bluuebunny/crossref_metadata_2025 huggingface dataset:
The influence of precipitation type on snowmelt partitioning between evapotranspiration and streamflow
Authors: Molotch Noah
Published: July 2024

An aspect of hydrologic sensitivity to climate change in snow dominated systems that has been moderately explored relates to the role of snowfall and snowpack accumulation in storing water throughout cold season months and releasing this water to terrestrial systems during warmer months when potential evapotranspiration (PET) is relatively elevated.  Importanlty, previous studies have not been able to document an explicit mechanism linking precipitation type to runoff production; yet several studies have noted that a sensitivity does exist.  Given that a shift from snowfall to rainfall is inevitable as the climate warms, this fundamental question as to how streamflow responds to a change in precipitation type is extremely timely and important for water resource management. Hence, one would expect that climate-related shifts toward earlier snowmelt or a shift from snowfall to rainfall would lead to an increased misalignment between the seasonality of surface water input (i.e. rainfall and snowmelt) and PET, and thus act to decrease annual partitioning to evapotranspiration and increase annual streamflow.  This shift from snowfall to rainfall, and it's associated influence on hydrologic partitioning will be refered to below as the 'energy-water-misalignment perspective'.  A second hypothesis, that is counter to the energy-water-misalignment perspective, has documented an increase in runoff partitioning with increased snowfall fraction which relates to a theorized greater effiency of soil-water drainage from snowmelt versus rainfall due to the high rate and duration of snowmelt; hereafter referred to as the 'snowmelt-rate perspective'.  In pursuit of this line of inquiry, this presentation will cover recent modeling and observation-based experiments that reveal the importance of both of the aforementioned runoff response perspectives.  The combination of these modeling and observation-based studies confirm that the energy-water misalignment perspective and the snowmelt-rate perspective both influence hydrologic partitioning sensitivity to precipitation type.  The resultant streamflow sensitivity to precipitation type and climate warming is therefore complex with the sign of the partitioning sensitivity being governed by the relative importance of the two paradoxical runoff response perspectives presented herein.  
30 years of observations of incoming radiative fluxes at the Lindenberg BSRN site: methods, applications and results
Authors: Wacker Stefan , Becker Ralf
Published: July 2024

The BSRN site was established at the Meteorological Observatory Lindenberg – Richard Aßmann Observatory (MOL-RAO) of the German Meteorological Service (DWD) in 1994. Since then, incoming shortwave and longwave irradiances have been observed continuously and with high accuracy. We will discuss the methods of operations and the uncertainties of the observations.The primary applications of the BSRN data are the i) validation of climate model projections and surface products derived from satellite data, and ii) monitoring of the shortwave and longwave radiative components and their changes with the best methods currently available. We will present such results from the radiation records. While the observed increase in the incoming longwave radiation of 3.5 Wm-2 per decade due to increasing air temperature, humidity and greenhouse gas concentrations is consistent with the corresponding projections from climate models, the interpretation of the continuous increase in total solar irradiance of 3.5 Wm-2 per decade since the 1990s - referred to as brightening - is more complex. The first period of this brightening may be solely attributed to the decrease in the aerosol load. However, aerosol loads have then stabilized at low levels at the beginning of the 21th century and thus the direct aerosol effect might have been less dominant in recent years. Instead, changes in cloudiness may have become more important. Indeed, calculations of the cloud radiative effect indicate a decrease in the magnitude over the past 30 years, which may imply a decrease in cloud cover, a shift towards a different cloud type and/or changes in microphysical cloud properties. However, these results are related to substantial uncertainties and thus are not conclusive.
Towards Met.3D version 2: Rapid exploration of numerical weather prediction data by means of interactive 3-D visualization
Authors: Rautenhaus Marc , Fischer Christoph , Vogt Thorwin , Beckert Andreas , Fuchs Susanne
Published: July 2024

Visualization is an important and ubiquitous tool in the daily work of weather forecasters and atmospheric researchers to analyse data from simulations and observations. The domain-specific meteorological visualization tool Met.3D (documentation including installation instructions available at https://met3d.readthedocs.org) is an open-source effort to make interactive, 3-D, feature-based, and ensemble visualization techniques accessible to the meteorological community. Since the public release of version 1.0 in 2015, Met.3D has been used in multiple visualization research projects targeted at atmospheric science applications, and has evolved into a feature-rich visual analysis tool facilitating rapid exploration of gridded atmospheric data. The software is based on the concept of “building a bridge” between “traditional” 2-D visual analysis techniques and interactive 3-D techniques powered by modern graphics hardware. It allows users to analyse data using combinations of feature-based displays (e.g., atmospheric fronts and jet streams), “traditional” 2-D maps and cross-sections, meteorological diagrams, ensemble displays, and 3-D visualization including direct volume rendering, isosurfaces and trajectories, all combined in an interactive 3-D context.In the past year, we have been able to significantly advance the Met.3D code base (available at https://gitlab.com/wxmetvis/met.3d) to make the tool more stable, usable, and to integrate visualization techniques not commonly available in other visualization tools. In this presentation, we present recent updates to the software and describe the status of our effort towards achieving a fully documented version 2.0. We demonstrate how Met.3D can be used for forecasting tasks, with examples including the utility of interactive visual analysis of 3-D cloud fields in combination with feature-based displays, and interactive visual analysis of trajectory data.
Radar Characteristics Of Wind Hazards Associated With Deep Moist Convection.
Authors: Najman Filip , Staněk Miloslav , Horák Jan
Published: July 2024

