bluuebunny/crossref_metadata_2025 huggingface dataset:
Downslope windstorms in the Scandinavian mountains from a kilometer-scale regional climate model
Authors: Jureša Patrik , Belušić Danijel , Hansen Felicitas , Marimbordes Sophie
Published: July 2024

Downslope windstorm (DW) is a mesoscale phenomenon with considerable impact on nature and human activities in mountainous regions. It is characterised not only by the descent of air down the lee slopes but also by wind acceleration on the windward side of a mountain. Climate studies of such complex and local events on larger scale are rare. In this work, a high-resolution climate model is used to study the DW characteristics in the present and future climate in the Scandinavian mountains.The HARMONIE-Climate (HCLIM) regional climate model is used on two nested grids with horizontal grid spacing of 12 and 3 km. The DW’s are identified using a two-step algorithm. First the favorable terrain for DW’s is marked, followed by the identification of DW events in these areas based on stability, horizontal and vertical wind speed. Synoptic circulation types (CT) are used to distinguish DW’s according to different large-scale environments. The Scandinavian mountains are divided into four subregions, and the analysis is focused on winter, which exhibits the highest frequency of DW’s.The simulation at 3-km grid spacing agrees considerably better with observations in DW-prone regions, indicating that such high resolution is needed to reproduce strong DW’s. The CT’s with highest frequency of DW occurrence are those with strong cross-mountain pressure gradient. For every CT, DW’s were additionally grouped as either cold or warm winds in the lee of a mountain, distinguishing between bora-like (cold) and foehn-like (warm) DW’s. Due to the considerable terrain complexity of the Scandinavian Mountains, a straightforward link between CT’s and the DWs’ effect on local warming or cooling is not evident. Results show that even in a single grid point DW’s can cause warming and cooling regardless of a CT or wind direction. We focus on one such exemplary grid point to highlight how different synoptic settings can lead to very similar local conditions but with opposite DW character. Additionally, insights into future climate projections of DW’s are presented.
How do we measure the impact of a Climate Service? Lessons learned from Klimaatlas, the Danish National Climate Atlas
Authors: Jønsson Asta Raagaard , Lauridsen Mette Høj , Stensballe Julie , Payne Mark R.
Published: July 2024

Climate services typically provide information with the goal of supporting climate adaptation at the local level: a successful climate service is therefore one that has impact on society and that is used to make decisions. While quantifying this impact is essential for ensuring the continued relevance of the service and for justifying continued funding, it is not clear how best this should be done. For example, standard metrics used within academia, such as the number of citations a paper or dataset receives, are not relevant, as most users of a climate service are not engaged in the publication of manuscripts. Similarly, standard web-oriented metrics such as number of page views do not reflect how the data is actually used to make decisions, and their representativeness of impact is often clouded by GDPR and privacy requirements. Here we describe our attempts to quantify the societal impact of Klimaatlas, the Danish National Climate Atlas. We centre our work around an analysis of the DK2020 climate action plans prepared by all 98 municipalities in Denmark, focussing on cases both where Klimaatlas has been used extensively and where it has not been used at all to understand the reasons for these outcomes. We also analyse the results of a user satisfaction survey and follow up interviews to get a better understanding of how Klimaatlas is used. The work shows that over 90% of all municipalities in Denmark use Klimaatlas to some degree. The impact analysis also revealed uses of Klimaatlas in other layers of government, in education, literature and private industry, many of which we were previously unaware of. Here we will focus on the lessons learnt from this exercise, and in particular on the need to think impact assessments directly into the design of climate services from the very start. Clearly expressed requirements for how to cite the data can also aid impact tracking, as well as privacy-compliant mechanisms to track usage and the use of standard identifiers such DOIs.
How well do dynamical seasonal forecasts capture soil-moisture atmosphere coupling?
Authors: Day Jonathan , Stockdale Tim , Vitart Frederic
Published: July 2024

Past observational studies and numerical experimentation suggest that spring soil moisture anomalies in the northern hemisphere can induce significant anomalies in local summer temperature and precipitation as well as in large-scale planetary wave patterns. This is particularly the case in regions where soil-moisture memory is long and there are strong soil-moisture-atmosphere interactions. However, recent studies, where soil-moisture initial conditions of dynamical forecasting systems are scrambled, have found more modest impacts suggesting that current seasonal forecasting systems may not accurately capture this source of potential predictability.Here we present an evaluation of two important aspects in the dynamical models that contribute to the multi-model seasonal forecasting system of the Copernicus Climate Change Service (C3S). Firstly, assessing the skill of soil-moisture in seasonal hindcasts and secondly analysing the realism of soil-moisture-atmosphere coupling strength. Both of which need to be well represented to achieve accurate predictions. These links between soil-moisture, evapotranspiration and temperature are investigated by way of correlation metrics to identify and compare locations where evaporation is soil-moisture limited or energy limited in seasonal hindcasts and observations.A key finding of this analysis is that in the summer hindcasts, initialised in May, tend to misrepresent the precise location of the so-called North America coupling “hotspot”, an important region for land-atmosphere coupling. The soil-moisture atmosphere coupling tends to be too weak in the models in the western USA and too strong in the east. Both the reasons for this misplacement and the impact of this on forecasts of temperature, precipitation and large-scale planetary waves will be investigated.  
Temporal variations of urban heat islands during COVID-19 lockdown in Prague, Czechia
Authors: Dogan Tugba , Urban Aleš , Hanel Martin
Published: July 2024

