OSA3.1 | Climate monitoring: data rescue, management, quality and homogenization
Climate monitoring: data rescue, management, quality and homogenization
Convener: Federico Fierli | Co-conveners: Dan Hollis, John Kennedy
Orals Tue2
| Tue, 09 Sep, 11:00–13:00 (CEST)
 
Room E3+E4
Posters P-Tue
| Attendance Tue, 09 Sep, 16:00–17:15 (CEST) | Display Mon, 08 Sep, 08:00–Tue, 09 Sep, 18:00
 
Grand Hall, P20–27
Orals Tue2 |
Tue, 11:00
Posters P-Tue
Tue, 16:00
Robust and reliable climatic studies, particularly those assessments dealing with climate variability and change, greatly depend on availability and accessibility to high-quality/high-resolution and long-term instrumental climate data. At present, a restricted availability and accessibility to long-term and high-quality climate records and datasets is still limiting our ability to better understand, detect, predict and respond to climate variability and change at lower spatial scales than global. In addition, the need for providing reliable, opportune and timely climate services deeply relies on the availability and accessibility to high-quality and high-resolution climate data, which also requires further research and innovative applications in the areas of data rescue techniques and procedures, data management systems, climate monitoring, climate time-series quality control and homogenisation.
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crowd-sourcing approaches or engaging citizen scientist volunteers, conserving, imaging, inventorying and archiving historical documents containing weather records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

Orals: Tue, 9 Sep, 11:00–13:00 | Room E3+E4

Chairperson: Dan Hollis
11:00–11:15
|
EMS2025-179
|
Onsite presentation
The contribution to the recovery of precipitation and temperature data before mid-1950 over the Italian territory by the Citizen Science project Cli-DaRe@School 
Veronica Manara, Bruno Arcuri, Michele Brunetti, Maria Carmen Beltrano, Giacomo Bertoldi, Yuri Brugnara, Daniele Cat Berro, Alessandro Ceppi, Alice Crespi, Jacopo Melada, Federico Mattia Stefanini, Francesco Sudati, Dino Zardi, and Maurizio Maugeri

Cli-DaRe@School is a Citizen Science project (https://aisam.eu/progetti/cli-dare-at-school/) aiming at digitising unexploited Italian meteorological observations still available only on paper or as scanned images. It was launched in 2022 as an initiative of the Italian Association of Atmospheric Sciences and Meteorology (AISAM) involving researchers, high-school students, and teachers.

Until now, the project focused on digitizing four monographs published by the Italian Hydrographic Service on monthly temperature and precipitation data for the Italian territory. The temperature data covers 1926-1955, while the precipitation data refers to the years before 1950. During two academic years, a team of more than 500 students from more than 10 high schools was engaged in supporting digitization in the framework of a national work trainee program for students, making about 7931 station records available. Each school was provided with pdf files containing the scanned pages to be digitized, along with spreadsheet templates for data entry, and related tutorials. Students also had the opportunity to participate in a training program offered by the project, which consisted of seminars and activities specifically designed for them. Their goal was to allow the students to delve into various aspects of climate change and to make them aware of the potential of the recovered data. Cli-DaRe@School, on the one hand, demonstrates the potential of high school students to make an enormous contribution to rescuing past meteorological data; on the other hand, it has a great educational value, offering young students an experience with climate data.

At the end of each year, we sent a questionnaire to both students and teachers to gauge their satisfaction with the project activities and to gather suggestions for the following years. The questionnaire revealed a good level of satisfaction among the teachers and students, with the most critical point being the number of hours devoted to digitization.

The quality control of the digitized data, partly automatic using an R code and partly manual, is finished for precipitation over the period 1921-1950, whereas it is still in progress for the previous years and for temperature. The station metadata proved to be a frequent source of errors, while the digitized precipitation and temperature records have a very low error rate. Project results have been published in the Bulletin of the American Meteorological Society (https://doi.org/10.1175/BAMS-D-24-0078.1). The newly rescued precipitation records for the period 1921-1950 are already freely available (https://doi.org/10.5281/zenodo.15084062) while, for the other periods, data publication is still in progress. In parallel, the application of Optical Character Recognition techniques to extend the data rescue also to the daily records is currently under evaluation.

