Jebb Q. Stewart

Publications

Corresponding Articles: 11

Jebb Q. Stewart authored and/or contributed to the following articles/publications.

A climatological study of thermally driven wind systems of the US Intermountain West

This paper investigates the diurnal evolution of thermally driven. plain-mountain winds, up- and down-valley winds, up- and downslope winds, and land-lake breezes for summer fair weather conditions in four regions of the Intermountain West where dense wind networks have been operated. Because of the diverse topography in these regions, the resu...

Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences

Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth ...

Institution National Oceanic and Atmospheric Administration - NOAA

Machine Learning for Targeted Assimilation of Satellite Data

Optimizing the utilization of huge data sets is a challenging problem for weather prediction. To a significant degree, prediction accuracy is determined by the data used in model initialization, assimilated from a variety of observational platforms. At present, the volume of weather data collected in a given day greatly exceeds the ability of as...

Institution National Oceanic and Atmospheric Administration - NOAA

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

Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges

Artificial Intelligence (AI) techniques have had significant recent successes in multiple fields. These fields and the fields of satellite remote sensing and NWP share the same fundamental underlying needs, including signal and image processing; quality control mechanisms; pattern recognition; data fusion; forward and inverse problems; and predi...

Institution National Oceanic and Atmospheric Administration - NOAA

Using Deep Learning to Emulate and Accelerate a Radiative Transfer Model

This paper describes the development of U-net++ models, a type of neural network that performs deep learning, to emulate the shortwave Rapid Radiative Transfer Model (RRTM). The goal is to emulate the RRTM accurately in a small fraction of the computing time, creating a U-net++ that could be used as a parameterization in numerical weather predic...

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...

NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)

We introduce the National Science Foundation (NSF) AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). This AI institute was funded in 2020 as part of a new initiative from the NSF to advance foundational AI research across a wide variety of domains. To date AI2ES is the only NSF AI institute focusi...

Institutions National Oceanic and Atmospheric Administration - NOAA National Center for Atmospheric Research - NCAR

Estimating full longwave and shortwave radiative transfer with neural networks of varying complexity.

Radiative transfer (RT) is a crucial but computationally expensive process in numerical weather/climate prediction. We develop neural networks (NN) to emulate a common RT parameterization called the Rapid Radiative Transfer Model (RRTM), with the goal of creating a faster parameterization for the Global Forecast System (GFS) v16. In previous wor...

Institution National Oceanic and Atmospheric Administration - NOAA