Low-level horizontal wind shear is one of the most prominent accompanying phenomena in convective thunderstorms. Hazards of low-level horizontal wind shear can often be life threathening. Among other things, it poses a serious threat to aviation, especially around airports during landings and takeoffs. Horizontal wind shear can also generate vertical vorticity, which can sometimes result in the development of mesovortices and tornadoes. Weather radars at the right places can help with detecting these phenomena by using doppler radar data, thus improving warning possibilities.In our poster, we will discuss the amount of horizontal wind shear along squall lines and regions of mesocyclone within supercells and their proximity. We quantify these regions in detail in terms of horizontal wind shear in relation to the environment in which the convection developed. The environment that determined the development of convection will be studied using proximity soundings or parameters derived from the WRF model or the ERA-5 reanalysis. The main goal of the poster will be to establish the relationship between the convective environment and the radar characteristics detected and computed from the Doppler velocity data.
Novel 3-D and AI-based weather forecast products based on open data
Authors: Fischer Christoph , Vogt Thorwin , Beckert Andreas , Fuchs Susanne , Radke Tim , Rautenhaus Marc
Published: July 2024

Recent developments in open data policies of meteorological agencies have much expanded the set of up-to-date forecast, reanalysis, and observational data that is publicly available to meteorological research and education. To make use of this open data, we have developed a set of 3-D and AI-based visualization products that extract and display meteorological information in novel ways.  

In this presentation, we present visualization products derived from publicly available data from operational agencies including the German Weather Service (DWD) and the European Centre for Medium-Range Weather Forecasts (ECMWF). All our visualizations are created using open-source software, mostly using the interactive 3-D visualization tool “Met.3D”. Met.3D has primarily been developed for rapid exploration of gridded atmospheric data by interactive means, and has recently been extended with capabilities for batch-creation of visualizations and animations. Met.3D supports a variety of visual displays from traditional 2-D maps to novel 3-D visualizations of, for example, clouds, jet streams, and weather fronts. In recent work, we also investigated explainable-AI-based feature identification algorithms to detect meteorological structures including fronts and tropical cyclones.  

We provide comprehensive documentation and ensure straightforward installation processes for easy adoption and use of the presented visualization products by the community, e.g., for use in weather forecasting courses at universities. In addition, we are currently developing a near-real-time website that comprehensively showcases our visualizations using current forecast and observation data. For reproducibility and further interactive visual analysis of the data, the website provides scripts and configurations that enable users to replicate the visualizations using a local Met.3D installation, thus providing additional benefit to users.
Weakly coupled data assimilation for climate predictions with ICON-XPP
Authors: Sgoff Christine , Pohlmann Holger , Brune Sebastian , Pham Trang Van , Schneidereit Andrea , Steinert Thorsten , Fröhlich Kristina
Published: July 2024

We present the development of the initialisation strategy for seasonal to decadal climate predictions based on the ICON-XPP model, within DWD’s Innovation in the Applied Research and Development (IAFE) program. The ICON-XPP model is based on several well-established model components:  the ICON-NWP, operational weather forecast model at the DWD, as atmospheric model, ICON-O as ocean model, JSBACH as land model and uses a hydrological discharge model. To develop a weakly coupled data assimilation system for ICON-XPP, we use the experience build on a former ICON-ESM version (Pohlmann et al 2023). In our weakly coupled data assimilation framework, we use two different assimilation methods for atmosphere and ocean. We initialize the ocean component of the climate system through a monthly assimilation of salinity and temperature profiles from the EN4 dataset. For this we use a localised singular evolutive interpolated Kalman filter implemented via the Parallel Data Assimilation Framework (PDAF, Nerger 2020). The atmosphere component is initialised by nudging temperature, pressure and horizontal wind fields of the ERA5 reanalysis. We conduct our experiments with a 25-member ensemble, which we put together from three different historical runs started from different states of our piControl run. In the atmosphere ICON-XPP is run as R2B5 (~80km resolution) with 130 vertical levels and in the ocean we use a resolution of ~40km (R2B6) with 72 vertical levels. Our assimilation experiments start from 1990 after a ten-year assimilation spin-up of the ocean. We show the results of our experiments with the weakly coupled data assimilation system and discuss its challenges.
Ecosystems of Climate Services in Latin America: examples from Guatemala and Chile
Authors: Muñoz Ángel G. , González Romero Carmen , García Alan , Campos Diego , Alabweh Zain
Published: July 2024