How to cite: Manara, V., Arcuri, B., Brunetti, M., Beltrano, M. C., Bertoldi, G., Brugnara, Y., Cat Berro, D., Ceppi, A., Crespi, A., Melada, J., Stefanini, F. M., Sudati, F., Zardi, D., and Maugeri, M.: The contribution to the recovery of precipitation and temperature data before mid-1950 over the Italian territory by the Citizen Science project Cli-DaRe@School, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-179, https://doi.org/10.5194/ems2025-179, 2025.

11:15–11:30
|
EMS2025-181
|
Onsite presentation
New Meteorological Observations Recovered from the Lighthouses on the Northern Coast of Catalonia (1853–1956) 
Marc J. Prohom, Mònica Herrero-Anaya, and Joan Ferrer-Godoy

The recovery of past meteorological data is essential for understanding the evolution of climate over time and detecting patterns of climate change. These historical records, often stored in physical or handwritten formats, provide valuable information for analyzing long-term trends, comparing extreme events, and validating current climate models.

The Meteorological Service of Catalonia, in collaboration with the Historical Archive of Girona, has catalogued and digitized a previously unpublished documentary collection that includes weather observations from a group of nine lighthouses located along the northern coast of Catalonia (in the province of Girona). The archive spans the period from 1853 to 1956, and a total of 44,168 images have been taken. This study provides a description of the archive and its potential uses.

The documentary collection is quite diverse, as it covers nearly a century. Generally, however, it consistently includes daily or sub-daily (three times a day) information on non-instrumental variables: sky conditions, wind direction and force, sea conditions, and visibility. For visibility, the observation of neighboring lighthouse lights was recorded, which makes it relatively easy to determine horizontal visibility in kilometers. In the case of sea conditions and wind force, the terminology used during the nineteenth and part of the twentieth century does not follow the international scales that later became widespread, but rather uses local terms, which will need to be standardized and harmonized. From the late 1890s onward, the lighthouses began incorporating instrumental observations, especially of extreme temperatures and daily precipitation. In some cases, such as the Sant Sebastià lighthouse (located in Palafrugell), the records include additional variables: atmospheric pressure, average wind speed (measured with a totalizing anemometer), and meteorological phenomena. From this period on, a series of daily report sheets also appear, detailing storms (start and end times, presence of lightning, hail, etc.). The collection also includes extensive correspondence between the lighthouses and the ministry responsible for public works.

The recovery of this archive will make it possible to extend already identified instrumental series and to analyze in greater detail and with greater accuracy the characteristics of maritime storm events in this part of the western Mediterranean. It will also help to contextualize these events in terms of their intensity, frequency, and duration, both in relation to current observations and to future projections from climate models.

How to cite: Prohom, M. J., Herrero-Anaya, M., and Ferrer-Godoy, J.: New Meteorological Observations Recovered from the Lighthouses on the Northern Coast of Catalonia (1853–1956), EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-181, https://doi.org/10.5194/ems2025-181, 2025.

Show EMS2025-181 recording (13min) recording
11:30–11:45
|
EMS2025-211
|
Onsite presentation
Producing a nearest neighbour check for station sunshine data as part of a station-satellite blended sunshine duration dataset for the UK 
Emily Carlisle, Philip Whybra, Dan Hollis, Josh Blannin, and Lizzie Good

Sunshine duration (SD) is an important climate variable in the UK, with UK Met Office records extending back to the 1800s. However, the station network is spatially and temporally inhomogeneous, and the number of stations declined considerably between 1990-2010. A project to create a blended UK sunshine duration dataset is currently underway and aims to address these issues through combining satellite and in situ measurements, with the goal to improve spatial representation and reduce uncertainties. Initial comparisons between the satellite and station sunshine duration values identified some days where the agreement is poor. Further investigation highlighted that the satellite data sometimes misidentify snow-covered ground as clouds, leading to clear-sky days having low sunshine duration, and that both satellite and station data struggled with Saharan dust events. Additionally, the comparisons indicated that there were some erroneous station sunshine values, and this highlighted the need for a preliminary quality control check for the station data. This study presents a nearest neighbour check that identifies incorrect sunshine duration values in the station data. The check compares stations with their nearest eight neighbours within 80km, limiting neighbours to those with similar elevations and proximity to the sea. This version of the nearest neighbour check uses conservative parameters to minimise false positives, with future work aimed at increasing the flexibility of these parameters. In the process of developing the nearest neighbour check, six stations were identified that had around a decade of sunshine duration values offset by a day, resulting in them being out of step with their nearest neighbours. The correction of these stations in the Met Office database has resulted in improved quality of the UK historical climate record, highlighting the benefit of utilising both station and satellite measurements to identify errors in both datasets.

