Incremental Analysis Updates in a Convective-Scale Ensemble Kalman Filter Using Minute-by-Minute Phased Array Radar Observations

Rapid-update data assimilation (DA) cycles, particularly during the early stages of the assimilation process, often suffer from physical imbalances that degrade the quality of analyses and lead to a rapid decline in forecast skill. This study evaluates the impact of combining the incremental analysis update (IAU) method with the ensemble Kalman filter (EnKF) on the assimilation of observations from a Multi-Parameter Phased Array Weather Radar. A series of experiments were conducted for two convective precipitation cases using a numerical weather prediction model with a 500-m horizontal grid resolution and a 1-min DA interval. The results show that the IAU strategy effectively mitigates the imbalances introduced by intermittent EnKF assimilation. Moreover, IAU maintains a slightly higher ensemble spread while still effectively constraining the analysis toward observations, enhancing ensemble diversity without sacrificing accuracy. The time-continuous, four-dimensional assimilation provided by IAU enables the model to gradually develop and refine convective structures during the forward integration, resulting in a more pronounced surface cold pool and deeper updrafts, thereby slowing down the rapid decline of forecast skills, particularly in high-reflectivity regions. This study indicates that for convective-scale rapid cycling assimilation at minute intervals, combining IAU with EnKF is a superior approach for improving precipitation forecasts.