Average Window Research Articles

Interactive effects between extreme temperatures and PM2.5 on cause-specific mortality in thirteen U.S. states

The extent and robustness of the interaction between exposures to heat and ambient PM2.5 is unclear and little is known of the interaction between exposures to cold and ambient PM2.5. Clarifying these interactions, if any, is crucial due to the omnipresence of PM2.5 in the atmosphere and increasing scope and frequency of extreme temperature events. To investigate both of these interactions, we merged 6 073 575 individual-level mortality records from thirteen states spanning seventeen years with 1 km daily PM2.5 predictions from sophisticated prediction model and 1 km meteorology from Daymet V4. A time-stratified, bidirectional case-crossover design was used to control for confounding by individual-level, long-term and cyclic weekly characteristics. We fitted conditional logistic regressions with an interaction term between PM2.5 and extreme temperature events to investigate the potential interactive effects on mortality. Ambient PM2.5 exposure has the greatest effect on mortality by all internal causes in the 2 d moving average exposure window. Additionally, we found consistently synergistic interactions between a 10 μg m-3 increase in the 2 d moving average of PM2.5 and extreme heat with interaction odds ratios of 1.013 (95% CI: 1.000, 1.026), 1.024 (95% CI: 1.002, 1.046), and 1.033 (95% CI: 0.991, 1.077) for deaths by all internal causes, circulatory causes, and respiratory causes, respectively, which represent 75%, 156%, and 214% increases in the coefficient estimates for PM2.5 on those days. We also found evidence of interactions on the additive scale with corresponding relative excess risks due to interaction (RERIs) of 0.013 (95% CI: 0.003, 0.021), 0.020 (95% CI: 0.008, 0.031), and 0.017 (95% CI: -0.015, 0.036). Interactions with other PM2.5 exposure windows were more pronounced. For extreme cold, our results were suggestive of an antagonistic relationship. These results suggest that ambient PM2.5 interacts synergistically with exposure to extreme heat, yielding greater risks for mortality than only either exposure alone.

Collocating wind data: A case study on the verification of the CERRA dataset

Abstract Wind resource assessment often relies on reanalysis data or meso-scale models due to the challenges and costs associated with on-site measurement campaigns, especially in offshore locations. While these datasets offer extensive spatial coverage, they often provide the desired parameters on a coarse grid only. The spatial averaging over the grid and the low frequency of the outputs hinders the model’s ability to capture the short-term wind speed variability and events with a time scale of a few minutes to a few dozen of minutes. In this paper we investigate the temporal scales of the wind events resolved by the Copernicus European Regional Re-Analysis (CERRA) dataset, ERA5, and an in-house regional down-scaling of ERA5 using the WRF model by comparing them against measurements. We propose a method to collocate the two sources of wind data (measured and modeled), focusing on the verification of CERRA with respect to first and second-order statistics in the North Sea. The results highlight the superiority of CERRA compared to ERA5 in estimating the wind speed distribution, improving the 95th percentile error from -0.85 to -0.19 ms−1 and the average bias from -0.286 to -0.004 m s−1 across all locations. The wind speed change over one hour is underestimated by both CERRA and ERA5 when collocated with 10-minute measurements at the full hour. However, hourly averaged measurements align well with CERRA’s wind speed variation predictions, while an averaging window of 3.5 hours is needed to align with ERA5’s hourly wind speed changes. The results showed that the synthetic wind speeds exhibit optimal correlation with the measurements when the measurements are averaged over 4.5 to 7 hours, depending on the modeled dataset. This time scale corresponds to a length scale of 160-250 km, assuming a mean wind speed of 10 m s−1, which falls within the meso-scale range. The study contributes to the understanding of model validation in offshore wind energy studies, with the potential to enhance wind resource assessment calculations and improve the results of long-term extrapolations.

Analysis of Diurnal Air Temperature Trends and Pattern Similarities in Highland and Lowland Stations of Italy and UK

ABSTRACTIn this study, an analysis of hourly air temperatures in four groups of 32 stations from the UK highland (5 stations), UK lowland (4 stations), Italian highland (11 stations), and Italian lowland (12 stations) at different altitudes was carried out over the period from 2002 to 2021. The study aimed to examine the trends of each hour of the day during this period, over different averaging time windows (10‐day, 30‐day, and 60‐day). The trends were computed using the Mann–Kendall trend test and Sen's slope estimator. The similarity of trends within and across the groups of stations was assessed using the hierarchical clustering with dynamic time warping technique. An additional analysis was conducted to show the correlation of trends among the group of stations using the correlation distance matrix. Hierarchical clustering and distance correlation analysis show trend similarities and correlations, also indicating dissimilarities among different groups. Using 30‐day averages, significant warming trends in specific months at the Italian stations are evident, especially in February, July, August, and December. The UK highland stations did not show statistically significant trends, but clear pattern similarities were found within the groups, especially in certain months. The ultimate goal of this article is to provide insights into temperature dynamics and climate change characteristics on regional and diurnal scales.

