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Heron colonies are dynamic components of wetlands. Therefore, their systematic monitoring is important for the management of both birds and wetlands. During the period 1988–2018, we counted breeding pairs of seven colonial breeding heron species at 65 colonies across 37 wetlands in Greece. We considered as annual variables of a population: (a) years since 1988, (b) Natura 2000 network inclusion, (c) protected area management authority overseeing, (d) wetland type (new or restored), and (e) new colonies (established after 2003). The Cattle Egret Bubulcus ibis and the Squacco Heron Ardeola ralloides had a positive breeding population trend. The Black-crowned Night Heron Nycticorax nycticorax, Little Egret Egretta garzetta, and Grey Heron Ardea cinerea had a negative trend, while the Purple Heron Ardea purpurea population was stable. The Great White Egret Ardea alba bred sporadically at only a few sites which precluded the evaluation of its population trend. The informative population variables differed among species, even of those at the same colony, which suggests trends are also affected by conditions at wintering grounds. The study highlights the need for the systematic monitoring of heron colonies and the protection of foraging/breeding areas in order to reverse the observed negative population trends.

Solar irradiance nowcasting and short-term forecasting are important tools for the integration of solar plants into the electricity grid. Understanding the role of clouds and aerosols in those techniques is essential for improving their accuracy. In this study, we introduce improvements in the existing nowcasting and short-term forecasting operational systems SENSE (Solar Energy Nowcasting System) and NextSENSE achieved by using a new configuration and by upgrading cloud and aerosol inputs, and we also investigate the limitations of evaluating such models using surface-based sensors due to cloud effects. We assess the real-time estimates of surface global horizontal irradiance (GHI) produced by the improved SENSE2 operational system at high spatial and temporal resolution (∼ 5 km, 15 min) for a domain including Europe and the Middle East–North Africa (MENA) region and the short-term forecasts of GHI (up to 3 h ahead) produced by the NextSENSE2 system against ground-based measurements from 10 stations across the models' domain for a whole year (2017). Results for instantaneous (every 15 min) comparisons show that the GHI estimates are within ±50 W m−2 (or ±10 %) of the measured GHI for 61 % of the cases after the implementation of the new model configuration and a proposed bias correction. The bias ranges from −12 to 23 W m−2 (or from −2 % to 6.1 %) with a mean value of 11.3 W m−2 (2.3 %). The correlation coefficient is between 0.83 and 0.96 and has a mean value of 0.93. Statistics are significantly improved when integrating on daily and monthly scales (the mean bias is 3.3 and 2.7 W m−2, respectively). We demonstrate that the main overestimation of the SENSE2 GHI is linked with the uncertainties of the cloud-related information within the satellite pixel, while relatively low underestimation, linked with aerosol optical depth (AOD) forecasts (derived from the Copernicus Atmospheric Monitoring Service – CAMS), is reported for cloudless-sky GHI. The highest deviations for instantaneous comparisons are associated with cloudy atmospheric conditions, when clouds obscure the sun over the ground-based station. Thus, they are much more closely linked with satellite vs. ground-based comparison limitations than the actual model performance. The NextSENSE2 GHI forecasts based on the cloud motion vector (CMV) model outperform the persistence forecasting method, which assumes the same cloud conditions for future time steps. The forecasting skill (FS) of the CMV-based model compared to the persistence approach increases with cloudiness (FS is up to ∼ 20 %), which is linked mostly to periods with changes in cloudiness (which persistence, by definition, fails to predict). Our results could be useful for further studies on satellite-based solar model evaluations and, in general, for the operational implementation of solar energy nowcasting and short-term forecasting, supporting solar energy production and management.

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting.

