bluuebunny/crossref_metadata_2025 huggingface dataset:
Horizontal and vertical wind speed sampling using multirotor UAS aircraft
Authors: Bramati Matteo , Schoen Martin , Savvakis Vasileios , Wang Yongtan , Bange Jens , Platis Andread
Published: July 2024

The utilization of multi-rotor UAS aircraft for atmospheric data collection is an expanding field, and one method employed for measuring atmospheric wind speed is the so called tilt angle method. This method correlates the tilt angle assumed by the multi-copter during hovering to compensate for aerodynamic drag forces due to the atmospheric wind.At the Umweltphysik Group of the Eberhard Karls Universität Tübingen, a cost-effective and easily replicable calibration method has been devised and tested. However, this approach overlooks the crucial vertical component of the wind, essential for calculating vertical turbulent fluxes.To address this limitation, a follow-up study proposes an analytical approach that involves calibrating relationships between wind speed and tilt angle, motor RPM and tilt angle, as well as vertical wind speed and RPM. The necessary data for this calibration can be obtained through telemetry from an open-source flight controller's log files and proper electronic speed controllers.Accurate calibration of these relationships is ensured through real-world flight testing to maintain high precision. Indoor tests or wind tunnel experiments might yield biased results due to interactions with walls, failing to accurately represent the aircraft's outdoor aerodynamic behavior.Considering environmental parameters such as air density is vital, as evidenced by notable differences between calibrations conducted in winter and summer. These variations underscore the necessity of accounting for environmental influences to avoid constant biases in the wind retrieval.Subsequently, the method will undergo further validation by flying the aircraft in close proximity to a sonic anemometer to assess its accuracy in measuring atmospheric parameters.
 Multidecadal variability of winter precipitation and snowfall frequencies in a Mediterranean mountain range
Authors: Durán Luis , González-Cervera Álvaro , Rodríguez-Fonseca Belén
Published: July 2024

The Mediterranean mountains are characterized by having most of its annual precipitation concentrated in winter and in the form of snow. They normally experience a summer drought with drier and hotter summers when compared with other mountain ranges. Therefore, snow is crucial as a water resource in these regions, providing water during the spring snowpack melting. On the other hand, according to the recent IPCC-AR6 report, the Mediterranean is a climate change hot spot, which threatens the future of snow in its mountains. Understanding the variability of winter precipitation and its underlying mechanisms on large time scales allow us to find predictability patterns, and thus to pursue mitigation and adaptation strategies.This study employs reconstructions of meteorological series from mountain stations in the Iberian Peninsula, utilising a downscaling methodology derived from ERA20C and ERA5 reanalyses. The methodology for reconstruction is based on an analog method, which is presented and discussed. The methods and code for the reconstructions are publicly available.  A comprehensive analysis of the low-frequency variability and trends of the resulting winter precipitation and the frequency of snow days has been conducted for the period 1900-2024. This analysis encompasses the investigation of teleconnection patterns associated with the low-frequency variability of winter precipitation and snowfall, as well as the frequency and nature of synoptic circulation types that lead to orographic precipitation and large-scale precipitation enhancement. The analysis and conclusions presented here provide a more comprehensive understanding of the mechanisms involved in the occurrence of decades with more or less winter precipitation and snowfall frequencies.
Assessment of impact of data assimilation of the regional ocean wave model
Authors: Oh Sang Myeong , Chang Pil-Hun , Park Jeong-Hyun , Kang Hyun-Suk , Cho Ik Hyun , Moon Il-Ju
Published: July 2024

Globally, 39% of the world's population lives within 100 km of the coast. Eight of the top ten largest cities in the world are located along the coast. In South Korea, 27% of the population also resides in coastal areas. Ocean waves, induced by sea winds, potentially endanger offshore infrastructure and threaten low-lying ecosystems and communities due to coastal erosion and flooding. More accurate prediction of ocean wave patterns, or sea states, is crucial for informed decision-making in mitigating these risks. Recently, many National Meteorological and Hydrological Services (NMHs) have been endeavoring to enhance the accuracy of ocean wave predictions by assimilating in-situ and remote sensing observations. The Korea Meteorological Administration (KMA) also initiated an ocean wave data assimilation system in 2021. However, this system has thus far only been adapted to a global-scale model. This study aims to install and evaluate the impact of ocean wave data assimilation on a regional scale. The regional ocean wave data assimilation system employs optimal interpolation based on WAVEWATCH-Ⅲ version 6.07 with a spatial resolution of 1/30° targeted for the East Asian region. Significant wave height data from five satellites in polar orbit, ocean data buoys, and coastal wave buoys were utilized. Numerical experiments for summer (2023JJA) and winter (2023/24DJF) reveal that the use of data assimilation reduced the root mean square error by 49.2% and 38.6%, respectively, for the initial fields of regional wave models. The assimilated initial fields improved ocean wave predictions by 12 hours in the KMA regional ocean wave model, which is consistent with previous research in the field. Particularly during Typhoon KHANUN in August 2023, there was a tendency to overestimate sea winds, which are input variables of the ocean wave model. Despite the use of overestimated sea winds, the regional ocean wave model utilizing data assimilation reduced the error by up to 94 cm.
Evaluation of ESD methods for climate change projections over Spain
Authors: Correa Carlos , Hernanz Alfonso , Rodríguez-Guisado Esteban
Published: July 2024

