OSA3.2 | Spatial climatology
Spatial climatology
Convener: Ole Einar Tveito | Co-conveners: Mojca Dolinar, Christoph Frei
| Mon, 04 Sep, 09:00–14:45 (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, 09:00
Tue, 16:00
Spatially comprehensive representations of past weather and climate are an important basis for analyzing climate variations and for modelling weather-related impacts on the environment and natural resources. Such gridded datasets are also indispensable for validation and downscaling of climate models. Increasing demands for, and widespread application of grid data, call for efficient methods of analyses to integrate the observational data, and a profound knowledge of the potential and limitations of the datasets in applications.

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.

Orals: Mon, 4 Sep | Lecture room B1.08

Chairperson: Ole Einar Tveito
Onsite presentation
Beatrix Izsák, Kinga Bokros, and Zita Bihari

It is a common phenomenon that a small but significant precipitation cell or supercell passes between two measuring stations, resulting in an inadequate record of the daily precipitation amounts of up to 100 mm falling on a small area between the measurements and observations. This renders the interpolation from measurements alone insufficient to provide a complete depiction of the actual weather conditions. Therefore, to reduce the deviations from the actual weather situation, supplementary background information, for instance satellite data series, forecast information or radar measurements is used. In many cases, heavy thunderstorms with high rainfall can cause flash floods, which have a number of known negative effects in both social and agricultural areas. To achieve more accurate interpolation, it is therefore essential to use radar background information, as it yields significant social and agricultural benefits and can even be used for hazard warning. As the annual, seasonal and daily precipitation sum data series are interpolated using the MISH software at the OMSZ (Hungarian Meteorological Service) Unit of Climatology, the 10-minute, hourly, etc. precipitation sums are also interpolated using the MISH system. MISH software incorporates background information automatically and produce verification statistics. In our presentation, we will not only illustrate the theoretical and practical application of the MISH software, but also provide an overview of these statistics. The data series were interpolated by MISH for the whole area of Hungary, with and without radar background information, and statistical methods were used to illustrate the extent to which the interpolation was improved by the radar product used as background, and the strong relationship between interpolation with and without background information.


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

How to cite: Izsák, B., Bokros, K., and Bihari, Z.: Interpolating intraday precipitation data with radar background information, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-248, https://doi.org/10.5194/ems2023-248, 2023.

Onsite presentation
Barbara Casati, Cristian Lussana, and Alice Crespi

Global reanalyses are routinely used to assess the state of the climate and to evaluate the ongoing climate change. The ERA5 global reanalysis has been compared against a high-resolution regional reanalysis (COSMO-REA6) by means of scale-separation diagnostics based on 2d Haar discrete wavelet transforms. Validation and inter-comparison of reanalysis products lead to a better understanding of their strengths and weaknesses, for a more robust interpretation of climate studies.

The presented method builds upon existing methods and enables the assessment of bias, error and skill for individual spatial scales, separately.  The energy scale-separation verification diagnostics (square-root energy, energy percentages, and their normalized bias) enable to assess the bias across the scales and differences in the scale structure of the reanalysis products.

A new skill score (evaluated against random chance) and the Symmetric Bounded Efficiency are introduced. These are compared to the Nash-Sutcliffe and the Kling-Gupta Efficiencies, evaluated on different scales, and the benefits of symmetric statistics are illustrated. We illustrate the enhanced diagnostic capabilities of the introduced scale-separation diagnostics while comparing ERA5 control and ensemble members to COSMO-REA6 precipitation reanalyses. Furthermore, the Symmetric Bounded Efficiency enables the comparison of gridded datasets with different resolutions, without penalizing the -more noisy- finer resolution products.

As expected, the wavelet statistics show that the coarser resolution ERA5 products underestimate small-to-medium scale precipitation compared to COSMO-REA6. The newly introduced skill score shows that the ERA5 control member, despite its higher variability, exhibits better skill in representing small-to-medium scales with respect to the smoother ensemble members. The Symmetric Bounded Efficiency is suitable for the inter-comparison of reanalyses, since it is invariant with respect to the order of comparison.

How to cite: Casati, B., Lussana, C., and Crespi, A.: Scale separation diagnostics and the Symmetric Bounded Efficiency for the inter-comparison of precipitation reanalyses, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-68, https://doi.org/10.5194/ems2023-68, 2023.

