bluuebunny/crossref_metadata_2025 huggingface dataset:
Scaling the vertical-velocity variance in the convective boundary layer forced by rapidly decaying surface heat flux after equilibrium breakdown
Authors: Elguernaoui Omar , Li Dan , Reuder Joachim
Published: July 2024

In the bulk of the convective boundary layer driven by surface heating, the vertical-velocity variance is known to scale with the convective velocity scale. This scaling relies on the quasi-equilibrium assumption that the surface heat flux (H) varies slowly compared to the adjustment time scale of the large scale convective eddies (or the eddy-turnover time scale). When the surface heat flux decays moderately or rapidly compared to the eddy-turnover time scale, a departure from the quasi-equilibrium regime is expected. A recent study proposed a parameter for describing such departure during the late afternoon transition: r = H-1dH/dt-1/t*, where t* is the eddy-turnover time scale. The quasi-equilibrium assumption applies when r>>1, and breaks down when r~1. Building on these results, we further investigate the scaling for the vertical-velocity variance in the regime r
Abnormal Climate and Market Economy: The Impact of Solar Radiation on Rice Prices during the 1830s Famine in Japan
Authors: Ichino Mika , Masuda Kooiti , Mikami Takehiko , Takatsuki Yasuo
Published: July 2024

How do societies respond to varying climates? This query not only piques academic curiosity but also holds the key to how our society can adapt to the ongoing climate change. To shed light on this, we focus on the Tenpō Famine, a pivotal event in the 1830s that was one of the most severe famines due to poor harvests caused by an abnormal climate. By investigating such a significant occurrence, we aim to provide insights into vulnerable areas in the modern era. We chose the Tenpō Famine for the case study because it falls within a period for which analytical data are readily accessible (e.g., old diaries and price records). It occurred nationwide with regional differences, which makes it valuable for gaining a broad picture of the effects of climate variation. To understand the impact of climate change on societies in the past, it is necessary to consider social and economic information from such periods. This involves reconstructing spatial patterns of climate variation at a higher temporal resolution than provided by annual data. Japan has a wealth of historical diaries from 1821 to 1850.   Using these records, we developed a method to estimate solar radiation, enabling the successful reconstruction of historical solar radiation for all seasons at a higher temporal resolution than provided in annual data. Weather descriptions were categorised to estimate solar radiation, making possible analyses of climate variability and its effects at the monthly time scale. This represents a novel approach for analysing climate impacts on agriculture and the economy during this period. We then correlated the reconstructed solar radiation to rice prices, revealing a significant relationship between climatic anomalies and economic stability, particularly during the summer growing season. The results show that lower solar radiation, indicative of poor weather conditions, corresponded to higher rice prices, particularly during the summers of 1833 and 1836. The temporal resolution of economic data has also improved recently. Hence, this study contributes to understanding historical climatic impacts on society by providing an even more refined temporal resolution than was previously available. This underscores the need to integrate climate data into economic analyses to better understand the dynamics of societies facing environmental challenges. Our study is particularly relevant in the context of contemporary climate change and provides historical perspectives that can inform current policies and adaptation strategies.
Diagnostics-based evaluation of Mediterranean extreme convective storms on high-resolution simulations under Destination Earth initiative
Authors: González-Alemán Juan Jesús , Gascon Estibaliz , Martin Daniel , Vannière Benoît , Maier-Gerber Michael , Jimenez Antonio , Calvo-Sancho Carlos , Viana Samuel , Calvo Javier
Published: July 2024

The Destination Earth (DestinE) is a flagship initiative of the European Commission to develop a highly accurate digital model of the Earth on a global scale. This model will monitor, simulate and predict the interaction between natural phenomena and human activities. DestinE will unlock the potential of digital modelling of the Earth system at a level that represents a real breakthrough in terms of accuracy, local detail, access-to-information speed and interactivity.The initial focus will be on the effects and prediction of climate change and extreme weather events, their socio-economic impact and possible adaptation and mitigation strategies. One of the subcomponents of DestinE is the On-Demand Extremes digital twin (DestinE_330), which will build a configurable framework for impact assessment and prediction of weather-induced extremes at sub-kilometre scales.Within the framework of this initiative and from diagnostics point of view, here we evaluate initial simulations of both the global (~4 km) and the regional weather-induced (~500 km) DestinE prototype components for the representation of high-impact and extreme convective weather over the Mediterranean region, with two specific problems which are highly sensible to increasing resolution and which anthropogenic climate change could worsen: A very high-impact static convective storm formed in front of Valencia (Spain) and a rare tropical-like convectively-driven cyclone (Medicane Ianos) over the Ionian Sea.The storm in Valencia, probably a mesoscale convective system, produced heavy precipitation (>100 mm in 2 hours) and reported the largest accumulation for one day in May for Valencia. The socioeconomic impact was also severe. This event had very low predictability in high-resolution convection-allowing models, being a challenge from a numerical modelling point of view. None of the national operational models nor global models over the region showed signals of convective activity with such features in the east of Spain.Ianos was a extremely rare Mediterranean tropical-like cyclone, behaving as a hurricane, that impacted the eastern Mediterranean on 17 and 18 September 2020, especially Greece, leaving severe damage with heavy rains (peak of >600 mm) and strong winds (peak of 195 km/h) and fatalities. Operational forecasts of this event were not highly valuable, thus it a another highly recommended case study.
Analyzing the Sensitivity of the LES PALM Model to Building Parameters Using an Archetype-Based Approach
Authors: Nowzamani Nooshin , Maronga Björn , van der Linden Lara , Bechtel Benjamin
Published: July 2024

The interaction between urban microclimate and building energy balance is pivotal in comprehending urban thermal behavior, with buildings' energy use contributing to waste heat that affects the intricate mass and momentum transactions characteristic of urban microclimates. Large-Eddy Simulation (LES) models have improved insights into these complex dynamics due to increased computational power. However, complexities and data uncertainties in real urban settings may compromise LES accuracy. Therefore, understanding model sensitivity to input data uncertainties is crucial for assessing potential deviations and prioritizing data collection parameters.This study scrutinizes the PALM model 6.0's sensitivity to building typologies and parameters within a residential quarter of Bochum, Germany, examining the trade-offs of modeling detail. Four divergent scenarios are considered. The baseline scenario presupposes homogeneity in building types across the model domain. The second scenario applies PALM’s standard building categories, predominantly delineating four types aligned with building age and building use. The third scenario encompasses an array of 28 building typologies, integrating the TABULA archetypes from the IEE Project 'Typology Approach for Building Stock Energy Assessment'. The first three scenarios maintain a consistent level of architectural specificity within the parent and the child domain, configured as a nesting structure. The fourth scenario distinguishes itself by combining PALM's predefined standard building typologies within the parent domain with the 28 distinct TABULA archetypes within the child domain. ​The underlying hypothesis suggests that increasing the detail in building parameters can potentially amplify the realism of urban environment simulations and augment the accuracy of the results. However, such advancements come with heightened computational demands and more extensive data requirements. The study emphasizes the importance of balancing these considerations to ascertain the most advantageous degree of detail, tailored to various simulation pursuits.
Exploring Tree Shade: Cooling Effects and Skin Temperature Recovery in Urban Environments
Authors: Peng Zhikai , Maiullari Daniela
Published: July 2024

This paper investigates the daytime microclimate of street trees and their potential cooling effects on pedestrians in a Dutch neighbourhood.Heat emissions from buildings, traffic, and industrial activities contribute to the warming of urban areas, intensifying the urban heat island effect. Street green infrastructure, like deciduous trees that provide shade, plays a crucial role in reducing heat stress and promoting pedestrian comfort in various domains. However, studying the physiological response to urban shade is challenging due to difficulties in controlling street-level meteorological variables and recruiting participants. We plan to use semi-controlled outdoor methods to study how shade affects bodies, with a limited sample size. This research aims to better understand the role of tree shades in adapting to urban heat and contributing to global goals for climate action (SDG 13), sustainable cities (SDG 11), and health and well-being (SDG 8).We conducted an explorative biometeorological study using a juvenile tree (Tilia x europaea, 12m in height) located in a social housing neighbourhood in Den Haag. The measurement campaign adopted mobile weather stations to gather meteorological data surrounding the Tilia tree. This took place from 10 am to 5 pm on a sunny day in July 2023. Two heat-stress trackers (Kestrel 5400) were used; one was placed in direct sunlight and the other in the shade of a tree. The shaded tracker was relocated every thirty minutes to adjust for the moving tree shade. Two measurements showed how tree shade cools the air by comparing differences in temperature, humidity, globe temperature, and wind speed between sun and shade. Additionally, sixteen iButton thermocron sensors were taped to different body parts of two participants according to international standard (ISO 9886:2004). We tested a sun-shade relay protocol, tracking skin temperature changes as subjects moved and sat between sun and shade every 20 minutes, across fifteen intervals from 11 am to 4 pm.The preliminary results are two-fold: 1) Heat stress analysis showed maximum UTCI and PET in the sun at around 3:30 pm were 36.3°C and 39.9°C, respectively, with tree shade significantly reducing UTCI by over 10°C and PET by over 15°C. 2) Heat recovery analysis revealed that the maximum skin warming rate in the sun (1.14°C/min) was higher than the cooling rate in the shade (-0.79°C/min). An additional interesting finding is that, while the PET contrast between sun and shade remains constant at 15°C from morning to afternoon, the skin's heat recovery capacity is compromised by approximately 1.24°C in the afternoon, possibly due to the overall increases in PET of around 3°C.The discussion and conclusions focus on the choice of outdoor thermal indices, particularly for urban shade studies, and their applicability for future research on dynamic thermal comfort and thermal alliesthesia.
Homogenization in mean and standard deviation (MASHv4.01)
Authors: Izsák Beatrix , Szentimrey Tamás
Published: July 2024

In essence the theme of homogenization can be divided into two subgroups, such as monthly and daily data series homogenization. These subjects are in strong connection with each other of course, for example the monthly results can be used for the homogenization of daily data. The earlier versions of our method MASH (Multiple Analysis of Series for Homogenization; Szentimrey) were developed for homogenization of the daily and monthly data series in the mean i.e. the first order moment. The software MASH was developed as an interactive automatic, artificial intelligence (AI) system that simulates the human intelligence and mimics the human analysis on the basis of advanced mathematics. The new version MASHv4.01 is able to homogenize also the standard deviation i.e. the second order moment. We remark if the data are normally distributed (e.g. mean temperature) then the homogenization of mean and standard deviation is sufficient since in case of normal distribution if the first two moments are homogenous then the higher order moments are also homogeneous.In our presentation, we present the application of the MASH4 software on real climate data. From 1 January 1901 to 31 December 2023, the daily mean temperature data series of 27 stations in Hungary were homogenized in both first and second moments. We present our results based on verification statistics. The developed automatic verification procedure of MASH makes it possible to test the homogenized time series systematically in order to clear up the uncertainty. The basic concept of the verification procedure is that confidence in the homogenized series may be increased by the joint comparative examination of the original and the homogenized series systems. The comparison is based on such adequate questions, which can also be formulated mathematically, consequently, a programmed statistical test procedure can be obtained to evaluate the quality of the homogenized series. The questions are related to the estimated inhomogeneity of the series before and after homogenization the comparison of the modification measure of the series with the estimated inhomogeneity of the original series, the representativity of the given station network in respect of the homogenization, the relation between the estimated inhomogeneities and the metadata.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.
The predictability of temperature related mortality in the summers of 2022 and 2023
Authors: Holmberg Emma , Quijal-Zamorano Marcos , Messori Gabriele , Ballester Joan
Published: July 2024

Europe has been identified as a heatwave hotspot, with numerous temperature records having been broken in recent summers. These temperature extremes exact a heavy toll on society; approximately 60,000 heat-related deaths have been estimated for the summer of 2022 alone. With projections showing extreme temperatures becoming more frequent, intense and longer in duration, there is a pressing need to further develop heat-warning systems to help protect, in particular, the most vulnerable members of society. Here we evaluate the skill of daily temperature related mortality forecasts in the context of current numerical weather prediction model capabilities. We consider the summers of 2022 and 2023 as case studies and find that temperature related mortality can, on average, be forecast skilfully up to lead times of approximately one week for these two summers, although we also note the increased predictability in south-western Europe in late-July 2022 coinciding with record breaking temperatures. We further discuss the implications that the non-linear relationship between temperature and temperature related mortality has on temperature related mortality forecast spread and errors, concluding that further developments in forecasting capabilities for extreme temperature events are of key importance for improving temperature related mortality forecasts. Finally, we highlight the implication of these results in a warming climate. In the absence of meaningful adaptation measures or considerable advances in numerical weather prediction capabilities, temperature related mortality forecasts will be associated with larger errors owing to increased climatological temperatures. We emphasise that continued work on understanding the predictability of temperature extremes and temperature related mortality is vital for the further development of heat-warning systems.
Integrating MODIS NDVI in the WRF model to improve simulations in the Iberian Peninsula: a case study in a Mediterranean grassland of central Spain.
Authors: Cicuéndez Victor , Carbone Juan , Ortiz-Corral Pablo , Inclán Rosa , Román-Cascón Carlos , Sastre Mariano , Yagüe Carlos
Published: July 2024

Vegetation has a strong influence on the interaction between the land surface and the overlying atmosphere, modifying the relative contribution of sensible and latent heat to the surface energy balance (SEB). Vegetation dynamics must be considered to improve atmospheric surface models, especially in the understanding of the land-atmosphere coupling.Nowadays, remote sensing is an excellent tool to monitor vegetation functioning at different spatial and temporal scales. Spectral indices obtained from remote sensing data, such as the Normalized Difference Vegetation Index (NDVI) are strongly related to photosynthesis and vegetation functioning. Thus, the role of vegetation in the energy partitioning processes could be assessed using these spectral indices.The Weather Research and Forecast Model (WRF) uses by default monthly values of the Green Vegetation Fraction (GVF) dataset derived from the NDVI of the NOAA Advanced Very High Resolution Radiometer (AVHRR) at 0.144° resolution for the period 1985-1990 to generate its simulations. The overall aim of this work is to integrate monthly MODIS NDVI values at 1 km resolution (MOD13A3 v061 product) in the WRF model to improve simulations. Validation of the simulations will be carried out using an Eddy Covariance flux tower with data from 2017 to the present, which belongs to the GuMNet network (https://www.ucm.es/gumnet), placed in a grassland ecosystem of Central Spain close to a forest influenced by the Guadarrama mountains (El Escorial, Madrid).Results show that including NDVI in the WRF model improves the simulations in the grassland ecosystem, especially in the estimation of turbulent heat fluxes. Preliminary results in this grassland environment showed that high-resolution NDVI was strongly correlated with latent (a) and sensible (b) heat, especially for three months (May-June-July). These three months coincided when the grassland showed its greatest p changes, presenting its maxima of biomass and then drying quickly during the following months due to the lack of soil water availability.In conclusion, this work shows that it is essential to include the vegetation dynamics to improve the atmospheric and land surface models, being a first step to use spectral indices as a proxy to improve the models.
Characterizing snow seasons through the application of a new Multivariate Snow Index 
Authors: Albalat Anna , Trapero Laura , Lemus-Canovas Marc , Pons Marc
Published: July 2024