Purpose: To compare testosterone castration levels between patients treated with the gonadotropin-releasing hormone (GnRH) antagonist, degarelix, and GnRH agonist. Materials and Methods: Patients with prostate cancer (PCa) of a single outpatient clinic were randomized (2:1) to receive degarelix (group A) or GnRH agonist (group B). The study evaluated testosterone and prostate-specific antigen (PSA) levels, patients’ age, Gleason score and the presence of metastases (nodal or bone). Testosterone and PSA levels were measured at 1st, 6th, 12th, and 18th months. Mann–Whitney test and Spearman correlation were used to investigate independent variable while standard multiple regression was performed to explore statistically significant correlations. Kruskal–Wallis test was used to compare testosterone levels at follow-up. Results: The study included 168 patients, 107 in group A and 61 in group B. Testosterone levels at 1st month were significantly lower in patients under GnRH antagonist than those receiving GnRH agonist (group A: 22 ng/dL vs. group B: 29 ng/dL, p=0.011). However, PSA values did not differ significantly between groups (group A: 0.130 ng/mL vs. group B: 0.067 ng/mL, p=0.261). In multivariate analysis, treatment with degarelix was an independent factor of lower testosterone levels at 1st month (p=0.013). Comparison of testosterone levels at 6, 12, and 18 months did not reveal any significant difference within each group. Conclusions: In patients with PCa who are candidates for androgen deprivation therapy, the administration of GnRH antagonist seems to achieve significantly lower testosterone levels compared to treatment with GnRH agonist at 1st month of treatment.

In August 2021, a historic heatwave was recorded in Greece which resulted in extreme wildfire events that strongly affected the air quality over the city of Athens. Saharan dust was also transferred over Greece on certain days of the same period due to the prevailing southern winds. The impact of these events on air quality and surface solar radiation is investigated in this study. Event characterization based on active and passive remote sensing instrumentation has been performed. The study shows that significantly increased levels of air pollution were recorded from the end of July to the first week of August. The smoke led to unusually high aerosol optical depth (AOD) values (up to 3.6 at 500 nm), high Ångström exponent (AE) (up to 2.4 at 440–870 nm), and a strong and negative dependence of single-scattering albedo (SSA) on wavelength that was observed to decrease from 0.93 at 440 nm to 0.86 at 1020 nm, while the dust event led to high AOD (up to 0.7 at 500 nm), low AE (up to 0.9 at 440–870 nm), and a positive dependence of SSA on wavelength that was observed to increase from 0.89 at 440 nm to 0.95 at 1020. Furthermore, the smoke plume was also detected over the PANhellenic GEophysical observatory of Antikythera on 7 August, which is about 240 km away from Athens. Increased AOD values (up to ∼ 0.90 at 500 nm) associated with a high fine-mode AOD (up to ∼ 0.85 at 500 nm) and decrease in SSA with wavelength suggested the dominance of fine biomass burning aerosols. The impact of dust and smoke on solar irradiance revealed significant differences in the spectral dependence of the attenuation caused by the two different aerosol types. The attenuation of solar irradiance in the ultraviolet (UV-B) spectrum was found to be much lower in the case of dust compared to smoke for similar AOD500 values. Differences were less pronounced in the near-infrared and visible spectral regions. The large AODs during the wildfires resulted in a decrease in the noon UV index by up to 53 %, as well as in the daily effective doses for the production of vitamin D (up to 50 %), in the daily photosynthetically active radiation (up to 21 %) and in the daily global horizontal irradiance (up to 17 %), with serious implications for health, agriculture, and energy. This study highlights the wider impacts of wildfires that are part of the wider problem for Mediterranean countries, whose frequency is predicted to increase in view of the projected increasing occurrence of summer heatwaves.

High-resolution wind datasets are crucial for ultra-short-term wind forecasting. Penetration of WT installations near urban areas that are constantly changing will motivate researchers to understand how to adapt their models to terrain changes to reduce forecasting errors. Although CFD modelling is not widely used for ultra-short-term forecasting purposes, it can overcome such difficulties. In this research, we will spatially extrapolate vertical profile LIDAR wind measurements into a 3D wind velocity field over a large and relatively complex terrain with the use of stationary CFD simulations. The extrapolated field is validated with measurements at a hub height of three WTs located in the area. The accuracy of the model increases with height because of the terrain anomalies and turbulence effects. The maximum MAE of wind velocity at WT hub height is 0.81 m/s, and MAPE is 7.98%. Our model remains accurate even with great simplifications and scarce measurements for the complex terrain conditions of our case study. The models’ performance under such circumstances establishes it as a promising tool for the evolution of ultra-short-term forecasting as well as for the evaluation of new WT installations by providing valuable data for all models.

