Modern spatial climatology seeks to improve the accuracy, coverage and utility of grid datasets. Prominent directions of the actual development in the field are the following:
• Establish datasets for new regions and extend coverage to larger, multi-national and continental domains, building on data collection and harmonization efforts.
• Develop datasets for more climate variables and improve the representation of cross-variable relationships.
• Integrate data from multiple observation sources (stations, radar, satellite, citizen data, model-based reanalyses) with statistical merging, machine learning and model post-processing.
• Extend datasets back in time, tackling the challenges of long-term consistency and variations in observational density.
• Improve the representation of extremes, urban climates, and small-scale processes in complex topography.
• Quantify uncertainties and develop ensembles that allow users to trace uncertainty through applications.
• Advance the time resolution of datasets to the sub-daily scale (resolve the diurnal cycle), building on methods of spatio-temporal data analysis.
This session addresses topics related to the development, production, and application of gridded climate data, with an emphasis on statistical analysis and interpolation, inference from remote sensing, or post-processing of re-analyses. Particularly encouraged are contributions dealing with new datasets, modern challenges and developments (see above), as well as examples of applications that give insights on the potential and limitation of grid datasets. We also invite contributions related to the operational production at climate service centers, such as overviews on data suites, the technical implementation, interfaces and visualisation (GIS), dissemination, and user information.
The session intends to bring together experts in spatial data analysis, researchers on regional climatology, and dataset users in related environmental sciences, to promote a continued knowledge exchange and to fertilise the advancement and application of spatial climate datasets.
This presentation addresses the challenge of generating high-resolution spatial wind climatologies for Switzerland, a region characterized by complex mountainous terrain and, for the purpose of mapping wind, a sparse measurement network. Accurately mapping wind patterns in such areas is thus inherently difficult but of great importance for supporting applications such as risk assessment or wind energy planning.
In this study we explore three different and complementary approaches to tackling this challenge:
- Model-only: a new test data set, the Swiss ICON Reanalysis-Light1-CH1 (REA-L), has recently been produced by MeteoSwiss for the period 2005-2024 at 1km mesh-size over the ICON-CH1-EPS domain. Long-term climatologies for mean wind and wind gusts have been produced from this data set.
- Machine-Learning: in an attempt to further downscale the ICON REA-L surface fields to sub-kilometer scale, a ML approach based on Gaussian Processes has been developped within obsweatherscale, an open-source Python package that integrates station measurements, high-resolution topographic descriptors, and auxiliary atmospheric predictors to produce a continuous spatial distribution of the target variable. The method captures nonlinear wind-terrain interactions, provides uncertainty estimates, and remains physically interpretable.
- Station Transfer: this approach follows classical statistics to estimate the full statistical distribution for mean wind and wind gusts at each grid point based on station measurements from 1981 – 2025 and topographic information.
The resulting outcomes are intercompared, verified, and explored regarding their consistency and accuracy, and to gather the pros and cons for each method. A special focus is on their suitability to accurately describe extremes. We present the resulting climatological fields as well as some case-study verification of selected wind events over Switzerland.
References:
Lloréns Jover, I., & Zanetta, F (2024). obsweatherscale: observation-conditioned ML downscaling of surface weather fields. GitHub repository: https://github.com/MeteoSwiss/obsweatherscale
Zanetta, F., Nerini, D., Buzzi, M., & Moss, H. (2025). Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks. Artificial Intelligence for the Earth Systems.
How to cite: Schwierz, C., Lloréns Jover, I., Isotta, F., Grams, C., Begert, M., and Arpagaus, M.: Towards a high-resolution wind climatology for Switzerland – a comparison of different methods , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-641, https://doi.org/10.5194/ems2026-641, 2026.
In recent years, machine learning has been used increasingly for climatological applications. It has been applied to traditional geostatistical problems, such as objective analysis, including reconstructing the value of an atmospheric variable at an unobserved location given observations at nearby locations. These approaches provide new possibilities for combining information from irregular observation networks and may offer advantages over classical interpolation methods, particularly in capturing nonlinear relationships.
In this study, we apply a neural network model to estimate hourly temperature and precipitation at a given location based on nearby observations. The aim is to estimate both the expected value and the associated uncertainty. The neural network is based on an approach originally developed for post-processing numerical weather prediction output, which provides probabilistic forecasts in the form of quantile functions. These quantile functions are represented as linear combinations of Bernstein basis polynomials, with coefficients predicted by the network. This representation allows for a flexible and consistent description of the predictive distribution while ensuring that the estimated quantiles are consistent with the observed data.
