Surface Wind Direction Research Articles

A Novel Method for the Estimation of Sea Surface Wind Speed from SAR Imagery

Wind is one of the important environmental factors influencing marine target detection as it is the source of sea clutter and also affects target motion and drift. The accurate estimation of wind speed is crucial for developing an efficient machine learning (ML) model for target detection. For example, high wind speeds make it more likely to mistakenly detect clutter as a marine target. This paper presents a novel approach for the estimation of sea surface wind speed (SSWS) and direction utilizing satellite imagery through innovative ML algorithms. Unlike existing methods, our proposed technique does not require wind direction information and normalized radar cross-section (NRCS) values and therefore can be used for a wide range of satellite images when the initial calibrated data are not available. In the proposed method, we extract features from co-polarized (HH) and cross-polarized (HV) satellite images and then fuse advanced regression techniques with SSWS estimation. The comparison between the proposed model and three well-known C-band models (CMODs)—CMOD-IFR2, CMOD5N, and CMOD7—further indicates the superior performance of the proposed model. The proposed model achieved the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), with values of 0.97 m/s and 0.62 m/s for calibrated images, and 1.37 and 0.97 for uncalibrated images, respectively, on the RCM dataset.

Sea surface wind retrieval from CFOSAT Rotating fan-beam scatterometer in ocean cyclones

ABSTRACT Rotating fan-beam scatterometer (RFSCAT) is a radar scatterometer system for sea surface wind vector measurement. Compared with other available scatterometers, RFSCAT can provide more combination of azimuth angles and incidence angles for a single WVC (wind vector cell), this observation mechanism is more conducive to the sea surface wind direction retrieval. In this paper, the NSCAT-4DS GMF (geophysical model function) with SST (sea surface temperature) correction, and the MSS (Multiple Solution Scheme) combination with a 2DVAR (2-dimensional variational analysis) are adopted to retrieve the sea surface winds from RFSCAT on the CFOSAT (Chinese-French Oceanography Satellite). The retrieved RFSCAT sea surface winds and ASCAT (advanced scatterometer) sea surface winds are compared and tested, and the feasibility of the RFSCAT measuring sea surface winds under high wind speeds is analysed. The results show that the RMSE of the RFSCAT sea surface wind speed using improved algorithm has decreased by 0.292 m s−1, the correlation coefficient has increased by 0.032, and the residual standard deviation has decreased by 0.194 m s−1. The RMSE of RFSCAT sea surface wind direction has decreased by 5.950°, the correlation coefficient has increased by 0.002, and the residual standard deviation has decreased by 5.567°. It is shown that the changes in winds RMSE using pre-improved and improved algorithms have statistical significance. In a word, the spaceborne Ku-band rotating fan-beam scatterometer can capture the winds structure of ocean cyclones. Although there may be high wind speeds saturation phenomena, the detecting sea surface winds at high wind speeds show preferable performance.

Observations of Sea Ice Edge Position in the Barents and Greenland Seas: Temporal Variability and Long‐Term Changes

AbstractSea ice edges play an essential role in the Earth's climate and weather systems through active atmosphere‐ice‐ocean interactions. However, our understanding of the observed positions of sea ice edges remains limited. The present study investigated, on seasonal and longer timescales from 1979 to 2023, the variability in sea ice edge positions in the Barents and Greenland Seas. We objectively derived the positions of sea ice edges based on satellite‐derived sea ice concentration gridded on a 25‐km resolution. In the Barents Sea, warm, narrow currents flow eastward along the northern and southern rims of the Central Bank. Until the mid‐2000s, sea ice edges fluctuated between these currents interannually during December–June, depending on the surface wind direction. However, after the mid‐2000s, the sea ice edges were mostly positioned near the northern current or farther to the north. Furthermore, sea ice edges during October–March were identified frequently near the warm current flowing along the continental slope in the northern Barents Sea after the mid‐2000s. In the Greenland Sea, sea ice edges were typically positioned near the East Greenland Current (EGC) throughout the year until 2006. However, sea ice edges in summer were located far to the west of the EGC after 2006. These observations suggest that the geographical relationship between sea ice edges and ocean currents has changed due to global warming.

