{
  "id": 14480302,
  "title": "Researchers improve weather forecasts looking two to six weeks out",
  "url": "https://urgent.news/2026/10/14/researchers-improve-weather-forecasts-looking-two-to-six-weeks-out",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-14T15:00:00.000Z",
  "source": {
    "name": "MIT News Research",
    "slug": "mit-news-research",
    "url": "https://news.mit.edu/2026/researchers-improve-weather-forecasts-looking-two-six-weeks-out-1014"
  },
  "original_language": "en",
  "account": "In 2021, Texas experienced its worst energy infrastructure failure, following three severe winter storms in February. At least 246 people died due to the disruptions, which caused shortages of food, water, heat, and electricity. If energy officials had more time to prepare for the storms, they could have mitigated the damage by bringing in more trucks of natural gas to affected areas. However, weather forecasts beyond two weeks are notoriously unreliable, making it difficult for energy operators, farmers, and emergency response officials worldwide to plan for potential issues.\n\nA team of researchers, including those from MIT, has developed a machine-learning model that significantly improves weather forecasting beyond two weeks. The team created a postprocessing layer that quantifies errors in existing forecasting models and adjusts their output. When applied to a gold-standard forecasting model, this technique doubled the forecasting accuracy over the two-to-six-week time frame, placing first in a prominent international forecasting competition. The researchers describe their approach in a new paper in Nature, showing that their method enables forecasts that beat climatologic averages between two and six weeks for the first time. This improvement is beneficial for farmers and members of the energy industry, as it allows them to better match supply with demand and predict extreme weather events, such as flooding or cold spells, which can have significant societal impacts.",
  "summary": "A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "MIT News AI",
        "title": "Researchers improve weather forecasts looking two to six weeks out",
        "url": "https://urgent.news/2026/10/14/researchers-improve-weather-forecasts-looking-two-to-six-weeks-out-14482236",
        "published": "2026-10-14T15:00:00.000Z"
      }
    ]
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}