We present results comparing the probabilistic predictions from the neural network with those obtained using traditional geostatistical methods such as kriging. The spatial filtering properties of the two methods are investigated using both synthetic Gaussian random fields and real observational data as input, allowing us to assess their performance under controlled as well as realistic conditions.
Furthermore, we evaluate the reconstruction method on a dense observation network that includes crowdsourced data, providing insight into its performance in data-rich and heterogeneous observational settings.
How to cite: Lussana, C., Miglio, J., Carrer, M., Bremnes, J. B., and Maugeri, M.: Quantile-based neural network reconstruction of temperature and precipitation over Norway, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-558, https://doi.org/10.5194/ems2026-558, 2026.
Within the framework of the Danube-ADAPT Enhancing Climate Data Cooperation for Evidence-based Adaptation Policy Making in the Danube Region project, 8 countries and 16 partners are jointly preparing a climatological database for the Danube region.
For meteorological and climatic studies, it is not only necessary that the quality of the data series is uniform and good over time, but also that we are able to derive reliable data for those places where meteorological observations have not been made. During interpolation, we naturally strive to ensure that the result is as close as possible to real state. At HungaroMet, a mathematical-statistical interpolation system called MISH was developed specifically for the interpolation of meteorological variables.
Within the framework of the Danube-ADAPT project, we are preparing a homogenized, grid-based climatological database covering nine meteorological elements for the entire Danube river basin. The database is generated applying uniform methods across the entire area, while homogenization is carried out using the MASH software, and interpolation is performed using the MISH software. The selected meteorological elements are the daily average temperature (1970-2024), minimum temperature (1970-2024), maximum temperature (1970-2024), precipitation sum (1970-2024), mean windspeed (2000-2024), wind maximum (2000-2024), air pressure (1970-2024), relative humidity (1970-2024) and global radiation (2000-2024). The MISH software consists of two main parts: modeling and interpolation. We use the data sets homogenized with MASH software to model the climate statistical parameters. The model results are used for interpolation.
The steps and results of the production of the final grid point database are presented in our presentation.
This paper was supported as part of Danube-ADAPT project, an Interreg Danube Region Programme project co-funded by the European Union.
How to cite: Izsák, B. and Szentes, O.: Interpolation with MISH-the climatological database of the Danube region-, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-263, https://doi.org/10.5194/ems2026-263, 2026.
High-resolution gridded climate datasets are increasingly needed to analyse local climate variability, assess weather related impacts, and support climate services at scales relevant to society. Yet official surface observation networks alone cannot resolve the fine-scale spatial patterns associated with urban climates, land use transitions, or small-scale extremes. At the same time, the growing availability of crowdsourced and IoT weather observations offers unprecedented spatial density, but their integration into spatial climatologies requires robust statistical methods capable of handling heterogeneous data sources, variable quality, and evolving observational coverage.
To address these challenges, the Royal Netherlands Meteorological Institute (KNMI) has developed an operational workflow for producing high-resolution spatial climatologies by integrating official surface observations with crowdsourced data. The approach builds on multi-fidelity Gaussian Process (GP) regression, in which official observations and personal weather station (PWS) data are combined while explicitly learning their relative error scales and biases. The workflow includes near real-time acquisition of crowdsourced data, basic quality control to remove implausible values, and interpolation using covariates such as land use fractions, terrain descriptors, and distance-to-coast. Scaling GP models to continental domains poses substantial computational challenges, as naïve implementations rely on full covariance matrices with prohibitive computational cost. This extension was made possible through collaboration with the Dutch National Research Institute for Mathematics and Computer Science (CWI), who developed scalable refinements including sparse covariance structures, Nyström-based low-rank approximations, Wendland-induced sparsity, and adaptive landmark selection. These innovations reduce the computational burden sufficiently to enable near real-time 1-km European fields.
Although designed for high-resolution mapping, the posterior fields produced by this system can be aggregated to hourly, daily, or longer periods, enabling the construction of spatially consistent climatological datasets with quantified uncertainty. From these aggregated fields, a wide range of standard climatological indices can be derived, including fixed-threshold metrics such as frost days, heatwave duration, or growing season length, while preserving spatial coherence across heterogeneous landscapes. The increased level of detail also improves the representation of urban climates and local extremes, offering finer-scale baselines for applications such as drought monitoring, energy planning, and hydrological modelling.
