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Building an Open-Source NOAA MRMS Radar Renderer in Python

David Taylor Jr. 2026年08月09日 11:38 2 次阅读 来源:Dev.to

When I started building Weather Experience , I wasn't planning to release an open-source project. I simply wanted to answer a question: Could I build a modern radar rendering pipeline using NOAA's publicly available MRMS data? That question led me down a rabbit hole of GRIB2 decoding, radar products, rendering pipelines, performance benchmarking, and ultimately the release of MRMS Renderer , the first open-source project from Taylor Creative Development. Why MRMS? NOAA's Multi-Radar/Multi-Sensor (MRMS) system provides an incredible amount of weather data. For my use case, I focused on the ReflectivityAtLowestAltitude product because it provides an excellent foundation for radar visualization. The challenge wasn't obtaining the data. The challenge was turning that data into something useful. The Pipeline MRMS Renderer performs the complete workflow: Discover the latest MRMS products directly from NOAA/NCEP Download and decompress GRIB2 data Decode the grid using ecCodes Process reflectivity values with NumPy Render transparent PNG radar frames Generate an animation manifest Display animated radar over OpenStreetMap using Leaflet Everything runs locally. The project intentionally does not provide a hosted radar service. Instead, it demonstrates how developers can work directly with NOAA's publicly available data. Performance One of the biggest questions I had at the beginning was performance. Could this realistically be done fast enough for a modern application? Rather than speculate, I wrote benchmarks. On my M4 Pro MacBook Pro over a standard Wi-Fi connection, the complete pipeline—from downloading the latest MRMS frame through rendering the finished PNG—consistently completed in around two seconds . The surprising result wasn't the renderer. The renderer itself was already highly optimized using NumPy vectorization. The largest source of latency turned out to be downloading the GRIB2 data itself. That finding helped shape later architectural decisions for Weather E

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