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Mineral dust is one of the most important aerosols when studying the radiative balance and climate of the planet. There are different dust emission schemes utilized by the atmospheric modeling communities, many of which disagree on basic output quantities such as mass of dust emitted and distribution of mass among size bins. In this work, we examined mineral dust emission from a leading model scheme, the Goddard Chemistry Aerosol Radiation and Transport (GOCART), as utilized in the Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2) Reanalysis and compared it to dust emissions calculated using wind measurements from ground based weather stations located in the Arabian Peninsula that are included in the National Oceanic and Atmospheric Administration's (NOAA) integrated surface database (ISD). An intercomparison of 10‐m wind speed is shown for the Arabian Peninsula region, differences of the observed and modeled wind field are quantified, and impacts of differences on dust emissions are calculated. This analysis shows 10‐m winds in the ISD were generally lower than MERRA‐2 winds, which propagated to dust emissions errors. Our estimate of one of the most significant mass impacts in dust emission is 0.178 Tg/year/grid box with a percent change of over 200% to the recalculated dust emissions from MERRA‐2. These differences in wind speed propagated to a difference in dust mass emitted by the use of a static source function which aids in scaling the mass emitted by the availability of dust in each grid. Additionally, the magnitude of these differences varies seasonally. Plain Language Summary: Dust emissions are related to the cube of wind speed as taken at 10 m above the surface of the planet. There is disagreement in the 10‐m wind speed values between measurements and model calculations. This leads to disagreement in models and measurements and a lowered ability to predict dust events. Inaccurate dust mass estimates contribute to the error in the calculations of the radiative balance of the planet. This study quantifies the differences in the 10‐m wind speeds and the impacts of these disagreements by holding all else constant besides the 10‐m wind speed while looking at the amount of dust emitted as calculated by leading dust emission schemes using values from measurements and models for 10‐m wind speed. Key Points: Evaluated 20 years of 10‐m wind speed integrated surface database observations against the MERRA‐2 Reanalysis wind fieldsLocated sites of agreement, seasonal bias, flatness, and overestimation of windsQuantified the impact of wind speed differences on dust emission at stations in the Arabian Peninsula [ABSTRACT FROM AUTHOR]

ABSTRACT In the North China Plain (NCP), the assessment of water surplus or deficit (WSD), which is calculated as precipitation minus reference evapotranspiration (ET0), holds significant implications for water resource management and agricultural irrigation decision‐making, given the region's long‐standing severe shortage of water resources. However, the magnitude, trend and climatic drivers of WSD remain poorly understood in the NCP. This study analysed the spatial and temporal characteristics of WSD, and quantified the contribution of climatic factors to WSD based on the sensitivity and contribution rate analysis methods with climatic data from 75 meteorological stations. The result showed that: (1) Annual WSD decreased mainly in northeastern NCP and increased significantly in southern NCP during 1961–2022. Annual WSD increased slightly from 1961 to 2022 at a rate of 1.63 mm a−2 mainly due to the more significant decrease (−1.88 mm a−2) in ET0 compared to precipitation (−0.25 mm a−2). (2) In terms of the sensitivity of WSD to climatic factors, relative humidity had the highest sensitivity, followed by net radiation, wind speed, precipitation and average air temperature. (3) Significant declines of wind speed were the most dominant factor affecting WSD variation in most part of NCP during most of a year, and net radiation of four stations in the western high‐elevation regions played the most important role. This study enhances comprehension of the impact of climate change on WSD in the NCP and provides a reference for improving management of agricultural water resources under NCP's evolving climatic conditions. [ABSTRACT FROM AUTHOR]

Wind speed is one of the main control factors of wind erosion and dust emissions, which are major problems in arid and semiarid regions of the world. Accurately simulating highly precise hourly wind speeds is critical and cost-efficient for land management decisions with the goal of mitigating wind erosion and land degradation. The Wind Erosion Prediction System (WEPS) is a process-based, daily time-step model that simulates changes in the soil–vegetation–atmosphere. However, to date, relatively few studies have been conducted to test the ability of the WEPS in simulating hourly wind speeds. In this study, the performance of the WEPS model was tested in the Inland Pacific Northwest (iPNW), where wind erosion is a serious problem. Hourly wind speeds were observed and simulated by the WEPS at 13 meteorological stations from 2009 to 2018 using the WEPS hourly wind speed probability histogram. Owing to increasing wind shear, the model is not as precise in reproducing high wind speeds. The WEPS inadequately simulated the hourly wind speeds at six of the 13 stations, with a low index of agreement (d < 0.5). The complex regional topography may be one of the reasons for this lack of agreement, because the WEPS's performance of interpolation relies on spatial distances and surface complexity. Therefore, we validated the model using another wind-speed database to eliminate the impact of spatial interpolation. The performance of the WEPS was improved after removing the impact of spatial interpolation, producing d values > 0.5 at nine of the 13 stations. Our results suggest that the WEPS can accurately simulate hourly wind speeds and assess wind erosion in the absence of interpolation, whereas the model may be uncertain when invoking spatial interpolation. Some evidence also suggests that the model may have a tendency to underestimate observed hourly wind speeds. Pragmatically, this suggests that model users should consider the possibility that WEPS may underestimate wind erosion risk in the iPNW and plan implementation of conservation practices accordingly. [ABSTRACT FROM AUTHOR]

Copyright of Afyon Kocatepe University Journal of Science & Engineering / Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi is the property of Afyon Kocatepe University, Faculty of Science & Literature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, the inter-relationship among 10 morphological characters, 8 climatic parameters and the content of total anthocyanins in the cob core of PMV 581 purple maize, cultivated and produced in three different places in Huanuco–Peru region, has been reported. This study of morphological characters was carried out using standard descriptors, both for the plant and the cob. Data on climatic parameters were obtained from three meteorological stations near the test locations. The total anthocyanin content (Acy), expressed as the glucoside-3-cyanidin concentration, has been determined by the differential pH method. From the statistical treatment of the data obtained, the following descriptors were found to be the most representative, given that they are poorly correlated with each other, but in general, depending on the localities: i/ (morphological) grain weight per cob GWC, plant length PL and cob core weight CCW; ii/ (climatic) minimum temperature Tmin, wind speed v and relative humidity RH. Between both types of descriptors, the best correlations occur for (CCW vs. Tmin) and (GWC vs. v). On the other hand, the total anthocyanin content Acy correlates very well with the CCW and Tmin descriptors. So, the highest concentration of Acy (684.2 mg/100 g) and also the highest CCW (38.6 g/cob) have been obtained in cobs of Winchuspata (W-Q), the coldest (Tmin = 7.89 °C) of the considered localities. On the contrary, the lowest concentration of Acy (603.7 mg/100 g) and also the lowest CCW 25.4 g/cob) have been obtained in cobs of Pistaloli (P-SA), the warmest (Tmin = 19.96 °C) of the three locations. The highest GWC value (139.4 g/cob) has been obtained in cobs of Marabamba (M-Y) where the wind speed v (4.13 m/s) was the highest of the locations considered. On the contrary, the lowest value of GWC (79.6 g/cob) has been obtained for cobs of Pistaloli (P-SA) where v was the lowest (1.19 m/s). In this context, it is important to propose studies on climatic variations' impact on different crop cycles, investigating how different agronomic management practices and the use of genetic identification/expression tools can optimize the anthocyanin production of purple maize, in order to facilitate the selection of new varieties for specific climatic conditions. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Quantification of the diffusive methane fluxes between the coastal ocean and atmosphere is important to constrain the atmospheric methane budget. The determination of the fluxes in coastal waters is characterized by a high level of uncertainty. To improve the accuracy of the estimation of coastal methane fluxes, high temporal and spatial sampling frequencies of dissolved methane in seawater are required, as well as the quantification of atmospheric methane concentrations, wind speed and wind direction above the ocean. In most cases, these atmospheric data are obtained from land-based atmospheric and meteorological monitoring stations in the vicinity of the coastal ocean methane observations. In this study, we measured wind speed, wind direction and atmospheric methane directly on board three research vessels in the southern North Sea and compared the local and remote atmospheric and meteorological measurements on the quality of the flux data. In addition, we assessed the source of the atmospheric methane measured in the study area in the German Bight using air mass back-trajectory assessments. The choice of the wind speed data source had a strong impact on the flux calculations. Fluxes based on wind data from nearby weather stations amounted to only 58 ± 34 % of values based on in situ data. Using in situ data, we calculated an average diffusive methane sea-to-air flux of 221 ± 351 µ mol m -2 d -1 (n = 941) and 159 ± 444 µ mol m -2 d -1 (n = 3028) for our study area in September 2019 and 2020, respectively. The area-weighted diffusive flux for the entire area of Helgoland Bay (3.78 × 10 9 m 2) was 836 ± 97 and 600 ± 111 kmol d -1 for September 2019 and 2020, respectively. Using the median value of the diffusive fluxes for these extrapolations resulted in much lower values compared to area-weighted extrapolations or mean-based extrapolations. In general, at high wind speeds, the surface water turbulence is enhanced, and the diffusive flux increases. However, this enhanced methane input is quickly diluted within the air mass. Hence, a significant correlation between the methane flux and the atmospheric concentration was observed only at wind speeds < 5 m s -1. The atmospheric methane concentration was mainly influenced by the wind direction, i.e., the origin of the transported air mass. Air masses coming from industrial regions resulted in elevated atmospheric methane concentrations, while air masses coming from the North Sea transported reduced methane levels. With our detailed study on the spatial distribution of methane fluxes we were able to provide a detailed and more realistic estimation of coastal methane fluxes. [ABSTRACT FROM AUTHOR]