Taken together, these developments show that crowdsourced weather observations can play a meaningful role in modern spatial climatology when combined with robust statistical methods and explicit uncertainty quantification. The approach demonstrates that we can now produce high-resolution maps across the European domain, a capability that is essential for representing local extremes, urban climates, and fine-scale land‑use transitions. In doing so, it supports the development of next-generation European climate products with enhanced spatial detail and traceable uncertainty.
How to cite: Garcia-Marti, I., Agdenstein, S., Angevaare, J., Crommelin, D., Hoekstra, R., Klein, R., Loukrezis, D., Mücke, N., van Ekris, J., van der Schrier, G., and Whan, K.: Unlocking km-scale European temperature fields with crowdsourced observations, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-393, https://doi.org/10.5194/ems2026-393, 2026.
Met Éireann provides 1 × 1 km monthly rainfall grids for Ireland from 1941 onwards, generated from more than 400 station observations using a regression-Kriging statistical gridding methodology. Work has been done recently to extend this dataset by generating historical rainfall grids back to 1855, when station coverage was sparse and spatially uneven. A key question in analysing Ireland’s historical climate is how this low density and particular spatial distribution influences the accuracy and reliability of these gridded rainfall products.
To address this question, we use Met Éireann’s post-1941 published rainfall grids as a benchmark to simulate historical station configurations over the 1855–1940 period. Subsampled networks are constructed to represent the varying station counts and spatial distributions consistent with the historic record. The current operational gridding method is applied to these reduced networks, and the resulting fields are evaluated against the full-network reference using quantitative metrics at both national and county scales. The county level analysis informs grid quality assessments where certain regions of the country can be more accurately gridded than others, and reasons for this can further investigated. This framework allows us to estimate reconstruction error, spatial bias, and temporal variability under historical scenarios with reductions in network density.
In addition, alternative gridding methods are implemented and compared to assess their performance under these sparse station distributions. The results offer methodological guidance for constructing historical rainfall grids and establish statistical thresholds for the minimum station density required for acceptable accuracy at county and national scales. These simulations also suggest principles which can be applied to gridding methodologies in general including importance of stations at elevation and the climatology to include in the gridding model. We present this simulation framework as a powerful tool for analysing the efficacy of historic rainfall networks.
How to cite: Liu, C., O'Sullivan, B., Coonan, B., and Ryan, C.: Quantifying Uncertainty in Historical Rainfall: A Simulation Study of Station Network Density and Design, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-359, https://doi.org/10.5194/ems2026-359, 2026.
The significant land-cover changes that occurred in many countries during the past century have driven strong temperature trends, either exacerbating or tempering the impacts of global climate change on public health, infrastructure, and ecosystems. These changes are generally not included in climate reconstructions, such that temperature changes may solely be attributed to large-scale changes. Indeed, it remains challenging to include land-cover change effects in high-resolution climate reconstructions due to the scarcity of historical meteorological and physiographic data.
We address these challenges by proposing a novel approach for historical climate reconstruction, explicitly integrating urbanization and forest-cover changes into gridded observational fields. The approach is demonstrated through the reconstruction of 1-km resolution monthly fields of near-surface daily minimum and maximum temperature starting in 1900 for Belgium, which saw pronounced changes in urban sprawl, as well as agricultural and forest cover. We rely on long homogenized series of air-temperature observations, global gridded temperature products, a dynamic land-cover map, and complementary information on the local impact of the land cover from models and local observations. Various sources of input data are employed in the generation of an ensemble dataset, providing a measure of the uncertainty on the estimates and the sensitivity to data source. For the recent decades, the new reconstruction is benchmarked against a country-wide reference gridded observational dataset spanning a shorter temporal extent.
To our knowledge, this reconstruction is the first century-long gridded temperature product at kilometric resolution including the effects of land-cover changes. The presented method provides a promising framework for disentangling land-cover change effects from background climate warming, and enhancing understanding of observed temperature trends.
How to cite: Tzvetkov, D., Van Schaeybroeck, B., De Frenne, P., Ghilain, N., Hamdi, R., Poelmans, L., Teller, J., and Caluwaerts, S.: Addressing the role of land-cover change in regional climate observation datasets: an example over Belgium (1900-2015), EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-138, https://doi.org/10.5194/ems2026-138, 2026.
