Met Éireann

Accurate extraction of crop planting structures is important for crop area and yield estimation, but complex and fragmented cropping patterns with overlapping phenology in the Hetao Irrigation District hinder reliable crop discrimination. This study proposes a hierarchical workflow that integrates vegetation masking with multi-source feature optimization for crop mapping. First, dual-temporal Sentinel-2 imagery (May and August) is used to generate a vegetation region-of-interest(ROI) mask via Otsu thresholding applied to the Normalized Difference Vegetation Index (NDVI), combined with pixel-wise maximum-value fusion to reduce phenology-driven omissions and background interference. Second, within the vegetation mask, Sentinel-2 spectral, vegetation-index, and texture features are combined with Sentinel-1 synthetic aperture radar (SAR) backscatter and SAR texture features to construct a multi-source feature set. Random Forest(RF) feature-importance ranking is used to select an effective feature subset, and four classifiers (RF, support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and convolutional neural network (CNN)) are compared under the same training/validation setting. The vegetation extraction achieves an overall accuracy of 91% (Kappa = 0.80). Using Sentinel-2 features only, the optimized subset with CNN attains the best performance (overall accuracy = 95%, Kappa = 0.93). Adding Sentinel-1 SAR texture features provides an additional improvement (overall accuracy = 96%, Kappa = 0.94), particularly for classes prone to confusion in fragmented plots. Area proportions derived from the final map are consistent with statistical yearbook data (percentage errors: maize 3.45%, sunflower 2.66%, wheat 0.11%, tomato 0.92%) under the study conditions. This workflow supports practical crop-structure monitoring in complex irrigation districts.

High-resolution convection-permitting climate simulations are essential for assessing climate risk at the national scale, but their high computational cost limits ensemble size and uncertainty sampling. In Ireland, national climate projections rely on multi-GCM, multi-RCM ensembles dynamically downscaled at considerable expense. Using the HARMONIE-Climate (HCLIM) model, we show that precipitation and temperature characteristics are comparable between 3 km and 5 km resolutions for ERA5-driven downscaled simulations produced using a nested GCM → 12 km HCLIM → CPM HCLIM approach over an Ireland–UK domain. This indicates that intermediate-resolution simulations can serve both as a cost-effective approach and alternatively as a basis for refinement using deep learning. Building on this result, we present initial findings from a deep learning model developed to emulate ≤3 km fields from 5 km ERA5-driven simulations, with a view to assessing whether this approach can provide high-resolution convection-permitting simulations more cost effectively for use in national climate projections.

Met Éireann, the Irish Meteorological Service, has historical climate records spanning nearly 200 years, many of them hand-written in observer logs. A majority of Ireland’s historical climate extremes (e.g. monthly maximum/minimum temperatures, wind gusts, highest and lowest monthly rainfall) have occurred before the use of sophisticated electronic instrumentation. This inherently raises concerns over the validity of these records compared to today’s modern standards. As such, herein we use a novel standardised procedure for verifying the monthly minimum temperature extremes across Ireland. This approach includes archival research, data rescue, contemporaneous observational and synoptic reports, and a thorough statistical analysis, in order to validate (or negate) the historical records. Through this standardised approach we have verified the veracity of all of the monthly minimum temperature records, including the all-time low temperature record for Ireland of -19.1°C which occurred at Markree Castle in January 1881.

