10.6084/m9.figshare.7592855.v1
Yunsoo Choi
Yunsoo
Choi
A Deep-Learning Driven Improved Ensemble Approach for Hurricane Forecasting
ESIP
2019
Machine Learning
Dynamical Models
Natural Hazards
Hurricane Harvey
ESIP Winter 2019
Atmospheric Sciences
Meteorology
Natural Hazards
2019-02-06 17:59:13
Presentation
https://esip.figshare.com/articles/presentation/A_Deep-Learning_Driven_Improved_Ensemble_Approach_for_Hurricane_Forecasting/7592855
<p>Tracking the path and forecasting the intensity of
hurricanes are challenging. Dynamical models produce a significant
model-measurement error. Accurate forecasting is very difficult to achieve
after landfall. For track forecasting (where the storm is going to go),
dynamical models are generally the best. For intensity forecasting, statistical
models generally perform better. We can combine the advantages of both models
using a machine learning ensemble approach. Machine learning models are
computationally efficient and are currently used widely for forecasting and
ensemble purposes. Deep Neural network (DNN) techniques comprise a popular and
powerful class of machine learning methods. We developed a three-step DNN-based
ensemble hurricane forecasting model using the output of eight dynamical
hurricane models being used in ATCF system for forecasting hurricane track and
intensity. We used all tropical cyclones in Atlantic Ocean from 2003-2016 and
tested the model for those in 2017. The preliminarily results of our model show
statistical advantages over NHC official forecasts. This presentation was given at the Earth Science Information Partners (ESIP) Winter Meeting in January 2019.</p>