How horizontal transport and turbulent mixing impact aerosol particle and precursor concentrations at a background site in the UAE

Abstract. The optical, physical, and chemical properties of aerosol particles have been previously studied in the United Arab Emirates (UAE), but there is still a gap in the knowledge of particle sources and in the horizontal and vertical transport of aerosol particles and their precursors in the area. To investigate how aerosol particle and SO2 concentrations at the surface responded to changes in horizontal and vertical transport, we used data from a 1-year measurement campaign at a background site where local sources of SO2 were expected to be minimal. The measurement campaign provided a combination of in situ measurements at the surface and the boundary layer evolution from vertical and horizontal wind profiles measured by a Doppler lidar. The diurnal structure of the boundary layer in the UAE was very similar from day to day, with a deep, well-mixed boundary layer during the day transitioning to a shallow nocturnal layer, with the maximum boundary layer height usually being reached around 14:00 local time. Both SO2 and nucleation-mode aerosol particle concentrations were elevated for surface winds coming from the east or western sectors. We attribute this to oil refineries located on the eastern and western coasts of the UAE. The concentrations of larger cloud condensation nuclei (CCN)-sized particles and their activation fraction did not show any clear dependence on wind direction, but the CCN number concentration showed some dependence on wind speed, with higher concentrations coinciding with the weakest surface winds. Peaks in SO2 concentrations were also observed despite low surface wind speeds and wind directions unfavourable for transport. However, winds aloft were much stronger, with wind speeds of 10 m s−1 at 1 km common at night and wind directions favourable for transport; surface-measured concentrations increased rapidly once these particular layers started to be entrained into the growing boundary layer, even if the surface wind direction was from a clean sector. These conditions also displayed higher nucleation-mode aerosol particle concentrations, i.e. new particle formation events occurring due to the increase in the gaseous precursor.

Using artificial neural networks and citizen science data to assess jellyfish presence along coastal areas

Abstract Jellyfish blooms along coastal areas can pose significant challenges for beach users and local authorities. Understanding the factors influencing jellyfish presence is crucial for effective management and mitigation strategies. In this study, citizen science data from the Andalusian coast (232 beaches, in 40 different localities) and machine learning techniques are used to investigate if the presence and absence of jellyfish along coastal areas can be predicted. A multi‐layer perceptron (MLP) neural network was employed to classify user comments regarding jellyfish presence or absence, achieving an accuracy of approximately 96%. The MLP model demonstrated robustness in handling non‐linear classification problems and noise, although it showed lower precision for predicting jellyfish presence, likely due to an imbalance in the dataset. Environmental data were also incorporated to characterise the influence of sea surface temperature, wind direction and wind speed on jellyfish distribution. The results align with previous studies, suggesting these environmental factors significantly impact jellyfish presence. Synthesis and applications. This research provides actionable recommendations for beach management. The implementation of continuous monitoring of sea surface temperature and wind conditions will enable more accurate predictions of jellyfish distribution. Adaptive management strategies that respond dynamically to environmental data will help mitigate the impact of jellyfish blooms on coastal tourism and public health.

Current dynamics to detect the spread of oil spills in coastal waters

Abstract Surface flow dynamics modelling is an important aspect of monitoring and detecting oil spills in marine environments, especially coastal waters. The model can predict the spatiotemporal distribution of oil spills in waters before remote sensing technologies such as radar and synthetic aperture radar (SAR) detect the oil spill distribution range. The purpose of this study is to model two-dimensional (2D) surface flow to predict the extent of oil spills, especially in shallow water or coastal areas. The accuracy of this ocean current distribution model was then verified using the case of an oil spill in East Aceh waters based on the results of the detection of synthetic aperture Interference images from the Sentinel-2B satellite Radar (InSAR) Technology from August 1st to September 20th, 2021. The results show that when the fluid dynamics used is residual flow, the oil distribution trajectory moves partly to the north and partly to the south toward the coast of the leak location. However, when mean current is applied, the distribution expands to the south toward the coast. Based on the state of the wind distribution when the oil spill occurred, it shows the same direction as the residual current model, but the influence of the wind is greater than the residual current. So, it can be concluded that oil spills in the waters of East Aceh are more influenced by a combination of mean currents and surface wind direction. These results were validated by the results obtained from satellite images. Therefore, the satellite image validation results indicate that mean flow provide a more accurate indication of oil spill distribution than the average flow in South Aceh waters.

