Data Assimilation and Coupled Modeling of Tropical and Midlatitude Systems: Improving Prediction of Extreme Weather Across Scales

Accurate prediction of high-impact weather and hydrometeorological extremes depends on advanced data assimilation and tightly coupled modeling across spatial and temporal scales. This presentation highlights recent progress in assimilating radar, lidar, satellite, and in situ observations to improve analyses and forecasts of tropical convection and cyclones, drawing on results from NASA’s Convective Processes Experiments (CPEX-AW and CPEX-CV). These studies demonstrate how enhanced observational constraints improve the representation of convective organization, moisture transport, and interactions between convection and the large-scale circulation.Complementary investigations from the midlatitude Cold Fog Amongst Complex Terrain (CFACT) campaign illustrate the critical roles of boundary-layer thermodynamics, surface heterogeneity, and complex terrain in governing local-scale predictability. Building on these efforts, recent developments in land–atmosphere data assimilation are presented, showing how coupled initialization of surface and atmospheric states strengthens cross-interface feedbacks and improves forecast skill from short- to subseasonal timescales.Together, these results bridge tropical, midlatitude, and land–atmosphere coupling research, highlighting the value of integrated observing systems and multiscale data assimilation for improving predictions of extreme precipitation and water-related hazards. The findings contribute to broader efforts aimed at advancing the predictability of high-impact weather across scales.