Implementation and Evaluation of Storm-Following 3DIAU for Hurricane Intensity Prediction Improvements in Operational HAFS

Abstract

Accurate hurricane intensity prediction remains a critical challenge in numerical weather prediction (NWP). This study implements and evaluates a newly developed Storm-Following Three-Dimensional Incremental Analysis Update (3DIAU) methodology for high-resolution regional hurricane models with storm-following nest capabilities. Built upon the feature-relative 4DIAU approach proposed by Lu and Wang (2021), https://doi.org/10.1175/MWR-D-21-0068.1, the method gradually introduces Data Assimilation (DA) increments relative to the storm's position, reducing spin-up imbalances and improving intensity predictions. Retrospective experiments were conducted over three Atlantic hurricane seasons (2021–2023) using the 2024 operational Hurricane Analysis and Forecast System (HAFS) version 2.0A configuration. Sensitivity experiments suggest that increment weighting should depend on storm strength. The storm-strength-dependent configuration yields an average improvement of 3% in intensity prediction skill, with modest gains in long-term track predictions. A case study further demonstrates that gradual, storm-relative adjustments mitigate disruptions caused by intermittent DA and enhance forecast performance. The Storm-Following 3DIAU will be incorporated into the 2025 operational HAFS V2.1 upgrade.