En Route Flight Time Prediction Under Convective Weather Events

2018 ◽  
Author(s):  
Guodong Zhu ◽  
Chris Matthews ◽  
Peng Wei ◽  
Matt Lorch ◽  
Subhashish Chakravarty
Keyword(s):  
Flight Time ◽  
Time Prediction ◽  
Weather Events ◽  
Convective Weather

Study of GOES-R Thermodynamic Indices for Short-Term Forecasting of Convective Weather Events Using Machine Learning

2021 ◽  
Author(s):  
Caroline Menegussi Soares ◽  
Gutemberg Borges França ◽  
Manoel Valdonel de Almeida ◽  
Vinícius Albuquerque de Almeida
Keyword(s):  
Machine Learning ◽  
Short Term ◽  
Weather Events ◽  
Short Term Forecasting ◽  
Convective Weather

scholarly journals A Simple and Flexible Method for Ranking Severe Weather Events

2006 ◽  
Vol 21 (6) ◽  
pp. 939-951 ◽  
Author(s):  
C. A. Doswell ◽  
R. Edwards ◽  
R. L. Thompson ◽  
J. A. Hart ◽  
K. C. Crosbie
Keyword(s):  
Secular Trends ◽  
Severe Weather ◽  
Weather Events ◽  
Multiple Variables ◽  
Convective Weather ◽  
The Impact ◽  
Severe Weather Event ◽  
Intended Use ◽  
Made In ◽  
Tornado Outbreak

Abstract The notion of an “outbreak” of severe weather has been used for decades, but has never been formally defined. There are many different criteria by which outbreaks can be defined based on severe weather occurrence data, and there is not likely to be any compelling logic to choose any single criterion as ideal for all purposes. Therefore, a method has been developed that uses multiple variables and allows for considerable flexibility. The technique can be adapted easily to any project that needs to establish a ranking of weather events. The intended use involves isolating the most important tornado outbreak days, as well as important outbreak days of primarily nontornadic severe convective weather, during a period when the number of reports has been growing rapidly from nonmeteorological factors. The method is illustrated for both tornadic and primarily nontornadic severe weather event day cases. The impact of the secular trends in the data has been reduced by a simple detrending scheme. The effect of detrending is less important for the tornado outbreak cases and is illustrated by comparing rankings with and without detrending. It is shown that the resulting rankings are relatively resistant to secular trends in the data, as intended, and not strongly sensitive to the choices made in applying the method. The rankings are also consistent with subjective judgments of the relative importance of historical tornado outbreak cases.

scholarly journals Verifying Supercellular Rotation in a Convection-Permitting Ensemble Forecasting System with Radar-Derived Rotation Track Data

2017 ◽  
Vol 32 (2) ◽  
pp. 781-795 ◽  
Author(s):  
Logan C. Dawson ◽  
Glen S. Romine ◽  
Robert J. Trapp ◽  
Michael E. Baldwin
Keyword(s):  
Ensemble Forecasting ◽  
Velocity Shear ◽  
Doppler Velocity ◽  
Model Ensemble ◽  
Model Data ◽  
Weather Events ◽  
Report Data ◽  
Forecasting System ◽  
Supercell Thunderstorm ◽  
Convective Weather

Abstract The utility of radar-derived rotation track data for the verification of supercell thunderstorm forecasts was quantified through this study. The forecasts were generated using a convection-permitting model ensemble, and supercell occurrence was diagnosed via updraft helicity and low-level vertical vorticity. Forecasts of four severe convective weather events were considered. Probability fields were computed from the model data, and forecast skill was quantified using rotation track data, storm report data, and a neighborhood-based verification approach. The ability to adjust the rotation track threshold for verification purposes was shown to be an advantage of the rotation track data over the storms reports, because the reports are inherently binary observations whereas the rotation tracks are based on values of Doppler velocity shear. These results encourage further pursuit of incorporating observed rotation track data in the forecasting and verification of severe weather events.

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