The study presents an evaluation and selection of global climate models for downscaled projections over Spain. The Spanish Meteorological Service (AEMET) is responsible for the elaboration of regionally downscaled climate projections over Spain according to the second National Adaptation Plan to Climate Change (PNACC-2 2021-2030). Regionally downscaled climate projections allow for increased spatial resolution compared to those provided by global climate models. AEMET develops regionally downscaled climate projections over Spain by applying empirical-statistical downscaling (ESD) methods to projections from a set of global climate models. Regionally downscaled climate projections are necessary for conducting impact and vulnerability studies that require data at high spatial resolution.The present work has two objectives. The first one is to evaluate and select a reduced ensemble of global climate models from CMIP6 in order to downscale their projections. The second objective is to compare different ESD methods in order to choose the best performing method for downscaling daily data of the following climatic variables of interest: maximum temperature, minimum temperature, and accumulated precipitation. The evaluation of global climate models was carried out using the GCMeval tool [1]. The comparison of ESD methods is based on the analysis of indicators such as bias, coefficients of temporal and spatial correlation, root mean square error, and future trends, and it was carried out using pyClim-SDM [2], the statistical downscaling software developed at AEMET. The results indicate the selection of eleven global climate models for downscaling and the choice of specific methods for each climatic variable: regression-analogues (MLR-ANA) for temperatures and eXtreme Gradient Boost (XGB) for precipitation. Additionally, bias correction using Quantile Delta Mapping is recommended to improve projections. This research lays a robust groundwork for future studies on climate change in Spain, highlighting its implications for the population, socio-economic sectors, and ecosystems.Ultimately, the key findings from the downscaling of projections over Spain using the representative ensemble of global climate models from CMIP6 and employing the resulting downscaling techniques from the prior assessment are presented. [1] Parding, K. M. et al., (2020), GCMeval – An interactive tool for evaluation and selection of climate model ensembles. Climate Services, Volume 18, 100167. https://doi.org/10.1016/j.cliser.2020.100167[2] Hernanz, A., et al. (2023), pyClim-SDM: Service for generation of statistically downscaled climate change projections supporting national adaptation strategies. Climate Services, Volume 32, December 2023, 100408. https://doi.org/10.1016/j.cliser.2023.100408  
Tracking arctic boundary layer evolution during on-ice flow using CMET balloons
Authors: Schindewolf Josua , Pithan Felix , Voss Paul
Published: July 2024

The atmospheric temperature profile in Arctic winter plays a key role in the observed Arctic amplification of global temperature changes. In the cold season, the Arctic atmospheric temperature and moisture profiles are a product of advection and transformation of air-masses from lower latitudes (Wexler, 1936, Curry, 1983). These poleward flowing air masses cool and dry over several days, losing the majority of their initial heat and moisture content along their trajectory. The occurrence of the resulting transition from an initially cloudy to a radiatively clear state (Stramler et al. 2011, Pithan et al. 2014) proves to be difficult to understand using Eularian, fixed in-space observations (e.g. Becker 2020, Lonardi 2024).Here we show results from recent controlled meteorological (CMET) balloon (Voss 2012, Roberts et al. 2016) launches from Ny-Alesund, Svalbard. The balloons followed advected air parcels downwind over the ocean beyond the sea-ice edge. CMET balloons can drift at a variable altitude for multiple days along a quasi-Lagrangian trajectory, continuously resolving the vertical structure of the arctic boundary layer (ABL) column.The observed low level ABL profiles show cooling (2.1 – 2.8K) within few hous after crossing onto the sea ice, leading to decoupling of the surface and an increase in near-surface relative humidity. The inversion layer depens by 100m over the course of 1-2 hours.The ABL soundings are combined with ice surface temperature satellite imagery to determine the radiative processes driving changes in cloud state and properties. We hypothesise that the boundary layer typically decouples during cold-season on-ice flow within hours of the ice edge, allowing for strong low-level wind-turning. The resulting near-surface flow limits low-level heat and moisture advection towards the central Arctic. According to our hypothesis, intrusions in which the boundary layer remains coupled after crossing the ice edge lead to a much stronger heat and moisture transport towards the central Arctic ocean.These observations provide a valuable step towards a better understanding of small-scale boundary layer processes, surface energy budgets and their representation in climate models.
Gridding crowd-sourced weather data: is quality control required?
Authors: de Baar Jouke H.S. , van der Schrier Gerard
Published: July 2024

