. New Bidirectional Ammonia Flux Model in an Air Quality Model Coupled With an Agricultural Model. Journal of Advances in Modeling Earth Systems. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(9): 2934-2957, (2019).

The TROPESS CrIS-SNPP L2 Ammonia for Reanalysis Stream, Summary Product contains the vertical distribution of the retrieved atmospheric state of ammonia (NH3), and formal uncertainties measured by the CrIS instruments on the Suomi-NPP satellite. The reanalysis stream summary product is global for the time period from 2015-12-01 to 2023-05-18. The NASA TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) project, uses an optimal estimation algorithm, known as the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES).The data files are written in the netCDF version 4 file format, and each file contains one day of data. The data have a spatial resolution of 14 km (CrIS nadir FOV), and are reported at 15 vertical levels from the surface to 0.1 hPa. The principal investigator for the TROPESS project is Kevin W. Bowman.

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).