How to cite: Carlisle, E., Whybra, P., Hollis, D., Blannin, J., and Good, L.: Producing a nearest neighbour check for station sunshine data as part of a station-satellite blended sunshine duration dataset for the UK, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-211, https://doi.org/10.5194/ems2025-211, 2025.

Show EMS2025-211 recording (13min) recording
11:45–12:00
|
EMS2025-128
|
Onsite presentation
Homogenisation and interpolation of daily snow depth time series in Slovenia from 1950 to 2020 
Gregor Vertacnik

Slovenian Environment Agency (ARSO) has recently renewed the homogenisation and interpolation of daily snow depth time series in Slovenia. After conducting quality control, the data was aggregated into monthly sums, resulting in 242 time series of new snow and 278 of total snow depth. Homogenisation was performed using the HOMER software tool, separately for three climate regions: littoral, interior, and higher elevations. Inhomogeneities were identified and removed through several iterations using various detection methods and station metadata.

Most of the time series were found to be homogeneous, with only a few stations exhibiting more than one break. Approximately one-third of the breaks are of unknown origin, while nearly half are due to site relocation. The relative data correction for inhomogeneities ranged from 5–30 % for new snow depth and 7–36 % for total snow depth.

Following monthly homogenisation, the original daily data was adjusted based on the ratio between the monthly homogenised data and the original data. Missing daily values were replaced with spatially interpolated values. The results of ordinary spatial interpolation were partially adjusted to better match the probability density function of the last homogeneous period.

Since the procedure was conducted separately for new and total snow depth, discrepancies between the two variables emerged. Consequently, the daily time series of both variables were harmonized both between the variables and within the time series. For new snow depth, the precipitation sum was also considered as a control variable. We aimed to align the snow cover depth with the precipitation amount and the new snow depth, while also accounting for the settling of the snow cover. During the alignment process, we prioritized corrections of interpolated values over measured ones. Harmonization was performed iteratively, converging towards the final result.

Homogenised time series of new and total snow depth show a very large and statistically significant negative trend of approximately –8 % and –9 % per decade, respectively, over the period from 1950 to 2020 throughout Slovenia. In spring and autumn, the trend is more pronounced at higher elevations, whereas in winter and on an annual level, it is stronger in the mountains.

Homogenised and interpolated time series have been used to calculate climate normals for the latest WMO standard reference period (1991–2020) and are planned to be used to improve climate projections for Slovenia.

Keywords: snow depth, climate change, homogenisation, spatial interpolation

How to cite: Vertacnik, G.: Homogenisation and interpolation of daily snow depth time series in Slovenia from 1950 to 2020, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-128, https://doi.org/10.5194/ems2025-128, 2025.

Show EMS2025-128 recording (16min) recording
12:00–12:15
|
EMS2025-232
|
Onsite presentation
Re-investigating Ireland’s minimum air temperature record value at Markree Castle, 16 January 1881 
John O'Sullivan, Mary Curley, Ciarán Kelly, and Jonathan McGovern

It is essential to have validated and trusted records of past climate extremes.

These records are used by planners and policymakers to help them make informed decisions regarding many different sectors - from construction projects to health budgets, from environmental legislation to infrastructure planning, for example.

They are also used to tune and improve climate models, leading to more reliable future projections in a changing climate. Assessing and improving on the abilities of climate models to reproduce these (by definition) rare events, provides a stronger basis from which better informed mitigation and adaptation measures against such potential future climate extremes can be taken.

To date, we have completed and published a reassessment of the overall maximum air temperature record for Ireland (in work which was presented at EMS 2024).

Continuing with our reassessment of national climate records, the aim of this current research is to re-examine Ireland’s overall minimum air temperature record – which was observed at Markree Castle on the 16th of January 1881.

We use recently digitised historical climate data from the Met Éireann archives and integrate advanced 20CRv3 sparse-input reanalysis data, station metadata, historical newspaper articles, and contemporaneous references from the examined timeframe. We also employ time series methods and extreme value theory to help us assess the veracity of the record.

This process will then be applied to other months and other climate variables in future work. This research underscores the significance of data rescue efforts in advancing our understanding of past climate extremes, and advocates for continued digitisation and analysis of historical climate data and metadata. By refining national air temperature records through the integration of historical data and advanced reanalysis techniques, the research contributes to a more comprehensive understanding of climate dynamics.

How to cite: O'Sullivan, J., Curley, M., Kelly, C., and McGovern, J.: Re-investigating Ireland’s minimum air temperature record value at Markree Castle, 16 January 1881, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-232, https://doi.org/10.5194/ems2025-232, 2025.

Show EMS2025-232 recording (15min) recording
12:15–12:30
|
EMS2025-625
|
Online presentation
EEAR-Clim: A high density stations-based dataset of daily meteorological data for the Extended European Alpine Region 
Giulio Bongiovanni, Michael Matiu, Alice Crespi, Anna Napoli, Bruno Majone, and Dino Zardi

Several observational products of key climate variables have been widely used to evaluate the ongoing effects of climate change in the European Alps, one of the most vulnerable and sensitive regions to the continuous warming of climate. However, the limited spatial coverage in most observational products and quality issues of data may strongly impact climate and hydrological studies results in terms of reliability, accuracy and precision. Although the collection and management of meteorological data for the whole Alpine area is a challenging task due to strong fragmentation and diversity of data sources, further efforts need to be dedicated to produce new harmonised, high-quality and high-resolution products able to permit a more robust assessment of climate change and its impacts.  

Here, we present a new observational dataset gathering in-situ daily measurements of key climate variables provided by a wide range of meteorological and hydrological services within the Extended European Alpine Region (EEAR). The dataset, originally including air temperature and precipitation time series recorded up to 2020, in its newest version has been expanded to incorporate more recent observations (2021-2024), additional historical records, and data of other climate variables such as relative humidity, wind speed and direction, global radiation, snow depth and surface pressure. The updated observational network includes over 10000 in-situ weather stations, providing extensive and consistent coverage both in space and elevation. 

Data collected are screened applying a comprehensive and deep quality control procedure for the identification of the most important critical data issues. Time series were thoroughly checked for internal and temporal consistency, and spatial coherence, facing the problem of outlier removal. Data homogeneity was assessed by a cross-comparison of break points identified with Climatol, Acmant and RH Test methods. The resulting inhomogeneous periods were adjusted through the quantile matching techniques.

A quantitative assessment of the present dataset highlights its added value in addressing key issues affecting state-of-the-art observational products, providing a powerful tool for a better understanding of Alpine climate changes and improving the reliability of future climate scenarios.

How to cite: Bongiovanni, G., Matiu, M., Crespi, A., Napoli, A., Majone, B., and Zardi, D.: EEAR-Clim: A high density stations-based dataset of daily meteorological data for the Extended European Alpine Region, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-625, https://doi.org/10.5194/ems2025-625, 2025.

Show EMS2025-625 recording (10min) recording
12:30–12:45
15 min Poster pitches
12:45–13:00
Discussion

Posters: Tue, 9 Sep, 16:00–17:15 | Grand Hall

Display time: Mon, 8 Sep, 08:00–Tue, 9 Sep, 18:00
P20
|
EMS2025-204
Rescue of Climate Data at the Slovenian Environment Agency 
Mateja Nadbath

The Slovenian Environment Agency (ARSO) has dedicated significant effort over the past twenty years to systematically climate data rescue in Slovenia. This data rescue activity will continue with the launch of the national project “SOVIR-Upgrade of the system for early warning and awareness-raising to weather-related emergencies and adaptation to them in a changing climate”. In the project, logbooks and reports will be imaged, the data will be keyed, and a document database will be established.

The goal is to create long-term time data series  for various climate analyses, preserve this observed data for future generations, ensure its easy accessibility and align our data management and archiving procedures with national legislation and the guidelines set by the WMO.