Context. For monitoring, analysing and forecasting the impact of weather and climate change on society, we see an increasing need for high-quality, high-resolution gridded weather and climate services. The official networks of the National Meteorological and Hydrological Services (NMHS) sample part of the land-use excluding, for example, urban areas, which motivated us to include third-party data which does sample urban areas.  We have provided the results in real-time as well as historical gridded data services.Approach. By including a statistical, simplified model of the observation error in our gridding approach (i.e. multi-fidelity regression Kriging), we did not include any advanced quality control (QC) of the crowd-sourced data. This might sound surprising in the context of weather and climate data,but is consistent with approaches in other scientific disciplines (e.g. marine engineering, aerospace engineering).Results. In this study, we investigate the quantitative effect of state-of-the-art quality control on the accuracy of gridded services, by analysing hourly temperature observations in The Netherlands for the year 2023. Our results indicate that quality control of crowd-sourced weather data can potentially increase the accuracy of straightforward – and commonly used – nearest-neighbour approximation, but generally deteriorates the accuracy of more advanced gridded services. This finding indicates that using strict QC procedures to turn crowd-sourced data into a dataset with similar fidelity as the NMHS-sourced data is not the way to go. Therefore, we do indeed continue to blend first-party data, crowd-sourced data and land-use data without applying any advanced quality control to the crowd-sourced data.Ecosystem. We emphasize that crowd-sourced weather data only improves services when it is blended with a high-quality network of NMHS data and possibly with land-use data. In other words, the availability of crowd-sourced data does not remove the need for high-quality NMHS observation networks. Therefore, we do not intend to present crowd-sourced data as a stand-alone product; rather, it lives in an integrated ecosystem.
Monitoring ground level nitrogen dioxide concentration in complex terrain areas using satellite Sentinel 5P total column observations
Authors: Campos Cristina , Sola Yolanda , Udina Mireia , Bech Joan , Trapero Laura
Published: July 2024

Air pollution is currently a major environmental issue to human health and natural ecosystems so improving air quality monitoring techniques, traditionally based on ground-based observation networks, is essential. Satellite remote sensing of air pollutants has made significant strides in recent years and now serves as a complementary data source alongside ground sensors. For example, different studies have explored the relationship between satellite-derived NO2 total column data and ground-level concentration but none of them focused on complex terrain areas. The aim of this work is to evaluate the feasibility of using NO2 column data from the Sentinel 5P satellite over complex terrain such as the Pyrenees Mountain area covering France, Spain and Andorra to estimate ground level values. For this purpose, a number of models considering the separation of temporal average and fluctuations are considered for both satellite and ground sensor data. The primary objective of these models is to enhance the signal-to-noise ratio. Initially, the periodicities are identified and subtracted from the original data, resulting in a residual series. These residual series are then filtered to eliminate noise while retaining the significant events. Finally, these new series are combined with the previously identified periodicity.  Preliminary results over Andorra show that our models can enhance Pearson's correlation between the temporal series of the satellite and ground sensor, improving it from 0.415 to 0.650. In addition, it has been found that the NO2 annual cycle in Andorra can be detected with a correlation of 0.950 between the model and the ground sensor NO2 series. Furthermore, a weekly cycle during winter has been detected in the Sentinel NO2 series too. These findings suggest that satellite estimates can identify days with high risk of exceeding NO2 ground level thresholds, enabling the creation of risk maps for areas lacking ground sensors. Such results could profoundly impact air quality monitoring in major towns located in valleys of mountain areas. Peak concentrations that deviate from average cycles have also been quantified. These deviations will be compared with other locations characterized by simpler topography to gain a deeper understanding of the limitations of satellite estimates. Subsequently, the next phase involves integrating these models into Machine Learning Algorithms to expand the application of Sentinel 5P data to complex terrain areas. This study is supported by the project “Towards a climate resilient cross-border mountain community in the Pyrenees (LIFE-SIP PYRENEES4CLIMA)”.
Inter-comparison and validation of high-resolution surface air temperature reanalysis fields over Italy
Authors: Cavalleri Francesco , Viterbo Francesca , Brunetti Michele , Bonanno Riccardo , Manara Veronica , Lussana Cristian , Lacavalla Matteo , Maugeri Maurizio
Published: July 2024

