标签：#Weather

Climate change is causing glaciers to retreat and become less stable, upping the odds of catastrophic collapses.

This year’s El Niño is shaping up to be the strongest on record. This is what impacts to expect and how bad it could get.

El Niño coupled with Saharan dust is making this year’s hurricane season a dud. But there’s still potential for dangerous storms.

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

With rising temperatures and drier conditions, fires are more likely to explode. And with more people living next to forests, the results can be catastrophic.

Its WeatherNext model, which will be open-sourced, can accurately predict both a storm’s track and intensity using lower-resolution weather data. Researchers don't yet fully understand how it does this.

"The models are forecasting something outside the envelope of anything we have ever observed."

WindBorne Systems has raised $37 million Series B round to scale its weather balloons and AI forecasts.

Heat waves are simultaneously hitting Europe, North America, and Asia, driven in part by the hot waters in the tropical Pacific—and climate change.

This climate phenomenon could cause a massive global temperature spike and cause economic losses of $10 trillion by 2032.

Livestreaming the channel through its app now starts at $5 per month.

Extreme heat coupled with humidity will make it feel like 109 degrees Fahrenheit as the holiday weekend approaches.

We run a prime directive on this stack: if a usable tool already exists, improve it; build our own only as a last resort, and when you keep your own, record why each alternative failed. This post is that audit for weather-mcp — a marine-weather MCP server — against the weather-MCP ecosystem, and the one capability change that fell out of it. The short version: three perfectly good weather MCP servers exist, and none of them does the thing a navigator actually needs. The reasons generalize to any "adopt an MCP server or keep your own" call, so the audit is the post. Then the fix — parsing a second NDBC file format to split swell from wind waves — is small enough to paste in full, and it surfaced data the standard file had thrown away. The problem, as you'd search it You want an agent to answer "what are the seas doing where we are?" and you go looking for a marine weather MCP. You find a few. Each one returns a forecast . None of them returns what a buoy 12 nautical miles away is measuring right now . That gap — forecast vs. observed — is the entire job, and it's the one thing the ecosystem skips. Here's what's on the shelf, and what each one is missing for marine use. The candidates Three real servers, all worth your time for what they're built for: cmer81/open-meteo-mcp ~13 tools, raw Open-Meteo JSON straight through weather-mcp/weather-mcp ~12 tools, own format, global; marine = Open-Meteo RyanCardin15/NOAA-Tides... CO-OPS stations: water levels + currents, not buoys And ours: sailingnaturali/weather-mcp 4 tools, Python, 2 runtime deps (httpx + mcp) get_marine_forecast Open-Meteo wind/swell/wind-wave/seas/pressure get_marine_forecast_premium Stormglass blend — 10 tokens/UTC-day, cache hits free get_nearest_buoy_observations NDBC observed wind + waves by lat/lon, with bearing + age get_stormglass_quota_status token-ledger read, no network Mapped against what a navigator needs: Capability ours open-meteo-mcp weather-mcp/weather-mcp NOAA-Tides Open-Meteo marine (swel

description: While most people chase political bets, these traders are dominating daily temperature...

description: Weather markets on Polymarket have become one of the hottest and most consistent...

From a wet winter in the Southwest to fewer Atlantic hurricanes, this is what to expect as a potential super El Niño takes shape.

The old app "still needs to be retired," AcuRite tells us.

Alt Carbon said the agreement followed more than a year of scientific review and due diligence, with Microsoft requiring additional verification and data-sharing measures.

We are exploring an experimental blockchain mechanism called "Proof of Weather" In the world of blockchain, various methods are used to achieve network consensus. The most well-known is Bitcoin’s Proof of Work (PoW). While PoW is an excellent mechanism, it has one major drawback. It consumes an enormous amount of electricity. At one point, I found myself wondering: Does blockchain really require such vast computational resources? Isn’t there something else that’s needed? This led to the creation of Dawn, the experimental cryptocurrency project I am developing, and an experimental blockchain mechanism called Proof of Weather. In this article, I will discuss: Why I decided to use weather How Proof of Weather works Security considerations Implementation in Rust How Does Proof of Work Work? Proof of Work is often explained as a mechanism where computers compete against each other in computational tasks. However, one important property of PoW is that it produces outcomes that are difficult to predict in advance. Miners repeatedly perform massive amounts of hash calculations, and only those who happen to meet the conditions can generate a block. This unpredictability plays a role in determining who can produce the next block. However, this process consumes enormous amounts of electricity worldwide. So I wondered: Aren’t there already phenomena in nature that are difficult to predict? Why Weather? Proof of Weather utilizes weather data as that unpredictable element. Of course, weather forecasts exist. However, Temperatures several days in the future Atmospheric pressure at specific locations Precipitation Wind speed and other factors cannot be predicted with absolute certainty. In particular, when combining observations from multiple locations, it becomes even more difficult to accurately calculate future values in advance. In other words, meteorological observations have the potential to be used as A real-world information source where future values cannot be fully predic

Meet the wet bulb globe temperature, the ominous measure that shows when it's too hot to go outside.