## 5 Weather Forecasting Models
### A Global Measurements Integration Model  
Tomorrow.io's technology enables the integration of all acquired measurements into a unified, cloud-centric global model. This approach ensures that weather forecasting models and their outputs are accessible worldwide, facilitating the development of numerous new solutions and use cases, as outlined below:  
1. Data Acquisition Measurements are continuously collected worldwide by Tomorrow.io's multi-sensor satellite constellation, which utilizes various remote sensing technologies onboard. 2. Data Transmission to Ground Stations  
The acquired data is relayed to a distributed network of commercial ground stations, such as those operated by Kongsberg Satellite Services (KSAT). KSAT, a Norwegian commercial entity, manages a global network of ground stations, specializing in receiving downlinks from polar-orbiting satellites and providing Earth observation services [29]. Its infrastructure includes 25 physical sites strategically located to ensure comprehensive coverage for polar-orbiting satellites. 3. Cloud-Based Data Processing and Accessibility  
The data received from the ground station network is transferred to a commercial cloud infrastructure, such as Amazon Web Services (AWS), as seen in Figure 7. This allows raw data to be processed by cloud-based analytics tools and ensures that weather forecasting models are accessible to any organization worldwide, enabling seamless integration into various services and applications. Figure 6: Weather Intelligence Alert Flowchart—Courtesy of Tomorrow.io. Data Processing Using Advanced Forecasting Models  
In the pre-final stage, vast amounts of collected data are fed into advanced weather forecasting models that utilize Artificial Neural Networks (ANNs) specifically designed [30] for remote sensing applications (see Figure 7). These models are required to operate in near-real time to enable the timely production of severe weather event warnings and other mission-critical outputs. Among the techniques likely employed are statistical regression approaches, which use large datasets of input-output pairs to empirically derive statistical relationships between atmospheric parameters. In simple cases, linear regression techniques based on second-order statistical moments (such as covariances) can be used to compute a linear fit that minimizes the sum of squared errors between the model and the observed data. However, due to the inherently complex and nonlinear nature of atmospheric systems, linear models are often insufficient. Consequently, nonlinear regression methods, including ANNs, are better suited for capturing the complex, high-dimensional relationships present in remote sensing datasets [31]. ANNs, as a specialized class of nonlinear regression operators, have proven highly effective for remote-sensing-based weather forecasting applications [32]. Dissemination to End-Users  
Finally, model predictions are integrated into Tomorrow.io's weather and climate security platform, where they are made accessible to end-users. This enables organizations to receive actionable, location-specific weather forecasts and severe weather alerts, enhancing preparedness and decision-making capabilities across industries. One could argue that such a model represents a significant advancement beyond the current capabilities of the Advanced Weather Interactive Processing System (AWIPS2), developed as an extension of the RSA Project under the National Oceanic and Atmospheric Administration (NOAA). AWIPS2 primarily serves to visualize site-specific weather data, providing decision-makers and monitoring personnel with improved situational awareness [33]. Figure 7: From Space to Software—Flowchart—Courtesy of Tomorrow.io. The emergence of near-real-time weather forecasting technologies is expected to have profound implications, not only for civilian sectors but also for defense and military operations.