Deep learning-based effective fine-grained weather forecasting model

• Published in: Pattern analysis and applications : PAA Vol. 24; no. 1; pp. 343 - 366
• Main Authors: Hewage, Pradeep, Trovati, Marcello, Pereira, Ella, Behera, Ardhendu
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2021; Springer Nature B.V
• Subjects: Autoregressive models; Computer Science; Deep learning; Error correction; Industrial and Commercial Application; Machine learning; Meteorological data; Neural networks; Numerical weather forecasting; Pattern Recognition; Regression models; Statistical analysis; Support vector machines; Weather forecasting; Long short-term memory; WRF; Neural network; Temporal convolutional networks; Weather prediction; Time-series data analysis

It is well-known that numerical weather prediction (NWP) models require considerable computer power to solve complex mathematical equations to obtain a forecast based on current weather conditions. In this article, we propose a novel lightweight data-driven weather forecasting model by exploring tem...