The results of the systematic data rescue activities up to this point are as follows:

  • An inventory of Slovenian meteorological reports has been created. This inventory includes historical climate reports stored in the ARSO and foreign archives. The inventory of Slovenian reports held in foreign archives is accessible on the EUMETNET DARE program's website. In the ARSO archive, meteorological reports spanning 400 running meters, including reports dating back to 1850.
  • Reports from 61 Slovenian stations, dated from 1849 to 1944, have been digitized from GeoSphere’s archive. In addition, reports for 70 Slovenian stations, dated from 1920 to 1947, have been imaged from ISPRA’s archive.
  • Historic meteorological data for Ljubljana, covering the years 1818 to 1856, has been recovered from digitized copies of the newspaper "Laibacher Zeitung", and this data has been keyed.
  • An inventory of meteorological station metadata has been conducted, including imaging of the documents and updating of the metadata database.
  • Most of the climate data from 1950 onward has been keyed into a database and it is publicly accessible on the website.
  • ARSO has established close cooperation with the National Archives.

The data rescue activities planned for the “SOVIR”, project in 2025–2026 include the following:

  • Professionals will image climate logbooks, precipitation reports, pluviograms, barograms, thermograms, and hygrograms, totaling over 5,5 million pages, dating from 1850 to 2023.
  • Experts will perform data keying for reports and logbooks from 1850 to 1949.
  • ARSO experts will conduct quality control of the data.
  • A document database will be created, consisting of over 700,000 files and requiring approximately 5 TB of storage.
  • A scanner will be acquired to facilitate future imaging tasks.
  • All activities will comply with national legislation regarding archives and archiving practices.
  • After digitization, logbooks and reports will be transferred to the National Archives, requiring approximately 200 running meters of space. An additional 200 running meters of meteorological reports will be stored in the ARSO archive.

How to cite: Nadbath, M.: Rescue of Climate Data at the Slovenian Environment Agency , EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-204, https://doi.org/10.5194/ems2025-204, 2025.

P21
|
EMS2025-385
The ReData project: engaging citizen scientists in the recovery and digitization of historical daily weather bulletins 
Veronica Manara, Alessandro Ceppi, Yuri Brugnara, Gabriele Buccheri, Goffredo Caruso, Luca Cerri, Maria Di Giovanni, Marco Giazzi, Ludovico Lapo Luperi, Luca Ronca, Elisa Sogno, and Maurizio Maugeri

In recent decades, many countries around the world have initiated climate data digitization projects. The aim of these projects is to preserve data recorded in paper documents, which are prone to deterioration, and to make them available to the scientific community. These new dataset will improve the accuracy of the description of the spatial distribution of particular events and will allow a reconstruction with a lower error of the evolution of the climate of the past. In this context, global reanalysis datasets play a crucial role, as their accuracy depends directly on the homogeneity and spatial distribution of the underlying historical observations. This study aims to design a new framework for the ReData (Recovery of Data) project, launched by the Meteonetwork Association in 2017. The aim of the project is to digitize the meteorological data collected by the Italian Royal Central Meteorological Office (RCMO) from 1879 to 1940 for the publication of its daily meteorological bulletins. The network used for this bulletin started with 11 meteorological stations and rapidly expanded to about 70 within a few decades. The use of telegraph technology to transmit observations in real time to the Central Office in Rome enabled the publication of the Daily Meteorological Bulletin, which also included observations from foreign stations and was one of the first steps towards international atmospheric monitoring. Currently, the RCMO daily bulletins available in digital form, as first result of the project, cover the period from December 1879 to December 1934, with the remaining years still to be scanned. In total, 55 years of data are accessible, encompassing 20,120 daily bulletins. Since the bulletins have been scanned page by page, over 84,000 scans have been performed. Given the number of meteorological variables recorded in the bulletins, which has increased over time, it is possible to estimate the amount of data that could potentially be digitized using ReData: more than 20 million data points. The project aims to digitize this huge amount of data through Citizen Science activities. Specifically, as a second outcome of the project, since the end of 2024 a website of the project is available on the online platform Zooniverse (https://www.zooniverse.org/projects/meteonetwork/redata). Here, volunteers from all over the world can contribute to the digitization of a station with data for 12 variables in only about 1 minute per day. Today, almost three years (1882-1884) have been completed for 37 stations, considering that each data is digitized three times.