The Urban Heat Island (UHI) effect has significant implications for human thermal comfort, urban ecosystems, and energy consumption. The COVID-19 global lockdown presented a unique opportunity to explore the impact of reduced air pollution emissions and Anthropogenic Heat Flux (AHF) on UHI. While some studies have proposed that the lockdown’s reduction in AHF led to a decrease in both Atmospheric UHI (AUHI) and Surface UHI (SUHI), these findings are susceptible to inherent uncertainties due to unaccounted weather variability and urban-rural dynamics.Our research provides a comprehensive analysis of the lockdown’s impact on AUHI and SUHI in Prague, Czechia. We selected days with similar weather conditions and compared the mean SUHI using MODIS satellite imagery and AUHI based on air temperature data from Prague weather stations during the lockdown period from March to April 2020 with a reference period spanning March to April 2017-2019.Our findings reveal that the lockdown period, compared to the reference period, was associated with a 15% (0.1 °C) reduction in SUHI in Prague’s urbanized areas and a 0.7 °C decrease in AUHI in the city center. Furthermore, we observed a 12% and 29% decrease in satellite-based aerosol optical depth and nitrogen dioxide, respectively. These observations support our hypothesis that the observed weakening of UHI effects is linked to the reduction in anthropogenic activities during the lockdown. In addition, our study shows the largest decrease in mean SUHI magnitude was in the periphery, an area characterized by predominantly rural land cover. This highlights the importance of considering urban-rural dynamics when attributing changes in SUHI to AHF.In conclusion, our study provides additional insights into the role of reduced anthropogenic activities in UHI dynamics during the COVID-19 lockdown. It offers policymakers a comprehensive understanding of how the complex interplay between urban and rural microclimate dynamics influences the SUHI phenomenon.
Analysis of a fire danger index over Southeast Europe
Authors: Szabó Péter , Pongrácz Rita , Bartholy Judit
Published: July 2024

In addition to the historically sensitive Mediterranean regions, northern parts of Southeast Europe are increasingly vulnerable to forest fires exacerbated by climate change. Higher temperatures, more frequent heat waves, and erratic rainfall patterns in the region foster conditions conducive to swift wildfire ignition and propagation. Human-induced factors further exacerbate the risk, while the ecological consequences of these fires are far-reaching, affecting biodiversity and human lives.The Forest Fire Danger Index is a tool for assessing wildfire risk under very dry soil conditions. It incorporates factors besides soil dryness such as daily wind speed, mean temperature, and humidity to assess the potential risk for wildfires. Elevated wind speed can hasten fire propagation, while low humidity and high temperatures increase the likelihood of ignition and accelerate spread in arid regions. To calculate soil dryness specifically developed for fire controls, we analyzed the widely-used Keetch-Byram Drought Index, which relies on daily precipitation, maximum temperature, and effectively captures the soil's evaporation-precipitation balance.For the calculations, we assessed E-OBS version 29, a state-of-the-art, quality-controlled and interpolated observational dataset on 0.1° horizontal resolution for the reference period of 1971–2023. For the future, we used an ensemble of regional climate model simulations from the Euro-CORDEX initiative, which takes into account future anthropogenic activity through three distinct RCP scenarios until 2100 (RCP2.6, RCP4.5, and RCP8.5).Results indicate that regions historically experiencing infrequent severe compound wildfire events, such as most of the northern lowland areas in Romania, Bulgaria, Serbia, Hungary, or Croatia, are exposed to significant changes with global warming. Particularly after 2060 in the case of the non-mitigation scenario of RCP8.5 compared to the immediate mitigation of RCP2.6. Consequently, these possibly high impacts require a substantial investment in fire monitoring, education, and a warning system in these countries as well.Acknowledgments: Research leading to this study has been supported by the European Climate Foundation (G-2309-66801), the Hungarian National Research, Development and Innovation Fund (K-129162), and the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014).
On the automated detection of the rotational center for the characterization of Mediterranean cyclones
Authors: Sebastianelli Stefano , D'Adderio Leo Pio , Sanò Paolo , Casella Daniele , Panegrossi Giulia , Herndon Derrick
Published: July 2024