Based on the long-time series of wind observation data and blueberry growth period data of 9 national meteorological stations in Liaoning province during 1991-2020, the optimal machine learning model was selected to extend the maximum wind speed time series, using the maximum wind speed and disaster information data of the corresponding stations, considering the frequency and duration comprehensively, the meteorological index thresholds of disaster risk caused by wind of different grades during the ripening period of blueberries were determined, and the risk levels of mild, moderate and severe wind disaster were formulated, and the time-space distribution characteristics of blueberry gale-induced disaster risk were analyzed by the frequency of disasters and the ratio of stations. Based on the information diffusion theory, the risk probability of gale disaster is evaluated. The results show that the random forest model had higher simulation accuracy and could better extend the time series of extreme wind speed. The risk threshold of gale disaster in the mature period of blueberry with different maturity was maximum wind speed ≥13.9m·s-1, and different grades of gale disaster were further divided, and the verification results were more in line with the actual situation. There were differences in the trend of the climatic tendency rate of the average maximum wind speed in the study area. The early and middle maturity periods showed an upward trend, while the late maturity period showed a downward trend, and the risk of gale disaster also decreases. The change trend of the number of gale days in the mature period of blueberry with different maturity was not completely consistent. Among them, Kuandian and Benxi station kept the increasing trend unchanged, and Xiuyan, Qingyuan and Xifeng stations kept the decreasing trend unchanged. The high wind disaster risk index experienced by different maturity blueberry maturity was not much different, ranging from 14 to 17 days, but the high wind disaster risk index between stations was quite different. Among them, Xifeng station had the most days in each maturity period, ranging from 22 to 31 days, while Kuandian station had less days in each maturity period, ranging from 4 to 6 days. The exceeding probability of gale disaster risk in different blueberry varieties at maturity stage in the study area was 42.6%, 40.4% and 32.3% from high to low, respectively. The greater the probability, the higher the risk of gale disaster. In general, the northwest region of Liaodong Green Economic Zone was a high-risk area for gale-induced disasters, with a wide range of disasters, high frequency and heavy degree. The return period of gale-induced disaster risk was more than 3 years. Among them, Xifeng and Qingyuan stations had the highest probability of gale risk in each mature period, followed by Fushun station and Benxi station. The whole study area is more suitable for planting late-maturing varieties of blueberry, planting early-maturing varieties and medium-maturing varieties of blueberry area, need to do a good job of gale prevention measures. [ABSTRACT FROM AUTHOR]

Dust storms are one of the important natural hazards that affect the lives of inhabitants all around the world, especially in North Africa and the Middle East. In this study, wind speed, wind direction, and air temperature patterns are investigated in one of the dustiest cities in Sistan Basin, Zahedan City, located in southeast Iran, over a 17-year period (2004–2020) using a WRF model and ground observation data. The city is located near a dust source and is mostly affected by local dust storms. The World Meteorology Organization (WMO) dust-related codes show that the city was affected by local dust, with 52 percent of the total dust events occurring during the period (2004–2021). The city's weather station reported that 17.5% and 43% were the minimum and maximum dusty days, respectively, during 2004–2021. The summer and July were considered the dustiest season and month in the city. Since air temperature, wind speed, and wind direction are important factors in dust rising and propagation, these meteorological factors were simulated using the Weather Research and Forecasting (WRF) model for the Zahedan weather station. The WRF model's output was found to be highly correlated with the station data; however, the WRF simulation mostly overestimated when compared with station data during the study period (2004–2020). The model had a reasonable performance in wind class frequency distribution at the station, demonstrating that 42.6% of the wind was between 0.5 and 2, which is in good agreement with the station data (42% in the range of 0.5–2). So, the WRF model effectively simulated the wind class frequency distribution and the wind direction at Zahedan station, despite overestimating the wind speed as well as minimum, maximum, and average air temperatures during the 17-year period. [ABSTRACT FROM AUTHOR]

Improving the accuracy of short-term wind speed predictions is crucial for mitigating the impact on power systems when integrating wind power into an electricity grid. This study developed a hybrid short-term wind speed prediction method, termed VMD–SSA–GRU, by combining variational mode decomposition (VMD) with gated recurrent units (GRUs) and optimizing it using a sparrow search algorithm (SSA). Initially, VMD was used to decompose the wind speed time series into subtime series. After reconstructing these subtime series, a GRU model was employed to establish separate prediction models for each series. Furthermore, an enhanced SSA was proposed to optimize the hyperparameters of the GRU model, which improved the prediction accuracy. Ultimately, the sub-series predictions were aggregated to produce the final wind speed prediction values. The predictive accuracy of this model was validated using the wind speed data measured at a meteorological station near a bridge site. The performance of the VMD–SSA–GRU model was compared with several other hybrid models, including those using wavelet transform, long short-term memory, and other neural networks. Comparably, the RMSE value of the VMD-SSA-GRU model was lower by 25.3%, 60.2%, and 61.7% in comparison to the VMD–SSA–LSTM, VMD–GRU, and VMD–LSTM models, respectively. The experimental results demonstrated that the proposed method achieved higher prediction accuracy than traditional methods. [ABSTRACT FROM AUTHOR]

