Abstract
The United States Air Force’s 45th Weather Squadron provides wind warnings, including those for downbursts, at the Cape Canaveral Air Force Station and Kennedy Space Center (CCAFS/KSC). This study aims to provide a Random Forest model that classifies thunderstorms’ downburst and null events using a 35-knot wind threshold to separate these two categories. The downburst occurrence was assessed using a dense network of wind observations around CCAFS/KSC. Eight dual-polarization radar signatures that are hypothesized to have physical implications for downbursts at the surface were automatically calculated for 209 storms and ingested into the Random Forest model. The Random Forest model predicted null events more correctly than downburst events, with a True Skill Statistic of 0.40. Strong downburst events were better classified than those with weaker wind magnitudes. The most important radar signatures were found to be the maximum vertically integrated ice and the peak reflectivity. The Random Forest model presented a more reliable performance than an automated prediction method based on thresholds of single radar signatures. Based on these results, the Random Forest method is suggested for continued operational development and testing.
Highlights
A downburst is characterized by the occurrence of divergent intense winds at or near the surface, which are produced by a thunderstorm’s downdraft [1,2]
The resultant True Skill Statistic (TSS) for the Random Forest model is 0.40, which is in the range of TSS values that are considered marginal for operational utility by the 45th Weather Squadron (45WS) (i.e., 0.3 to 0.5) [24]
This study presented a Random Forest classification method for downburst forecasting around the CCAFS/KSC
Summary
A downburst is characterized by the occurrence of divergent intense winds at or near the surface, which are produced by a thunderstorm’s downdraft [1,2]. A number of observational [4,5,6,7,8,9] and modeling [10,11,12,13,14] studies have been conducted to reveal the structure, dynamics, microphysics, and environmental conditions associated with a variety of convective downbursts Precipitation microphysical processes such as precipitation loading [10], melting hailstones [6,12,15], and evaporation of raindrops [10,14,16] are important for downburst generation. [19] used radar and environmental variables as input to different machine learning techniques to predict surface straight-line convective winds
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