In this session, we welcome contributions (oral and poster) in the following major topics:
• Climate monitoring , including early warning systems and improvements in the quality of the observational meteorological networks
• More efficient transfer of the data rescued into the digital format by means of improving the current state-of-the-art on image enhancement, image segmentation and post-correction techniques, innovating on adaptive Optical Character Recognition and Speech Recognition technologies and their application to transfer data, defining best practices about the operational context for digitisation, improving techniques for inventorying, organising, identifying and validating the data rescued, exploring crowd-sourcing approaches or engaging citizen scientist volunteers, conserving, imaging, inventorying and archiving historical documents containing weather records
• Climate data and metadata processing, including climate data flow management systems, from improved database models to better data extraction, development of relational metadata databases and data exchange platforms and networks interoperability
• Innovative, improved and extended climate data quality controls (QC), including both near real-time and time-series QCs: from gross-errors and tolerance checks to temporal and spatial coherence tests, statistical derivation and machine learning of QC rules, and extending tailored QC application to monthly, daily and sub-daily data and to all essential climate variables
• Improvements to the current state-of-the-art of climate data homogeneity and homogenisation methods, including methods intercomparison and evaluation, along with other topics such as climate time-series inhomogeneities detection and correction techniques/algorithms, using parallel measurements to study inhomogeneities and extending approaches to detect/adjust monthly and, especially, daily and sub-daily time-series and to homogenise all essential climate variables
• Fostering evaluation of the uncertainty budget in reconstructed time-series, including the influence of the various data processes steps, and analytical work and numerical estimates using realistic benchmarking datasets
Knowledge about past climate and extreme weather events is an essential part of understanding future climate change and variability. KNMI is involved in multiple data rescue efforts to uncover the past, which we would like to highlight.
KNMI manages and maintains the web-based portal (https://datarescue.climate.copernicus.eu) with practical information, current data rescue (DARE) projects and metadata inventories that originated from initiatives of both the World Meteorological Organization (WMO) and Copernicus Climate Change Service (C3S). News items with developments from the community are published on this portal on a bi-monthly basis. One of the main features is the opportunity to highlight your data rescue project. By making your efforts known and publicly available, chances are decreased that the same data is rescued twice by other groups. Additionally, we encourage owners of rescued data to share this in a global repository, such as the C3S Global Land And Marine Observations Database (GLAMOD), so that the valuable data will not get lost again and is made available for use in e.g. the ERA6 reanalysis.
The C3S Data rescue work package contributes and monitors efforts using Artificial Intelligence (AI) and Deep Learning for Optical Character Recognition (OCR) to aid data rescue efforts. The development of a data rescue image repository by C3S is in line with future methods to retrieve valuable meteorological data from scanned paper records with OCR.
Within the context of the International Panel on Deltas, Coastal areas, and Islands (IPDC) project, KNMI will advise the National Meteorological Services (NMSs) on the islands of Aruba, Curacao, and St. Maarten on digitization efforts.
How to cite: van den Besselaar, E., van der Schrier, G., and van der Schee, M.: Facilitating data rescue initiatives, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-464, https://doi.org/10.5194/ems2026-464, 2026.
Met Eireann, Ireland's National Meteorological Service, maintains the National Climate Archive, a repository of paper and digital climate data dating from the 1800s to the present day. The archive contains an extensive collection of historical weather observations such as hourly and daily meteorological registers, daily and monthly rainfall registers and weather diaries. In the last decade efforts have been made to image and transcribe the paper records to make it available for climate analyses. The data rescue projects to date have involved collaboration with another government organisation, Irish universities and a citizen science project. These projects have involved manual data entry, which is a very time-consuming process, for example it takes approximately six hours to transcribe one month of daily climate data from one station. As part of our efforts to enhance efficiency, we are currently evaluating the use of machine learning optical character recognition (ML-OCR).
The archives also hold paper charts such as barograms, anemograms, thermograms, pluviograms and hygrograms. The pluviograms are very important as they provide us with sub-daily rainfall measurements and vital information on historical high intensity short duration events. Although substantial manual effort has been undertaken to extract the information from the pluviograms, a considerable volume remains unrecovered. To address this, we have investigated a novel computer vision–based approach for converting the analogue data into digital records. This method is also applicable to other chart types.
This presentation will outline past data rescue projects that have been undertaken and the approaches taken to rescue the data. It will discuss current data rescue projects including Met Eireann's Irish Weather Rescue project-a citizen science project to rescue historical daily rainfall observations from stations across Ireland as well as research on the use of machine learning for transcribing climate observations and digitising charts.
