# Machine Learning Methods in Weather and Climate Applications: A Survey
###### Abstract  
With the rapid development of artificial intelligence, machine learning is gradually becoming popular for predictions in all walks of life. In meteorology, it is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction--a field of growing importance across multiple sectors, including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modeling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field. machine learning; weather prediction; climate prediction; meteorological forecasting; survey 2023  
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and typhoons is instrumental in disaster management, offering the possibility of early warnings and thereby mitigating loss of life and property [3]. Although climate prediction is often ignored by human beings in the short term, it has a close relationship with Earth's life. Global warming and the subsequent rise in sea levels constitute critical challenges with far-reaching implications for the future of our planet. Through sophisticated climate modeling and forecasting techniques, we stand to gain valuable insights into the potential ramifications of these phenomena, thereby enabling the development of targeted mitigation strategies. For instance, precise estimations of sea-level changes in future decades could inform rational urban planning and disaster prevention measures in coastal cities. On an extended temporal scale, climate change is poised to instigate considerable shifts in the geographical distribution of numerous species, thereby jeopardizing biodiversity. State-of-the-art climate models integrate an array of variables--encompassing atmospheric conditions, oceanic currents, terrestrial ecosystems, and biospheric interactions--to furnish a nuanced comprehension of environmental transformations [4]. This integrative approach is indispensable for the formulation of effective global and regional policies aimed at preserving ecological diversity. Economic sectors such as agriculture, fisheries, and tourism are highly susceptible to the vagaries of climate change. Elevated temperatures may precipitate a decline in crop yields, while an upsurge in extreme weather events stands to impact tourism adversely. Longitudinal climate forecasts are instrumental in guiding governmental and business strategies to adapt to these inevitable changes. Furthermore, sustainable resource management, encompassing water, land, and forests, benefits significantly from long-term climate projections. Accurate predictive models can forecast potential water scarcity in specific regions, thereby allowing for the preemptive implementation of judicious water management policies. Climate change is also implicated in a gamut of public health crises, ranging from the proliferation of infectious diseases to an uptick in heatwave incidents. Comprehensive long-term climate models can equip public health agencies with the data necessary to allocate resources and devise effective response strategies.