# Machine Learning Methods in Weather and Climate Applications: A Survey
## 7 Discussion
### Overall Comparison  
In this section of our survey, we delineate key differences between our study and existing surveys, thereby underscoring the unique contribution of our work. We contrast various time scales--short-term versus medium-to-long-term climate predictions--to substantiate our rationale for focusing on these particular temporal dimensions. Additionally, we draw a comparative analysis between machine learning approaches and traditional models in climate prediction. This serves to highlight our reason for centering our survey on machine learning techniques for climate forecasting. Overall, this section not only amplifies the distinctiveness and relevance of our survey but also frames it within the larger scientific discourse. **Comparison to existing surveys. ** Compared to existing literature, our survey takes a unique approach by cohesively integrating both short-term weather forecasting and medium-to-long-term climate predictions--a dimension often underrepresented. While other surveys may concentrate on a limited range of machine learning methods, ours extends to nearly 20 different techniques. However, we recognize our limitations, particularly the challenge of providing an exhaustive analysis due to the complexity of machine learning algorithms and their multifaceted applications in meteorology. This signals an opportunity for future research to delve deeper into specialized machine-learning techniques or specific climatic variables. In contrast to many generalized surveys, our study ventures into the technical nuances of scalability, interpretability, and applicability for each method. We also make a conscious effort to incorporate the most recent advances in the field, although we acknowledge that the pace of technological change inevitably leaves room for further updates. In sum, while our survey provides a more comprehensive and technically detailed roadmap than many existing reviews, it also highlights gaps and opportunities for future work in this rapidly evolving interdisciplinary domain. **Short-term weather prediction vs. medium-to-long-term climate prediction. ** Short-term weather predictions focus on immediate atmospheric conditions within a time span of hours to days. This is a contrast to medium-to-long-term climate predictions, which aim to forecast broader patterns in weather, temperature trends, and precipitation averages over extended timeframes of months to decades. The goals underlying these two forms of prediction also diverge significantly. Short-term forecasts are usually operational in nature, aimed at immediate public safety or aiding sectors like agriculture and industry, whereas medium-to-long-term predictions typically inform strategic and policy-oriented planning for various societal sectors, including agriculture, energy, and urban development. This comparison extends to the variables considered in the predictive models. Short-term weather predictions often hone in on localized states like temperature, humidity, wind speed, and precipitation. On the other hand, medium-to-long-term climate predictionsscrutinize a wider array of variables, such as average temperature shifts, sea-level rise, and the general patterns of extreme weather events, often on a global or regional scale. Regarding methodologies, machine learning techniques such as neural networks, random forests, and support vector machines are frequently deployed in the realm of short-term weather prediction, owing to their prowess in swiftly analyzing large datasets. In contrast, for medium-to-long-term climate predictions, machine learning generally complements traditional physics-based models, serving a supplementary role to handle the complexities and uncertainties inherent in longer-range forecasts. Finally, each type of prediction comes with its own set of challenges. Short-term forecasts grapple with issues related to the accuracy and granularity of the data and the speed of its dissemination to the public.