ASSESSMENT OF SOLAR PV OUTPUT PERFORMANCE WITH VARYING TILT ANGLES AND WEATHER DATA FROM ERA5: CASE OF MUNTINLUPA CITY, PHILIPPINES

Abstract. Solar photovoltaic (PV) technology has been gaining popularity in the Philippines as an alternative source of sustainable energy. In such technology, module tilt angle and weather conditions are among the system parameters that have substantial impacts on PV system performance. Previous studies have considered either tilt angles or weather conditions, but not the combined impact of these two parameters on solar PV power output. The objective of this study is to examine the effects of weather variables and tilt angle on the output of solar photovoltaic (PV) systems in Muntinlupa City, the Philippines. Three 120W monocrystalline solar PV panels were used and set up to three different tilt angles (i.e., 5°, 10°, and 15°). The fifth generation of the ECMWF's global climate and weather reanalysis (ERA5) dataset was used to gather hourly weather information such as surface solar radiation, wind speed, wind direction, relative humidity, ambient temperature, and total precipitation. Three principal components (PC), which together account for 95% of the variability, were identified using principal components analysis (PCA), which was used to address multicollinearity among the weather parameters. To assess the effects of tilt angle, time, and PCs on solar PV production, analysis of covariance (ANCOVA) was carried out. Results show that all weather variables, except for wind speed and total precipitation, have a significant impact on solar PV production with configuration producing the best results. Moreover, a significant difference in mean solar PV production was observed among the three tilt angles. From 6:00 AM to 2:00 PM, solar PV output gradually increases and declines thereafter. Outputs this study can help in optimizing the design and configuration of solar PV systems in the Philippines by considering weather variables and module tilt angle. Lastly, this study provides useful information for system designers, installers, and policymakers in improving energy generation and utilization, encouraging the use of renewable energy sources, and advancing sustainable energy objectives.

Seasonal variability of coastal pH and CO2 using an oceanographic buoy in the Canary Islands

Ocean acidification, caused by the absorption of carbon dioxide (CO2) from the atmosphere into the ocean, ranks among the most critical consequences of climate change for marine ecosystems. Most studies have examined pH and CO2 trends in the open ocean through oceanic time-series research. The analysis in coastal waters, particularly in island environments, remains relatively underexplored. This gap in our understanding is particularly important given the profound implications of these changes for coastal ecosystems and the blue economy. The present study focuses on the ongoing monitoring effort that started in March 2020 along the east coast of Gran Canaria, within the Gando Bay, by the CanOA-1 buoy. This monitoring initiative focuses on the systematic collection of multiple variables within the CO2 system, such as CO2 fugacity (fCO2), pH (in total scale, pHT), total inorganic carbon (CT), and other hydrographic variables including sea surface salinity (SSS), sea surface temperature (SST) and wind intensity and direction. Accordingly, the study allows the computation of the CO2 flux (FCO2) between the surface waters and the atmosphere. During the study period, stational (warm and cold periods) behavior was found for all the variables. The lowest SST values were recorded in March, with a range of 18.8-19.3°C, while the highest SST were observed in September and October, ranging from 24.5-24.8°C. SST exhibited an annual increase with a rate of 0.007°C yr-1. Warmer months increased SSS, while colder periods, influenced by extreme events like tropical storms, led to lower salinity (SSS=34.02). The predominant Trade Winds facilitated the arrival of deeper water, replenishing seawater. The study provided insights into atmospheric CO2. Atmospheric fCO2 averaged 415 ± 4 µatm (2020-2023). Surface water fCO2sw presented variability, with the highest values recorded in September and October, peaking at 437 µatm in September 2021. The lowest values for fCO2sw were found in February 2021 (368 µatm). From 2020 to 2023, surface water fCO2sw values displayed an increasing rate of 1.9 µatm yr-1 in the study area. The assessment of fCO2sw decomposition into thermal and non-thermal processes revealed the importance of SST on the fCO2sw. Nevertheless, in the present study, it is crucial to remark the impact of non-thermal factors on near-shallow coastal regions. Our findings highlight the influence of physical factors such as tides, and wind effect to horizontal mixing in these areas. The CT showed a mean concentration of 2113 ± 8 μmol kg-1 and pH at in-situ temperature (pHT,IS) has a mean value of 8.05 ± 0.02. The mean FCO2 from 2020 to 2023 was 0.34 ± 0.04 mmol m-2 d-1 (126 ± 13 mmol m-2 yr-1) acting as a slight CO2 source. In general, between May and December were the months when the area was a source of CO2. Extrapolating to the entire 6 km2 of Gando Bay, the region sourced 33 ± 4 Tons of CO2 yr-1.

