June 14, 2022
Research Highlight

Online Diagnostics Make Analyzing Modeled Atmospheric Processes Easier and More Efficient

Newly developed tool helps researchers analyze process-level relationships in atmospheric models

Photograph of a bright blue sky with wispy clouds

A new tool can help researchers understand how much different atmospheric processes contribute to the formation of ice clouds without performing tedious coding or archiving huge amounts of data.

The Science

Numerical models used to predict the weather and climate must represent complex relationships between many atmospheric phenomena (processes). Analyzing these relationships during experiments often requires tedious coding, while performing analysis after experiments can involve archiving huge amounts of data. A new, flexible tool developed as part of the Energy Exascale Earth System Model (E3SM) eliminates these obstacles and substantially simplifies workflows for performing detailed relationship analysis.

The Impact

Accurately simulating the relationships between atmospheric processes is crucial for improving the predictive skill of weather and climate models. This new tool makes it much easier for researchers to analyze model behavior at a fundamental level. It can help speed up model development and lead to better tools for weather and climate predictions. More accurate predictions can help decision makers prepare for future changes in weather and climate.


Large-scale simulations can provide researchers with important information about atmospheric processes. However, analyzing data during and after simulations to understand the complex relationships between these processes can require extensive amounts of data and coding. Researchers added new and flexible data structures to E3SM to capture process-level information during simulations on a supercomputer. They developed general algorithms for process tracking and conditional sampling based on typical use cases. These flexible data structures and generalized algorithms in this new tool allow E3SM users to perform online diagnosis without tedious coding or archiving large amounts of data. The tool has other convenient features, such as its ability to automatically calculate various statistics. It allows users to sample model data in multiple ways within a single simulation, improving the efficiency of such analysis.

This new paper provides a detailed description of the tool and demonstrates its usage through three examples: a global view of the sources and sinks of dust, the relationship between sea salt emission and wind speed at ocean surfaces, and changes in relative humidity caused by different atmospheric processes under conditions where ice cloud formation can happen. The tool was developed as part of E3SM, but the algorithms can be adapted for use with other weather and climate models.

PNNL Contact

Hui Wan, Pacific Northwest National Laboratory, hui.wan@pnnl.gov


This research was supported primarily by the Department of Energy Office of Science’s Scientific Discovery through Advanced Computing (SciDAC) program via the partnership between Biological and Environmental Research (BER) and the Advanced Scientific Computing Research (ASCR) programs in Earth System Model Development. One of the coauthors was supported by the Biological and Environmental Research program through the E3SM project.

Published: June 14, 2022

Wan, H., Zhang, K., Rasch, P. J., Larson, V. E., Zeng, X., Zhang, S., and Dixon, R. “CondiDiag1.0: a flexible online diagnostic tool for conditional sampling and budget analysis in the E3SM atmosphere model (EAM),” Geoscientific Model Development, 15, 3205–3231, (2022). [DOI: 10.5194/gmd-15-3205-2022]