bluuebunny/crossref_metadata_2025 huggingface dataset:
Risicobeheersing bij strategievorming: een exploratief onderzoek
Authors: Emanuels Jim , Brockhoff Gerard
Published: July 2023

Een onvoldoende rationeel strategisch besluitvormingsproces kan leiden tot onlogische strategische keuzes. Risicobeheersings­kaders als het COSO-raamwerk geven geen expliciet inzicht in de risico’s bij strategievorming. Op basis van de literatuur werken wij 2 typen risico’s uit: (1) het gebruiken van beperkte of onjuiste informatie en (2) het onvoldoende zorgvuldig wegen van strategische opties. Deze risico’s valideren wij in een exploratief onderzoek onder bestuurders. Wij concluderen dat de door hen genoemde risico’s gedeeltelijk corresponderen met de risico’s uit de literatuur. De bestuurders zien echter ook risico’s die samenhangen met onvoldoende pragmatisme in strategievorming en signaleren niet expliciet de in de literatuur naar voren gebrachte risico’s van een suboptimale groepsdynamica en dominant strategisch discours.
An Analysis of English Student Needs in the Development of a Hybrid Learning Teaching Material Model Based on Religious Moderation Values
Authors: Anggaira Aria Septi , Sari Yeasy Agustina
Published: July 2023

Technology plays a very important role in today's learning process. Online learning is not an option anymore but an obligation that must be carried out, especially during the Covid-19 Pandemic. Hybrid learning is one of the online learning that is widely used at all levels of education. This research is the initial research from development research that will develop a hybrid learning model based on religious moderation values in the teaching media course. The respondents in this study were 89 students majoring in English education at Institut Agama Islam Negeri (IAIN) Metro Lampung. The data was obtained through the results of the questionnaire. The result showed that the students needed a hybrid learning teaching material model based on religious moderation values in the teaching media course. It was evidenced by the results of the questionnaire, namely as many as 80% of students state that they need a hybrid learning teaching material model based on religious moderation values in the eyes of teaching media lectures.
Keywords: Development of Hybrid Learning, Religious Moderation Values, Teaching Material
The Influence of Social-Emotional Development on Early Childhood Learning Outcomes During the Covid-19 Pandemic
Authors: (Imam Ghozali) Tukadi , Jannah Siti Roudhotul , Sugiarto Sugiarto
Published: July 2023

This article aimed to analyze the effect of social-emotional development on children's learning outcomes from 2019 until 2022. We shocked by the covid 19 virus, which requires parents to be more emotional to children and impact on children's social-emotional behavior. The research was conducted to determine the effects of social development on children's social-emotional behavior. The research used qualitative with case study approach through interviews with thematic analysis on twenty mothers who have children attending kindergarten in the East Lampung district. Using structural interview, the mothers were asked questions about the impact of children's social-emotional behavior through interviews. The results analysis shoed that that the children's socio-emotional behavior was less cooperative because children rarely play together, lack tolerance, lack socializing with friends was limited by studying at home, children's emotions sometimes feel bored and sad, children feel homesick for friends and teachers and children are also recorded as experiencing verbal violence because of the overall learning process.

Keywords: Social-Emotional Development, Learning Outcomes, childhood Education
Trying Perpetrators: Denazification Trials and Support for Democracy in West Germany
Authors: Capoccia Giovanni , Pop-Eleches Grigore
Published: July 2023

We study the effects of transitional justice (TJ) programs that punish large numbers of human rights violators through the lenses of social psychology theories on how individuals respond to punishment in allocative situations, including how defendants in court trials evaluate their
verdicts. We analyze subnational variation in procedures and outcomes of denazification trials in West Germany during 1946-1947. Consistently with established findings in social psychology, we find that procedural justice and the distributive fairness of outcomes can compensate for the anti-democratic
attitudinal effects of being a defendant in a TJ trial. We also find evidence that procedural justice influences the democratic attitudes of family members of TJ defendants. The study has implications for contemporary cases of TJ programs that affect large numbers of perpetrators.
Pengabdian Kepada Masyarakat Mahasiswa KKM STAI Pelabuhanratu Melalui Kegiatan Pendidikan Mengajar PAUD, RA, MTs Dan MA Di Desa Bantargebang Bantargadung Kabupaten Sukabumi
Authors: Hidayat Rifki , Sanria Peri , Ghifari Azril Fiqru
Published: July 2023