Perceiving Synchrony: Determining Thermal-tactile Simultaneity Windows.

In cutaneous displays in which both tactile and thermal signals are presented, it is important to understand the temporal requirements associated with presenting these signals so that they are perceptually synchronous. Such synchrony is important to provide realistic touch experiences in applications involving object recognition and social touch interactions. In the present experiment the temporal window within which tactile and warm thermal stimuli are perceived to occur at the same time was determined. A Simultaneity Judgment Task was used in which pairs of tactile and thermal stimuli were presented on the hand at varying stimulus onset asynchronies, and participants determined whether the stimuli were simultaneous or not. The results indicated that the average simultaneity window width was 1041 ms. The average point of subjective simultaneity (PSS) was -569 ms, indicating that participants perceived simultaneity best when the warm thermal stimulus preceded the tactile stimulus by 569 ms. These findings indicate that thermal and tactile stimuli do not need to be displayed simultaneously for the two stimuli to be perceived as being synchronous and therefore the timing of such stimuli can be adjusted to maximize the likelihood that they will both be perceived.

A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection.

A real-time and reliable automatic detection system for epileptic seizures holds significant value in assisting physicians with rapid diagnosis and treatment of epilepsy. Aiming to address this issue, a novel lightweight model called Convolutional Neural Network-Reformer (CNN-Reformer) is proposed for seizure detection on long-term EEG.The CNN-Reformer consists of two main parts: the Data Reshaping (DR) module and the Efficient Attention and Concentration (EAC) module. This framework reduces network parameters while retaining effective feature extraction of multi-channel EEGs, thereby improving model computational efficiency and real-time performance. Initially, the raw EEG signals undergo Discrete Wavelet Transform (DWT) for signal filtering, and then fed into the DR module for data compression and reshaping while preserving local features. Subsequently, these local features are sent to the EAC module to extract global features and perform categorization. Post-processing involving sliding window averaging, thresholding, and collar techniques is further deployed to reduce the false detection rate (FDR) and improve detection performance. On the CHB-MIT scalp EEG dataset, our method achieves an average sensitivity of 97.57%, accuracy of 98.09%, and specificity of 98.11% at segment-based level, and a sensitivity of 96.81%, along with FDR of 0.27/h, and latency of 17.81 s at the event-based level. On the SH-SDU dataset we collected, our method yielded segment-based sensitivity of 94.51%, specificity of 92.83%, and accuracy of 92.81%, along with event-based sensitivity of 94.11%. The average testing time for 1[Formula: see text]h of multi-channel EEG signals is 1.92[Formula: see text]s. The excellent results and fast computational speed of the CNN-Reformer model demonstrate its potential for efficient seizure detection.

Field assessments on the impact of CO2 concentration fluctuations along with complex-terrain flows on the estimation of the net ecosystem exchange of temperate forests

Abstract. CO2 storage (Fs) is the cumulation or depletion in CO2 amount over a period in an ecosystem. Along with the eddy covariance flux and wind-stream advection of CO2, it is a major term in the net ecosystem CO2 exchange (NEE) equation. The CO2 storage dominates the NEE equation under a stable atmospheric stratification when the equation is used for forest ecosystems over complex terrains. However, estimating Fs remains challenging due to the frequent gusts and random fluctuations in boundary-layer flows that lead to tremendous difficulties in capturing the true trend of CO2 changes for use in storage estimation from eddy covariance along with atmospheric profile techniques. Using measurements from Qingyuan Ker Towers equipped with NEE instrument systems separately covering mixed broad-leaved, oak, and larch forest towers in a mountain watershed, this study investigates gust periods and CO2 fluctuation magnitudes and examines their impact on Fs estimation in relation to the terrain complexity index (TCI). The gusts induce CO2 fluctuations for numerous periods of 1 to 10 min over 2 h. Diurnal, seasonal, and spatial differences (P < 0.01) in the maximum amplitude of CO2 fluctuations (Am) range from 1.6 to 136.7 ppm, and these differences range from 140 to 170 s in a period (Pm) at the same significance level. Am and Pm are significantly correlated to the magnitude of and random error in Fs with diurnal and seasonal differences. These correlations decrease as CO2 averaging time windows become longer. To minimize the uncertainties in Fs, a constant [CO2] averaging time window for the Fs estimates is not ideal. Dynamic averaging time windows and a decision-level fusion model can reduce the potential underestimation of Fs by 29 %–33 % for temperate forests in complex terrain. In our study, the relative contribution of Fs to the 30 min NEE observations ranged from 17 % to 82 % depending on turbulent mixing and the TCI. The study's approach is notable as it incorporates the TCI and utilizes three flux towers for replication, making the findings relevant to similar regions with a single tower.