The reference evapotranspiration (ETo) is essential for water-consuming in agriculture and land-water cycle research. The synoptic data from meteorological stations can provide reliable ground data for ETo estimation with the FAO-56 Penman-Monteith equation. However, the five primary variables this equation needs, including maximum temperature (Tmax), minimum temperature (Tmin), sunshine duration (SSD), wind speed (Wind), and relative humidity (RH), often experience severe data loss due to force majeure events in synoptic data. The data loss would directly introduce severe data gaps to the complex records for ETo. Machine learning algorithms can fill various data gaps with low error rates, however, to achieve high data quality, the algorithms must be selected properly to deal with the distinct types of data loss and train independently. Here, based on the data characters, we investigated and classified data gaps from the synoptic dataset into 2 major types: the common, minor data loss gaps including Tmax loss/Tmin loss/SSD loss/Wind loss/RH loss/Wind and SSD loss/Wind and RH loss, and the other 19 types of data loss which is more severe in information loss but barely occurred. Our results show that the XGBoost model achieved the best accuracy in all 3 machine learning models with high statistic levels. For the other 19 types of data gaps, the LSTM models were trained separately for each site and achieved average R², RMSE, and nRMSE at 0.9, 0.5 mm d-1, and 38 % for the total 2419 stations. Thus, we propose a high-quality, gap-filled daily ETo dataset during 1951–2021 for China with the proportion of large errors (the data with daily ETo errors more than 1.5 mm d-1) below 0.2 %. Our results also reveal that the entanglement degree between synoptic variables varies a lot from region to region in China. Although most research indicates that wind speed is not very important for ETo estimation with machine learning models, our findings reveal that wind speed played a more significant role in ETo estimation in most areas of China during the years before the 21st century. Still, the impact of wind speed on ETo has also been alleviated in recent years. This ETo dataset for China is available online at https://doi.org/10.5281/zenodo.11496932 (Zhou et al., 2024). [ABSTRACT FROM AUTHOR]

In this study, we investigate the spatial transport characteristics of cold-front-type sandstorms with different intensities in the Hexi Corridor based on the hourly observation data from 13 ground-based meteorological stations in the Hexi Corridor, the data in the upper and lower layers from the Meteorological Information Comprehensive Analysis And Process System, and the three-dimensional aerosol observation data of the Vertical Feature Mask product obtained from the Cloud-Aerosol Lidar with Orthogonal Polarization of the United States. The results show that the influence range and horizontal transport distance of cold-front-type sandstorms in the Hexi Corridor are determined by the intensity, thickness and width of the upper-level troughs and the intensity of cold fronts. Generally, cold fronts pass through the Hexi Corridor from noon to evening and stay in this region for a long time or move slowly, which is favorable to the horizontal long-distance transport of sandstorms. The intensity of the 200 hPa upper-level jets is directly proportional to both the influence range and transport distance of sandstorms which primarily occur on the left side of the exit area of an upper-level jet. The strengthening of upper-level jets induces the formation of the vertical circulation cell at middle and low levels. The ascending branch of the circulation cell lifts sand-dust particles from the surface to the upper layers, and the descending branch transports upper-level momentum to the near-surface. This circulation situation increases the near-surface wind speed and forms sandstorms. The occurrence time and descending branch of the secondary circulation cell caused by upper-level jets are important indicators for predicting the occurrence time and falling area of sandstorms. Dust aerosols are mainly concentrated at a height of 2–6 km,.,and they can reach the height of 12 km. When the dust is concentrated at the height of 2–8 km, and the wind speed of 500 hPa and 300 hPa reaches 28 and 32 m s− 1 or above respectively, the cold front sandstorm can realize the long distance transport of thousands of kilometers. [ABSTRACT FROM AUTHOR]

The air temperature lapse rate (TLR) is one of the essential parameters for glacio-hydrological studies. However, TLR estimations are limited in the glacierised regions of Himalaya due to a scarcity of long-term observations. Therefore, a dense in-situ monitoring network over a high Himalayan region is needed to estimate the TLR accurately. Here, in-situ air temperature data is obtained from Automatic Weather Stations (AWS) installed over the Chandra basin, western Himalaya, from October 2020 to September 2022. This data is used to estimate the TLR by regressing the air temperature with the corresponding elevations. We estimated the mean annual TLR of 3.8 ± 0.3 °C km-1 for the entire Chandra basin, significantly less than the standard environmental lapse rate (6.5 °C km-1). We found substantial seasonal variability in each TLR time series. The maximum TLR is 5.8 ± 0.2 °C km-1 during the summer, and the minimum is -1.6 ± 0.1 °C km-1 during winter, comparing all the meteorological stations. Further, we observe strong diurnal fluctuations of TLR, which has maximum and minimum values during 10:00 to 18:00 hrs and 20:00 to 09:00 hrs, respectively. The study highlights that the temporal variability of TLR is site-specific and strongly correlated with wind speed, relative humidity, and radiation fluxes. Furthermore, a temperature-index model is used to assess the implications of TLR by estimating glacier mass balance. This study highlights the importance of considering observed TLR to accurately model surface mass balance over the glacierised Himalayan region. [ABSTRACT FROM AUTHOR]

The subject of the analysis were the climatic and bioclimatic conditions of the mountainous areas in Central Europe from 2005 to 2022. The study was conducted based on meteorological data from 4 stations located in 2 mountain ranges in Central Europe, which were obtained from the Ogimet database. The analysis examined the course of mean air temperature, winter days, precipitation, snow cover, wind speed, horizontal visibility, as well as the number of days with thunderstorms and fog. Subsequently, bioclimatic indices were analyzed based on the wind chill index (WCI) and the climate severity index by Osokin (So). The results confirm an increase in mean air temperature and a decrease in the number of winter days. The most significant change in mean annual air temperature was recorded at the Carphatian stations: Varful Omu (0.59 °C/10 years) and tomnica (0.49 °C/10 years). The largest change in the WCI value was recorded at the Carpathian station Varful Omu (51 W/m²/10 years), while the So index was 1.4/10 years. [ABSTRACT FROM AUTHOR]

As a frequent hazard, tropical cyclones have a great impact on the social and economic development of China, which is close to the origin of tropical cyclones in the western Pacific Ocean. The primary objective of this study was to construct a comprehensive risk assessment model for tropical cyclone hazards based on natural influencing factors, informing recommendations for hazard prevention and mitigation in affected regions. This research focused on tropical cyclones that made landfall in mainland China and Hainan from 1949 to 2023, utilizing hazard risk theory and classical extreme value theory. The wind speed and rainfall data during the peak cyclone periods (June to October) from 1997 to 2021 gathered from various meteorological stations, as well as altitude and vegetation cover data, were examined. Hierarchical analysis and ArcGIS spatial analysis methods were employed to study the characteristics of the spatiotemporal distribution of landfalling tropical cyclones and the comprehensive risk of tropical cyclone hazards, and the regions of China were delineated according to these methods. The results showed that, during the period from 1949 to 2023, the overall number of landfalling tropical cyclones decreased in a fluctuating manner, while the intensity of the cyclones increased. Furthermore, severe typhoons tended to occur more frequently in the summer than autumn with time, intensifying the challenge to resist short-term hazards. Moreover, the hazard-causing factors in areas affected by tropical cyclones displayed an increasing trend from north to south and from west to east. In detail, the regions sensitive to natural hazards were primarily located in the central part of Liaoning province, Tianjin, central and eastern Hebei province, Shandong province, eastern Henan province, central and northern Anhui province, Jiangsu province, and Shanghai, which are characterized by flat terrain and relatively low vegetation cover. Overall, the comprehensive risk of tropical cyclone hazards showed a geographical distribution that decreases from south to north and from east to west, with coastal cities in provinces such as Hainan, Guangdong, Guangxi, Fujian, and Zhejiang—including Haikou, Zhanjiang, Xiamen, Beihai, and Taizhou—exhibiting the highest levels of risk. [ABSTRACT FROM AUTHOR]