Surface air temperature (t2m) data are essential for understanding climate dynamics and assessing the impacts of climate change. Reanalysis products, which combine observations with retrospective short-range weather forecasts, can provide consistent and comprehensive datasets. ERA5 represents the state-of-the-art in global reanalyses and supplies initial and boundary conditions for higher-resolution regional reanalyses designed to capture finer-scale atmospheric processes. However, these products require validation, especially in complex terrains like Italy.This study analyzes the capability of different reanalysis products to reproduce t2m fields over Italy during the 1991-2020 period. The analyses encompass ERA5, ERA5-Land, the MEteorological Reanalysis Italian DAtaset (MERIDA), the Copernicus European Regional ReAnalysis (CERRA), and the Very High-Resolution dynamical downscaling of ERA5 REAnalysis over ITaly (VHR-REA_IT).The validation we conduct pertains to both the spatial distribution of 30-year seasonal and annual normal values and the daily anomaly records. Each reanalysis is compared with observations projected onto its respective grid positions and elevations, overcoming any model bias resulting from an inaccurate representation of the real topography.Key findings reveal that normal values in reanalyses closely match observational values, with deviations typically below 1°C. However, in the Alps, winter cold biases sometimes exceed 3°C and show a relation with the elevation. Similar deviations occur in the Apennines, Sicily, Sardinia. VHR-REA_IT, on the contrary, presents a warm summer bias of about +3 °C on average over the Po valley. Daily anomalies generally exhibit lower errors, with MERIDA showing the highest accuracy. Moreover, when aggregating daily anomalies to annual time scales, the errors in the anomaly records rapidly decrease to less than 0.5 °C.The results of this study empower reanalysis users across multiple sectors to gain a more profound insight into the capabilities and constraints of different reanalysis products. This knowledge, in turn, enables them to make well-informed choices when incorporating these products into their research and practical applications.
Case studies of extreme values of residual load.
Authors: Chabert Lucie , Späth Stephan , Baumgart Marlene , Riemer Michael
Published: July 2024

The residual load is defined by energy demand minus energy production from renewable sources (wind and solar power). High values of residual load correspond to a high energy demand that cannot be covered by renewable energies and must be compensated with fossil energies. On the opposite, low/negative values of residual load correspond to a surplus of renewable energies with a too low demand. In this case, energy must be exported, or wind turbines and solar panels curtailed.We compute the residual load using a demand model and a solar and wind power model. For the solar power model, we use the CAMS solar energy dataset interpolated to the position of all single solar panels installed in Germany and model the solar power output with the PVlib Python package. The wind power model is using ERA5 windspeed and air density interpolated to turbine location and turbine hub height to be later fed in the power curve of all single turbines in Germany. The demand model is a linear regression of 2m-temperature from ERA5, industry production index, day-of-week, public holidays and installed solar capacity. The residual load is then given by: ResLoad = Demand - (Wind + Solar)We look at both tails of the distribution of the residual load. High values of residual load are mostly observed in winter when temperatures and renewable energy production are low (mostly during blocked regimes). These events correspond to high energy prices. Negative values of residual load are mostly observed on Sundays, public holidays and in summer, where demand is low and temperatures and solar and wind power production are high. They usually correspond to negative energy prices. Both events described above are critical for the energy industry. Skillful forecasts of these events in the sub-seasonal range help to reduce stress to the energy market. The results presented herein lay the foundation for a more comprehensive, statistical analysis of the predictability characteristics of residual load extremes.
Global Warming Influence on a Giant Hailstorm in Spain via the Pseudo-Global Warming Approach
Authors: Calvo-Sancho Carlos , Montoro-Mendoza Ana , Taszarek Mateusz , González-Alemán Juan Jesús , Díaz-Fernández Javier , Farrán José Ignacio , Sastre Mariano , Santos-Muñoz Daniel , Luna María Yolanda , Martín María Luisa
Published: July 2024

On August 30th, 2022, a giant hailstorm with hailstones reaching up to 12 cm occurred in the northeastern Spain. This Spanish giant hailstorm record event caused, in addition to the damage to roofs, cars, and croplands, 67 injuries and even one fatality. During the event, the weather pattern over Europe was a quasi-omega block in the Western Mediterranean with a narrow cut-off low over the center-eastern of France, inducing the development of a very short-wave trough in extreme northeastern Spain. Such setup, the typical summer thermal-low and very high Mediterranean SSTs, promoted vorticity advection and a high amount of moisture in low-levels.In this study, the climate change effect in the hail-favorable environment, in which hailstone growth was promoted, is analyzed by applying the Pseudo-Global Warming Approach (PGWA). The PGWA is also applied to study the response to global warming during the last part of the 21st century to the giant hailstorm development. Three climatic models from the CMIP6 (EC-EARTH3, CESM-WACCM, and MRI-ESM2-0) and the PiClim, historical, and SSP5-8.5 scenarios are used to obtain the climate change increment [PRESENT - PiClim and FUTURE-PRESENT) needed in the PGWA. The increments are computed for all the prognostic variables and added to ERA5 to be used as initial/boundary conditions in the simulations obtained from the WRF-ARW model. A control simulation is performed using the ERA5 initial conditions without perturbation to compare it with the preindustrial-like and the future-like climates. The results in this first climate change attribution study to giant hailstorms indicate that the environment in a preindustrial-like climate would have been less conducive to convective hazards with a significant reduction in the studied thermodynamic parameters. The hailstorm event considering the preindustrial-like climate would have been less severe than the real event in the present climate. Considering a future-like climate, the results also indicate an enhancement in the thermodynamic variables. Large amounts of instability would be available in the scenario used, exceeding the values of the actual event. Although the hailstone diameter would not increase, the probability of very large hailstone events would increase by ~30%. Therefore, very large hail events would be more likely in hail-favorable supercells at the end of the century.The use of the PGWA in high-impact severe weather events allows for a better understanding of how these events could change with global warming. This knowledge could help to improve the present early-warnings systems.  
Combined Effect of ENSO and AO on Winter Temperatures of the Korean Peninsula on Subseasonal Time Scales
Authors: Lee Woo-Seop , Kim Gaeun , Yang Sinil
Published: July 2024

