Weather Forecasting System Market Size & Share, by Component Type; by Forecast Range (Nowcast, Short-Range Forecast, Medium-Range, Long-Range); Application (Weather Observing Systems, Weather Satellites, Weather Stations, Drones, Weather Balloons); End-Use (Agriculture, Transportation, Aviation, Energy & Utilities, Marine, BFSI, Meteorology, Weather Service Providers) - Global Supply & Demand Analysis, Growth Forecasts, Statistics Report 2024-2036

Growth Drivers

• Increasing Climate Change- Long-term shifts in temperature and weather patterns are referred to as climate change. These changes caused by natural processes, such as fluctuations in the solar cycle. According to the data provided by the National Centers for Environmental Information, the worldwide surface temperature in June 2022 was 0.87°C (1.57°F) above the 20th century estimate, the sixth highest in the 143-year record. This month was also 0.08°C (0.14°F) colder than the warmest June ever noted, which occurred in 2019.
• Increasing Occurrences of Tropical Cyclones- Tropical cyclones carry one of the largest threats to property and human life, even in the early phases of their formation. They contain a variety of dangers that each have the potential to seriously harm people and property. As per a report, worldwide, around 5 tropical storms with winds >_ 39 mph have occurred in the month of June 2022.
• Surge in Heavy Precipitation – The incidences of heavy rainfall or precipitation have been increasing on a global scale, which lead to the requirement of a precise prediction of such events. As per the data provided by the United States Environmental Protection Agency, by July 2022, global precipitation has grown at a rate of 0.04 inches per decade on average as of 1901, while precipitation in the contiguous 48 states has raised at a rate of 0.20 inches per decade.
• Escalating Use of AI for Weather Forecasting- Since the researchers are utilizing varied ways of AI i.e., deep learning, machine learning, and neural networks to predict and analyze the weather conditions, the process of weather forecasting has become swift and cheaper. For instance, Deep Weather, a computer that costs around USD 10170 is efficient to make the same forecast in 100 milliseconds, as compared to the outdated way of weather forecasting, which takes three hours and is significantly more expensive.

Challenges

• Lack of Efficient Automation
• Barriers of Complexities in Climate Models
• High Cost for the Installation of the Weather Forecasting System