Onsite presentation
Louis Frey and Christoph Frei

Today, most climate data sets derived by spatial analysis of station data have a maximum time resolution of one day. This is limiting in environmental modelling applications. For example, plant transpiration and snow melt depend on temperature non-linearly. Modelling these processes using daily mean temperatures only is therefore questionable. Our objective is to improve this situation and to advance spatial analysis of temperature into the sub-daily time scale. At this high temporal resolution, surface temperature fields are highly organized not just spatially but also temporally. This requires a new modelling approach that allows information from observations to also flow over time, not just over space like in traditional spatial climate analysis.

An attractive framework for this methodological extension is spatiotemporal (ST) statistical modelling. In this presentation we propose, and experiment with, a dynamic linear model (DLM). A DLM can be thought of as an extension of Kriging with external drift (KED, a Gaussian spatial process), where the trend coefficients are assumed to be serially correlated, i.e. smooth in time. These time series represent the characteristic variations of the temperature field, such as a diurnal cycle with a varying amplitude and phase, or the gradual variation of the height and amplitude of a temperature inversion.

In our study we develop and configure a DLM for surface air temperatures in Switzerland. The model can represent non-linear temperature profiles, such as inversions or well-mixed boundary layers. Our configuration involves several profiles across the domain to account for large-scale variation and across-ridge contrasts of the temperature distribution. We also account for mesoscale cold-air pooling with a tailor-made predictor. All model components are allowed to vary quasi harmonically to account for the diurnal variation.

We apply the DLM to hourly temperature observations from about 200 stations in Switzerland. Experiments are made for a set of test episodes including typical weather conditions like summertime high pressure and wintertime inversion situations. We find that the DLM reproduces physically plausible temperature distributions and evolutions during these weather conditions. The developments are continuous and reveal, for example, the generation, diurnal oscillation and dissolution of an inversion layer over several days. We also find a quite robust performance with respect to erroneous and missing observations. In a comparison with a traditional spatial-only analysis (sequentially applied KED), the DLM exhibits some added value. This benefit will be demonstrated in the presentation. In summary, the DLM framework turns out to be flexible and promising for the development of sub-daily temperature datasets, in Switzerland and likely in other regions of the world.

How to cite: Frey, L. and Frei, C.: Spatiotemporal Analysis of Hourly Surface Air Temperature – an Application in Switzerland, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-555, https://doi.org/10.5194/ems2023-555, 2023.

Online presentation
Lisa Kunert, Monika Rauthe, Christoph Brendel, Armin Rauthe-Schöch, and Thomas Deutschländer

In order to be able to analyse the past weather and climate, high-resolution (5 km x 5 km) observational gridded data sets over long periods are required. The HYRAS data sets are based on daily data from meteorological stations in Germany and its river catchment areas for the parameters daily minimum, mean, and maximum temperature, relative humidity, global radiation and precipitation. Hence all for the water sector important parameters are included and the HYRAS grids thus allow many applications in the field of climatology and hydrology as well as in other sectors affected by climate change. The data covers the period from 1951 to 2020, except precipitation, which is on the one hand daily updated for the area of Germany and on the other hand covers the period since 1931 with a horizontal resolution of 1 km x 1 km. The data sets have been developed at Deutscher Wetterdienst (DWD) in two large research programs (KLIWAS and BMDV network of experts) financed by the German Federal Ministry for Digital and Transport. Nowadays, the DAS (Deutsche Anpassungsstrategie) core service “Climate and Water”, which is an operational climate service for climate and water in Germany, is responsible for the operational production of the HYRAS data sets.


With the new version of HYRAS (v5.0), the input station data control has been considerably improved. Digitisation and data control made it possible to use more and qualitatively better station data compared to the previous versions. New data sources (precipitation totaliser data in the alpine region) improved the background field used for the interpolation of precipitation. For a broader usability of the dataset, the netCDF attributes have been revised. This allows the data to be read in more easily by common software packages like ArcGIS, QGIS and Panoply. Due to the longer time period covered by the new version, the climatological long-term averages for the most recent climatological period 1991-2020 can now be provided.

We will give an overview of the HYRAS data sets and show a few examples of validation. Furthermore, we will provide some insights into broad applications of these data sets.

How to cite: Kunert, L., Rauthe, M., Brendel, C., Rauthe-Schöch, A., and Deutschländer, T.: Hydrometeorological raster datasets (HYRAS) - Description and new developments, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-40, https://doi.org/10.5194/ems2023-40, 2023.

Onsite presentation
Anna-Maria Tilg, Johann Hiebl, Angelika Höfler, and Anna Rohrböck

Spatially comprehensive information of climate variables that extends over many decades is essential for various modelling, reanalysis, monitoring, and planning applications, like snow cover modelling, drought monitoring, model evaluation or environmental planning. For the territory of Austria, a gridded dataset called SPARTACUS is produced by the national weather service of Austria, by spatially analysing daily station observations. This operational climate-monitoring dataset has a spatial resolution of 1 km to 1 km and provides data since 1961 with a temporal resolution of one day and further temporal aggregates (month, season and year).

