Impact of Rapid-Scan-Based Dynamical Information From GOES-16 on HWRF Hurricane Forecasts

Abstract

Observations of dynamical information in the upper levels of tropical cyclones at high spatiotemporal resolutions are rare but very important to the analysis and prediction of the storm evolution and landfall impacts. These observations are now becoming routinely available from the new generation of geostationary weather satellites. Understanding and optimizing the utilization of that information in numerical weather prediction models is a vital step toward simulating tropical cyclone behavior and improving forecasts. The Advanced Baseline Imager (ABI) onboard GOES-16 is providing high spatial and temporal resolution images that can be targeted on North Atlantic tropical cyclones. In addition to a full-disk scan every 10 min and a CONUS scan every 5 min, the ABI also has a flexible “mesoscale scan” mode featuring limited moving domains at 1-min intervals. The mesosector can focus on a targeted storm center with a 10°×10° domain coverage that follows the storm movement. Using this 1-min ABI imagery to track cloud motions, automated algorithms have been developed to produce enhanced, high-resolution atmospheric motion vectors (AMVs) during a targeted tropical cyclone event. These high spatiotemporal AMVs represent estimates of the wind field around the storm and can provide critical dynamical information on the targeted storm and its near environment. This information can help improve the representation of the initialized vortex in numerical model analyses. To study the impact of the enhanced AMV observations on numerical weather prediction, the Hurricane Weather Research Forecast (HWRF) model is used in a series of assimilation and forecast experiments. Three destructive Atlantic hurricane cases from 2017, Harvey, Irma, and Maria, are chosen as case studies. The results show that the assimilation of the enhanced AMVs from GOES-16 consistently improves the HWRF hurricane track and size forecasts, and have mixed impacts on intensity forecasts. These results augment previously published studies on optimizing the quantitative use of new generation geostationary satellite rapid-scan observations for improving high impact weather forecasts.