[HTML][HTML] Interpretable deep learning for spatial analysis of severe hailstorms

DJ Gagne II, SE Haupt, DW Nychka… - Monthly Weather …, 2019 - journals.ametsoc.org
Abadi, M., and Coauthors, 2016: Tensorflow: A system for large-scale machine learning.
Proc. 12th USENIX Symp. on Operating Systems Design and Implementation (OSDI'16) …

Multi-Radar Multi-Sensor (MRMS) severe weather and aviation products: Initial operating capabilities

TM Smith, V Lakshmanan, GJ Stumpf… - Bulletin of the …, 2016 - journals.ametsoc.org
Abstract The Multi-Radar Multi-Sensor (MRMS) system, which was developed at the
National Severe Storms Laboratory and the University of Oklahoma, was made operational …

[HTML][HTML] Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles

DJ Gagne, A McGovern, SE Haupt… - Weather and …, 2017 - journals.ametsoc.org
Forecasting severe hail accurately requires predicting how well atmospheric conditions
support the development of thunderstorms, the growth of large hail, and the minimal loss of …

[HTML][HTML] A radar-based climatology of mesoscale convective systems in the United States

AM Haberlie, WS Ashley - Journal of Climate, 2019 - journals.ametsoc.org
A Radar-Based Climatology of Mesoscale Convective Systems in the United States in: Journal of
Climate Volume 32 Issue 5 (2019) Jump to Content Logo Logo Logo Logo Logo Logo …

[HTML][HTML] An objective high-resolution hail climatology of the contiguous United States

JL Cintineo, TM Smith, V Lakshmanan… - Weather and …, 2012 - journals.ametsoc.org
An Objective High-Resolution Hail Climatology of the Contiguous United States in: Weather
and Forecasting Volume 27 Issue 5 (2012) Jump to Content Jump to Main Navigation Logo …

An algorithm for the detection and tracking of tropical mesoscale convective systems using infrared images from geostationary satellite

T Fiolleau, R Roca - IEEE transactions on Geoscience and …, 2013 - ieeexplore.ieee.org
This paper focuses on the tracking of mesoscale convective systems (MCS) from
geostationary satellite infrared data in the tropical regions. In the past, several automatic …

[HTML][HTML] Machine learning for real-time prediction of damaging straight-line convective wind

R Lagerquist, A McGovern, T Smith - Weather and Forecasting, 2017 - journals.ametsoc.org
Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind in:
Weather and Forecasting Volume 32 Issue 6 (2017) Jump to Content Jump to Main Navigation …

Using machine learning to generate storm-scale probabilistic guidance of severe weather hazards in the Warn-on-Forecast system

ML Flora, CK Potvin, PS Skinner… - Monthly Weather …, 2021 - journals.ametsoc.org
A primary goal of the National Oceanic and Atmospheric Administration Warn-on-Forecast
(WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for …

[HTML][HTML] Classifying convective storms using machine learning

GE Jergensen, A McGovern… - Weather and …, 2020 - journals.ametsoc.org
Aggarwal, SK, and LM Saini, 2014: Solar energy prediction using linear and non-linear
regularization models: A study on AMS (American Meteorological Society) 2013–14 solar …

[HTML][HTML] Application of object-based time-domain diagnostics for tracking precipitation systems in convection-allowing models

AJ Clark, RG Bullock, TL Jensen… - Weather and …, 2014 - journals.ametsoc.org
Accadia, C., Mariani S., Casaioli M., Lavagnini A., and Speranza A., 2003: Sensitivity of
precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor …