European cloud infrastructure is helping national meteorological and hydrological services address the challenges of data growth, operational resilience and artificial intelligence
Weather forecasting has always been driven by scientific progress. Every new generation of satellites, weather radars, hydrological sensors and numerical weather prediction models has improved the ability to understand and predict the atmosphere. Yet each technological advance has also created a new operational challenge: the exponential growth of environmental data.
Today, national meteorological and hydrological services are expected to process observations from satellites, radar networks, in-situ stations, aircraft, drones, ocean buoys and IoT sensors while simultaneously operating increasingly sophisticated numerical weather prediction models. At the same time, governments, emergency authorities, researchers and commercial users expect faster access to forecasts, warnings and climate information than ever before. Artificial intelligence is adding another dimension, demanding enormous datasets and unprecedented computing power for model development and operational deployment.
Meteorology has therefore become one of the world’s most demanding data-intensive disciplines. The question is no longer simply how to generate more accurate forecasts. Increasingly, it is how to collect, process, archive, distribute and secure rapidly growing volumes of environmental information while maintaining operational resilience and controlling infrastructure costs.
These challenges are familiar to every national meteorological service. Rapid data growth, increasing IT complexity, lifecycle management of operational and historical datasets, the transition toward AI-supported forecasting, and the need for secure and sovereign digital infrastructure have become strategic priorities across Europe and beyond. At the same time, institutions must ensure interoperability with international standards, provide open access to many datasets and protect information that is critical for public safety against cyber threats and geopolitical risks.
Cloud as operational infrastructure
Traditionally, meteorological organizations have addressed these demands through continuous investment in on-premises computing centers and high-performance computing facilities. While supercomputers remain indispensable for numerical weather prediction, maintaining the supporting ICT infrastructure has become increasingly resource intensive. Hardware refresh cycles, storage expansion, cybersecurity, software maintenance and specialist personnel compete directly with investments in forecasting science and service development.
Cloud computing has emerged not as a replacement for operational forecasting systems but as an extension of them. Modern cloud platforms provide elastic computing resources, scalable storage, integrated data management and AI-ready environments capable of supporting research and operational services. Rather than forcing institutions to build and maintain every component themselves, cloud-native infrastructure enables meteorologists, hydrologists and climate scientists to concentrate on developing new forecasting techniques, improving warning systems and delivering value to society.
For organizations responsible for critical national infrastructure, however, not every cloud environment is suitable. Operational meteorology requires long-term reliability, predictable performance, compliance with regulatory requirements and complete confidence in the management of sensitive environmental data. This has driven growing interest in sovereign cloud infrastructures, where technology, operations, support and legal jurisdiction remain under trusted European control.
Experience gained through Europe’s flagship programs
Guidance from the World Meteorological Organization, together with operational initiatives led by organisations including ECMWF, EUMETSAT and the European Space Agency, reflects a broader transition toward cloud-enabled meteorological services. Across Europe, operational programs are increasingly combining high-performance computing with cloud-native technologies to support data management, AI development and the dissemination of environmental information.
The evolution of sovereign cloud infrastructure is reflected in several European Earth observation and meteorological programs. Over the past decade, European cloud providers have developed operational platforms capable of supporting multipetabyte repositories, large-scale data processing and mission-critical services for scientific and governmental organizations.
Experience gained through long-term collaborations with organizations such as EUMETSAT, ECMWF and the European Space Agency, as well as programs including Destination Earth Data Lake, Copernicus Climate Data Store, WEkEO and CREODIAS, demonstrates that sovereign cloud technologies have evolved into operational infrastructure supporting European meteorology.
These projects demonstrate that cloud technologies have moved well beyond experimentation. They now form part of Europe’s operational meteorological ecosystem, supporting the storage, processing and dissemination of environmental information on a continental scale. The experience gained while developing these infrastructures has also shaped a portfolio of cloud solutions designed specifically for national meteorological and hydrological organizations.
Integrating operational meteorological services
One of the most significant operational challenges is bringing together heterogeneous data originating from numerous observation systems and transforming it into actionable information.
