Christina E. Kumler

Publications

Corresponding Articles: 5

Christina E. Kumler authored and/or contributed to the following articles/publications.

Tropical and Extratropical Cyclone Detection Using Deep Learning

Extracting valuable information from large sets of diverse meteorological data is a time-intensive process. Machine-learning methods can help to improve both speed and accuracy of this process. Specifically, deep-learning image-segmentation models using the U-Net structure perform faster and can identify areas that are missed by more restrictive...

Institution National Oceanic and Atmospheric Administration - NOAA

Using deep learning to nowcast the spatial coverage of convection from Himawari-8 satellite data

Predicting the timing and location of thunderstorms (“convection”) allows for preventive actions that can save both lives and property. We have applied U-nets, a deep-learning-based type of neural network, to forecast convection on a grid at lead times up to 120 minutes. The goal is to make skillful forecasts with only present and past satellite...

Institution National Oceanic and Atmospheric Administration - NOAA

Improving Satellite Observation Utilization for Model Initialization with Machine Learning: An Introduction and Tackling the “Labeled Dataset” Challenge for Cyclones Around the World

One of the National Oceanic and Atmospheric Administration (NOAA) goals is to provide timely and reliable weather forecasts to support important decisions when and where people need it for safety, emergencies, planning for day-to-day activities. Satellite data is essential for areas lacking in-situ observations for use as initial conditions in N...

Machine Learning: Defining Worldwide Cyclone Labels for Training

In this paper we present a procedure for labeling both tropical and extratropical cyclones. The procedure is developed based off of a set of strict heuristics for the purpose of creating a worldwide labeled dataset for cyclones. The heuristics are defined from time, pressure, vorticity, and gradient thresholds without an explicit terrain cut-off...

Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data

The forecast of tropical cyclone trajectories is crucial for the protection of people and property. Although forecast dynamical models can provide high-precision short-term forecasts, they are computationally demanding, and current statistical forecasting models have much room for improvement given that the database of past hurricanes is constan...