Deep Learning Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders

Remote sensing data play a critical role in improving numerical weather prediction (NWP). However, the physical principles of radiation dictate that data voids frequently exist in physical space (e.g., subcloud area for satellite infrared radiance or no-precipitation region for radar reflectivity)....

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Veröffentlicht in:	Monthly weather review Jg. 150; H. 8; S. 1977 - 1991
Hauptverfasser:	Wang, Yueya, Shi, Xiaoming, Lei, Lili, Fung, Jimmy Chi-Hung
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Washington American Meteorological Society 01.08.2022
Schlagworte:	Accuracy Analysis Approximation Barotropic mode Data assimilation Data augmentation Data collection Deep learning Density Error reduction Experiments Fields Initial conditions Machine learning Mathematical analysis Numerical prediction Numerical weather forecasting Physical states Precipitation Probability distribution Radar Radar data Radar reflectivity Radiance Radiation Rain Reconstruction Reflectance Remote observing Remote sensing Resolution Simulation Voids Vorticity Vorticity equations Weather forecasting
ISSN:	0027-0644, 1520-0493
Online-Zugang:	Volltext
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