Unified AI Weather Forecasting Pipeline thru Aurora, Foundry, and Microsoft Planetary Computer Pro
Weather shapes some of the most critical decisions we make, from protecting our critical infrastructure and global supply chains, to keeping communities safe during extreme events. As climate variability becomes more volatile, an organization’s ability to predict, assess, and plan their response to extreme weather is a defining capability for modern infrastructure owners & operators. This is especially true for the energy and utility sector — even small delays in preparations and response can cascade into massive operational risk and financial impacts, including widespread outages and millions in recovery costs. Operators of critical power infrastructure are increasingly turning to AI-powered solutions to reduce their operational and service delivery risk. “As the physical risks to our grid systems grow, so too does our technological capacity to anticipate them. Artificial intelligence has quietly reached a maturity point in utility operations-not just as a tool for optimization, about as a strategic foresight engine. The opportunity is clear: with the right data, infrastructure, and operational alignment, AI outage prediction utility grid strategies can now forecast vulnerabilities with precision and help utilities transition from reactive to preventive risk models.” – Article by Think Power Solutions Providing direct control of their data and AI analytics allows providers to make better, more actionable insights for their operations. Today, we’ll demonstrate and explore how organizations can use the state-of-the-art Aurora weather model in Microsoft Foundry with weather data provided by Microsoft Planetary Computer (MPC), an Azure based geospatial data management platform, to develop a utility industry-specific impact prediction capability. Taking Control of your Weather Prediction Microsoft Research first announced Aurora in June 2024, a cutting-edge AI foundation model enabling locally executed, on-demand, global weather forecasting, and storm-trajectory prediction generated from publicly available weather data. Two months later, Aurora became available on Microsoft Foundry elevating on-demand weather forecasting from a self-hosted experience to managed deployments, readying Aurora for broader enterprise and public adoption. Aurora’s scientific foundations and forecasting performance were peer‑reviewed and published in Nature, providing independent validation across global benchmarks. Its evolution continues with a strong commitment to openness and interoperability: In November 2025, Microsoft announced plans to open-source Aurora to accelerate innovation across the global research and developer community. Building upon the innovation and continued development of Aurora, today we are showcasing how organizations can operationalize this state-of-the-art capability with Microsoft Planetary Computer and Microsoft Planetary Computer Pro. By bringing together the vast public geospatial data stores in Planetary Computer, with the private data managed by Planetary Computer Pro, organizations can unify their weather prediction and geospatial data in a single platform, simplifying data processing pipelines and data management. This advancement allows enterprise customers to take control of their own weather forecasting on their own timeline. A Unified Weather Prediction Data Pipeline In addition, a key pain-point for energy and utility companies is the inability to reliably ingest, store, and operationalize high-volume weather data. Model inputs and outputs often sit scattered across fragmented pipelines and platforms, making decisions difficult to trace, reproduce, and reference over time. For example, referenced in articles, many utility companies have to pull public data from various silos, maintain GIS layers in another, and run operational planning in a separate environment—forcing teams to manually stitch together forecasts, assets, and risk assessments, introducing delays exactly when rapid decisions matter most. With the MPC Pro + Microsoft Foundry pipeline, utility companies transition from fragmented, manual workflows to a single operating platform – where the value lies in a seamless end-to-end data-to-model pipeline. Users can leverage Aurora on Microsoft Foundry alongside Microsoft Planetary Computer Pro’s geospatial data platform to unlock the following unified workflow: Source near real time weather data from Planetary Computer Run Aurora in Microsoft Foundry Fuse weather prediction results with geospatial data in Planetary Computer Pro for rapid assessment and post processing A Ready-to-use reference architecture This reference architecture provides a reusable pattern for operationalizing frontier weather models with Microsoft Planetary Computer Pro and Microsoft Foundry. Our architecture feeds updated global weather data, hosted by Microsoft Planetary Computer, to the Microsoft Foundry hosted model, then fuses those prediction results with enterprise geospatial