How to cite: Manara, V., Ceppi, A., Brugnara, Y., Buccheri, G., Caruso, G., Cerri, L., Di Giovanni, M., Giazzi, M., Luperi, L. L., Ronca, L., Sogno, E., and Maugeri, M.: The ReData project: engaging citizen scientists in the recovery and digitization of historical daily weather bulletins, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-385, https://doi.org/10.5194/ems2025-385, 2025.

P22
|
EMS2025-637
Using Machine Learning based solution for quality control of UK monthly rainfall 
Stephen Packman and Emily Carlisle

The UK Met Office is an authoritative and trusted source of UK climate information and the custodian of the national historical climate records. We maintain a long-term time series for temperature, precipitation and other climate variables dating back to 1884 for monthly temperature and 1836 for monthly precipitation. The quality control of climate observations is essential for accurate assessment of the impacts of a changing climate. There are indications that a warmer atmosphere will result in increased intensity of rainfall and in order to monitor this our precipitation observations must be of a very high quality

 

Quality control of observations is a key challenge for precipitation due to its high temporal and spatial variability. The small-scale features and discontinuities in the field make it difficult to distinguish between erroneous values and true measurements. HadUK-Grid is the primary product used by the Met Office for producing areal statistics for monitoring the UK climate. The current quality control of data used by this product uses a simple range check to remove negative or extreme values and a check based on the distortion of the curvature field which assesses neighbouring station values.

 

Here we describe a machine learning (ML) based approach to the quality control of monthly rainfall values, with the primary goal of detecting and removing anomalies. The ML model incorporates a range of features including station metadata, spatial relationships with nearby stations, geographical variables such as elevation and proximity to coastlines, and past observations. This gives the model spatial context, allowing it to identify and preserve true extreme rainfall totals while removing the anomalous “bullseyes”. By training a model on historical data with known anomalies, the system learns to detect complex error patterns that traditional methods may overlook. The outcome is a more consistent and reliable set of UK monthly rainfall statistics that can be used to monitor the UK climate.

How to cite: Packman, S. and Carlisle, E.: Using Machine Learning based solution for quality control of UK monthly rainfall, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-637, https://doi.org/10.5194/ems2025-637, 2025.

P23
|
EMS2025-698
Quality control of precipitation data at GeoSphere Austria 
Niko Filipovic

Rain gauge measurement network of the Austrian national weather service is operated by GeoSphere Austria and comprises about 270 weather stations, most of which are equipped with weighing rain gauges and a smaller number with tipping bucket rain gauges. Each gauge is additionally equipped with a precipitation monitor that detects the beginning and the end of precipitation events. Precipitation data are checked for plausibility and completeness in several steps within a framework of an automated quality control tool called AQUAS (short for Austria Quality Service). The software was developed in 2016 at ZAMG (now GeoSphere Austria) in Vienna as part of the quality management in the area of real-time processing of near-surface observation data.
The basis for quality control procedure is formed by standard methods for checking meteorological and climatological data in accordance with the WMO recommendation (e.g. plausibility check, temporal, spatial and internal consistency check, etc.); in addition, test procedures are developed that take into account the specific errors of the measuring devices.  In AQUAS, individual system components are designed to test the incoming observation parameters in real time – in the case of precipitation data with a time resolution of 1 minute - as well as on the basis of daily data.
Based on the quality control of precipitation data, the structure of AQUAS and an example of its operational use are presented. An algorithm for checking precipitation data from 1-minute weighing gauge measurements is demonstrated that detects spurious precipitation events and missing gauge precipitation based on combination of the precipitation monitor observations and the total weight changes of the rain gauge. The advantage of this method is that the software errors of the weighing gauge are largely intercepted by comparison with an independent measurement. This algorithm currently supports the experts in manual quality control of precipitation data. After thorough improvements and tests, it is planned to integrate it into a semi-automatic quality control system.

How to cite: Filipovic, N.: Quality control of precipitation data at GeoSphere Austria, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-698, https://doi.org/10.5194/ems2025-698, 2025.

