# Machine Learning Methods in Weather and Climate Applications: A Survey
## 5 Short-Term Weather Forecast
### Result Analysis  
**MetNet:** According to the MetNet experiment, at the threshold of 1 mm/h precipitation rate, both MetNet and NWP predictions have high similarity to ground conditions. Evidently, MetNet exhibits a forecasting capability that is commensurate with NWP, distinguished by an accelerated computational proficiency that generally surpasses NWP's processing speed.  
**FourCastNet:** According to the FourCastNet experiment, FourCastNet can predict wind speed 96 h in advance with extremely high fidelity and accurate fine-scale features. In the experiment, the FourCastNet forecast accurately captured the formation and path of the super typhoon Shanzhu, as well as its intensity and trajectory over four days. It also has a high resolution and demonstrates excellent skills in capturing small-scale features. Particularly noteworthy is the performance of FourcastNet in forecasting meteorological phenomena within a 48 h horizon, which has transcended the predictive accuracy intrinsic to conventional numerical weather forecasting methodologies. This constitutes a significant stride in enhancing the veracity and responsiveness of short-term meteorological projections.  
**GraphCast:** According to the GraphCast experiment, GraphCast demonstrates superior performance in tracking weather patterns, substantially outperforming NWP in various forecasting horizons, notably from 18 h to 4.75 days, as depicted in Figure 3b. It excels at predicting atmospheric river behaviors and extreme climatic events, with significant improvement seen in longer-term forecasts of 5 and 10 days. The model's prowess extends to accurately capturing extreme heat and cold anomalies, showcasing not just its forecasting capability but a nuanced understanding of meteorological dynamics, thereby holding promise for more precise weather predictions with contemporary data.  
**PanGu:** According to the PanGu experiment, PanGu can almost accurately predict typhoon trajectories during the tracking of strong tropical cyclones Kong Lei and Yu Tu and is 48 h faster than NWP. The advent of 3D Net further heralds a momentous advancement in weather prediction technology. This cutting-edge model outperforms numerical weather prediction models by a substantial margin and possesses the unprecedented ability to replicate reality with exceptional fidelity. It's not merely a forecasting tool but a near-precise reflection of meteorological dynamics, allowing for a nearly flawless reconstruction of real-world weather scenarios.  
In Table 3, \"forecast-timeliness\" represents the forecasting horizon of each model, indicating their ability to predict weather up to certain future days. In meteorology, z500 refers to the height at the 500 hPa isobaric level, which is critical for understanding atmospheric structures and weather systems. Model evaluation often employs RMSE (Root Mean Square Error) and ACC (Anomaly Correlation Coefficient) to gauge prediction accuracy and correlation with actual observations. Lower RMSE and higher ACC values indicate better model performance. Among GraphCast, PanGu, and IFS, PanGu exhibits the highest accuracy with an ACC of 0.872 for a 7-day forecast timeliness. GraphCast, while having a longer forecast timeliness of 9.75 days, has an ACC of 0.825 and an RMSE of 460, showing a balance between a longer forecasting duration and decent accuracy. Apart from this, introducing GPU data and prediction speed can provide crucial reference information for model selection, especially in scenarios with limited resources or where rapid responses are required. This aids in finding a balance between efficiency and effectiveness, offering support for successful forecasting.