Climate change has been intensifying its adverse effects, especially in today's world, prompting an examination of the impact of varying green space ratios on microclimate and outdoor thermal comfort for Erzurum, a winter city. The study area was determined as the Yıldızkent district, one of the new settlements in the city of Erzurum. Microclimate data in te area were measured hourly for one year using the Davis Vantage Pro-2 weather station. Within the Yıldızkent zoning plan, five scenarios were created to increase the green space ratio, namely 10%, 20%, 30%, 40%, and 50%. Simulations for a total of six scenarios, including the existing situation, were modeled for the outdoor thermal comfort using the ENVI-met BIO+Science program for the hottest (summer) and coldest (winter) periods of 2021. While temperature, relative humidity, and wind speed did not differ across scenarios in the green space scenarios, the simulations indicated positive results for both winter and summer in terms of mean radiant temperature (Tmrt) in the 10% green space ratio scenario and physiological equivalent temperature (PET) in the 30% green space ratio scenario. For the 10% green space ratio, Tmrt increased by 0.2 ℃ in winter and decreased by an average of 0.2 ℃ in summer compared to the existing situation. In PET analyses, while winter showed similar results for all scenarios, in summer, the 30% green space ratio resulted in a 0.3 ℃ decrease compared to the existing situation. The study concludes that increasing the green space ratio has a positive contribution to the microclimate and brings about differences in outdoor thermal comfort. However, for accurate results in such studies, simulation analyses for each area with different microclimate characteristics are necessary. The results of this study will guide neighborhood-scale research, and incorporating a 30% green space ratio in landscape designs yields positive climate-related outcomes. [ABSTRACT FROM AUTHOR]

Overshooting tops (OTs), prominent signatures within deep convective storms, are produced by intense updrafts and are closely linked to heavy rainfall, strong winds, and other severe weather conditions. Using an OT dataset derived from multiyear observations of precipitation radar on board the Global Precipitation Measurement core observatory as a reference, the performances of two commonly used OT detection algorithms are evaluated for the Himawari-8 and Fengyun-4A satellites. The results indicate that the infrared contour-based algorithm based on Himawari-8 is the most effective for objective OT detection in eastern China. It exhibits a probability of detection (POD) of 62.1% and a false-alarm ratio (FAR) of 36.6%, outperforming others by achieving a greater POD and a lower FAR. Furthermore, based on the severe weather records from surface meteorological stations and nearby OT detections, a strong relationship is revealed between GEO-detected OTs and the occurrence of short-term heavy rainfall (e.g., ≥20 mm h−1) and extreme wind speed (e.g., ≥17.2 m s−1) events. The OT matched percentages for these events are 61.8% and 54.0%, respectively. This suggests that GEO satellite-based OT data can serve as an important objective product for forecasters to increase their understanding of severe convective storms. [ABSTRACT FROM AUTHOR]

Reanalysis datasets provide a continuous picture of the past climate for every point on Earth. They are especially useful in areas with few direct observations, such as Siberia. However, to ensure these datasets are sufficiently accurate they need to be validated against readings from meteorological stations. Here, we analyse how values of six climate variables—the minimum, mean and maximum 2‐metre air temperature, snow depth (SD), total precipitation and wind speed (WSP)—from three reanalysis datasets—ERA‐Interim, ERA5 and ERA5‐Land—compare against observations from 29 meteorological stations across Siberia and the Russian Far East on a daily timescale from 1979 to 2019. All three reanalyses produce values of the mean and maximum daily 2‐metre air temperature that are close to those observed, with the average absolute bias not exceeding 1.54°C. However, care should be taken for the minimum 2‐metre air temperature during the summer months—there are nine stations where correlation values are <0.60 due to inadequate night‐time cooling. The reanalysis values of SD are generally close to those observed after 1992, especially ERA5, when data from some of the meteorological stations began to be assimilated, but the reanalysis SD should be used with caution (if at all) before 1992 as the lack of assimilation leads to large overestimations. For low daily precipitation values the reanalyses provide good approximations, however they struggle to attain the extreme high values. Similarly, for the 10‐metre WSP; the reanalyses perform well with speeds up to 2.5 ms−1 but struggle with those above 5.0 ms−1. For these variables, we recommend using ERA5 over ERA‐Interim and ERA5‐Land in future research. ERA5 shows minor improvements over ERA‐Interim, and, despite an increased spatial resolution, there is no advantage to using ERA5‐Land. [ABSTRACT FROM AUTHOR]

Ensembles of meteorological quantities obtained from numerical models can be used for forecasting weather variables. Unfortunately, such ensembles are often biased and under-dispersed and therefore need to be post-processed. Ensemble model output statistics (EMOS) is a widely used post-processing technique to reduce bias and dispersion errors of numerical ensembles. In the EMOS approach, a full probabilistic prediction is given in the form of a predictive distribution with parameters depending on the ensemble forecast members. Parameters are then estimated and substituted, thus obtaining a so-called estimative predictive distribution. Nonetheless, estimative distributions may perform poorly in terms of the coverage probability of the corresponding quantiles. This work proposes the use of predictive distributions based on a bootstrap adjustment of estimative predictive distributions, in the context of EMOS models. These distributions are calibrated, which means that the corresponding quantiles provide exact coverage probabilities, in contrast to the estimative distributions. The introduction of the bootstrap calibrated procedure for EMOS is the innovative aspect of this study. The performance of the suggested calibrated EMOS is evaluated in two simulation studies, comparing the different predictive distributions by means of the log-score, the continuous ranked probability score, and the coverage of the corresponding predictive quantiles. The results of these simulation studies show that the proposed calibrated predictive distributions improve estimative solutions, both reducing the mean scores and producing quantiles with exact coverage levels. The good performance of the new calibrated EMOS is further stressed in two real data applications, one about maximum daily temperatures at sites located in the Veneto region (Italy) and the other one about wind speed forecasts at weather stations over Germany. [ABSTRACT FROM AUTHOR]

Reference crop evapotranspiration (ET0) is a key factor in ecohydrological processes. Studying the variation trend of ET0 in arid river valleys and its influencing factors is not only helpful to understanding the response of dry and hot river valleys to hydrological processes under the background of climate change but also has important guiding significance for the efficient allocation of soil and water resources and the stable maintenance of the ecosystem in this area. Based on the daily meteorological data of three representative meteorological stations in the middle Dry-hot Valley of the Jinsha River from 1988 to 2019, the ET0 variation and its influencing factors in the middle Dry-hot Valley of the Jinsha River are analyzed by quantitative and qualitative methods. The results showed that (1) the ET0 in the middle and middle of the Dry-hot Valley of Jinsha River showed a significant fluctuating trend (Z > 1.98), and the linear change rates were examined in Huaping, Yuanmou, and Panzhihua. (2) Grey correlation analysis and principal component analysis mutually verify that daily mean temperature is the most influential meteorological factor. (3) The sensitivity of ET0 to the change in meteorological factors in the middle section and its sub-sections is as follows: daily average temperature, daily relative humidity, daily average wind speed, and sunshine hours. ET0 is the most sensitive to the change in daily average temperature, followed by the strengthening of daily average wind speed and the reduction in daily relative humidity, and the sensitivity of ET0 to the change in sunshine hours is the least. (4) Among the regions, the meteorological factors that contributed the most to the increase in ET0 in Huaping, Panzhihua, and Yuanmou were daily average wind speed (6.086%), daily average wind speed (8.468%) and daily average temperature (3.869%), respectively. The meteorological factors that contributed the least were sunshine hours. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study's main objective was to analyse daily wind speed data in Brazil collected on‐site from 1961 to 2020 and assess changes in two 30‐year climatological normals, denoted as P1 and P2. Data were sourced from 54 INMET‐managed meteorological stations, with missing data filled using the bootstrap expectation maximization algorithm. Imputation performance was evaluated, and results suggested that the 10% imputed level was most reliable, showing low RMSE values (below 0.5), SD values near 1, and r values exceeding 0.93. This method proved suitable for reconstructing historical wind speed data in Brazil. The analysis included the application of the Mann–Kendall test to detect linear trends and Sen's slope test to quantify trend intensity, both at a 5% significance level. Positive trends during P2 were observed in the Amazon basin and Brazil's semiarid region, while negative trends increased along the coast and in the Southeast region. Average wind speeds during P1 were higher than in P2. Comparing the two climatological normals revealed significant positive trends in some regions during P2, with an increase in average monthly variation of up to 0.85 m·s−1. The average slopes of significant trends were −1 and 1 m·s−1·decade−1 for all periods. The results indicated high wind magnitudes, with maximum power density of 778.08 W·m−2 for the NEB region, 170.90 W·m−2 for the SEB region and 401.67 W·m−2 for the SUB, making these regions promising for future wind power projects. These findings have the potential to enhance the optimization of renewable wind energy generation in Brazil. [ABSTRACT FROM AUTHOR]

