# ERA5 Reanalysis

Many global forecast archives store fields every six hours, while applications need hourly states. WeatherBridge reconstructs the interior state as query-conditioned bidirectional second-order transport between the two anchors, plus multiscale and spherical-harmonic residual corrections, and reaches the lowest aggregate error at held-out query hours on the 2020 ERA5 benchmark: normalised RMSE 0.07431 at six hours, 48.6% below linear interpolation's 0.144 and 9.0% below WeatherDCAE-14M. The advantage is not field-uniform; WeatherDCAE-14M remains better for geopotential and MSLP.

Many global forecast archives store fields every six hours, while applications need hourly states. WeatherBridge reconstructs the interior state as query-conditioned bidirectional second-order transport between the two anchors, plus multiscale and spherical-harmonic residual corrections, and reaches the lowest aggregate error at held-out query hours on the 2020 ERA5 benchmark: normalised RMSE 0.07431 at six hours, 48.6% below linear interpolation's 0.144 and 9.0% below WeatherDCAE-14M. The advantage is not field-uniform; WeatherDCAE-14M remains better for geopotential and MSLP.

Subseasonal forecasts, two to six weeks ahead, have long hovered near the climatological average because errors compound, and operational dynamical ensembles and even state-of-the-art probabilistic AI routinely underperform climatology at longer subseasonal leads. Probabilistic bias correction uses machine learning to fix the forecast distribution itself rather than only a single point estimate. It doubles the subseasonal skill of an AI forecasting system and beats the operationally debiased dynamical model for 91% of pressure, 92% of temperature and 98% of precipitation targets; in the 2025 real-time competition its global forecasts ranked first for all variables and lead times.

Subseasonal forecasts, two to six weeks ahead, have long hovered near the climatological average because errors compound, and operational dynamical ensembles and even state-of-the-art probabilistic AI routinely underperform climatology at longer subseasonal leads. Probabilistic bias correction uses machine learning to fix the forecast distribution itself rather than only a single point estimate. It doubles the subseasonal skill of an AI forecasting system and beats the operationally debiased dynamical model for 91% of pressure, 92% of temperature and 98% of precipitation targets; in the 2025 real-time competition its global forecasts ranked first for all variables and lead times.