Constructing a cross-component background error covariance for strongly coupled land-atmosphere data assimilation

Land surface temperature (LST) is the key variable in land–atmosphere interaction, having an important impact on weather and climate forecasting. Although there have been advances in data assimilation within land-atmosphere coupled models, weakly coupled assimilation remains predominant. This means that the cross-component interactions between land and atmosphere are not adequately considered during the assimilation process, making it difficult to achieve consistent analysis between the land and atmospheric variables.