Recent studies exploiting satellite observations characterized both the thermodynamic and the microphysical structure of Mediterranean cyclones supposed to go through a tropical transition (Medicanes) with particular reference to the role of deep convection in the development of the warm core. In analogy with what happens in tropical cyclones (TC), the surface wind field is useful to characterize Medicanes, as it could give additional information on their evolution.The purpose of our work is the characterization of the surface wind field of Medicanes by means of the correct identification of Medicane’s rotational center. The methodology we developed exploits the computation of the standard deviation of the horizontal surface wind direction also taking into account the wind speed field. This methodology is based on the observation that very close to the center the wind direction is highly variable in space due to the formation of the cyclonic vortex.The Automated Rotational Center Hurricane Eye Retrieval (ARCHER) algorithm, developed by the TC group at CIMSS/University of Wisconsin-Madison, is widely used for the correct identification of a TC’s center of rotation. Since Mediterranean cyclones often exhibit satellite-based phenomenological features typical of TCs we will also try to investigate the applicability of ARCHER algorithm to these cases.To retrieve the surface wind field over the sea, data provided by the Advanced SCATterometer (ASCAT) real-aperture radar onboard MetOp satellites and by the Wind Radar (WindRAD) onboard of Feng Yun FY-3E satellite series are used. For both sensors the surface winds field estimation is related to the roughness of the sea surface through the back-scattered electromagnetic signal. We exploit all the available ASCAT and WindRAD overpasses for the Medicanes occurred in the last decade.A key feature that characterizes the surface wind field of a Medicane is the radius of maximum wind (RMW). Following the definition provided for TCs, the RMW is defined as the distance between the location where the maximum wind speed occurs and the Medicane’s center of rotation. A further objective will be to show how the different methodologies used for the determination of the Medicanes' rotational center influence the RMW computation as the cyclones evolves to its mature phase and to what extent the RMW can be used as a proxy for the cyclone’s intensification.Preliminary results show that our methodology is more reliable during the mature phase when the Medicane is more organized showing a closed cyclonic structure associated to strong near-surface winds with a quasi-calm area in its center, and that the RMW decreases as the medicanes intensify. This work is part of the ESA project “Earth Observations as a cornerstone to the understanding and prediction of tropical-like cyclone risk in the Mediterranean (MEDICANES)”.
Can seasonal forecasts be used to quantify the hazard of extreme meteorological droughts?
Authors: Buccellato Marco , Ruggieri Paolo , Porcù Federico
Published: July 2024

The characterization of hydrometeorological extreme events can be hindered by limited availability of observations of past meteorological conditions. Seasonal forecasts, commonly used to represent the temporal evolution of predictable components of the Earth system over periods of a few weeks or months, have recently been applied to obtain information about extreme events and their dynamical mechanisms. These forecasts typically have higher spatial resolution than climate projections and can provide a better description of the complex and nonlinear interactions among Earth system components. This study presents an application of seasonal forecasts for studying meteorological drought across Europe, with a focus on precipitation deficits over Germany. Following a rigorous workflow, the realism of seasonal hindcasts from five contributors to the Copernicus Climate Change Service (C3S) is assessed through various steps including bias correction, statistical characterization and stability check of the outputs. When examining physical credibility of simulated extremes, the mean 500 hPa geopotential anomaly field associated with the most intense meteorological droughts over Germany shows common large-scale features across different models, suggesting that seasonal forecasts can serve as a powerful tool for better understanding the drivers of climate extremes at the regional scale and the role played by the involved teleconnections. Furthermore, chance assessment for the period 1993-2016 indicates that harmful and unprecedented meteorological droughts, such as those observed in 2018 and 2022, could have been anticipated using seasonal ensembles. The refined methodology can be successfully downscaled to various regions of the European domain, providing hydrologists and engineers with an actionable tool for evaluating regional drought hazard.
Sensitivity analysis in pollution dispersion within street canyons
Authors: Moreira Beltrami Gabriel , Beicker Rafael , Gonçalves dos Santos Rogério , Jorba Oriol , Mateu Armengol Jan
Published: July 2024

