Opportunities to sell or provide data into the EWS value chain With the surge in commercial satellite imagery, IoT networks and AI capabilities, businesses are now in a prime position to offer valuable weather- related data to the EWS ecosystem. The volume of commercial satellite imagery has grown significantly. In 2010, the average number of satellite launches per year went from under 200 to over 1,200.24 As of 2020, approximately 100 terabytes (TB) of earth imaging data were being collected every day, and the value of the earth imaging market is expected to quadruple between 2023 and 2030.25 These developments provide an unprecedented wealth of high-resolution data that can be integrated into EWS. Additionally, AI-powered analytics enable businesses to process vast amounts of satellite and sensor data in real-time, generating actionable insights for governments, industries and customers. Private IoT networks increasingly provide localized, real-time weather data that can enhance the precision and accuracy of official meteorological systems. For example, companies that develop satellite technology or IoT-based sensors are uniquely positioned to sell (or otherwise provide) their data streams to NMHS or other stakeholders involved in EWS. By offering this data, businesses can create new revenue streams while contributing to improved forecasting and risk management for the public and private sectors. For example, a company specializing in the aerospace and defence sector acts as an upstream provider of near real-time satellite data and related services. The company showcased its ability to assess mountainous region risks effectively by providing high-resolution topographic information. Technological advancements in data integration and forecasting Technological advancements in data assimilation have led to significant improvements in weather forecasting models. Over the past five decades, the global observing system, which includes surface observation stations and satellite-based data, has dramatically expanded. Deriving insights from this data is facilitated by the continued progress in computational power and AI witnessed in recent years. As businesses tap into these technological advancements, they can enhance the precision of their weather-related services and offer more reliable insights to their customers. For example, a business using ECMWF’s enhanced model forecasts could offer tailored risk assessments for industries such as insurance or agriculture, where precise weather predictions are crucial for managing climate risks. 3.2 Barriers to business participation in EWS Barrier 1: Gaps in data granularity, quality and usefulness Despite significant advances in weather data collection and forecasting, businesses still face challenges when applying weather insights to their specific operational needs. Many companies require highly granular data to make decisions (e.g. for individual facilities or supply chain nodes) rather than relying on broader city- or region-level insights. However, the high cost of obtaining data and the lack of compelling evidence to demonstrate a strong return on investment limit widespread adoption. Businesses are hesitant to invest heavily in asset- specific insights until the business case is clearer. In many regions of the world, even city- or regional- level data is sparse or unavailable. Global disparities in weather observation capabilities, particularly in developing regions, pose a significant challenge to accurate weather modelling. Gaps in weather observations weaken forecasting models’ ability to provide precise, localized predictions and reduce the accuracy of hydrometeorological forecasts. The latter are critical for effective decision-making, especially for industries that rely on real-time weather data to protect assets and ensure operational continuity. Globally, closing these gaps and improving forecasting precision will require significant investment. Barrier 2: Regulatory and policy barriers A significant barrier to private sector engagement in EWS is the lack of clear governance, guidance and enabling policies from governments. In many countries, the private sector’s role in contributing to or using the EWS value chain remains unclear. This is due to restrictive or poorly defined policies developed without adequate consultation with key stakeholders – such as NMHS in its role as the authoritative voice on weather, climate, water and related environmental services and warnings. In turn, this contributes to overlapping responsibilities and reduces the private sector’s ability to use EWS outputs fully, ultimately impeding the long- term development and sustainability of national EWS initiatives. For instance, a major telecommunications company highlighted that regulations developed without consulting mobile network operators failed to create cohesive national EWS frameworks. These overly complex policies discourage private sector investment and hinder collaboration between businesses and governments. A significant barrier to private sector engagement in EWS is the lack of clear governance, guidance and enabling policies from governments. Catalysing Business Engagement in Early Warning Systems 14

Catalysing Business Engagement in Early Warning Systems 2025