Data / Science Data Products / VAPs

SYNOPWEAREG

Synoptic Weather Regime Classification VAP products

Evaluation VAP

SynopWeaReg leverages self-organizing maps, a type of unsupervised machine learning algorithm, to classify large-scale synoptic weather regimes over selected regions at ARM sites. This classification technique identifies patterns in atmospheric circulation by grouping similar daily meteorological fields, such as geopotential height, into distinct regime types without relying on predefined labels or human supervision.

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By providing an objective and data-driven framework for identifying synoptic-scale variability, SynopWeaReg enables users to stratify observational or model data based on prevailing large-scale weather patterns. This stratification is especially useful for understanding how atmospheric processes vary across different circulation regimes and for interpreting site-based measurements in the context of broader meteorological influences.

In addition, SynopWeaReg empowers researchers to isolate the influence of large-scale circulation on cloud properties, precipitation, and regional weather systems. This capability is critical for a wide range of scientific investigations, including evaluating the performance of numerical weather and earth system models, understanding regime-dependent cloud-aerosol-precipitation interactions, and improving physical parameterizations.

Ultimately, integrating SynopWeaReg into workflow analyses can enhance the interpretability of observational data, improve model diagnostics, and contribute to more accurate weather and earth system predictions by accounting for the dominant synoptic forcing mechanisms.