The current operational version covers the parameters minimum and maximum air temperature, precipitation amount and sunshine duration. Parameter-specific methods are used to interpolate the surface in-situ observations. An overview of the applied geostatistical interpolation methods including key results from evaluation will be presented, focusing on temperature and precipitation. Moreover, requirements on input station data, like constant station networks over time, will be specified. Limitations in the analysis of such a gridded observation dataset, due to for example conditional biases, will be pointed out as well.

A gridded dataset of air humidity is currently under development. Such a dataset is highly relevant, for example for the investigation of droughts or snowmaking potential. The actual spatial analysis is performed for the parameter of dew-point depression, which allows adapting the interpolation method previously used for air temperature. Preliminary findings of the current approach including results from the evaluation will be shown. They might be relevant for similar projects in other regions with complex topography.

How to cite: Tilg, A.-M., Hiebl, J., Höfler, A., and Rohrböck, A.: Overview of applied statistical interpolation methods in SPARTACUS, a spatial dataset of the surface climate in Austria, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-404, https://doi.org/10.5194/ems2023-404, 2023.

Onsite presentation
Christoph Frei, Michael Begert, and Francesco A. Isotta

Many planning applications require statistical information about the climate at the scale of a point. In civil engineering, for example, the design of heating and cooling systems relies on estimates of the occurrence of cold/warm extremes at the location of the building. An emerging practice of climate service providers is to derive such information from interpolation-based or reanalysis-based climate datasets on a fine (km-scale) grid. Yet, such datasets are poorly suited for this purpose. The effective resolution is limited and, as a consequence, this data represents regional area-mean conditions, not the point scale. As a result, the frequency of extremes is underestimated, and cross-parameter relationships are compromised. Here, we propose and illustrate a statistical procedure to derive point-scale climate data, at any point in space, without the artefacts of traditional optimal-prediction-based interpolation.

The proposed method, denoted “station transfer”, derives a climate series for the target location by transferring the observations of a suitable station and applying an adjustment, so that the result is representative for the target location. The choice of station and the adjustment are fully data driven. Unlike conventional interpolation, where concomitant observations in the neighborhood are combined, the “station transfer” insists on a single station as the origin of an adjustment, which is less invasive and better preserves point-scale tail statistics and cross-parameter relationships. We introduce a stochastic model for the transfer where the adjustments and the areas of representativity are jointly estimated from the entire station network. The method is illustrated with an example application that predicts point-scale surface air temperatures over the territory of Switzerland, using data from 65 stations.

The development and experimental application of this presentation are preliminaries of an upcoming project, where MeteoSwiss will derive new building-design climate basics for the Swiss Society of Engineers and Architects. But “station transfer” may become a much more widely used complement to conventional gridding, because of the wide-spread request for point-scale and high-frequency climate information in civil planning. 

How to cite: Frei, C., Begert, M., and Isotta, F. A.: Deriving point-scale climate data for arbitrary locations – The “station transfer” method and an example application, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-47, https://doi.org/10.5194/ems2023-47, 2023.

Coffee break
Chairperson: Christoph Frei
Onsite presentation
Guido Fioravanti, Sara Martino, Michela Cameletti, and Andrea Toreti

Gridded observational datasets of the main climate variables are essential in climate science. However, common interpolation approaches (e.g., classical kriging-based methods), often lack of a proper propagation and representation of uncertainty. In this study, a Bayesian spatio-temporal regression model based on the Integrated Nested Laplace Approximation (INLA) and the Stochastic Partial Differential Equation (SPDE) is introduced. Although the effective use of INLA and SPDE is documented in several envitonmental studies, their use among climate practitioners is still quite limited. Here, based on high-resolution monthly 2-meter maximum (Tmax) and minimum (Tmin) air temperature, we employ INLA and SPDE to derive gridded monthly temperature climatologies for Italy both for the most recent standard 30-year period (1991–2020) and three previous standard periods (1961-1990, 1971-2000, 1981-2010). Our regression model includes three spatial predictors (elevation, latitude and distance to sea) and a linear time effect accounting for the  temporal trend in the observed monthly temperatures. A Matern field is used to capture the residual spatio-temporal correlation. Because of the large space-time domain of our study, the regression analysis is run separately for each month (January-December) and for each variable (Tmax/Tmin). Despite its simplicity, this approach provides a flexible model to produce accurate continuous gridded surfaces equipped with model-based uncertainties. Through simulation, we generate  a distribution of plausible gridded surfaces of Tmax and Tmin monthly means, which we summarize through measures of central tendency (posterior mean)  and variability (standard deviation). We use the standard deviation maps to investigate how uncertainty affects our estimates of the 1991-2020 monthly climatologies and where, and the 95% credible intervals maps to assess the regions where the  1991-2020 period is significantly warmer than the previous 30-year standard periods.



