# Machine Learning Methods in Weather and Climate Applications: A Survey
## 4 Taxonomy of Climate Prediction Applications
### Classification of Climate Prediction Methods
As such, global forecasts might not capture localized weather events as accurately as regional forecasts. However, global models are crucial for understanding large-scale atmospheric dynamics and providing the boundary conditions necessary for regional models. **ML and ML-Enhanced Types. ** We categorize models into ML and ML-Enhanced types. In ML type, algorithms are directly applied to climate data for pattern recognition or predictive tasks. These algorithms typically operate independently of traditional physical models, relying instead on data-driven insights garnered from extensive climate datasets. Contrastingly, ML-Enhanced models integrate machine learning techniques into conventional physical models to optimize or enhance their performance. Fundamentally, these approaches still rely on physical models for prediction. However, machine learning algorithms serve as auxiliary tools for parameter tuning, feature engineering, or addressing specific limitations in the physical models, thereby improving their overall predictive accuracy and reliability. In this survey, ML-enhanced was divided into three categories: bias correction, down-scaling, and emulation [66]. **Model. ** Within each time scale, models are further categorized by their type. These models include: Specific Models: These are unique or specialized neural network architectures developed for particular applications. _Specific DNN Models:_ Unique or specialized neural network architectures developed for particular applications. _Hybrid DNN Models:_ These models use a combination of different neural network architectures, such as LSTM + CNN. _Single DNN Models:_ These models employ foundational Deep Neural Network architectures like ANNs (Artificial Neural Networks), CNNs (Convolutional Neural Networks), and LSTMs (Long Short-Term Memory networks). _Non-Deep-Learning Models:_ These models incorporate machine learning techniques that do not rely on deep learning, such as Random Forests and Support Vector Machines. **Technique. ** This category specifies the underlying machine learning or deep learning technique used in a particular model, for example, CNN, LSTM, Random Forest, Probabilistic Deep Learning, and GAN. _CNN._ A specific type of ANN is the Convolutional Neural Network (CNN), designed to automatically and adaptively learn spatial hierarchies from data [67]. CNNs comprise three main types of layers: convolutional, pooling, and fully connected [68]. The convolutional layer applies various filters to the input data to create feature maps, identifyingspatial hierarchies and patterns. Pooling layers reduce dimensionality, summarizing features in the previous layer [69]. Fully connected layers then perform classification based on the high-level features identified [70]. CNNs are particularly relevant in meteorology for tasks like satellite image analysis, with their ability to recognize and extract spatial patterns [71]. Their unique structure allows them to capture local dependencies in the data, making them robust against shifts and distortions [72]. _LSTM._ Long Short-Term Memory (LSTM) units are a specialized form of recurrent neural network architecture [42]. Purposefully designed to mitigate the vanishing gradient problem inherent in traditional RNNs, LSTM units manage the information flow through a series of gates, namely the input, forget, and output gates. These gates govern the retention, forgetting, and output of information, allowing LSTMs to effectively capture long-range dependencies and temporal dynamics in sequential data [42]. In the context of meteorological forecasting, the utilization of LSTM contributes to a nuanced understanding of weather patterns as it retains relevant historical information and discards irrelevant details over various time scales [42]. The pioneering design of LSTMs and their ability to deal with nonlinear time dependencies have led to their outstanding robustness, adaptability, and efficiency, making them an essential part of modern predictive models [42].