This session offers a platform to showcase the utilisation and value of weather information all the way from its origins to the utilisation by end users. Use cases span civil authorities, transportation, tourism, building management, energy, agriculture, and many other sectors.
The session aims further to evaluate the benefits of investments into the meteorological value chain, to build the scientific and political basis for public and private investments, and to share best practices on how the weather value chain can improve the value delivered to society though the wide range of activities, ranging from observations to forecasting and climate services. The session further explores the opportunities for increased collaboration between public, private and academic actors within the Weather Enterprise.
MeteoIQ collects, harmonizes, and processes meteorological data from a wide range of providers, forming the backbone of several operational services. These data sources include in situ weather station observations, numerical weather prediction (NWP) outputs, emerging AI-based forecast products, as well as radar and satellite-derived datasets. The modalities for accessing these data vary considerably. They range from fully unrestricted open-data platforms to restricted or commercial services with heterogeneous licensing schemes, formats, and delivery mechanisms.
Over recent years, the broader shift towards open data in meteorology has created significant opportunities for innovation, interoperability, and downstream service development. At the same time, it has introduced new challenges for service providers. These include inconsistencies in metadata standards, varying levels of data quality control, lack of versioning, and uncertainties related to long-term availability and reliability. Such issues can directly affect the robustness of value-added services and the reproducibility of derived products.
In this contribution, we present a service-provider perspective on best practices for meteorological data sharing. Drawing from our operational experience and the requirements of our users, we identify key principles that facilitate efficient data reuse and integration. These include standardized and well-documented metadata, stable and predictable access interfaces (e.g., APIs), transparent data provenance, clear licensing conditions, and consistent update cycles. We also highlight the importance of communication with data re-users as well as offering prompt and competent operational support.
Furthermore, we discuss how these best practices not only reduce technical barriers but also foster trust and collaboration between data providers and downstream users. By illustrating practical examples from our workflows, we aim to contribute to ongoing discussions on how to make meteorological data more accessible, reliable, and usable for a wide range of applications, from research to commercial services.
How to cite: Schulze, D., Müller, E., and Hoffmann, J.: Best practices on data sharing from a service provider perspective, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-622, https://doi.org/10.5194/ems2026-622, 2026.
Weather is increasingly harnessed, both directly and indirectly, to generate economic, social, and environmental value. Despite this widespread dependence, the opportunities and benefits of weather remain poorly conceptualised and instead, focus is typically around value as it applies to risk reduction. However, across many sectors, decision‑making identifies value in weather conditions and information that relates to creating opportunities and maximising benefits. Direct uses of weather include applications such as wind energy generation or snow for ski tourism, while indirect uses include weather conditions determining the timing of agricultural activities such as sowing or harvesting. These direct and indirect contributions of weather align closely with established concepts of natural resources and ecosystem services. However, weather itself has received limited explicit attention as a resource or service within scientific and economic frameworks.
In this study, we build on theoretical foundations from the ecosystem services literature to conceptualise weather as both a resource and a service, and to clarify the distinction between the two. We propose definitions that emphasise how weather can be directly utilised to create value or indirectly enable value across an array of systems. To establish these definitions, we undertook a systematic literature review to identify how weather is used and harnessed across sectors, including energy, agriculture, transport, tourism, construction, and health. Thematic analysis from this literature was cross mapped to existing theoretical concepts in the literature around other natural resources and services. From this, we developed a new framework for defining weather resources and weather services, which can be applied in a variety of contexts that employ a benefit‑focused framing rather than a hazard‑centric approach.
Applying this framework allows for the identification of the distribution and reliability of weather resources and services, providing a foundation for assessing their economic, social, and environmental value. Such an approach is increasingly important given the increasing reliance on weather‑dependent systems (e.g. energy, water, food). At the same time, anthropogenic climate change is altering the distribution, timing, and reliability of weather, reshaping the availability and performance of weather resources and services in ways that are not yet fully understood. Explicitly conceptualising weather as a resource and service is therefore essential for anticipating future risks and opportunities to maximise economic opportunities, but also for supporting adaptation and mitigation strategies, and informing decision‑making in a rapidly changing climate.
How to cite: Gallant, A., Stubbington, A., and Xie, S.: A framework for identifying weather resources and services, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-758, https://doi.org/10.5194/ems2026-758, 2026.
The WMO states that between 1970-2019, over sixty percent of economic losses due to weather-, climate- and water-related disasters were reported for developed economies. While these economic losses were equivalent to less than 0.1% of the gross domestic product (GDP) for most of the disasters, it still resulted in billions of dollars lost per decade. When there are losses, there are also opportunities and innovations. National meteorological services have begun to take a stronger leadership role in improving the economy and providing services that enhance forecasting, provide earlier warnings, safer societies, and utilize climate research for future planning.
The Met Office is advancing its leadership through investment and focus on their weather programs. An independent 2024 assessment by London Economics estimated the Met Office will deliver £56 billion in benefit to the UK economy over the next decade. This value depends entirely on the quality and breadth of weather intelligence and establishes that investment in observation infrastructure is not merely a technical choice but an economic one.
The Met Office partnered with Synoptic Data, whose cloud-native Data-as-a-Service (DaaS) platform aggregates real-time surface observations from thousands of stations across diverse third-party networks in the UK, Ireland, and wider Europe. Synoptic handles data ingest, automated quality control, format standardization, and secure API-based dissemination — delivering a single, consistent, high-quality observation stream directly into Met Office operational systems, without the overhead of managing individual network relationships.
The Met Office continues to recognize the value of third party data, including citizen science observations, as a critical element of the observation infrastructure required for modern forecasting. Developments in artificial intelligence and machine learning are demonstrating their ability to digest large volumes of data and open up high resolution modeling of the urban environment — a region poorly sampled by traditional NMHS-owned networks.
This presentation will discuss the collaboration and DaaS approach, which represents a replicable, cost-effective framework for NMHSs seeking to expand observational coverage without proportional increases in coordination overhead. By framing observation data access as an enabler of economic value — not just a technical function — this partnership offers a model for how the global meteorological community can justify and scale investment in observation infrastructure.
How to cite: Wilson, E. and Taylor, J.: Investing in Observations: How Public-Private Partnerships and Weather Data Infrastructure Produces Economic Benefit, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-387, https://doi.org/10.5194/ems2026-387, 2026.
This study presents a comprehensive global analysis of the budgets of National Hydro-Meteorological Services (NHMS) over the period 2020–2025, across more than 150 countries spanning Europe, Asia, Africa, North and South America and Oceania. The research has collected the extent and development of NHMS financing, and made them comparable in common currency. Where available , the budget are distinguished between public (government) funding and service-generated revenue.
The study also presents the personnel resources, and evaluates the expense per employee, and summarises differences between countries of different economic zones.
The study proposes a combination of methodologies to find and analyse NHMS budgets and employment, and is the first such worldwide, which makes NHMS investment and employment comparable amongst countries.
For better understanding of the funding structures, the countries are divided into different clusters of climate, GDP per capita and NHMS funding. The study thereby shows differences in the NHMS funding levels between countries. Despite their high socio-economic value- estimated to yield benefits at least ten times greater than investment costs , the NHMS their funding levels remain relatively modest and unevenly distributed globally. Available evidence indicates that NHMS budgets typically represent only a very small fraction of GDP, averaging approximately <0.1 to 10 US$/capita in sampled countries, with relatively lower ratios in low-income and some very large nations .
As complement to the NHMS funding, data from previous studies on private weather services are included to assess the level public in relation to private investment.
The study reveals four major structural patterns.
First, public funding is the dominant financial source of NHMS funding worldwide.
Second, service revenue—derived from specialized data services, aviation meteorology, and private-sector partnerships—is a secondary component, with significant regional variation depending on regulatory frameworks and market maturity.
Third, the balance between operational expenditure and capital investment is skewed: a large share of budgets is consumed by operational and staffing costs, leaving limited resources for innovation, digital transformation, and expansion of service capabilities .
Fourth, the private sector is only present in countries with higher NHMS funding.
As conclusion, the study proposes some KPIs (key Performance Indicators) to measure the investment of countries into meteorological services, which can be used to compare the effectiveness of meteorological services and levels of optimal resourcing in each country. The study will further provide a basis to review the effectiveness of public policies to develop NHMS public, as well as private Weather services for the larger benefit of the economy and society.
How to cite: Gutbrod, K. G.: Development of National Hydro-Meteorological Services (NHMS) budget worldwide from 2020-2025, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-773, https://doi.org/10.5194/ems2026-773, 2026.
MeteoGate supports and builds collaboration throughout the meteorological community by making data easy to share, find, access, and then use to deliver value.
Whether an academic researcher, NMS operational forecaster, private weather service developer, or a member of the public who simply enjoys weather data, MeteoGate makes it easy to find what data is available. MeteoGate can give a preview of the data for users to check it is what they need before downloading it. Once found, data access using APIs or bulk download is through the secure MeteoGate API Gateway. Users can sign up for notifications so they don’t miss new or updated datasets becoming available.
MeteoGate was developed at part of the RODEO project, presented at EMS2023 (ES1.5 RODEO Project – bringing more European meteorological data open for all users). The design was based on the EU’s Data Space Support Centre’s blueprint with the development work to create this meteorological data space co-funded by EUMETNET and EU. The RODEO project also responded to the requirements of the EU Directive (2019/1024) on Open Data and the Reuse of Public-sector Information and its Implementing Regulation. A key aim of this legislation and project was to boost the re-use and combination of open public data across the EU, particularly the High Value Datasets: Weather observation data, climate data, warnings, weather radar data and Numerical Weather Prediction (NWP) data.
MeteoGate is also aligned with the objectives of the global meteorological community, notably the World Meteorological Organisation’s (WMO) Unified Data Policy which commits WMO Member Nations to supporting free and open exchange of meteorological data. MeteoGate builds on the WMO Information System 2.0 (WIS 2.0), utilising the capabilities of their Global Discovery Catalogue and Global Message Broker. Owned by EUMETNET, the MeteoGate system is operated and maintained by the Finnish Meteorological Institute, providing reassurance that the underpinning infrastructure is effectively, sustainably, and securely run for our community.
As well as MeteoGate vastly increasing the amount of meteorological data available for all, an active discussion forum has been established. With engagement from data providers, the user community, and MeteoGate support team, this providing a key platform to assist increased collaboration between all actors within the Weather Enterprise.
MeteoGate is already up and running, supporting better services and information to decision makers. However, the development journey is far from over. Although initially created with a focus on sharing High Value Datasets, MeteoGate has been designed to share any hydro-meteorological data which uses WMO data standards and formats. Through showcasing the benefits of the investment in innovative development of a federated, cloud-based infrastructure, opportunities can be explored for increasing and widening the available data for the benefit of all, and the services to support effective use of that data.
How to cite: Wardle, J.: MeteoGate – A one-stop shop for sharing, discovering and accessing meteorological data, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-515, https://doi.org/10.5194/ems2026-515, 2026.
AI forecasts have some impressive high-level statistics; less clear is how useful they are in operational forecasting for end users.
Many went into winter 2025/26 excited to see if AI models (including the ECMWF AI ensembles) would have better skill at forecasting the significant atmospheric pattern shifts during the winter. For energy end users the question was especially whether they would forecast any potential cold outbreaks more rapidly, or more reliably, than NWP model output. And how they handled associated wind, and so renewable output.
Here we take a few case studies over the last winter and compare the ECMWF AI and NWP ensemble forecasts for 2m temperature and 100m wind over NW Europe.
Winter 2025/26 had some significant stratospheric activity, including an early sudden stratospheric warming in Nov which many operational meteorologists (and some academics) regard as influential on the winter temperatures for Europe throughout winter, tho by no means the only driver! This raises the question of whether a lack of (upper) stratosphere in the ECMWF AI ensembles impacted forecast skill. We will look at how well the ECMWF AI ensembles fared in capturing changes propogating from the upper stratopshere.
ECMWF will release new models for NWP and AI in spring 2026 with the new AI model including a 10hPa level for better representation of the stratosphere. At the time of writing this abstract, it’s unclear when AI v2 test data will be available from: if possible we will look to see whether a better representation of the stratosphere in the next generation of the ECMWF AI model improved performance during stratospherically-driven events of last winter.
How to cite: Finney, I.: Comparing AI and NWP operational forecasts for the energy sector for winter 2025/26, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-68, https://doi.org/10.5194/ems2026-68, 2026.
The World Meteorological Organization’s Early Warnings for All (EW4All) initiative aims to ensure that every person is protected by life-saving early warning systems by 2027. However, maritime navigation, particularly in offshore and remote ocean regions, remains relatively underserved. This gap is driven by sparse observational coverage, intermittent connectivity, and a lack of tailored, context-aware alerting tools adapted to the needs of sailors and maritime operators.
Weather fronts are key drivers of hazardous marine conditions, often associated with abrupt wind shifts, strong gusts, and convective activity. Despite their importance, automated front detection remains challenging due to the lack of a universally accepted scientific definition and inconsistencies in the meteorological variables available in different numerical weather prediction models.
To address these challenges, we have developed a front detection system that directly ingests and processes raw forecast data from multiple numerical weather prediction providers (e.g., ICON, ECMWF, GFS) to identify and refine warm and cold fronts. The system highlights the most hazardous parts of the frontal zones expected along maritime routes.
The detection module combines the Thermal Front Parameter (TFP) diagnostic with Machine Learning techniques to locate and classify fronts at each forecast timestep. A complementary processing step is further used to emphasize the most active and potentially dangerous segments of each front. The detection is performed independently on each model, with the methodology adaptively configured to the specific variables and resolutions available in each dataset.
