A review of the global operational geostationary meteorological satellites
Geostationary meteorological satellite data and products are proven to be indispensable in
operational weather monitoring and forecasting for various sectorial applications and …
operational weather monitoring and forecasting for various sectorial applications and …
MSTCGAN: Multiscale time conditional generative adversarial network for long-term satellite image sequence prediction
Satellite image sequence prediction is a crucial and challenging task. Previous studies
leverage optical flow methods or existing deep learning methods on spatial–temporal …
leverage optical flow methods or existing deep learning methods on spatial–temporal …
Learning spatial-temporal consistency for satellite image sequence prediction
As an extremely challenging spatial–temporal sequence prediction task, satellite image
sequence prediction has various and significant applications in real-world scenarios …
sequence prediction has various and significant applications in real-world scenarios …
WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning
The Earth's weather system encompasses intricate weather data modalities and diverse
weather understanding tasks, which hold significant value to human life. Existing data-driven …
weather understanding tasks, which hold significant value to human life. Existing data-driven …
Mcsip net: Multichannel satellite image prediction via deep neural network
Satellite image prediction is important in weather nowcasting. In this article, we propose a
novel multichannel satellite image prediction network (MCSIP Net) for predicting satellite …
novel multichannel satellite image prediction network (MCSIP Net) for predicting satellite …
SCENT: A new precipitation nowcasting method based on sparse correspondence and deep neural network
Precipitation nowcasting is an important research topic in meteorology, which relates to
many aspects of people's life and social development. Under the combined influence of …
many aspects of people's life and social development. Under the combined influence of …
Characterising and predicting the movement of clouds using fractional‐order optical flow
Estimating cloud motion with complex background through the sequence of satellite images
plays an important role in weather forecasting. This motion can be used for characterization …
plays an important role in weather forecasting. This motion can be used for characterization …
TinyPredNet: a lightweight framework for satellite image sequence prediction
Satellite image sequence prediction aims to precisely infer future satellite image frames with
historical observations, which is a significant and challenging dense prediction task. Though …
historical observations, which is a significant and challenging dense prediction task. Though …
A novel sequence-to-sequence based deep learning model for satellite cloud image time series prediction
Satellite cloud imagery is pivotal for meteorologists in characterizing weather patterns,
detecting climate anomaly regions, and predicting rain effects. The task of satellite cloud …
detecting climate anomaly regions, and predicting rain effects. The task of satellite cloud …
Prediction of satellite image sequence for weather nowcasting using cluster-based spatiotemporal regression
The flawed characterization of transitions between different meteorological structures is
often regarded as one of the largest sources of error in weather forecasting. This paper …
often regarded as one of the largest sources of error in weather forecasting. This paper …