World Meteorological Organization

Global water systems are under increasing pressure from climate change, hydrological extremes, and competing demands on limited freshwater resources. Robust, interoperable data and data sharing infrastructures are essential to advance scientific understanding, support operational forecasting, and inform effective water management and climate adaptation strategies. The Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) and the Global Hydrological Status and Outlook System (HydroSOS) represent complementary international efforts to unify and leverage hydrological data for science and service delivery. ROBIN, coordinated by the UK Centre for Ecology & Hydrology, addresses critical gaps in global streamflow observations by integrating long-term, near-natural catchment data to form a global Reference Hydrometric Network. This open-access dataset, comprising >3000 of quality-controlled streamflow records from near-natural catchments spanning diverse climates and geographies, is shared under common standards and protocols to enable global-scale climate change trend detection, model evaluation, and large-scale hydrological research. The initiative also fosters long-term collaboration among researchers and institutions, promoting shared infrastructure, code libraries, and harmonised metadata to support international scientific agendas. ROBIN is aligning its data-sharing processes with the GRDC, ensuring interoperability and complementarity between datasets, and strengthening the long-term international hydrological data legacy. Through collaboration with the FRIEND-Water and EUROFRIEND networks, ROBIN is increasing its emphasis on social hydrology, enabling analyses that better capture human–water interactions, vulnerability, and adaptation alongside climate-driven hydrological change. The World Meteorological Organization’s HydroSOS builds operational capacity for standardised hydrological status assessments and sub-seasonal to seasonal outlooks across spatial scales. HydroSOS enhances national and regional water information systems by linking local observations, forecasts, and global services to produce consistent products for water resources management, disaster risk reduction, and climate resilience. Developed and supported by the UKCEH, the HydroSOS portal provides open access to these standardised “change from normal” assessments, enabling transparent comparison of hydrological conditions across countries and regions. It emphasises the value of consistent and standardised hydrological information among National Meteorological and Hydrological Services, contributing to united global water information frameworks and decision support tools. Together, ROBIN and HydroSOS exemplify synergistic efforts to overcome current and historical fragmentation in hydrological data and services. By promoting open data practices, harmonised standards, and shared technical capacity, these initiatives enable a more integrated global hydrological data ecosystem that supports science, policy, and operational services in the face of evolving environmental challenges and a need to improve resilience to extreme hydrological events both now and in the future.

As climate change increasingly impacts ecosystems, economic activity, and human well-being, Europe is accelerating the development of coherent and effective climate action under the umbrella of the Paris Agreement and the EU Green Deal. This research evaluates the dynamics and effectiveness of existing mitigation and adaptation policies and governance frameworks at the European Union level and within eight European countries: Austria, the Czech Republic, Denmark, France, Germany, Italy, Romania, and the United Kingdom.This work combines an extensive review of primary EU and national policy documents in force in 2025, with insights from 42 semi-structured interviews with key informants, including experts from European institutions, national decision-makers, research organisations, and civil society representatives. The interviews provide critical insights into actual implementation dynamics, political priorities and challenges, and the institutional barriers that shape climate action beyond formal planning commitments.The analysis focuses on the core pillars of climate policy, examining objectives, timeframes, and sectoral coverage across key socio-economic sectors such as energy, transport, agriculture, health, infrastructure, and water management. A focal point is the tension between sustainable development, mitigation and adaptation, as mitigation policies are relatively mature and supported by robust EU monitoring, while adaptation remains heterogeneous and under-prioritised or under-financed. The findings reveal significant gaps in the operationalisation of climate strategies, short-termism and polarisation or weak institutional cooperation, despite the steady development of the formal strategic settings, the limited but useful involvement of civil society, the limited actual focus on societal issues and legislative gaps, among others.Our findings inform the development of the CROSSEU Decision Support System, ensuring the tool reflects real-world governance constraints and stakeholder needs. By identifying where synergies emerge and where institutional bottlenecks constrain progress, the research supports the translation of climate-socio-economic risks data into actionable, socially-informed decision pathways. Furthermore, the strong national specificities highlight the need for understanding the local context and the relevance of comparative research for fostering equitable and resilient climate strategies at the European scale.This research was funded by the "Cross-sectoral Framework for Socio-Economic Resilience to Climate Change and Extreme Events in Europe (CROSSEU)" project, under the European Union’s Horizon Europe Programme (Grant agreement No. 101081377).