Climate services ecosystems are dynamic complex network of institutions, agents, information, knowledge, products and services functioning as a unit, at any (or across multiple) spatial or temporal scale(s), with the objective of supporting decision-making to enhance the resilience to a changing climate, and support countries and institutions to achieve their adaptation and mitigation goals while optimising available resources (González Romero et al., BAMS, sub-judice). Similarly to natural ecosystems, climate services ecosystems are self-contained and self-adjusting, having the ability to adapt in response to changes in the network or system (MilleniumEcosystemAssessment, 2003). Ideally, the more interconnected and interdependent the elements of the climate service ecosystem are, the higher the value and resilience of its network (e.g. Sawai 2013; Watts and Strogatz 1998).Here, ecosystems of climate services in Guatemala and Chile are analysed and contrasted, using the González Romero et al (BAMS, sub-judice) approach, based on Dynamic Causal (Bayesian) Network Theory. Bayesian Network Analysis here aims at infering probabilities under changing conditions, like changes induced by programs, policies, budgets, institutional changes or any other external interventions. The causal assumptions that can be inferred from Bayesian Network Analysis identify relationships that remain invariant when external conditions change, allowing for the assessment of these changes, predictions of plausible scenarios and evaluation of counter-factuals and testable scenarios (e.g. Pearl, 2009). The assessment of causality derived from a Bayesian Network Analysis implies that the influence of one event onto another is stable and autonomous, so the change in one of them would necessarily result on a change in the linked event.This research also involves an assessment of how optimal each network of climate services is, including potential improvements, and also an intercomparison -whenever possible- between the two sample ecosystems.
Tools and data for planning and operating a climate-proof European power system
Authors: Schyska Bruno , von Bremen Lueder , Witte Francesco , Zech Matthias , Medjroubi Wided , Schroedter-Homscheidt Marion
Published: July 2024

To support evidence-based decision-making, energy system modelers in research, administration and industry rely on the best-available meteorological and/or climatological information on a variety of scales, from the local distribution grid level to the European transmission system, and from short-term forecasts for the operation to climate scenarios for making investment decisions. However, there is a lack of knowledge among energy system modellers about available meteorological data sets, their characteristics and the implications of using different data sets for grid planning and adequacy assessment activities. Standardised tools and methods to add the analysis of climate change and/or climate uncertainty to user workflows or to conduct tailored bias correction, validation or downscaling tasks rarely exist. The energy systems modelling and climate modelling communities can be considered at least partially disconnected. This observed disconnect hinders energy system modellers in fully making use of the available meteorological information, prevents them from tapping the full potential of the data and, consequently, potentially leads to sub-optimal or inefficient decisions.In this presentation, we introduce approaches for deriving relevant information for grid planning and adequacy assessment from climate simulations to support overcoming the current disconnect between the two disciplines. In particular, we show how results obtained from energy system simulations can be linked to certain characteristics of the meteorological input, e.g. the occurrence of extreme events or prevailing weather regimes, through running and evaluating a semi-operational energy system modeling workflow for a great number of weather scenarios. Furthermore, using insights gained from this analysis we derive a criticality measure for certain weather situations and show how criticality can be used to group weather years limiting thereby the  weather scenarios required for adequacy assessment and/or grid planning purposes. For this study, we use meteorological data from the new Pan-European Climatic Data Base (PECD version 4.1) and, as one of the first studies, climate projections and historical simulations from the Destination Earth Digital Twin for Climate Adaptation.
Harmonisation of historical and future emission data for climate and air quality modelling
Authors: Grawe David , Alyuz Ummugulsum , Arghavani Somayyeh , Boorman Penny , Finardi Sandro , Halenka Tomas , Radice Paola , Samland Mailin , Sokhi Ranjeet , Troccoli Alberto
Published: July 2024