We evaluated the performance of the Thornthwaite (ThW) method using two gridded climate datasets to estimate monthly average daily potential evapotranspiration (PET). The PET estimated from two gridded series were compared to PET and to reference evapotranspiration (ETo) determined, respectively, through the ThW and Penman-Monteith model parameterized on Food and Agriculture Organization–Irrigation and Drainage paper No 56 (PM-FAO56) using data from weather stations. The PET by ThM was based on monthly air temperature series (1961–2010) from two gridded datasets (Global Historical Climatology Network-GHCN and University of Delaware-UDel) and 21 weather stations of the National Institute of Meteorology (INMET) located in Southeastern Brazil. The ETo PM-FAO56 used monthly climate series (1961–2010) on sunshine duration, air temperature, relative humidity, and wind speed from weather stations of the INMET. The PET estimated using UDel gridded series was better overall performance than the GHCN series. Differences in altitude, latitude, and longitude were the main geographic factors determining the performance of the PET estimates using gridded climate series. Depending on the factors, some locations require bias correction, especially locations more than 10 km away from the grid point. The gridded datasets are an alternative for locations without climatic series data or with low-quality non-continuous data series. [ABSTRACT FROM AUTHOR]

This study employs copula functions to establish the dependency structure of the joint distribution among rainfall intensity, wind speed, and wind direction in Qatar. Based on a Vine Copula, the trivariate distribution between rainfall intensity, wind speed, and wind direction is found to exhibit a root-mean-square error (RMSE) of 0.0072 on the observed vs. modeled cumulative probabilities using ranked normalized observations. It is also found that the winter Shamal winds are most pronounced during rainfall. However, a secondary component of easterly winds known as the Kaus winds is also found to exert an important influence. This wind pattern is observable during rainfall at all the selected stations, albeit with minor variations. It is also found that rainfall stations where the rainfall is obstructed in any way from northwest to north and from east to southeast significantly influence the rainfall measurements. Specific rain gauges in Qatar are found to be situated in disrupted surroundings, such as meteorological stations close to passing traffic, where road spray could infiltrate the rain gauge funnel, impacting the accuracy of rainfall measurements. The study results necessitated the relocation of approximately half of these roadside gauges to mitigate wind-induced biases from road spray. An evaluation of operations is recommended for approximately 80 meteorological stations responsible for measuring rainfall in Qatar. The methodology devised in this study holds potential for application to other Middle Eastern countries and regions with similar climates. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization makes the phenomenon of urban heat islands even more serious. Predicting the impact of land cover change on urban heat island has become one of the research hotspots. Taking Wuhan, China, as an example, this study simulated the land type change in 2020 through the Cellular Automata-Markov-Chain (CA-Markov) model. The urban heat island in 2020 was simulated and analyzed in conjunction with the Weather Research & Forecasting Model (WRF), and the simulation results of wind velocity and temperature were confirmed using weather station observation data. Based on this, the land cover and urban heat island of Wuhan in 2030 were predicted. The temperature was found to be well-fit by CA-Markov simulated land use data, with an average inaccuracy of about 2.5°C for weather stations. Wind speed had a poor fitting effect; the average error was roughly 2 m/s. The built-up area was the center of the high temperature area both before and after the prediction, the water was the low temperature area, and the peak heat island happened at night. According to the forecast results, there will be more built-up land in 2030, and there will be a greater intensity of heat islands than in 2020. [ABSTRACT FROM AUTHOR]

Malaysian wind maps used in wind analysis and engineering design are dated back to the last 50 years of wind data. Cotemporally, the mean wind speed was used as the basic wind design and due to changes in the weather condition worldwide, wind monitoring assumption and structure design are jeopardized. Therefore, this study aims to map the wind based on the trend of the highest wind speed recorded. The wind speed was analyzed based on a trend basis. The study included 42 Malaysian Metrological Department weather stations and the annual wind speed was acquired based on the monthly highest wind speed (1990–2019). The data were then processed using the 95% confidence interval method to determine the mean of the month, and the annual wind trendline was obtained. The trendline was then clustered using Ward's method with the assistance of the high-level programming language, PYTHON v3.11.6. The clustering analysis produced two clusters for the Malaysian Peninsula and two for Sabah and Sarawak. The recommendation is to use the highest wind speed recorded on the map as the design wind speed. The recommendation is expected to help experts to compensate for the uncertainties in wind speed during the design stage and avoid incidents in the field. [ABSTRACT FROM AUTHOR]

While previous work on the climatology of Northern China has focused on mean wind speed, wind gusts have received comparatively less attention but are equally important to various users. In this paper, an observed hourly maximum gust wind speeds (HMGS) dataset across North China has been created by using time series from 174 meteorological stations. The dataset offers superior quality, high spatiotemporal near-surface HMGS series for North China spanning from 2015 to 2022. The objective of this study is first to improve our understanding of the spatiotemporal gusts climatology in North China by analyzing the observed gust data. Second, we aim to supplement the observational data by using gust analysis and forecast data with a high spatial–temporal resolution from model simulations. The spatial characteristics of the seasonal cycle of the simulated analysis of mean HMGS and the performance in predicting gusts based on the geographical locations and elevations of the validation stations were investigated by comparing it with the observations. Results indicate the following: 1) Wind direction and intensity are affected by the terrain and climate conditions of different weather stations. Stations situated along the Bohai Bay coastal region and at higher-elevation areas of North China exhibit a higher mean HMGS than those located in the coastal and inland plains. 2) The probability density function curves for wind speed and wind direction exhibit notable variations across different elevation intervals. The contribution of moderate and strong gust wind speeds increases gradually with increasing altitude, while the gust directions in mountainous areas exhibit relatively consistent patterns due to the increased exposure to synoptic-scale forcing at higher elevations. 3) The nowcasting prediction system analysis of mean HMGS provides a higher horizontal resolution that is capable of capturing the contrasts between land and sea, as well as the influence of high HMGS associated with large-scale circulations in high-elevation regions. Significance Statement: The purpose of this study is to better understand the spatiotemporal gust climatology in North China and the performance of the model-simulated gust analysis and forecast data. This is important because gusts conditions differ due to varying topographic and climatic conditions of different weather stations. Our results provide a valuable insight into the climatological variations of HMGS, their drivers, and identify the deficiencies in the model-simulation gusts. [ABSTRACT FROM AUTHOR]