The Mediterranean, and particularly its Eastern basin, is a crossroad of air masses advected from Europe, Asia and Africa. Anthropogenic emissions from its megacities meet over the Eastern Mediterranean, with natural emissions from the Saharan and Middle East deserts, smoke from frequent forest fires, background marine and pollen particles emitted from ocean and vegetation, respectively. This mixture of natural aerosols and gaseous precursors (Short-Lived Climate Forcers—SLCFs in IPCC has short atmospheric residence times but strongly affects radiation and cloud formation, contributing the largest uncertainty to estimates and interpretations of the changing cloud and precipitation patterns across the basin. The SLCFs’ global forcing is comparable in magnitude to that of the long-lived greenhouse gases; however, the local forcing by SLCFs can far exceed those of the long-lived gases, according to the Intergovernmental Panel on Climate Change (IPCC). Monitoring the spatiotemporal distribution of SLCFs using remote sensing techniques is important for understanding their properties along with aging processes and impacts on radiation, clouds, weather and climate. This article reviews the current state of scientific know-how on the properties and trends of SLCFs in the Eastern Mediterranean along with their regional interactions and impacts, depicted by ground- and space-based remote sensing techniques.

Particular matter (PM) constitutes one of the major air pollutants. Human exposure to fine PM (PM with a median diameter less than or equal to 2.5 μm, PM2.5) has many negative and diverse outcomes for human health, such as respiratory mortality, lung cancer, etc. Accurate air-quality forecasting on a regional scale enables local agencies to design and apply appropriate policies (e.g., meet specific emissions limitations) to tackle the problem of air pollution. Under this framework, low-cost sensors have recently emerged as a valuable tool, facilitating the spatiotemporal monitoring of air pollution on a local scale. In this study, we present a deep learning approach (long short-term memory, LSTM) to forecast the intra-day air pollution exceedances across urban and suburban areas. The PM2.5 data used in this study were collected from 12 well-calibrated low-cost sensors (Purple Air) located in the greater area of the Municipality of Thermi in Thessaloniki, Greece. The LSTM-based methodology implements PM2.5 data as well as auxiliary data, meteorological variables from the Copernicus Atmosphere Monitoring Service (CAMS), which is operated by ECMWF, and time variables related to local emissions to enhance the air pollution forecasting performance. The accuracy of the model forecasts reported adequate results, revealing a correlation coefficient between the measured PM2.5 and the LSTM forecast data ranging between 0.67 and 0.94 for all time horizons, with a decreasing trend as the time horizon increases. Regarding air pollution exceedances, the LSTM forecasting system can correctly capture more than 70.0% of the air pollution exceedance events in the study region. The latter findings highlight the model’s capabilities to correctly detect possible WHO threshold exceedances and provide valuable information regarding local air quality.

Aerosol optical depth (AOD) constitutes a key parameter of aerosols, providing vital information for quantifying the aerosol burden and air quality at global and regional levels. This study demonstrates a machine learning strategy for retrieving AOD under cloud-free conditions based on the synergy of machine learning algorithms (MLAs) and ground-based solar irradiance data. The performance of the proposed methodology was investigated by applying different components of solar irradiance. In particular, the use of direct instead of global irradiance as a model feature led to better performance. The MLA-based AODs were compared to reference AERONET retrievals, which encompassed RMSE values between 0.01 and 0.15, regardless of the underlying climate and aerosol environments. Among the MLAs, artificial neural networks outperformed the other algorithms in terms of RMSE at 54% of the measurement sites. The overall performance of MLA-based AODs against AERONET revealed a high coefficient of determination (R2 = 0.97), MAE of 0.01, and RMSE of 0.02. Compared to satellite (MODIS) and reanalysis (MERRA-2 and CAMSRA) data, the MLA-AOD retrievals revealed the highest accuracy at all stations. The ML-AOD retrievals have the potential to expand and complement the AOD information in non-existing timeframes when solar irradiances are available.

