Periodic Reporting for period 1 - SKYFORA (Next-generation weather intelligence for more accurate decision making throughout the economy)
Periodo di rendicontazione: 2023-02-01 al 2024-01-31
Accurate weather forecasting is vital for various industries, including utilities, agriculture, transport, construction, and disaster risk management. The World Bank estimates that these weather-sensitive industries could potentially benefit by approximately €160 billion annually from improved forecasting. Weather's impact is increasing due to climate change, creating demand for advanced weather monitoring solutions from public and private sectors.
Private weather companies face challenges due to the high cost of weather observation infrastructure, relying heavily on public agencies' data. Quality and coverage vary by region, hindering comprehensive forecasting. However, the growing global network of 5G base stations offers an untapped opportunity. Equipped with dual-band GNSS receivers, they capture navigation signal delays caused by atmospheric interference, which can be processed for precise weather forecasting.
Skyfora aims to unlock this potential, providing previously unattainable weather insights both hyperlocal and global scales using proprietary technology. Skyfora extracts diverse weather parameters from GNSS signal delay data, creating 3D weather tomography and AI-based forecasts. Utilizing 5G base stations and StreamSonde technology for data collection, Skyfora's GNSS-based infrastructure will offer superior spatial coverage and observation accuracy.
Unlike current methods, GNSS signals arrive from all directions, enabling 3D weather volume estimation. The expanding 5G network ensures data collection even in challenging locations, improving local forecasting and global climate monitoring.
Skyfora's WeatherCTScan software will process delay data, combining it with external weather information for calculations. High-resolution weather data will construct 3D tomography models for real-time weather forecasts with unprecedented accuracy, down to a single square kilometer. Additionally, this data aids in assessing seasonal climate variations and extreme weather events, benefiting public safety and aiding industry-specific planning.
Skyfora's innovative approach will empower public institutions to issue timely warnings and coordinate emergency services. In the private sector, it will enable tailored, high-quality weather analysis for weather-sensitive industries, offering substantial economic benefits.
Private weather companies face challenges due to the high cost of weather observation infrastructure, relying heavily on public agencies' data. Quality and coverage vary by region, hindering comprehensive forecasting. However, the growing global network of 5G base stations offers an untapped opportunity. Equipped with dual-band GNSS receivers, they capture navigation signal delays caused by atmospheric interference, which can be processed for precise weather forecasting.
Skyfora aims to unlock this potential, providing previously unattainable weather insights both hyperlocal and global scales using proprietary technology. Skyfora extracts diverse weather parameters from GNSS signal delay data, creating 3D weather tomography and AI-based forecasts. Utilizing 5G base stations and StreamSonde technology for data collection, Skyfora's GNSS-based infrastructure will offer superior spatial coverage and observation accuracy.
Unlike current methods, GNSS signals arrive from all directions, enabling 3D weather volume estimation. The expanding 5G network ensures data collection even in challenging locations, improving local forecasting and global climate monitoring.
Skyfora's WeatherCTScan software will process delay data, combining it with external weather information for calculations. High-resolution weather data will construct 3D tomography models for real-time weather forecasts with unprecedented accuracy, down to a single square kilometer. Additionally, this data aids in assessing seasonal climate variations and extreme weather events, benefiting public safety and aiding industry-specific planning.
Skyfora's innovative approach will empower public institutions to issue timely warnings and coordinate emergency services. In the private sector, it will enable tailored, high-quality weather analysis for weather-sensitive industries, offering substantial economic benefits.
In Work Package 2, the Skyfora team has been actively engaged in meetings with various telecom company operators and related stakeholders, with ongoing negotiations in progress. Skyfora was invited to World Economic Forum in Davos by EIC, where we unveiled Skyfora GNSS Meteorology innovation publicly.
Work Package 3 activities related to design and development of both hardware and software components are on-going. Hardware development team is working on mapping out the GNSS antenna elements and crafting an architecture that prioritizes key functionalities. The GNSS signal processing has made significant progress, encompassing data from various external and in-house sources, including multiple receiver vendors and countries. Corrections have been refined and improved from the prototype version, enhancing the system's accuracy and reliability. The current project status involved the on-going development of a secondary test set-up intended for utilization by the EMS. To enhance efficiency and scalability, discussions are underway with the local EMS regarding potential increases in capacity and pricing policies. These initiatives aim to fortify our testing capabilities and streamline production processes.
In addition to GNSS data, Skyfora has successfully developed and implemented other weather data sources for conversion into weather data. While these sources are operational, the team is still working on implementing advanced machine learning techniques to further enhance the quality and precision of the derived weather information.
The AI algorithm's computational performance has seen significant enhancements. The team has established a robust development infrastructure that enables rapid and efficient training and testing of model generations. This infrastructure streamlines the development process, ensuring that AI models can be iterated and optimized effectively. Also a prototype has been developed for deep-learning-based tomography, alongside with some data visualizations.
One of the most promising developments is the continuous improvement in model accuracy. Skyfora's models consistently outperform alternatives across various critical metrics, signifying their capability to provide superior weather forecasting and insights.
Work Package 3 activities related to design and development of both hardware and software components are on-going. Hardware development team is working on mapping out the GNSS antenna elements and crafting an architecture that prioritizes key functionalities. The GNSS signal processing has made significant progress, encompassing data from various external and in-house sources, including multiple receiver vendors and countries. Corrections have been refined and improved from the prototype version, enhancing the system's accuracy and reliability. The current project status involved the on-going development of a secondary test set-up intended for utilization by the EMS. To enhance efficiency and scalability, discussions are underway with the local EMS regarding potential increases in capacity and pricing policies. These initiatives aim to fortify our testing capabilities and streamline production processes.
In addition to GNSS data, Skyfora has successfully developed and implemented other weather data sources for conversion into weather data. While these sources are operational, the team is still working on implementing advanced machine learning techniques to further enhance the quality and precision of the derived weather information.
The AI algorithm's computational performance has seen significant enhancements. The team has established a robust development infrastructure that enables rapid and efficient training and testing of model generations. This infrastructure streamlines the development process, ensuring that AI models can be iterated and optimized effectively. Also a prototype has been developed for deep-learning-based tomography, alongside with some data visualizations.
One of the most promising developments is the continuous improvement in model accuracy. Skyfora's models consistently outperform alternatives across various critical metrics, signifying their capability to provide superior weather forecasting and insights.
Not applicable at this stage of the activities.