bluuebunny/crossref_metadata_2025 huggingface dataset:
ZAMG Data Hub – Open access to high value data sets 
Authors: Dautz Erika , Teubner Irene , Auer Martin , Beck Alexander , Pechstein Fabian , Schöberl Julia , Stuxer Bernhard , Lang Daniel
Published: June 2022

With new open data policies in place, the European Commission aims to speed up the development of innovative services and products, thereby playing a significant role in economic growth. In particular, the reuse of so-called high value data sets is expected to bring remarkable benefits to society and economics. By being defined as high value information, meteorological data sets need to be made available by public sector bodies free of charge and with minimal legal restrictions, published via APIs and in machine readable formats.

Austria's national weather service Zentralanstalt für Meteorologie und Geodynamik (ZAMG) is aiming to realize those directives by publishing public sector data on the ZAMG Data Hub. Data sets are made available for the public in a step wise approach, which has started in 2021 with providing station measurement data and raster data sets and continues with the preparation of forecasts. Besides the open data sector, the ZAMG Data Hub provides commercial data for registered users and serves as a data platform for universities and ZAMG internal users. The main services and interfaces for the end user are a CKAN based web portal, a REST API, and an associated Metadata Hub, which are connected to the underlying Zarr and S3 file storage infrastructure by state-of-the-art technologies and components. The numerous use cases and actors, the large amount of data to be stored and processed together with the demand of offering a highly available and reliable infrastructure brings challenging questions regarding the architecture of the system and its components. Key aspects are the need for a powerful access control system, scalable and performant data access and easy-to-use metadata information. Besides continuous improvements, ZAMG wants to provide further data sets and implement additional data services to fulfill open data directives and maximize customer satisfaction. 
Multivariate Ensemble Sensitivity Analysis for an Extreme Weather Event Over Indian Subcontinent
Authors: George Babitha , Kutty Govindan
Published: June 2022

Ensemble forecasts have proven useful for diagnosing the source of forecast uncertainty in a wide variety of atmospheric systems. Ensemble Sensitivity Analysis (ESA) uses ensemble forecasts to evaluate the impact of changes in initial conditions on subsequent forecasts. Ensemble sensitivity is often used as a simple univariate regression as it approximates the analysis covariance matrix with diagonal elements on predicting the response to an initial perturbation. On the contrary, the multivariate ensemble sensitivity computes the ensemble sensitivities by incorporating the contribution from the full covariance matrix. In this work, the precipitation forecast responses of an extreme rainfall event from both univariate and multivariate ensemble sensitivity methods are analyzed. The ensemble forecasts and analyses are generated using an ensemble Kalman Filter (EnKF) coupled with the Advanced Research version of the Weather Research and Forecasting (WRF) model. Based on the results obtained the univariate ensemble sensitivity analysis shows broadly distributed sensitivity patterns, while the multivariate sensitivity analysis generally exhibits organized sensitivity patterns. The perturbation initial condition experiment applied to both methods proves that the ensemble sensitivity estimated by multivariate is more realistic compared to univariate ensemble sensitivity. In the presence of added model error, using the Stochastic Kinetic Energy Backscatter Scheme (SKEBS) it is found that the forecast response estimated by multivariate sensitivity compares better with the actual model response. Further, it is identified that if the localization used in multivariate is not sufficient its performance is contaminated by the occurrence of spurious correlations. The impact of lead times on both methods shows that multivariate provides better performance than univariate mainly at longer lead times when nonlinearity becomes important. The use of convection-permitting ensemble forecasts reveals that the multivariate ensemble sensitivity with localization ameliorates the sensitivity estimates in convective scales.
Thunderstorm formation in the Mediterranean: a cloud modelling sensitivity analysis to SST anomalies
Authors: Hourngir Dario , Burlando Massimiliano
Published: June 2022

Windstorms constitute nowadays one of the most dangerous hazards worldwide, representing the costliest natural hazard in Europe between 1980 and 2015 and ranking second for overall losses and fourth in terms of the number of human casualties. Among all the extreme phenomena associated to intense deep convective storms, they have the largest impacts on human health, structures, natural environments, as well as transport and energy infrastructures. Local non-synoptic phenomena as tornadoes and thunderstorm outflows are often responsible for the strongest winds, and according to IPCC projections these episodes are expected to further worsen soon in many parts of the world because of global warming. The warming of the Earth’s surface temperature, indeed, is expected to enhance in the future the convective activity at the base of thunderstorms, and therefore some studies project increasing intensity, for example in the Mediterranean region, with a forecast trend of more frequent and more severe synoptic and convective windstorms. However, given the great unpredictability of thunderstorm events, the study of their behaviour is still a challenge, especially from a numerical point of view. In this context, a study based on the explicit cloud modelling of thunderstorms is performed to investigate the mechanisms that underlie deep convective development and intensification in the Ligurian Sea, which is one of the areas in the Mediterranean most prone to severe convective episodes, particularly during the autumn. High-resolution simulations, performed using the Bryan Cloud Model Version 1 (CM1), are focused on the intense thunderstorm event and its associated downburst that hit the city of Genoa in the morning of the 14th of August 2018. The processes responsible for its development and that triggered its intensification are studied, as well as the forcing due to the complex topography, which is thought to have played a role in the enhancement of the thunderstorm outflow at the ground. Preliminary results show some discrepancies between idealized simulations without and with the orography of this area, in terms of intensity and duration of the event. Additionally, the possible role of the sea surface temperature (SST) discontinuities in the convection triggering, through the downward momentum mixing (DMM) mechanism, is investigated as a contributing factor to the thunderstorm development, together with the SST anomalies, observed warmer than average during the occurrence of this severe weather episode by satellite measurements.
Deriving future climate reference data for the Swiss building sector
Authors: Wehrli Kathrin , Gubler Stefanie , Settembrini Gianrico , Sidler Franz , Kotlarski Sven
Published: June 2022

Buildings constructed today will be exposed to climatic changes during their lifetime. Changes in the outdoor climate also affect the indoor climate in buildings where people spend a substantial part of their day. In addition, the building sector significantly contributes to energy demand and greenhouse gas emissions. Therefore, it is vital to design buildings in a way that ensures comfortable indoor temperatures today and in the future while minimizing energy use. For Switzerland, an additional warming of 2-3 degrees Celsius by mid-Century is projected without climate change mitigation. According to the Swiss Climate Scenarios CH2018, heatwaves are set to increase significantly in frequency, intensity and duration, whereas coldwaves will be less common. To adapt to these changes and design buildings optimally for a warmer future climate, climate projections tailored to the needs of the building sector are required.

To serve this demand and together with partners from universities and the industry, MeteoSwiss has created a reference dataset for the future climate for use in building simulations. For this, observations were combined with climate change information from the CH2018 scenarios. The resulting reference data represent the possible future climate at locations across Switzerland. Hourly and physically consistent data for the future for the variables temperature, humidity, wind and solar radiation have been calculated. These represent typical years and warm summer years. The data are available for the near future and the middle of the century and are based on two different greenhouse gas emission scenarios: 'no climate change mitigation' (RCP8.5) and 'concerted climate change mitigation efforts' (RCP2.6). The data are also available for four extra-urban and urban station pairs, which allows evaluating the urban heat island effect.

The described reference data were compiled in close collaboration with users and allows them to design buildings tailored to a warmer climate. Findings from using this data in building simulations allow inferring requirements for the architecture and operation of the buildings and show an increasing demand for cooling systems in the future.
Development of a very high resolution solar radiation cadaster for estimating solar energy potential across the entire federal state of Salzburg, Austria
Authors: Göbel Matthias , Schlögl Matthias , Lehner Sebastian , Krautgasser Fabian , Olefs Marc
Published: June 2022

The effect of local shadows cast by e.g. infrastructure or vegetation is of paramount importance for accurately estimating the local solar energy potential. Given the high spatial and temporal resolution that is required to provide accurate estimates of solar radiation components at very fine scales, deriving a solar cadaster across large areas is a computationally demanding undertaking. Here we present the development of a solar cadaster for the Austrian federal state of Salzburg, covering an area of more than 7.150 km² at a spatial resolution of 0.5 meters and a temporal resolution of 1 hour.

The dataset is based on the radiation model STRAHLGRID, which calculates near-surface direct and diffuse solar radiation on horizontal, real and arbitrarily inclined surfaces in the spectral range 0.3-3 μm, their component sum (global radiation), and sunshine duration. Results are provided at a spatial resolution of 100 m and a temporal resolution of 15 minutes across the national territory of Austria in near real-time. The model takes atmospheric turbidity, cloudiness, terrain shading, multiple / terrain reflections and ground albedo feedbacks into account. It integrates temporal changes of atmospheric turbidity by including precipitable water (water vapor transmittance) and visibility fields (aerosol transmittance) as obtained from the nowcasting model INCA as well as a cloud raster based on measured sunshine fraction and satellite data. The underlying digital elevation model (DEM) used as input in STRAHLGRID and INCA has a spatial resolution of 100 m.

The downscaling procedure used for providing a consistent solar radiation cadaster is based on combining the 100 m radiation data with a very high resolution digital surface model (VHR-DSM) obtained from airborne laser scanning. First, a test reference year is computed by aggregating the 15 min data to one hour and averaging across all years between 2006 and 2021 on an hourly basis. Second, diffuse and direct radiation on the horizontal surface are upsampled using bilinear interpolation. Third, radiation datasets are modified based on the VHR-DSM. Diffuse radiation is corrected using an updated version of the sky view factor based on the original DEM and the VHR-DSM. Direct radiation is corrected using shade maps from the VHR-DSM derived from the horizon angle and the solar position, computed using azimuth angle steps of 1 degree and time steps of 4 minutes. In addition to applying the shadow mask, direct radiation on the real surface is obtained using a correction term for the tilted surface based on slope, aspect and solar position. Finally, global radiation is computed as the sum of diffuse and direct radiation, which is of high relevance for spatial energy planning and solar energy applications at diverse scales.
Statistical post-processing of COSMO-REA6's wind speeds at hub heights using a diagnostic wind model and neural networks
Authors: Brune Sebastian , Keller Jan D.
Published: June 2022

The correct representation of wind speeds at hub height (100m above ground) is becoming more and more important with respect to the expansion of renewable energies. Since there are only a few long-term measurements at hub heights available in Europe, we rely on wind speed estimates from reanalyses. Reanalyses provide a physically consistent state of the atmospheric dynamics over long periods, but are not able to represent local effects due to their limited horizontal resolution. We perform a post-processing of the wind speeds from the regional reanalysis COSMO-REA6 in Central Europe based on a combined physical and statistical approach. The physical basis is provided by downscaling wind speeds with the help of a diagnostic wind model, which reduces the horizontal grid spacing by a factor of eight compared to COSMO-REA6 (to approx. 800m) and considers different vertical atmospheric stabilities. While the downscaled wind fields might be better in line with the orography, the data still has inherent uncertainties (e.g., fit of the COSMO-REA6 input to the orography, errors in COSMO-REA6, assumptions in the wind model) and thus may still deviate considerably from the observations.

Therefore, in a second step, a statistical correction based on various reanalysis parameters as predictors. These corrections are performed using a neural network approach as well as a generalized linear model as reference. Although only few measurements by masts or lidars are available at hub heights, a reduction of wind speed RMSE of up to 30% can be achieved depending on location. A comparison with radiosonde observations also confirms the added value of combining the physical and statistical approach in wind speed post-processing.
A very high spatial resolution database for heating and cooling degree-days in Catalonia (1971-2100): observations and climate projections.
Authors: Aparicio Albert , Altava-Ortiz Vicent , Barberia Laura , Barrera-Escoda Antoni , Rius Anna
Published: June 2022

Future climate projections for the Mediterranean area point out to an important increase in temperature during this century, independently on the considered emission scenario. This projected increase will have a significant impact on temperature-related climate indices, such as heating and cooling degree-days, and also, the economic costs to maintain climate comfort within buildings, especially for summer. These indices are key outside temperature-based indices to evaluate the energy consumption of buildings. 

In this work, a very high spatial resolution database for heating (HDD) and cooling degree-days (CDD) has been performed from different temperature data: The network of automatic weather station from the Meteorological Service of Catalonia from 2006 to 2015 (138 stations in 32,000 km2), and non-automatic weather station data from 1971 to 2015. It has been taking as a baseline 15 and 18 °C for HDD, and 21 and 25 °C as thresholds for CDD. 

Data from automatic weather stations allow us to compute these indices with a high temporal accuracy (hourly or sub-hourly time scales), but they are only available with a highly-dense network for the last few 15 years. Otherwise, data from non-automatic weather stations allows us to analyse a wider temporal coverage, but only with daily-mean temperature data. Thus, in areas with a complex topography, as it is the case of Catalonia, important differences at annual scale can appear when computing HDD and CDD from daily or hourly-mean values, especially for zones prone to thermal inversion situations. Hourly data leads to increase HDD and CDD for each considered threshold. The differences among them reach annual-mean values higher than 200 ºC-days for heating degree-days and 150 ºC-days for cooling degree-days. 

Taking into account statistically downscaled climate projections at 1-km spatial resolution from 3 IPCC-AR5 global climate simulations forced by RCP4.5 and RCP8.5 emission scenarios, it is expected a significant decrease in HDD for the Pyrenees, up to 2000-2400 ºC-days in 2100, for 15 and 18 ºC thresholds, respectively. Meanwhile, it is projected an important increase in CDD for the Ebro Valley, up to 400-600 ºC-days in 2100, for 25 and 21 ºC thresholds, respectively.

 
Sensitivity analyses and validation of an ENVI-met microclimate model for a greened urban study area in Cologne Südstadt under various typical weather conditions
Authors: Eingrüber Nils , Schneider Karl , Korres Wolfgang , Löhnert Ulrich
Published: June 2022

Increasing heat exposure and heat stress due to global warming is a major problem in cities, and adaptation strategies to improve the urban microclimate are urgently needed. Successful climate change adaptation requires data, models and scenario analyses to evaluate the potentials and thermal effects of measures like green infrastructures, facade and roof greenings, shadings or light surfaces. In order to be able to estimate the effectiveness of different adaptation strategies in agglomeration areas, a physically-based 3D ENVI-met model was setup to simulate the urban microclimate of a 16 ha study area in Cologne Südstadt which is characterized by dense development in the east as well as an urban park area in the western part. In order to assess and test the model outputs, various sensitivity analyses were implemented, especially with regard to the effects of different meteorological forcing data. We compared the output data i) when using measured radiation (incoming direct and diffuse shortwave radiation and incoming longwave irradiance), and ii) when using measurements of the relative cloud cover of low-level, medium-level and high-level clouds as radiative forcing of the model. Radiation is measured with a diffusometer consisting of two pyranometers with and without a shadow ring and a pyrgeometer. The instruments are installed at our meteorological station (Campbell Scientific) in the urban park. The relative cloud cover for the different cloud levels is derived from the measurements of a close-by ceilometer. In addition to the sensitivity analyses, a model validation is carried out based on reference data of a densely distributed weather station network consisting of 33 NETATMO weather sensors and ultrasonic anemometers in the study area, which have previously been checked for long-term stability and consistency in the field and under laboratory conditions. On the basis of air temperature, relative humidity, wind speed and wind direction measurements, the quality of the ENVI-met model outputs is evaluated for statistical goodness of fit, and it is checked how accurate the ENVI-met model can represent and reproduce the actual measurements at the heterogeneous NETATMO test sites. The model validations and sensitivity analyses are accomplished for various typical weather conditions in order to show how the model quality differs between low-exchange high-pressure conditions and mixed weather conditions with different dominant wind directions. Further research will use this validated ENVI-met model to simulate and analyse the effects and potentials of various hypothetical adaptation measures taking into account climate change projections until 2099, and different scenarios will be compared in order to mitigate future heat stress and improve the urban microclimate.
GeoE3 - combining meteorological data with geospatial and statistical data
Authors: Visa Mikko
Published: June 2022

GeoE3 (Geospatially Enabled Ecosystem for Europe) is a project co-financed by the Connecting Europe Facility of the European Union. It aims to connect existing national, regional and cross-border services and data sets including meteorological, statistical and geospatial such as building data or road network data. This simplifies meaningful analysis and visualization in a national and cross-border context.

GeoE3 develops tools and APIs that will merge available information from national sources. It demonstrates data and service interoperability and creates dashboards and visualizations for an improved understanding of data from a variety of sources. To produce tangible results that can serve as best practices for other domains, disciplines, and areas, GeoE3’s tools and services address compelling use cases. The action will simplify the discovery of relevant data and improve services through the adoption of the latest standards with emphasis on Web APIs and linked data principles.

The three use cases include Solar Energy, Smart Cities and Electric Cars. Investigation of the solar energy potential and energy efficiency of buildings is based on detailed 3D building data, digital elevation models, climate normals, observations and forecasts. The electric cars use case is based on evaluation and prediction of the energy consumption of electric cars as well as providing new services for the European C-ITS platform (Cooperative Intelligent Transport Systems). This use case will use 2D and 3D road data, weather and traffic data, road signs and speed limits. Smart Cities explores renewable energy potential where a planned development area is considered and deals with the optimization of the efficiency of urban expansion.

From a meteorological domain perspective GeoE3 will deliver an implementation of the OGC EDR API built on top of Finnish Meteorological Institute open source SmartMet Server data server. Focus during this year will be on datasets necessary to fulfill the GeoE3 project use cases.
Postprocessing of gridded precipitation forecasts using conditional generative adversarial networks and quantile regression
Authors: Hemri Stephan , Bhend Jonas , Spirig Christoph , Nerini Daniele , Moret Lionel , Furrer Reinhard , Liniger Mark A.
Published: June 2022

Probabilistic predictions of precipitation call for rather sophisticated postprocessing approaches due to its low predictability, high spatio-temporal variability and highly positive skewness. Moreover, the large number of zeros makes the generation of physically realistic postprocessed forecast scenarios using standard approaches like ensemble copula coupling (ECC) rather difficult. In addition to classical statistical approaches, recently, machine learning based methods gained increasing popularity in the field of postprocessing of probabilistic weather forecasts.

In this study, we compare conditional generative adversarial network (cGAN) based postprocessing of daily precipitation with a quantile regression based approach. Recent publications on applying cGAN to precipitation forecasts have shown its potential to generate forecast scenarios that improve forecast skill and cannot be distinguished from observed data in terms of spatial structure (Harris et al., 2022; Price and Rasp, 2022). While we use ECC to generate physically realistic forecast scenarios from quantile regression, cGAN does not need any additional ECC steps. For training and verification, we use COSMO-E ensemble forecasts with a grid resolution of about 2 km over Switzerland and the corresponding CombiPrecip observations, which are a gridded blend of radar and gauge observations. Preliminary results obtained by using loss functions that are tailored to precipitation postprocessing as proposed by Harris et al. 2022 confirm the potential of cGAN for precipitation also for our study domain. Using cGAN, we aim to generate realistic looking forecast scenarios while also increasing forecast skill compared to COSMO-E. Furthermore, we provide a comparison of multivariate verification measures between COSMO-E, cGAN and quantile regression, which does increase forecast skill at the expense of relying on an additional ECC step to generate forecast scenarios. 

 

References: 

Harris, L., McRae, A. T., Chantry, M., Dueben, P. D., & Palmer, T. N. (2022). A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts. arXiv preprint arXiv:2204.02028.

Price, I., & Rasp, S. (2022). Increasing the accuracy and resolution of precipitation forecasts using deep generative models. arXiv preprint arXiv:2203.12297.

 
Characterizing Optimal Atlantic-European Blocking Precursors Using a Linear Inverse Model 
Authors: Madsen Maria , Wirth Volkmar , Riemer Michael , Grams Christian
Published: June 2022

Commonly studied atmospheric phenomena are the persistent, quasi-stationary high-pressure systems known as atmospheric blocking. During the boreal winter, these blocking anticyclones often lead to downstream cold temperature extremes and have been associated with processes varying on longer sub-seasonal timescales such as the Madden Julian oscillation (MJO) and the stratospheric polar vortex. The Atlantic-European region has recently been characterized by four types of blocked regimes, each with differing downstream sensible weather impacts and forecast skill. Key to better predictability of these Atlantic-European blocked weather regimes is identifying remote influences on sub-seasonal timescales.

Recently, linear inverse models (LIMs) have been successfully used to quantify slowly varying precursors to large-scale circulations patterns in the Northern Hemisphere. A LIM is an empirical model using lag covariance statistics to approximate dynamical properties and the evolution of specific atmospheric variables contained in a prescribed atmospheric state vector. The evolution of the state vector is approximated by separating slowly evolving linearized dynamics, as well as a linear approximation to nonlinear dynamics, from presumably unpredictable white-noise forcing. In this work, a LIM is employed to identify optimal precursors on sub-seasonal time scales for the life-cycle of Atlantic-European blocking events during the extended boreal winter (November-March). Included in the LIM are tropical heating anomalies related to the MJO or the El Niño-Southern Oscillation (ENSO), extratropical influences from Rossby wave activity, and the stratospheric polar vortex. Optimal extratropical and tropical precursors for the varying types of blocking events, as well as the LIM’s skill in forecasting European-Atlantic blocking, are presented.  
Radar-derived variability of ice water content (IWC), total number concentration (Nt), and mean volume diameter (Dm) for improved parameterizations 
Authors: Scharbach Tobias , Trömel Silke
Published: June 2022

Radar polarimetry and recently developed microphysical retrievals offer great potential for evaluating and improving the parameterizations of numerical models. In this study, we intend to inform the subgrid parameterizations of the ICON general circulation model (ICON-GCM), in particular to assess and improve the spatial heterogeneity of ice water content at ICON subgrid scales. Quasi-vertical profiles (QVPs) generated by azimuthal averaging of various polarimetric radar variables from Plan Position Indicators (PPIs) acquired during standard conical scans at antenna elevation angles of 18° successfully reduce statistical errors, especially for phase-based measurements such as specific differential phase (KDP). This methodology provides the ideal data basis for various robust polarimetric microphysical retrievals of IWC, Nt, and Dm. However, the use of QVPs reduces the information on sub-grid scale variability compared to higher-resolution PPIs. Thus, an important question is how much averaging is required for robust estimates of IWC, Nt, and Dm and how we can separate spatial variability from noise. Spatial variabilities or azimuthal standard deviations and statistical errors of different retrievals are analyzed using measurements of the polarimetric X-band radar in Bonn, Germany (BoXPol). Statistical errors are quantified by the standard error of the mean (σmean), calculated via Gaussian error propagation using Bienaymé's law. σmean is used to investigate the extent to which the azimuthal window size of 360° in the QVP methodology can be reduced while still ensuring acceptable statistical errors of IWC, Dm, and Nt. Range-defined QVPs (RD-QVPs), a variant making use of different antenna elevation angles, can further reduce the statistical errors due to the larger sample size. Shannon's information entropy is exploited within a new method to test the distributions of polarimetric variables and retrievals for homogeneity within PPIs. This is key for the use of σmean to ensure that samples are drawn from the same distribution. Statistics of a large BoXPol data set are presented and compared to simulations of ICON-GCM. Finally, an attempt is made to improve the specific threshold used in ICON-GCM for the onset of aggregation (particle diameters > 0.1 mm are defined as snow) by estimating the particle size distributions (PSDs) assuming an exponential function.
Specific and localized heat warning system built upon normalized analysis of the UHI for the city of Karlsruhe – Results of GreenLung project
Authors: Gangwisch Marcel , Matzarakis Andreas
Published: June 2022

Global climate change is increasingly threatening our life in cities due to intensified heat waves and is thereby stressing the resilience of our cities and our society. Multiple descriptions of action plans (e.g., Sustainable development goals, Sendai framework for Disaster Risk Reduction 2015 – 2030 and Heat-health action plans), reflect the public awareness of these threats with the demand for action. Local administrations must implement long-term but also short-term adaptation measures, to increase the thermal resilience of our cities to protect the urban population, especially the vulnerable group.

This contribution will demonstrate how meteorological observations from traverse measurement campaigns, reflecting the urban heat island effect, can be utilized in the aforementioned action plans.

The observations are comprehensively preprocessed to generalize from prevailing weather conditions to observe the typical, thermal response of the urban morphology for authochtonous weather conditions during heat waves. Absolute values of meteorological conditions are no longer considered, as they are mapped to a relative and normalized scale. A subsequently fitted, Generalized Additive Model, considering the local urban structures (gray and green infrastructure), provides this information spatially for the entire urban area. The spatial prediction can be combined with local risk and protection factors (location of vulnerable facilities (e.g., hospitals, nursing homes, kindergartens) and protective infrastructure (e.g., drinking fountains)) to create a valuable tool for short-term protection of vulnerable and exposed populations during a heat wave.

The short-term adaptation (for days) mainly addresses the people's behavior with respect to heat and is often implemented by information and warning systems. The German Meteorological Service operates a national heat warning system at community level based on the forecasts of the ICON-EU model (~6.5km, < 72h). The official heat warnings are derived from perceived temperature for severe and extreme heat and are statistically related to heat mortality. Based on the previously, generalized results, a specific heat warning system on city level (e.g., city of Karlsruhe) is implemented upon the national heat health warning system. The specific warning system is coupled to the national system and provides small-scale warnings and information for short-term adaptation under consideration of spatially resolved vegetation and urban structures. These warnings provide valuable recommendations of action for vulnerable and exposed people on urban district level in a citizen-oriented manner via the municipal systems.
Building storylines for applications: what have we learned in the EUCP project?
Authors: Fung Fai , Goddard Christopher , McSweeney Carol , Crocker Tom , Matte Dominic , Ballinger Andrew , Hegerl Gabi , O'Reilly Christopher , Weisheimer Antje , Van der Wiel Karin , Wilcke Renate
Published: June 2022

The European Climate Prediction system (EUCP) project aimed to lay the foundation for a future regional climate prediction system for Europe. An important element of this is the role of narrative or storylines approaches in data production and scientific investigation, as well as a user product. We will present the results of our investigations which sought to understand the potential advantages and challenges in developing physically based climate storylines as part of a climate service by addressing the questions

What are climate storylines and where are they useful?

How could storylines bring together various outputs of EUCP state-of-the-art climate science attempting to reduce uncertainty and complexity in climate projections, and seamlessly combine them with decadal predictions?

What are the challenges of producing them as a service?

The body of EUCP work included two case studies of co-producing storylines as a user product, revealing the potential usefulness for applications. These included the heritage and water supply management sectors which are at different stages of adaptation management. We also reflect on the potential of event-based future storylines: one using a convection-permitting model to provide attribution statements for the Copenhagen flooding event in July 2011 and another using large ensembles to construct storylines of the European summer 2018 drought under different pseudo-global warming. Novel scientific studies were also performed which could form the scientific building blocks of climate storylines including an algorithmic clustering approach and approaches to producing more realistic climate variability. Finally, we will present a tool for performing multiple lines of evidence assessments that could aid storylines development.
FAIRness of micrometeorological data: New community challenge?
Authors: Lalić Branislava
Published: June 2022

"Findability, Accessibility, Interoperability and Reusability – the FAIR principles – intend to define a minimal set of related but independent and separable guiding principles and practices that enable both machines and humans to find, access, interoperate and re-use data and metadata" (FAIR Data Maturity Model Working Group, 2020). In the further text term "FAIRness" will be used to describe how data meet FAIR principles, while FAIRNESS (capital letters) is the COST CA20108 action title.

The long-term need for a FAIR micrometeorological data is strongly boosted by two independent but equally important issues: a) full awareness of the time, effort, and money lost due to the lack of FAIR data in general (it is quantified in EC PwC EU Services report (PwC EU Services, 2018): "…the minimum true cost of not having FAIR data … is 78% of the Horizon 2020 budget per year) and b) urgent need to develop tailored adaptation and mitigation measures in rural and urban areas to reduce negative effects of adverse weather and climate change.