THE RELATIONSHIP OF THE DISTRIBUTION OF SEA SURFACE TEMPERATURE WITH RAINFALL AND WIND IN THE WATERS OF WEST SUMATRA

This research was carried out in February-March 2023 in the West Sumatra waters. It aimed to determine the variability of sea surface temperature, rainfall intensity, wind direction, and speed in the West Sumatra waters. Sea surface temperatures were taken from three stations, namely: in the coastal waters of Pariaman City, Padang-Pariaman Regency, and Bungus, Padang. The survey method was used, which was directly observed in the field. The results showed that the Sea Surface Temperature of the study areas ranged from 29.25oC-30.28oC, while the rainfall went from 211.97-499.89 mm, and the dominant speed was between 0.50-2.10 m/s. The correlation analysis between rainfall and sea surface temperatures in Station 1 yielded an "r" value of 0.871, a powerful correlation category, and the determination coefficient (R2) was 0.579 or 57.9% during the western season. In the east season, the "r" value was 0.321, in the moderate correlation category, and the determination coefficient was 0.103 or 10.3%. At Station 2, the correlation coefficient (r) was 0.788, a powerful correlation category, and the determination coefficient (R2) was 0.622 or 62.2% during the western season. In the east season, the "r" value was 0.699, a strong correlation category, and the determination coefficient was 0.488 or 48.8%. At Station 3, the "r" value was 0.688, a strong correlation category, and the determination coefficient was 0.477 or 47.7% during the western season. In the east season, the "r" value was 0.743, a strong correlation category, and the determination coefficient was 0.579 or 57.9%

Characteristics and mechanisms of near-surface negative atmospheric electric field anomalies preceding the 5 September 2022, Ms 6.8 Luding earthquake in China

Abstract. A magnitude 6.8 strike-slip earthquake (EQ) struck Luding, Sichuan Province, China, on 5 September 2022, resulting in significant damage to nearby Ganzi Prefecture and the city of Ya'an. In this research, the near-surface atmospheric electric field (AEF) recorded at four sites 15 d before the Luding EQ was analyzed and differentiated, and multisource auxiliary data including precipitation, cloud base height, and low cloud cover were used at the same time. Nine possible seismic AEF anomalies at four sites were obtained preliminarily. Accordingly, microwave brightness temperature (MBT) data, which are very sensitive to the surface dielectrics and are closely related to the air ionization, together with surface soil moisture, lithology, and a 3D-simulated crustal stress field, were jointly analyzed to confirm the seismic relations of the obtained negative AEF anomalies. The geophysical environment for crustal high-stress concentration, positive charge carrier transfer, and surface accumulation was demonstrated to exist and to meet the conditions necessary to generate local negative AEF anomalies. Furthermore, to deal with the spatial disparities in sites and regions with potential atmospheric ionization, near-surface wind field data were employed to scrutinize the reliability of the AEF anomalies by comprehensively analyzing the spatial relationships among surface charges accumulation areas, wind direction and speed, and the AEF sites. Finally, four negative AEF anomalies were deemed to be closely related to the Luding EQ, and the remaining five possible anomalies were ruled out. A possible mechanism of negative AEF anomalies before the Luding EQ is proposed: positive charge carriers were generated from the underground high-stress concentration areas and then transferred to and accumulated on the ground surface to ionize the surface air, thus disturbing the AEF above the ground. This study presents a method for identifying and analyzing seismic AEF anomalies and is also beneficial for the examination of the pre-earthquake coupling process between the coversphere and the atmosphere.

Flapping about: trends and drivers of Australian cownose ray (Rhinoptera neglecta) coastal sightings at their southernmost distribution range

