Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data
Traditional deep learning-based prediction methods predominantly rely on weather radar data to quantify precipitation, often neglecting the integration of the thermal processes involved in the formation and dissipation of precipitation, which leads to reduced prediction accuracy. To address this lim...
Saved in:
Main Authors: | Hao Wang, Rong Yang, Jianxin He, Qiangyu Zeng, Taisong Xiong, Zhihao Liu, Hongfei Jin |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/17/2/238 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Nowcasting of Russian manufacturing output using business survey data
by: R. E. Gartvich
Published: (2023-12-01) -
Historical rainstorm in Hong Kong on 7–8 September 2023: Diagnosis, forecasting and nowcasting
by: Hiu Ching Tam, et al.
Published: (2025-01-01) -
An Operational Geomagnetic Baseline Derivation Method for Magnetic Observatories Located in Mid‐Latitudes
by: Veronika Haberle, et al.
Published: (2024-12-01) -
Quantifying the Location Error of Precipitation Nowcasts
by: Arthur Costa Tomaz de Souza, et al.
Published: (2020-01-01) -
On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport
by: Décio Alves, et al.
Published: (2024-01-01)