# Machine Learning Methods in Weather and Climate Applications: A Survey
## 4 Taxonomy of Climate Prediction Applications
### Classification of Climate Prediction Methods
_Random forest._ A technique used to adjust or correct biases in predictive models, particularly in weather forecasting or climate modeling. Random Forest (RF) is a machine learning algorithm used for various types of classification and regression tasks. In the context of bias correction, the Random Forest algorithm would be trained to identify and correct systematic errors or biases in the predictions made by a primary forecasting model. _Probabilistic deep learning._ Probabilistic deep learning models in weather forecasting aim to provide not just point estimates of meteorological variables but also a measure of uncertainty associated with the predictions. By leveraging complex neural networks, these models capture intricate relationships between various features like temperature, humidity, and wind speed. The probabilistic aspect helps in quantifying the confidence in predictions, which is crucial for risk assessment and decision-making in weather-sensitive industries. _Generative adversarial networks._ Generative Adversarial Networks (GANs) are a class of deep learning models composed of two neural networks: a Generator and a Discriminator. The Generator aims to produce data that closely resembles a genuine data distribution, while the discriminator's role is to distinguish between real and generated data. During training, these networks engage in a kind of \"cat-and-mouse\" game, continually adapting and improving--ultimately with the goal of creating generated data so convincing that the Discriminator can no longer tell it apart from real data. _Graph Neural Network._ Graph Neural Network(GNN ) are designed to work with graph-structured data, capturing the relationships between connected nodes effectively. They operate by passing messages or aggregating information from neighbors and then updating each node's representation accordingly. This makes GNNs exceptionally good at handling problems like social network analysis, molecular structure analysis, and recommendation systems. _Transformer._ A transformer consists of an encoder and a decoder, but its most unique feature is the attention mechanism. This allows the model to weigh the importance of different parts of the input data, making it very efficient for tasks like text summarization, question answering, and language generation. **Name. ** Some models are commonly cited or recognized under a specific name, such as PanGu or FourCastNet. Some models are named after their technical features. **Event. ** The type of weather or climatic events that the model aims to forecast is specified under this category. This could range from generalized weather conditions like temperature and precipitation to more extreme weather events. **Selection Rationale. ** In the next section, we will discuss the related reasons. In the short term, we choose three specific ones (PanGu; GraphCast and FourCastNet) as analysis targets according to the model type. And we also analyze the MetNet, which is a hybrid DNNs Model. The other hybrid DNNs Model (ConsLSTM) is one part of MetNet. Inthe medium-to-long term, we choose the probabilistic deep learning model (Conditional Generative Forecasting). It has more extensive applicability compared with the other one in the probabilistic deep learning category. The probabilistic deep learning method is also a minority machine learning method that could be used in medium-to-long-term prediction. In addition, we also selected three machine learning-enhanced methods for Down-scaling: bias correction and emulation. In general, our survey includes established models recognized for their utility in applying machine learning to meteorological tasks and cutting-edge complex models viewed from a computer science standpoint as state-of-the-art.