The Australian cownose ray (Rhinoptera neglecta) is an understudied batoid that occurs along Australia's north and east coasts. Currently classified as Data Deficient on the IUCN Red List of Threatened Species, major knowledge gaps exist regarding the species' geographic range, habitat use and the drivers influencing its presence in coastal Australian waters. Sightings of R. neglecta were collected during systematic aerial surveys conducted along 980 km (~47%) of the New South Wales (NSW) coastline between 2017 and 2019. North-bound surveys were flown 500 m offshore, whilst return surveys were flown along the beach/sea interface (inshore or nearshore). Using generalized additive models and a set of nine predictors, we examined the relationship between the spatio-temporal occurrence of R. neglecta, their group size and the biophysical environment at the southernmost extent of their distribution. Results for the presence/absence (44.20% deviance explained) and group size of R. neglecta observed offshore and inshore (42.58 and 41.94% deviance explained, respectively) highlighted latitude, day of year, sea surface temperature, rainfall, wind speed, and wind direction as common influences to the three models. The models indicated R. neglecta were more likely to be present in the northern half of NSW during spring and summer months. However, larger group sizes were more likely to be observed in more southern regions during the same seasons, regardless of whether they were observed offshore or inshore. Group size is also likely influenced by more localized conditions, such as SST and tidal flows. This study represents the largest attempt to date to decipher the spatial ecology of R. neglecta and provides insights into the spatio-temporal distribution and relative abundance of the species along the full extent of the NSW coastline, extending the species' known distribution by over 70 km southward.

Performance evaluation of the WRF model under different physical schemes for air quality purposes in Buenos Aires, Argentina

This work presents the performance evaluation of the Weather Research and Forecasting (WRF) model to estimate surface wind speed and direction, air temperature, and water vapor mixing ratio considering 22 configurations at high spatial resolution (1 km) during one week in winter and one week in spring, in order to determine the best-performing schemes for air quality purposes in the Metropolitan Area of Buenos Aires, Argentina. Results show that the use of urban schemes mostly affects wind speed and temperature. The single-layer urban canopy model (UCM) coupled with the Boulac planetary boundary layer (PBL) scheme exhibits the best results for wind speed. Wind direction and water vapor mixing ratio are more sensitive to the land surface model scheme, with results slightly improving with the Noah-MP land surface model. Wind speed and direction errors are larger when the former is lower. When removing from the analysis wind speed values below 2.6 m s–1 for the winter week and 3.1 m s–1 for the spring week, the root mean square errors for wind direction decreased between 50 and 72% of the original value, depending on the configuration and week. Overall, under the studied conditions, configurations including Noah-Mp land surface model or the combination of a simple UCM with BouLac PBL are suitable for air quality applications, as they reproduce both temperature and water vapor mixing ratio relatively well, with errors below 10% and Correlation values above 0.7, and are the best performing configurations for wind direction and speed, respectively.

Digitizing weather observations from World War II US naval ship logbooks

AbstractThe number and coverage of weather observations over the oceans were considerably reduced during World War II (WW2) due to disruptions to normal trade routes. The observations that do exist for this period are often unavailable to science as they are still only available as paper records or scanned images. We have rescued the detailed hourly weather observations contained in more than 28,000 logbook images of the US Navy Pacific Fleet stationed at Hawai'i during 1941–1945 to produce a dataset of more than 630,000 records. Each record contains the date and time, positional information and several meteorological measurements, totalling more than 3 million individual observations. The data rescue process consisted of a citizen science project asking volunteers to transcribe the observations from the available images, followed by additional quality control processes. This dataset not only contains hourly weather observations of air temperature, sea surface temperature, atmospheric pressure, wind speed and wind direction, mainly in the Pacific Ocean but also includes some observations from the Atlantic and Indian Oceans. The new observations are found to be of good quality by inter‐comparing independent measurements taken on ships travelling in convoy and by comparing with the 20th‐Century Reanalysis. This dataset provides invaluable instrumental weather observations at times and places during WW2, which fill gaps in existing reconstructions.

Green infrastructure modelling for UHI control to urban thermal comfort: a case study of Temanggung urban area

ABSTRACT The Temanggung urban area in Central Java (Indonesia) already has considerable urban Green Infrastructure (GI), but the phenomenon of Urban Heat Island (UHI) has expanded to 268% in the last seven years. This study explores the theoretical gaps related to urban thermal comfort by combining tree management modelling as the independent variable and existing urban morphology as the dependent variable to identify the right place for the right tree using ENVI-met v5 simulations. We use the Universal Thermal Climate Index (UTCI) to determine urban thermal comfort, and our results showed a temperature decrease from 37°C (strong heat stress) to 32.5°C–35.5°C (moderate heat stress) and an average decrease of 1°C–3.5°C. These results were obtained by simulating the placement pattern of substations, land use zoning, tree size composition, paying attention to the type of road surface material, wind direction and speed and direction of road orientation, average air temperature, and location of building masses. This study provides a basis for consideration to assist local governments and researchers in managing GI in Temanggung Urban Area to create comfortable urban thermal comfort for their residents.