P24
|
EMS2025-361
Homogenizing the Temporally and Spatially Extended Hungarian Wind Speed Database (1961–2024) 
Kinga Bokros, Beatrix Izsák, Mónika Lakatos, and Rita Pongrácz

High-quality wind speed datasets are essential for climate research, energy planning, and risk assessment. In Hungary, a homogenized and quality-controlled wind speed database covering the period from 1997 to the present has supported numerous climatological analyses. This study presents a significant extension of the Hungarian wind speed database – both temporally (extending the data back to 1961 and forward to 2024) and spatially (from 89 to 125 stations) – to improve long-term trend analysis and regional representativeness.

The homogenization process is conducted in three steps using the Multiple Analysis of Series for Homogenization (MASH) method. First, the period 1961–2024 is homogenized for a subset of 40 stations with long-term records. As many new stations were installed and measurement methodologies changed around the mid-1990s, the second step involves homogenizing an expanded dataset with 71 additional stations for the 1997–2024 period (a total of 111 stations). Finally, the full network of 125 stations is incorporated for the 2013–2024 period, enabling high-resolution spatial analysis for the most recent decade. During homogenization, the detected inhomogeneities of the three station networks are harmonized.

By increasing both the spatial density and temporal coverage of the database, this work contributes to a more robust characterization of the Hungarian wind climate. The final homogenized dataset will be made accessible to the scientific and operational communities, supporting applications in renewable energy planning, infrastructure resilience, agriculture, and urban adaptation to climate variability.

This presentation will detail the methodology—including station selection, metadata, quality control, and homogenization procedures—and highlight key findings from the expanded wind speed climatology.

The present study was carried out within the framework of the Széchenyi Plan Plus program with the support DIMOP Plusz-2.3.1-23-2023-00001 project and 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.: Homogenizing the Temporally and Spatially Extended Hungarian Wind Speed Database (1961–2024), EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-361, https://doi.org/10.5194/ems2025-361, 2025.

P25
|
EMS2025-473
The Impact of Land Cover Changes Surrounding Meteorological Stations on Measurements and Observations Using Landsat Satellite Data 
Monika Hajto, Bożena Łapeta, Tobiasz Górecki, and Zbigniew Ustrnul

Meteorological station metadata are essential for assessing the homogeneity of climatic measurement and observation time series. Among these metadata, land cover characteristics in the vicinity of the station are particularly important. Changes in land cover around a meteorological station can influence measurement outcomes, thereby compromising the homogeneity of climatic data.
Additionally, the geographical location of meteorological stations may be moved. Depending on the distance of relocation, the structure and composition of land cover surrounding the station can vary significantly. Substantial changes in the proportions of land cover classes near a meteorological station may considerably affect the recorded measurements and observations, further disrupting data homogeneity.
This study employed two satellite-derived land cover datasets with a spatial resolution of 30 meters, both based on Landsat imagery: GLanCE (NASA, 2001–2019) and GLC_FCS30D (AIRI CAS, 1985–2022). Changes in land cover structure were analyzed for 62 synoptic meteorological stations in Poland from 1985 to 2022 at both local (1 km and 2 km) and mesoscale (10 km and 30 km) radii. The analysis of land cover changes was also performed for the main wind directional sectors. The analysis distinguished stations that remained stationary from those that changed geographical location one or more times. For two selected synoptic meteorological stations—one stationary and one relocated—an assessment was conducted to determine the potential influence of land cover changes on recorded meteorological variables, using homogeneity tests and machine learning techniques to detect anomalies.

Keywords: meteorological station metadata, land cover changes, data homogeneity, climatic time series

References:
Friedl, M. A., Woodcock, C. E., Olofsson, P., Zhu, Z., Loveland, T., Stanimirova, R., Arevalo, P., Bullock, E., Hu, K.-T., Zhang, Y., Turlej, K., Tarrio, K., McAvoy, K., Gorelick, N., Wang, J. A., Barber, C. P., & Souza, C. (2022). Medium Spatial Resolution Mapping of Global Land Cover and Land Cover Change Across Multiple Decades From Landsat. Frontiers in Remote Sensing, 3. https://www.frontiersin.org/articles/10.3389/frsen.2022.894571
Zhang, X., Zhao, T., Xu, H., Liu, W., Wang, J., Chen, X., and Liu, L. (2024). GLC_FCS30D: the first global 30-m land-cover dynamic monitoring product with a fine classification system from 1985 to 2022 using dense time-series Landsat imagery and continuous change-detection method, Earth Syst. Sci. Data, 16, 1353–1381. https://doi.org/10.5194/essd-16-1353-2024

How to cite: Hajto, M., Łapeta, B., Górecki, T., and Ustrnul, Z.: The Impact of Land Cover Changes Surrounding Meteorological Stations on Measurements and Observations Using Landsat Satellite Data, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-473, https://doi.org/10.5194/ems2025-473, 2025.

