# Machine Learning Methods in Weather and Climate Applications: A Survey
###### Abstract
Table 1 elucidates the diverse applications of weather forecasting across multiple sectors and time frames. In the short-term context, weather forecasts are instrumental for agricultural activities such as determining the optimal timing for sowing and harvesting crops, as well as formulating irrigation and fertilization plans. In the energy sector, short-term forecasts facilitate accurate predictions of output levels for wind and solar energy production. For transportation, which encompasses road, rail, aviation, and maritime industries, real-time weather information is vital for operational decisions affecting safety and efficiency. Similarly, construction projects rely on short-term forecasts for planning and ensuring safe operations. In the retail and sales domains, weather forecasts enable businesses to make timely inventory adjustments. For tourism and entertainment, particularly those involving outdoor activities and attractions, short-term forecasts provide essential guidance for day-to-day operations. Furthermore, short-term weather forecasts play a pivotal role in environmental and disaster management by providing early warnings for floods, fires, and other natural calamities. In the medium-to-long-term scenario, weather forecasts have broader implications for strategic planning and risk assessment. In agriculture, these forecasts are used for long-term land management and planning. The insurance industry utilizes medium-to-long-term forecasts to prepare for prospective increases in specific types of natural disasters, such as floods and droughts. Real estate sectors also employ these forecasts for evaluating the long-term impact of climate-related factors like sea level rise. Urban planning initiatives benefit from these forecasts for effective water resource management. For the tourism industry, medium-to-long-term weather forecasts are integral for long-term investments and for identifying regions that may become popular tourist destinations in the future. Additionally, in the realm of public health, long-term climate changes projected through these forecasts can inform strategies for controlling the spread of diseases. In summary, weather forecasts serve as a vital tool for both immediate and long-term decision-making across a diverse range of sectors. Short-term weather prediction.Short-term weather forecasting primarily targets weather conditions that span from a few hours up to seven days, aiming to deliver highly accurate and actionable information that empowers individuals to make timely decisions like carrying an umbrella or postponing outdoor activities. These forecasts typically decrease in reliability as they stretch further into the future. Essential elements of these forecasts include maximum and minimum temperatures, the likelihood and intensity of various forms of precipitation like rain, snow, or hail, wind speed and direction, levels of relative humidity or dew point temperature, and types of cloud cover such as sunny, cloudy, or overcast conditions [18]. Visibility distance in foggy or smoky conditions and warnings about extreme weather events like hurricanes or heavy rainfall are also often included. The methodologies for generating these forecasts comprise numerical simulations run on high-performance computers, the integration of observational data from multiple sources like satellites and ground-based stations, and statistical techniques that involve pattern recognition and probability calculations based on historical weather data. While generally more accurate than long-term forecasts, short-term predictions are not without their limitations, often influenced by the quality of the input data, the resolution of the numerical models, and the sensitivity to initial atmospheric conditions. These forecasts play a crucial role in various sectors, including decision-making processes, transportation safety, and agriculture, despite the inherent complexities and uncertainties tied to predicting atmospheric behavior.