Online Diagnostics Make Analyzing Modeled Atmospheric Processes Easier and More Efficient
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.
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.