Pelaksanaan kegiatan kuliah kerja mahasiswa di STAI Pelabuhanratu dilaksanakan selama 3 pekan pada tanggal 02-22 agustus 2022 di Desa Bantargebang Kecamatan Bantargadung Kabupaten Sukabumi. Salah satu program pada kegiatan tersebut adalah pengabdian dalam bentuk pendidikan yaitu mengajar di PAUD, RA, MTs dan MA. Program mengajar ini sesuai dengan tujuan akhir dari perkuliahan di perguruan tinggi itu sendiri, ditambah lagi karena adanya beberapa kendala yang lebih menguatkan untuk melaksanakan program ini. Dan ini juga akan menjadi penting dimana dalam proses pengajaran ini akan memberikan suasana baru kepada peserta didik belum lagi mengenai metode dan strategi dalam mengajar yang akan lebih beragam. Oleh karena itu, dengan hal-hal tersebut akan dilaksanakan program pendidikan dalam bentuk mengajar. Tujuan dari program ini untuk membantu dan meringankan kendala yang dihadapi satuan pendidikan, dan juga untuk memberikan metode dan strategi yang beragam dalam mengajar. Karena salah satu peranan terpenting dalam suatu satuan pendidikan itu adalah pengajar atau guru, dan untuk mencapai tujuan dari pada pembelajaran itu dapat dilakukan dengan pemilihan metode dan strategi ketika mengajar. Adapun hasil dari kegiatan ini antara lain: 1) kegiatan belajar mengajar di setiap satuan pendidikan berjalan lancar karena adanya guru yang mengajar dari mahasiswa, 2) peserta didik mendapatkan suasana baru dalam belajar sehingga lebih menumbuhkan semangat belajar, dan 3) dengan menerapkan berbagai metode dan strategi dalam mengajar, peserta didik lebih cepat dan dapat memahami pelajaran serta aktif dalam proses kegiatan belajar mengajar.
PENCIPTAAN MANUSIA BERDASARKAN KEJADIAN 1:26-28 SEBAGAI EVALUASI TERHADAP PERILAKU TRANSGENDER DALAM PERSEPSI UMAT KRISTEN
Authors: Sondopen Dorce , Gloria Grace Anggraini , Gultom Horas Fransiskus
Published: July 2023

Abstract: This article describes the study of human creation as an evaluation of transgender behavior in the perception of Christians. So much exciting and controversial news has appeared on social media and is circulating among churches and the community. This news is being debated especially among Christians, giving rise to pro and contra thoughts on the existence of transgender people in general. Where transgender behavior is indeed a form of deviation from social norms. This study uses a qualitative descriptive method by explaining the terms used in the title of the article, where literature and journals are the main objects in obtaining information as descriptive data in this study. The purpose of this article is to answer some of the following questions: What is meant by the creation of humans based on Genesis 1:26-28? How is transgender behavior seen from the perception of Christians? How is the creation of humans based on Genesis 1:26-28 as an evaluation of transgender behavior from the perception of Christians? Starting from the theoretical reference, the important points of the research results in this study are: (1) The creation of humans based on the context of events 1:26-28 aims to relate to other creations and also to fellow humans where God wants humans to multiply and multiply to fulfill earth. (2) Transgender behavior in terms of the perception of Christians is behavior that is contrary to the principles and teachings of the Bible, which arises as a result of sin and must be prohibited because it deviates from social norms (3) According to the context of Genesis 1:26-28, humans are created according to image and likeness of God. The Bible teaches that every human being must be grateful for every gender that God has given him by not trying to change or carry out sexual deviations that contaminate the body such as transgender. The human body is God's temple which must be cared for and cared for to glorify God. Transgender perpetrators, on the one hand, are sinful according to the Bible, but on the other hand, they still have to be loved and served with God's love so that they are aware, repent and return to the right path, with normal behavior and glorify God.
A GÊNESE DO ESTADO E SEU CONCEITO EM NIETZSCHE
Authors: Silva Luciano Olavo da
Published: July 2023

Muitos filósofos dedicaram seus melhores esforços para explicar a gênese do Estado. Entre eles está Nietzsche, cujo pensamento político é altamente polêmico e disruptivo. O presente artigo assume a tarefa de demonstrar que o pensamento nietzschiano acerca da gênese do Estado é original e não se enquadra em nenhuma das escolas tradicionalmente estudadas acerca do assunto (teoria da força, teoria da origem natural e teoria contratualista), o que gera profundas implicações teóricas em relação à sustentabilidade e possibilidades das relações humanas (sociais e políticas) que se desenvolvem em seu território, assim como em relação ao próprio conceito de Estado.
A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations
Authors: Aboelyazeed Doaa , Xu Chonggang , Hoffman Forrest M. , Liu Jiangtao , Jones Alex W. , Rackauckas Chris , Lawson Kathryn , Shen Chaopeng
Published: July 2023

Abstract. Photosynthesis plays an important role in carbon,
nitrogen, and water cycles. Ecosystem models for photosynthesis are
characterized by many parameters that are obtained from limited in situ
measurements and applied to the same plant types. Previous site-by-site
calibration approaches could not leverage big data and faced issues like
overfitting or parameter non-uniqueness. Here we developed an end-to-end
programmatically differentiable (meaning gradients of outputs to variables
used in the model can be obtained efficiently and accurately) version of the
photosynthesis process representation within the Functionally Assembled
Terrestrial Ecosystem Simulator (FATES) model. As a genre of
physics-informed machine learning (ML), differentiable models couple
physics-based formulations to neural networks (NNs) that learn parameterizations
(and potentially processes) from observations, here photosynthesis rates. We
first demonstrated that the framework was able to correctly recover multiple assumed
parameter values concurrently using synthetic training data. Then, using a
real-world dataset consisting of many different plant functional types (PFTs), we
learned parameters that performed substantially better and greatly reduced
biases compared to literature values. Further, the framework allowed us to
gain insights at a large scale. Our results showed that the carboxylation
rate at 25 ∘C (Vc,max25) was more impactful than a factor
representing water limitation, although tuning both was helpful in
addressing biases with the default values. This framework could potentially
enable substantial improvement in our capability to learn parameters and
reduce biases for ecosystem modeling at large scales.
Reconstructing land temperature changes of the past 2,500 years using speleothems from Pyrenean caves (NE Spain)
Authors: Bartolomé Miguel , Moreno Ana , Sancho Carlos , Cacho Isabel , Stoll Heather , Haghipour Negar , Belmonte Ánchel , Spötl Christoph , Hellstrom John , Edwards R. Lawrence , Cheng Hai
Published: July 2023

