Enhancing Tropical Weather Forecasts with Constrained Data Assimilation

Forecasting precipitation in tropical regions is challenging because of substantial errors in both the models and the initial conditions. The interaction between tropical waves and convection suggests possible predictability. Therefore, an accurate representation of these waves in the models and initial conditions is important for increasing the accuracy of precipitation forecasts. This study intends to improve the predictive reliability of the ICON (Icosahedral Nonhydrostatic) global model for tropical weather events, such as tropical waves and the Madden-Julian Oscillation (MJO). We initially analyze the ability of data assimilation (DA) to conserve total energy, enstrophy, moist static energy, and other physical properties. Then, we implement an advanced DA technique, the Quadratic Programming Ensemble (QPEns), with a moist static energy constraint. Preliminary findings show that the moist static energy constraint, together with accurate wind and humidity data, decreases forecast errors and improves tropical wave representation. This induces more reliable long-term precipitation forecasts.