Long-range weather model updates

🌍 The latest World Meteorological Organization El Niño/La Niña Update brings important news for climate-sensitive sectors: 🔹 Current cooler-than-average Pacific sea surface temperatures are expected to return to neutral conditions (neither El Niño nor La Niña) between March and May 2025 — with a 60% probability, rising to 70% by April-June 2025. 🔹 The risk of El Niño redeveloping in this period is negligible, but uncertainty remains high due to the spring predictability barrier, a well-known challenge for long-range climate forecasts. Why should businesses care? 👉 Seasonal climate forecasts like this are not just scientific updates — they are powerful risk management tools that translate into economic savings worth millions of dollars for industries like #agriculture, #energy, #transport, #supplychains, and #insurance. 👉 Understanding how ENSO patterns will evolve helps businesses: ✅ Plan for #climaterisks and supply chain disruptions ✅ Anticipate shifts in #wateravailability, #energydemand, and #cropyields ✅ Align operational strategies to climate-sensitive markets (#commodities, #foodsecurity, #renewableenergy production) ✅ Enhance disaster preparedness and reduce costly damage from climate extremes Even in a warming world, businesses can no longer rely solely on historical weather patterns — they must actively integrate climate intelligence into decision-making. 📊 The latest forecast also comes in the context of record-breaking heat: January 2025 was the hottest January ever recorded, despite the presence of weak La Niña conditions since December 2024. What’s next? WMO’s Global Seasonal Climate Updates (GSCU) provide even broader insights — covering key climate drivers like the North Atlantic Oscillation, Indian Ocean Dipole, and tropical Atlantic temperatures — all critical for regional weather and climate risks that matter to business continuity and resilience. ✅ Takeaway for leaders: Climate intelligence is a competitive advantage. Use it to future-proof your business strategy and build climate resilience into your operations. 💡 Want to stay ahead of #climaterisks and opportunities? Follow #WMO for ongoing updates and insights. https://lnkd.in/e-hXFXPC

How far in advance can we reliably predict the weather? The answer isn't simple. It depends on many factors: the weather model used, the time of year, geographical location, and—most importantly—the type of weather pattern we're trying to predict. Stable weather regimes (like a persistent high-pressure system bringing clear skies) are generally much easier to forecast than dynamic or unstable situations. While a deep dive into forecast verification would require several posts (it’s a complex field involving statistics and mathematics), I’d like to show a recent example: the development of a strong—though fortunately short-lived—heatwave over Northern Europe. In the animation below, I’ve visualized temperature forecasts at about 1.5 km altitude, using outputs from a weather prediction model initialized at different times, from June 3 to June 12. Each forecast includes multiple lines, representing different ensemble members (scenarios with equal probability). I’ve also added the climatology to help highlight the heatwave signal. The forecast is for Hamburg, Germany. What stands out is that as early as June 6, the model began hinting at a heat spike around June 13—more than a week in advance. From that point on, the intensity of the heatwave became clearer, while the timing and duration continued to adjust until the final forecast. Had this been a low-pressure system or a moving front, the forecast would have looked much more chaotic. Looking at the early frames of the animation, you might wonder: is there any value in such long-range forecasts, when they seem so uncertain? The answer is yes. The spread of scenarios helps us quantify uncertainty and assess predictability. Saying how likely something is—even with uncertainty—is far more informative than saying nothing at all. Finally, a good model isn’t one that shows no change from run to run—it’s one where the actual evolution falls within the range of predicted scenarios. In the end, every forecast is just one of many possible paths the atmosphere might take.

Announced today in the WSJ!  Our AI-based long-range weather forecasting model, NeuralGCM is helping 38 million farmers in India, enabling them to make more informed decisions about when to plant their crops for the season. Hundreds of millions of smallholder farmers across the tropics depend on information about when the monsoon season will come each year. However, accurate forecasting of when the monsoon will begin, especially at long lead times and at local scales, has remained a century-old challenge. When we open-sourced NeuralGCM, we hoped the community would use this new model to power their own innovative applications. The University of Chicago, in collaboration with India's Ministry of Agriculture and Farmers’ Welfare, did just that – they used our NeuralGCM model in combination with models from ECMWF to text forecasts directly to farmers each week. Here's how this can help farmers: -- Beyond traditional weather models: For years, accurate long-range monsoon forecasting has been a challenge. AI-driven models like NeuralGCM learn from decades of historical weather data, making them more efficient to run. -- Tangible economic and social impact: Existing research from the University of Chicago shows that accurate monsoon forecasts can almost double a farmer's annual income. By putting actionable information directly into the hands of tens of millions, this initiative is helping farmers strengthen their resilience against climate variability. This project demonstrates the immense potential of AI to create solutions that directly benefit communities. It’s a huge achievement by the Indian Ministry of Agriculture and a model for how to put people first in the age of AI. I am so proud and thankful to our team – Olivia Graham, Stephan Hoyer, Shreya Agrawal, Mansi Kansal–  whose passion and commitment has kept us focused on the impact we can drive in agriculture.  Yet another testament to the value that AI can bring to our earth. Read the full story: WSJ: https://lnkd.in/ge9ujZTt Google blog: https://lnkd.in/gDqsPYvS

