Deep Learning Approach for the Detection of Areas Likely for Convection Initiation

Convection Initiation (CI) leading to the development of thunderstorms can cause significant meteorological impacts on a variety of aspects of human society. The early detection of convection initiation can lead to planning and preparation. Previous research has shown there are indicators that can be observed through satellite remote sensing which can provide lead times of up to potentially 1 hour plus in advance of convective storms being detected by radar. Through a partnership with the Central Weather Bureau (CWB) of Taiwan, a team at the Global Systems Division (GSD) with the NOAA Earth System Research Laboratory (ESRL) has built upon previous research using Deep Neural Networks for detection of Regions of Interest to identify and assign probability of Convection developing in specific areas.

Research for this project has investigated different neural network architectures to address temporal information without using recurrent neural networks and how to use multiple radiance observations to increase our accuracy beyond a typical RGB type image. This presentation will provide an overview of our research efforts at ESRL into the application of deep learning to improve satellite data processing, discussion of the challenges we face, and next steps to further our research.