Dry and hot Foehn wind weather often occurs in Urumqi, China, due to its canyon terrain. This directly impacts the lives and health of local people. Using surface meteorological variables (including the hourly wind, temperature, humidity, and pressure) measured in situ at the Urumqi Meteorological Station and ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts in the past 15 years (2008–2022), the characteristics of Foehn wind and their relationship with EI Niño and extreme high-temperature events in Urumqi are analyzed. The results show that the annual distributions of Foehn wind present a fluctuating pattern, and the highest frequency occurred in 2015. Compared to the summer (July) and winter (February) seasons, Foehn wind occurs most frequently in spring (March, April, May) and autumn (September, October, and November). Daily variations in Foehn wind occur most frequently from 9:00 a.m. to 14:00 p.m. In particular, high levels are found at 10:00 a.m. and 11:00 a.m. in April and May. In 2011, 2012, and 2014, the average wind speed of FW exceeded 6 m/s, and the lowest average wind speed was 3.8 m/s in 2021. The temperature and relative humidity changes (ΔT and ΔRH) caused by Foehn wind are the most significant in winter and when Foehn wind begins to occur. The high-temperature hours related to Foehn wind weather in Urumqi represented 25% of the total in the past 15 years. During the EI Niño period, the amount of Foehn wind in Urumqi significantly increased; The correlation coefficient beteewn slide anomaly of Foehn days and the Oceanic Niño Index is as high as 0.71. Specifically, Foehn wind activity aggravates extreme high-temperature events. This study provides indications for Foehn wind weather forecasting in Urumqi. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Biologia Marina y Oceanografía (RBMO) is the property of Universidad de Valparaiso, Facultad de Ciencias del Mar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The northernmost region of South America has great potential for wind energy production and is a strategic area for the energy transition. We analysed data from ERA5-Land global reanalysis from 1980 to 2020 to determine the wind power potential in this area. Our aim was to identify areas with a high potential for wind energy that could be used to supply energy in case of drought in the region or increases in fuel prices. We compared data from 19 meteorological stations located in Colombia with ERA5-Land reanalysis data to generate reliable information for the feasibility and operation of wind farms. According to our analysis, the Colombian Caribbean and Coastal Plain have monthly average wind speeds at 90 m ranging from 4.85 to 10.34 m/s and 3.79 to 6.62 m/s, respectively. Based on wind power density calculations, these regions have the highest wind power density in our study, with a monthly average of between 250 and 399 W/m2. The Caribbean Region had a 69% increase in wind power density from January to July compared with the annual average. The wind power density in the plains increased from January to March, while the Mountain Region had higher anomalies from May to October. The Alta Guajira and Paraguaná Peninsula have great potential to generate wind energy due to their privileged geographical location. Although ERA5 offers valuable information about wind availability in time and space, it cannot represent the influence of local effects on wind patterns for the Mountain Region. [ABSTRACT FROM AUTHOR]

Intensification of Typhoon Chanthu (2021) along the eastern coast of Taiwan was accompanied by pronounced asymmetry in eyewall convection dominated by wavenumber-1 features, as observed by a dense radar network in Taiwan. The maximum wind speed at 3-km altitude, retrieved from radar observations, exhibited a rapid increase of approximately 18 m s−1 within an 11-h period during the intensification stage, followed by a significant decrease of approximately 19 m s−1 within 8 h during the weakening stage. Namely, Chanthu underwent both rapid intensification (RI) and rapid weakening (RW) within the 24-h analyzed period, posing challenges for intensity forecasts. During the intensifying stages, the region of maximum eyewall convection asymmetry underwent a sudden cyclonic rotation from the eastern to the northern semicircle immediately after the initiation of terrain-induced boundary inflow from the south of the typhoon, as observed by surface station data. This abrupt rotation of eyewall asymmetry exhibited better agreement with radar-derived vertical wind shear (VWS) than that derived from global reanalysis data. This finding suggests that the meso-β-scale VWS is more representative for tropical cyclones than meso-α-scale VWS when the terrain-induced forcing predominates in the environmental conditions. Further examination of the radar-derived VWS indicated that the VWS profile pattern provided a more favorable environment for typhoon intensification. In summary, Chanthu's RI was influenced by the three factors: 1) terrain-induced boundary inflow from the south of the typhoon, observed by surface station data; 2) low-level flow pointing toward the upshear-left direction; and 3) weak upper-level VWS. Significance Statement: Tropical cyclone intensity change has been an important issue for both real-time operation and research, but the influence of terrain on intensity change has not been fully understood. Typhoon Chanthu (2021) underwent a significant intensity change near the complex terrain of Taiwan that was observed by a dense radar network. This study analyzes 24 h of radar and weather station data to investigate Chanthu's evolution. The analyses indicate that the complex terrain affected the low-level flow near the TC. Such a change in flow pattern provided additional boundary inflow and a relatively favorable vertical wind shear pattern for TC intensification. [ABSTRACT FROM AUTHOR]

The traditional assessment of wind resource considers the ambient factors like temperature, pressure and humidity as a secondary parameter, and focus on the statistically evaluation of wind magnitude and direction. Nevertheless, the evaluation requires a deeper analysis of the above ambient factors, because they are thermodynamically linked, and the energy flux among them will affect the wind dynamics. This research aims to analyse the interactions of temperature, pressure, and humidity variables with the wind speed with an inverse approach, it means that wind will be controlled by the ambient variables. We conducted a statistical analysis using data from a meteorological station located in the University of the Coast in Barranquilla, Colombia. The methodology is based on a Design of Experiments-Analysis of Variance (DOE-ANOVA) with a 3² factorial design. The estimated effects from the DOEANOVA results were utilized to generate standardized effect equations, for evaluating the response of wind speed during changes of the studied ambient variables (factors). The results evidenced that the applied methodology provided information about the non-linear interactions of the analysed variables, and the standardized effect equations were tested against a liner regression model with satisfactory results. [ABSTRACT FROM AUTHOR]

This publication presents a comprehensive hydrometeorological data set for three research sites in the upper Rofental (1891–3772 ma.s.l., Ötztal Alps, Austria) and is a companion publication to a data collection published in 2018: https://doi.org/10.5194/essd-10-151-2018 (Strasser et al., 2018). The time series presented here comprise data from 2017 to 2023 and originate from three meteorological and snow-hydrological stations at 2737, 2805, and 2919 ma.s.l. The fully equipped automatic weather stations include a specific set of sensors to continuously record snow cover properties. These are automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water content, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow transport. They are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. We present data for temperature, precipitation, humidity, wind speed, and radiation fluxes and explore the continuous snow measurements by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations is demonstrated with examples of measured wind speeds, snow drift rates and redistributed snow amounts during an event in December 2020. The data complement the scientific monitoring infrastructure in the research catchment and represent a unique time series of high-altitude mountain weather and snow observations. They enable comprehensive insights into the dynamics of high altitude snow processes and are collected to support the scientific community, as well as operational warning and forecasting services. [ABSTRACT FROM AUTHOR]

Wind power production plays an important role in achieving UN's (United nations) Sustainable development goal (SDG) 7 - affordable and clean energy for all; and in the increasing global transition towards renewable and carbon neutral energy, understanding uncertainties associated with wind and turbulence is extremely important. Characterization of wind is not straightforward due to its intrinsic intermittency: activity of the becomes increasingly concentrated at smaller and smaller supports as the scale de15 creases. When it comes to power production by wind turbines, another complexity arises the influence of rainfall, which only a limited number of studies have addressed so far suggesting short term as well as long-term effects. To understand this, the project RWTurb (https://hmco.enpc.fr/portfolio-archive/rw-turb/; supported by the French National Research Agency, ANR-19-CE05-0022) employs multiple 3D sonic anemometers (man20 ufactured by Thies), mini meteorological stations (manufactured by Thies), and disdrometers Parsivel2, manufactured by OTT) on a meteorological mast in the wind farm of Pays d'Othe (110 km south-east of Paris, France; operated by Boralex). With this simultaneously measured data, it is possible to study wind power and associated atmospheric fields under various rain conditions. Variations of wind velocity, power available at the wind farm, power produced by wind turbines and air density are examined here during rain and dry conditions using the framework of Universal Multifractals (UM). UM is a widely used, physically based, scale invariant framework for characterizing and simulating geophysical fields over wide range of scales which accounts for the intermittency in the field. Since rated power acts like an upper threshold in statistical analysis of empirical wind power, efforts were made to use the theoretical available power as proxy to see the difference. From an event based analysis, differences in UM parameters were observed between rain and dry conditions for the fields illustrating the influence of rain. This is further explored using joint multifractal analy-sis and an increase in correlation exponent was observed between various fields with an increase in rain rate. Here we also examine the possibility of difference in power production according to type of rain (convective or stratiform) as well as various regimes of wind velocity. While examining time steps according to wind velocity, power curves showed different regions of departure from state curve according to the rain rate. [ABSTRACT FROM AUTHOR]