Abstract. Reconstructing of past temperatures at regional scales during the Common Era is necessary to place the current warming in the context of natural climate variability. Here we present a composite record of oxygen isotope variations during last 2500 years based on eight stalagmites from four caves in the central Pyrenees (NE Spain) dominated by temperature variations, with precipitation playing a minor role. The dataset is compared with other Iberian reconstructions that show a high degree of internal coherence with respect to variability at the centennial scale. The Roman Period (especially 0–200 AD), the Medieval Climate Anomaly, and part of the Little Ice Age represent the warmest periods, while the coldest decades occurred during the Dark Ages and most of the Little Ice Age intervals (e.g., 520–550 AD and 1800–1850 AD). Importantly, the LIA cooling or the MCA warming were not continuous or uniform and exhibited high decadal variability. The Industrial Era shows an overall warming trend although with marked cycles and partial stabilization during the last two decades (1990–2010). The strong coherence between the speleothem data, European temperature reconstructions and global tree-ring data informs about the regional representativeness of this new record as Pyrenean past temperature variations. Solar variability and major volcanic eruptions appear to be the two main drivers of climate in southwestern Europe during the past 2.5 millennia.
An improved dynamic bidirectional coupled hydrologic-hydrodynamic model for efficient flood inundation prediction
Authors: Shen Yanxia , Zhu Zhenduo , Zhou Qi , Jiang Chunbo
Published: July 2023

Abstract. To improve computational efficiency while maintaining numerical accuracy, coupled hydrologic-hydrodynamic models based on non-uniform grids are used for flood inundation prediction. In those models, a hydrodynamic model using a fine grid can be applied for flood-prone areas, and a hydrologic model using a coarse grid can be used for the rest of the areas. However, it is challenging to deal with the separation and interface between the two types of areas because the boundaries of the flood-prone areas are time-dependent. We present an improved Multigrid Dynamical Bidirectional Coupled hydrologic-hydrodynamic Model (IM-DBCM) with two major improvements: 1) automated non-uniform mesh generation based on the D∞ algorithm was implemented to identify the flood-prone areas where high-resolution inundation conditions are needed; 2) ghost cells and bilinear interpolation were implemented to improve numerical accuracy in interpolating variables between the coarse and fine grids. A hydrologic model, two-dimensional (2D) nonlinear reservoir (NLR) model was bidirectionally coupled with a 2D hydrodynamic model that solves the shallow water equations. Three cases were considered to demonstrate the effectiveness of the improvements. In all cases, the mesh generation algorithm was shown to efficiently and successfully generate high-resolution grids only in those flood-prone areas. Compared with the original M-DBCM (OM-DBCM), the new model had lower RMSEs and higher NSEs, indicating that the proposed mesh generation and interpolation were reliable and stable. It can be adapted adequately to the real-life real flood evolution process in watersheds and provide practical and reliable solutions for rapid flood prediction.
The contribution of residential wood combustion to the PM2.5 concentrations in the Helsinki Metropolitan Area
Authors: Kangas Leena , Kukkonen Jaakko , Kauhaniemi Mari , Riikonen Kari , Sofiev Mikhail , Kousa Anu , Niemi Jarkko V. , Karppinen Ari
Published: July 2023

Abstract. This article has investigated the contribution of residential wood combustion (RWC) to the fine particulate matter (PM2.5) concentrations in the Helsinki Metropolitan Area (HMA) for six years, from 2009 to 2014. We have used the PM2.5 concentrations measured at eight air quality measurement stations. The dispersion of emissions on an urban scale was evaluated with multiple source Gaussian dispersion models UDM-FMI and CAR-FMI, and on a regional scale using the chemical transport model SILAM. The overall agreement of the predicted concentrations with measurements of PM2.5 was good or fairly good for all stations and years, e.g., at the permanent residential station the daily average values of the index of agreement ranged from 0.69 to 0.81, and the fractional bias values ranged from −0.08 to 0.11, for the considered six years. Both the measured and predicted daily averaged concentrations showed increasing trends towards the lower temperature values. The highest predicted annual averaged concentrations in the region occurred in the vicinity of major roads and streets, and the suburban residential areas, to the northwest, north and northeast of the city centre. The average concentrations attributed to RWC in winter were up to 10- or 15-fold, compared to the corresponding concentrations in summer. During the considered six-yearly period, the spatially highest predicted fractions of RWC of the annual PM2.5 concentrations ranged from 12 to 14 %. In winter, the corresponding contributions ranged from 16 to 21 %. The RWC contribution was higher than the corresponding urban vehicular traffic contribution at all the residential stations during all years. The study has highlighted new research needs for the future, in particular (i) the modelling of the RWC emissions that would be explicitly based on the actual ambient temperatures, and (ii) the modelling of the impacts of the most important holiday periods on the emissions from RWC.
Merging two city-level weather-sensor networks to analyse urban heat island conditions in Tallinn, Estonia
Authors: Karolin Kaarel , Hoy Andreas , Dorling Stephen
Published: July 2023