This study investigates the combined impact of the El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO) on the variability of winter temperatures in South Korea at the subseasonal time scale. This is achieved by analyzing hindcast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) database. Six composite cases are selected based on thresholds for each index: El Niño with positive AO (EP), El Niño with negative AO (EN), La Niña with positive AO (LP), La Niña with negative AO (LN), positive AO only (PA), and negative AO only (NA). Results from reanalysis data indicate that these climate factors can serve as predictors for predicting South Korea's temperature up to 4 weeks in advance. The study confirms that ENSO significantly influences the strength of AO's impact on temperature anomalies, depending on whether they are in phase or out of phase. For instance, during El Niño periods, there's a notable increase in mean temperature anomalies due to positive geopotential height (GPH) anomalies and warm temperature advection over South Korea in the EP case. Conversely, during La Niña periods, there's a significant decrease in mean temperature anomalies due to negative GPH anomalies and cold temperature advection over South Korea in the LN case. The ECMWF S2S hindcast exhibits reasonable ability to replicate circulation patterns over East Asia up to 3 weeks in advance. It also adequately predicts weekly mean temperature anomalies over South Korea in the EP, LN, and PA cases. This suggests that the combination of ENSO and AO indices can contribute to improved subseasonal forecasting of winter temperatures in South Korea.
Challenges in characterizing air quality with low-cost sensors
Authors: Sola Yolanda , Udina Mireia , Esbrí Laura , Bech Joan , Llasat Maria del Carmen
Published: July 2024

Air pollution is a great health concern for the governments, but also for the population, especially in urban environments. Particulate matter (PM) levels are regulated by European standards and are therefore continuously monitored by official measurement networks with precision instruments that are regularly calibrated. Although citizens often have access to open data, these networks are not usually dense. During the last years, low-cost sensors for measuring air quality have become popular, allowing the establishment of citizen science networks in which the population takes an active attitude in capturing measurements.In the framework of the European project I-CHANGE, the Barcelona Living Lab on Extreme Events  has used eight Smart Citizen Kits of the company Fablab, with different low-cost sensors including the Plantower PMS5003 that estimates the concentration of PM10, PM2.5, PM1 (referring the number to the maximum radius of the measured particles, in micrometers). The sensors were initially installed alongside an official instrument GRIMM EDM 180 providing PM10 and PM2.5 for 3 weeks, to intercompare the ability of these devices to measure PM. These sensors together with low-cost weather stations have been distributed in different schools in Barcelona, in the context of a citizen science campaign with a double objective: to analyze the use of this type of sensors to have more detailed information on pollution in areas of Barcelona with different characteristics and to raise awareness in the school community and promote changes in habits in response to the European Green Pact.The results of the intercomparison show that the instruments have a good performance for PM2.5 estimation, with an average determination coefficient (R2) of 0.84 with the official instrument, when comparing 10-minute averages. On the other hand, the instruments have worse quality in the estimation of PM10 (R2=0.64) as could be seen during the Saharan dust intrusion that affected the city. The laser-based measurement system does not allow a good characterization of coarse particles. Despite these differences with the official data, the agreement among low-cost sensors was good (R2 higher than 0.95 for PM10 and PM2.5), so the variations detected when displayed separately can be relied upon. During the time they have been located in schools, starting in January 2023 the longest series, they have allowed to monitor PM concentrations in different areas of the city. The comparison of PM evolution between official instruments and low-cost sensors during high concentration events have shown that the latter can have an informative and pedagogical role in raising public awareness on air quality.This study is supported by the project “Individual Change of HAbits Needed for Green European transition (I-CHANGE)”. I-CHANGE has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement 101037193.
Offshore wind project at MET Norway
Authors: Palm Anne Hesby , Albertsen Camilla Garshol
Published: July 2024