Climate change and air quality research are closely related research areas that have often been investigated with different objectives. However, addressing both topics as a joint approach can lead to synergies and help to avoid counteracting effects, where mitigating one may exacerbate the other. The convergence of methods and approaches is necessary to consider on the one hand the climate trend and forcing in mitigation scenarios applied by the air quality community and on the other hand to better understand and describe the impact of short-lived climate pollutants on the regional climate.The project FOCI aims to analyse non-CO2 forcings on both climate and air quality and therefore requires a joint approach. A core task of the project is the application of regional climate and urban scale models driven by global earth system models to describe continental to urban scale air quality under present and future climate conditions. The results of these processes will be used to investigate possible mitigation and adaptation scenario options.The present and future anthropogenic emissions required for such model investigations need to be consistent with CMIP6 historical climate reconstruction and future scenario simulations. CMIP6 has been based on CEDS emissions that are therefore the necessary reference for FOCI activities. One critical aspect is that pollutants considered in CEDS do not include particulate matter (PM2.5 and PM10), but only its black carbon (BC) and organic carbon (OC) components. This is understandable considering the objective for which CEDS has been built, but it would cause a significant underestimation of particulate matter concentrations and raises the need to define a method to estimate the non-speciated PM2.5 and PM10 emissions from the available information.In order to derive PM emissions for CEDS we investigate a number of approaches based on the use of different proxies from the EDGAR database. In particular, we consider the feasibility of deriving PM2.5 from BC and OC as the main components of fine particulate matter. For emission sectors where BC and OC is not available in the emission inventories, we explore the possibility of using alternative proxies including NOx. By combining the different approaches we derive PM2.5 emissions for CEDS at a spatial grid resolution of 0.1 degree and compare these for each main emissions sector.These estimated dataset of consistent CEDS and particulate matter emissions are used in the FOCI project numerical models to describe continental to urban scale air quality under present climate conditions. We also discuss the implications of employing our approach to derive consistent emissions for particulate matter in conjunction with SSP emission data for projections to ultimately evaluate the impact of key radiative forcers on climate and societal systems.
WINSENT – wind energy research in mountainous and complex terrain
Authors: Rettenmeier Andreas
Published: July 2024

The research test site WINSENT supports answering questions regarding wind energy technology developments, to align wind energy with nature conservation and to gather a huge amount of open data with respect to the research wind turbines (RWT), environment and to meteorology.ZSW, as a member of the southern German wind energy research cluster WindForS, has been developed and realized together with further cluster partners the globally unique wind energy research platform WINSENT at a site in mountainous and complex terrain. The research site is characterised by its location on an unwooded, exposed plateau downstream of a steep slope in the Swabian Alb. The steep slope results in higher wind speeds, turbulence and oblique currents. The seasonal change in vegetation on the Albtrauf influences the flow characteristics additionally.Two RWT, each with a rated output of 750 kilowatts and a hub height of 73 metres have been erected and put into operation. Their rotor diameter is 54 metres, giving a total height of 100 metres. One of the unique selling points of the RWT is that the scientists have unrestricted access to the complete operation and control as well as to the construction data of the turbines in order to analyse their behaviour in detail. In addition to technological tests and investigations on the RWT, special emphasis is placed on the permanent recording of meteorological parameters at the test site and in the surrounding area. In particular, the microclimatic influences of the topography on the wind - and thus ultimately on the wind turbines - is a key focus of research.Meteorological masts of 100m height are positioned in front of and behind each research turbine to measure the inflow and wake wind field. The top anemometer is as high as the top blade tip position of the turbines and the masts are equipped with various meteorological sensors as well as with bat microphones and bird/insects detection sensors. In addition to the met masts, eddy-covariance systems, ceilometer and rain sensors etc. gather a huge amount of environmental data. The measured data are used amongst others e.g. for the development and validation of a simulation chain, which couples mesoscale and microscale domains using WRF, LES and detailed CFD simulation.Furthermore three lidar systems installed in the vicinity of the test site also record the wind flow in front of and behind the wind turbines. As a synchronised ensemble, the lidar devices can be designed as a virtual measuring mast and can also measure the wind flow far in front of the escarpment or directly behind the wind turbines.As a research and development platform, the test site will be available to both natural and engineering scientists in the course of future projects.
New visualization and analysis tools for 3D Lightning Mapping Array data
Authors: van der Velde Oscar , Romero David , López Jesús , Montanyà Joan , Pineda Nicolau , Fabró Ferran
Published: July 2024

