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.