The European mountain bioregion are potentially vulnerable to the effects of climate change, both from an ecological and a socio-economic perspective. At the end of the 21st century, the level of greenhouse gases will play a fundamental role in emission scenarios and temperature increases. In recent decades, the snow depth in the European mountain regions like the Pyrenees has tended to decrease due to a rise in average temperature and precipitation variability. Several sectors, such as snow tourism and hydroelectric power generation, have been affected by this changing snow trends. In order to follow the winter snow seasons, a multivariate snow index (MSI) has been proposed to characterize the climatic variability of the snowpack in time and space, with the aim of detecting extreme changes. This new index consists in the objective characterization of the snowdepth anomaly and is based on high/low quantiles of daily maximum snow height distributions to characterise the seasons in terms of surplus or scarcity. This approximation is more suitable for making comparisons between regions and detecting variations in climatic extremes and could be applied to snow data sets from any region, at different spatial scales and for both the present and future climate. The analysis of extreme trends in duration and intensity helps to identify areas where the decrease in these parameters are more relevant and statistically significant. In this sense the proposed indicators will complement the classical snow indicators that are currently monitored, helping to provide a better regional characterization of the temporal evolution of the snowdepth.
Thunderstorm prediction using convolutional neural networks to support air traffic management in Switzerland
Authors: Attinger Roman , Aznar-Siguán Gabriela , Barras Hélène , Landmann Johannes , Waber Jivan , Wehrli Kathrin , Exterde Szilvia
Published: July 2024

Adverse weather conditions substantially affect aviation operations. In 2023, weather accounted for the largest fraction of en-route air traffic delays in the European network [1]. Alongside low visibility and strong winds, thunderstorms are one of the main causes for these delays. This is exacerbated during the summer months when both convective activity and air traffic demands are high. To anticipate the adverse effect of thunderstorms on air traffic, accurate information on the location and timing of convective initiation as well as on the duration of convective activity are required. However, convective developments at time-scales greater than the nowcasting range still pose a great challenge even for convection-resolving numerical weather prediction (NWP) models.To support air traffic management, MeteoSwiss is developing a range of novel products in close collaboration with the Swiss air navigation service provider and airports. Specifically, machine learning (ML) based approaches to forecast wind, visibility [2], and thunderstorms are developed that exploit the full potential of NWP data and observations. Moreover, solutions to improve the interpretability and usefulness of these probabilistic forecasts are implemented [3].The presented work provides probabilistic thunderstorm predictions together with an estimation of cloud top height up to 33 hours in advance. The icosahedral non-hydrostatic (ICON) model, which is the newly operational convection-resolving ensemble prediction system at MeteoSwiss, forms the data basis of the approach. Thunderstorm probabilities are derived from relevant NWP parameters using convolutional neural networks (CNNs) as they are highly efficient in identifying spatial relationships in data. We train both a U-Net and fully connected ResNet50 model on ICON re-forecast data from the convective seasons of the previous three years. The objective is defined as a binary classification problem using ground-based lightning observations as the target variable. Predictions are provided in the greater Alpine region and are updated in accordance with the operational NWP system every 3 hours.We discuss the feature selection procedure for which we use a tree-based ML model to identify the most meaningful model ensemble statistics. We compare the performance in terms of skill and reliability of the CNN approaches with the direct model output of ICON for different lead times. Finally, insights and challenges regarding the use of the new product in operational air traffic flow and capacity management are presented.[1] https://www.eurocontrol.int/publication/performance-review-report-prr-2023-consultation[2] abstract Wehrli et al., 2024, submitted to EMS 2024.[3] abstract Landmann et al., 2024, submitted to EMS 2024.
On the use of disdrometer data for characterization of precipitation episodes in the Basque Country
Authors: Gaztelumendi Santiago , Egaña Joseba , Aranda José Antonio
Published: July 2024