The 3D Lightning Mapping Array (LMA) is a regional network of VHF antenna stations spaced 5-50 km apart that detect lightning pulses in the 60-66 MHz band. The pulses are timed by GPS. Time-of-arrival reconstruction results in 3D pulse locations, revealing the lightning leader channels (of negative polarity) inside the cloud and toward ground. The detection range of a sensitive network can reach over 250 km from the center. LMAs are used for lightning science, nowcasting of severe weather and airspace safety.In 2011, the UPC Ebro 3D Lightning Mapping Array in Spain was the first LMA to be installed outside the USA and has collected data for about 11 years. This system was replaced in 2023 by 13 (up to 15) stations spread out mostly across western Catalonia, operated on solar power. Additionally, the Meteorological Service of Catalonia started operating their LMA network in 2024. Each network can operate independently, but data from both networks can be combined to offer the best spatial coverage and resolution of real-time lightning monitoring.As the duration of processing time-of-arrival into lightning location data goes up exponentially with the number of stations in the network, smart approaches of combining small groups of stations in parallel and pooling the solutions are investigated.This poster will focus on the capabilities of newly designed interactive lightning visualization and analysis tools for the LMA, developed using the Julia programming language.It is a suite of tools that focus on (a) lightning flash analysis and general browsing of activity, (b) storm activity evolution analysis, and (c) network performance analysis based on the data. Lightning flash analysis focused on the structure of the lightning flash. Flash grouping is based on DBSCAN clustering, and sparkle activity near the cloud top can be isolated similarly. An automated leader speed using the Theil-Sen slope estimator of points runs efficiently on a large dataset, and can be used to highlight regions of positive and negative polarity in the cloud. The tool also can read cloud-to-ground stroke data from other networks, and Meteosat Third Generation Lightning Imager or GOES Geostationary Lightning Mapper (GLM) to be compared with LMA. The storm activity analysis tool will be based on local 3D flash density and its evolution in time and aims to study trends in 3D activity, rather than the ubiquitously used general flash rate. Lightning leader speed, polarity and the repetition rate of leaders in flashes may hold new information that could turn out to be useful in monitoring the thunderstorm state and potential for severe weather.
Multi-scale analysis of the meteorological processes forcing African dust ablation and transport in the extreme mid-march 2022 case
Authors: G. Orza Jose A. , Kaplan Michael L.
Published: July 2024

We examine the complex meteorological processes involved in the development of multiple dust storms in the middle of March 2022 over North Africa by means of multi-level high-resolution reanalysis datasets, and satellite and surface observations. These dust storms ultimately transported a remarkable amount of dust into Europe, and in particular to the Iberian Peninsula.The analyses show that baroclinic Rossby Wave Breaking (RWB) events over two regions: the eastern North Atlantic and the western Eurasian border, resulted in the creation of a downscale confluence of low-level jets that ablated dust in the Sahara Desert. One of these active baroclinic regions involved multiple wave-breaks over western Russia and the Middle East that transported unseasonably cold air southwestward into Egypt and Sudan. A second active baroclinic region occurred along the North African Atlantic Coast where rapid downstream dispersion of energy was focused in response to very intense cyclogenesis over Labrador. The cold pool which propagated into northwestern Africa and cold air over northeastern Africa effectively sandwiched hot Saharan air in between the Hoggar and Atlas Mountain Ranges. The fronts separating these air masses became juxtaposed with the two mountain ranges creating ideal conditions for blocking and multiple barrier jets’ genesis. These barrier jets each combined with larger scale semi-geostrophic low-level jet formation, resulted in the ablation of dust into two separate large plumes both of which were transported poleward out of North Africa into Europe around a upper-level cutoff-low near Iberia slowly displacing downstream.The blocked RWB forcing the penetration of cold air over eastern Africa constitutes a substantial difference with respect to other more frequent events where RW trains intensify, penetrate equatorward into North Africa and propagate eastward.Work funded by MCIN/AEI/ 10.13039/501100011033 under Grant PID2020-115153RB-I00 (rROSSETA Project).
Exploring the use of urban imagery for representation of urban fabric in the SLUCM
Authors: Kalverla Peter , Donnelly Claire , Steeneveld Gert-Jan , Timmermans Wim
Published: July 2024

Cities experience the urban heat island effect (UHI), i.e. cities are warmer than the countryside. Climate change and rapid urbanization means UHI effects are projected to become more frequent, and better UHI modelling and warning strategies against adverse human thermal comfort are thus needed. Urban temperatures vary locally due to the presence of buildings, trees, water bodies that affect the absorbed solar radiation, the wind speed and evapotranspiration. Urban weather models typically use so called urban canopy models to estimate the energy and radiation balance of facets like roofs, walls and roads, and calculate temperatures and wind speeds in streets canyons. For successful modelling, these models need high-resolution urban morphology information as surface boundary conditions, like building height, street width, tree positions, building material and radiation properties of roofs, roads and building walls. Recent model development focused on mapping buildings and trees following the so called NUDAPT approach to quantify intra-urban UHI patterns, but spatial information about radiation properties of roofs, roads and building walls has not been mapped nor incorporated in urban canopy models.In the Urban-M4 project we aim to collect high-resolution radiation information of walls and roads, from widely available data sources such as street view imagery, public databases and airborne observations. These gridded data will feed into hectometric WRF model simulations for Amsterdam and Enschede (The Netherlands) for hot summer days. Here we present a framework for the project as well as the experimental setup for our WRF simulations and some initial results from sensitivity experiments gauging the effect of changing radiation information on urban heat in Amsterdam.
UAV-based Studies of Temperature and Wind in and above a Deciduous Forest (and a Forest Clearing) in the Region of the Weser Valley
Authors: Wrenger Burkhard , Cuxart Joan
Published: July 2024