How to cite: Curley, M., Ryan, C., Jayakumari, D., Coonan, B., McGovern, R., and Bliss, J.: Data rescue activities at Met Éireann (the Irish National Meteorological Service), EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-538, https://doi.org/10.5194/ems2026-538, 2026.
Historical meteorological observations are crucial for better assessing past climate variability and trends in the frequency, intensity, duration, and distribution of extreme weather events, and for placing the current climate change into historical context. Specifically, long-term high-resolution data at daily and hourly scales are essential for a more accurate assessment of past and rare extreme weather events. Historical meteorological observations are crucial for generating climate products, such as reanalysis and gridded datasets.
Ireland has a great heritage of historical instrumental meteorological observations. This presentation will primarily focus on four meteorological collections, which have been rescued from the original paper sources and digitally preserved, including methodologies and examples of data application for climate research:
1) Meteorological observations from over 40 locations in Ireland registered from 1783 to 1854 and preserved in the archives of the Royal Irish Academy.
2) Meteorological observations from over 70 locations in Ireland registered from 1808 to 1939 and rescued from newspapers.
3) Meteorological observations from Dunsink Observatory from 1818 to 1850.
4) Meteorological observations from Ulster, including the long-term series registered at the Linen Hall (1796 to 1895) and Queen’s College Belfast (1850-1919).
Observed variables include air temperature, maximum and minimum air temperatures, dry and wet bulb temperatures, sea temperature, rainfall, pressure, wind direction and force, maximum air temperature in the sun, humidity, cloud cover, cloud form, tension of vapour, and state of the weather as qualitative remarks. It is very important to make these observations published in newspapers available since the majority of the original manuscripts are not traceable.
Many well-known historical extreme weather events in Ireland, such as extreme air temperatures and storms, are documented in historical meteorological records.
The metadata and data from these meteorological collections have been rescued and will be made available as open access in forthcoming peer-reviewed publications and digital datasets.
How to cite: Mateus, C.: Data rescue of early historical meteorological observations from Ireland, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-708, https://doi.org/10.5194/ems2026-708, 2026.
At Met Éireann, the Irish Meteorological Service, part of our mission is analysing Ireland’s climate. This involves the assessment of Ireland’s climate extremes which include monthly maximum and minimum air temperatures, highest sustained wind speeds and highest wind gusts, highest and lowest monthly rainfall, sunshine duration, and highest and lowest atmospheric pressure. However, as our historical climate records span nearly 200 years, many of these climate extremes occurred when observations were reported in hand-written observer logs and are therefore unsuitable for our current validation methods. As such, herein we use a novel standardised operating procedure (SOP) for verifying the monthly minimum and maximum temperature extremes across Ireland. This includes a comprehensive search of archival newspaper records and meteorological reports published at the time of the records. We also employ data rescue techniques when needed and use contemporaneous observational and synoptic reports to investigate the larger scale atmospheric conditions observed during the period in question to see the favourability of inducing the extreme weather reported. Finally, we completed a thorough statistical analysis using a generalized extreme value distribution to put the record into context against a longer observational record. Through this standardised approach we have verified the veracity of 11 out of 12 of the original monthly minimum air temperature records, including the all-time low temperature record for Ireland of -19.1°C which occurred at Markree Castle in January 1881. Our methods resulted in the rejection of the standing March minimum record and has since been replaced by a new verified record for the month. Ten out of 12 of the monthly maximum air temperature records were validated, including the all-time high temperature record for Ireland of 33.3°C which occurred at Kilkenny Castle in June 1887, but March and December were rejected and replaced by newly verified records. This presentation will give a brief overview of this standardised approach, followed by case studies of a record that stood up to the SOP and one that was rejected by it.
How to cite: Evans, C., O'Sullivan, J., and Curley, M.: The Reassessment of Monthly Minimum and Maximum Temperature Extremes Across Ireland, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-317, https://doi.org/10.5194/ems2026-317, 2026.
Approximately 25 years ago the Met Office designed and implemented a database of station-based monthly climate statistics using data from the UK's land network of weather stations. This database enables climate monitoring activities by providing pre-calculated monthly values for the entire record at each site.
The variables include temperature, rainfall, sunshine duration, wind speed, pressure, humidity, snow and cloud cover. Statistics derived from these variables include monthly means and totals, highest and lowest daily values within each month, and counts of threshold exceedances (e.g. number of days with air temperature below 0°C).
After 25 years this legacy code is being retired and a modern replacement system designed that will be written in Python.
In this presentation we will describe the progress of this work, with particular focus on:
- The source of the input data (and how this relates to the business logic)
- The database table structure (and why we decided to change this)
- The design of the software (including how it can be configured)
- Synchronisation with the raw data (through database triggers and version numbers)
- Improved traceability (of both calculated values and rescued data)
- Population of the new database table (including data volumes and run times)
We will also address data licensing considerations and highlight some of the downstream impacts of the new system on user applications.