Urban areas are characterised by a (compared to rural surroundings) elevated temperature level, which is specifically problematic during heat waves. Climate change is increasingly exacerbating the problem. We investigate urban heat island conditions (UHI) for the coastal city of Tallinn, located at the southern shore of the Gulf of Finland in the north-eastern part of the Baltic Sea region. For that, we merge observations of two city-level weather sensor networks, available from June 2022 in hourly resolution. One network was designed to investigate the value of green (compared to grey) spaces from a climate perspective (18 sensors), while the other one consists of weather sensors alongside the road network of Tallinn (>50 sensors). Both networks are supported by quality-controlled data provided by the national weather service of Estonia.
Major attention will be put to quality-checking and comparisons of observations generated by both networks, paving the way for analysing the data from an urban climate perspective – considering local climate zones and land-use factors as well as the influence of the sea. We will be focussing on heat conditions during the 2022 summer, with additional analyses planned for winter 2022/23.
The results of this study will help city planners optimising climate adaptation in Tallinn and support the communication of weather- and climate-related risks – especially linked to heat conditions – to the citizens of the city. Being the Green Capital of Europe during the year of 2023, our investigations will provide unique data for decision-makers related to green city planning and UHI reduction in Tallinn.
Trends in precipitation variability over Europe on different datasets
Authors: Beranová Romana , Huth Radan , Plavcová Eva
Published: July 2023

Different types of meteorological data (station, gridded, reanalysed) are known to have different statistical characteristics for higher order moments, extremes and trends. While the analysis of long-term changes in mean values and extremes is often studied, changes in precipitation variability are rather on the fringes of attention. We use data from stations across Europe (ECA&D project), gridded data (E-OBS, Regen), and several reanalyses (NCEP/NCAR, 20CR, ERA5). We examine the long-term changes in precipitation characteristics in different data sources over Europe. We calculate differences between the datasets and attempt to identify causes of these differences and the specific behavior of the datasets. In addition to the analysis of the trend of the seasonal totals and the probability of precipitation, we focus also on the trends of a day-to-day variability of precipitation. We consider wet-to-wet and dry-to-dry transition probabilities as a measure of short-term precipitation variability. Long-term trends of seasonal values of precipitation variables and their statistical significance are calculated by non-parametric methods (Mann-Kendall test, Kendall statistic). The analysis is conducted on a seasonal basis, with emphasis on winter and summer. We find that each of the datasets has its advantages and drawbacks. Trends in the reanalyses deviate considerably from the observed datasets. This is mainly because of the changes in the type and amount of assimilated data over time. The weakness of the gridded datasets is the unstable number of stations entering the interpolation in time. The main disadvantage of the station data is the lack of representativeness of some climate stations.
Analysis and evolution of the meteorological terminology from the update of the “essay on a Catalan meteorological vocabulary (1948)”
Authors: Mazon Jordi , Pino David , Hernández Antonio , Costa Marcel
Published: July 2023

Glossaries, meteorological terminologies and dictionaries play an important role in disseminating scientific knowledge of the atmospheric sciences. Currently hundreds of glossaries and meteorological terminologies can be found in many languages that cover many regions and countries. However, few of them were published before 1950 (Miloslav et al., 2022). One of the oldest publications is the Catalan meteorological terminology (Fontserè, 1948), wrote in Catalan language, an unofficial language those years, to include Catalan as a scientific language. Despite it was written in 1941, was not published until 1948 due to political reasons.
Based on the original work done by Fontserè (1948), an update of the meteorological terminology in Catalan has been done including new terms and language expressions that have appeared in the period from 1948 to 2023, after 75 years after the original publication. Some of these new terms were defined by the authors previously, such us the anthropocloud (https://en.wikipedia.org/wiki/Anthropogenic_cloud ; Mazon et al., 2012), the flash heat (https://glossary.ametsoc.org/wiki/Flash_heat, Mazon et al., 2014), or the meteodiversity (Mazon and Pino, 2017), among others.
An analysis about the number and the topics of the new terminology included in the updated version has been performed to evaluate the temporal evolution of the meteorological and climatology terminology and the knowledge of the atmospheric sciences since 1948. For instance, lot of terms on climate change and numerical simulation included in the new version cannot be found in the original work. An update of such old terminology is an opportunity to understand how meteorological language has changed, to disseminate the meteorological and climatological sciences, and also to quantify and understand the interest focus on these atmospheric sciences during the past decades.
 
