Weather Forecasting Services Market Size & Share, by Organization Size (Large Enterprises, Small & Medium Enterprises); Forecast (Nowcasting, Short-range, Medium-range, Long-range); Industry - Global Supply & Demand Analysis, Growth Forecasts, Statistics Report 2025-2037

Growth Drivers

• Advanced technological innovations: Observational ecosystem IT ecosystem comprising communication, storage, and all internal and external interfaces are the two main infrastructural elements that determine the performance of NEWP systems. Recent examples of such developments are space-based measurements for winds, clouds, and precipitation using lidar and radar technologies. Remote sensing technologies such as microwave sounders and infrared imagers, combined with cutting-edge ground-based observational networks including the bedrock, have successfully provided accurate weather conditions and have improved synoptic-scale forecasts.
  Increasing public-private partnerships (P3s) has improved the penetration of government resources for the development of Doppler radar and interoperable network communications for marine and land rescue, support, and management operations. In June 2019, WMO introduced the Open Consultative Platform (OCP) to foster innovation in the Next Generation of Weather and Climate Intelligence with the participation of public and private sector stakeholders, thus, imploring a community-wide approach. Furthermore, NOAA plays a pivotal role in sharing government facilities, expertise, and intellectual property with private entities under Cooperative Research and Development Agreements (CRADAs) and Economic Development Administration (EDA) programs.
  Private players are leveraging technology to diversify product/services offerings. In March 2024, Google LLC unveiled the Scalable Ensemble Envelope Diffusion Sampler (SEEDS), an AI-powered technology for probabilistic forecasts using diffusion models. Some other Google’s downstream applications include MetNet-3 and GraphCast, generating plausible weather predictions. The private sector is also investing in economic technologies, typically built upon research advances for constellations of nanosatellites and other short-lifetime missions. In June 2024, Kineis, a satellite connectivity provider and operator for the IoT deployed an IoT constellation of 25 nanosatellites in Asia Pacific.
• Ongoing product launches and emergence of smart computers: HPC efficiency and focus on performance-per-energy cost are the overarching driving factors for numerical weather prediction design considerations. Accurate predictions are attainable with high-resolution computationally intensive models such as GPUs. In March 2024, NVIDIA launched the Blackwell GPU catering to weather predictions, electronic design automation, and generative AI – all emerging opportunities for the company.
  Smart computers are highly used in data-driven models and simulated weather forecasts for knowledge discovery and predictive modeling. This assures weather prediction accuracy. World Metrics in 2024 estimated that the United States dominated the globally installed supercomputers by 33.8%. Moreover, because supercomputers can now evaluate high-resolution weather forecasts and climate models more accurately, weather predictions will be more precise. Forecasters can better understand long-term weather trends with the use of complex software models and simulator-based techniques. This will also increase the high performance computing market share in the forecast years.

Challenges

• Nonlinear weather: Weather forecasting models use real-time oceanic and atmospheric conditions to predict future weather because weather conditions change frequently. A weather forecast considers cloud cover, temperature, air pressure, humidity, and wind speed. Weather forecasting models consider a wide range of factors, such as latitude and elevation, closeness to water sources, geographic location, and urban constructions.
• Complex process and lack of specialized models: There is less availability of resources and data about weather data in several regions. This acts as a restraining factor to produce accurate weather predictions in many regions. Attributed to which, extreme weather events like tornadoes, hurricanes, and many more.