The trend of urbanization is increasing due to socioeconomic factors, leading to a decline in air quality and heightened exposure for individuals. Modeling dispersion of emissions within street canyons is crucial for understanding  local-scale air pollution exposure  indicators and devising effective strategies to enhance urban air quality. However, microscale urban air quality simulations are complex and involve a large number of interacting physical processes. Phenomena taken into consideration in simulations of urban pollution dispersion largely vary from one study to another. In this context, we aim at identifying the most influential parameters governing this phenomenon.  First, we present a comprehensive database of pollution dispersion in street canyon by perturbing the most important parameters. In this study, these parameters include wind speed, background concentration, emission rates, background turbulence intensity, temperature disparity between building surfaces and the atmosphere, as well as model-specific parameters. We employ COMSOL Multiphysics for the computational fluid dynamic simulations. To keep feasible computational times and create a large database, turbulence is modeled by means of the Reynolds Averaged Navier-Stokes (RANS) k-ε technique and the domain is considered two-dimensional.  This simulation bank is used to develop a surrogate model based on Gradient Boosting Machine and the Principal Component Analysis to reduce system complexity. This surrogate model offers a solution to carry out a multivariate global sensitivity analysis. To this end, we compute the Sobol Indexes, providing insights into the global contributions of input factors to the resulting pollution concentrations at each point of the physical domain.  Results show that, as expected, background concentration dominates the pollution field far from the emission point. The temperature of building surfaces plays a secondary role, acting mainly on the interface between the street canyon cavity and the flow above the buildings. Emission plays a fundamental role, especially in the vertical concentration profile. Other  model-specific parameters have a minor role when compared to the other physical variables. Wind speed plays a key role, along with temperature, in determining how pollution exits the cavity. By integrating advanced simulation techniques with rigorous sensitivity analyses, this study aims to provide valuable insights into the factors influencing air quality in urban street canyons. Such insights are crucial to perform model calibration, and to understand the implications of typical approximations such as non-buoyancy flows.
Statistical summaries for streamed climate data
Authors: Grayson Katherine , Thober Stephan , Roura Adserias Francesc , Lacima-Nadolnik Aleksander , Sharifi Ehsan , Doblas-Reyes Francisco
Published: July 2024

Projections from global climate models (GCMs) are regularly used to create information for climate adaptation policies and socio-economic decisions. As demand grows for accuracy in these projections, GCMs are being run at increasingly finer spatiotemporal resolution to better resolve physical processes and consequently reduce uncertainty associated with parametrizations. Yet this increase in resolution and the consequent size of the data output makes the current state-of-the-art archives (e.g., CORDEX, CMIP) unfeasible. Moreover, the current archival method has left some data consumers without their required data due to the limited number of variables stored and their lower frequency (e.g., monthly means). Initiatives like Destination Earth are investigating the novel method of data streaming, where user applications can be run as soon as the required data is produced by the climate models. Data streaming allows users to access the climate data at the highest frequency possible (e.g., hourly) and native resolution in near real model run-time. This provides an unprecedented time-scale reduction to access the climate data compared with the current simulation paradigm and the possibility of using variables and frequencies not previously available.Yet the advent of data streaming in the climate community poses its own set of challenges. Often users require climate data that spans long periods. For example, many hydrological impact models require daily, monthly or annual maximum precipitation values, while in the wind energy sector, accurate distributions of the wind speed over long periods are essential. Obtaining statistics for periods longer than the time the climate model output is accessible can no longer be done using traditional statistical algorithms. This introduces the one-pass problem; how to compute summaries, diagnostics or derived quantities that only see each data point once (i.e., pass through the data one time)?We present here a detailed analysis on the use of one-pass algorithms to compute statistics on streamed climate data. Unlike traditional two-pass methods, one-pass algorithms do not have access to the full time series of data needed to estimate the statistic; instead, they process data incrementally every time that the model outputs new time steps. While these algorithms have been adopted in other fields such as online trading and machine learning, they have yet to find a foothold in climate science, mainly because they have not been necessary until now. Here we show how one-pass algorithms can be harnessed for use in Earth system digital twins, generating the statistics required by users with minimal loss in accuracy and bypassing unfeasible storage requirements.
Multidecadal variability of the ENSO teleconnection to early-winter temperature in Europe
Authors: Fernández-Castillo Pablo , Losada Teresa , Rodríguez-Fonseca Belén , García-Maroto Diego , Durán Luis , Mohino Elsa
Published: July 2024