All National Meteorological Services must routinely replace legacy IT systems such as this if they are to remain fit for purpose in the long term. Our new system is designed to be modern, simple, consistent, efficient, traceable, flexible and scalable. We aim for it to provide a robust foundation for climate monitoring activities over the next 25 years!
How to cite: Packman, S., Whybra, P., Hollis, D., Kendon, M., Carlisle, E., and Doherty, A.: Updating our Climate Monitoring Database to a simple, consistent and traceable solution , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-779, https://doi.org/10.5194/ems2026-779, 2026.
The Royal Meteorological Institute of Belgium (RMIB) holds a vast collection of digitized climate archives dating back to 1833. However, most of these historical observations have been digitized but not transcribed in machine-readable format. Therefore, this wealth of information remains unusable for most climate applications. In this project, we propose to fill that gap by transcribing and quality-controlling in-situ measurements - including surface pressure, temperature, humidity, from the longest Belgian observation series of Brussels/Uccle. The project, funded by the C3S National Collaboration Programme, will be conducted in collaboration with the Free University of Brussels (VUB) which developed the MeteoSaver transcription tool for rescuing historical climate data. MeteoSaver is an open-source software based on AI/ML techniques and includes built-in quality control modules.
The project has two main objectives : (1) making new historical climate data available in the C3S Data Rescue repository for further use in the ECMWF reanalysis systems and for climate applications in general and (2) to enhance the flexibility and user-friendliness of the MeteoSaver tool and promote its use for climate data rescue, supported by new documentation and online training.
The main impact of the project lies in its contribution to a better understanding of past climate evolution. By incorporating newly recovered historical data into climate reanalysis, the project will help refine their accuracy and extend their temporal coverage further back in time. Targeted outreach will promote the rescued data to fuel climate research, empower national weather institutes in data rescue initiatives, and raise Belgian society’s awareness of the societal value of preserving historical climate information.
For EMS2026, the objectives and first steps in transcribing the data will be presented.
How to cite: Ingels, R., Brehon, M., Muheki, D., and Thiery, W.: Saving Climate Records In BElgium (SCRIBE) project – First steps in transcribing sub-daily climatic parameters starting from 1833 for Brussels/Uccle station with MeteoSaver, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-201, https://doi.org/10.5194/ems2026-201, 2026.
AgroClima and DataClima: a simple and interactive way to access climate data in Portugal. V. Pires (vanda.cabrinha@ipma.pt), C. Pereira (carlos.pereira@ipma.pt), R. Deus ricardo.deus@ipma.pt). Portuguese Sea and Atmosphere Institute, I.P.
ABSTRACT
Public and scientific interest in climate products has increased significantly in the last decade, driven not only by greater societal awareness of climate change but also by the growing frequency of extreme weather events. In this context, IPMA, as the national authority on climate matters, reinforces its role as a reference entity in the monitoring, analysis, and communication of climate variability and climate change. To this end, IPMA has developed two complementary platforms (DataClima and AgroClima) that integrate innovative climate modelling methodologies with in situ observational data. Together, these platforms constitute essential tools for civil society, providing reliable, high-quality, and easily accessible climate information. They support continuous climate monitoring and facilitate climate change adaptation, strengthening planning and decision-making processes across multiple socioeconomic sectors. The analysis of in situ observation data was carried out according to the standards of the World Meteorological Organization (WMO).
The information accessible through this two digital platform, which detailed information for the various regions of the country, aggregated by different Territorial Units, based on daily meteorological observation data from the IPMA network (in situ and remote) and numerical weather prediction (numerical model).
Dataclima is an interactive digital platform that provides access to modelled and observed climate indicators for the mainland and island regions of Portugal. Modelled climate indicators are available for the period from 1981 to the present day for different territorial units, with the aim of supporting various sectors of activity. The AgroClima platform facilitates digital access to climatological and agroclimate indicators, empower users in their interpretation, and provide up-to-date information and agroclimate warnings based on established thresholds.
How to cite: Pires, V., Pereira, C., and Deus, R.: AgroClima and DataClima: a simple and interactive way to access climate data in Portugal, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-377, https://doi.org/10.5194/ems2026-377, 2026.
Reliable climate monitoring depends on observational networks that are not only long-term and high-quality, but also representative of local environmental conditions. In regions such as the Basque Country, the predominance of automatic weather stations (AWS) presents both an opportunity and a challenge for climate applications, as these networks are primarily designed for real-time weather monitoring rather than climate analysis.