Norway has an ambition to assign areas for 30 GW offshore wind production by 2040. The first project areas for offshore wind at Utsira Nord and Sørlige Nordsjø II were announced last year 2023. Other areas that may be suitable for offshore wind have been analyzed and we expect high activity on the Norwegian continental shelf regarding offshore wind in the near future.The Norwegian Meteorological institute (MET Norway) wants to support renewable energy. For the maritime sector, offshore wind will need meteorological and oceanographic data and services. Today MET Norway delivers products and services to the offshore industry. The offshore wind project at MET Norway has an overall goal to further develop the services to fit the needs for offshore wind. This service includes statistical weather data, offshore forecasts delivered and visualized through a web portal called Luna, data delivered though Luna-API and also services such as providing a weather brief in video meetings and dedicated meteorologists. For the weather forecasting services through the web portal, the project focuses on delivering wind forecasts at hub height, a good way to present forecasts of ocean surface current with ensemble data and make maritime observations available. Both monitoring and forecasting of lightning in a wind park are planned topics to work with as well as icing on wind turbines.As well as predicting wind conditions in turbine heights, another purpose of these services is to give operators working with offshore wind the necessary information to perform operations and maintenance related to the wind park with the lowest possible risk for personnel and equipment. The offshore forecasting services at MET Norway plays an important role to develop waves and ocean models that further benefits the society. In addition, providing this service gives MET Norway a valuable competence in maritime forecasting. This project will prepare MET Norway for the green industry and the needs of MET data, products and services in the future.
Actionable Climate Information to take decisions: Firts insights of the co-created climate indices for cultural outdoor activities in Catalonia
Authors: Olano Pozo Jon Xavier , Boqué-Ciurana Anna , Saladié Borraz Òscar
Published: July 2024

Climate change impacts every aspect of human and ecological systems, yet its effects on cultural outdoor activities remain underexplored, an oversight this project aims to start correcting. Specifically, this research focuses on the Catalan tradition of "Castellers," which involves constructing human towers, a deeply rooted cultural practice in some Catalan regions symbolising community strength and cooperation. Recognizing the potential vulnerability of this tradition to changing climate conditions, this study develops a tailored analytical framework to assess these impacts, building upon the co-creation of climate service methodology by local agents outlined in Font et al. (2021).To capture the specific impacts of climate change on the "castellers," we conducted workshops with different "colles" (the groups that build the towers) to adapt the existing methodology better to fit the unique needs of this cultural practice. These collaborations have led to the creation of a specific indicator designed to measure the sensitivity of the "castellers" to environmental changes.A key component of our research methodology involved measuring Essential Climate Variables (ECVs) during tower-building exhibitions, among the general description based on station and reanalysis data. Instruments were set up in the “places” where these events typically take place to collect real-time environmental data, such as temperature, humidity, and wind speed, during the performances. This approach allowed for the accurate capturing of the climatic conditions faced by participants during their performances, providing a robust dataset for analysing the potential impacts of climate variability on this activity.The initial findings from this data collection indicate that even slight changes in ECVs can influence the performance and safety conditions of the "castellers." With these insights, the project seeks to equip organizers, participants, local government bodies in their capacity as risk managers, and the audience with valuable information to make informed decisions that help mitigate risks and ensure the sustainability of this intangible cultural heritage. This study highlights the vulnerability of cultural practices to climate change and sets a precedent for similar research into other areas of intangible heritage. 
Large-scale Influences on Tropical Cyclone Activities in a Superparameterized GCM Aquaplanet Experiment
Authors: Chow Tsun Ngai , Tam Chi Yung , Chung Eric Tsz Shun
Published: July 2024

Understanding and predicting tropical cyclone (TC) activities are important for mitigating TC risks to human society. However, general circulation models (GCM) are integrated at moderate horizontal resolutions (~50 km) in climate projections; TC characteristics in these runs tend to be unrealistic and very sensitive to how convection is parameterized. The so-called multiscale modeling framework provides a way to improve GCMs by inserting a cloud resolving module- but only within each model grid column to “super-parameterize” convective-related processes.Here we compared the TC activities in the super-parameterized Community Atmosphere Model (SPCAM) v5 with those in the convectively parameterized CAM (CPCAM, v5; Zhang and McFarlane deep convection and Park and Bretherton shallow convection schemes), in aquaplanet, perpetual summer experiments. It was found that SPCAM had a TC frequency 4-5 times higher than that produced by CPCAM. In SPCAM the genesis potential index and 500-hPa pressure velocity were also significantly stronger, while the SPCAM ventilation index was found to be weaker off the equator than that in CPCAM, indicating an environment more conducive for TC genesis. More TC “seeds” were also found in SPCAM compared to CPCAM. For individual TCs, there were stronger maximum 10-m wind speed and lower minimum sea level pressure, while the wind-pressure relationship was also more realistically captured, in SPCAM compared to CPCAM. Further inspection of the GCMs’ general circulation revealed an Inter-topical Convergence Zone (ITCZ) farther (closer) to the equator, with more (less) frequent ITCZ breaking in SPCAM (CPCAM). SPCAM also exhibited enhanced equatorial Rossby and mixed Rossby-gravity wave activities, as well as tropical depression-type systems, in its model environment. Implications were drawn for applying a multiscale modeling framework in GCMs to reduce uncertainties in climate and extreme event modeling.
Exploring the relationships between satellite-observed cloud albedo and ERA5 variables to improve data driven short-term irradiance forecast
Authors: Chea Nicolas , Cros Sylvain , Tuomiranta Arttu , Badosa Jordi , Meddahi Amar , Haeffelin Martial , Guillon Sébastien
Published: July 2024