High-performance, superhydrophobic, durable photonic structure coating for efficient passive daytime radiative cooling

Passive daytime radiative cooling (PDRC) is an innovative and energy-free cooling technology that automatically cools the surface of an object by reflecting sunlight and emitting heat into outer space without the need for external energy inputs. However, PDRC materials often face issues such as surface contamination and poor long-term outdoor durability. Herein, a photonic structure coating with high PDRC performance, superhydrophobic property, and high outdoor durability was designed and prepared using a phase separation strategy. The photonic structure coating achieves a solar reflectance ∼97.6 % and an average atmospheric window (AW) emissivity of ∼93 %. Under direct sunlight (800 W/m2), the coating exhibits good PDRC performance, with an average temperature drop of ∼13 °C and a maximum temperature drop of up to ∼20 °C. The rough and porous surface of the coating can adsorb air, reducing the solid-liquid adhesion and endowing the coating with super-hydrophobic properties. The incorporation of a small amount of fluoroalkyl silanes into the coating provides water resistance, resulting in a water contact angle (WCA) of ∼155.1° and sliding angle (SA) of ∼2.3°, meeting the need for self-cleaning. Furthermore, the coating exhibits superior durability, including resistance to acid and alkali, UV aging, abrasion, and scratching. All these merits render this photonic structure coating great potential for real-world applications.

The Slope of the Phillips Curve and the Optimal Average Inflation Targeting Window

AbstractThe slope of the Phillips curve has been shown to be flatter in recent years, including in Australia. How does this impact the optimal targeting window for a central bank conducting an average inflation targeting policy? This article shows that, in the face of the flattening of the Phillips curve, it is desirable to increase the targeting window. Doing so would lead to lower inflation fluctuations and to a non‐trivial welfare gain. In a persistently higher‐than‐target inflation, high‐interest‐rate environment, this implies that the central bank should keep the nominal interest rate higher for longer.

Impact of internal phase volume on the physical, morphological and mechanical characteristics of emulsion templated scaffolds

Objectives: The high porosity of tissue engineering scaffolds is advantageous as they provide a high degree of infiltration of nutrients, enable cell penetration, and support vascularisation. However, the mechanical strength is also critical for providing structural support to the defect site throughout the regeneration process. In this study, we aimed to establish a relationship between internal phase volume and emulsion-templated scaffolds' physical, morphological and mechanical characteristics. Methods: In this work, tetra methacrylate functionalised polycaprolactone (4PCLMA) polymers were synthesised via ring-opening polymerisation followed by methacrylation. 4PCLMA-based emulsion templated matrices with 60%, 75% and 82% internal phase volumes were fabricated (P60, P75, and P82). These scaffolds' densities, porosities, average pore and window sizes, degree of interconnectivity values, and mechanical properties were investigated. Results: Increasing internal phase volume reduced the density of the foams by almost two-fold. No direct correlation was observed between average pore size and internal phase volume. Both the average window sizes and the degree of interconnectivity values increase with increasing internal phase volume. Compression modulus values are calculated as 0.46±0.04 MPa, 0.23±0.02 MPa and 0.14±0.01 MPa for P60, P75, and P82, respectively. Increasing internal phase volume from 60% to 82% caused a more than 2-fold reduction in the stiffness of the emulsion-templated matrices. Conclusions: Accordingly, by reporting on this experimental framework, we established a relationship between internal phase volume and the physical, morphological and mechanical characteristics of 4PCMA-based scaffolds to precisely engineer these characteristics for specific tissue engineering applications.

Neural populations in the language network differ in the size of their temporal receptive windows.

