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How did weather impact global solar production in 2025? Use our interactive map to explore irradiance anomalies, La Niña effects, and regional solar generation trends worldwide. Explore global solar irradiance trends, anomaly maps, and daily generation variability to see how weather shaped solar production in your region.

The shift from centralised thermal generation to distributed renewable generation is fundamentally changing grid architecture. Managing this transition isn't just about building capacity - it requires a new data layer: granular, scalable forecasting that provides the observability to operate thousands of independent generators as a coordinated system.

January saw above-normal solar across East Asia under weak La Niña, but storm-driven cloud and rain reduced irradiance in the Philippines.

Storm systems and late-month snowfall reduced solar irradiance across much of Europe in December 2025, while central regions benefited from clearer conditions

December irradiance anomalies followed a classic La Niña setup, with a ridge over the Southwest driving strong solar gains and deficits across northern regions.

Analysis by the Electricity Authority revealed forecast errors were affecting spot prices by up to $273 million annually. After examining global approaches, they implemented a hybrid forecasting arrangement - and selected DNV as the centralised forecast provider following an 8-week competitive trial.

Forecasts for early 2026 point to a mixed solar outlook. While Australia and eastern China are likely to experience above-average irradiance, Europe and parts of the Americas may see below-average conditions early in the year.

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Solar generation potential across Europe showed a clear east-west divide in November, driven by persistent low-pressure systems affecting eastern regions.

TMY variants - there's no universal right choice. This guide explains what they are, why selection introduces uncertainty, and how non-specialists can transition to time series data for more reliable yield estimates.

Solcast has introduced new parameters for PM2.5 and PM10 particulate matter concentrations in its API to improve the accuracy of photovoltaic (PV) soiling loss modelling globally. This integration supports data-driven soiling analysis by combining particulate data with irradiance and weather information for better energy yield predictions.