Downburst Prediction Applications of GOES over the Western United States

TL;DR

This paper evaluates the effectiveness of the Microburst Windspeed Potential Index (MWPI), a GOES satellite-based tool, in predicting downburst wind events over the western U.S., showing significant correlation with observed wind gusts.

Contribution

It provides an updated assessment and validation of the MWPI algorithm, demonstrating its operational utility and accuracy in downburst prediction.

Findings

MWPI shows a significant correlation (r > 0.6) with observed wind gusts.

MWPI has a low mean error (< 1 kt) in wind speed prediction.

Case studies confirm MWPI's effectiveness in operational settings.

Abstract

Over the western United States, the hazards posed to aviation operations by convective storm-generated downbursts have been extensively documented. Other significant hazards posed by convective downbursts over the intermountain western U.S. include the rapid intensification and propagation of wildfires and the sudden generation of visibility-reducing dust storms (haboobs). The existing suite of GOES downburst prediction algorithms employs the GOES sounder to calculate potential of occurrence based on conceptual models of favorable environmental thermodynamic profiles for downburst generation. Previous research has demonstrated the effectiveness of the Dry Microburst Index (DMI) as a prediction tool for convectively generated high winds. A more recently-developed diagnostic nowcasting product, the Microburst Windspeed Potential Index (MWPI) is designed to diagnose attributes of a favorable downburst environment: 1) the presence of convective available potential energy (CAPE), and 2) the presence of a deep surface-based or elevated mixed layer with a large temperature lapse rate. This paper presents an updated assessment of the MWPI algorithm, case studies demonstrating effective operational use of the MWPI product, and recent validation results. MWPI data were collected for downburst events that occurred during the 2014 convective season and were validated against surface observations of convective wind gusts as recorded by wind sensors in high-quality mesonetworks. Favorable validation results include a statistically significant correlation (r > 0.6) and low mean error (< 1 kt) between MWPI values and confirmed downburst wind speeds measured in situ.