How to cite: Fioravanti, G., Martino, S., Cameletti, M., and Toreti, A.: Climate variables interpolation by using INLA and the SPDE approach   , EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-25, https://doi.org/10.5194/ems2023-25, 2023.

Online presentation
Emilio Romero-Jiménez, Luna Cepeda-Ventura, Matilde García-Valdecasas Ojeda, Juan José Rosa-Cánovas, David Donaire-Montaño, Feliciano Solano-Farías, Yenny Toro Ortiz, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, and María Jesús Esteban-Parra

Mountainous regions are especially vulnerable to the effects of climate change. Therefore, it is essential to study these areas, which contain an invaluable diversity of species and resources. Meanwhile, the southern part of the Iberian Peninsula (IP), as a semi-arid region, is particularly susceptible to climate change. Consequently, the mountainous area of the IP, Sierra Nevada (SN), is an area of interest that needs more research.

Currently, there are different sources of meteorological, observational data for SN. There are several stations scattered across the area. However, certain difficult-to-access areas, located in the highest peaks of the IP, exhibit a lack of data. This issue can be solved by applying spatial interpolation techniques, such as the Regionalisierung der Niederschlagshöhen (RegNie) method. The result of this method is a gridded dataset for the complete region of study. Nevertheless, the coherence of the available observational data is not always guaranteed, so that the statistical analysis of the RegNie results may show low quality gridded data. There is an ongoing effort to create a curated database for the area, known as Climanevada, which is used in this research.

The aim of this study is to create a gridded meteorological database using the RegNie method, containing at least precipitation and temperature data, for SN and its surrounding area. To achieve this goal, the available data for the period 1990-2020 has been deeply analyzed, searching for errors or discardable data. Once this step was complete, the most adequate grid size and resolution were chosen, considering the specific physical characteristics of the area. The whole process is described in this study, together with the description of the features of the final gridded dataset.

Keywords: Sierra Nevada, precipitation, temperature, gridded data, RegNie.

This research was financed by the project “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AIe-Services University of Granada-SierraNevada”(LifeWatch-2019-10-UGR-01), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program2014-2020 (POPE), LifeWatch-ERIC action line; the project P20_00035 funded by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades; and by the project PID2021-126401OB-I00 funded by MCIN/AEI/ 10.13039/501100011033/FEDER Una manera de hacer Europa.

How to cite: Romero-Jiménez, E., Cepeda-Ventura, L., García-Valdecasas Ojeda, M., Rosa-Cánovas, J. J., Donaire-Montaño, D., Solano-Farías, F., Toro Ortiz, Y., Gámiz-Fortis, S. R., Castro-Díez, Y., and Esteban-Parra, M. J.: Creating a gridded climate database in Sierra Nevada, in the southern Iberian Peninsula, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-463, https://doi.org/10.5194/ems2023-463, 2023.

Onsite presentation
Aleksandar Sekulic, Milan Kilibarda, and Petar Bursac

Daily gridded meteorological datasets are an important source of information for analysis of historical weather and many other research areas since they have no gaps in the spatio-temporal domain they cover. Most of the daily gridded meteorological datasets represent reanalysis or estimations from different remote sensing sensors or are generated by downscaling procedures. There are none or a very few high resolution datasets on regional or global scale that are based on observational data and space-time geostatistics, i.e. that takes into account spatio-temporal correlation between observations. The only such dataset for Europe is the E-OBS dataset at 10 km spatial resolution, but it uses spatial correlation only. Therefore, a daily gridded meteorological dataset for Europe at 1 km spatial resolution, named MeteoEurope1km, is created, initially covering the 1991–2020 period. Spatio-temporal regression kriging (STRK), an interpolation method that combines multiple linear regression for trend modeling and space-time kriging for the estimation of the residuals, is used for interpolation of maximum, minimum, and mean daily temperature. Combination of GHCN-daily, ECA&D, and SYNOP observations from OGIMET service is used as an observational dataset, with previous removal of duplicated stations and outliers, while geometric temperature trend, digital elevation model and topographic wetness index are used as auxiliary variables. Accuracy assessment (leave-one-station-out cross-validation) shows high accuracy of the STRK models for daily temperature. Coefficient of determination for all three parameters is greater than 97% and root mean square error is less than 1.5°C. Future work will be oriented towards increasing the temporal extent of the MeteoEurope1km to the 1961–present period, interpolation of other daily meteorological variables, and improving performance of STRK models for daily temperature at higher altitudes, since the accuracy is lower due to a well known problem with lack of stations. The same methodology will be applied for interpolation of daily sea level pressure, while spatial machine learning methods, such as Random Forest Spatial Interpolation, will be used for interpolation of daily precipitation because of its complex nature.