Enhanced standardization and integration between data bases&sets of micrometeorological measurements that are part of research projects or local/regional observational networks established for special purposes (agrometeorology, urban microclimate monitoring) by increasing FAIRness of data create a strong background for future research and modeling studies as well as a European Micrometeorological database.

Addressing identified challenges requires an effective transboundary network of researchers, stakeholders (extension services and environmental agencies, local authorities and ministries, SME), and civil society (specialized and general public) from Europe and beyond to identify and fill knowledge gaps, standardize, optimize and promote new environmental-tailored measurement and control procedures, enhance research effectiveness and improve dissemination. Therefore, FAIRNESS Cost action gathers 94 researchers, scholars, students, and stakeholders from 25 countries and 20 different specializations to work together to achieve goals set together.

Literature

FAIR Data Maturity Model Working Group. (2020). FAIR Data Maturity Model. Specification and Guidelines (1.0). https://doi.org/10.15497/rda00050

PwC EU Services. The cost of not having FAIR research data (2018) DOI 10.2777/02999.
An observational study of the Inn Valley exit jet
Authors: Sedlmeier Katrin , Kossmann Meinolf , Paunovic Ivan , Bock Lothar , Nitsche Oliver , Mühlbacher Gudrun
Published: June 2022

Previous studies have found a pronounced nocturnal low-level jet at the exit of the Inn Valley north of the valley contraction near Schwaigen which reaches into the Alpine foreland for several tenth of kilometers (e.g. Pamperin and Stilke, 1985 as part of the MERKUR experiment or a model study by Zängl, 2004). The exit jet forms under nocturnal stably stratified atmospheric conditions and is interpreted as a transition from subcritical to supercritical hydraulic flow.

As part of the pre-experiment of the TEAMx programme in June to August 2022, we will conduct measurements to corroborate and extend the previous findings on the formation and maintenance of the Inn Valley exit jet. For this, a wind lidar will be deployed north of the valley contraction. Further, during selected days, radiosondes will be launched at the site of the wind lidar, accompanied by drone measurements. Additionally, the operational network of the German Meteorological Service (DWD) will be supplemented by ground station measurements of wind, temperature and humidity along the valley and at one higher elevated station between Kufstein and the wind lidar site. At selected stations, pressure, radiation and precipitation measurements will be conducted additionally. These will run until the completion of the main TEAMx campaign in 2024/2025.

 This contribution will show some preliminary results of the measurement campaign.

 

References:

Zängl, G. "A reexamination of the valley wind system in the Alpine Inn Valley with numerical simulations." Meteorology and Atmospheric Physics 87.4 (2004): 241-256.

Pamperin, H., and G. Stilke. "Nächtliche Grenzschicht und LLJ im Alpenvorland nahe dem Inntalausgang." Meteorologische Rundschau 38.5 (1985): 145-156.
ACCORD aerosols
Authors: Rontu Laura , Sekula Piotr , Sljivic Ana
Published: June 2022

ACCORD is a NWP consortium for development of limited-area short-range numerical weather prediction (NWP) system, representing 26 European and North-African national weather services. Various cloud microphysics and radiation schemes available in ACCORD framework are being developed to account for aerosol impact on the cloud, precipitation and fog formation as well as on the direct and indirect aerosol effects on radiation transfer. As a short-range limited area NWP system, ACCORD does not aim at fully integrated representation of weather-aerosol-chemistry dynamics but relies on external climatological and near-real-time aerosol data, currently obtained from Copernicus Atmosphere Monitoring Service (CAMS). Preliminary results of single-column and full model experiments in cases of dust intrusion and very clean air illustrate the interactions, sensitivities and uncertainties of the forecast, related to the choices of aerosol source data, parametrization schemes and their settings.

Currently, the HARMONIE-AROME model within ACCORD uses monthly climatologies of total aerosol optical depth at 550 nm of four aerosol types (land, sea, urban and desert) for radiation parametrizations only. These data come in coarse global grid and represent the average conditions. Radiation impacts of dust and carbon aerosols, that can be significant especially during dust intrusions and wildfires, cannot really be taken into account. On the other hand, currently the aerosol impact on liquid and ice cloud formation and evolution are not taken into account at all. The most important aerosol impacts on cloud microphysics are presumably related to the water-soluble aerosol like sulfates and sea salt, that are abundant but regionally variable in the atmosphere.

