# Machine Learning Methods in Weather and Climate Applications: A Survey
###### Abstract
Our survey extends this current understanding by including recent advances in the application of machine learning techniques such as High-Resolution Neural Networks and 3D neural networks, representing the state-of-the-art in this multidisciplinary domain. This survey endeavors to serve as a comprehensive review of machine learning techniques applied in the realms of meteorology and climate prediction. Previous studies have substantiated the efficacy of machine learning methods in short-term weather forecasting [23]. However, there exists a conspicuous dearth of nuanced research in the context of medium-to-long-term climate predictions [24]. The primary objective of this survey is to offer a comprehensive analysis of nearly 20 diverse machine-learning methods applied in meteorology and climate science. It is worth noting that our selection criteria are twofold: we include classic models in the application of machine learning to meteorology, as well as, from a computer science perspective, represent recent state-of-the-art complex models. We categorize these methods based on their temporal applicability: short-term weather forecasting and medium-to-long-term climate predictions. This dual focus uniquely situates our survey as a bridge between immediate weather forecasts and longer climatic trends, thereby filling existing research gaps summarized as follows:  
* Limited Scope: Existing surveys predominantly focus either on short-term weather forecasting or medium-to-long-term climate predictions. There is a notable absence of comprehensive surveys that endeavour to bridge these two-time scales. In addition, current investigations tend to focus narrowly on specific methods, such as simple neural networks, thereby neglecting some combination of methods. * Lack of model details: Many existing studies offer only generalized viewpoints and lack a systematic analysis of the specific model employed in weather and climate prediction. This absence creates a barrier for researchers aiming to understand the intricacies and efficacy of individual methods. * Neglect of Recent Advances: Despite rapid developments in machine learning and computational techniques, existing surveys have not kept pace with these advancements. The paucity of information on cutting-edge technologies stymies the progression of research in this interdisciplinary field. By addressing these key motivations, this survey aims to serve as a roadmap for future research endeavors in this rapidly evolving, interdisciplinary field. **Contributions of the Survey. ** The contributions of this paper are as follows. * Comprehensive scope: Unlike research endeavors that restrict their inquiry to a singular temporal scale, our survey provides a comprehensive analysis that amalgamates short-term weather forecasting with medium- and long-term climate predictions. Intotal, 20 models were surveyed, of which a select subset of eight were chosen for in-depth scrutiny. These models are discerned as the industry's avant-garde, thereby serving as invaluable references for researchers. For instance, the PanGu model exhibits remarkable congruence with actual observational results, thereby illustrating the caliber of the models included in our analysis
* In-Depth Analysis: Breaking new ground, this study delves into the intricate operational mechanisms of the eight focal models. We have dissected the operating mechanisms of these eight models, distinguishing the differences in their approaches and summarizing the commonalities in their methods through comparison. This comparison helps readers gain a deeper understanding of the efficacy and applicability of each model and provides a reference for choosing the most appropriate model for a given scenario. * Identification of Contemporary Challenges and Future Work: The survey identifies pressing challenges currently facing the field, such as the limited dataset of chronological seasons and complex climate change effects, and suggests directions for future work, including simulating datasets and physics-based constraint models.