Understanding whether and why observed flood hazards are changing remains a central challenge for hydrology. While event-based attribution has advanced rapidly, robust attribution of changes in observed flood series remains difficult due to the integration of exogenous climatic forcing with endogenous catchment change (e.g. urbanisation, landuse change) and data quality challenges (especially for extremes). Here we develop and apply a causal-chain framework to detect, attribute, and scale changes in annual maximum floods using observational data. Taking the Shannon catchment in Ireland as an exemplar, we i) identify causal chains to reconstruct annual maximum instantaneous discharge from flood-relevant precipitation indices, ii) separate climate-driven and residual components of observed change, iii) evaluate the emergence of a climate change signal in causal chains by regressing (multiple linear regression) local precipitation indices onto global mean surface temperature (GMST), and iv) employ these results to scale local changes in flood magnitude to observed and future changes in GMST. Results show that increases in flood magnitude across the catchment are predominantly climate-driven, with multi-day precipitation totals representing antecedent conditions, particularly annual 30-day maxima and the number of very wet days in winter, emerging as the dominant causal pathways. These precipitation indices exhibit detectable warming-related signals that have emerged from variability and explain a substantial proportion of observed increasing flood trends at all sites (ranging between 28 and 93 percent). Residual trends highlight the role of endogenous catchment factors, especially data quality, changing hydrometric conditions and arterial drainage. By linking local flood discharge directly to GMST via causal chains, the framework quantifies catchment-specific flood sensitivity expressed as percentage change per degree of warming, enabling scaling to future warming levels. Results indicate that flood sensitivity per degree increase in GMST varies substantially across catchments, ranging from 8 to 18 percent per degree warming across the catchment sample. The approach provides an observation-based framework for flood attribution, leveraging established methods to bridge trend detection, process understanding, and climate scaling. Moreover, the approach can identify sensitive catchments, sentinel indices for monitoring floods and help better inform adaptation strategies. The approach is readily transferable to other catchments and hydrological extremes.

ABSTRACT In this research, data rescue and analysis of available meteorological archives from the late‐19th century were carried out to re‐investigate Ireland's minimum air temperature record of −19.1°C at Markree Castle on 16th January 1881. In addition to analysing this data in detail, we also conducted a comprehensive investigation of newspaper archives and meteorological documents published at the time of the record. The month of January in 1881 was exceptional across Ireland and Britain, with unprecedented frost and blizzard conditions across both islands. Many places saw record low temperatures during this period, with some stations setting records that stand to the present day. A thorough analysis of the rescued data, inspectors' reports, and station metadata for Markree showed compelling support for the record temperature. Following careful consideration of all of the evidence from both the metadata and the data analysis, our recommendation is that Ireland's long‐standing record minimum air temperature value of −19.1°C at Markree on 16th January 1881 should continue to stand.

As the volume of meteorological observations continues to grow, automating the quality control (QC) process is essential for timely data delivery. This study evaluates the performance of three machine learning algorithms—autoencoder, variational autoencoder, and long short-term memory (LSTM) autoencoder—for detecting anomalies in air temperature data. Using expert-quality-controlled data as ground truth, all models demonstrated anomaly detection capability, with the LSTM outperforming others due to its ability to capture temporal patterns and minimize false positives. When applied to raw data, the LSTM achieved 99.6% accuracy in identifying valid observations and replicated 79% of manual flags, with only five false negatives and six false positives over a full year. Its sensitivity to subtle meteorological changes, such as those caused by rainfall or cloud cover, highlights its robustness. The LSTM’s performance using a three-day timestep, combined with basic QC checks in SaQC (System for Automated Quality Control), suggests a scalable and effective solution for automated QC at Met Éireann, with potential for expansion to include additional variables and multi-station generalization.

Effective flood forecasting and warning systems depend on robust hydrometric networks tailored to meet the functional, geographical, and operational demands of real-time monitoring. This study outlines the ideal requirements for hydrometric data collection and transmission systems to support the National Flood Forecasting and Warning Service (NFFWS). Key considerations include spatial and temporal coverage, gauge placement in flood-prone areas, and sufficient resolution to align with hydrological models.Recommendations emphasize the need for durable, reliable infrastructure designed for extreme conditions, adherence to international standards, and regular maintenance protocols. Priority gauges—those critical for public safety, hydrological model calibration, and flood risk management—should be safeguarded, with enhancements in accuracy, redundancy, and telemetry for real-time data transmission. Network resilience is bolstered by backup power, dual communication systems, and fault alerts.Another critical component for flood forecasting is the sub-daily rainfall network. High-resolution, high-quality rainfall data at sub-daily intervals are essential for capturing short-duration, intense rainfall events that can lead to flash flooding. The design of a sub-daily rainfall network should prioritize strategic placement of gauges in areas with high rainfall variability and known flood risk areas. Real-time data collection and transmission are paramount to ensure timely updates for flood forecasting models. Integration of automated quality control processes can further enhance the reliability of sub-daily rainfall data.We propose systematic reviews of network performance, integration of historical and real-time data archives, and automated quality control to improve data reliability. Advanced metadata management and API-based data dissemination enhance usability for stakeholders. These standards ensure hydrometric networks remain integral to flood forecasting, minimizing flood risks and improving public safety. The findings provide a blueprint for developing resilient, effective hydrometric networks that address the evolving needs of flood forecasting and warning services.