Surface solar irradiance short-term forecasts (up to 6h) are crucial for solar energy-related applications, including energy trading, grid management, and energy storage solutions.Operational current forecasting methods for this time horizon are based on meteorological satellite imagery analysis. However, some atmospheric processes, such as the early stages of convection or cloud dissipation, cannot be considered if the forecast model depends solely on this type of observation. As a result, these processes are ignored, leading to significant forecast errors.The objective of this study is to identify the NWP output variables that most significantly influence the evolution of satellite-observed cloud albedo at few hours scale. The findings are expected to facilitate the integration of these variables as ancillary inputs in a deep-learning-based forecasting model, which could improve the performance of intraday forecasts based on satellite imagery.In this work, we explore the relationship between cloud albedo derived from the HRV channel of the Meteosat Second Generation’s (MSG) SEVIRI instrument and meteorological variables from the ERA5 reanalysis, specifically focusing on the Paris, France, area. A preliminary exploratory data analysis is conducted to highlight the specific context of the study, using descriptive statistics and visual representations.Subsequently, we implement an XGBoost model to forecast cloud albedo, identifying the meteorological variables that most effectively contribute to the estimation under varying cloud regimes, meteorological situations and seasonal variations.The findings of this study reveal significant variability in the predictability of cloud albedo when using certain NWP outputs, particularly under specific cloud regimes such as low clouds. This analysis has enabled us to select a restricted group of variables, including total column water and surface pressure, which were the most influential in forecasting cloud albedo, reducing the need for costly extensive experimentation with different variable configurations in a future deep learning forecasting framework.
Seamless Probabilistic Nowcasting and Short-Range Forecasting for High-Impact Weather Events
Authors: Atencia Aitor , Dabernig Markus , Muschinski Thomas
Published: July 2024

This abstract presents a new forecasting system aimed at providing seamless and calibrated predictions of high-impact weather events, tailored to the needs of hydrological services, forecasters, and civil protection agencies for effective early-warnings. Focused on thunderstorms, flash floods,  extreme temperatures and gusts, the system aims to create a probabilistic nowcasting and short-range forecasting. In order to capture local events such as thunderstorms or wind gusts, the gridded forecasts have a spatial resolution of 500m and a temporal resolution up to 5 min and tailored (scaled) to weather warnings using all the available information at Geosphere Austria.
The first step is creating a probabilistic analysis using data-driven approaches and introducing uncertainty due to data density and external factors such as altitude, orographic factors for temperature or radar beam for spatial distribution of precipitation in high resolution. Afterwards, a radar-based nowcasting technique is employed, leveraging data-driven approaches to account for uncertainty due to external factors such growth and decay for precipitation.
Machine learning allows to merge probabilistic nowcasting with a collection of NWP models, ensuring a robust prediction framework by using the probabilistic analysis.  This merging of various sources of information facilitates seamless transitions between nowcasting and NWP-based forecasts with the goal to improve the reliability of early warnings.
By providing probabilistic forecasts tailored to specific weather warnings, the system is flexible enough to provide specific weather information to different end-users. The information tailored to specific tasks (such as probability of exceedance) of the final probabilistic method is verified against the individual components of the model chain to guarantee the added value of the system in helping the decision making in high-impact weather situations.
Annual probabilities of extreme air temperatures in the past and future climate of Finland
Authors: Laapas Mikko , Jylhä Kirsti , Ruosteenoja Kimmo
Published: July 2024

Both extremely low and high temperatures can be relevant for the operation of nuclear power plants (NPPs). For example, temperatures higher than 40 °C might endanger safe shutdown of an NPP (STUK, 2011). The MAWECLI project aims to provide reliable estimates about the likelihood of extreme events affecting NPPs in the changing climate of Finland.In this study, we evaluate the annual probabilities of low and high temperature extremes in the past and future climate of Finland. To produce estimates for events with low annual probabilities (2%, 1%, 0.2%, and 0.1%), we utilise statistical extreme value analysis, employing both the generalized extreme value distribution with block maxima approach and the generalized Pareto distribution with peak-over-threshold approach. Furthermore, any potential non-stationarity in the time series is addressed by utilising distribution parameter covariates based on linear trends identified from the time series.For the past, the extreme value analysis is performed for 35 weather stations across Finland. The analysis is based on a 60-year period of observations (1963-2022), which is common for all stations, as well as longer station-specific historic records, starting from 1844 for the longest operational weather station. In addition to instantaneous extreme temperatures, estimates are also calculated for high (or low) temperatures that have prevailed for time periods of six and 24 hours.For the future projections, bias-corrected daily mean temperature data from 25 CMIP6 global climate models (GCM) is utilised (Ruosteenoja and Jylhä, 2023). To produce extensive data sets for 20-year periods representing current (1999-2018) and mid-century (2041-2060) climatic conditions, 20-year time series of each climate model were combined to create a 500-year ensemble depicting the climatic conditions of each period. The use of this 500-year ensemble enables more robust estimates in comparison to extreme value analysis relying on only 20-year data periods for each individual GCM. Our results show higher warming for extremes compared to changes in mean temperatures, the difference increasing towards smaller annual probabilities, i.e., more rare events.References:Ruosteenoja, K., Jylhä, K. Average and extreme heatwaves in Europe at 0.5–2.0 °C global warming levels in CMIP6 model simulations. Clim Dyn 61, 4259–4281 (2023). https://doi.org/10.1007/s00382-023-06798-4STUK, 2011. European Stress Tests for Nuclear Power Plants. National Report. Finland 3/0600/2011 2011 Tomi Routamo (ed.) Radiation and Nuclear Safety Authority 2011. Available at: https://www.ensreg.eu/sites/default/files/EU_Stress_Tests_-_National_Report_-_Finland.pdf
Validation of AERONET and SKYNET columnar aerosol size distributions by comparison with aircraft in situ measurements at Cape Verde
Authors: Garcia-Suñer Meritxell , Estellés Víctor , Marenco Franco , Ryder Claire , Kumar Gaurav , Momoi Masahiro , Campanelli Monica , O'Sullivan Debbie , Brooke Jennifer , Buxmann Joelle
Published: July 2024