Solar irradiance forecasting plays a crucial role in integrating large quantities of intermittent solar power. Forecasting systems are commonly evaluated using metrics like root‐mean‐ square error (RMSE) and skill scores. However, these metrics aggregated over larger data sets do not adequately assess the prediction of ramp events, which are critical for many applications. This article introduces a novel, simple, and adaptable ramp rate metric that analyzes ramp events between successive lead times within forecasts. A case study on ramp rate mitigation in PV systems benchmarks suitable ramp thresholds for various solar irradiance components. The capabilities and limitations of deterministic and probabilistic forecasts from two all‐sky imager‐based models are evaluated for ramp prediction. A state‐of‐the‐art data‐driven vision transformer End2End model excels in RMSE and skill scores but performs poorly in ramp prediction. Conversely, a novel generative forecasting model combined with a convolutional neural network‐based irradiance model shows superior ramp prediction, achieving an F1 score of ≥0.7 for critical ramp events. This study underscores the importance of suitable ramp rate metrics and highlights the potential of generative models for enhancing ramp forecasting. [ABSTRACT FROM AUTHOR]

Within the 9th European Framework programme, since 2021 EUROfusion is operating five tokamaks under the auspices of a single Task Force called ‘Tokamak Exploitation’. The goal is to benefit from the complementary capabilities of each machine in a coordinated way and help in developing a scientific output scalable to future largre machines. The programme of this Task Force ensures that ASDEX Upgrade, MAST-U, TCV, WEST and JET (since 2022) work together to achieve the objectives of Missions 1 and 2 of the EUROfusion Roadmap: i) demonstrate plasma scenarios that increase the success margin of ITER and satisfy the requirements of DEMO and, ii) demonstrate an integrated approach that can handle the large power leaving ITER and DEMO plasmas. The Tokamak Exploitation task force has therefore organized experiments on these two missions with the goal to strengthen the physics and operational basis for the ITER baseline scenario and for exploiting the recent plasma exhaust enhancements in all four devices (PEX: Plasma EXhaust) for exploring the solution for handling heat and particle exhaust in ITER and develop the conceptual solutions for DEMO. The ITER Baseline scenario has been developed in a similar way in ASDEX Upgrade, TCV and JET. Key risks for ITER such as disruptions and run-aways have been also investigated in TCV, ASDEX Upgrade and JET. Experiments have explored successfully different divertor configurations (standard, super-X, snowflakes) in MAST-U and TCV and studied tungsten melting in WEST and ASDEX Upgrade. The input from the smaller devices to JET has also been proven successful to set-up novel control schemes on disruption avoidance and detachment.

In 2021 JET exploited its unique capabilities to operate with T and D–T fuel with an ITER-like Be/W wall (JET-ILW). This second major JET D–T campaign (DTE2), after DTE1 in 1997, represented the culmination of a series of JET enhancements—new fusion diagnostics, new T injection capabilities, refurbishment of the T plant, increased auxiliary heating, in-vessel calibration of 14 MeV neutron yield monitors—as well as significant advances in plasma theory and modelling in the fusion community. DTE2 was complemented by a sequence of isotope physics campaigns encompassing operation in pure tritium at high T-NBI power. Carefully conducted for safe operation with tritium, the new T and D–T experiments used 1 kg of T (vs 100 g in DTE1), yielding the most fusion reactor relevant D–T plasmas to date and expanding our understanding of isotopes and D–T mixture physics. Furthermore, since the JET T and DTE2 campaigns occurred almost 25 years after the last major D–T tokamak experiment, it was also a strategic goal of the European fusion programme to refresh operational experience of a nuclear tokamak to prepare staff for ITER operation. The key physics results of the JET T and DTE2 experiments, carried out within the EUROfusion JET1 work package, are reported in this paper. Progress in the technological exploitation of JET D–T operations, development and validation of nuclear codes, neutronic tools and techniques for ITER operations carried out by EUROfusion (started within the Horizon 2020 Framework Programme and continuing under the Horizon Europe FP) are reported in (Litaudon et al Nucl. Fusion accepted), while JET experience on T and D–T operations is presented in (King et al Nucl. Fusion submitted).

Coastal wetlands are considered as systems of high avian diversity and are usually used for livestock production throughout the world. In this study, the diversity and seasonal abundance of avian species were monitored for two years on a monthly basis in a coastal grazing land in Evros Delta (Greece). The effects of livestock (cattle) presence and different classes of vegetation cover on the number of bird species were also investigated. A total of 96 bird species belonging to 29 families were recorded. The most commonly encountered species was the Eurasian skylark Alauda arvensis. The cattle presence was not significantly correlated (p>0.05) with the abundance of recorded bird species. On the contrary, patches with vegetation cover 25.1 - 50.0% and 50.1 - 75.0 % were used by more bird species in relation to patches with cover ≤25.0% or >75.0%. We concluded that the use of livestock grazing to preserve the desired vegetation cover (25 – 75%) is a promising management tool.

