Premier Analytics Consulting at the American Meteorological Society (AMS) 2026 Madison Summit

The rapid expansion of cloud-hosted environmental datasets through open data initiatives has changed how climate and weather data are accessed and analyzed; however, challenges remain in efficiently interfacing with large-scale repositories for operational and analytical workflows. This study presents a cloud-native Python framework for scalable access, processing, and analysis of environmental datasets stored in object storage systems including Amazon S3, Microsoft Azure, and Google Cloud Storage. The framework integrates Xarray, Dask, and fsspec to enable lazy loading, parallel computation, and efficient subsetting of high-resolution, multi-dimensional gridded datasets. It implements metadata-driven access, chunked data retrieval, caching, and optimized indexing using cloud-hosted datasets including CMIP6 and the operational NOAA RTMA dataset. Case studies demonstrate on-demand time series generation, spatial subsetting, and interactive visualization workflows without requiring full dataset downloads. Performance considerations and trade-offs between data formats (NetCDF, Zarr, GRIB, GRIB2) are evaluated across use cases, data structures, and access patterns in both research and operational contexts. This work establishes a Python framework for integrating cloud-based environmental datasets into forecasting workflows, research applications, and decision-support systems, with extensions to AI-enabled workflows. It enables scalable machine learning data pipelines through efficient extraction, alignment, and processing of multi-resolution environmental datasets. This approach supports supervised and unsupervised workflows, including resolution enhancement and pattern identification, for climate and environmental AI applications.