# Machine Learning Methods in Weather and Climate Applications: A Survey
## 4 Taxonomy of Climate Prediction Applications
### Climate Prediction Milestone Based on Machine-Learning  
In this subsection, we surveyed almost 20 methods of machine learning applications for weather prediction and climate prediction. These methods are representative and common. We listed them in the following timeline shown in Figure 1. The journey of machine learning applications in climate and weather prediction has undergone significant transformations since their inception.  
**Climate prediction methods before 2010**. The earliest model in this context is the Precipitation Neural Network Prediction Model, published in 1998. This model serves as an archetype of Basic DNN Models, leveraging Artificial Neural Networks to offer short-term forecasts specifically for precipitation in the Middle Atlantic Region. Advancing to the mid-2000s, the realm of medium-to-long-term predictions saw the introduction of ML-Enhanced Non-Deep-Learning Models, exemplified by KNN-Down-scaling in 2005 and SVM-Down-scaling in 2006. These models employed machine learning techniques like K-Nearest Neighbors and Support Vector Machines, targeting localized precipitation forecasts in the United States and India, respectively. In 2009, the field welcomed another medium-to-long-term model, CRF-Down-scaling, which used Conditional Random Fields to predict precipitation in the Mahanadi Basin.  
**Climate prediction methods from 2010-2019.** During the period from 2010 to 2019, the field of weather prediction witnessed significant technological advancements and diversification in modeling approaches. Around 2015, a notable shift back to short-term predictions was observed with the introduction of Hybrid DNN Models, exemplified by ConsvLSTM. This model integrated Long Short-Term Memory networks with Convolutional Neural Networks to provide precipitation forecasts specifically for Hong Kong. As the decade progressed, models became increasingly specialized. For instance, the 2017 Precipitation Convolution prediction model leveraged Convolutional Neural Networks to focus on localized precipitation forecasts in Guang Dong, China. The following year saw the emergence of the Stacked-LSTM-Model, which utilized Long Short-Term Memory networks for temperature predictions in Amsterdam and Eindhoven.  
**Climate prediction methods from 2020.** Fast forward to 2020, the CapsNet model, a Specific Model, leveraged a novel architecture known as Capsule Networks to predict extreme weather events in North America. By 2021, the scope extended to models like RF-bias-correction and the sea-ice prediction model, focusing on medium-to-long-term predictions. The former employed Random Forests for precipitation forecasts in Iran, while the latter utilized probabilistic deep learning techniques for forecasts in the Arctic region. Recent advancements as of 2022 and 2023 incorporate more complex architectures. Cycle GAN, a 2022 model, utilized Generative Adversarial Networks for global precipitation prediction. PanGu, a 2023 release, employed 3D Neural Networks for predicting extreme weather events globally. Another recent model, FourCastNet, leverages a technique known as AFNO to predict extreme global events. Furthermore, in 2022, this year also witnessed the introduction of DeepESD-Down-scaling and CNN-Bias-correction models, both utilizing Convolutional Neural Networks to predict local temperature scales and perform global bias correction, respectively.