We address a need for hydrometeorological early warning and information systems (EWIS) in Southern Africa. In the project 'Co-Design of Hydrometeorological Information system for Sustainable Water Resource Management in Southern Africa' (Co-HYDIM-SA) we want to enhance water security in the two transboundary regions: Cuvelai-Cunene and Notwane (Namibia and Angola; Botswana and South Africa).
The Global Precipitation Climatology Centre (GPCC) has many years of experience in hosting an operational and publicly available global drought monitoring service with the drought index GPCC-DI. The main challenge of drought monitoring in the focus region is data scarcity. Only few parameters are available and gaps in time series from stations are often present. That’s why, we work on a flexible data input in the operational system, where we can decide which data source should be used. For precipitation we mainly rely on the gridded GPCC dataset based on station data, whereas for temperature the gridded dataset from the Climate Prediction Center (CPC) is used. Furthermore, we plan to include satellite products (GIRAFE, CHIRPS, GPCP) and reanalysis (ERA5-Land) datasets. For the data acquisition and the implementation of the product, the collaboration with stakeholders in the focus region is essential. Therefore, they are included in the decision making and informed about our progress. The ‘co-design’ approach is an essential part of the project and is achieved by a close partnership with local Universities and a regular contact to the stakeholders.
At the EMS26 we want to present the Co-HYDIM-SA project, our findings and challenges we have encountered. Until today, we have calculated time series for the two Drought Indices (SPI, SPEI) and compared them with specific drought events. In general, the indices are consistent with the described droughts. One disadvantage of the SPI is that it has limitations during the dry season, especially for short term data aggregation. Whereas, the SPEI is characterized by its all-year round usability, due to the integration of potential evapotranspiration in addition to the precipitation data. As a next step, we will compare the grid data to station time series and evaluate the results
by calculating skill scores.
How to cite: Appenheimer, J., Rustemeier, E., Ziese, M., and Finger, P.: Meteorological Drought Monitor for two transboundary regions in Southern Africa, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-338, https://doi.org/10.5194/ems2026-338, 2026.
This work assesses the relationship between the IPMA-computed NAO (North Atlantic Oscillation) and EA (East-Atlantic) indices, determined using ERA5 reanalysis, and climate variability over mainland Portugal. Additionally, it aims to operationalise both indices for routine production and public dissemination. Both indexes were computed using a methodology based on NOAA reference computations of NAO and EA. The results show that NAO-IPMA index shows a moderate-to-strong correlations with the NOAA reference index and with precipitation anomalies over mainland Portugal, particularly during the extended winter season (November to March; NDJFM), when the influence of the North Atlantic Oscillation (NAO) is most pronounced.
This way, the relationship between the NAO-IPMA index and precipitation anomalies over mainland Portugal is significant between December and March, with negative correlations ranging from −0.6 to −0.7, peaking in February. Thus, it is highlighted the key role of the NAO in modulating winter precipitation in mainland Portugal, with negative (positive) NAO phases associated with wetter (drier) conditions. In contrast, the relationship with mean air temperature is generally weak. The joint analysis of the NAO-IPMA and EA-IPMA indices with the rainfall anomaly proxy index (Standardized Precipitation Index; SPI-3) supports these results, showing that wetter periods tend to occur under negative NAO conditions or when negative NAO coincides with a positive EA. In contrast, drier periods are associated with positive NAO or the combination of a positive NAO and negative EA.
The assessment of the seasonal forecasting system ECMWF SEAS5.1 indicates limited predictive skill for the NAO and EA, with moderate correlations only for the forecast month (lead time = 0) between January and March, followed by a rapid decline in skill as lead time increases. Overall, the NAO-IPMA and EA-IPMA are robust indexes for climate monitoring and interpretation of precipitation variability over mainland Portugal, particularly during winter, even though seasonal predictability remains limited. Nevertheless, the operational implementation and combined use of the NAO and EA indices, together with proxy indicators for rainfall anomalies, such as the SPI, can help understand atmospheric conditions leading to wetter or drier periods, thereby supporting improved monitoring and prediction of climate variability in mainland Portugal.
How to cite: Pereira, C., Antunes, S., and Deus, R.: Operationalisation of the NAO and EA indices for climate monitoring and seasonal variability assessment in Mainland Portugal, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-669, https://doi.org/10.5194/ems2026-669, 2026.