Contemporary Earth and space science relies on complex infrastructures that connect observations, data transmission, processing systems and digital services. A critical but often under-recognized component of this system is access to radio-frequency spectrum, which enables both the acquisition of observations and the real-time exchange of data from meteorological satellites, weather radars, radiosondes, space weather sensors and other Earth observation systems. The radio-frequency spectrum is a physically limited and increasingly contested resource.The World Meteorological Organization (WMO), a specialized agency of the United Nations with 193 Members, provides the global coordination framework for observing and data systems through the WMO Integrated Global Observing System (WIGOS), the WMO Information System (WIS) and the WMO Integrated Processing and Prediction System (WIPPS). These systems support standardized observations, global data exchange and the delivery of operational services that underpin numerical weather prediction, climate monitoring, hydrology and environmental applications worldwide.Within this framework, the WMO Space Programme coordinates international activities related to the availability and use of satellite data and products, capacity development, space weather coordination, and cooperation on radio-frequency spectrum use. This includes engagement with scientific and regulatory communities through the Expert Team on Radio-Frequency Coordination, contributions to international technical studies, development of joint guidance (e.g., WMO–International Telecommunication Union (ITU) handbooks), and coordinated preparations for the World Radiocommunication Conference 2027 (WRC-27).This presentation frames spectrum coordination as a core element of Earth observation data systems and highlights its role in maximizing the economic, social and environmental value of global meteorological infrastructures, including societally critical initiatives such as Early Warnings for All (EW4All).

Flooding is one of Africa’s most impactful natural disasters, significantly affecting human lives, infrastructure, and economies. This study examines the spatial and temporal distribution of historical flood events across the continent from 1927 to 2020, with a focus on fatalities, affected populations, and economic damage. Data from the Emergency Events Database (EM-DAT), the fifth generation of bias-corrected European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) observational datasets were used to calculate extreme precipitation indices—Consecutive Wet Days (CWD), annual precipitation on very wet days (R95PTOT), and Annual Maximum Precipitation (AMP). Spatial analysis tools and the Mann–Kendall test were used to assess trends in flood occurrences, while Pearson correlation analysis identified key meteorological drivers across 16 African capital cities for 1981–2019. A flood frequency analysis was conducted using Weibull, Gamma, Lognormal, Gumbel, and Logistic probability distribution models to compute flood return periods for up to 100 years. Results reveal a significant upward trend with a slope above 0.50 floods per year in flood frequency and impact over the period, particularly in regions such as West Africa (Nigeria, Ghana), East Africa (Ethiopia, Kenya, Tanzania), North Africa (Algeria, Morocco), Central Africa (Angola, Democratic Republic of Congo), and Southern Africa (Mozambique, Malawi, South Africa). Positive trends (at 99% significance level with slopes ranging between 0.50 and 0.60 floods per year) were observed in flood-related fatalities, affected populations, and economic damage across Regional Economic Communities (RECs), individual countries, and cities of Africa. The CWD, R95PTOT, and AMP indices emerged as reliable predictors of flood events, while non-stationary return periods exhibited low uncertainties for events within 20 years. These findings underscore the urgency of implementing robust flood disaster management strategies, enhancing flood forecasting systems, and designing resilient infrastructure to mitigate growing flood risks in Africa’s rapidly changing climate.

Climate change poses urgent public health risks from rising global temperatures and extreme weather events, including heatwaves, droughts, and floods, which disproportionately affect vulnerable populations. To address the current silos embedded in climate, environmental, and public health monitoring and surveillance systems, climate-smart public health (CSPH) creates an integrated platform for action across these sectors, enabling more rapid and efficient responses to climate-related public health challenges. In this Personal View, we introduce the concept of CSPH, a data-driven framework designed to monitor, assess, and adapt to climate-related health impacts. CSPH incorporates surveillance, risk assessment, early warning systems, and resilient health-care infrastructure to address the evolving challenges of climate change. The framework adopts an iterative, community-centred model that responds to local needs and incorporates feedback from health-care providers and policy makers. CSPH also leverages data science and artificial intelligence to address a wide range of health concerns, including infectious diseases, non-communicable diseases, nutrition, and mental health. We applied this framework in Madagascar, a region highly vulnerable to climate impacts, where poverty, malnutrition, and frequent extreme weather events make climate adaptation particularly urgent. Early data analysis has shown strong climate sensitivity in important diseases such as malaria and diarrhoea, which could enable preparedness efforts to target some regions more efficiently. CSPH provides a pathway to enhance resilience in such settings by improving the capacity of public health systems to withstand and respond to climate-related stressors.