Measurements are essential to provide information on the actual state of the atmosphere in order to improve our understanding of atmospheric processes and their role in water cycle and the climate system. In this paper we focus on measurements from optical disdrometers which seek to improve our understanding of complexity of precipitations processes at surface.In this work we focus on the use of the basque disdrometer network a novel and evolving network with operational purpose. Deployed by the Basque Government, this network currently comprises more than 10 Parsivel OTT-2 disdrometers positioned across various locations in the Basque Country. Optical disdrometers function by gauging the degree of light obstruction caused by particles traversing a laser beam. When raindrops intercept the beam, a sensor detects a reduction in light intensity, which is then converted into an electric signal by a photodiode. This reduction in intensity corresponds to the size of the raindrops impeding the beam. Moreover, by analyzing the duration of this reduced intensity, the descending velocity can be estimated. Consequently, such instrumentation provides us with both raw information, such as raindrop size and velocity distribution, and derived data, including rain intensity, hydrometeor classification, reflectivity, visibility, etc., recorded every minute.In this paper, we present a study focused on analyzing the key characteristics of precipitation episodes in the Basque Country. This analysis involves incorporating information derived from various aggregated indicators, which are based on selected event statistics such as maximum, minimum, mean, median, mode, percentiles, and others. These statistics are applied to various event variables, including duration, number of particles, rain intensity, total rainfall, raindrop size distribution, and more.To accomplish the analysis, we prepare a comprehensive dataset spanning three years (2021-23) of precipitation episodes derived from minute-by-minute raw data recorded by the disdrometer network.  The event raw data undergoes proper filtering and processing before being aggregated into precipitation episodes. These episodes are subsequently grouped based on shared characteristics or factors that facilitate analysis, such as predominant precipitation type, total precipitation amount, maximum intensity or season, among other categories. The ultimate goal is to identify patterns and common characteristics.
Model Evaluation and Future Projections of Regional Winds in the Iberian Peninsula: Cierzo, Levante and Poniente
Authors: Ortega María , Gutiérrez Claudia , López-Franca Noelia , Molina María Ofelia , Cabos William , Sein Dmitry , Sánchez Enrique
Published: July 2024

Regional winds are caused by small-scale pressure differences in a way that important air flows can arise in a very small and specific region. Sometimes an orographic feature, such as a channel like the Ebro Valley or the Strait of Gibraltar, lead the wind, due to mass conservation, to acquire a certain specific range of directions and considerable speed. For some regional winds in the Iberian Peninsula, such as Cierzo, Levante and Poniente, there are quantitative works on their properties through high-resolution reanalyses, but their possible changes under the influence of climate change have not been studied with models. This work proposes to investigate the capacity of several climate models, validated against reanalysis, to study Cierzo, Levante and Poniente main characteristics in the common period 1995-2011. To this end, regional wind algorithms to detect the flows have been proposed. Three models (REMO, MPIOM-REMO and CNRM-RCSM4) have been selected based on evaluation results. Then, a study of Cierzo, Levante and Poniente future changes under RCP 8.5 emissions scenario (2006-2099) has been carried out. This has been compared with its historical period (1950-2005). Results suggest that spatial resolution is key to detecting these winds, especially in inland flows such as Cierzo, but that the internal physics of each model are also a source of variation beyond 10-km spatial resolution. Low temporal resolutions introduce errors in regional wind days calculation, while coupling effects will depend on each flow. In general, all models are capable of simulating historical Cierzo (100-130 days), Levante and Poniente (150-160 days) frequencies similar to observations. Trend study suggests that Cierzo extension could decrease by 1.5% in a statistically significant manner by the end of the century. Results also show a strong increase of 10-20 annual Levante events depending on the model. Levante extension will vary significantly, although the models do not agree on trend sign. Poniente shows a weakening of its characteristics for all models. Specifically, a decrease in the number of annual Poniente events of 5-20 days is detected.
On the response of extreme Tropical Cyclones to Anomalous Upper Ocean Thermal Structure 
Authors: Wellmeyer Evan , Ricchi Antonio , Ferretti Rossella
Published: July 2024

This study investigates the Rapid Intensification (RI) and Maximum Intensity (MI) of Hurricanes Wilma and Rita (2005), focusing on the impact of Sea Surface Temperature (SST), SST anomalies (SSTA), Ocean Heat Content (OHC), and the Ocean Mixed Layer Depth (OMLD) on tropical cyclone intensity and trajectory. Utilizing numerical model simulations, the research aims to quantify the ocean's influence on tropical cyclone intensity, particularly under different OHC scenarios. Additionally, the study examines how these variations influence the trajectory of tropical cyclones (TCs). By exploring these relationships, this research contributes to a better understanding of the complex interplay between oceanic conditions and tropical cyclone behavior. Numerical simulations are performed using the Weather Research and Forecasting (WRF) model coupled with a simplified 1D Ocean Model. Simulations performed include a control with initialization consistent with observations, removing the SSTA, modulating the OMLD (doubling and halving), and modulating SST initialization fields at –3, –2, –1, +1, +2, +3 Celsius. Simulations successfully reproduce the RI phase and intensity trajectory of Hurricane Wilma and Rita, with the SST variations significantly impacting both intensity and track. Preliminary results (under ideal atmospheric conditions) indicate higher OHC scenarios result in a lower northerly/easterly component of the TCs displacement. The SSTA produces an average 27% lower Central Sea Level Pressure (CSLP), a 30% difference in minimum CSLP, and a mean difference in maximum wind speed of approximately 6%. Additionally, SSTA enhances the deepening rate during the RI phase by about 47%, increases the total surface heat flux by approximately 19%, and produces a 10% increase in accumulated grid scale precipitation. 
Analysis of Turbulence Kinetic Energy Dynamics in Complex Terrain
Authors: Rohanizadegan Mina , Petrone Richard , Pomeroy John , Kosovic Branko
Published: July 2024