El Niño-Southern Oscillation (ENSO) is the main mode of climate variability at interannual timescales, impacting the climate of many regions worldwide through atmospheric teleconnections. Characterising these teleconnections is relevant to improve seasonal prediction skill over the regions affected by ENSO. However, the early-winter (November-December; ND) North Atlantic and European (NAE) atmospheric circulation response to ENSO and its associated impacts on surface air temperature in Europe remain unclear. Hence, the ENSO early-winter teleconnection to temperature in Europe is analysed in this study. Unlike most of the previous studies addressing this teleconnection, our analysis is carried out in different time periods, taking into account the climate shifts that occurred in the Pacific in the 1970’s and in the 2000’s. This fact allows for the possibility of a non-stationary teleconnection due to changes in the mean circulation. The study also aims at analysing the ability of the European Centre for Medium Range Weather Forecasts’ (ECMWF) seasonal forecast system System 5 (SEAS5) to reproduce the ENSO early-winter teleconnection. Results show that ENSO has a significant impact on the early-winter temperature in Europe, with a clearly non-stationary behaviour of the teleconnection. Between the 1950s and mid-1970s, impacts on temperature were restricted to the far north of Europe, and since the late 1970s, they have extended to much of the rest of Europe, with the largest impact since the 2000s being located in the southwest. In this region, ENSO shows significant correlation with the leading mode of seasonal early-winter temperature variability. These changes are related to interdecadal changes in the ENSO wave train pattern that reaches the NAE region. ECMWF’s SEAS5 captures reasonably well the spatial pattern of the teleconnection, although with a notably weaker signal compared to observations. Overall, these results may contribute to enhance early-winter seasonal predictability over Europe.
Future changes in global drought fractality
Authors: Galiano Lorena , Monjo Robert , Royé Dominic , Martin-Vide Javier
Published: July 2024

Drought is one of the extreme events that will be increasingly present in the context of climate change. As revealed in recent studies, meteorological droughts will become the principal factor modulating compound hot-dry events and analysis; thereof is therefore fundamental with regard to understanding future climate patterns. The average citizen knows little of geometry, but it plays an essential role in the characteristics of the droughts, by means of "fractional lengths". We analysed the fractality of the meteorological droughts under the most recent climate change scenarios. In addition, a new criterion for defining the beginning and end of meteorological droughts is established, which allows the analysis of seasonal variability. A temporal fractality measure based upon the Cantor set reveals consensual changes in the behavior of droughts worldwide. Most regions will undergo a slight increase in fractality (up to +10% on average), particularly associated with an acceleration of the hydrological cycle and the Hadley cell expansion, with a shift towards the higher latitudes of the tropical edge in both hemispheres. Geometrical measures were applied to the dry spells (
Comparative Evaluation of WRF and WRF-Solar for solar irradiance in South Korea
Authors: Yoon Ji Won , Kim Hyunsu , Lee Seungyeon , Park Seon Ki
Published: July 2024

The accurate forecasting of the solar irradiance is crucial for the grid stability, energy management, and maximizing the efficiency of the solar energy systems. The increasing reliance on solar energy as a key element of its renewable energy strategy highlights the need for accurate solar irradiance forecasting not only globally but also in South Korea. However, the variability of solar irradiance greatly influenced by the weather conditions poses challenges to its integration into the energy grid. To address this issue, we simulate two numerical weather prediction models---Weather Research and Forecasting (WRF) and WRF-Solar---during high-demand summer seasons for electricity and then utilize various observational data, such as satellite and in-situ measurements, to investigate the prediction performance of the solar irradiance. The WRF-Solar model was specialized for the solar irradiance prediction and fully integrates cloud-aerosol-radiation feedback to enhance solar irradiance prediction performance. Through the evaluation of these models, we aim to enhance the prediction accuracy of the solar irradiance in South Korea. Preliminary findings indicate that WRF-Solar exhibits better performance compared to the WRF model in South Korea. These results emphasize the potential benefits of integrating the solar radiation physics and specialized algorithms into forecasting models. By doing so, WRF-Solar shows potential in overcoming the challenges associated with solar irradiance forecasting. In the future, we need more research to better understand the abilities and limitations of WRF-Solar, and to improve its forecasting accuracy. With this knowledge, we can make solar irradiance predictions more accurate and help solar energy grow in South Korea.
UV spectra and atmospheric parameters measured with ultraviolet solar radiation instruments in EURAMET BIOSPHERE campaigns
Authors: Doppler Lionel , Bolsée David , Van Laeken Lionel , Zuber Ralf
Published: July 2024

