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.

Acknowledgement:

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.

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.

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.

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.

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.

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.

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.

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.

ACKNOWLEDGEMENTS
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.

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.

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.

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.

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.