The system is implemented within our platform, where front-related hazards are integrated directly into route planning and displayed as targeted warnings along the user’s trajectory. This approach enables improved anticipation of hazardous conditions, supporting safer and more efficient navigation. More broadly, it illustrates how combining meteorological expertise, data processing, and application-driven design can enhance the practical value of weather information within the maritime sector.
How to cite: Zygarlowska, E., Dumard, C., and Rochut, B.: Automated Detection of Weather Fronts for Maritime Safety, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-513, https://doi.org/10.5194/ems2026-513, 2026.
Verifying the quality of weather warnings is important for improving warnings, building trust, and enabling stakeholders to choose the right course of action. But what makes a warning valuable from a user’s point of view?
The recipients of weather warnings, such as emergency response teams, commercial actors, or the general public, may each prioritize different aspects of weather warnings. For instance, some may value the correct intensity of an event over its correct timing, while others may prefer correct location over the correct duration. In addition, the trade-off between over- and under-forecasted weather events may vary between stakeholders as some might prioritize minimizing missed events while others prefer not having too many false alarms. To better capture these individual preferences, this work showcases the efforts made at the German Meteorological Service (Deutscher Wetterdienst, DWD) in the context of the renewal of its warning system (i.e., the RainBoW program: “Risk-based, Application-oriented and Individualizable Provision of Optimized Warning Information”) to understand how users perceive the quality of warnings.
To explore what properties of a warning are the most important to the general public - the largest group of recipients of weather warnings - we conducted two non-representative surveys during two ‘open house’ DWD events. Participants recorded how satisfied they were with forecasts if specific attributes such as intensity, timing, location, or persistence were incorrect and affected the outcome of their planned activities.
In addition to this public survey, a workshop with stakeholders from disaster response teams provided further insights into their specific needs and interests towards forecast quality.
With this information from different stakeholders, we aim to develop verification methods that take these preferences into account when evaluating the accuracy of a warning. The development of new verification methods may be supported by user reports of the current weather collected through the official DWD weather app.
A key component of any user-oriented verification approach is an effective communication of the results. This requires translating complex statistical information into clear practical information that is accessible and meaningful for non-specialist audiences.
How to cite: Sauter, C., Wapler, K., Feige, K., Gehlen-Zeller, M., and Primo, C.: Towards User-Oriented Verification of Weather Warnings: Insights from Public and Stakeholder Preferences, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-348, https://doi.org/10.5194/ems2026-348, 2026.
This paper proposes a structured economics process to define the need and optimal scope of meteorological measurements for a specific area. Rather than relying on generic standards, environmental zoning or purely technical considerations, the approach links measurement requirements directly to the economic value of weather-sensitive decisions, enabling targeted and cost-effective investments in observation systems.
The proposed framework is based on five key evaluation steps:.
First, the economic annual value of decisions influenced by weather at the site is quantified. Use cases provided for these decisions are agricultural operations, energy production and distribution, construction planning, and outdoor sports.
Second, the proportion of these decisions that can be improved through enhanced meteorological information is assessed, using the Use cases pr This dimension captures the degree to which better temporal or spatial resolution, increased accuracy, or additional variables can lead to measurable changes in operational choices and outcomes.
Third, the expected lifetime of the measurement system is considered, ensuring that long-term benefits are properly accounted for in the evaluation.
Fourth, the full lifecycle cost of the measurement solution - including installation, operation, maintenance, data processing, and system depreciation - is incorporated.
Fifth, the RoI of the investment can be calculated for different measurement options.
By integrating these four decision criteria into the decision process, the paper develops a simple decision model that estimates the net economic benefit of a given measurement configuration. The approach allows for comparison between alternative system designs, ranging from minimal sensor deployments to advanced, multi-parameter observation networks. It also provides some guidelines to quantify the economic value of use cases. It further supports sensitivity analyses to account for uncertainty in economic valuations, technological performance, and environmental variability.
The results demonstrate that the economic justification for meteorological measurements is highly site-specific and strongly dependent on sectoral context, rather than only on environmental or technical factors. In high-value environments - such as precision agriculture, renewable energy systems, or critical infrastructure - relatively small improvements in weather information can yield substantial economic returns, justifying more sophisticated and costly measurement systems. Conversely, in lower-value contexts, simpler and more cost-efficient solutions should be sought.
The framework also allows the economic evaluation of existing measurement networks and provides a transparent and replicable methodology for decision-makers, investors, and service providers to define meteorological measurement needs in economic terms. It shifts the focus from technology-driven deployment to value-driven design, thereby supporting more efficient allocation of resources and enhancing the impact of weather information on operational and strategic decision-making across sectors.
How to cite: Gutbrod, K. G. and Bader, N.: A new process based on economics to define the regional need for weather measurements., EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-774, https://doi.org/10.5194/ems2026-774, 2026.
Supporters & sponsors