The European Partnership on Metrology (EPM) joint research project BIOSPHERE investigates how the increasing ionization of the atmosphere, caused by extraterrestrial radiation fields (cosmic rays and solar UV radiation) and amplified by anthropogenic emissions, affects the human and ecological health of our planet. One main aspect is how these extraterrestrial radiation fields impact the stratospheric ozone (enforced depletion), resulting in an increase of Ultraviolet (UV) radiation on the ground.To assess the influence of extraterrestrial radiation fields on atmospheric parameters and on solar UV radiation on the Earth surface, BIOSPHERE organizes four measurement campaigns with colocated SCR (Secondary Cosmic Rays) measurements (revealing the cosmic rays events) and solar UV radiation measurements, in GHI (global horizontal irradiance) and DNI (direct-sun normalized irradiance) geometries of observation. From the DNI UV radiation measurements we extract some atmospheric parameters: the total ozone column (TOC) in dobson units (DU) and the aerosol optical depth (AOD) for the UV wavelength range (290 nm - 400 nm).The measurement campaigns took place at Athens (near urban site) in summer, in Brussels (urban site) in winter, in Milesovka (Czech Republic, rural mountain site) in spring/summer and will take place in Lindenberg (North-East Germany, rural flatland site) in autumn.In addition to the correlation study confronting SCR measurements to TOC, AOD and UV measurements, we focus here on the quality of the UV radiation and atmospheric parameters’ measurements during the first campaigns: UV radiation (GHI) and especially the UV index from spectroradiometers (array spectrometers: Gigahertz-Optik BTS2048-UV-S-WP, Bentham DTMc300 double monochromators) are compared to measurements from UVB pyranometers and  a multi-channel filters radiometer (GUV-511 from BIOSPHERICAL Inc.) on the same site. TOC and AOD measurements done with UV DNI spectral measurements with an array spectrometer (Gigahertz-Optik BTS-Solar based on BTS2048-UV-S-WP) are compared to Brewer and photometer measurements done on geographically close sites. This survey analyzes the measurement differences, considering the instrumentation itself, the measurement retrieval procedures, and the spatial heterogeneity of the atmospheric parameters when the measurements that are compared to each other are not done at the same site.
Past and future changes in the spatiotemporal distribution of heat stress in Madrid
Authors: Sanchez Beatriz , Martilli Alberto , Santiago Jose Luis , Rivas Esther , Martin Fernando , Royé Dominc , Carbone Juan , Yagüe Carlos
Published: July 2024

One relevant effect of urbanization is the modification of atmosphere-surface interactions, which modulate urban microclimate and human thermal comfort. To quantify the impact of heat on the human body, more comprehensive biometeorological indices, such as the Universal Thermal Climate Index (UTCI), are commonly employed accounting for not only air temperature influence but also the variability of other relevant weather variables such as wind speed, air humidity and radiation. The irregularity of urban morphology (e.g. building height and layout) across the city leads to high spatial heterogeneity of the micrometeorological variables, in particular on wind speed and solar radiation (e.g. shading effects). Therefore, analyzing the impact of urban geometry and the past changes in urban land cover on heat stress contributes to understanding the potential risks that urban residents might face considering the future urban growth and future climate.The purpose of the present work is to investigate the impact of urban development and climate on outdoor thermal comfort in Madrid for summer weather conditions under past and future climate. A modeling study is conducted using the Weather, Research and Forecasting (WRF) model adapted to estimate the heat and momentum exchanges between buildings and atmosphere (BEP-BEM urban scheme), as well as the recent development incorporated into BEP-BEM to quantify heat stress through UTCI values and its subgrid variability. Past urban scenarios are performed considering the realistic urban expansion and morphology from 1970 to 2020, and the expected urban development is used for the future scenario. The model evaluation is conducted against observations showing an overall good performance of the model in predicting near-surface meteorological parameters. Even though the urban layout has barely changed in the center of Madrid over the last 50 years, results show an increase in the UTCI values due to the influence of the surrounding urban expansion. In addition, these results show the relative contribution of urbanization and climate effects on the heat stress changes across the city under the past and future climates.
Scale-Dependent Rossby Wave Dynamics and Alaskan Ridge Amplification: Impacts on Pan-Atlantic Compound Extreme Events
Authors: Strigunova Iana , Messori Gabriele
Published: July 2024

Our work focuses on the dynamic drivers of cold spells in North America and wet and windy extremes in Europe, which we refer to as pan-Atlantic compound surface extremes. In particular, we consider the role of different atmospheric dynamical pathways in leading to an intensification of the Alaskan Ridge, which previous work has highlighted as a key precursor to the pan-Atlantic extremes. We specifically emphasise the role of Rossby wave activity acting on various spatial and temporal scales. The separation is performed on the ERA5 data projected into the normal-mode functions, as one of the available wave decomposition techniques. We select planetary (zonal wavenumbers 1-3) and synoptic (zonal wavenumbers 4-8) components to understand what processes are related (or dominant) at different Rossby wave scales. One potentially important mechanism for Alaskan Ridge amplification is related to stratospheric wave reflection events, which are associated with the vertical propagation of planetary-scale waves, and occur approximately two weeks before the peak of North American cold spells. On a shorter timescale (within 10 days), Rossby wave trains with smaller spatial scales are enhanced 5 days prior to the peak. Preliminary analysis suggests that the enhancement is likely to result from the merging of meridionally and zonally propagating waves within the modulation of the planetary waves. Our results highlight the intricate connection between different dynamical processes in the atmosphere and amplified Alaskan Ridges, which in turn are a precursor to pan-Atlantic compound extremes. The dynamical and statistical link to surface extremes could aid in predicting such events.
Homogenization and assessment of observed near-surface wind speed across Argentina, 1961-2022
Authors: Poggi Maria Mercedes , Bettolli Maria Laura , Azorin-Molina Cesar , Skansi Maria de los Milagros
Published: July 2024