References:
Fontserè E., 1948: Assaig d'un vocabulari meteorològic català. Institut d'Estudis Catalans. Barcelona.
Mazon J., Costa M., Pino D., Lorente J., 2012: Clouds caused by human activities. Weather, 67, 11, 302–306.
Mazon J., Pino D., and Barriendos M., 2014: Rapid and sudden advection of warm and dry air in the Mediterranean Basin. Nat. Hazards Earth Syst. Sci., 14, 235–246, doi:10.5194/nhess-14-235-2014.
Mazon J., Pino D. (2017) Meteodiversity: a new concept for quantifying meteorological diversity. Weather 72(5):143–145
Müller, M., Kocánová B., and Zacharov P., 2022: Meteorological Glossaries and Dictionaries: A Review of Their History and Current State. Bull. Amer. Meteor. Soc., 103, E157–E180, https://doi.org/10.1175/BAMS-D-20-0295.1.
Toward improvement of cloud representation in the Eurasian continental interior in CPS
Authors: Chiba Jotaro , Adachi Yukimasa , Kubo Yutaro , Komori Takuya , Hirahara Shoji
Published: July 2023

 In February 2022, the Japan Meteorological Agency started operating its third-generation seasonal ensemble prediction system (CPS3; Coupled Prediction System version 3; Hirahara et al. 2023). CPS3 is based on an atmosphere/ocean/land/sea-ice-coupled model; the atmosphere model has a resolution of TL319 (approx. 55 km) L100 (up to 0.01 hPa) and the ocean model has 60 layers with a global resolution of 0.25° x 0.25°. Along with the widely improved performance of each model, prediction skills of CPS3 are significantly better than those of the previous system. There is, however, still room for improvement, including a positive IOD-like SST bias in the Indian Ocean, a positive surface air temperature bias over the interior of Eurasia, a negative cloud cover bias in subtropical ocean stratocumulus regions, and an arctic radiation flux bias. Cumulus and cloud parameterization errors are some of the primary causes of these biases. Focusing on the Eurasian continental interior, predicted cloud cover is fewer than observed amount throughout the year, and diurnal and seasonal variations are insufficiently represented. To represent cloud condensation, CPS3 has a cloud parametrization using a diagnostic method based on Smith (1990), where spatial fluctuations in cloud water variables that cannot be resolved on a grid scale are represented by assuming a specific Probability Density Function (PDF). In CPS3, a symmetric top-hat PDF (JMA 2022) is used. Although detrain from cumulus is included in cloud water content, fluctuations in cloud water variables due to cumulus are not fully considered, which is one of the possible causes of the negative bias in cloud cover. To address this issue, representation of asymmetries of the fluctuation in cloud water variables due to cumulus in the PDF can be adapted. At this meeting, we will report on the above topics and the progress of development for the next CPS.
How to Choose Credible Ensemble Members of Temperature and Precipitation Long-Range Forecasts for Summer in Central Europe?
Authors: Kliegrova Stanislava , Belda Michal , Metelka Ladislav , Štěpánek Petr
Published: July 2023

Long-range forecasts provide information about expected future atmospheric and oceanic conditions averaged over periods of one to three months and are attractive for many sectors.
Dynamical forecasting employs full, three-dimensional models of the climate to explicitly simulate possible changes in the atmosphere and ocean over the next few months based on current conditions.  Ensembles of simulations of possible weather scenarios are run and provide probabilities of how likely it is that a season will be wetter, drier, warmer, or colder compared to the average for that period of the year.  Is it possible to choose the most credible (or at least more credible) members of ensembles?
This study considers four seasonal forecasting systems available in the Copernicus Climate Change Service (C3S) archive which provide near-surface air temperature and precipitation data at 1°by 1°spatial resolution: European Centre for Medium-range Weather Forecast System SEAS5 (ECMWF), Météo – France System 8 (MF), Deutscher Wetterdienst GCFS 2.1 (DWD) and Centro Euro-Mediterraneo sui Cambiamenti Climatici SPSv3.5 (CMCC). We focus on summer forecasts (the starting date is May 1st) in the period 1993–2016 (the longest period of hindcasts common to all systems) and the domain of the Czech Republic in Central Europe (latitude 47-52°N, longitude 11-20°E). E-OBS daily gridded observational datasets for precipitation and temperature at 0.25°spatial resolution are used as a reference.
Each model has several tens ensemble members of the long-range forecast. Based on correlations between modeled and observed air temperature a precipitation patterns in the first days, we try to find the most credible (or at least more credible) ensemble members for the domain of the Czech Republic for further processing.
Changes in potential evapotranspiration and atmospheric evaporative demand in Slovakia since 19
Authors: Labudová Lívia , Szabóová Kristína , Turňa Maroš
Published: July 2023

