[HTML][HTML] Making the black box more transparent: Understanding the physical implications of machine learning
A McGovern, R Lagerquist, DJ Gagne… - Bulletin of the …, 2019 - journals.ametsoc.org
Making the Black Box More Transparent: Understanding the Physical Implications of Machine
Learning in: Bulletin of the American Meteorological Society Volume 100 Issue 11 (2019) Jump …
Learning in: Bulletin of the American Meteorological Society Volume 100 Issue 11 (2019) Jump …
[HTML][HTML] Interpretable deep learning for spatial analysis of severe hailstorms
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) …
Proc. 12th USENIX Symp. on Operating Systems Design and Implementation (OSDI'16) …
Forecasting different types of convective weather: A deep learning approach
A deep learning objective forecasting solution for severe convective weather (SCW)
including short-duration heavy rain (HR), hail, convective gusts (CG), and thunderstorms …
including short-duration heavy rain (HR), hail, convective gusts (CG), and thunderstorms …
[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 …
Climate Volume 32 Issue 5 (2019) Jump to Content Logo Logo Logo Logo Logo Logo …
Leveraging modern artificial intelligence for remote sensing and NWP: Benefits and challenges
SA Boukabara, V Krasnopolsky… - Bulletin of the …, 2019 - journals.ametsoc.org
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 …
These fields and the fields of satellite remote sensing and NWP share the same fundamental …
Improving atmospheric river forecasts with machine learning
WE Chapman, AC Subramanian… - Geophysical …, 2019 - Wiley Online Library
This study tests the utility of convolutional neural networks as a postprocessing framework
for improving the National Center for Environmental Prediction's Global Forecast System's …
for improving the National Center for Environmental Prediction's Global Forecast System's …
[HTML][HTML] Using a 10-year radar archive for nowcasting precipitation growth and decay: A probabilistic machine learning approach
Andersen, H., J. Cermak, J. Fuchs, R. Knutti, and U. Lohmann, 2017: Understanding the
drivers of marine liquid-water cloud occurrence and properties with global observations …
drivers of marine liquid-water cloud occurrence and properties with global observations …
Application of machine learning to large hail prediction-The importance of radar reflectivity, lightning occurrence and convective parameters derived from ERA5
This study presents a concept for coupling remote sensing data and environmental variables
with machine learning techniques for the prediction of large hail events. In particular, we …
with machine learning techniques for the prediction of large hail events. In particular, we …
NCAR's real-time convection-allowing ensemble project
Beginning 7 April 2015, scientists at the US National Center for Atmospheric Research
(NCAR) began producing daily, real-time, experimental, 10-member ensemble forecasts …
(NCAR) began producing daily, real-time, experimental, 10-member ensemble forecasts …
[HTML][HTML] Forest-based and semiparametric methods for the postprocessing of rainfall ensemble forecasting
M Taillardat, AL Fougères, P Naveau… - Weather and …, 2019 - journals.ametsoc.org
Forest-Based and Semiparametric Methods for the Postprocessing of Rainfall Ensemble
Forecasting in: Weather and Forecasting Volume 34 Issue 3 (2019) Jump to Content Jump to …
Forecasting in: Weather and Forecasting Volume 34 Issue 3 (2019) Jump to Content Jump to …