Argentina, a country located in the southern part of South America, has a large wind resource. However, renewable wind energy at the national level is under-exploited, with less than 15% of the country's energy coming from a renewable source. Existing studies are generally focused on the analysis of winds for particular stations or regions such as the Patagonia, where intense and persistent winds dominate throughout the year. Nevertheless, the study of wind and its socioeconomic and environmental implications is still lacking in Argentina.This study aims to fill the research gap of quality controlling and homogenizing wind speed measurements from a meteorological network of 117 conventional stations from the National Meteorological Service of Argentina (SMN), using the R package Climatol. Here we will also correct the biases introduced by new automatic weather stations, which are planned to progressively replace the conventional ones in the near future. Moreover, metadata about the type, changes and calibrations in anemometers (30% of the stations used cup anemometers) is rescued to support the corrections of detected breakpoints by the Standard Normal Homogeneity Test (SNHT). Additionally, the quality controlled and homogenized database is used here to assess for the first time (i) the spatio-temporal climatology and (ii) the long-term trends and multidecadal variability of near-surface mean wind speed across Argentina for 1961-2022. This research is highly important as there is only little evidence of changes in surface winds in the Southern Hemisphere; where both observations and projections show positive trends, denoting an interhemispheric asymmetry because of the declining (stilling) of winds observed in the Northern Hemisphere.
Anisotropic turbulence in marine cumulus clouds
Authors: Krol Stanislaw , Wacławczyk Marta , Jędrejko Paweł , Malinowski Szymon
Published: July 2024

Clouds are such manifestations of turbulent flow in the atmosphere, where different physical processes and effects such as radiation, entrainment, microphysical interactions, non-zero gradients of vertical profiles of temperature and wind speed influence the flow at different scales. When analysing turbulence characteristics in clouds, one often assumes homogeneity, isotropy, and equilibrium. For such complicated systems as clouds, in which many thermodynamical and microphysical processes take place, these assumptions might fall short. If the turbulence characteristics are calculated along short windows, which still capture bigger scales of the order of the size of the cloud, then deviations from isotropy are observed in cloudy regions of the flight leg.In this work, anisotropy is studied using anisotropy invariant maps. An example of anisotropy invariant map is the Lumley's triangle, which is a type of plot where theoretically possible values of invariants of anisotropic stress tensor are contained in a triangle figure. By studying the values of the invariants, one may connect the results to ideal geometrical cases such as elongation or contraction along one of the dimensions. Studying the time dependence of the values of the invariants gives an insight into different regimes of anisotropy in clouds.The data under consideration comes from the EUREC4A campaign, where Twin-Otter aircraft performed penetrations of marine cumulus clouds near Barbados in 2020. Conditional statistics are made using the RQA method to differentiate between the cloud and its dynamical surroundings. By employing that differentiation, the extent of the anisotropy is studied. The effect of the averaging window is also considered. The preliminary results suggest that the turbulence in clouds is anisotropic to a degree.
Precipitation Phase Prediction in Norway Using Neural Networks
Authors: Ersland Olav , Nipen Thomas Nils , Seierstad Ivar Ambjørn
Published: July 2024

We introduce a neural network for predicting precipitation phases across Norway. This method is trained on observation data from 52 airports throughout the country, with data from 3 winter seasons. One of our main concerns is to have a method that works well in operation on Yr.no. It needs to be fast, and give results that are intuitive, so that it is easy to judge whether the method gives reasonable predictions. The neural network uses predictors from the MetCoOp Ensemble Prediction System (MEPS) that forecasts weather across Norway. We use three predictor variables: air temperature at 1500 meters above the ground, air temperature at 200 meters above the ground, and the wet bulb temperature at 2m above the ground. The model output is the probability of snow for any given point in time and space, given that there is precipitation present. The problem itself is a classification problem, so we have used a TensorFlow model with a sigmoid activation function in the output layer, together with a binary cross entropy loss function. Other than that, we have tested different configurations of hidden layers. It turns out that a simple configuration performs well and is robust with respect to overfitting.  Until now, MET Norway has used a simple threshold based method in operation, which is based on the wet bulb temperature at 2m height above the ground. This neural network approach offers a simple and data driven alternative to this, and improves the forecast in difficult weather scenarios. To evaluate the method, we apply metrics such as accuracy and brier score, to compare our results with former methods.
Validation of satellite thermal infrared dust aerosol optical depth : challenges and results
Authors: Vandenbussche Sophie , Moses Vishal David , De Mazière Martine
Published: July 2024

