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
| Mon, 04 Sep, 14:45–15:30 (CEST)|Lecture room B1.08
| Attendance Tue, 05 Sep, 16:00–17:15 (CEST) | Display Mon, 04 Sep, 09:00–Wed, 06 Sep, 09:00|Poster area 'Day room'
Orals |
Mon, 14:45
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crowd-sourcing approaches or engaging citizen scientist volunteers, conserving, imaging, inventorying and archiving historical documents containing weather records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: Mon, 4 Sep | Lecture room B1.08

Chairpersons: Federico Fierli, Dan Hollis
Onsite presentation
Uwe Pfeifroth, Marc Schröder, Nathalie Selbach, and Rainer Hollmann

In recent decades climate variability and change have caused impacts on natural and human systems on all continents. Observations are needed to understand and document these impacts and its causes. Such observations are increasingly based on remote sensing from satellites which offer global scale and continuous coverage. Only long-term and consistent observations of the Earth system allow us to quantify climate variability and change and their impacts on the natural and human dimension.

Since more than 20 years, the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF, https://www.cmsaf.eu) develops capabilities for a sustained generation and provision of Climate Data Records (CDRs) derived from primarily operational meteorological satellites. The product portfolio of the CM SAF comprises long time series of Essential Climate Variables (ECVs) related to the energy and water cycles as defined by the Global Climate Observing System (GCOS). Currently available CM SAF CDRs include, among others, surface and top of the atmosphere radiative fluxes, cloud products, surface albedo as well as latent heat flux/evaporation, precipitation and freshwater flux over the global ice-free oceans. The recent CDR versions cover the WMO reference period from 1991-2020, and several of the CM SAF CDRs even have a temporal coverage of more than 40 years. In order to serve applications with strong timeliness requirements, CM SAF also produces so-called Interim Climate Data Records (ICDRs), which are typically released within a few days of the observations. All products are well-documented, carefully validated and have been externally reviewed prior to product release.

After a short introduction to CM SAF and an overview of available and upcoming CDRs and ICDRs from CM SAF, the presentation will focus on new releases of CDRs and ICDRs of clouds and radiation. The product portfolio was enhanced to also include the surface radiation budget and recent retrieval developments rely on artificial intelligence techniques. Examples from validation and applications using CM SAF data records will also be introduced.

How to cite: Pfeifroth, U., Schröder, M., Selbach, N., and Hollmann, R.: Climate Monitoring SAF: Sustained Generation of Satellite-Based Climate Data Records, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-142, https://doi.org/10.5194/ems2023-142, 2023.

Onsite presentation
Niko Filipović

The station network of the Austrian Weather Service GeoSphere Austria comprises about 260 semi-automatic weather stations called TAWES. The observation data collected by TAWES are checked for plausibility and completeness in several steps. The main quality control is based on an automated tool called AQUAS (short for Austria Quality Service). The software was developed at ZAMG (now GeoSphere Austria) in Vienna as part of quality management in the area of real-time processing of near-surface observation data.
The basis for quality control is formed by standard methods for checking meteorological and climatological data (e.g. plausibility check, temporal, spatial and internal consistency check, etc.), which are continuously improved and further developed within the framework of AQUAS. The individual system components are designed to test the incoming observation parameters in real time. For the greatest possible flexibility, each parameter can be processed autonomously, independently of the other measured variables of a weather station. In addition, data from other sources such as radar and satellite data as well as data from numerical weather prediction models and data from other measurement networks, like hydrological network or any other third-party network, can also be implemented in AQUAS.
Some examples for the operational use of AQUAS and the current state of research on quality control procedures are presented. As an example, a method for real-time quality control of 10-minute wind speed data is shown, which detects doubtful wind gust peaks. Another example on the basiss of daily data shows a method using daily sums of global radiation and sunshine duration to detect instrument malfunctions.

How to cite: Filipović, N.: AQUAS - A Data Quality Control tool at GeoSphere Austria, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-583, https://doi.org/10.5194/ems2023-583, 2023.