PCAPS Open Session: Forecast value cycles for Polar Research Operations: A workshop was held as part of the annual Steering Group meeting of the WMO World Weather Research Program project “Polar Coupled Analysis and Prediction for Services” (PCAPS). The Open Session gathered about 50 Arctic and Antarctic stakeholders to share perspectives on polar forecasting challenges, operational needs, and opportunities for collaboration.

Newborns, children, and pregnant and postpartum populations are among the most at risk from exposure to extreme heat. Heat Health Action Plans (HHAPs) are promoted by the World Health Organization to manage public health risks of heat. Still, limited research exists on how well current HHAPs address the needs of women and children in the context of increasingly frequent heat events. We identified national and subnational HHAPs published between January 2004 and July 2024 using various search channels. We extracted content to assess whether and how HHAPs included actions and indicators related to pregnant, postpartum, or breastfeeding individuals; newborns; and children. We identified 83 eligible HHAPs from 24 countries, predominantly from high-income (49%) or lower-middle-income (47%) economies, with none from low-income economies. Most HHAPs identified children as a key population to protect (83%), with fewer naming pregnant individuals (52%). Even fewer mentioned newborns (39%) or postpartum and breastfeeding individuals (14%) as at-risk groups. We identified five broad activity categories targeting maternal, newborn, and child health (MNCH), with 'informing, education, and awareness raising' (77%) and 'improving care in health services or school settings' (59%) being the most common. However, no HHAP comprehensively addressed MNCH risks during extreme heat, and monitoring mechanisms were inadequate for assessing the impact of heat on MNCH. This is the first review mapping MNCH content in HHAPs. Comprehensive action plans must incorporate targeted strategies for at-risk MNCH populations to ensure equitable health outcomes during heat events. While many HHAPs focus on behaviour change messages, structural and policy changes are needed to build broader resilience. Strengthened in-country monitoring mechanisms and global support for better documentation are essential to build an evidence base.

<strong class="journal-contentHeaderColor">Abstract.</strong> Black carbon (BC) is a key atmospheric forcer due to its interaction with solar radiation and clouds. However, accurately quantifying and understanding the impact of atmospheric aging on BC properties and radiative forcing remains a major challenge. To address this, the AIDA aRCtic Transport Experiment (ARCTEx) project simulated BC aging under quasi-realistic Arctic conditions in the AIDA (Atmospheric Interactions and Dynamics in the Atmosphere) chamber. Four distinct scenarios were simulated based on reanalysis data, representing summer and winter conditions at both low and high altitudes, to capture the variability in BC aging processes during Arctic transport. In the first part of the paper, we define the meteorological conditions characterizing norward transport under different scenarios and describe the technical solutions to simulate 5-day transport in the AIDA chamber. In the second part of the work, we assess the evolution of fundamental properties including density, morphology and mixing state observed during the aging process. The ARCTEx project demonstrates that large facilities such as AIDA can successfully reproduce environmental conditions, enabling a gradual aging process that closely follows the natural timescales observed in the atmosphere. Our experiments revealed that temperature strongly influences the aging timescale and the evolution of BC&rsquo;s diameter, effective density and coating thickness. Low-altitude scenarios exhibited rapid aging, resulting in fully-coated, compact BC particles within 39 &ndash; 98 hours, corresponding to 50&deg; N and 80&deg; N respectively. In contrast, high-altitude transport was characterized by slow aging, with limited coating and compaction, even after 115 hours of simulation. These findings provide valuable insights into the temporal evolution of BC properties during Arctic transport. In forthcoming work, we will report the implications of this evolution on climate-relevant properties such as light absorption and activation as cloud droplets and ice crystals. Together, these studies aim to enhance the representation of BC aging in climate models, reducing uncertainties in Arctic radiative forcing estimates.

