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

Professional training, papers, and presentations delivered at the AMS 2026 Madison Summit in Madison, Wisconsin.

AMS 2026 Madison Summit, held in Madison, Wisconsin from August 2 through August 7, 2026, will include professional training and technical conference participation by Premier Analytics Consulting, LLC. Ryan Paul Lafler, our CEO and Lead Consultant, will deliver a full-day, hybrid Python workshop focused on efficient workflows for accessing, analyzing, visualizing, and indexing petabyte-scale environmental datasets in the cloud.

Premier Analytics Consulting's conference contributions focus on scalable, cloud-native environmental data systems for climatological, meteorological, forecasting, and decision-support workflows. In addition to the full-day training, Ryan will present a published paper and presentation on a cloud-native Python framework for scalable access and analysis of environmental data, featuring practical methods using tools such as Xarray, Dask, fsspec, Kerchunk, Zarr, cloud object storage, CMIP6, and NOAA RTMA datasets.

Hands-On Training Workshop

Training developed and delivered by the Premier Analytics Consulting team.

Efficient Python Workflows for Accessing, Analyzing, Visualizing, and Indexing Large Environmental Datasets in the Cloud

 Ryan Paul Lafler; Miguel Angel Bravo 

Date: August 2, 2026

Time: 8:00 AM - 4:00 PM CT

Location: Madison, Wisconsin and Online

Training Workshop Description

Join us for an immersive, hybrid, full-day Python workshop designed to equip attendees with applied skills in accessing, querying, processing, visualizing, and indexing large-scale environmental datasets stored in public cloud object storage systems. This workshop focuses on real-world climatological and meteorological use cases, featuring datasets from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and NOAA’s Real-Time Mesoscale Analysis (RTMA), with access via Google Cloud Storage (GCS) and Amazon S3. Attendees will learn how to efficiently build scalable Python pipelines for exploring, filtering, and visualizing high-resolution climate datasets across time and space. Hands-on examples will demonstrate how to extract time series for point locations and areal shapes, visualize multi-dimensional model output, and apply optimization strategies for handling big cloud-hosted environmental data. New in this 2026 edition: we introduce powerful cloud-native indexing techniques using Kerchunk, enabling virtual Zarr access to GRIB2, NetCDF, and HDF5 files without full conversion. Attendees will gain practical experience managing rate limiting, leveraging metadata-aware access patterns, and scaling workflows using tools such as Xarray, fsspec, Dask, and Kerchunk.

Published Technical Papers

Technical papers authored and published by the Premier Analytics Consulting team.

16.3 | A Cloud-Native Python Framework for Scalable Access and Analysis of Environmental Data for Forecasting and Decision Systems

 Ryan Paul Lafler ​

Abstract

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.

Tags:  Xarray, Dask, fsspec, Python, CMIP6, NOAA RTMA, cloud-native, machine learning pipelines, AI workflows, scalable analytics, forecasting workflows, decision support, data infrastructure, NetCDF, Zarr, GRIB, GRIB2 ​

Section: Weather Analysis and Forecasting

AMS 2026 Madison Summit Highlights

See the Premier Analytics Consulting team in action

Efficient Python Workflows for Accessing, Analyzing, Visualizing, and Indexing Large Environmental D

Efficient Python Workflows for Accessing, Analyzing, Visualizing, and Indexing Large Environmental D

Full-day, hybrid training seminar taught by Ryan Paul Lafler at the AMS 2026 Madison Summit on Python workflows for accessing, analyzing, and visualizing multi-dimensional CMIP6, NOAA RTMA, and similar petabyte-scale climatological and meteorological data repositories in the cloud.

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Expert services and solutions in AI, data engineering, full-stack systems, advanced statistical analysis, enterprise GIS, and open-source modernization.

Premier Analytics Consulting helps organizations deliver secure, modern, and data-driven solutions across AI, data engineering, full-stack architectures, advanced statistical analysis and reporting, enterprise GIS, environmental informatics, and open-source modernization. We welcome opportunities for contracting, subcontracting, technical partnerships, advisory support, training, and project-based assignments, and support clients with specialized implementation, analytical workflows, solution architecture, and modern data system development across business, enterprise, research, and public-sector environments.

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