Our contribution is aimed on the investigation of changes in humidity characteristics at the most homogeneously considered stations in Slovakia. We used data on the Penman and the Budyko – Zubenokova´s potential evapotranspiration, Tomlain's irrigation indicator and the SPEI to investigate changes in the moisture characteristics. Tomlain's indicator is calculated as the annual sums of the difference between precipitation and potential evapotranspiration (PET) data. The PET for this indicator was estimated using to the Budyko-Zubenokova´s method. The SPEI is used in operational way for meteorological drought monitoring in Slovakia. The PET considered in the SPEI is estimated according to the Penman-Montheit method, which is also recommended by WMO. To obtain the regions with negative atmospheric evaporative demand, we used the relative SPEI using the coefficients of the theoretical distribution estimated for the spatial averaged data of the PET and precipitation. These coefficients were applied to calculate the SPEI using the station data. The stations were chosen to represent the northern, south-western and eastern parts of Slovakia, as they cover a very diverse orography and they have the longest series of observations. Three reference periods were compared, namely the 1961-1990, 1981-2010 and the most recent reference period, 1991-2020. The main goal of the work was to investigate how the potential evapotranspiration characteristics were changed and to what extent the country has a drying tendency. The comparison of the different reference periods has shown that the annual precipitation sums increased slightly, but their increase does not cover the increase of the PET resulting in the drying tendency. The highest negative change was observed in the south-western and south-eastern part of Slovakia hitting the lowland regions with intensive agricultural land use.
Benefits of precursor simulations with a drag-porosity model as inlet conditions for LES of atmospheric flow within urban canopy
Authors: Bucquet Quentin , Calmet Isabelle , Perret Laurent
Published: July 2023

The study of wind flow characteristics within urban environments is of prior interest for climate, meteorology and air pollution issues. Previous works have shown that within the urban areas the turbulent transfers of momentum, heat and mass between the Atmospheric Boundary Layer (ABL) and the urban canopy are governed by the interactions between large- and small-scale turbulent structures. Hence, to accurately compute flow characteristics at the neighbourhood scale, it is required to compute canopy-generated turbulence, as well as the turbulent structures prevailing in the ABL. This wide range of scales presents a major issue when performing unsteady simulations of turbulent flows such as Large-Eddy Simulations (LES), since the flow field specified at the inlet conditions the downstream development of the flow. In ABL numerical models, the most common method is to use a multi-scale approach based on successive grid-nesting from mesoscale to the local scale. When performing multi-scale nesting to study urban canopy flows, the urban obstacles are generally explicitly taken into account in the smallest high-resolution domain only, larger domains being not refined enough for a realistic representation of the canopy elements. However, it has been shown that adding roughness elements in the larger domain had a noticeable impact on the flow dynamics and statistics within the small domain (Wiersema et al., Mon. Wea. Rev., 2020).
This study addresses the problem of the generation of realistic unsteady atmospheric inflow conditions on an idealised urban-like canopy consisting of a staggered array of cubes of constant height and packing density. Here we investigate the performance of using drag-porosity based simulations as precursor calculations for obstacle-resolving LES. The objective is to show that by using a precursor simulation with a less-costly canopy modelling method as inlet, LES of ABL flows within and above a canopy of explicitly resolved obstacles is possible with minimum flow adjustment in the inlet region.
The LES atmospheric model ARPS (Advanced Regional Prediction System) is used with an Immersed Boundary Method (IBM) to model the flow over building-like obstacles. In order to assess the proposed approach, three inflow conditions are explored and compared : (1) a periodic precursor calculation over a large domain covered by cube array modelled with IBM, (2) a synthetic turbulence inflow obtained from purposely deteriorating flow information of the periodic case (without modifying moments and spectra), and (3) a drag-porosity precursor calculation (in which the urban canopy is modelled as a porous media depending on canopy averaged morphological characteristics). To the authors’ knowledge, the use of an urban-designed drag model as precursor calculation for an obstacle-resolving simulation is the first to be performed within a single atmospheric solver.
Identifying Efficient Ensemble Perturbations for Initializing Probabilistic Subseasonal‐To‐Seasonal Prediction
Authors: Demaeyer Jonathan
Published: July 2023

Nowadays, weather forecasts systems are probabilist, based on ensemble of model integrations starting from different initial conditions. How to define efficient sets of initial conditions is now a well settled problem for weather forecasts, but still an open question for longer forecast ranges. Here, a method to construct initial conditions which produce reliable ensemble forecasts at the particularly challenging subseasonal-to-seasonal forecast range is presented. These initial conditions are obtained by perturbing the analysis with random perturbations projected onto the Koopman and Perron-Frobenius operators’ eigenfunctions, which describe the time-evolution of observables and probability distributions of the system dynamics, respectively. In practice, the perturbations are projected on approximations of these eigenfunctions provided by the Dynamic Mode Decomposition data-driven algorithm, potentially allowing this method to be applied to high-dimensional state-of-the-art prediction models. The effectiveness of this approach is illustrated in the framework of a low-order coupled ocean-atmosphere model, and by comparing it to other well-known ensemble initialization methods based on the Empirical Orthogonal Functions of the model trajectory and on the backward and covariant Lyapunov vectors of the model dynamics. Explanations are provided on why this method is effective and could be applied to operational forecasting models.
References

