Using Ensemble Data Assimilation to Explore the Environmental Controls on the Initiation and Predictability of Moist Convection

Abstract

Abstract Atmospheric deep moist convection has emerged as one of the most challenging topics for numerical weather prediction, due to its chaotic process of development and multi-scale physical interactions. This study examines the dynamics and predictability of a weakly organized linear convective system using convection permitting EnKF analysis and forecasts with assimilating all-sky satellite radiances from a water vapor sensitive band of the Advanced Baseline Imager on GOES-16. The case chosen occurred over the Gulf of Mexico on 11 June 2017 during the NASA Convective Processes Experiment (CPEX) field campaign. Analysis of the water vapor and dynamic ensemble covariance structures revealed that meso-α (2000-200 km) and meso-β (200-20 km) scale initial features helped to constrain the general location of convection with a few hours of lead time, contributing to enhancing convective activity, but meso-γ (20-2 km) or even smaller scale features with less than 30-minute lead time were identified to be essential for capturing individual convective storms. The impacts of meso-α scale initial features on the prediction of particular individual convective cells were found to be classified into two regimes; in a relatively dry regime, the meso-α scale environment needs to be moist enough to support the development of the convection of interest, but in a relatively wet regime, a drier meso-α scale environment is preferable to suppress the surrounding convective activity. This study highlights the importance of high-resolution initialization of moisture fields for the prediction of a quasi-linear tropical convective system, as well as demonstrating the accuracy that may be necessary to predict convection exactly when and where it occurs.