Despite long knowing what brain areas support language comprehension, our knowledge of the neural computations that these frontal and temporal regions implement remains limited. One important unresolved question concerns functional differences among the neural populations that comprise the language network. Here we leveraged the high spatiotemporal resolution of human intracranial recordings (n = 22) to examine responses to sentences and linguistically degraded conditions. We discovered three response profiles that differ in their temporal dynamics. These profiles appear to reflect different temporal receptive windows, with average windows of about 1, 4 and 6 words, respectively. Neural populations exhibiting these profiles are interleaved across the language network, which suggests that all language regions have direct access to distinct, multiscale representations of linguistic input-a property that may be critical for the efficiency and robustness of language processing.

Assessing a machine learning-based downscaling framework for obtaining 1km daily precipitation from GPM data

Hydro-meteorological monitoring through satellites in arid and semi-arid regions is constrained by the coarse spatial resolution of precipitation data, which impedes detailed analyses. The objective of this study is to evaluate various machine learning techniques for developing a downscaling framework that generates high spatio-temporal resolution precipitation products. Focusing on the Hai River Basin, we evaluated three machine learning approaches—Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Back Propagation (BP) neural networks. These methods integrate environmental variables including land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), Precipitable Water Vapor (PWV), and albedo, to downscale the 0.1° spatial resolution Global Precipitation Measurement (GPM) product to a 1 km resolution. We further refined the results with residual correction and calibration using terrestrial rain gauge data. Subsequently, utilizing the 1 km annual precipitation, we employed the moving average window method to derive monthly and daily precipitation. The results demonstrated that the XGBoost method, calibrated with Geographical Difference Analysis (GDA) and Kriging spatial interpolation, proved to be the most accurate, achieving a Mean Absolute Error (MAE) of 58.40 mm for the annual product, representing a 14 % improvement over the original data. The monthly and daily products achieved MAE values of 11.61 mm and 1.79 mm, respectively, thus enhancing spatial resolution while maintaining accuracy comparable to the original product. In the Hai River Basin, key factors including longitude, latitude, DEM, LST_night, and PWV demonstrated greater importance and stability than other factors, thereby enhancing the model's precipitation prediction capabilities. This study provides a comprehensive assessment of the annual, monthly, and daily high-temporal and high-spatial resolution downscaling processes of precipitation, serving as an important reference for hydrology and related fields.

Building and validating trend-based multiple sclerosis case definitions: a population-based cohort study for Manitoba, Canada

ObjectiveThis study aims to (1) build and validate model-based case definitions for multiple sclerosis (MS) that use trends (ie, trend-based case definitions) and (2) to apply dynamic classification to identify...

A hypothesis on ergodicity and the signal‐to‐noise paradox

AbstractThis letter raises the possibility that ergodicity concerns might have some bearing on the signal‐to‐noise paradox. This is explored by applying the ergodic theorem to the theory behind ensemble weather forecasting and the ensemble mean. Using the ensemble mean as our best forecast of observations amounts to interpreting it as the most likely phase‐space trajectory, which relies on the ergodic theorem. This can fail for ensemble forecasting systems if members are not perfectly exchangeable with each other, the averaging window is too short and/or there are too few members. We argue these failures can occur in cases such as the winter North Atlantic Oscillation (NAO) forecasts due to intransitivity or regime behaviour for regions such as the North Atlantic and Arctic. This behaviour, where different ensemble members may become stuck in different relatively persistent flow states (intransitivity) or multi‐modality (regime behaviour), can in certain situations break the ergodic theorem. The problem of non‐ergodic systems and models in the case of weather forecasting is discussed, as are potential mitigation methods and metrics for ergodicity in ensemble systems.

Self-rectifying NiOX/WOX heterojunction synaptic memristor for crossbar architectured reservoir computing system

In this study, we examine an ITO/NiOX/WOX/Pt p-n heterojunction memristor for neuromorphic applications as a synaptic crossbar array. The transition in the depletion region at the interface between p-type NiOX and n-type WOX crucially influences the self-rectifying characteristics of the device depending on the voltage polarity. Furthermore, long- and short-term memory coexist depending on the switching voltage condition, thus enabling various neuromorphic applications, such as reservoir computing and the use of the synaptic device at the off-chip trained network. The reliable operational characteristics are confirmed by obtaining an average memory window (>9) and rectification ratio (>58) between device-to-device and cycle-to-cycle. Furthermore, synaptic functions were successfully implemented, such as spike-rate-dependent plasticity, spike-number-dependent plasticity, paired-pulse facilitation, post-tetanic potentiation, paired-pulse depression, and post-tetanic depression, in conjunction with repeatability. Ultimately, reservoir computing is accomplished based on the I–V nonlinearity and short-term memory characteristics. A high pattern recognition rate (>96.2 %) is achieved in MNIST tasks using 16 reservoir states, thus affirming the trustworthiness of the reservoir computing system. With its comprehensive approach to synaptic applications for neuromorphic computing systems, the heterojunction device highlights its considerable potential to advance artificial neural networks in conjunction with novel memristor technology directions.