Online presentation
June Madariaga Navarro, María Ibáñez Herrera, and María de las Mercedes Maruri Machado

Waves are one of the variables that most condition coastal maritime activities. Coastal observation systems are concentrated in ports, where economic activity is centred. Recent studies assess the effects of climate change on productivity or impacts on structures and their negative repercussions on the economy. But it is not only seaports that are affected, but there are also many coastal maritime activities in the food, tourism, energy and private sectors, etcetera, whose economy is highly conditioned by the ocean-meteorology and its changes.  In the Basque Country, many towns face the sea and the highest density centres are on or near the sea.

Through the study of observations of coastal phenomena we can improve our warning systems and provide useful information for the management of coastal maritime activities.

In order to optimise and improve the safety of these workplaces, knowledge of the ocean-meteorological conditions is required to adapt the work and make it safer. Knowledge of the environment requires the implementation of observation networks, modelling of the environment and integration of existing observations. The Basque coast and beaches have a great morphological variability from west to east, making it a very attractive study area. Furthermore, land-sea interactions are complex because they are intermediate, complex areas, where very local phenomena occur, observed with different networks, administration, and competences.

The University of the Basque Country, in collaboration with the Tecnalia research centre and the Basque Government emergency and meteorology department, is planning, studying, and implementing the installation of a coastal buoy in order to obtain better information on sea conditions in an area with a high level of coastal maritime activity, in front of busy beaches and close to the perimeter of the port of Bilbao.

The collaboration between the manufacturer (Zunibal S.L.), university (UPV/EHU), research centre (TECNALIA) and Basque Government (DAEM) for the exploitation of the information and study of the time series and how to use the information in different applications, are presented in this work.

How to cite: Madariaga Navarro, J., Ibáñez Herrera, M., and Maruri Machado, M. D. L. M.: Coastal buoys, uses in the coastal maritime field., EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-288, https://doi.org/10.5194/ems2023-288, 2023.

Posters: Tue, 5 Sep, 16:00–17:15 | Poster area 'Day room'

Display time: Mon, 4 Sep 09:00–Wed, 6 Sep 09:00
Chairpersons: Federico Fierli, Dan Hollis
Thomas Möller, Tina Leiding, Axel Andersson, Florian Imbery, and Thomas Junghänel

Historic observational data records are an important contribution for climate reconstructions and analysis of past weather events. Particularly in remote and data sparse regions, such as the open ocean, newly rescued data can significantly improve the knowledge about weather and climatic conditions in earlier decades and centuries.

Deutscher Wetterdienst (DWD) holds several collections of original historical weather records from land stations and ships. They comprise not only observations from Germany, but also of the global oceans and land stations in many parts of the world.

All German state-owned meteorological observations beginning with the Prussian Meteorological Institute in 1848 are collected in the main archive of DWD in Offenbach.

DWD’s branch office in Hamburg holds the marine archive starting with the collections of the German Naval Observatory, 'Deutsche Seewarte', which existed from 1868 to 1945. It includes marine data records from ships, as well as land stations in many parts of the world (e.g. from former German colonies) and signal stations situated at the coasts of the North and Baltic Sea.

With the further expansion of the IT infrastructure, high temporal resolution data have increasingly become the focus of climate research in recent years. Thus, the processing of such historical data is also increasingly necessary. The digitization of recording strips from pluviographs, for example, is currently one focal point of the data rescue activities at DWD.

The documentation, digitisation and quality check of the enormous quantity of handwritten journals of all four data archives is still ongoing. The digitised data will be freely accessible to all interested scientists and are also continuously submitted to international data archives, such as ICOADS and ISPD. Through these data sets, the data are also an important input for regional and global reanalyses.

The presentation will give an overview of the historical archives of Deutscher Wetterdienst and will show the recent progress of the digitization efforts and ongoing analysis of the data.

How to cite: Möller, T., Leiding, T., Andersson, A., Imbery, F., and Junghänel, T.: Data rescue of national and international meteorological observations at Deutscher Wetterdienst, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-268, https://doi.org/10.5194/ems2023-268, 2023.

Barbara Chimani, Oliver Bochniček, Michele Brunetti, Manfred Ganekind, Juraj Holec, Beatrix Izsák, Mónika Lakatos, Melita Perčec Tadić, Veronica Manara, Maurizio Maugeri, Pavel Stastny, Olivér Szentes, and Dino Zardi

HISTALP is a long-term climatological database for the Greater Alpine Region, including monthly data of precipitation, temperature, sunshine duration and air pressure. The activities leading to this dataset started in 1997 with the final database being in place in 2003. Since then annual updates of the data are done. The dataset is freely available for research and education and frequently used in different climate related studies.