Meteorological influences on PM2.5 variation in China using a hybrid model of machine learning and the Kolmogorov-Zurbenko filter

Quantitatively evaluating the contribution of meteorology and emissions to PM2.5 reduction during the Three-year Action Plan to Fight Air Pollution (2018–2020) is important to guide future emission control measures in China. In this study, a Kolmogorov-Zurbenko (KZ) filter and an artificial neural network (ANN) combined model was used to separate the meteorology-influenced PM2.5 concentration by considering seven meteorological factors including boundary layer height, surface pressure, surface net solar radiation, total precipitation, atmospheric temperature, wind speed and wind direction in China. To compensate for the black-box nature of the ANN, the Shapley additive explanation (SHAP) value was introduced to explain the effect of each meteorological factor on long-term PM2.5 concentration. Results showed that PM2.5 concentration decreased by 14% from 2018 to 2020 in China with annual average concentrations of 37.19, 35.28, and 31.94 μg/m3, respectively. Although the decline in PM2.5 was mainly due to emission reductions (58% for all of China), the contribution of prevailing meteorological conditions should not be ignored, especially in some regions such as Northeast China, which had the highest contribution of meteorology (58%) on PM2.5 reduction. In general, the contributions of meteorological conditions to the long-term trend of PM2.5 varied greatly by region and season. SHAP analysis indicated that temperature, surface pressure and boundary layer height were the dominant meteorological factors affecting long-term PM2.5 with contributions of 65%, 11% and 6% on average. We also found that regions with larger average PM2.5 concentration were more affected by emission changes than by meteorology. Therefore, we recommend the continuation of emission control measures in heavily polluted regions such as North and Central China.

NEW FUNCTIONALITIES OF THE WEIGH-IN-MOTION SYSTEM: iWIM SOLUTION

This paper presents new functionalities for systems weighing vehicles in motion, which result from the integration of various measurement technologies and the use of a precise registration and data processing system. The utilized registration track provides accurate measurements of basic vehicle parameters such as axle load and vehicle speed and makes it possible to determine the location of the vehicle passing the weighing station, calculate the width of the tire tread, and detect twin (double) wheels. A key functionality is the assessment of the reliability of the measurement, taking into account, among other factors, vehicle movement dynamics and the ambient conditions during the measurements. For this purpose, additional measurements of road surface temperature and wind speed and direction were introduced. The technological solutions used and the proprietary data processing algorithms are described. Tests carried out to verify the effectiveness of the proposed algorithms and to assess the significance of the influence of the isolated factors permitted confirmation of the validity of the proposed solutions.

Unambiguous Wind Direction Estimation Method for Shipborne HFSWR Based on Wind Direction Interval Limitation

Due to its maneuverability and agility, the shipborne high-frequency surface wave radar (HFSWR) provides a new way of monitoring large-area marine dynamics and environment information. However, wind direction ambiguity is problematic when using monostatic shipborne HFSWR for wind direction inversion. In this article, an unambiguous wind direction measurement method based on wind direction interval limitation is proposed. The two first-order spectral wind direction estimation methods are first presented using the relationship between the normalized amplitude differences or ratios of the broadened Doppler spectrum and the wind direction. Moreover, based on the characteristic of a small wind direction estimation error in a large included angle between the spectral wind direction and the radar beam, the wind direction interval is obtained by counting the distribution of radar-measured wind direction within this included angle. Furthermore, the eliminated ambiguity of wind direction is transformed to judge the relationship between the wind direction interval and the two curves, which represent the relationship between the spreading parameter and the wind direction. Therefore, the remote sensing monitoring of ocean surface wind direction fields can be realized by shipborne HFSWR. The simulation results are used to evaluate the performance of the proposed method and the multi-beam sampling method for wind direction inversion. The experimental results show that the errors of wind direction estimated by the multi-beam sampling method and the equivalent dual-station model are large, and the proposed method can improve the accuracy of wind direction measurement. Three widely used wave directional spreading models have been applied for performance comparison. The wind direction field measured by the proposed method under a modified cosine model agrees well with that observed by the China-France Oceanography Satellite (CFOSAT).