The possibility to use near-real-time aerosol as an external source in the high-resolution NWP system requires that the model is adapted to use these data in an optimal way. For radiation, this means better definition of the aerosol optical properties depending on the species and wavelengths as well as on the real-time atmospheric humidity at different elevations. For the single-band radiation schemes, aerosol optical depths of the aerosol mixture for short-wave and long-wave bands are obtained as weighted averages, based on the data of aerosol mass mixing ratios and the prescribed aerosol inherent optical properties. Considering cloud-precipitation microphysics, droplet number concentration and specific cloud ice content depend on aerosol. These influence the cloud, precipitation and fog evolution and also, via the cloud particle size, the radiation transfer in clouds.  
Introducing a daily updated Leaf Area Index in a mesoscale Numerical Weather Prediction model
Authors: Szintai Balázs , Tóth Helga , Kullmann László
Published: June 2022

In current state-of-the-art Numerical Weather Prediction (NWP) models Leaf Area Index (LAI) is considered as an external parameter where monthly values are derived from long-term averages. Such an approach is not capable of describing vegetation anomalies e.g. during severe droughts, when LAI values (especially over non-irrigated grasslands and croplands) could be considerably lower than long-year averages of the selected month. A solution for this inaccuracy is presented in this study, with the aim to use satellite LAI observations in the NWP model. The main difficulty with such an approach is that high resolution (e.g. that of Sentinel) satellite vegetation products have a time lag of 10-15 days. To overcome this the following system was developed: satellite vegetation observations are assimilated in an offline land data assimilation system which is capable to deliver a soil and vegetation state analysis 10 days prior the actual date (T-10d). From T-10d we integrate the offline surface model with prognostic vegetation until the current date; and the resulting vegetation state (at time T) could be merged with the operational analyses of the NWP model.

In the present study the AROME-Hungary NWP system is used, which is run operationally at the Hungarian Meteorological Service (OMSZ) at 2.5 km horizontal resolution. For the simulation of surface processes the offline SURFEX land surface model is applied, which in its present version utilizes the ISBA-Ags simplified photosynthesis scheme to simulate LAI prognostically. In SURFEX-offline an Extended Kalman Filter method is used to assimilate Leaf Area Index satellite measurements, from the OLCI sensor of Sentinel-3.

The capabilities of the system and the impact of LAI change on the weather forecast produced by AROME-Hungary are presented during summer 2021. In 2021 a cold spring was followed by very hot and dry summer in the Carpathian Basin. During July and August a severe drought occurred over Southern Hungary and Northern Serbia, and consequently maize fields over large areas were significantly underdeveloped (irrigation is very limited in the region). This LAI anomaly was well captured by the SURFEX-offline system. Results show that LAI can have an impact on the weather forecast produced by AROME in summer anticyclonic cases. Most affected variables are 2 m temperature and precipitation.

Based on the encouraging results it is planned that this system for the improvement of LAI fields in AROME is going to be implemented in the operational NWP chain of OMSZ in near future.

 
Atmospheric mass variations from high-resolution, non-hydrostatic atmospheric models and their influence on dealiasing of GRACE/GRACE-FO data sets
Authors: Friederichs Petra , Dixit Shashi , Kracheletz Magdalena , Springer Anne , Mielke Christian , Kusche Jürgen , Hense Andreas
Published: June 2022

We will present our atmospheric physics contribution to the New Refined Observations of Climate Change from Spaceborne Gravity Missions (NEROGRAV) research group, which is funded by the German Research Foundation (DFG). The goal of NEROGRAV is to develop new analysis methods and modeling approaches to improve the current data analysis of the GRACE and GRACE-FO missions. Our contribution to this research unit is to is to (re)analyze data fields on horizontal grid sizes below 10 km from non-hydrostatic regional atmospheric models: the 1995-2019 COSMO-REA6 regional reanalysis and the July 2021 ICON-EU/D2 analyses. From these models, the mass densities of dry air and all water phases (gaseous, liquid clouds and rain, icy snow, sleet, and hail) are available in each of the 40-50 vertical layers, so that the total local and column-by-column atmospheric mass density can be calculated without using the hydrostatic assumption.

Current non-hydrostatic, high-resolution atmospheric models run under some idealizations (e.g., assumption of a perfect sphere, spatially constant gravitational acceleration, approximation of a flat atmosphere) that are inconsistent with the real measurements of the GRACE/GRACE-FO missions. Furthermore, high-resolution atmospheric model data are currently only available within the CORDEX-EU domain, which covers the North Atlantic-European region, or an even smaller domain. The results to be presented will answer the following questions: (1) Do the model idealizations cause discrepancies with respect to the GRACE/GRACE-FO measurements that are larger than the sum of the actual measurement uncertainties and the uncertainties resulting from the remaining dealiasing models? (2) Can the idealizations be corrected to achieve better agreement with the GRACE/GRACE-FO data without sacrificing the underlying model dynamics, e.g. by introducing additional gradients in the models? (3) Do extreme weather events, such as heavy rainfall observed in the Ruhr/Ahr/Erft/Maas basin in July 2021, cause mass variations that exceeds typical GRACE/GRACE-FO uncertainties? And finally, (4) does high-resolution but regional atmospheric mass variability leave a statistically significant fingerprint in the global loading coefficients?