Mineral dust is a major component of tropospheric aerosols, and plays a significant role in the Earth’s energy balance (both directly and indirectly through impact on clouds), air quality, health, transportation, and solar energy. Although being natural particles, their emission mechanisms are linked to the ground surface state (vegetation cover, soil type and moisture) and the surface winds, which are all impacted by human activities and climate change. The radiative impact of dust depends on many parameters, such as total amount, vertical distribution, particle size and composition.The Mineral Aerosol Profiling from Infrared Radiances (MAPIR) algorithm focuses on retrieving dust total amount (in terms of Aerosol Optical Depth or AOD) and vertical distribution from satellite observations in the thermal infrared (TIR), by the Infrared Atmospheric Sounding Interferometer (IASI) satellite instrument. As far as aerosols are concerned, the TIR spectral range has the advantage of being sensitive almost exclusively to mineral aerosols (with specific absorption around 10µm), making it intrinsically specific to mineral dust and volcanic ash.We have recently developed a new version of the MAPIR algorithm (version 5.1), requiring validation. As there is currently no validation data obtained in the thermal infrared, we can only use aerosol data obtained in the solar spectral range. Comparing data obtained with totally different sensors, using a different spectral range (therefore sensitive to different types of particles) and different types of algorithms includes significant challenges. In this contribution, we will discuss those challenges. We will show comparisons of MAPIR dust AOD with AErosol RObotic NETwork (AERONET) Standard Deconvolution Algorithm (SDA) coarse mode AOD, and focus on some specific events where we observe a large difference. For those events, additional satellite data (e.g. Cloud-Aerosol Lidar with Orthogonal Polarization - CALIOP, MODerate resolution Imaging Spectroradiometer - MODIS) will be used for in-depth analysis of the event and investigating the plausible causes for discrepancy.
Probabilistic forecasts of winter road maintenance operations based on machine learning
Authors: Kämäräinen Matti , Haikonen Minna , Tuhkalainen Elina
Published: July 2024

Planning the scale of necessary road maintenance actions based on outputs from numerical weather prediction (NWP) models can be challenging. By employing machine learning, these actions can be estimated more objectively. 

A machine learning model was developed to predict the hourly probability of salting and snow removal operations across different road maintenance categories. This model provides a three-day forecast of maintenance probabilities over a five-kilometer pixel grid covering mainland Finland. 

The model was developed collaboratively by the Finnish Meteorological Institute and Destia, Finland's largest infrastructure service company. This development was part of the JääVe project, funded by the National Emergency Supply Agency. The model uses a gradient boosting approach and is trained on reports of snow removal and salting operations provided by Destia. Inputs to the model include MEPS NWP model outputs, cyclical temporal variables, and static landscape features. 

The model has learned to account for varying regional and seasonal characteristics. For example, it predicts different probabilities for coastal versus inland areas and adjusts for variations between the sparse northern regions and the more populated southern settlements. The model's predictions are comprehensive, extending beyond the road station locations used for fitting to cover the whole Finland. 
 
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Assessment of Engineering Models for Large-Scale Cluster Wakes Using In Situ Airborne Measurements
Authors: zum Berge Kjell , Centurelli Gabriele , Dörenkämper Martin , Bange Jens , Platis Andreas
Published: July 2024

Germany is to be climate-neutral by 2045, with important interim targets set for 2030. This includes a 65% reduction in greenhouse gas emissions compared to 1990 and a significant increase in the proportion of renewable energies in the electricity mix to at least 80% by 2030. This share currently stands at around 56%. A key element of this strategy is the Renewable Energy Sources Act (EEG 2023), which provides for an accelerated expansion of renewable energies. This includes wind and solar energy, with particularly ambitious targets for offshore wind energy: at least 30 gigawatts by 2030, 40 gigawatts by 2035 and 70 gigawatts by 2045. This strong expansion of offshore wind energy brings with it several challenges. The biggest is certainly the space required in the German Bight and in the Baltic Sea. Studies are already showing long wake lengths of wind farms and wind farm clusters, which can reach wind farms located downstream and thus reduce their energy production. With the increasing expansion of wind energy in the German Bight, the influence of wind farms on each other will increase and lead to greater power losses. This influence must be considered in future planning. The models often used by industry are so-called engineering models. They have low computational costs due to their fast calculations but are made for power yield calculations within a wind farm and not for modelling the wake in the far field. An approach was taken to use different setups of this model to evaluate the results against measurement flights of a research aircraft in the lee of large wind farm clusters in the German Bight. As part of the X-Wakes project, the wakes of wind farm clusters were measured and evaluated under different meteorological conditions using the D-IBUF research aircraft operated by the Technische University of Braunschweig. This data was then used to evaluate the performance of the Weather Research and Forecasting Model with Wind Farm Parameterization (WRF-WF) and the engineering model "FOXES" (Farm Optimization and eXtended yield Evaluation Software) with four different setups. 