The Southwestern portions of the United States experience dust events frequently due to the arid and semi-arid environments and close proximity to multiple deserts. On February 26, 2023, a dust event was initiated in New Mexico due to strong winds aloft mixing down to the surface. The dust intensified as it moved eastward into West Texas, and turned into a dust storm (visibility < 1 km) in multiple locations. This study examined the meteorological characteristics of this dust storm using 21 meteorological stations and examines the impacts on PM2.5 and/or PM10 (particulate matter with an aerodynamic diameter < 10 and 2.5 µm) concentrations using 19 air quality stations. The dust event lasted up to 18 hours, and locations experienced dust storm conditions from five minutes up to 65 minutes, with even zero visibility. The highest wind speed and wind gust recorded during the dust were 27.3 m s-1 and 37 m s-1 respectively. This dust had a strong impact on the air quality in the area, as very high PM values were recorded across the region, and nine of the PM stations exceeded the EPA daily threshold. The maximum hourly PM2.5 and PM10 concentrations recorded were 518.4 µg m-3 and 9,983 µg m-3 respectively. For some locations (e.g., Lubbock Texas), these high PM2.5 concentrations were the highest ever recorded, highlighting the significance of this dust storm. [ABSTRACT FROM AUTHOR]

Heat waves and extreme weather events caused by climate change increase people's need for predictable, healthy, and appropriate thermal thresholds in urban areas. The Mediterranean region, where alarming effects are expected, poses a danger to many species and threatens the quality of human life. In the research, predictions were made according to SSP 245 and SSP 585 scenarios from CNRM-CM6-1 climate models using the data of meteorological stations for 2020 in the Eastern Mediterranean region via CMIP6 and WorldClim database. The study aims to predict the change in the bioclimatic comfort situation of the region at 20-year intervals until 2100, depending on the periods. The highest annual temperatures seen in the area are 18–20 °C. In the 2100 estimations, areas with a value of 22–24 °C according to SSP 245 and 24–26 °C according to SSP 585 are modeled spatially. While the largest area in the basin today is the area with a humidity range of 62–64%, according to SSP 245, in 2100 predictions, the largest area will be 23% with a humidity level of 56–58%. While the wind speed in the area is currently 0.5–1 m/s, it decreases to 0–0.5 m/s in 36% of the area, according to SSP 585. According to the ETv index, quite cool areas are effective on a 36% area surface. However, in the 2100, compared to the SSP 245, the most comprehensive range is the slightly cool areas with 40%. According to SSP 585, mild areas will have a share of 42%. Warm areas, the most critical class of the index, will begin to form. According to DI, the field has a 58% share in the cold class. According to SSP 585, hot areas have a rate of 26%, and comfortable areas have a rate of 52%. Findings of thermal disturbance variation can help develop solutions to conditions in the context of the climatic values of the region. [ABSTRACT FROM AUTHOR]

Determination of optimal location for wind farm is a challenging task influenced by several decision criteria and factors subject to human. This case study done proposes an integrated decision support framework to investigate the spatial wind farm suitability based on F-AHP (Fuzzy-Analytical Hierarchy Process) together with Q-GIS (Quantum-Geographic Information System) tool. Firstly, spatial analysis and integration of nine-exclusion factors were included to identify the unsuitable sites. Next, F-AHP-based MCDM (Multi Criteria Decision Making) analysis was employed to obtain criteria weights of seven evaluation factors. Unlike traditional AHP method, the proposed F-AHP method is much flexible to deal with intermediate linguistic variable. F-AHP results reveal that wind speed is the most important criteria followed by power Tr. lines with weighted value of 33.05% and 20.95% respectively. The thematic suitability map was developed on Q-GIS platform to classify from "least suitable" to "most suitable" classes. The final suitable map indicates 26.09 km2 and 138.42 km2 area are most suitable and highly suitable respectively for wind farm establishment. This study is also supported with ground measurement wind resource data collected through a WMS (Weather Monitoring Station) installed at "most suitable" region of suitability indexed class. [ABSTRACT FROM AUTHOR]

Profound research on the characteristics of the long-term persistence of wind is greatly significant for understanding the characteristics of wind speed mechanisms as well as for avoiding disasters caused by wind. In the current study, we selected the hourly 10 min wind speed series between 2017 and 2021 from 105 nation-level meteorological stations in Xinjiang and investigated the spatiotemporal variations in the long-term persistence of wind speed in different regions of Xinjiang and in different seasons using detrended fluctuation analysis. The main findings are as follows: (1) The wind speed in Xinjiang shows noticeable annual and seasonal variations, exhibiting satisfactory long-term sustainability. Winter has the best long-term sustainability, followed sequentially by spring, autumn, and summer because of wind speed stability. (2) The long-term persistence of hourly wind speed in Xinjiang exhibits remarkable regionality, with regions with strong wind superior to the remaining regions. (3) The long-term persistence of wind speed within the same season is primarily associated with wind speed magnitude and the dispersion degree between 90% and 100% of the wind speed numerical values. A higher wind speed indicates better long-term persistence. At the same speed, the more discrete the numerical values in the 90–100% distribution range, the better the persistence. [ABSTRACT FROM AUTHOR]

Dust storms are one of the major environmental hazards affecting the Middle East countries, and largely originate in vast deserts and narrow dried lake beds. This study analyzes the inter-annual variation in dust weather conditions from 2000 to 2020 using data obtained from ten meteorological stations located around dried (completely or partly) lakes in Northwest (Urmia Lake) and South (Bakhtegan Lake) Iran. Since the wind regime is one of the most important factors controlling dust emissions in the dust source areas, wind speed simulations from the Weather Research and Forecasting (WRF) Model for 134,113 grid points covering the Middle East area, with a resolution of 5 km, were analyzed and compared with wind measurements at the stations around Urmia and Bakhtegan Lakes from 2005 to 2015. The analysis shows that the annual number of dust days was highly variable, presenting a significant increase at the stations around Urmia Lake during 2008–2011 and at the stations around Bakhtegan Lake in 2007–2012. Eleven years of WRF simulations of the mean diurnal wind patterns revealed that the highest 10 m wind speed occurred mostly around the local noon (12 to 15 UTC), generally coinciding with the majority of the reported dust codes within this time frame, as a result of the association between wind speed and dust emissions (dust weather conditions) around these lake basins. Consequently, accurate wind simulation has high importance for unbiased numerical prediction and forecasting of dust conditions. The comparison between the measured mean monthly 10 m wind speed and WRF-simulated 10 m wind speed revealed that the model overestimated wind data in all the stations around the Bakhtegan Lake but performed better at reconstructing the wind speeds at stations around Urmia Lake. Furthermore, notable differences were observed between measured and simulated wind directions, thus leading to uncertainties in the simulations of the dust-plume transport. [ABSTRACT FROM AUTHOR]

