Comparative Analysis of ERA5 Model Levels and Pressure Levels Over the Continental U.S.

Reanalyses data is recurrently used as a surrogate for observed atmospheric data in numerous scientific applications, representing robust historical environmental profiles derived through data assimilation. Nevertheless, its performance and suitability for different atmospheric phenomena have been seldom assessed. Among all reanalyses, the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) stands out as a favored choice for downscaling, case studies, and convective analysis, in part due to its high temporal and spatial resolution. ERA5 is used frequently in two vertical level versions: model level (ML) and pressure level (PL) data. The ML consists of 137 hybrid sigma-pressure vertical levels, 20 of which are on the lowest kilometer, while the PL consists of 37 pressure levels ranging from 1000 hPa (near surface) to 1 hPa (about 80 km). Additionally, the accessibility to PL data drives its preference among users rather than higher-resolution ML products. This is particularly relevant in applications for dynamic downscaling, case applications, or studies using convective parameters. Comparing the ML and PL data allows us to assess these two versions’ relative performance and biases, providing essential insights into the accuracy and applicability of each ERA5 version.

To evaluate the performance of both ERA5 versions, relative errors in output parameters are assessed as compared to the Integrated Global Radiosonde Archive version 2 (IGRA2). For this analysis, the dataset consists of historical atmospheric profile observations from over 200 distributed stations throughout the continental U.S. To ensure a representative comparison, exhaustive quality control is undertaken to address data issues within the IGRA2 dataset. The relative errors are evaluated from a multi-approach perspective, exploring directly observed quantities (temperatures, dewpoint temperature, winds), commonly used atmospheric stability indices (convective available potential energy; CAPE, and convective inhibition energy; CIN), and kinematic parameters. Furthermore, performance for stable and unstable environments is compared to evaluate the relative performance under convective scenarios. Finally, to identify regime-specific biases, we use Self Organizing Maps (SOM), as a clustering technique, to identify the different groups of locations with similar error distributions.

This research enhances the understanding of ERA5's utility and the implications of selecting specific vertical-level versions for future studies. Additionally, it underscores the importance of considering error distributions, topography, and regional variations in utilizing reanalysis data for scientific applications, particularly those related to severe weather conditions. Preliminary results illustrate that the ML version provides a superior option in certain scenarios, particularly close to topography changes or where large vertical or horizontal gradients are found. Such nuances can significantly impact the precision in calculating atmospheric indexes like CAPE or CIN and the position of storms simulated through downscaling.