Demaeyer, J., Penny, S. G., & Vannitsem, S. Identifying efficient ensemble perturbations for initializing subseasonal-to-seasonal prediction. Journal of Advances in Modeling Earth Systems, 14, e2021MS002828, 2022. https://doi.org/10.1029/2021MS002828
Vannitsem, S., J. Demaeyer, L. De Cruz, M Ghil, Low-frequency variability and heat transport in a low-order nonlinear coupled ocean-atmosphere model. Physica D, 309, 71-85, 2015. https://doi.org/10.1016/j.physd.2015.07.006
Impact of ENSO teleconnections on the probabilistic prediction of the atmosphere at seasonal-to-decadal time scales: A reduced-order model perspective
Authors: Vannitsem Stéphane
Published: July 2023

The dynamics of the atmosphere (and of the climate system) is known to display the property of sensitivity to initial conditions. This property has considerable impact on our abilities to make predictions at short and medium-range weather time scale, and at seasonal-to-decadal ranges. This implies that climate predictions are in essence probabilistic problems that should be tackled with appropriate tools. Since the nineties, considerable efforts have been addressed to develop such an information, often through ensemble forecasts based on multiple model integrations starting from different initial or boundary conditions. Such an approach is now well settled for weather forecasts, but still in its infancy for seasonal to decadal forecasts.
The impact of both initial condition and boundary condition errors on ensemble forecasts is extensively explored in a reduced-order coupled extratropical ocean-atmosphere model -- that was developed over the years -- forced by a Tropical model (Vannitsem et al, 2015, 2021), with a particular emphasis on the impact of Tropical teleconnections on extratropical predictability. In this perfect model framework, the analysis reveals that the potential of teleconnections in improving the quality of climate predictions could only be realized provided that the Tropical dynamics is accurately predicted. The improvements that can be expected by ensemble and temporal averages are also explored.
References
Vannitsem, S., J. Demaeyer, L. De Cruz, M Ghil, Low-frequency variability and heat transport in a low-order nonlinear coupled ocean-atmosphere model. Physica D, 309, 71-85, 2015. https://doi.org/10.1016/j.physd.2015.07.006
Vannitsem, S., Demaeyer, J., & Ghil, M. Extratropical low-frequency variability with ENSO forcing: A reduced-order coupled model study. Journal of Advances in Modeling Earth Systems, 13, e2021MS002530, 2021. https://doi.org/10.1029/2021MS002530.
 
 
Urban Climate Measurements in Hannover (Germany)
Authors: Krugmann Gabriele , Bauditz Stefanie , Tinz Birger , Möller Thomas
Published: July 2023

In the city of Hannover, capital of the federal state Lower Saxony, the urban climate was investigated with different methods:
- Stationary measurements at five locations
- Mobile measurements by cars in three campaigns during summer conditions
- Mobile measurements by trams
- Simulation of the temperature distribution in the urban area with an urban climate model
Project duration: June 2017 to December 2020
Project partners: German Weather Service, City of Hannover, State of Lower Saxony, ÜSTRA Hannoversche Verkehrsbetriebe AG
Stationary measurements (temperature, relative humidity, wind) at three stations in the dense urban area, additionally at two stations in the surrounding area. The temperature measurements show on average up to 1.0 K higher temperatures at the urban stations. The differences are greater at night than during the day, and greater in spring and summer than in autumn and winter. The inner city shows significantly more days with heat (summer days, heat days, tropical nights) and fewer frost days than in the surrounding area.
Profile measurement runs were carried out with a passenger car on two fixed routes through Hannover. Each measurement campaign lasted two to three days, during which measurement runs were carried out at three fixed times. Measurements of air temperature and relative humidity took place continuously during the trip. The comparison of the averaged temperatures of the measurement runs with the surrounding area shows hardly any differences during the afternoon measurements. During the early morning runs, however, there were large differences of up to 4.3 K and up to 3.2 K during evening runs.
Tram measurements were made throughout the entire route network during regular service. Three trams were equipped with measuring devices (for temperature and relative humidity), in one streetcar interior measurements were also made. The temperature distribution was evaluated for various route sections. Especially the inner city, commercial areas, residential areas and urban green areas were considered. The tram measurements are presented in detail in another paper.
Simulation of the temperature distribution in the urban area of Hannover with the urban climate model MUKLIMO_3: The simulations show the different extension of the urban heat island during the day. In the afternoon, striking temperature differences are shown within a small area. Hot inner-city areas are directly adjacent to cool areas characterized by water bodies, meadows and parks. In the late evening, overheated urban areas occupy significantly more space, and the cool  areas have decreased. The urban heat island is most extensive in the early morning hours. Almost the entire urban area is overheated, only at the city boundary are still cool regions.
Automated processing of webcam digital imagery for near-real-time identification of drought impacts on vegetation
Authors: Bláhová Monika , Kudláčková Lucie , Fischer Milan , Poděbradská Markéta , Trnka Miroslav
Published: July 2023

