Western North American FLEXPART Back Trajectory 2016 Data

WNA-FLEXPART-BackTraj-2016 is the 2016 Western North America Back Trajectory data using the FLEXible PARTicle (FLEXPART) dispersion model. Data collection for this product is complete.

Backward simulations of airmass transport using a Lagrangian Particle Dispersion Model (LPDM) framework can establish source-receptor relationships (SRRs), supporting analysis of source contributions from various geospatial regions and atmospheric layers to downwind observations. In this study, we selected receptor locations to match gridded ozone observations over Western North America (WNA) from ozonesonde, lidar, commercial aircraft sampling, and aircraft campaigns (1994-2021). For each receptor, we used the FLEXible PARTicle (FLEXPART) dispersion model, driven by ERA5 reanalysis data, to achieve 15-day backwards SRR calculations, providing global simulations at high temporal (hourly) and spatial (1° x 1°) resolution, from the surface up to 20 km above ground level. This product retains detailed information for each receptor, including the gridded ozone value product, allowing the user to illustrate and identify source contributions to various subsets of ozone observations in the troposphere above WNA over nearly 3 decades at different vertical layers and temporal scales, such as diurnal, daily, seasonal, intra-annual, and decadal. This model product can also support source contribution analyses for other atmospheric components observed over WNA, if other co-located observations have been made at the spatial and temporal scales defined for some or all of the gridded ozone receptors used here.

These data products were generated with support from NASA’s Atmospheric Composition Campaign Data Analysis and Modeling program and the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center (award SMD-20-28429430). The critical role of in situ measurements collected by many individual research teams, as well as contributions in model development and intermediate analyses, is gratefully acknowledged.

For further information, please contact the authors of the manuscripts describing the datasets. Information is included in the ReadMe files, which are provided in the Related URLs.