This study investigates the applicability of using the sky information from an all-sky imager (ASI) to retrieve aerosol optical properties and type. Sky information from the ASI, in terms of Red-Green-Blue (RGB) channels and sun saturation area, are imported into a supervised machine learning algorithm for estimating five different aerosol optical properties related to aerosol burden (aerosol optical depth, AOD at 440, 500 and 675 nm) and size (Ångström Exponent at 440–675 nm, and Fine Mode Fraction at 500 nm). The retrieved aerosol optical properties are compared against reference measurements from the AERONET station, showing adequate agreement (R: 0.89–0.95). The AOD errors increased for higher AOD values, whereas for AE and FMF, the biases increased for coarse particles. Regarding aerosol type classification, the retrieved properties can capture 77.5% of the total aerosol type cases, with excellent results for dust identification (>95% of the cases). The results of this work promote ASI as a valuable tool for aerosol optical properties and type retrieval.

Atmospheric water vapor is an important greenhouse gas, mainly distributed in the lower tropospheric levels where its concentration varies significantly in space and time; consequently, so does precipitable water. This work uses information from thermal infrared images to model precipitable water (PW) under clear skies. PW is measured using a portable sun-photometer and thermal images obtained through a high-cost thermal infrared camera. PW depends on the zenith-point temperature (Tb) exhibiting a non-linear positive exponential relationship, with systematic and dispersion errors of 0.04 mm and 1.9 mm.

The study presents results of the research project ASPIRE (atmospheric parameters affecting spectral solar irradiance and solar energy). A new dataset has been created for Athens, Greece, for one year (December 2020–November 2021), consisting of detailed measurements of spectral solar irradiance (SSI) from 300 to 1020 nm and its influencing parameters (clouds, aerosols, ozone, nitrogen dioxide, sulfur dioxide, water vapor). The new dataset is explored, and major results are presented. The datasets are available to scientists from interdisciplinary scientific communities for research and education purposes. Here, we present an overview of different studies within ASPIRE, dealing with effects of different aerosol types on solar measurements and PV-related outputs and also the evaluation of a nowcasting solar model under different atmospheric conditions.

We conducted a theoretical analysis of the relationship between red-to-blue (RBR) color intensities and aerosol optical properties. RBR values are obtained by radiative transfer simulations of diffuse sky radiances. Changes in atmospheric aerosol concentration (parametrized by aerosol optical depth, AOD), particle’s size distribution (parametrized by Ångström exponent, AE) and aerosols’ scattering (parametrized by single scattering albedo—SSA) lead to variability in sky radiances and, thus, affect the RBR ratio. RBR is highly sensitive to AOD as high aerosol load in the atmosphere causes high RBR. AE seems to strongly affect the RBR, while SSA effect the RBR, but not to such a great extent.

Nowadays, all-sky imagers (ASI) provide valuable information regarding the sky’s state, and they have been extensively used in cloud detection, segmentation, and solar forecasting studies. In this study, global and diffuse horizontal irradiances (GHI and DHI) are modeled using a Convolutional Neural Network (CNN) and Red–Green–Blue (RGB) information retrieved through ASI images. The predicted GHI and DHI underestimated observations with systematic biases of −1.8 W m−2 and −0.5 W m−2, while the dispersion errors were 82.7 W m−2 and 39.8 W m−2, respectively. The correlation coefficient was high, approaching 0.95 and 0.85 for GHI and DHI.

Radiative transfer modeling is used to investigate the effect of aerosol optical properties and water vapor on cloud-free sky radiances at various atmospheric conditions. Simulations are generated by changing the most critical aerosol optical properties, namely aerosol optical depth, Ångström exponent, the single-scattering albedo, the precipitable water, and the solar zenith angle (SZA) in three different spectral ranges: ultraviolet A, visible, and near-infrared.

