Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Foundation models for weather and climate data understanding: A comprehensive survey
As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …
sciences is increasingly adopting data-driven models, powered by progressive …
Weatherbench 2: A benchmark for the next generation of data‐driven global weather models
WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting
benchmark proposed by (Rasp et al., 2020, https://doi. org/10.1029/2020ms002203) …
benchmark proposed by (Rasp et al., 2020, https://doi. org/10.1029/2020ms002203) …
A survey on diffusion models for time series and spatio-temporal data
The study of time series is crucial for understanding trends and anomalies over time,
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …
On some limitations of current machine learning weather prediction models
M Bonavita - Geophysical Research Letters, 2024 - Wiley Online Library
Abstract Machine Learning (ML) is having a profound impact in the domain of Weather and
Climate Prediction. A recent development in this area has been the emergence of fully data …
Climate Prediction. A recent development in this area has been the emergence of fully data …
Wildfire spreading prediction using multimodal data and deep neural network approach
Predicting wildfire spread behavior is an extremely important task for many countries. On a
small scale, it is possible to ensure constant monitoring of the natural landscape through …
small scale, it is possible to ensure constant monitoring of the natural landscape through …
Ai foundation models for weather and climate: Applications, design, and implementation
Machine learning and deep learning methods have been widely explored in understanding
the chaotic behavior of the atmosphere and furthering weather forecasting. There has been …
the chaotic behavior of the atmosphere and furthering weather forecasting. There has been …
Fu**-Extreme: Improving extreme rainfall and wind forecasts with diffusion model
Significant advancements in the development of machine learning (ML) models for weather
forecasting have produced remarkable results. State-of-the-art ML-based weather forecast …
forecasting have produced remarkable results. State-of-the-art ML-based weather forecast …
A deep learning approach for forecasting thunderstorm gusts in the Bei**g-Tian**-Hebei region
Y Liu, L Yang, M Chen, L Song, L Han, J Xu - Advances in Atmospheric …, 2024 - Springer
Thunderstorm gusts are a common form of severe convective weather in the warm season in
North China, and it is of great importance to correctly forecast them. At present, the …
North China, and it is of great importance to correctly forecast them. At present, the …
3D‐Var data assimilation using a variational autoencoder
Data assimilation of atmospheric observations traditionally relies on variational and Kalman
filter methods. Here, an alternative neural network data assimilation (NNDA) with variational …
filter methods. Here, an alternative neural network data assimilation (NNDA) with variational …
DiffESM: Conditional emulation of temperature and precipitation in Earth system models with 3D diffusion models
Earth system models (ESMs) are essential for understanding the interaction between human
activities and the Earth's climate. However, the computational demands of ESMs often limit …
activities and the Earth's climate. However, the computational demands of ESMs often limit …