How to cite: Sekulic, A., Kilibarda, M., and Bursac, P.: MeteoEurope1km: a high-resolution daily gridded meteorological dataset for Europe for the 1991–2020 period, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-422, https://doi.org/10.5194/ems2023-422, 2023.

Onsite presentation
Jouke de Baar, Gerard van der Schrier, Wouter Knap, and Else van den Besselaar

Question. Currently, we already provide an E-OBS pan-European gridded data set for global radiation. However, improvements might be made in the reliability of the estimate in areas with a low network density, in the effective spatial resolution as well as in the reliability of the ensemble dispersion. In this work, we use lessons learnt from earlier work (Frei et al, IJC, 2015) and the E-OBS wind grid, and apply them to the radiation grid, in order to see if we can achieve these improvements. 

Approach. Our approach is to use aggregated regression kriging. First, we use linear regression to fit a monthly background field of the observed global radiation as a function of several covariates. In order to allow for the inclusion of multiple covariates, yet avoid over-fitting, we use forward selection linear regression to construct the monthly background field. Apart from covariates like longitude, latitude, distance to coast and altitude, we include monthly-varying PCA modes of reanalysis-based radiation patterns. Then, we use kriging to regress the daily anomalies with respect to the monthly background field. 

In order to provide the gridded data set as an ensemble of 20 members, we run a 20-fold bootstrapping loop over the entire gridding procedure. Because this still leads to an underestimation of the grid uncertainty – or ensemble dispersion – we apply ensemble dispersion improvement auto-tune (EDIT) as an ensemble post-processing step. 

Results. We provide a gridded data set of daily global radiation at a resolution of 0.1x0.1 and 0.25x0.25 degrees longitude and latitude. The standard data set consists of ensemble mean and ensemble inter-quantile range, however, the full ensemble can be made available upon request. A comparison against the available EUMETSAT satellite data will be made. The data set will be provided for the period 1950-present, with monthly updates, and will be available for download from the E-OBS website. 

How to cite: de Baar, J., van der Schrier, G., Knap, W., and van den Besselaar, E.: An improved E-OBS pan-European gridded data set for global radiation, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-171, https://doi.org/10.5194/ems2023-171, 2023.

Online presentation
Johann Hiebl, Quentin Bourgeois, Anna-Maria Tilg, and Christoph Frei

Grid datasets of sunshine duration at high spatial resolution and extending over many decades are required for many quantitative applications in regional climatology and environmental change, including the modelling of droughts and snow/ice covers, the evaluation of clouds in numerical models, the mapping of solar energy potentials, and many more. Here, we present a new gridded dataset of relative sunshine duration, and derived absolute sunshine duration, developed for the territory of Austria at a grid spacing of 1 km, and extending back until 1961 at daily time resolution. The dataset complements the collection of SPARTACUS climate monitoring datasets, operationally produced by the national meteorological service of Austria. The presentation will outline the efforts made into a consistent station dataset, the methodological solution adopted in this complex topography region, and it will highlight key results from an evaluation with a user-orientated viewpoint.

The big challenges in the construction of the dataset were (a) issues of consistency in the available station data, notably the inhomogeneities related to changes in measurement devices and practices, and (b) limitations posed by the scarcity of long series and the high variation of cloudiness in the study region. The challenges were addressed by special efforts to correct evident breaks in the station series and by adopting an analysis method that combines the station data with information from satellite data. The methodology merges the data sources non-contemporaneously, i.e. using statistical patterns distilled over a short period, which allowed involving satellite data even for the early periods of our dataset.

The resulting fields contain plausible mesoscale structures, which could not be resolved by the station network alone. On average, the daily analysis explains 81 % of the spatial variance in daily sunshine duration at the stations (cross-validation). Comparison to other datasets showed that the new dataset meets a similar standard in temporal consistency, in spite of the limited data quality over such a long period. Our evaluation revealed a slight systematic underestimation (−1.5 %) and a mean absolute error of 9 %. The average error is larger during winter, at high altitudes and around the 1990s. We also find that the dataset exhibits a conditional bias, related to the involved uncertainties, which can lead to considerable systematic errors (up to 15 %) when calculating sunshine-related climate indices.

