Despite a consensus between models on the likely impact of clouds and precipitation across North America, when we look back on similarly neutral-to-weak La Niña years in recent history, we see a less consistent pattern, including some areas of Texas having the opposite anomaly, experiencing more clouds and precipitation. This analysis shows the inherent uncertainty in seasonal-range forecasting and also highlights that La Niña is not the only phenomenon affecting seasonal climate conditions. For example, changes in tracks of arctic storms can also influence winter conditions over the U.S, but these changes are not predictable more than around three to four weeks ahead at most.

Solcast produces these figures by tracking clouds and aerosols at 1-2 km resolution globally, using satellite data and proprietary AI/ML algorithms. This data is used to drive irradiance models, enabling Solcast to calculate irradiance at high resolution, with typical bias of less than 2%, and also cloud-tracking forecasts. This data is used by more than 350 companies managing over 300 GW of solar assets globally.
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