Exploration of the spatiotemporal variability in precipitation gradients (PGs) is important for understanding the spatial differences in precipitation and optimizing hydrological process simulations. Here, the spatiotemporal distribution of precipitation and PG was analyzed in the Tianshan and Qilian Mountains based on national meteorological station data from 1961 to 2017. The relationships between PG and relative humidity and wind speed were explored. The results show that precipitation exhibits a significant increasing trend during the last decades. Precipitation in the Tianshan Mountains is greater in the north and west, the maximum precipitation height (MPH) occurs at 1942.5 m a.s.l., and the precipitation change rate is higher in the west than in the east. Precipitation in the eastern Qilian Mountains is greater, the MPH is approximately 2850 m a.s.l., and the precipitation change rate decreases from the middle to the western and eastern sections. There are noticeable differences in PG between the two mountain areas. Annually, the PG in the Tianshan and Qilian Mountains, except for the Yili Valley, has increased significantly. The PG change rate in the western Qilian Mountains is higher. Monthly, the PG increases most significantly from June to August. Precipitation is positively correlated with elevation in the Tianshan Mountains from April to October and in the western and eastern sections of the Qilian Mountains every month, while that in the middle Qilian Mountains decreases with increasing elevation from November to January. The PG in both mountain regions experienced abrupt changes around the 1970s–1980s, and most areas increased significantly after the abrupt changes, showing a clear humidification phenomenon. Except for some subregions, PG is directly proportional to relative humidity and inversely proportional to wind speed. [ABSTRACT FROM AUTHOR]

Hazard studies based on thousands of synthetic tropical cyclone (TC) events require a validated model representation of the surface wind field. Here, we assess three different TC parametric vortex models with input from four along-track parameter studies of the TC size and shape, based on statistical formulation of the relationships to observed TC intensity, geographic location, and forward transition speed. The 12 model combinations are compared to in situ 10-min observed surface mean wind speeds for 10 TCs that made landfall over Queensland, Australia, which occurred over the period 2006–17. Empirical wind reduction factors to reduce gradient winds to the surface are recalculated for the more recent TCs at both offshore (ocean, small islands, reefs, and moorings) and onshore (land) locations. To improve the wind comparisons over ocean and land, a secondary reduction factor was developed based on an inland decay function. Pearson correlations for the unadjusted modeled peak wind speed from 118 instances of a TC passing a weather station sit between a range of 0.57 and 0.65 for the 12 model combinations. Using the secondary reduction factor based on the inland decay function increases the range of correlation to 0.74–0.81. Based on the assessment of the instances of peak surface wind speed correlations, bias, and root-mean-square error, along with the correlation 48 h around the peak, the top-ranked performing model combination for the region was an along-track parameter study with a double-vortex model, both previously tested for the South Pacific basin. Significance Statement: When assessing tropical cyclone hazards, users are presented with several simplified parametric models to describe the surface wind field of tropical cyclones. These parametric models are used routinely for risk assessment of cyclonic winds, as well as for input to surge and wave models used in coastal hazard assessments. Differences between the models include the formulation of the parametric cyclone model, the way winds above the boundary layer are specified at the surface and along-track parameters that describe the cyclones' size and shape. Of the 12 model combinations investigated in this study, the top-ranked performing model combination for the region was an along-track parameter equation with a double-vortex model, which were both tested previously for the South Pacific basin. Analysis is performed to show unadjusted modeled winds overestimate observed 10-min surface winds over the ocean by around 13% (median) and over land by around 73.9% (median), which is resolved in this study with a secondary empirical wind reduction factor. These findings will support future modeling of tropical cyclone winds for multiple applications, including regional risk assessment and coastal hazard studies. [ABSTRACT FROM AUTHOR]

The average time interval adopted by the historical wind climate observation data of different meteorological stations worldwide is not uniform, leading to a conversion difficulty of the average time interval in the extreme wind speed estimation process. Early observation equipment requires manual intervention, leading to discontinuous data and estimated deviation. This study analyzed wind speed data recorded by 11 meteorological stations in normal wind climate areas to obtain average wind speed sequences with different average time intervals. The extreme wind speeds for some return periods were calculated. Subsequently, the influence of the average time intervals on the prediction results of extreme wind speeds was analyzed. Simultaneously, the data were extracted into a 2 min average wind speed series with hourly, 6-hourly, and no-spacing observation. The estimation results of extreme wind speed for the return periods based on these three data series were calculated and compared in detail. Extreme wind speed for a given return period decreases with an increase in the average time interval in normal wind climate areas and approximates an exponential attenuation. The difference between the extreme wind speed corresponding to the continuous observation series and that corresponding to the observation data series with a spacing of 1 or 6 h meets the Rayleigh distribution. This study proposes a conversion formula for extreme wind speed with different average time intervals in normal wind climate areas. A set of correction methods for wind speed observation data with observation spacings was provided for more accurate estimation results. [ABSTRACT FROM AUTHOR]

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The structural performance of an asphalt pavement is substantially affected by its temperature state. The in-depth temperature is usually measured through drilling a hole in the pavement. As an alternative, researchers have proposed temperature prediction models considering attributes like air temperature, depth, time, location, solar intensity, wind speed, and relative humidity. But procuring data on many attributes is tedious for the field professionals and at times is not available locally in developing countries. It was observed that most of the existing models predict maximum and minimum temperatures, whereas few models predict the temperature at a fixed depth. This paper presents a pavement temperature prediction model which uses air temperature of preceding hours, time in a day, and depth of the measurement. These attributes are easily available in developing countries. Data were collected through an instrumented track and from a weather station. Contrary to the use of 1–5-day air temperature, it was found that the average air temperature of the preceding few hours is sufficient in predicting the pavement temperature. The prediction accuracy and validation results of the model were found good as compared to some prevailing models. [ABSTRACT FROM AUTHOR]

A multifractal analysis based on the time series of temperature, pressure, relative humidity, wind speed, and wind direction was performed for 16 weather stations located in the hydrographic basin of the Guadalupe River in Baja California, Mexico. Our analysis included a 38-year dataset from MERRA-2 database, we investigated the multifractal nature of daily time series data for climatic variables associated with the Santa Ana Winds. We employed the Multifractal Detrended Fluctuation Analysis (MFDFA) method to extract multifractal complexity parameters ( α 0 , ∆ α , and r ). This was adequate to evaluate the multifractality of the time series that represented the conditions of the phenomenon's occurrence. From the estimation of the generalized Hurst exponent (hq), it was possible to characterize the time series of the meteorological variables in terms of the characteristics of persistence, anti-persistence, or randomness. Finally, the values corresponding to the parameters and characteristics of the multifractal spectrum or singularities can be used as quantitative and qualitative indicators to describe the dynamics of meteorological processes during the occurrence of the Santa Ana winds in the Guadalupe basin. [ABSTRACT FROM AUTHOR]

The paper deals with wind speeds of extreme wind events in Poland and the descriptions of their effects. Two recent estimations developed by the Institute of Meteorology and Water Management in Warsaw and by Halina Lorenc (a previous co-worker of the institute) are presented here. Their strong and weak points are briefly described. The 37 annual maximum gusts of wind speeds measured at all meteorological stations between 1971 and 2007 are analysed by an extremal probability paper, block-maxima (BM), and peak-over-threshold (OVT) approaches. Based on the measured and estimated wind speeds (taken from existing literature), the authors suggest new estimations for extreme winds that may occur in Poland. Shortly, Poland will construct important structures, such as a central air terminal and (some) nuclear power plants, so knowledge about extreme winds in our country is significant for engineers who will design these structures. [ABSTRACT FROM AUTHOR]