Probabilistic forecasting for isolated thunderstorms using a genetic algorithm: The DC3 campaign. Issue 1 (14th January 2014)

• Record Type: Journal Article
• Authors: Hanlon, Christopher J.; Young, George S.; Verlinde, Johannes; Small, Arthur A.; Bose, Satyajit
• Is Part Of: Journal of geophysical research. Volume 119:Issue 1(2014:Jan.)
• Page Start: 65
• Page End: 74
• Publication Date: 2014-01-14
• Subjects: Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2013JD020195
• Languages: English
• ISSNs: 2169-897X

Abstract

<abstract abstract-type="main" id="jgrd50880-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="jgrd50880-para-0001">[1] Researchers on the Deep Convective Clouds and Chemistry (DC3) field campaign in summer 2012 sought airborne in situ measurements of isolated thunderstorms in three different study regions: northeast Colorado, north Alabama, and a larger region extending from central Oklahoma through northwest Texas. Experiment objectives required thunderstorms that met four criteria. To sample thunderstorm outflow, storms had to be large enough to transport boundary‐layer air to the upper troposphere and have a lifetime long enough to produce a large anvil. The storms had to be small enough to sample safely and isolated enough that experimenters could distinguish the impact of a particular thunderstorm from other convection in the area. To aid in the optimization of daily flight decisions, an algorithmic forecasting system was developed that produced probabilistic forecasts of suitable flight conditions for each of the three regions. Atmospheric variables forecast by a high‐resolution numerical weather prediction model for each region were converted to probabilistic forecasts of suitable conditions using fuzzy logic trapezoids, which quantified the favorability of each variable. In parallel, the trapezoid parameters were tuned using a genetic algorithm and the favorability values of each of the atmospheric variables were weighted using a logistic regression. Results indicate that the automated forecasting system shows predictive skill over climatology in each region, with Brier skill scores of 16% to 45%. Averaged over all regions, the automated forecasting system showed a Brier skill score of 32%, compared to the 24% Brier skill score shown by human forecast teams.</p> </abstract>