Satellite imagery, radar observations, automatic weather stations, hydrological measurements and numerical model outputs all differ in format, update frequency and spatial resolution. Integrating these datasets while maintaining traceability, quality control and interoperability requires considerably more than storage capacity.
One approach to address this challenge is the use of integrated cloud-native platforms that combine data ingestion, processing, archiving and distribution within a single operational environment. Such platforms provide scalable computing, modern data governance, workflow orchestration and collaborative tools while reducing the operational burden associated with maintaining complex ICT infrastructure. Commercial implementations of such platforms are already available. One example is CloudFerro’s Meteo Data Platform.
Preserving today’s observations for tomorrow’s science
While operational forecasting receives much attention, historical observations are becoming equally valuable.
Climate research, model validation, reanalysis projects and AI training increasingly depend on rapid access to decades of archived environmental data. At the same time, maintaining these archives using traditional high-performance storage can become prohibitively expensive.
Long-term cloud archives are increasingly being designed specifically for meteorological datasets. Beyond providing economical storage, these environments combine metadata cataloging, secure access management and lifecycle policies that balance accessibility with storage efficiency. Historical observations remain readily accessible for scientific studies, climate services, operational verification and future AI applications while ensuring long-term integrity and compliance. As climate adaptation becomes a global priority, the importance of reliable environmental archives will only continue to grow. Commercially available long-term archive solutions are increasingly for meteorological organizations to address these requirements. CloudFerro’s Meteo Long Term Archive represents one implementation designed for large-scale environmental datasets.
Preparing operational meteorology for the AI era
Artificial intelligence is rapidly becoming one of the defining themes of modern meteorology.
The World Meteorological Organization has encouraged the development of AI and machine learning technologies for environmental monitoring and prediction, recognizing their potential to complement traditional numerical weather prediction. Yet operational AI depends on far more than algorithms alone.
Training, validating and deploying machine learning models requires powerful GPU resources, scalable storage, efficient data pipelines and environments capable of supporting continuous model development. Many meteorological organizations possess the scientific expertise but lack infrastructure that can easily scale to meet rapidly changing computational demands.
To support operational AI, cloud environments increasingly integrate GPU resources, scalable storage, workflow orchestration and AI development services into a single platform. Several cloud providers are integrating GPU resources, workflow orchestration and AI development environments into unified platforms. CloudFerro’s MeteoAI represents one implementation of this broader industry trend.
Looking beyond infrastructure
The future of meteorology will continue to be driven by scientific innovation, but digital infrastructure is becoming an equally critical component of operational success.
Future meteorological services must simultaneously support numerical weather prediction, AI-driven analytics, climate research, open data policies and resilient warning systems while managing ever-growing data volumes and increasingly complex ICT environments. Success will depend on platforms that combine scalability, interoperability, security and operational reliability without diverting scientific expertise toward infrastructure management.
As meteorology enters the AI era, sovereign cloud infrastructure is increasingly becoming an important component of operational meteorological ecosystems. Experience gained through European flagship programs demonstrates that, when implemented appropriately, such platforms can strengthen resilience, improve scalability and enable meteorological organizations to focus on their primary mission: delivering accurate forecasts, timely warnings and reliable environmental services.
Weather forecasting has always depended on observations, scientific expertise and international cooperation. As meteorology enters the AI era, trusted sovereign cloud infrastructure is emerging as a fourth essential pillar. By combining operational resilience, scalable computing and secure management of environmental data, it enables meteorological organizations to focus on their primary mission: transforming environmental observations into timely forecasts, effective warnings and better decisions that protect lives, infrastructure and economies.
Rafał Lewandowski is a meteorological technology expert with more than 25 years’ experience in weather radar systems, remote sensing and meteorological observation technologies. He played a key role in the development, operation and modernization of Poland’s national weather radar network, including the introduction of dual-polarization radar technology. He currently serves as senior business development manager at CloudFerro, where he is responsible for the meteorological and hydrological markets, combining extensive domain expertise with the development of cloud-based solutions for weather, water and Earth observation applications.