Boundary layer processes and turbulence in a complex terrain are influenced by thermally driven flows, as well as dynamical or non-local winds. We investigate the variability in turbulence kinetic energy (TKE) with elevation, and topography in a shallow high mountain valley in the Canadian Rockies. The Fortress Mountain Research Basin in the Kananaskis Valley, Alberta, was chosen for this study. Data from three high-frequency eddy-covariance systems at a north-west-facing slope location, and at two ridgetops at the south and north valley side walls were used for the analysis, and combined with large-eddy simulations (LES). The observed data and simulations focused on a sunny summer day when turbulence was well-developed, and cross-ridge flows interacted with thermally driven circulations. The observed TKE time series compared reasonably well with simulations at the north-west-facing slope and southern ridgetop. The model was then used to evaluate the vertical and horizontal TKE budget equation. Analysis of the TKE budget showed that horizontal shear driven by interactions of cross-ridge flows with the up-valley flow could be an important source of TKE production on the north-west-facing slope station in the Fortress Valley. At the northern ridgetop, both model and observations showed no contributions from the vertical production terms, while model showed a significant contribution from the horizontal shear production to TKE at this location. The correlation between the TKE at the valley station and the wind speed at a different location above the valley suggests that both horizontal and vertical exchange processes are an important part of TKE production mechanisms in this high mountain valley.
Climate change adaptation through citizen participation: Simulation of the effect of willingness to act on the heat mitigation potential in urban neighborhoods with different social milieu composition 
Authors: Eingrüber Nils , Schneider Karl , Nehren Udo , Dlugoß Verena
Published: July 2024

The urban population is particularly affected by the consequences of climate change. The increasing frequency and intensity of extreme weather events such as heat, drought, extreme precipitation and flooding has significant negative effects on human comfort, well-being, health and mortality rate. Sustainable urban development therefore requires the implementation of climate change adaptation measures as well as the acceptance, activation and participation of urban dwellers. The feasibility and effectiveness of climate change adaptation measures vary spatially due to the given local conditions. Additionally, the awareness, ability and willingness to act and pay differ between social milieus. The aim of this study is to analyse how a difference in the milieu-related willingness to act and operational ability to implement climate change adaptation measures affects the heat mitigation potential in two neighbourhoods in the city of Cologne/Germany with a significantly different social milieu composition. To investigate the relationship between the degree of willingness to act and the cooling effects of technical and nature-based solutions for heat mitigation, scenario analyses are performed using the physically-based, 3D gridded urban microclimate model ENVI-met for a neighbourhood in Cologne Suedstadt which is dominated by the social milieus of performers, post-materialists, neo-ecologists and conservatives. The model was parameterized based on field measurements and remote sensing data, and has been validated by a setup quality-controlled, densely-distributed microclimate sensor network in the study area. The agent-based scenarios represent a different percentage of residents willing to implement climate change adaptation measures in their living environment. These measures include facade greenings, roof greenings, cooling building materials and light surfaces. To identify the microclimate sensitivity, a scenario with a willingness to act of 0%, 25%, 50%, 75% and 100% of all dwellers in the neighbourhood is designed in the model domain. Simulation results show that the willingness to act has a significant influence on the cooling effect and thus the heat mitigation potential in this study area. In future research, the willingness to act in this upper-middle class neighborhood will be compared to a neighbourhood in Cologne Muelheim which has a significantly different social structure and is dominated by consumer-hedonism and traditional milieus. It will also be investigated how citizen science approaches, participation and activation measures can change the willingness to act and the acceptance of climate change adaptation measures in these contrary urban neighborhoods. 
A hybrid statistical-dynamical approach for seasonal prediction of the boreal winter stratosphere
Authors: Gargiulo Federico , Ruggieri Paolo , Famooss Paolini Luca , Di Sabatino Silvana
Published: July 2024