How to cite: Hiebl, J., Bourgeois, Q., Tilg, A.-M., and Frei, C.: A grid dataset of daily sunshine duration for Austria since 1961, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-414, https://doi.org/10.5194/ems2023-414, 2023.

Onsite presentation
Fabian Lehner, Tatiana Klisho, and Herbert Formayer

Climate impact research sometimes needs climatological input data at a much higher spatial resolution than that of typical meteorological data sets (e.g. reanalysis, regional climate models, gridded observational data sets). We contribute climate data to a large interdisciplinary project “FORSITE II”, where forest site classification maps are produced for three provinces of Austria under the influence of climate change. The climate data involves many climate indicators relevant for vegetation with a spatial resolution of 10x10 m, leading to about 400 million grid cells for the research domain. Complex climate indicators such as potential evapotranspiration calculated with the Penman-Monteith equation need temperature, wind speed, humidity, and radiation as input.

Most meteorological variables are highly correlated with elevation, especially on a regional scale. Thus, for the downscaling to 10 m, a local vertical gradient of the climate indicator is calculated. Combining the vertical gradient with the difference of the topography on the fine and coarse grid yields the climate indicator on the high-resolution grid with a clear imprint of the fine topography.

Radiation-based indicators require a more advanced approach: For realistic shading effects of the topography, a solar radiation model is used to calculate potential direct radiation on both the coarse and the 10x10 m topography on each day of the year. Indirect radiation is directly downscaled with the difference of the sky view factors between coarse and fine resolution. These two downscaling methods are then applied to actual daily indirect and direct radiation, which was provided on a 100x100 m grid by GeoSphere Austria.  

How to cite: Lehner, F., Klisho, T., and Formayer, H.: Downscaling of climate indicators to 10x10 m resolution, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-132, https://doi.org/10.5194/ems2023-132, 2023.

Onsite presentation
Marc J. Prohom, Mercè Barnolas, and Javier Martín-Vide

Climate normals have two main functions in climate studies: (1) become an implicit predictor of the conditions most likely to be experienced in the near future at any location, and (2) be a stable reference against which to compare long-term changes in climate observations. When normals are expressed as georeferenced climate data in a regular grid, then we talk of digital climate atlases.

Here we describe the steps followed to develop the new Climate Atlas of Catalonia (1991-2020), the reference tool to provide information to the government, regional authorities, businesses and citizens about the characteristics of Catalonia's climate during the last thirty years. First, a description is made of the database used (daily mean, maximum and minimum temperature, and precipitation), which presents a much higher density of climate information than other previous periods, including data from automatic weather stations, and from high mountain and remote areas, about which there was very little knowledge of its climatology. Secondly, the quality control procedure that has been applied is detailed, and the subsequent homogeneity analysis (by means of ACMANTv5), which has made it possible to correct artificial biases in the series. Finally, the interpolation methodologies that have been tested are described, and the one that has finally been chosen for the generation of the digital cartography.

The final product includes high-resolution digital cartography (1 km for precipitation, and 100 m for temperature) at different time scales (monthly, seasonal, and annual) and for the four essential climate variables mentioned above, which will be freely available through the SIG portal of the Government of Catalonia (https://sig.gencat.cat/visors/hipermapa.html). To conclude, the work includes a comparison of the results obtained between the periods 1961-1990 and 1991-2020, making visible the warming experienced in the region and some changes in rainfall patterns.

How to cite: Prohom, M. J., Barnolas, M., and Martín-Vide, J.: The process of creating the new Climate Atlas of Catalonia (1991-2020), EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-406, https://doi.org/10.5194/ems2023-406, 2023.

Lunch break
Chairpersons: Dan Hollis, Federico Fierli
Onsite presentation
Ole Einar Tveito

When assessing long term climate trends and variability it is important to analyse data that are not disturbed by external factors that might lead to misleading trends. It is also urgent to analyse consistent series that cover the entire time period. Since observation networks are continuously changing not too many complete series are available for a centennial long analysis. 

Gridded climate monitoring data are often based on all available data for each timestep, which might disturb the spatial and temporal consistency. In order to investigate such impacts we have constructed a long term gridded dataset based on homogenised input data.  165 raw temperature series and 323 raw precipitation series fr Norway covering the entire period 1901-2020 were constructed by an extraction from a gridded data set of monthly climate anomalies, which was based on all available observations. The constructed series were homogenised applying the automatic procedure in Climatol. 