Forecasting operation of a chiller plant facility using data-driven models

In recent years, data-driven models have enabled accurate prediction of chiller power consumption and chiller coefficient of performance (COP). This study evaluates the usage of time series Extreme Gradient Boosting (XGBoost) models to predict chiller power consumption and chiller COP of a water-cooled chiller plant. The 10-second measured data used in this study are from the Intelligent Building Agents Laboratory (IBAL), which includes two water-cooled chillers. Preprocessing, data selection, noise analysis, and data smoothing methods influence the accuracy of these data-driven predictions. The data intervals were changed to 30 s, 60 s, and 180 s using down-sampling and averaging strategies to investigate the impact of data preprocessing methods and data resolutions on the accuracy of chiller COP and power consumption models. To overcome the effect of noise on the accuracy of the models of chiller power consumption and COP, two data smoothing methods, the moving average window strategy and the Savitzky-Golay (SG) filter, are applied. The results show that both methods improve the predictions compared to the baseline, with the SG filter slightly outperforming the moving average. Particularly, the mean absolute percentage error of the chiller COP and power consumption models improved from 4.8 to 4.9 for the baseline to 1.9 and 2.3 with the SG filter, respectively. Overall, this study provides a practical guide to developing XGBoost data-driven chiller power consumption and COP prediction models.

The Utility of Expert-Rated and Self-Report Assessments of Youth Psychopathic Traits for Predicting Felony Recidivism Among Formerly Incarcerated Youth

Psychopathic traits assessed with expert-rated instruments are well-established predictors for both general and violent felony recidivism outcomes in adjudicated youth. However, it is not clear whether self-report assessments of psychopathic traits show similar predictive value. The current study evaluated both expert-rated (i.e., the Hare Psychopathy Checklist: Youth Version [PCL:YV]) and self-report measures (i.e., the Childhood Psychopathy Scale, Inventory of Callous Unemotional Traits, Antisocial Process Screening Device, and the Youth Psychopathic Traits Inventory) assessing youth psychopathic traits for predicting general and violent felony recidivism among formerly incarcerated high-risk male adolescents ( n = 275). Scores on both expert-rated and self-report instruments were significantly associated with a shorter time to felony rearrest into early adulthood (with an average follow-up window of 11 years). However, the expert-rated PCL:YV, but not self-report measures, provided significant utility for predicting violent felony arrest. These results suggest that in general, youth psychopathic traits, regardless of the specific instrument used, are predictors of general felony rearrest outcomes, while the expert-rated PCL:YV alone provides utility for predicting the most severe crimes (i.e., violent felonies). The strengths and limitations of the assessment modalities are discussed.

Efficient daytime radiative cooling films originating from controllable multi-scattering effect

Passive radiative cooling technology has received extensive attention as an environmentally friendly cooling solution. However, existing radiative coolers often suffer from complexities in preparation and high costs, limiting their practical applications. Here, a novel porous structure with randomly distributed microspheres is fabricated in a low-cost and simple method known as solvent volatilization-induced phase separation, employing polyvinylidene fluoride (PVDF) and monodispersed SiO2 microspheres. Utilizing the regulation of parameters such as the size of the pores and microspheres, controllable multi-scattering of light by the microstructures is achieved. The porous PVDF/SiO2 film, with an average solar reflectance of 93.8% and an average atmospheric window emissivity of 0.958, is self-supporting and exhibits excellent flexibility and mechanical strength. When applied to closed and windowed models, the porous PVDF/SiO2 film effectively lowers the internal temperatures by an average of 7.0 °C and 4.6 °C in summer, respectively, and by an average of 6.7 °C and 4.4 °C in spring. This work provides a viable strategy for achieving efficient radiative cooling and improving building energy management in a simple, cost-effective, and scalable manner.

