A Deep Learning-Based Wind Field Nowcasting Method With Extra Attention on Highly Variable Events

Article Engineering, Electrical & Electronic

A Spatio-Temporal Neural Network for Fine-Scale Wind Field Nowcasting Based on Lidar Observation

Hang Gao et al.

Summary: This article presents an indirect wind field nowcasting scheme based on lidar observation. The proposed network, consisting of an encoder-forecaster network and a mask branch, is trained and evaluated using lidar observations at Hong Kong International Airport in 2020. Comprehensive comparison with other methods demonstrates the good performance of the network in capturing spatio-temporal features and achieving fine-scale wind field nowcasting results with high efficiency.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2022)

Add to Collection

Article Geochemistry & Geophysics

CMODH Validation for C-Band Synthetic Aperture Radar HH Polarization Wind Retrieval Over the Ocean

Yiru Lu et al.

Summary: The study validates the performance of the new HH-polarized geophysical model function CMODH for C-band synthetic aperture radar ocean surface wind speed retrieval. The wind speeds estimated using CMODH are consistent with buoy measurements and show good accuracy and consistency in the validation with in-situ data. Compared to other models, CMODH achieves the smallest root mean square error for wind speed retrieval.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021)

Add to Collection

Article Multidisciplinary Sciences

Skilful precipitation nowcasting using deep generative models of radar

Suman Ravuri et al.

Summary: Advanced nowcasting methods using deep generative models with radar observations can provide accurate and operationally useful precipitation predictions, overcoming the limitations of traditional approaches. These models show improved forecast quality, consistency, and value, making them a valuable tool for various sectors reliant on weather-dependent decision-making.

NATURE (2021)

Add to Collection

Proceedings Paper Computer Science, Cybernetics

A Spatio-Temporal Prediction Method of Wind Energy in Green Cloud Data Centers

Jing Bi et al.

Summary: Accurate and reliable prediction of wind speed data is crucial in optimizing resources in cloud data centers. This study introduces a spatio-temporal prediction method that outperforms traditional time series forecasting approaches by considering spatial and temporal features of the data. The proposed method effectively removes noise interference and achieves better prediction accuracy using real-world datasets.

2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) (2021)

Add to Collection

Article Green & Sustainable Science & Technology