New gridded climatological maps were added into daily operation at Slovak Hydrometeorological Institute (SHMI) during March 2025. They comprise the following products: Minimum, Maximum, Mean daily air temperature [°C], Daily precipitation sum [mm] and Daily sum of potential evapotranspiration [mm]. The maps are updated daily at 10 a.m. (CET) and are freely available for the public. The temperature maps are created using method of Frei (2014) which is suitable for areas with complex orography since it has been originally used for territories of Switzerland and Austria. Precipitation map is created using two-step interpolation procedure. In this case, the computation combines background monthly normal precipitation fields created by kriging with external drift (KED) and daily field created by Inverse Distance Weighting (IDW) method. Daily sum of potential evapotranspiration is derived from aforementioned maps using modified Hargreaves method (Droogers & Allen 2002). The map products are published at the website in a user-friendly form. E.g. tooltip helps to read the exact interval values from the map to ensure a correct understanding of colour scale used in the maps. Additionally, a videotutorial explaining each product and all its utilities should improve the overall understanding of displayed data by general public.
In consequence, to daily operational climatological maps, newly updated drought monitoring products were added to operation during June 2025. Following products were updated, or newly created: Standardized precipitation-evapotranspiration index (SPEI), Standardized Precipitation Index (SPI), both for 30-day period, Precipitation balance in comparison with normal of 90-day precipitation sum, Percentual difference of precipitation in comparison with normal of 90-day precipitation sum and duration of drought. SPEI and SPI indices include a 7-day forecast based on A-LAEF (days 1-3) and ECMWF IFS models (days 4-7). Instead of a deterministic forecast, an ensemble forecast is used, showing the mean, 10th, and 90th percentiles of the ensemble to illustrate the forecast uncertainty.
Third part of new products represents new bioclimatological products, namely Universal Thermal Comfort Index (UTCI) and Heat index (HI) and fire risk products, namely Fire Weather Index (FWI) and Fuel moisture index (FMI). These products have 7 day forecast as well, based on ALADIN NWP model with 2 km resolution (day 1-3) and ECMWF model (day 4-7).
Acknowledgment
This work was supported by project Clim4Cast (Central European Alliance for Increasing Climate Change Resilience to Combined Consequences of Drought, Heatwave, and Fire Weather through Regionally-Tuned Forecasting; CE0100059) co-funded by European Union funds (ERDF – Interreg Central Europe).
How to cite: Labudová, L., Holec, J., Štefánik, D., Mikulová, K., Balek, J., and Bartošová, L.: New gridded daily operational map products for Slovakia, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-271, https://doi.org/10.5194/ems2026-271, 2026.
This talk describes a high-resolution dataset of daily minimum (TN), mean (TG), and maximum (TX) near-surface temperatures in Poland from 1951 to 2020 with a 1 km2 spatial resolution developed using radial basis functions applied to quality-controlled observations from 347 ground weather stations at the Institute of Meteorology and Water Management – National Research Institute. The dataset was evaluated on a seasonal, monthly, and station basis by leave-one-out cross-validation (LOO-CV) to ensure the ability to reproduce the original variability in the characteristics. The findings indicate TG performs best in winter and spring, with high correlation and low root-mean-square deviation. Although TX's performance is slightly below average, it still shows relatively good agreement, especially in autumn. The method is less successful at accurately capturing TN, with the best results seen in winter. The analysis also confirms that the standard deviation of temperatures, a measure of variability, is consistent between observed and interpolated data. All variables exhibit biases of no more than 0.03 and minimal interannual variability. Pearson's correlation coefficient is very high, ranging from 0.94 to 0.98. The difference between the 5th and 95th percentiles suggests a slight underestimation of Q95 and an overestimation of Q05. There is a noticeable increase in seasonal RMSD variability with altitude, with relatively small interannual differences at lower altitudes and the greatest at the highest altitude class. Mean values are preserved, and the interpolated data's standard deviation is slightly lower. Finally, an example of the application of the resulting gridded product in the field of climate change is shown.
The validation results demonstrate the dataset's accuracy and suitability for climatological applications. However, some limitations and potential areas for improvement have been identified. Notably, while computationally efficient, the RBF interpolation method may smooth out extremes and underestimate spatial variability, particularly in areas with complex terrain.
This open-access dataset is crucial for climate change impact studies at smaller scales and can serve a wide range of users, including researchers, administrative bodies, and society. One important application of such a dataset is as reference data for bias correction of regional dynamical downscaling results (e.g., the EURO-CORDEX initiative) to develop effective adaptation and mitigation strategies.
