Release Notes

MindSpore Earth 0.3.0 Release Notes

[STABLE] PreDiff: PreDiff is a novel latent diffusion model (LDM)-based method for short-term precipitation nowcasting. This approach proposes a two-stage pipeline specifically designed for data-driven Earth system forecasting models. Additionally, it introduces a knowledge control mechanism to guide the denoising diffusion process in the latent space. This mechanism ensures that the generated predictions align more closely with domain-specific knowledge, thereby enhancing the credibility of the model's forecasts.

[RESEARCH]: G-TEAM is a data-driven earthquake early warning model whose core architecture combines the sequence modeling capability of Transformer and the spatial information extraction capability of GNN, so that the model can not only capture the time sequence characteristics of seismic waveforms, but also use the propagation relationship of seismic waves in the station network to improve the prediction precision of magnitude and epicenter location. It can quickly provide epicenter location, magnitude and seismic intensity distribution within 3 seconds after an earthquake occurs.

[STABLE] GraphCast: The graphcastTp mode has been newly added. In the downstream medium-term precipitation cases, a grid of 0.5°×0.5° is adopted, and ERA5 data with an input resolution of 360×720 is used for training.
[STABLE] SKNO: A new SKNO model has been added, which integrates the KNO model and the SHT operator. The SKNO model is developed based on the 16-year assimilation reanalysis dataset ERA5. It can predict global meteorological conditions with a temporal resolution of 6 hours and a spatial resolution of 1.4 degrees. The prediction results include indicators such as temperature, humidity, and wind speed.

Thanks to the following developers for their contributions:

hsliu_ustc, hong-ye-zhou, liulei277, kevinli123, Zhou Chuansai, alancheng511, Cui Yinghao, xingzhongfan, cmy_melody, Liu Ruoyan

Contributions to the project in any form are welcome!