Quality-assured aerosol optical properties (AOP) with high spatiotemporal resolution are vital for the accurate estimation of direct aerosol radiative forcing and solar irradiance under clear skies. In this study, the sky information from an all-sky imager (ASI) is used with machine learning (ML) synergy to estimate aerosol optical depth (AOD) and the Ångström Exponent (AE). The retrieved AODs (AE) revealed good accuracy, with a dispersion error lower than 0.07 (0.15). The retrieved ML AOPs are used to estimate the DNI by applying radiative transfer modeling. The estimated ML DNI calculations revealed adequate accuracy to reproduce reference measurements with relatively low uncertainties.

Particulate matter (PM) is one of the major air pollutants that has adverse impacts on human health. The aim of this study is to present an alternative approach for retrieving fine PM (particles with an aerodynamic diameter less than 2.5 μm, PM2.5) using artificial intelligence. Ground-based instruments, including a hand-held Microtops II sun photometer (for aerosol optical depth), a PurpleAir sensor (for PM2.5), and Rotronic sensors (for temperature and relative humidity), are used for the machine learning algorithm training. The retrieved PM2.5 reveals an adequate performance with an error of 0.08 μg m−3 and a Pearson correlation coefficient of 0.84.

Energy nowcasting is a valuable asset in managing energy loads and having real-time information on solar irradiation availability. In this study, we evaluate the spectrally integrated outputs of the SENSE system for solar irradiance nowcasting for the period of the ASPIRE (atmospheric parameters affecting spectral solar irradiance and solar energy) campaign (December 2020–December 2021) held in Athens, Greece. For the needs of the campaign, several ground-based instruments were operating, including two pyranometers, a pyrheliometer, a cloud camera, a CIMEL sunphotometer, and a precision spectral radiometer (PSR). Global horizontal irradiance (GHI) estimations were more accurate than direct normal irradiance (DNI). SENSE estimations are provided every 15 min, but when comparing bigger time intervals (hours-days), the statistics improved. A dedicated assessment of the SENSE’s inputs is performed in respect to ground-based retrievals, considering cloud conditions (from a sky imager), AOD, and precipitable water vapor from AERONET. The factor that established the larger errors was the visibility of the solar disc, which cannot be defined by the available sources of model inputs. Additionally, there were discrepancies between the satellite estimation of the clouds and the ground picture, which caused deviations in results. AOD differences affected more the DNI.

Low-cost sensors (LCSs) for particulate matter (PM) concentrations have attracted the interest of researchers, supplementing their efforts to quantify PM in higher spatiotemporal resolution. The precision of PM mass concentration measurements from PMS 5003 sensors has been widely documented, though limited information is available regarding their size selectivity and number concentration measurement accuracy. In this work, PMS 5003 sensors, along with a Federal Referral Methods (FRM) sampler (Grimm spectrometer), were deployed across three sites with different atmospheric profiles, an urban (Germanou) and a background (UPat) site in Patras (Greece), and a semi-arid site in Almería (Spain, PSA). The LCSs particle number concentration measurements were investigated for different size bins. Findings for particles with diameter between 0.3 and 10 μm suggest that particle size significantly affected the LCSs’ response. The LCSs could accurately detect number concentrations for particles smaller than 1 μm in the urban (R2 = 0.9) and background sites (R2 = 0.92), while a modest correlation was found with the reference instrument in the semi-arid area (R2 = 0.69). However, their performance was rather poor (R2 < 0.31) for coarser aerosol fractions at all sites. Moreover, during periods when coarse particles were dominant, i.e., dust events, PMS 5003 sensors were unable to report accurate number distributions (R2 values < 0.47) and systematically underestimated particle number concentrations. The results indicate that several questions arise concerning the sensors’ capabilities to estimate PM2.5 and PM10 concentrations, since their size distribution did not agree with the reference instruments.