Helped by anticipated El Nino, marine heatwave aerial coverage increases from 23% now to 37% by late 2026 according to NOAA. Combining NOAA and Climate Impact Company (CIC) identification of current global sea surface temperature anomalies (SSTA) and the International Multi-Model Ensemble (IMME) SSTA forecast for APR/MAY/JUN 2026 renders expansion of marine heatwave (MHW) risk for 2026. According to NOAA, aerial coverage of global MHW’s is 23% in FEB-26 and forecast to increase to 30% by mid-year and 37% by end of 2026. El Nino development will, in part add to the global oceanic warming in 2026. Despite the warming, two notable cooler trends into mid-year are forecast including the North Atlantic warm hole (NAWH) south of Greenland and an expansive Amundsen Sea WH westward to the Ross Sea. The catalyst to warm holes is freshwater runoff from rapid ice-melt from polar land masses (Greenland and Antarctica). Large MHW’s and WH’s are significant contributors to regional climate and must be considered, along with ENSO, to generate climate forecasts (or explain previous climate observations).   CIC identifies MHW’s outside of the tropics (NOAA includes the tropics). Likely the largest influence on climate in the northern hemisphere as mid-2026 approaches is the semi-permanent (since 2018) expansion of the Kuroshio MHW east of Asia running northeastward past the Dateline. Strong MHW’s during the warm season are often well-correlated to stronger than normal high pressure ridging in the middle troposphere which increases risk of anomalous heat and drought affecting nearby land masses (implicating Japan and possibly China). Similarly, MHW’s off Baja California and strengthening in the Gulf of Mexico and eastward could elevate subtropical ridge intensity affecting Mexico and possibly the Southern U.S. during warm season.   A plethora of MHW’s located in the southern hemisphere will have tendency to weaken or become less organized as the winter climate arrives. Exceptions are MHW’s southeast of Australia and in the Southern Indian Ocean (according to the IMME forecast). In the mid-troposphere, large low-pressure troughs have tendency to form across or downwind WH’s. Significant upper troughing is possible given the size of the Amundsen/Ross WH which increases risk of chilly air masses emitted into South America during the winter season. 

Long-range models are increasingly hinting at a very active winter weather pattern for the Eastern U.S. as we head into January. A more consistent signal is emerging that the Jan 6 - Jan 20 window could feature significant snow potential and sustained cold, especially across the Mid-Atlantic and Northeast. Exact timing and which corridors would be impacted are still uncertain at this range – but historically, this type of large-scale setup has been associated with some of the most memorable winter storms of the past few decades. The best way to illustrate this is with the 500 mb height anomaly forecast below. It's a fancy way of showing what the atmosphere is doing at a higher level. Quick nerdy breakdown of why this pattern matters: → Strong west-based -NAO (Greenland block) The deep reds and pinks over Greenland indicated a blocking high-pressure system. This "block" disrupts the jet stream and can cause storms to slow down or amplify along the East Coast – a key ingredient for larger, high-impact snow events. → Western U.S. ridging Models are showing periods of higher pressure building in the West. This ridge helps storms "lift" northward, increasing the odds of heavier snowfall in the Mid-Atlantic and Northeast rather than suppressed systems sliding harmlessly offshore. → Deep eastern U.S. trough A pronounced trough over the Eastern U.S. supports a colder, stormier pattern. When this aligns with Greenland blocking, the atmosphere becomes much more efficient at producing high-impact winter weather. Is a major East Coast snowstorm guaranteed? No. Is this one of the most favorable large-scale winter patterns we've seen in the last ~5 years? Yes. We'll gain much more clarity as we move into the New Year – but this is a pattern worth watching closely. Prepare your operations for high impact winter weather with WeatherOptics.