P26
|
EMS2025-129
Exploring the use of the Basque Country AWS network for climate monitoring 
Roberto Hernandez, Maialen Martija, Maddalen Iza, and Santiago Gaztelumendi

Effective climate studies and monitoring heavily depend on access to extensive, high-resolution, and long-term instrumental climate data. However, our ability to understand, detect, predict, and address climate variability and change at finer spatial scales—beyond global ones—is currently limited by the scarcity and accessibility of high-quality, long-term climate records and datasets. In this context, it is important to highlight the ongoing transition from manual observation networks to fully automated systems, with such systems now predominating in much of Europe, including the Basque Country.

In this paper, we describe efforts undertaken to assess the conditions and circumstances under which automatic stations deployed across our region can be used for climate monitoring. Specifically, we focus on the automatic measurement network operated by the Basque Meteorological Agency (Euskalmet), which currently includes around 130 automatic weather stations (AWS) providing real-time data on various variables across the region at 10-minute intervals. While the network is primarily oriented towards real-time surveillance of severe weather events, including flooding, measurements are far from ideal from the climatic perspective.

Nonetheless, some stations have been operational since the early 1990s, providing relatively long data series spanning 20 to 30 years at various locations, which could offer valuable local climate data. However, it is well-known that many factors influence the usability and value of such data—not only the length and quality of the data, but also its overall representativeness, which can be affected by local environmental conditions.

Our ultimate goal is to provide insights into the use of AWS data from the analyzed network for local-scale climate monitoring. To achieve this, we identify stations that most closely align with WMO climate observation criteria, select those with the best possible data quality, implement procedures to construct essential climate data series—including data curation and homogenization—generate derived climate indicators for anomaly and trend analysis, and conduct validation studies to draw conclusions.

How to cite: Hernandez, R., Martija, M., Iza, M., and Gaztelumendi, S.: Exploring the use of the Basque Country AWS network for climate monitoring, EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-129, https://doi.org/10.5194/ems2025-129, 2025.

P27
|
EMS2025-137
New data and improved data processing for ECA&D and ICA&D  
Karlijn Zaanen, Else van den Besselaar, Gerard van der Schrier, and Marlies van der Schee

The International Climate Assessment & Dataset (ICA&D) contributes to the provision of climate services in regions across the world. ICA&D provides both climate data and tools for climate monitoring. It supports the WMO Regional Climate Centers in carrying out their mandatory functions. 

In WMO Regional Association VI (Europe and the Middle East), the European Climate Assessment & Dataset (ECA&D) has fulfilled this role for the last 25 years. It is what the ICA&D concept was derived from. ICA&D has already been implemented by the Indonesian meteorological service as the Southeast Asian Climate Assessment & Dataset (SACA&D) and is currently in development in the Caribbean, West Africa and Southern Africa regions.  

ICA&D facilitates the sharing of daily meteorological surface observations from countries in a given region with meteorological services in that region and with scientists worldwide, and derives climate monitoring products from these observations. Examples are the climate indices of extremes, such as the number of warm or dry days, which can be monitored over time to assess climate change.  

In the last few years, EU regulations have led to the sharing of more and more national weather data in Europe. A large number of new daily time series became available for ECA&D, also in previously data sparse areas such as Italy. This increase in data also requires us to upgrade our data processing techniques to be able to efficiently and reliably serve the information products. 

Here we will give an overview of the new data that’s available for ECA&D, the plans for improved data processing and the status of the implementation of ICA&D systems around the world. 

How to cite: Zaanen, K., van den Besselaar, E., van der Schrier, G., and van der Schee, M.: New data and improved data processing for ECA&D and ICA&D , EMS Annual Meeting 2025, Ljubljana, Slovenia, 7–12 Sep 2025, EMS2025-137, https://doi.org/10.5194/ems2025-137, 2025.