Four-dimensional variational data assimilation

Four-dimensional variational data assimilation (4D-Var) is a technique used in weather forecasting and climate modeling to improve predictions. It combines observational data (like temperature and wind measurements) with mathematical models to find the best estimate of the current state of the atmosphere over time. By considering data across four dimensions—three spatial dimensions plus time—4D-Var adjusts the model to minimize differences between the model predictions and actual observations. This process enhances the accuracy of forecasts, helping scientists better understand and predict weather patterns and climate changes.