The next climate breakthrough may not be a new turbine or battery. It may be a better baseline dataset. 🌍 Copernicus ECMWF has started ERA6 production — the next flagship climate reanalysis after ERA5. Why that matters is not the headline alone, but what sits underneath it: • 📍 14 km vs 31 km resolution means a much sharper view of storms, coastal dynamics, land–atmosphere interactions, and local weather risk. That matters for wind/solar siting, grid planning, insurance, and infrastructure resilience. • 🕒 Hourly reconstructions stretching back 75+ years create a stronger historical baseline for trend analysis, asset stress-testing, and climate-risk decisions that are still too often made on weak local proxies. • 🌊 First-time ocean–atmosphere coupling in ECMWF’s flagship reanalysis improves system coherence. That is a big deal for coastal risk, storm surge work, shipping, offshore wind, and any analysis where disconnected datasets distort reality. • 📚 +50% to more than doubling of some observations versus ERA5 is a reminder that climate intelligence is only as good as the data rescue, correction, and assimilation behind it. • 🤖 In the middle of the AI wave, ECMWF makes an important point: the best ML models still depend on robust physics-based training data. Better AI starts with better ground truth. My take: ⚡ We spend a lot of time talking about transition technologies, and not enough time talking about measurement infrastructure. But better data changes real money decisions: where to build, how to insure, what to finance, how to model risk, and which assets stay competitive under climate stress. For energy, transport, and physical-risk teams, ERA6 is not “just another dataset”. It is an upgrade to the decision layer. 📈 Watch next: 👀 • Late 2027: first decades of ERA6 data expected • Early 2028: first four decades expected to be downloadable Who will benefit first from ERA6-quality data? A) renewables & grids B) insurers & infrastructure investors C) logistics / shipping / coastal risk teams #ClimateData #Copernicus #ECMWF #EnergyTransition #ClimateRisk #Infrastructure #AI

NOAA Research Air Resources Laboratory’s (ARL) HYSPLIT model is one of the most extensively used atmospheric transport and dispersion models in the scientific community. HYSPLIT, used for over 25 years with many updates throughout that time, can be used to forecast dispersion of chemical releases into the atmosphere, smoke from wildfires, ash from volcanic eruptions, and even balloons that may be drifting over our country. The most recent update, HYSPLIT v9, has now been approved for implementation into the National Weather Service's National Centers for Environmental Prediction operations. HYSPLIT v9 provides significant enhancement to the code, the addition of a transfer coefficient matrix capability (which represents the amount of materials moving from one area into another) for volcanic ash and radiological releases, and stronger integration with global weather prediction models.  #NOAA #research https://lnkd.in/ePkVkGBn

Forecasts suggest a neutral to weak La Niña this season, but dry conditions and clear skies suggest a potential upside for solar production. Despite the consensus between models from the European Centre for Medium-Range Weather Forecasts and the Met Office for the likely impact on clouds and precipitation across North America, when we look at similarly neutral-to-weak La Niña years in recent history, we see a less consistent pattern, including some areas such as Texas having the opposite anomaly - more clouds and precipitation. The current consensus between modeling agencies leans towards “neutral” conditions (i.e. neither El Niño or La Niña), but on the La Niña side of the spectrum. Whilst it is hard to guarantee a long range weather forecast like this - signals are looking good for solar production in the US this winter. Check out what La Niña might mean for your assets, and read our full analysis in the weekly update from Solcast, a DNV company in pv magazine Global today.

Weather forecasting is complicated and relies on physics-based equations and the nuanced expertise of trained forecasters. These general circulation models, which run multiple numerical simulations of the atmosphere, need a lot of computing power that is available only at high-performance supercomputing facilities. Google’s DeepMind has introduced an AI-based diffusion model called GenCast which uses machine learning to generate ensemble-based forecasts. The model provides probability-driven projections instead of the usual deterministic “one outcome fits all” approach. https://buff.ly/4g86bNw The model is efficient in anticipating extreme weather events, “even those outside the data it was trained on”. The model is trained on historical weather data from 1979 to 2018, and computation speed is its strong suit. GenCast requires only 8 minutes to generate forecasts. The model needs to be more accurate - for now it provides updates every 12 hours. In this era of climate change, the ability to predict unprecedented and severe events, quickly, is all the more crucial. GenCast surpassed the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble in 97% of evaluated metrics. This includes accurately tracking tropical cyclones and forecasting extreme events. The integration of AI in weather forecasting will complement, not replace, the work of human meteorologists. #deepmind #google #AL #ML #weather #forecasting #predictive #analytics #meteorology #GenCast #climate #climatechange