The subseasonal-to-seasonal (S2S) variability of the Northern Hemisphere Stratospheric Polar Vortex (SPV) is primarily influenced by the vertical propagation of Rossby waves. These waves can trigger intense events known as Sudden Stratospheric Warmings (SSWs), characterized by warming and reversal of zonal winds in the SPV area.  SSW effects extend beyond the stratosphere, impacting the troposphere for several weeks. This study investigates the predictability of the boreal winter stratosphere, focusing particularly on the relationship between lower-stratosphere wave activity (LSWA) – represented by meridional eddy heat fluxes at 100-hPa – which serves as an indicator of vertical propagation of Rossby waves, and the intensity of the wintertime SPV. Specifically, we hypothesize that improving the predictability of the North Atlantic Oscillation (NAO), a dominant climate mode influencing winter circulation in the Northern Hemisphere, will enhance prediction accuracy in the SPV region. A poor representation of model variability in the North Atlantic area, a known problem referred to as signal-to-noise paradox, limits the capacity of ensemble-based seasonal prediction systems (SPSs) in forecasting the NAO. To partially overcome this issue, we adopt a subsampling approach, already used in recent studies, with the idea of applying statistical methods to a dynamical prediction reducing the ensemble size of a SPS. This is performed with the use of four variables studied during the autumn season strongly correlated with the wintertime NAO, referred as predictors. The results demonstrate one more time the effectiveness of this approach in increasing the predictability of NAO index and its variability within SPSs. Furthermore, the analysis evidences an enhanced ability of the models in predicting the intensity of zonal-mean zonal winds in the SPV region with correlation prediction skill for wintertime SPV going from 0.39 to 0.64 for a 3 model multi-ensemble. We find that the hybrid statistical-dynamical approach improves the ability of SPSs in predicting the number of SSW days, defined as days during a winter season presenting an inversion of zonal-mean zonal winds in the SPV region, according to SSW definition. Additionally, our results suggest that these improvements are partly explained by an increased ability of SPSs in reproducing the vertical propagation of Rossby waves during December and January (DJ), especially over two critical regions across Eurasia. This result highlights the importance of the DJ LSWA for the seasonal prediction of the SPV. 
Gridded datasets of climate indices: comparison of two approaches
Authors: Tilg Anna-Maria , Habibi Maral , Chimani Barbara , Greilinger Marion
Published: July 2024

Gridded data is highly appreciated for many climate related applications due to their spatial information compared to single station locations. The quality and additional value of a gridded dataset is depending on the method itself and on the amount of additional information, e.g. topographical data, used for the interpolation. Furthermore, there is an ongoing discussion on the implications of using gridded datasets of primary climate parameters like temperature or precipitation to derive gridded datasets of climate indices. Within the project SDGHUB (https://www.sdghub.at/), we are exploring the influence of different approaches on the final gridded climate index dataset. The focus is on the frequently used climate index of hot days (days with a maximum air temperature above or equal to 30 °C). In the first approach the hot days were computed from the gridded climate dataset SPARTACUS (Hiebl and Frei, 2016), while in the second approach the hot days were directly interpolated considering station values. SPARTACUS is a national gridded dataset of Austria, available on a daily basis with a 1 km-spatial resolution via the DataHub of GeoSphere Austria (https://data.hub.geosphere.at/). It covers the period from 1961 onwards and includes the parameters of maximum, minimum and mean air temperature and rain amount.  For the second approach the efficacy of several geostatistical interpolation methods, including Kriging with External Drift (KED), Ordinary Kriging (OK), and Regression Kriging (RK), with a particular emphasis on their ability to represent the spatial variability of hot days was explored. As KED allows to include external variables to improve the interpolation results, we decided to proceed with that method. Furthermore, we tested different data transformation methods and variogram models to improve further the results.  As for SPARTACUS, the direct interpolation of hot days was done on a 1 km-scale for Austria.To evaluate the performance of the two respective approaches a comparison with independent station data was done.The presentation will include information about the two approaches, details about the direct interpolation of hot days and provide insights into the evaluation and differences of the two datasets. ReferencesHiebl J, Frei C (2016) Daily temperature grids for Austria since 1961 – concept, creation and applicability. Theor Appl Climatol 124:161–178. https://doi.org/10.1007/s00704-015-1411-4AcknowledgementThe project SDGHUB is funded by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility and Technology (BMK) via the ICT of the Future Program - FFG No 892212.
A systematic approach for flood-impact data retrieval in urban floods modelling
Authors: Lombardo Margherita , Totaro Vincenzo , Chiaravalloti Francesco , Petrucci Olga
Published: July 2024

