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pro vyhledávání: '"MCGOVERN, AMY"'
Although generative artificial intelligence (AI) is not new, recent technological breakthroughs have transformed its capabilities across many domains. These changes necessitate new attention from educators and specialized training within the atmosphe
Externí odkaz:
http://arxiv.org/abs/2409.05176
This report documents the process that led to the NSF Workshop on "Sustainable Computing for Sustainability" held in April 2024 at NSF in Alexandria, VA, and reports on its findings. The workshop's primary goals were to (i) advance the development of
Externí odkaz:
http://arxiv.org/abs/2407.06119
The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updraft proxies, like overshooting top area from satellite images, have be
Externí odkaz:
http://arxiv.org/abs/2310.09392
Machine learning (ML) models are becoming increasingly common in the atmospheric science community with a wide range of applications. To enable users to understand what an ML model has learned, ML explainability has become a field of active research.
Externí odkaz:
http://arxiv.org/abs/2211.10378
With increasing interest in explaining machine learning (ML) models, the first part of this two-part study synthesizes recent research on methods for explaining global and local aspects of ML models. This study distinguishes explainability from inter
Externí odkaz:
http://arxiv.org/abs/2211.08943
Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. In order to fill the dearth of resources covering neural networks with a meteor
Externí odkaz:
http://arxiv.org/abs/2211.00147
Publikováno v:
Artificial Intelligence for the Earth Systems (2023)
Heat waves are projected to increase in frequency and severity with global warming. Improved warning systems would help reduce the associated loss of lives, wildfires, power disruptions, and reduction in crop yields. In this work, we explore the pote
Externí odkaz:
http://arxiv.org/abs/2205.10972
Publikováno v:
Weather and Forecasting 37 (2022) 1509-1529
Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unavailable to meteorology students and are not required to become a meteo
Externí odkaz:
http://arxiv.org/abs/2204.07492
Given the growing use of Artificial Intelligence (AI) and machine learning (ML) methods across all aspects of environmental sciences, it is imperative that we initiate a discussion about the ethical and responsible use of AI. In fact, much can be lea
Externí odkaz:
http://arxiv.org/abs/2112.08453
A primary goal of the National Oceanic and Atmospheric Administration (NOAA) Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0-3 h) severe weather forecasts. Maximizing t
Externí odkaz:
http://arxiv.org/abs/2012.00679