New gridded data sets were established applying the homogenised data series as input. These data sets were compared to a similar gridded dataset based on the raw non-homogenized data series. Analysis of the regional time series of the datasets shows similar temporal variability and trends. The gridded data based on the homogenised data shows less local spatial variability than those based on the raw data series. This means that anomalies due to inhomogeneous data are reduced, and that false local trends are removed. The construction and homogenization of climate data series therefore provides more consistent data for gridding, and leads to better and more reliable gridded data sets for assessing spatio-temporal climate trends.

How to cite: Tveito, O. E.: Homogenised input series for generating gridded data, does it matter?, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-513, https://doi.org/10.5194/ems2023-513, 2023.

Onsite presentation
Jouke de Baar, Irene Garcia-Marti, Gerard van der Schrier, and Theo Brandsma

Question. Starting from the mid 20th century, one might say that there has been a social contract between society and science: in most cases, society provides science with funding and a large amount of autonomy, while science provides society with authoritative research conclusions and recommendations (Gibbons, Nature, 1999). However, we might build a more meaningful partnership between society and science if we find and expand alternative ways of structuring this social contract. We expect that by finding more and new ways of collaborating directly with society on the content level, we can help create a situation of partnership. Importantly, such partnership can result in an increased shared feeling of ownership and responsibility for next generation climate research.  

From this line of thought, for example, we can make the best use of observational data collected by national meteorological services (NMSs) in combination with crowd-sourced data like collected by the Weather Observations Website (WOW-NL) network. Presently, we investigate if including such crowd-sourced data, provided by society, can enable us to deliver high-volume high-resolution gridded climate data sets, zooming in to local and urban scales. 

Approach. In the Community Climatology approach, we use multi-fidelity regression kriging to blend official NMS observations with crowd-sourced observations and high-resolution covariates. We think this can evolve into a new partnership between society and science, where volunteers find a meaningful way to directly contribute to improved monitoring capabilities of climate change and the quantification of extreme events. 

The development of our approach has been done in close collaboration with end users, so that we provide these high-resolution gridded data sets in a way that caters to their needs. An important lesson we already learned here is that providing gridded uncertainty estimates is one of the essential requirements of various end users. 

On a technical level, we consider the significant bias and noise in the volunteer observations. We include a simplified observation model description in the likelihood of our Bayesian updating process. Using cross-validation, we tune this observation model to the data under consideration. As such we exploit data-driven estimate of observational error, since we learn the bias and noise of the observations in each time slice. 

Results. Although we are in the early stages of applying this approach to longer time spans and a variety of variables, we already see that the contribution from volunteer observations is indeed a meaningful addition to these gridded data sets, for five different user-requested climate variables in the Netherlands. We provide this data set at a spatial resolution of 0.01 x 0.01 degrees (approximately 1 x 1 km) and at an hourly time resolution. Initial results show a significant increase of accuracy of these data sets due to the blending approach. 

How to cite: de Baar, J., Garcia-Marti, I., van der Schrier, G., and Brandsma, T.: Community Climatology – Towards a new social contract in climate research?, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-172, https://doi.org/10.5194/ems2023-172, 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: Ole Einar Tveito, Mojca Dolinar, Christoph Frei
Francesco Sioni, Agostino Manzato, Gabriele Fasano, Arturo Pucillo, and Cristian Lussana

Temperature (T), pressure (p) and density (ρ) are the three fundamental variables that describe the local behaviour of the atmosphere through the ideal gas law. This work aims: 1) to investigate the mutual dependence of these variables near the Earth’s surface in the real-world atmosphere (i.e. not in idealized conditions); 2) to assess the physical conditions that lead to positive or negative correlations R(p,T).

Possible applications of this study are: i) to improve the knowledge of the interdependence between meteorological variables at the local level, at least for the stations considered; ii) more in general, to classify stations objectively (e.g. dividing valley floor stations from top stations), according to the relationship between physical parameters; iii) identify implausible observations of meteorological variables. 

First of all, from hourly data it is found that temperature and pressure are weakly related if compared to density and temperature, which show a correlation close to 1. In fact, temperature and density depend only on local thermodynamic conditions, while pressure is representative of the properties of an entire vertical column of air. Moreover, from a temporal variation perspective, it is found that, on average, the normalized hourly change in pressure is very low with respect to temperature and density temporal variations. 

A second result we present is that scatterplots of hourly pressure versus temperature reveal a triangular shape for various stations we considered at different latitudes in extratropical areas. For all the locations it was always possible to fit the upper side of the triangle with a line of the same slope and with an intercept proportional to the mean pressure of the station. This suggests that a physical boundary exists in the extratropics, above which combinations of pressure and temperature cannot be observed. An exception are the high mountain stations, for which the scatterplots show a strong correlation very similar to what is found in the free atmosphere. Also the behaviour in the tropics is different, with a density-plot closer to a 2D gaussian distribution; however, the fitted line still represents an upper limit.

