Atmospheric G2

To the folks in #Texas - its been a pretty up and down winter, but are y'all ready for #summer?! We are forecasting the hottest March 25th high temperature in ERCOT (by population weighted aggregate) since 1960, tied with 2020. Our insights tool on the new Atmospheric G2 Trader can help users see where our forecast stacks up against other years on that date back to 1960 and provides a variety of analytics related to that date - see below! Our sales team has been diligently moving clients over to the new website that we have been working so long and hard on and we are so excited for you to see what its all about. Feel free to each out to any of us on the team for more info and to see what other new tools and features we are rolling out! James Caron Amy Hodgson Olivia Birch Todd Crawford Andrew Pedrini #atmosphericg2 #ercot #energy #weather

The rollout of various new AI weather models over the last year has been something of a blur and, now that the excitement of a cold winter is behind us, we thought it would be time to offer some thoughts from our unique perspective as a leading voice in the energy markets. 1. The AI models are quite useful, but are still not as good, in aggregate, as the better legacy NWP models, especially when looking at fields like 500 mb GPH. Discussions with our operational forecasters, who are in the trenches every day, suggest that the AI models are still used secondarily to the legacy models - "I don't use it other than a gut check/reference". My personal experience is that I still do not consult the AI models nearly as much as a good high-resolution NWP model/ensembles. Perhaps that will evolve with time, but that is the current perspective from those with an extreme level of skin in the game, those who are highly motivated to produce an accurate forecast. 2. However, there are many situations where the legacy models are still severely flawed, especially for 2-meter temperatures, where the AI models add considerable value. We know that the calculation of 2-meter temperatures in the legacy NWP models is a complex process involving highly imperfect parameterizations of surface energy exchanges/fluxes, which is especially complicated and difficult at night. Given that AI models are effectively very mathematically sophisticated analog models, trained on actual observations, they are not crippled by the same biases/errors that the legacy NWP models are. Further, there are certain well-known situations where even the best legacy models do poorly, such as southward-moving shallow and dense cold air masses in the lee of the Rockies and Appalachians, and we've seen multiple instances this past winter where AI models do astoundingly well, while legacy models can be 20-30 degrees off with mistimed cold fronts, etc. 3. The value of AI models relative to legacy models decreases with forecast horizon. An examination of forecast accuracy suggests that AI models can outperform legacy models in the 1-7 day window, but fall off considerably behind that. This applies when comparing both deterministic and ensemble mean solutions. In summary, we are excited to see the continued investment in this space, and are continuing to follow developments as we work to optimally integrate the new models into our product suite. However, we do caution that these new models are a complement, not a replacement, for legacy NWP models, at least for now. #atmosphericg2 #ai #weather

The rollout of various new AI weather models over the last year has been something of a blur and, now that the excitement of a cold winter is behind us, we thought it would be time to offer some thoughts from our unique perspective as a leading voice in the energy markets. 1. The AI models are quite useful, but are still not as good, in aggregate, as the better legacy NWP models, especially when looking at fields like 500 mb GPH. Discussions with our operational forecasters, who are in the trenches every day, suggest that the AI models are still used secondarily to the legacy models - "I don't use it other than a gut check/reference". My personal experience is that I still do not consult the AI models nearly as much as a good high-resolution NWP model/ensembles. Perhaps that will evolve with time, but that is the current perspective from those with an extreme level of skin in the game, those who are highly motivated to produce an accurate forecast. 2. However, there are many situations where the legacy models are still severely flawed, especially for 2-meter temperatures, where the AI models add considerable value. We know that the calculation of 2-meter temperatures in the legacy NWP models is a complex process involving highly imperfect parameterizations of surface energy exchanges/fluxes, which is especially complicated and difficult at night. Given that AI models are effectively very mathematically sophisticated analog models, trained on actual observations, they are not crippled by the same biases/errors that the legacy NWP models are. Further, there are certain well-known situations where even the best legacy models do poorly, such as southward-moving shallow and dense cold air masses in the lee of the Rockies and Appalachians, and we've seen multiple instances this past winter where AI models do astoundingly well, while legacy models can be 20-30 degrees off with mistimed cold fronts, etc. 3. The value of AI models relative to legacy models decreases with forecast horizon. An examination of forecast accuracy suggests that AI models can outperform legacy models in the 1-7 day window, but fall off considerably behind that. This applies when comparing both deterministic and ensemble mean solutions. In summary, we are excited to see the continued investment in this space, and are continuing to follow developments as we work to optimally integrate the new models into our product suite. However, we do caution that these new models are a complement, not a replacement, for legacy NWP models, at least for now. #atmosphericg2 #ai #weather

Strat/Tropo #coupling – #Early #Stratospheric #Final #Warming #analogs indicate #cold #risks for #April in #Europe. https://lnkd.in/dcD4RwQX Analog years show a surface response towards high-latitude ridging, which skewed things colder especially for Europe and at times E #Canada in April, while generally #warm for E #Asia, among a weaker signal for the #States. Subscribe to Atmospheric G2 solutions for your #weather #impacting #markets. My colleagues Rhian Pearson, Edward Vallee, Olivia Birch, Amy Hodgson, James Caron and I are here for questions and inquiries. AG2 website: https://lnkd.in/drMsBQCG AG2 on LinkedIn: https://lnkd.in/dnKKJNKR AG2 on X: https://lnkd.in/dMGefV4p #energy #trading #energyprice #spring #temperatures #power #gas #renewables #commoditytrading #lng #weatherimpacts #stratosphere #weatherforecasting

C3S #Summer #2025 #outlook – where the highest #risks of #heatwaves and #droughts? https://lnkd.in/dAuaXjAj Ahead of the issuance of the first official summer 2025 #AG2 #forecasts coming later this week, we took a look at the latest C3S release. The highest risks of combined anomalous #warmth and #dryness are projected through C-NW #States, C-E #Europe and #Japan, while less warm and/or wetter conditions are resolved through the mid-#Atlantic to SE #US, NW Europe, and #China/ #Korea. The #ENSO signal is also discussed. Stay tuned for Atmospheric G2 guidance on Summer 2025, featuring our technical discussions, coming out soon. For that, subscribe to our #seasonal solutions or reach out to myself or my colleagues Amy Hodgson, Olivia Birch, Rhian Pearson, Edward Vallee, James Caron. AG2 website: https://lnkd.in/drMsBQCG AG2 on LinkedIn: https://lnkd.in/dnKKJNKR AG2 on X: https://lnkd.in/dMGefV4p #energy #trading #energytrading #power #heating #heatdome #heat #energymarket #temperatures #renewables #hydro #precipitation #rainfall #weatherforecasting #riskmanagement