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting. [ABSTRACT FROM AUTHOR]

The European turtle dove (Streptopelia turtur) is an Afro-Palearctic migrant whose populations have declined by 79% from 1980 to 2014. In 2018, the International Single Species Action Plan for the Turtle Dove (ISSAP) was developed with the goal of enabling, by 2028, an increase in turtle dove numbers along each of the three migration flyways (western, central, eastern). To achieve this, the illegal killing of turtle doves, a critical threat to the species, has to be eradicated. The Ionian Islands off the west coast of Greece lie on the eastern flyway and are considered a major turtle dove poaching hot-spot during spring migration. Quantifying wildlife crime, however, is challenging. In the absence of a reliable protocol for monitoring spring poaching levels, the agencies tasked with tackling the problem have no means of assessing the effectiveness of the anti-poaching measures and adapting them if required. Using passive acoustic monitoring (PAM) methods, we recorded gun hunting intensity at known turtle dove poaching sites during the 2019–2022 spring migrations (2–10 sites/season) with unprecedented spatial and temporal resolution. Based on published gunshot to killed/injured bird ratio for similar species (corroborated with discussions with local hunters) and an estimate of the proportion of hunting sites monitored by our PAM grid (using gunshot detection range estimates from control gunshots), we estimated that in 2021, up to 57,095 turtle doves were killed or injured across five Ionian Islands (Zakynthos, Paxi, Antipaxi, Othoni, and Mathraki). The 2022 estimate was almost half, but it is unclear as to whether the change is due to a decline in poachers or turtle doves. We propose ways of improving confidence in future estimates, and call for a temporary moratorium of autumn turtle dove hunting in Greece—as per ISSAP recommendation—until spring poaching is eradicated and the eastern flyway population shows signs of a full recovery. Finally, we hope our findings will pave the way for the development of PAM grids at turtle dove poaching hot-spots across all migration flyways, contributing to the global conservation of the species.

Hybrid concentrated solar thermal power (CSP) and photovoltaic (PV) plants are gaining relevance because they combine their advantages: easy installation and low cost of PV plus dispatchability of CSP. This paper presents results of a techno-economic modelling of this hybrid approach for sites in Jordan, Tunisia and Algeria. Local boundary conditions such as meteorology, cost and electricity demand have been considered to determine the best configurations for these three sites. Different CSP technologies with thermal energy storage have been selected. Hybridization with natural gas has also been included. The optimization is done towards minimizing the LCOE while covering the electrical demand 24/7. Results are presented for different CO2 emissions ranges, as the use of fossil fuel has a strong impact on the LCOE and for environmental reasons, it may be preferred to be kept to a minimum. For most of the cases analyzed, the fraction of energy from PV that leads to minimum LCOE is lower than the energy from CSP. It is shown that for countries with a high fuel price, the use of natural gas reduces the LCOE until a share from this source of about 20%. A higher integration of fossil fuel for sites rich in solar irradiation is considered not advantageous if the price of natural gas is above EUR 40/MWh.

High-resolution wind datasets are crucial for ultra-short-term wind forecasting. Penetration of WT installations near urban areas that are constantly changing will motivate researchers to understand how to adapt their models to terrain changes to reduce forecasting errors. Although CFD modelling is not widely used for ultra-short-term forecasting purposes, it can overcome such difficulties. In this research, we will spatially extrapolate vertical profile LIDAR wind measurements into a 3D wind velocity field over a large and relatively complex terrain with the use of stationary CFD simulations. The extrapolated field is validated with measurements at a hub height of three WTs located in the area. The accuracy of the model increases with height because of the terrain anomalies and turbulence effects. The maximum MAE of wind velocity at WT hub height is 0.81 m/s, and MAPE is 7.98%. Our model remains accurate even with great simplifications and scarce measurements for the complex terrain conditions of our case study. The models' performance under such circumstances establishes it as a promising tool for the evolution of ultra-short-term forecasting as well as for the evaluation of new WT installations by providing valuable data for all models. [ABSTRACT FROM AUTHOR]