Between the German cities of Höxter and Holzminden, the landscape ascends in the eastern direction from the Weser river at roughly 100 m a.s.l. up to 500 m a.s.l. in the Solling natural area. The slope is forested with deciduous trees with a typical height of approx. 30 m. In order to study the vertical profiles of temperature and wind in the forest, we operated an Unmanned Aerial Vehicle (UAV) flying from ground level up to approx. 100 m agl. Fifteen flights near sunset between summer 2019 and spring 2020 are analysed revealing effects of the foliation and downslope flows. Using rough estimates of turbulent fluxes and sensible heat and momentum based on the vertical profiles allows us to produce leaf area density profiles for the leafy and the leafless case. We compare the results with existing theories both for the average structure of the forest and the vertical profiles.For the cases studied here, the forest exerts a drag on the wind and the wind decreases markedly in a 30-m-layer above the canopy. The drag is maximal at the upper canopy and the presence of the leaves increases this effect. Due to the slope situation, downslope flows can be detected and enhance the mixing near the surface. Based on the work by Yi [1] we can split the wind speed into contributions from the forest and the slope.The estimation of turbulent fluxes is rough due to the limited data sample during the flights, but it is in agreement with the profiles of the mean variables. The momentum fluxes as a function of height allows the estimation of the profile of leaf area density as a fingerprint of the average structure of the forest. The results show that for a two storeys forest as present in the our region of interest, assuming a logarithmic profile of the variables does not reflect the measurements. [1] C. Yi, R. K. Monson, Z. Zhai, D. E. Anderson, B. G. Allwine, A. A. Turnipsseed, S. T. Burns: Modeling and measuring the nocturnal drainage flow in a high-elevation, subalpine forest with complex terrain. Journal of Geophysical Researcj, Vol. 110, D22303 (2005)
Temperature trends and uncertainties over peninsular Spain and the Balearic Islands
Authors: Garrido Juan Luis , González-Rouco Jesús Fidel , Brunet Manuela , Sigró Javier , Herrera Sixto , Chazarra-Bernabé Andrés , Serrano-Notivoli Roberto , Beguería Santiago , González-Hidalgo José Carlos , Peña-Angulo Dhais , Rodríguez-Camino Ernesto , Rodríguez-Guisado Esteban , Peral Candela , García Marta , García-Bustamante Elena
Published: July 2024

Six gridded datasets of mean, minimum and maximum temperatures and one dataset of 22 individual sites distributed over mainland Spain and the Balearic Islands are compared. The gridded datasets vary in spatial resolution, temporal coverage and interpolation procedures as well as in the number of individual predictor sites used to create them. The six gridded datasets start in 1901 (1951) in the longest (shortest) case and extend until present or some date within the last decade. The spatial resolution ranges from 0.11◦ to 1 km. The site-resolved dataset includes the 22 longest data records over Spain, reaching back until the mid-19th century and thus complementing the temporal analysis of this study. The availability of datasets with different levels of predictor data, methodologies and final characteristics allows for an assessment of the observational uncertainties in temperature climatologies and trends at the local/regional scales.The structure of the spatial patterns for the climatologies of the mean, minimum and maximum temperatures is quite consistent across datasets, with the coldest temperatures over areas of the Pyrenees and mountain ranges in winter and the hottest ones over the Guadalquivir Valley in summer. However, notable differences are observed at a regional scale, particularly over the highest elevation regions where average discrepancies greater than 3 ◦C are found. Mean temperatures increase since 1970s between 1-2 ◦C, and uncertainties range from 0.2 to 0.5 ◦C across datasets. The spatial distribution of trends shows that warming is widespread but there are notable differences in magnitude, spatial distribution and even sign of the trends depending on the database considered. The relevant variability estimated among datasets leads to a low confidence on where the warming peaks annually and seasonally. A Principal Component  Analysis is used to filter out signals that account for low amounts of regional variance and focus on those responsible for the long term responses. The first principal component reproduces the trends in each dataset and season, and is consistent with the forced response to external forcing found at global and hemispherical scales. This PC accounts for a large percentage of variance in each dataset and season. The corresponding spatial patter has uniform sign and its regional variability varies considerably across datasets with differences being larger in scale than the resolution of the datasets. The results can be relevant for model evaluation and detection and attribution studies over this region.
A low-cost approach to develop Weather, Water and Climate Services (WWCS) in rural areas of Tajikistan
Authors: Bellprat Omar , Spirig Chistoph , Orlowsky Boris , Nasibov Afzalsho , Kassam Shinan , Yatimov Safarali
Published: July 2024