This study presents a comprehensive framework to assess the suitability of the Basque Country AWS network for climate monitoring purposes. The network, operated by Euskalmet, consists of more than one hundred stations providing high-frequency observations, with many records extending over two decades. The methodology integrates multiple components to ensure data reliability and representativeness. A systematic network characterization is carried out, followed by an automated site classification based on World Meteorological Organization (WMO) guidelines. This classification incorporates geospatial information, including land use, digital elevation models, canopy height, and geomorphological analysis using geomorphons, complemented by in-situ inspections.
Climate time series of key variables such as temperature and precipitation are processed through advanced quality control and homogenization techniques, including the ACMANT method. Breakpoint detection is supported by metadata analysis to identify non-climatic influences such as instrumentation changes or station relocation. The representativeness of the AWS network is evaluated through comparisons with reference climatological datasets, allowing the identification of stations suitable for climate monitoring applications. To support operational use, a system of station fact sheets has been developed, integrating metadata, quality indicators, homogenization status, and spatial context for each station. These tools enhance traceability and facilitate the integration of AWS data into climate services.
The results demonstrate that, while AWS networks are not initially designed for climate monitoring, a significant subset of stations can provide valuable and consistent climate information when properly evaluated, processed, and contextualized within a robust methodological framework.
How to cite: Hernandez, R., Iza, M., Martija-Díez, M., and Gaztelumendi, S.: Assessment of the Basque Country Automatic Weather Station Network for Climate Monitoring: Quality Control, Homogenization and Site Classification, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-575, https://doi.org/10.5194/ems2026-575, 2026.
In a naturally ventilated wooden thermometer screen, such as the Stevenson-type screen, which is still used in many weather stations, anomalous temperature and/or humidity readings may occur, particularly in calm conditions and when exposed to direct sunlight. This is due to insufficient ventilation and the resulting relatively high thermal inertia caused by the microclimate that develops within the housing. In accordance with WMO recommendations, forced ventilation using a fan has become standard practice when measuring temperature and relative humidity in a thermometer screen.
The ventilation system at weather stations is continuously monitored during data collection, and repairs are arranged should any problems arise. To assess the urgency of the repairs required, comparative measurements were carried out at the GeoSphere Austria weather station in Vienna, comparing an actively ventilated thermometer screen with a naturally ventilated one. Over a period of more than two years, between June 2023 and October 2025, measurements of air temperature and humidity inside the screens, as well as other meteorological parameters in the vicinity of the screen’s location, were carried out, including global radiation, duration of sunshine and wind speed.
The screens have been compared for different weather conditions (wind regime, radiation conditions, etc.), magnitude of the difference between aspirated and naturally ventilated screens is estimated, and the relationship with other parameters is investigated. Essentially, the known characteristics, like proportional bias – the increase in the temperature as solar radiation increases and/ or wind speed decreases – are generally confirmed. Weather-related variations are examined in greater detail, and the resulting conclusions for operational purposes are drawn.
How to cite: Filipovic, N.: Intercomparison of air temperature measurements in aspirated and naturally ventilated thermometer screens, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-638, https://doi.org/10.5194/ems2026-638, 2026.
Our temperature, precipitation, wind speed, humidity, global radiation and air pressure data sets, homogenized with the MASH software, are systematically renewed annually at the Climate Research Department of HungaroMet Hungarian Meteorological Service. Likewise, our wind direction database is updated every year. In this poster, we will explain the steps of homogenizing wind direction. However, it should be taken into account that we are not talking about a scalar, but a vector quantity, so wind direction must be treated consistently together with wind speed. The combined homogenization of daily wind speed and wind direction is solved with the MASH method, the mathematical background of which ensures the success robustness of the homogenization procedure.
High-quality wind direction datasets are essential for climate research, energy planning, and risk assessment. In Hungary, a homogenized, completed and quality-controlled wind direction database covering the period from 1997 to the present has supported numerous climatological analyses. We aim to extend this database both temporally (back to 1961 and forward to 2024) and spatially (from 89 to 127 stations), in order to improve long-term trend analysis and regional representativeness. The extended dataset is expected to provide a more consistent basis for analysing prevailing wind patterns and their long-term variability across Hungary. Through systematic homogenization and quality control, the project provides a consistent, high-resolution dataset for scientific and operational use.
Acknowledgement:
The present study was carried out within the framework of the EKÖP-KDP-24 University Excellence Scholarship Program Cooperative Doctoral Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation fund.
How to cite: Bokros, K., Izsák, B., Lakatos, M., and Pongrácz, R.: Wind direction homogenization with MASH software, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-763, https://doi.org/10.5194/ems2026-763, 2026.
Supporters & sponsors