Eventually, hourly data are aggregated at different temporal scales and correlations are computed: the analysis shows a hourly sinusoidal variation of the correlation R(p,T) during the day, reaching a maximum in the afternoon and a minimum in the night, that can be found in all the extratropical stations considered.

How to cite: Sioni, F., Manzato, A., Fasano, G., Pucillo, A., and Lussana, C.: On pressure and temperature correlation patterns, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-143, https://doi.org/10.5194/ems2023-143, 2023.

Christoph Brendel, Monika Rauthe, and Thomas Deutschländer

Within the research program BMDV Network of Experts of the Federal Ministry for Digital and Transport, the Hydrometeorological raster datasets (HYRAS) of the Deutscher Wetterdienst (DWD) have been extended by one variable: the global radiation. Previously, precipitation, temperature (daily mean, maximum, minimum), and relative humidity data were available, covering Germany and its river catchments from 1951- 2020 with a spatiotemporal resolution of 5km x 5km and 1 day.

The HYRAS datasets are frequently used in various applications, such as analyzing current and historical climate conditions, as well as for the adjustment of the bias in climate model data which results in more accurate climate change impact assessments. Another application of the HYRAS datasets is the usage as input data for a water balance model. Since global radiation is an important parameter to correctly model evapotranspiration, together with the other HYRAS variables, the global radiation leads to more realistic results of the long-term development of the modeled runoff as well as high-flow and low-flow situations in river basins. Therefore, a gridded dataset of global radiation should extent as far back into the past as possible to include trends and decadal variability.

The interpolation methodology and the availability of suitable measurement data have a significant impact on data quality and thus the suitability of the gridded dataset for the aforementioned applications. Station measurements of global radiation are available only very sporadically for the period 1951-1980 for Germany and its river catchments, especially at the very beginning of the time series. Thus, station measurements of sunshine duration were also considered to interpolate the HYRAS global radiation dataset. These measurements are widely available and cover the entire area, making them a valuable resource for the interpolation. To convert the sunshine duration measurements into global radiation, a simple linear regression method according to Angstrom and Prescott can be used, which shows good results for monthly but not for daily values. Especially a large bias for low global radiation values is caused by using the simple linear regression. To improve the quality of this method the total column cloud liquid water content from ERA5 reanalysis data has been implemented as additional predictor. 

After a short overview about dataset generation, a comparison between the new HYRAS global radiation dataset, station measurements of global radiation and further reference datasets like the ERA5 reanalysis and the CM SAF SARAHv2.1 satellite dataset will be presented. 

How to cite: Brendel, C., Rauthe, M., and Deutschländer, T.: The new-high resolution HYRAS global radiation dataset for Germany and its river catchments, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-295, https://doi.org/10.5194/ems2023-295, 2023.

Ole Einar Tveito and Reidun Gangstø

Due to climate change, droughts are becoming more frequent and severe in many parts of the world. The drought experienced in Norway during 2018 caused significant repercussions, particularly in the agricultural industry. Norway's rising temperatures and altered precipitation patterns are attributed to climate change. While annual precipitation has increased and precipitation events have become more intense, summer precipitation is not expected to increase everywhere in Norway, and evaporation will rise with temperatures. The impact of these changes on the risk of drought in different regions of Norway remains uncertain.

To address this uncertainty, we undertook an analysis of the frequency, extent, and duration of summer (April-September) drought in Norway, utilizing several meteorological drought indices such as the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) at timescales ranging from 3 to 12 months. We have developed monthly gridded datasets of SPI and SPEI based on gridded daily precipitation and potential evapotranspiration. The potential evapotranspiration is estimated by the Penman-Monteith formula. Using these datasets, we calculated trends and compared the last two official normal periods (1991-2020 and 1961-1990) to analyze historical changes. Furthermore, we compared future climate projections with the 1991-2020 period to identify potential future changes. 

Preliminary results did not indicate significant changes in occurrence of severe droughts in Norway. The two drought indices applied shows the same temporal variability, and the increase of total summer precipitation balances the increased potential evapotranspiration.  There is however a slight increase in the SPEI drought conditions compared to SPI. 

Our research has implications for Norway's agricultural sector and natural resource management. A better understanding of past and future drought changes can aid in the development of effective adaptation strategies.

How to cite: Tveito, O. E. and Gangstø, R.: Changes in drought occurrence due to climate change in Norway, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-587, https://doi.org/10.5194/ems2023-587, 2023.