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GTC ON-DEMAND

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Abstract:
The numerical simulations underlying our weather forecasts and climate projections depend on a large set of sub-models called parameterization schemes to represent unresolved processes in the Earth system. Many existing parameterizations are computationally intensive or contain simplifying assumptions that lead to biases or artifacts in the simulations. We'll talk about how machine learning models can address both problems by emulating a more complex parameterization or learning to represent a process from long records of detailed observations. We will describe machine learning parameterizations of the conversion of cloud water to rain and the transfer of energy between the surface and atmosphere, and we'll compare these against existing approaches for these problems.
The numerical simulations underlying our weather forecasts and climate projections depend on a large set of sub-models called parameterization schemes to represent unresolved processes in the Earth system. Many existing parameterizations are computationally intensive or contain simplifying assumptions that lead to biases or artifacts in the simulations. We'll talk about how machine learning models can address both problems by emulating a more complex parameterization or learning to represent a process from long records of detailed observations. We will describe machine learning parameterizations of the conversion of cloud water to rain and the transfer of energy between the surface and atmosphere, and we'll compare these against existing approaches for these problems.  Back
 
Topics:
Climate, Weather & Ocean Modeling, AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9245
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Abstract:
Attendees will learn how deep learning models identify severe weather hazards, how deep learning severe weather diagnosis compares with other machine learning methods, and what weather features deep learning considers most important for determining whether a storm will produce severe weather or not. Severe weather hazards, such as tornadoes, hail, high winds, and flash floods, cause billions of dollars in property damage and injure or kill hundreds of people in the U.S. each year. Improved forecasts of the potential for severe weather enables decision makers to take actions to save lives and property. Machine learning and deep learning models extract spatial information from observations and numerical weather prediction model output to predict the probability of severe weather based on whether or not some form of severe weather was reported by the public. Convolutional neural networks and generative adversarial networks are compared against principal component analysis encodings to determine how much skill deep learning adds over traditional methods. The deep learning models are interrogated to identify important variables and spatial features for severe weather prediction.
Attendees will learn how deep learning models identify severe weather hazards, how deep learning severe weather diagnosis compares with other machine learning methods, and what weather features deep learning considers most important for determining whether a storm will produce severe weather or not. Severe weather hazards, such as tornadoes, hail, high winds, and flash floods, cause billions of dollars in property damage and injure or kill hundreds of people in the U.S. each year. Improved forecasts of the potential for severe weather enables decision makers to take actions to save lives and property. Machine learning and deep learning models extract spatial information from observations and numerical weather prediction model output to predict the probability of severe weather based on whether or not some form of severe weather was reported by the public. Convolutional neural networks and generative adversarial networks are compared against principal component analysis encodings to determine how much skill deep learning adds over traditional methods. The deep learning models are interrogated to identify important variables and spatial features for severe weather prediction.  Back
 
Topics:
Advanced AI Learning Techniques, Climate, Weather & Ocean Modeling, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8455
Streaming:
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