Vegetation plays a crucial role in the land surface water and energy balance modulating the interactions and feedback with climate at the regional to global scale. The availability of unprecedented Earth observation products covering recent decades (and extended up to real-time) are therefore of paramount importance to better represent the vegetation and its time evolution in the land surface models (LSMs) used for offline analysis/initialization and for the seasonal-to-decadal predictions.&amp;#160;Here, we integrate realistic vegetation Leaf Area Index (LAI) variability from latest generation satellite campaigns, available through Copernicus Land Monitoring Service (CLMS), in three different LSMs that conducted the same coordinated set of offline land-only simulations forced by hourly atmospheric fields derived from the ERA5 atmospheric reanalysis. The experiment implementing realistic interannually-varying LAI (SENS) is compared with simulations utilizing a climatological LAI (CTRL) to quantify the vegetation feedback and the effects on the simulation of near-surface soil moisture.The results show that the inter-annually varying LAI considerably affects the simulation of near-surface soil moisture anomalies in all three models and over the same water-limited regions, but surprisingly the effects diverge among models: compared with ESA-CCI observations, the near-surface soil moisture anomalies significantly improve in&amp;#160; one of the three LSMs (HTESSEL-LPJGuess) while the other two (ECLand and ISBA-CTRIP) display opposite effects with significant worsening of the anomaly correlation coefficients. It is found that the enhanced simulation of near-surface soil moisture is enabled by the positive feedback that is activated by the effective vegetation cover (EVC) parameterization, implemented only in HTESSEL-LPJGuess. The EVC parameterization works such that the effective fraction of the bare soil being covered by vegetation does vary with LAI following an exponential function constrained by available satellite observations. The increased (reduced) soil-moisture limitation during dry (wet) periods produces negative (positive) LAI and therefore EVC anomalies, which in turn generate a dominating positive feedback on the near-surface soil moisture of HTESSEL-LPJGuess by exposing more (less) bare soil to direct evaporation from the sub-surface layer. On the other hand, in the EC-Land and ISBA-CTRIP models, EVC is fixed in time as it cannot vary with LAI and so the positive feedback described cannot be activated. The only feedback on near-surface soil moisture anomalies that operates&amp;#160; in these two models is negative and comes from the reduced (increased) transpiration related to the negative (positive) LAI anomalies.Simply prescribing observed vegetation data into LSMs does not guarantee the introduction of the correct coupling and feedback on climate. In this respect, this multi-model comparison experiment demonstrates the fundamental role of the inclusion of the underlying vegetation processes in LSMs. Ignoring the proper representation of the vegetation processes could lead to unrealistic (and even the opposite effects compared with observations) behaviour in reanalysis and climate predictions.

Air quality forecasting is essential for protecting public health and the environment. The World Meteorological Organization (WMO) has developed initiatives like the Global Air Quality Forecasting and Information System (GAFIS), the Sand and Dust Storms Warning Advisory and Assessment System (SDS-WAS) and the Vegetation Fire and Smoke Pollution Warning Advisory and Assessment System (VFSP-WAS) to enhance forecasting capabilities. &amp;#160; In this context, low-cost air quality sensor systems (LCS) are transformative tools in modern air quality management, offering new opportunities to complement traditional monitoring methods. By integrating LCS data with established systems, such as satellite observations and reference-grade instrumentation, the reliability and applicability of air quality data for forecasting can be significantly enhanced.&amp;#160; A key strength of LCS is their ability to expand the spatial and temporal reach of monitoring networks. However, their use must address inherent limitations in accuracy and precision. Co-locating LCS with reference-grade monitors is essential to quantify uncertainties and ensure data quality. This calibration step enables the deployment of LCS in advanced applications like air quality forecasting. Successfully implementing LCS networks for global, regional, or urban forecasting requires careful planning to ensure adequate spatial coverage, data quality, and timely updates, as well as seamless integration with other systems for actionable insights. &amp;#160; The World Meteorological Organization (WMO) has been instrumental in coordinating global efforts to optimize LCS deployment. Through guidelines, best practices, and integration frameworks, the WMO supports national and regional initiatives to enhance air quality management. A recent WMO report (WMO, 2024) underscores the importance of incorporating LCS into comprehensive monitoring frameworks for supporting air quality forecasting and reanalysis applications.&amp;#160; Ongoing advancements in LCS technology and standardization&amp;#8212;led by organizations like WMO &amp;#8212;are vital to unlocking their full potential. These efforts promise a more equitable and effective approach to air quality management, ensuring that LCS contribute meaningfully to global strategies for monitoring and forecasting. The present contribution will overview the main outcomes of the WMO&amp;#8217;s 2024 report on the use of LCS for air quality forecasting and reanalysis applications.&amp;#160;