The occurrence of severe drought episodes, especially changes in their intensity and frequency, is considered one of the major impacts of changing climate. Consequences of these changes manifest on many levels ranging from ecological to societal, through all the drought types (meteorological, hydrological, agricultural, or socioeconomic). In relation to this development, the need to build complex drought early warning, monitoring, predictive, and impact assessment systems is also becoming more important. Even though many such systems are already in place, relying on complex modeling, remote sensing data, or participative data collection, we often lack the possibility of fast, near-real-time, and straightforward drought manifestation mapping with good spatiotemporal coverage. The proposed methodology presented in our study aspires to cover this gap. The presented work aims to develop a method for early warning and identifying drought impacts on vegetation based on automated interpretation of imagery from an RGB camera network. Thanks to the cooperation with Windy.com as the main partner, we are able to access the largest global web camera network with continuous time series of imagery in daily, weekly, and monthly timestep. In the first step, we prepared a training database for automated image categorization to detect vegetation in each camera image. To perform this step, we manually classified over two thousand camera images and defined the presence of vegetation and three subtypes of vegetation – forest, grassland, and agricultural vegetation. Thanks to this training dataset, we are able to categorize any other imagery in the database automatically, thanks to machine learning algorithms. After labeling images, we created time series for cameras focusing on vegetation operating during daylight hours. To lower the computational load, we focused only on vegetated parts of images. Therefore, for each camera, we created a time series and defined masks of green vegetated areas. The mask definition is based on HSV color model transformation, with statistically defined thresholds to detect green color. All image time series are subsequently masked, and the RGB image is normalized. Finally, the RGB greenness index, as (2*G)/(R+B), is computed for each masked vegetation area to assess spatiotemporal changes in greenness in the focus area of each camera. With the ability to monitor continuous camera imagery in weekly timestep, combined with the global scope of the webcam network, the proposed approach brings an operative tool to monitor current vegetation conditions. Thanks to the fully automated processing of real-time images, we are able to cover underrepresented areas and include those in drought and drought impact monitoring networks without bringing additional costs.
Enhancing quantitative precipitation estimation in the NWP using a deep learning model
Authors: Liu Haolin , Fung Jimmy C.H. , Lau Alexis K.H.
Published: July 2023

Precise quantification of precipitation is crucial for effective planning and minimizing property damage or loss of human life caused by extreme weather events, especially under the rapidly changing climate. Currently, quantitative precipitation forecasting (QPF) in numerical weather prediction (NWP) models rely heavily on parameterization schemes for microphysics, boundary layers, cumulus, etc., rather than directly solving physical-based governing equation sets to predict fundamental variables such as temperature, wind speed, and humidity. These parameterization schemes introduce significant uncertainties in precipitation forecasting due to the limited knowledge of precipitation processes, which bottlenecks the performance of precipitation forecasting in NWP models.
To overcome this challenge, we propose a deep learning model based on Vision-Transformer that directly ingests fundamental meteorological variables solved by NWP models as predictors and maps them quantitatively to the precipitation map from a satellite-merged precipitation product. In this study, we conducted Weather Research and Forecasting (WRF) model simulations at 27km grid resolution for five years from 2017 to 2021 over China and the southeast region of Asia, and we used simulation results for the wettest season from June to September in 2017-2019 as training data, while validating and testing the model performance on data from 2020 and 2021. The deep learning model aims to circumvent uncertainties in physical parameterization schemes, which are due to the incomplete understanding of physical processes, and directly reproduce the high-resolution satellite rainfall observation product, the Climate Prediction Center morphing method (CMORPH) data.
Our evaluation results on the test dataset show that the deep learning model effectively extracts features from meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed deep learning model can provide vital insights for capturing precipitation-triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations.
Challenges and Opportunities Using Impact Data for Impact-based Forecast Verification.
Authors: Wyatt Faye , Robbins Joanne
Published: July 2023

The collection and management of data describing socio-economic impacts resulting from hydrometeorological hazards is critically important. Such Impact Data may be used to contextualize upcoming hazardous events, provide situational awareness, and track the efficacy of mitigating actions. Importantly, Impact Data can provide the observations of impacts necessary to evaluate the performance of Impact-based Forecasts and Warnings (IbFW).  There are many sources of publicly accessible impact data available including international organizations, governments, academic institutions, and news websites. Differences in the purpose, methodologies, format, and quality of collection across agencies has led to a fragmented and inconsistent picture of impacts to date, limiting their utility.
To understand the implications of using freely available impact data for the purpose of IbFW evaluation, seven freely available sources of impact data were assessed, and six types of bias interrogated (geographic, hazard, temporal, threshold, accounting, and systemic biases). 'Our analysis shows that different types of bias are present across the data sources investigated The biases affect the coverage, severity, timing, geographic scale, and accuracy of impact observations which influence their utility in IbFW evaluation. Despite these challenges, our assessment suggests that using a range of data sources together can provide a more holistic observation of the impacts of hydrometeorological events. Here we present results of the interrogation of Impact Data for IbFW evaluation, and the processing steps required to harmonize Impact Data across multiple sources for utilization within an evaluation workflow. Initial results of a routine objective IbFW evaluation approach are also presented, and opportunities and challenges of using Impact Data for IbFW evaluation highlighted.