Severe weather and climate change take a high toll on the most vulnerable population of Tajikistan. Every year, droughts, flooding or avalanches and unsustainable land and water use management practices cause food insecurity and affect the lives of exposed rural communities. Weather, Water and Climate Services (WWCS) that support agronomic decisions and early warning systems can greatly reduce socio-economic and environmental impacts on vulnerability and well-being. However, a lack of public and private resources and local capacities have so far inhibited their development. In response to this urgent need, a consortium of Swiss federal institutions, the WMO and ICARDA, led by CARITAS Switzerland (CACH) and with co-funding from CACH and the Swiss Agency for Development and Cooperation, support governmental and private sector partners in Tajikistan to develop WWCS. The project improves the observational database by deploying a large number of low-cost automated weather stations complemented by citizen observations in remote areas. These stations enable post-processing of forecasts and thereby the development of reliable services tailored to local user-needs. This presentation will give an overview of the achieved results during the first phase of the project, demonstrating the added value from benchmark and demonstration sites for improved irrigation practices, early warning of frost, and increased agricultural production yield due to timely sowing and harvesting of crops. These results will consequently put into the institutional context of Tajikistan and the challenges faced by the national actors in hydrometeorology. The need for few but highly qualified (and relatively expensive) staff, with capacity to maintain and develop further these WWCS is a key main concern. An innovative perspective is finally presented to overcome these challenges, by targeting public-private partnerships.
Simulation of marine stratocumulus using the super-droplet method: numerical convergence and comparison to a double-moment bulk scheme using SCALE-SDM 5.2.6-2.3.1
Authors: Yin Chongzhi , Shima Shin-ichiro , Xue Lulin , Lu Chunsong
Published: July 2024

Abstract. The super-droplet method (SDM) is a Lagrangian particle-based numerical scheme for cloud microphysics. In this work, a series of simulations based on the DYCOMS-II (RF02) setup with different horizontal and vertical resolutions are conducted to explore the grid convergence of the SDM simulations of marine stratocumulus. The results are compared with the double-moment bulk scheme (SN14) and model intercomparison project (MIP) results. In general, all SDM and SN14 variables show a good agreement with the MIP results and have similar grid size dependencies. The stratocumulus simulation is more sensitive to the vertical resolution than to the horizontal resolution. The vertical grid length DZ ≪ 2.5 m is necessary for both SDM and SN14. The horizontal grid length DX < 12.5 m is necessary for the SDM simulations. DX ≤ 25 m is sufficient for SN14. We also assess the numerical convergence with respect to the super-droplet numbers. The simulations are well converged when the super-droplet number concentration (SDNC) is larger than 16 super-droplets per cell. Our results indicate that the super-droplet number per grid cell is more critical than that per unit volume at least for the stratocumulus case investigated here. Our comprehensive analysis not only offers guidance on numerical settings essential for accurate stratocumulus cloud simulation but also underscores significant differences in liquid water content and cloud macrostructure between SDM and SN14. These differences are attributed to the inherent modeling strategies of the two schemes. SDM's dynamic representation of aerosol size distribution through wet deposition markedly contrasts with SN14's static approach, influencing cloud structure and behavior over a 6 h simulation. Findings reveal sedimentation's crucial role in altering aerosol distributions near cloud tops, affecting the vertical profile of cloud fraction (CF). Additionally, the study briefly addresses numerical diffusion's potential effects, suggesting further investigation is needed. The results underscore the importance of accurate aerosol modeling and its interactions with cloud processes in marine stratocumulus simulations, pointing to future research directions for enhancing stratocumulus modeling accuracy and predictive capabilities.
High-resolution long-term average groundwater recharge in Africa estimated using random forest regression and residual interpolation
Authors: Pazola Anna , Shamsudduha Mohammad , French Jon , MacDonald Alan M. , Abiye Tamiru , Goni Ibrahim Baba , Taylor Richard G.
Published: July 2024