Due to the long existence of the HISTALP dataset, a new homogenisation activity was due in order to ensure the homogeneity of the updated time series. This was done in the course of the creation of a new version of the gridded precipitation dataset of the Greater Alpine region (LAPrec, https://surfobs.climate.copernicus.eu/dataaccess/access_laprec.php) within an international Copernicus project. Before starting the homogenisation activity, the existing data was revisited and an exchange with the data owners on the original data took place. This lead to corrected, historical original data as well as to the replacement of some stations used in HISTALP. Homogenisation was mainly done within the four climate regions of HISTALP (www.zamg.ac.at/histalp), with some special networks e.g. for especially long time series. The results were compared to the former version of homogenised HISTALP-precipitation data as well as to national homogenised datasets.

The analyses of the resulting dataset on trends and data range support the idea of a generally good quality of the homogenisation. For all comparisons with other homogenised datasets the timing of the homogenisation had to be taken in to account, additional differences were to be expected due to the availability and selection of reference series, choice of homogenisation method and availability of metadata (especially for interactive methods). The results of the comparison show that in the national homogenisations more breaks were detected than in the HISTALP one. This was to be expected due to the higher number of highly correlated reference series. Overall, the comparison gives confidence in the HISTALP dataset and its homogenisation. Nevertheless, for a small number of stations strong differences between the different homogenisations have been detected. Those will be assessed by a future step.

How to cite: Chimani, B., Bochniček, O., Brunetti, M., Ganekind, M., Holec, J., Izsák, B., Lakatos, M., Perčec Tadić, M., Manara, V., Maugeri, M., Stastny, P., Szentes, O., and Zardi, D.: Revisiting HISTALP Precipitation dataset, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-452, https://doi.org/10.5194/ems2023-452, 2023.

Olivér Szentes, Mónika Lakatos, and Rita Pongrácz

A more accurate understanding of climate and its changes requires temporally and spatially representative climate databases. However, measurement conditions change frequently: relocation of stations, instrument changes, changes in measurement time, changes in environmental conditions can all cause inhomogeneities in the data series, and therefore homogenization is needed.

For homogenization of data series, quality control and filling in the missing values we use the MASH (Multiple Analysis of Series for Homogenization) procedure (MASHv3.03 software) at the Climate Department of the Hungarian Meteorological Service (OMSZ). Inhomogeneities are estimated using monthly data series. Monthly, seasonal and annual inhomogeneities are harmonized in all MASH systems, constructed for homogenization of various station systems which consist of stations with different length of data. After homogenization, we have temporally representative data series.

However, weather stations are not evenly distributed, the station network consists of both densely and sparsely covered subregions. In order to estimate the values of meteorological variables at points where no measurements are available, a spatial interpolation method must be used. Our gridded climate datasets are generated using the MISH method (MISHv1.03 software). After interpolation, we have spatially representative climate database.

Currently, the start of the Hungarian precipitation climate database is 1901, but the beginning of regular precipitation measurements started decades earlier, so it is possible to extend the precipitation database in time. In addition, the 131 datasets from the first half of the 20th century that are currently used can be significantly extended, as there are still many undigitized datasets before the 1950s. The collection of monthly precipitation data stored still on paper made it possible to use many more stations from the first half of the 20th century than before, and thus, the precipitation patterns in Hungary in the second half of the 19th century can be analyzed.

In this poster presentation, we will present the new precipitation station systems used for homogenization, the most important verification statistics of the homogenization of precipitation data series, and analyze the gridded spatial means (national averages for Hungary) from the beginning of the measurements to the present.


The research presented was carried out within the framework of the Széchenyi Plan Plus program with the support RRF 2.3.1 21 2022 00008 project.

How to cite: Szentes, O., Lakatos, M., and Pongrácz, R.: Long-term homogenized and gridded precipitation data for Hungary, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-376, https://doi.org/10.5194/ems2023-376, 2023.