Jaczewski, A., Marosz, M., and Miętus, M.: PL1GD-T – gridded data of the mean, minimum and maximum daily air temperature (2 m) for the Polish area at a resolution of 1 km×1 km and the period 1951–2020, Data repository of IMGW-PIB, https://doi.org/10.26491/imgw_repo/PL1GD-T, 2024.
Jaczewski, A., Marosz, M., and Miętus, M.: PL1GD-T: a high-resolution gridded daily air temperature dataset for Poland, Earth Syst. Sci. Data, 17, 3857–3871, https://doi.org/10.5194/essd-17-3857-2025, 2025.
How to cite: Jaczewski, A., Marosz, M., and Miętus, M.: PL1GD-T: a high-resolution gridded daily air temperature dataset for Poland, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-290, https://doi.org/10.5194/ems2026-290, 2026.
The newly developed Digital Climate Atlas of Hungary allows users to view different climate variables and derived indicators for Hungary, including their past averages and projected future values, across various spatial scales, presented in both maps and graphs.
The climate of the present and recent past is described using homogenized (MASH) and gridded (MISH) observations from the HungaroMet network, except sunshine duration, for which satellite-based products were used. Climate data for 36 climate parameters (basic variables and derived indicators) are provided at a spatial resolution of about 1×1 km, generally covering 1901–2024, with shorter periods for global radiation and wind speed (2001–2024), sunshine duration (1986–2024), and soil moisture (5×5 km, 1991–2020). Values are aggregated to monthly, seasonal, and annual scales based on standard climatological periods (1961-1990, 1971-2000, 1991-2020).
Future climate change is described by the 10 km resolution projections of two regional climate models, ALADIN-Climate and REMO using two anthropogenic scenarios, RCP4.5 and RCP8.5. The simulations cover the 21st century with a special focus on the periods of 2041–2070 and 2071–2100. Projected changes for 25 climate indicators (mostly on annual, but also seasonal and monthly scales) are expressed relative to the 1971–2000 reference period, with uncertainty represented by the minimum and maximum changes across simulations.
In addition to the maps, graphs of monthly means and long time series as well as statistics of the measurement records are presented. The observation data are updated annually, while new projections are published in line with the research progress. The Atlas is linked also to the climate service portal of the HungaroMet Hungarian Meteorological Service, from where some requests directly lead to pre-selected information for different sectors.
Acknowledgements:
The research presented was carried out within the framework of the Széchenyi Plan Plus program with the support of the RRF 2.3.1 21 2022 00014 project.
How to cite: Lakatos, M., Szépszó, G., and Bihari, Z. and the Hungarian Climate Atlas Team: Digital Climate Atlas of Hungary, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-737, https://doi.org/10.5194/ems2026-737, 2026.
Accurate characterization of climate variability and hydrometeorological risk in the Southeast Asia relies
on robust observational baselines. The Southeast Asian Climate Assessment & Dataset (SACA&D) serves
as the primary regional repository, providing not only an extensive archive of daily station records but also
the derived climate extreme indices essential for monitoring climate change impacts. However, as is
common in the long-term stewardship of large-scale climate archives, integrating evolving data streams-
historical records alongside centralized daily observation systems-inherently introduces metadata
complexities.
Within the SACA&D database, the ingestion of these various data streams has yielded over 10,703 station
metadata entries covering the period 1980-2025. These entries largely consist of fragmented time series,
coordinate misalignments, and reused station identifiers corresponding to a core network of
approximately 200 active physical stations in Indonesia. Spatial interpolation of such unconsolidated
records generates potentially spurious trends and spatial density, compromising the reliability of
downstream gridded products.
To maximize the climatological utility of these observations, this study implements a data stewardship and
harmonization framework to prepare the in-situ baseline for the next-generation gridded dataset (SA-
OBSv3e). Following the protocols outlined in the European Climate Assessment & Dataset (ECA&D)
Algorithm Theoretical Basis Document (ATBD), extensive data rescue, deduplication, and quality control
efforts were applied. By resolving overlapping station IDs, removing invalid date entries, and merging
fragmented records belonging to identical physical locations, we reconstructed continuous, long-term
precipitation time series.