A hybrid learning approach to model the diversity of window-opening behavior

The diverse window-opening behaviors of individuals can result in significant differences in indoor thermal environments, air quality, and energy utilization. However, the majority of existing studies focus on constructing an average window operation model, thus overlooking the diversity of behaviors. Current methods for addressing behavioral diversity face challenges with integration into building performance simulation software and are highly dependent on data scale. To address these limitations, this study proposes a novel approach that combines unsupervised learning (K-Means) and supervised learning (Light Gradient Boosting Machine, LightGBM) for modeling the diverse window-opening behaviors. Furthermore, the SHapley Additive exPlanations (SHAP) was employed to interpret the predictive model. This study yielded four key findings: 1) There were 12 different window-opening behavior patterns. Interestingly, 65 % of the residents’ window-opening behaviors were not influenced by environmental factors but were instead a matter of personal habit. 2) Using random sampling to divide the dataset may pose a risk of data leakage. The time series cross-validation method is more suitable for evaluating the performance of the window state prediction model. 3) Under the time series sampling strategy, the LightGBM model incorporating behavioral diversity improved the prediction accuracy by 1.3%–10.4 % compared to the standalone LightGBM model. Notably, when the daily average window opening time was used as a clustering feature in the LightGBM model (Cluster(T)-LightGBM), the accuracy reached 87.1 %. 4) The SHAP feature analysis highlighted high-intensity window-opening categories, outdoor temperature, and indoor CO2 concentration as the most pivotal predictors.

Vegetation and Evapotranspiration Responses to Increased Atmospheric Vapor Pressure Deficit across the Global Forest

A forest is vulnerable to drought and plays important roles in the regulation of carbon and water cycling in a terrestrial ecosystem. Atmospheric vapor pressure deficit (VPD) has been identified as an increasingly major factor in plant functioning and has been established as a main contributor to recent drought-induced plant mortality, independent of other drivers associated with climate change. However, most previous studies have focused on the effects of climate warming and CO2 enrichment on vegetation growth, without considering the effects of an increased VPD on vegetation growth and evapotranspiration (ET) in forest ecosystems. This could lead to a large uncertainty in estimating the variability in forest carbon sinks. Based on the long-term satellite data, we investigated the response of the leaf area index (LAI) and ET to the VPD via a partial correlation analysis in this study. We also examined the temporal variability in the partial coefficients within a ten-year moving window. The results showed that over 50% of the region displayed a negative partial correlation between the LAI, ET, and VPD, and those pixels were mainly concentrated in North America and the plains of Eastern Europe. Regions with a negative trend of partial correlation in both the LAI and ET are mostly located in the plains of Eastern Europe and the Siberian Plain of western Russia, while the positive trend is mainly in South America. The plains of Eastern Europe are becoming drier, which was proved by the interannual trend of the Standardized Precipitation Evapotranspiration Index (SPEI) and soil water content (SWC). Additionally, the LAI and ET in those areas exhibited a significant positive correlation with the SWC based on the moving window average. This study suggests that the role of the VPD on vegetation will become increasingly prominent in the context of future climate change for the forest ecosystem.

Statistical downscaling of GCMs wind speed data for trend analysis of future scenarios: a case study in the Lombardy region

Near-surface wind speed is a key climatic variable, affecting many sectors, such as energy production, air pollution, and natural hazard. Lombardy region of Italy is among the European areas with lowest average wind speed, leading generally to low air quality and wind energy potential. However, it is also one of the most affected area by tornadoes in Italy. Here we investigate possible changes in wind circulation as due to prospective global warming. We analysed wind speed WS under future scenarios (SSP1-2.6 and SSP5-8.5) from six Global Climate Models (GCMs) until 2100, tuned against observed WS data. We employed a statistical downscaling method, namely Stochastic Time Random Cascade (STRC) to correct locally GCMs outputs. Three statistical tests, i.e. Linear Regression, Mann Kendall, Moving Window Average, were carried out to analyse future trends of: annual WS averages, 95th quantile (as an indicator of large WS), and the number of days of calm wind per year (NWC). The proposed STRC algorithm can successfully adjust the mean, standard deviation, and autocorrelation structure of the GCM outputs. No strong trends are found for the future. The chosen variables would all display non-stationarity, and the 95th percentile display a positive trend for most of the stations. Concerning NWC, notable discrepancies among GCMs are seen. The STRC algorithm can be used to successfully adjust GCMs outputs to reflect locally observed data and to then generate credible long-term scenarios for WSs as a tool for decision-making.