Meteorological experts achieved promising results in improving the effectiveness of neural weather networks in precipitation forecasting by incorporating precipitation-optimized objectives into the loss function. Howe...
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ISBN:
(数字)9798331505813
ISBN:
(纸本)9798331505820
Meteorological experts achieved promising results in improving the effectiveness of neural weather networks in precipitation forecasting by incorporating precipitation-optimized objectives into the loss function. However, since neural networks are not based on explicit physical processes, meteorological experts may lack understanding and trust in the model’s predictions, and they cannot perform bias correction by adjusting physical parameters. Additionally, precipitation events are highly imbalanced, with heavy rainfall events being relatively rare but of greater importance. Therefore, traditional metrics for evaluating deep models are insufficient to fully assess precipitation forecasting performance. In this paper, we present a neural weather network visual analysis system designed to help domain experts understand and comprehensively compare the impact of different loss functions on neural networks. We customize the model evaluation process based on the characteristics of precipitation forecasting tasks and provide a reference for bias correction using historically similar data. To validate our approach, we perform two case studies using real-world reanalysis datasets, with feedback from domain experts further confirming its effectiveness.
Sea surface temperature (SST) is critically important for understanding ocean dynamics and supporting various marine activities, making accurate short-term SST forecasting highly significant. However, accurately model...
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A numerical investigation of the influence of pitch angle on vertical axis wind turbine (VAWT) aerodynamic performance is carried out using the finite volume method. The attack angle of the wind turbine is studied und...
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A numerical investigation of the influence of pitch angle on vertical axis wind turbine (VAWT) aerodynamic performance is carried out using the finite volume method. The attack angle of the wind turbine is studied under a speed ratio lambda = 3D 4, and the pitch angle of the wind turbine is adjusted to optimize the working attack angle and hence the aerodynamic performance of the turbine. The flow is assumed to be 2D fully turbulent and the fluid is incompressible, and turbulence is modeled by the k-w SST. The sliding mesh technique is adopted. The present study shows that an appropriate pitch angle can apparently improve the wind turbine power-coefficient.
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