The Sunphotometer Airborne Validation Experiment (SAVEX-D) was carried out in the Cape Verde archipelago, along with the ICE-D (Ice in Clouds Experiment - Dust) campaign, led by the Met Office and the Universities of Leeds and Manchester (United Kingdom) in summer 2015. The aim of the SAVEX-D experiment was to verify and validate the retrievals of both AERONET’s and SKYNET’s ground based sun-photometers for columnar aerosol optical properties, particularly the aerosol size distributions, with the use of airborne in situ measurements. To this end, a Cimel CE318 (AERONET) and a Prede POM01 (SKYNET) were installed at Praia (Cape Verde), and their retrievals were compared with those obtained with several in-situ instruments equipped in the FAAM BAe-146 research aircraft, such as PCASP, CDP and 2DS. In addition, data from AERONET’s long-term site on Sal was employed. The motivation behind this experiment was related to the non-negligible discrepancies between the retrieved aerosol properties from AERONET and SKYNET networks reported in previous studies. In fact, it is essential to better characterise aerosol retrievals from sun-photometers, since these are employed as inputs in climatological studies, aerosol model verifications and validation of satellite products. The location of the site, downwind of the Sahara Desert (a major source of dust aerosols) is a key aspect of the experiment. In the two flights that were performed (16th and 25th August 2015), dust dominated the aerosol mixture, with a measured AOD about 0.4 - 0.6. Cimel and Prede measurements were inverted through different available versions of the AERONET and SKYNET algorithms. Among the retrieved aerosol properties, aerosol size distribution is of particular interest, as it can be compared to the vertically integrated size distributions obtained from the in-situ instrumentation in the aircraft. From the results, it is observed that both AERONET version 2 and different versions of Skyrad algorithm from SKYNET are consistent in the interval 0.2 - 2.0 µm. However, discrepancies between networks become more noticeable at the extremes of the distributions. In addition, it is shown that AERONET underestimates the size distribution of coarse particles, and that differences in the coarse mode are significant when non-sphericity is considered in the MRI version of the Skyrad inversion algorithm. Further plans include the comparison with new versions of both AERONET and SKYNET algorithms, and GRASP retrievals obtained from inversion of both Cimel and Prede measurements.
On Monthly Mean Surface Wind Speed Data Homogenization and Trend Assessment
Authors: Wang Xiaolan , Feng Yang , Isaac Victor , Vincent Lucie , Hartwell Megan
Published: July 2024

Using hourly surface wind speed data from 155 stations in Canada, this study first developed a homogenized monthly mean wind speed dataset for the period of 1953-2023, which was then used to characterize observed changes in surface wind speed in Canada. The hourly data were first quality controlled and adjusted for non-standard anemometer heights before being used to calculate monthly mean wind speed series. To identify artificial discontinuities, the monthly mean wind speed series were subject to a semi-automated comprehensive data homogenization procedure, which uses a combination of station metadata and multiple statistical tests with and without using reference series. Reference series used include up to four best significantly-correlated neighbour stations’ data series, the ensemble mean series of monthly wind speed taken from the Twentieth Century Reanalysis version 3 (20CRv3), and monthly mean geostraphic wind speeds derived from homogenized surface pressure data. The results from the automated procedure were then reviewed manually using metadata and visual inspection of the multiphase regression fits with expert judgement. As a result, all the 155 data series were identified to have one or more artificial discontinuities, which were diminished by quantile matching adjustments. Anemometer height change, station joining, relocation, instrument changes/problems were found to be the main causes of data inhomogeneities. The homogenized dataset for 1953-2023 shows wind stilling in region from northern British Columbia (BC) to southern Yukon-Northwest Territories and from southern Prairies to Quebec-Labrador, which was matched with wind strengthening in the region from southern-central BC to the Rocky Mountains, and in Newfoundland and the high Arctics. The trend pattern of in-situ wind speed data bear substantial similarity to that of both the  ERA5 and 20CRv3 reanalysis wind speed data.