Predictability and Genesis of Hurricane Karl (2010) Examined through the EnKF Assimilation of Field Observations Collected during PREDICT

Abstract

Abstract The genesis of Hurricane Karl (2010) is explored using analyses and forecasts from a cycling ensemble Kalman filter (EnKF) that assimilates routinely collected observations as well as dropsonde measurements that were taken during the Pre-Depression Investigation of Cloud Systems in the Tropics (PREDICT) field campaign. A total of 127 dropsonde observations were collected from six PREDICT flight missions over a 5-day period before and during Karl’s genesis. EnKF analyses that take into account the PREDICT dropsondes provide a detailed four-dimensional overview of the evolving kinematic and thermodynamic structure within the pregenesis disturbance. In particular, the additional field observations are found to increase the low- and midlevel circulation and column moisture in the EnKF analyses and reduce the position error of the low-level vortex. Deterministic forecasts from these analyses show a 24-h improvement in predicting genesis over a control experiment that omits the dropsonde observations. In ensemble forecasts for this event, the more accurate analyses translate into a higher confidence in predicting the intensification of Karl; that is, data assimilation experiments also suggest that initial condition errors at the mesoscale pose large challenges for predicting genesis, thus highlighting the need for improved observation networks and more advanced data assimilation methods.