This study extended the archive’s temporal coverage to December 2025, yielding over 153 million quality-
controlled daily precipitation values across the network. At the upcoming conference, we will present the
fully harmonized SACA&D dataset. We will demonstrate that resolving these inherent metadata
anomalies and consolidating the fragmented network is a mandatory prerequisite for developing the
upcoming high-resolution SA-OBS v3e dataset.
How to cite: Suwondo, A., Ettema, J., van der Schrier, G., van den Besselar, E., and Pradana, R. B.: From Fragments to Footprints: Harmonizing Southeast Asian Climate Assessment & Dataset (SACA&D)for the Next-Generation Regional Grid Data, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-815, https://doi.org/10.5194/ems2026-815, 2026.
Spatial interpolation of meteorological variables represents a key component of operational climatology at the Czech Hydrometeorological Institute (CHMI). A variety of interpolation approaches are currently used, ranging from simple deterministic techniques to more advanced regression-based and geostatistical methods, implemented both in specialized software and open-source environments.
This study presents a systematic comparison of selected interpolation methods applied to the Czech Republic, including inverse distance weighting (IDW), approaches based on local linear regression (LLR, Clidata-DEM), multiple weighted linear regression (MWLR) and kriging implemented in R and Python. The tested methods differ in their treatment of topographic predictors and spatial relationships, ranging from purely distance-based approaches to regression- and terrain-informed models
The evaluation is based on a dataset of daily temperature, precipitation and snow cover measurements from approximately 200 climatological stations over a five-year period. Interpolation performance is assessed using cross-validation strategies with systematically removed stations (5–10%) and stratification across elevation zones (150–1600 m a.s.l.) and selected geographical regions. Additional independent validation is performed using observations from stations not included in the interpolation process.
The study aims to quantify the relative performance of individual methods under varying geographical and climatic conditions, with a particular focus on the added value of terrain-informed and regression-based approaches. The results provide guidance for optimizing interpolation procedures in operational climate data production and contribute to the development of high-resolution gridded climate datasets in Central Europe.
This work was financially supported by the Ministry of the Environment through institutional support under the Long-term Concept of the Development of a Research Organization, and by the Technology Agency of the Czech Republic within the project SS02030040 – Prediction, Evaluation and Research for Understanding National sensitivity and impacts of drought and climate change for Czechia. Further we acknowledge support from the AdAgriF project – Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation (CZ.02.01.01/00/22_008/0004635) and the ACECE project (24-14581L) – Atmospheric Circulation and weather Extremes in Central Europe and their representation in climate models, funded by the Czech Science Foundation (GA ČR).
How to cite: Stepanek, P., Fiala, R., Chuchma, F., and Srubkova, A.: Comparison of spatial interpolation methods for operational climatology in the Czech Republic, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-502, https://doi.org/10.5194/ems2026-502, 2026.
The rapid warming of Arctic regions has severe consequences for nature and environment. To understand the impact of climate change on terrestrial ecosystems climate indicators, or climate state variables are used to monitor and explain the connections between weather, climate and components of the ecosystems.
Arctic ecosystems are vulnerable to climatic end environmental changes. The number of species is relatively few, and they depend very much on each other. Weather and/or climate events causing abrupt change in one component will often have a direct consequence on several other components within the entire system.
Within the COAT (Climate-ecological observatory for Arctic Tundra, www.coat.no/en/) several climate indicators addressing these challenges for different ecosystem components have been identified and defined. These indicators consist of time aggregated statistics that summarize climate or weather-related events for a specific season or time periods of the year that typically describe plausible explanation for variability in ecosystems. These indicators represent input variables to conceptual ecosystem component models, e.g. for different types of vegetation, herbivores or ungulates.
To get a better understanding a pan-Arctic approach is initiated to investigate the trends and variability in a selection of these indicators. We have identified five regions in the European Arctic, three in Greenland (Nuuk, Disko, Zackenberg), one on Spitsbergen and one north-eastern Norway. Each of these regions are defined as squares of 200x200 km.
The climate indicators are estimated from a suite of gridded datasets, both regional reanalyzes, hindcasts and observation grids. The indicators are estimated for each grid cell in the input datasets and then aggregated to regional scale.
In this presentation we present a comparison of the derived indicators, highlighting the differences between the various input datasets. We also analyze the temporal variations in and between the various regions to better understand how climate variability can be used to describe ecosystem responses within the areas of interest.
How to cite: Tveito, O. E.: Assessing gridded climate datasets to derive regional climate state variables across the Arctic, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-810, https://doi.org/10.5194/ems2026-810, 2026.
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