Marine Protected Areas (MPAs) are vital for preserving marine biodiversity and supporting sustainable resource management. However, the intensifying impacts of future temperature extremes, such as marine heatwaves (MHWs), pose significant risks to marine ecosystems. This study utilizes high-resolution climate projections under the SSP585 scenario (AWI-CM3) to examine 21st century changes in MHWs, defined as extreme deviations from the seasonally varying long-term temperature trend, within global MPAs, focusing on the surface to a depth of 300 meters. Our findings reveal that, while the frequency of MHWs is expected to decline, their intensity and duration are projected to increase, exacerbating accumulative impacts. On average, accumulative intensity is anticipated to rise by approximately 30 %, surpassing the global average increase of 25%. Notably, substantial increases are identified in mid- and high-latitude MPAs below the surface, while in the surface layer, such increases are observed in tropical MPAs. These results highlight the urgent need to understand how marine organisms and habitats will adapt to escalating heat stresses. Such insights are critical for ensuring the resilience and sustainability of marine ecosystems in the face of future climate challenges.

The establishment of a National Framework for Climate Services in Ireland followed a low risk, yet high impact “project-first” approach to understand the national relevance and appetite for a coordinated approach to climate services. Rather than begin with a National Framework for Climate Services, and be unsure of its interest or value, this “project-first” approach allowed Met Éireann to identify relevant stakeholders, evaluate engagement and discover potential problems. As projects are time bound, this was a low-risk method of determining appetite for a more permanent national solution to provision and sharing of climate information.Following the publication of the National Adaptation Plan and recommendations from the Irish Climate Change Advisory Council, there was an unprecedented requirement for accessible and robust climate information to support decision making and adaptation planning. At this time the Irish climate services landscape was fragmented and climate information for Ireland was developed in an ad hoc, project-to-project basis. There were no clear mechanisms or structures in place for establishing and maintaining sustainable climate services and partnerships between researchers, providers, policy makers and end users. Met Éireann saw the need for both the development of a common set of climate projections to help Irish society speak the same climate language and the establishment of a permanent national framework for climate services. This led to a national research call (to build national capability), from Met Éireann and in 2021 the TRANSLATE project was founded. TRANSLATE became the seed that led to the establishment of this permanent national framework.National collaboration, knowledge sharing, and co-production of data and services are central to the TRANSLATE ethos. A key challenge of this project was bringing the whole Irish climate science and services community together and subscribe to a collaborative approach. Rather than work in isolation and leveraging Met Éireann’s position as the National Met Service, an organisation that naturally bridges the science-policy-user interface, Met Éireann brought together the national community – (all of whom would be required under a successful national framework) - under the TRANSLATE banner. This “all-Ireland” approach was so effective that it has led to the establishment of a permanent National Framework for Climate Services.Here we describe an “all-Government” approach to building permanent mechanisms and structures for developing and maintaining sustainable and scalable climate services and partnerships across the science-policy-user interface by establishing an inclusive National Framework for Climate Services. We explore the challenges, barriers and successes in establishing and operationalising of a National Framework for Climate Services in Ireland.

Near real-time aerosol fields from the Copernicus Atmospheric Monitoring Services (CAMS), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF), are configured for use in the HARMONIE-AROME Numerical Weather Prediction model. Aerosol mass mixing ratios from CAMS are introduced in the model through the first guess and lateral boundary conditions and are advected by the model dynamics. The cloud droplet number concentration is obtained from the aerosol fields and used by the microphysics and radiation schemes in the model. The results show an improvement in radiation, especially during desert dust events (differences of nearly 100 W/m2 are obtained). There is also a change in precipitation patterns, with an increase in precipitation, mainly during heavy precipitation events. A reduction in spurious fog is also found. In addition, the use of the CAMS near real-time aerosols results in an improvement in global shortwave radiation forecasts when the clouds are thick due to an improved estimation of the cloud droplet number concentration.