A Deep Neural Network to Globally Forecast the Track and Intensity of Tropical Cyclones

Recorded Presentation
Recording

• 11B.2: A Deep Neural Network to Globally Forecast the Track and Intensity of Tropical Cyclones

• Boston Convention and Exhibition Center
• – 156A

• Hammad Usmani
  • Georgia Institute of Technology
    Atlanta, GA, USA
• Aadil Habibi
  • Univ. of Central Florida
    Orlando, FL, USA
• Daanish Habibi
  • Univ. of Central Florida
    Orlando, FL, USA

Tropical cyclones are the most devastating weather phenomenon and the IPCC’s 2018 “Special Report on Global Warming of 1.5°C” there’s evidence that extreme tropical events are likely to worsen. With machine learning, producing skillful forecasts becomes possible provided the right data. We can create a deep neural network that fully utilizes recurrent and convolutional layers using the IBTrACS database and the NCEP/NCAR Surface Temperature imagery. This artificial intelligence is accompanied by a standard web application that can be used operationally to produce forecasts. The study develops under a permissive license that allows reuse and maintains open source. The study follows the I18n internationalization format to assist with global dissemination. The model produces near-instant track and intensity forecasts for Atlantic, Indian, and Pacific Ocean tropical cyclones and has shown skill over the NHC’s statistical baseline for Atlantic tropical storms. One of the goals of the research is to provide both professional and amateur meteorologists access to extreme tropical cyclone forecasts to assist with emergency scenarios.