Floods are one of the main natural disasters that affect environment and ecosystems, and an increasing number of evidences highlights how climate change and urbanization are contributing to exacerbate the risk related to their occurrences. A parallel and coupled investigation of meteo-hydrological causes and of impacts on societies is therefore of primary importance for allowing a reliable analysis of flood events and improving current emergency management techniques. Within this framework, flood modelling assumes a key role in developing an accurate knowledge of rain-related impacts, especially in the urban fabric. Recent advances in flood modelling have enabled hyper-resolution street-level studies, offering significant potential for flood reconstruction, nowcasting, and forecasting. However, the calibration of flood models faces challenges due, among others, to limited availability of flood-related measurement, particularly in ungauged basins or during extreme events. While low-complexity and low-resolution inundation models may suffice to be calibrated on estimated flow discharges, space-time variability of rainfall patterns and hydrodynamic information, namely flood depths and flow velocities, become crucial for accurate simulations in urban areas. To address this issue, there is a growing need for accurate information, prompting research into alternative documentary sources concerning the impact of past floods and solutions for dataset merging. Moving from a consolidated catalogue of historical damaging hydrogeological events (ASICal), this work explores the potential of a composite meteorological and impact-based data collection for pilot case studies in Calabria region (southern Italy). Information gathered from different data sources and characterized by different levels of detail, from pixel (radar/satellite) scale up to the street level, have been systematically retrieved and organized. In our study we review and highlight (i) the abilities of weather radar in characterizing rainfall dynamics in time and space, and (ii) how photos and video posted on social media represent valuable sources for quantitative information offering insights up to the house number level, despite the limitation of the need for a visual inspection and interpretation of their contents. Results are supportive of the opportunities arising from the proposed approach in the collection of impact data from multiple sources and different levels of detail, which represents a key task for the improvement of the emergent flood related studies based on high-resolution hydrodynamics and hydrological approaches to rainfall classification and modelling.
Docker container in DWD's Seamless INtegrated FOrecastiNg sYstem (SINFONY)
Authors: Zacharuk Matthias , Ulbrich Sven , Josipovic Lukas , Welzbacher Christian , Blahak Ulrich
Published: July 2024

At Deutscher Wetterdienst (DWD) the SINFONY project has been set up to develop a seamless ensemble prediction system for convective-scale forecasting with forecast ranges of up to 12 hours. It combines Nowcasting (NWC) techniques with numerical weather prediction (NWP) in a seamless way for a number of applications. Historically the operational NWC and NWP forecasts are generated on separate IT-Infrastructures. To reduce data transfer between both infrastructures and to reduce the complexity of SINFONY those NWC components, which solely rely on NWP pre-products, are ported to the NWP infrastructure using software container.With this aim in view a container image containing all relevant NWC components is created in a CICD oriented procedure. The respective containers are integrated into DWD’s development and operational code bases and executed on DWD’s HPC using apptainer. The integration into DWD’s development code base is completed already and currently used for further development of the data assimilation procedure.A major innovation of SINFONY is the rapid update cycle (RUC), an hourly refreshing NWP procedure with a forecast range of 12 hours. Currently RUC is in a preoperational stage and subsequent calculations using NWC components together with the subsequent combination of RUC forecasts with NWC forecasts is still executed on the NWC infrastructure. The RUC will be implemented to the operational forecasting system at the end of 2024 and together with that our aim is to implement the containers to the RUC for two applications. At this step the containers have to meet even harder performance requirements and above that a reliable update and support workflow must be established, since it will then be part of Germanys national critical infrastructure.
European-wide climate indicators for 1961-2020 derived from daily data at 30x30 arc sec resolution
Authors: Lehner Fabian , Klisho Tatiana , Maier Philipp , Formayer Herbert
Published: July 2024

High-resolution climate data provide great benefits for ecological and environmental climate impact studies where data need to represent local climate conditions. Especially in mountainous terrain, high resolution can accurately resolve mountain valleys and peaks and thus better represent topography-related aspects of meteorological variables. CHELSA-W5E5 v1.1 fulfills the need for daily data at a high spatial resolution of 30x30 arcseconds, but only covers the period 1979-2016 and does not provide certain daily variables necessary for more complex estimations of the water budget with potential evapotranspiration. Given that the current climate is usually referred to 1991-2020, a period already significantly influenced by anthropogenic climate change, the previously established standard period of 1961-1990 is still recommended for observing long-term climate development. This is particularly relevant in forestry where the main growth phases of current trees date back several decades. To extend the temporal coverage of CHELSA-W5E5 v1.1 from the initial span of 38 years (1979-2016) to 60 years (1961-2020) across Europe, we used ERA5-Land data set and applied quantile mapping with CHELSA on each grid cell. Variables such as wind speed and vapor pressure deficit are only provided on a climatological monthly basis from the similar data set CHELSA V2.1 and had to be newly generated for daily values. The result of this procedure is a 60-year long representation of Europe’s historical climate useful for applications in ecological and environmental sciences.A strength of this data set is its temporal consistency as it is based on ERA5-Land, and high spatial resolution as it is based on CHELSA. Averaged climate indicators for the periods 1961-1990 and 1991-2020 are available for download (DOI 10.5281/zenodo.10623854). Validation shows that CHELSA lacks accurate temperature values in some mountainous locations, as shown with weather station data in the Alps. Thus, it is desirable for future versions of CHELSA to improve the accuracy of the vertical lapse rate in mountainous terrain for a more precise representation of valleys. 