Abstract. Groundwater recharge is a key hydrogeological variable that informs the renewability of groundwater resources. Long-term average (LTA) groundwater recharge provides a measure of replenishment under the prevailing climatic and land-use conditions and is therefore of considerable interest in assessing the sustainability of groundwater withdrawals globally. This study builds on the modelling results by MacDonald et al. (2021), who produced the first LTA groundwater recharge map across Africa using a linear mixed model (LMM) rooted in 134 ground-based studies. Here, continent-wide predictions of groundwater recharge were generated using random forest (RF) regression employing five variables (precipitation, potential evapotranspiration, soil moisture, normalised difference vegetation index (NDVI) and aridity index) at a higher spatial resolution (0.1° resolution) to explore whether an improved model might be achieved through machine learning. Through the development of a series of RF models, we confirm that a RF model is able to generate maps of higher spatial variability than a LMM; the performance of final RF models in terms of the goodness of fit (R2=0.83; 0.88 with residual kriging) is comparable to the LMM (R2=0.86). The higher spatial scale of the predictor data (0.1°) in RF models better preserves small-scale variability from predictor data than the values provided via interpolated LMMs; these may prove useful in testing global- to local-scale models. The RF model remains, nevertheless, constrained by its representation of focused recharge and by the limited range of recharge studies in humid, equatorial Africa, especially in the areas of high precipitation. This confers substantial uncertainty in model estimates.
Tower-based C-band radar measurements of an alpine snowpack
Authors: Brangers Isis , Marshall Hans-Peter , De Lannoy Gabrielle , Dunmire Devon , Mätzler Christian , Lievens Hans
Published: July 2024

Abstract. To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains for the period of 2021–2023. The experiment objective was to improve understanding of the sensitivity of Sentinel-1 C-band backscatter radar signals to snow. The data were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The data show that scattering is present throughout the snow volume, although it is limited for low snow densities. Contrasting layer interfaces, ice features and metamorphic snow can have considerable impact on the backscatter signal. During snowmelt periods, wet snow absorbs the signal, and the soil backscatter becomes negligible. A comparison of the vertically integrated tower radar data with Sentinel-1 data shows that both systems have similar temporal behavior, and both feature an increase in backscatter during the dry-snow period in 2021–2022, even during weeks of nearly constant snow depth, likely due to morphological changes in the snowpack. The results demonstrate that C-band radar is sensitive to the dominant seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy, melt–freeze cycles and snow wetness may complicate satellite-based snow depth retrievals.
Prevalence of Peer Bullying among Adolescents in the Turkish Republic of Northern Cyprus and Determination of Affecting Factors
Authors: Abiç Veli , Bilgiç Nurcan
Published: July 2024

The aim of this study was to investigate the prevalence of peer bullying among adolescents the Turkish Republic of Northern Cyprus (TRNC) and the factors affecting it. The research is descriptive type research. The target population of the study comprises 3016 students who study in the Famagusta district of TRNC during 2018-2019 academic year. A sample of this research was detected by using the stratification sample method. In this study used, peer bullying scale was used for students developed by Olweus. It was detected that male students are both victims and bullies in peer bullying more than female students; vocational high school students are more victims of peer bullying than other students; 6th and 7th grade students and students who do not live with their parents are more victims. The study found that students who had bad relationships with their parents were more likely to become bullies. Nurses must observe the main reason for this problem by visiting both victim and bully students’ houses.
Evidence-Based Social-Emotional Learning Intervention Programs for Preschool Children: An Important Key to Development and Learning
Authors: Aksoy Pinar , Gresham Frank M.
Published: July 2024

The preschool years, spanning from birth to age six, are crucial periods for acquiring social-emotional learning (SEL) skills. An effective way to address social-emotional learning deficits is to implement evidence-based intervention programs. The purpose of this article is to review specific evidence-based social-emotional learning intervention programs for preschool children, drawing from resources such as books, peer-reviewed journal articles, and conference proceedings. In this context, five social-emotional learning intervention programs, including First Step to Success, I Can Problem Solve, Incredible Years, PATHS, and Strong Start Pre-K, documented in the social-emotional learning literature, were reviewed. Each program was evaluated based on its target group, duration, focus, delivery method, and experimental effects. The reviews found that play-based activities, puppets and stories, role-playing, and family involvement were common components of these programs. The majority of programs focused on problem-solving skills, and all had positive effects on preschoolers’ social-emotional learning skills. Based on the reviews, it was underlined that social-emotional learning intervention programs are effective when their content, process, and delivery methods are developmentally appropriate for the target group. Accordingly, it was seen that the widespread implementation of evidence-based social-emotional learning intervention programs is essential to improve the basic social-emotional learning skills of preschool children and to address their potential deficits. Continuous evaluation and refinement of social-emotional learning intervention programs, guided by feedback from educators, parents, and researchers, was also highlighted as crucial to increase their impact. By prioritizing the implementation of evidence-based social-emotional learning intervention programs with appropriate components and delivery methods, the stakeholders of education can facilitate the holistic development of preschool children and lay a strong foundation for their future social-emotional well-being.