# Machine Learning Methods in Weather and Climate Applications: A Survey
## 4 Taxonomy of Climate Prediction Applications
### Classification of Climate Prediction Methods  
To provide a deeper level of understanding regarding the various weather prediction methods discussed, we have organized them into classifications in Table 2. These classifications are made according to multiple criteria that encompass Time Scale, Type, Model, Technique, Name, Region, and Event. This structured approach aims to offer readers an easy way to compare and contrast different methods, as well as to gain insights into the specific contexts where each method is most applicable. Figure 1: Applications: of machine-learning on climate prediction milestone [(41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61)]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Time Scale & **Spational Scale** & **Type** & **Model** & **Technology** & **Name** & **Event** \\ \hline \multirow{8}{*}{Short-term weather prediction} & \multirow{8}{*}{Global} & \multirow{8}{*}{Ml.} & \multirow{8}{*}{Special DNN Models} & AFNO & Front-Search [6] & Extreme Events \\ \cline{5-8}  & & & & 3D Neural Network & Fast-Gen [6] & Extreme Events \\ \cline{5-8}  & & & & Vision Transformers & Climax [50] & Temperature \& Extreme Event \\ \cline{5-8}  & & & & Swin Transformer & SwinVNN [82] & \\ \cline{5-8}  & & & & U-Transformer & Fux [6] & Temperature \& Precipitation \\ \cline{5-8}  & & & & CLKCN [64] & Temperature & \\ \cline{5-8}  & & & & Graph,Cat [8] & & \\ \cline{5-8}  & & & & Transformer & Fong [85] & Extreme Events \\ \cline{5-8}  & & & & CapsNet [6] & & \\ \cline{5-8}  & & & & & Precipitation & \\ \cline{5-8}  & & & & CNN & Prediction [6] & Precipitation \\ \cline{5-8}  & & & & ANN & Precipitation Neural & Precipitation \\ \cline{5-8}  & & & & Network prediction [4] & & Precipitation \\ \cline{5-8}  & & & & LSTM & Stacked-LSTM Model [44] & Temperature & \\ \cline{5-8}  & & & & & ConvLSTM [42] & Precipitation \\ \cline{5-8}  & & & & & CNN [43] & Precipitation \\ \cline{5-8}  & & & & & MetNet [46] & Precipitation \\ \hline \multirow{8}{*}{Medium-to-long-term climate prediction} & \multirow{8}{*}{Global} & \multirow{8}{*}{Single DNN models} & \multirow{8}{*}{Problaits-deep learning} & \multirow{8}{*}{Conditional  
**Time Scale. ** Models in weather and climate prediction are initially divided based on their temporal range into 'Short-term' and 'Medium-to-long-term'. Short-term weather prediction focuses on the state of the atmosphere in the short term, usually the weather conditions in the next few hours to days. Medium-to-long-term climate prediction focuses on longer time scales, usually the average weather trends over months, years, or decades. Weather forecasts focus on specific weather conditions in the near term, such as temperature, precipitation, humidity, wind speed, and direction. Climate prediction focuses on long-term weather patterns and trends, such as seasonal or inter-annual variations in temperature and precipitation. In the traditional approach, weather forecasting usually utilizes numerical weather prediction models that predict weather changes in the short term by resolving the equations of atmospheric dynamics; climate prediction usually utilizes climate models that incorporate more complex interacting feedback mechanisms and longer-term external drivers, such as greenhouse gas emissions and changes in solar radiation. **Spatial Scale. ** Regional meteorology concerns a specified geographic area, such as a country or a continent, and aims to provide detailed insights into the weather and climate phenomena within that domain. The finer spatial resolution of regional models allows for a more nuanced understanding of local geographical and topographical influences on weather patterns, which in turn can lead to more accurate forecasts within that particular area. On the other hand, global meteorology encompasses the entire planet's atmospheric conditions, providing a broader yet less detailed view of weather and climate phenomena. The spatial resolution of global models is generally coarser compared with regional models.