All-sky imagers (ASIs) can be used to model clouds and detect spatial variations of cloud attenuation. Such cloud modeling can support ASI-based nowcasting, upscaling of photovoltaic production and numeric weather predictions. A novel procedure is developed which uses a network of ASIs to model clouds and determine cloud attenuation more accurately over every location in the observed area, at a resolution of 50 m × 50 m. The approach combines images from neighboring ASIs which monitor the cloud scene from different perspectives. Areas covered by optically thick/intermediate/thin clouds are detected in the images of twelve ASIs and are transformed into maps of attenuation index. In areas monitored by multiple ASIs, an accuracy-weighted average combines the maps of attenuation index. An ASI observation’s local weight is calculated from its expected accuracy. Based on radiometer measurements, a probabilistic procedure derives a map of cloud attenuation from the combined map of attenuation index. Using two additional radiometers located 3.8 km west and south of the first radiometer, the ASI network’s estimations of direct normal (DNI) and global horizontal irradiance (GHI) are validated and benchmarked against estimations from an ASI pair and homogeneous persistence which uses a radiometer alone. The validation works without forecasted data, this way excluding sources of error which would be present in forecasting. The ASI network reduces errors notably (RMSD for DNI 136 W/m2, GHI 98 W/m2) compared to the ASI pair (RMSD for DNI 173 W/m2, GHI 119 W/m2 and radiometer alone (RMSD for DNI 213 W/m2), GHI 140 W/m2). A notable reduction is found in all studied conditions, classified by irradiance variability. Thus, the ASI network detects spatial variations of cloud attenuation considerably more accurately than the state-of-the-art approaches in all atmospheric conditions.

Despite the success of hand-crafted features in computer visioning for many years, nowadays, this has been replaced by end-to-end learnable features that are extracted from deep convolutional neural networks (CNNs). Whilst CNNs can learn robust features directly from image pixels, they require large amounts of samples and extreme augmentations. On the contrary, hand-crafted features, like SIFT, exhibit several interesting properties as they can provide local rotation invariance. In this work, a novel scheme combining the strengths of SIFT descriptors with CNNs, namely SIFT-CNN, is presented. Given a single-channel image, one SIFT descriptor is computed for every pixel, and thus, every pixel is represented as an M-dimensional histogram, which ultimately results in an M-channel image. Thus, the SIFT image is generated from the SIFT descriptors for all the pixels in a single-channel image, while at the same time, the original spatial size is preserved. Next, a CNN is trained to utilize these M-channel images as inputs by operating directly on the multiscale SIFT images with the regular convolution processes. Since these images incorporate spatial relations between the histograms of the SIFT descriptors, the CNN is guided to learn features from local gradient information of images that otherwise can be neglected. In this manner, the SIFT-CNN implicitly acquires a local rotation invariance property, which is desired for problems where local areas within the image can be rotated without affecting the overall classification result of the respective image. Some of these problems refer to indirect immunofluorescence (IIF) cell image classification, ground-based all-sky image-cloud classification and human lip-reading classification. The results for the popular datasets related to the three different aforementioned problems indicate that the proposed SIFT-CNN can improve the performance and surpasses the corresponding CNNs trained directly on pixel values in various challenging tasks due to its robustness in local rotations. Our findings highlight the importance of the input image representation in the overall efficiency of a data-driven system.

Solar forecasting constitutes a critical tool for operating, producing and storing generated power from solar farms. In the framework of the International Energy Agency’s Photovoltaic Power Systems Program Task 16, the solar irradiance nowcast algorithms, based on five all-sky imagers (ASIs), are used to investigate the feasibility of ASIs to foresee ramp events. ASIs 1–2 and ASIs 3–5 can capture the true ramp events by 26.0–51.0% and 49.0–92.0% of the cases, respectively. ASIs 1–2 provided the lowest (

PM2.5 measurements using a network of lost-cost sensors were conducted during 2017–2019 in the greater area of Patras, Greece. The average PM2.5 concentration in all sites during the study period was 9.4 μg m−3, varying from 6.2 μg m−3 in the background areas to 12.8 μg m−3 at the city center. The site with the peak PM2.5 levels was not located in an area with high traffic density but rather in a square with pedestrian-only zones and a high restaurant density. The highest PM2.5 concentrations were observed during the colder period (November–March) due to high emissions from residential wood burning for heating purposes. The measurements of the sensors were used to estimate the importance of regional and local PM2.5 sources. During the warm period, regional transport dominated, contributing approximately 80–85% of the PM2.5 in the city center; however, during the colder period, the local sources were responsible for approximately half the PM2.5. The network operated reliably during this multiyear study. Such measurements provide, at a very low cost, valuable insights not only about the temporal and spatial variability of PM2.5 in a city but also about its sources, including the role of regional transport.