context for analysis, decision-making, and action. Each component plays a distinct role in ensuring forecasts are timely, scalable, and directly usable within operational workflows. Near Real-Time Weather Data Microsoft Planetary Computer automatically ingests, indexes, and distributes up-to-date global weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF) four times per day. This fully managed data pipeline ensures that the latest atmospheric datasets are continuously refreshed, standardized, and readily accessible, eliminating the need for manual data acquisition or preprocessing. Storing and Centralizing Public and Private Geospatial Data on Microsoft Planetary Computer Pro Microsoft Planetary Computer Pro enables utility operators to store, manage, and access both public and private geospatial datasets within a single Azure platform. With a Microsoft Planetary Computer Pro GeoCatalog, organizations can centralize ECMWF weather data alongside infrastructure and location data to support downstream analyses. Microsoft Foundry Hosts and Runs Weather Prediction Model on Demand Microsoft Foundry provides model access and the infrastructure required to support execution of Aurora and other weather forecasting models. Users can provision Aurora inference endpoints on their own dedicated compute. After provisioned, the user would be able to open the python notebook and run the model to execute weather forecasts on demand. Weather Forecast Outputs are Fused with Existing Data Sources on Microsoft Planetary Computer Pro Aurora’s weather prediction outputs are seamlessly integrated back into Microsoft Planetary Computer Pro, where they are fused with existing public or private geospatial datasets. This makes forecast results immediately accessible for visualization, post-processing, and analysis—such as identifying assets at risk, estimating localized impact, informing operational response plans, or pre-positioning needed assets for quick recovery. By combining AI-driven forecasts with geospatial context, organizations can move from raw predictions to actionable insights in a single workflow. This solution also provides organizations with a centralized platform to store and catalog geospatial data for future traceability. Unified Weather Prediction Demonstration This demonstration visualizes the forecast storm track (Figure 2), along with projected damage impact along the storm path and associated coastal surge areas (Figure 3 & 4). This enables users to assess asset exposure, anticipate damage due to winds, pre-position crews, and proactively protect critical infrastructure—helping reduce outage duration, lower operational costs, and improve grid resilience. & Powerplants) Getting Started The python notebook supports tracking of historical storm events, forecasting real-time storm trajectories, and overlaying critical power infrastructure structure data from OpenStreetMap to visualize overlap. To get started, deploy this solution in your Azure environment to begin generating weather forecasts and storm-track predictions. The code and documentation for running this notebook are available in the linked GitHub Repo. Sample output for you to explore are linked within this HTML. For additional resources, visit the following MS Learn pages: Microsoft Planetary Computer Pro Microsoft Foundry The interoperability between ‘GeoAI models + data platform’ extends far beyond weather prediction. It empowers organizations to take control of their geospatial data; to generate actionable insights on their own timeline, and to meet their own specific needs. With Microsoft Planetary Computer and Microsoft Foundry together, organizations will unify their enterprise geospatial data, and unlock its value with powerful, and state of the art AI solutions.Unlocking the Power of Spatial Data: Azure Cache for Redis Geospatial Indexing
In the digital age, spatial data management and analysis have become integral to a wide array of technical applications. From real-time tracking to location-based services and geospatial analytics, efficient handling of spatial data is pivotal in delivering high-performance solutions.Spatial SQL with PostGIS and Postgres: What makes spatial queries special?
When you are working with spatial data using the PostGIS extension to Postgres, most of the Standard Query Language (SQL) you end up with is just standard SQL—table joins, selects, inserts, updates, and so on—not spatial sorcery. But there is a small percentage of SQL in PostGIS that makes spatial queries a little bit special! I have collected a few examples to this blog post.Importing spatial data to PostGIS
If you’re new to PostGIS and want to try it out, one of the first things you’ll want to do is to import some data into your database. I want to provide an overview of how to accomplish this. The post provides an introduction to some common data import terminology, details on where to find spatial data and tools for importing data to PostGIS, and specific instructions for how to use each tool to perform the import.