Techno-economic assessment of offshore wind energy potential at selected sites in the Gulf of Guinea

Offshore wind power has been found to stand out among the most dynamic renewable energy technologies. With its long coastal line, Nigeria has an overwhelming advantage in developing marine energy resources to relieve the power crisis effectively. This work analyzed and characterized observation data of sea-surface wind speed and direction at 30-minute intervals between 1979 and 2015 at five synoptic offshore stations in the Gulf of Guinea. The seasonal variations in hourly surface wind speed and directions as well as the Weibull distribution of wind speed and wind power at 100 m hub height were examined. The wind shears, capacity factors, and accumulated energy outputs for seven offshore wind turbine types were determined for the selected locations. In addition, the economic analysis of the selected offshore turbines using levelized cost of energy was carried out, while sensitivity analysis of the total levelized cost of energy to key input parameters was further determined. The results revealed large spatial and temporal variations in wind speed and wind power in the Gulf of Guinea. The most viable offshore site for wind energy exploitation was Agbami (the deepest offshore site), while Bonny (the shallow coastal site) had the least. The findings established very good fits (having mean bias (between −0.08 ms−1 and −2.44 ms−1), percentage bias (between −0.47% and −13.98%), correlation coefficients (between 0.97 and 0.98), Chi-square (between 0.2 and 1.2), and root mean square error (between 1.2 ms−1 and 3.1 ms−1)) between Weibull distribution and the actual wind data. The wind turbines with the highest and the lowest wind power densities, capacity factors, and power outputs across the seasons and sites were V236-15.0 MW and Siemens SWT113, respectively. The levelized cost of energy was considered for the deep waters due to the moderately high-capacity factors. The highest values ranged between 101.48 and 137.12 USD/MWh at Sea Eagle with V236-15 MW and V117-4.2 MW, respectively, while the lowest ranged from 52.29 to 69.66 USD/MWh at Agbami with V236-15 MW and Siemens (SWT113), respectively. The exploitation of Nigeria’s offshore wind resources could also be dedicated to producing renewable hydrogen and can serve to meet the country’s ambitious targets set for carbon neutrality by 2060.

Local-to-regional methane emissions from the Upper Silesian Coal Basin (USCB) quantified using UAV-based atmospheric measurements

Abstract. Coal mining accounts for ∼12 % of the total anthropogenic methane (CH4) emissions worldwide. The Upper Silesian Coal Basin (USCB), Poland, where large quantities of CH4 are emitted to the atmosphere via ventilation shafts of underground hard coal (anthracite) mines, is one of the hot spots of methane emissions in Europe. However, coal bed CH4 emissions into the atmosphere are poorly characterized. As part of the carbon dioxide and CH4 mission 1.0 (CoMet 1.0) that took place in May–June 2018, we flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 and CO2 mole fractions 150–300 m downwind of five individual ventilation shafts in the USCB. In addition, we also measured δ13C-CH4, δ2H-CH4, ambient temperature, pressure, relative humidity, surface wind speed, and surface wind direction. We used 34 UAV flights and two different approaches (inverse Gaussian approach and mass balance approach) to quantify the emissions from individual shafts. The quantified emissions were compared to both annual and hourly inventory data and were used to derive the estimates of CH4 emissions in the USCB. We found a high correlation (R2=0.7–0.9) between the quantified and hourly inventory data-based shaft-averaged CH4 emissions, which in principle would allow regional estimates of CH4 emissions to be derived by upscaling individual hourly inventory data of all shafts. Currently, such inventory data is available only for the five shafts we quantified. As an alternative, we have developed three upscaling approaches, i.e., by scaling the European Pollutant Release and Transfer Register (E-PRTR) annual inventory, the quantified shaft-averaged emission rate, and the shaft-averaged emission rate, which are derived from the hourly emission inventory. These estimates are in the range of 256–383 kt CH4 yr−1 for the inverse Gaussian (IG) approach and 228–339 kt CH4 yr−1 for the mass balance (MB) approach. We have also estimated the total CO2 emissions from coal mining ventilation shafts based on the observed ratio of CH4/CO2 and found that the estimated regional CO2 emissions are not a major source of CO2 in the USCB. This study shows that the UAV-based active AirCore system can be a useful tool to quantify local to regional point source methane emissions.

Predicting Dust-Storm Transport Pathways Using a Convolutional Neural Network and Geographic Context for Impact Adaptation and Mitigation in Urban Areas

Dust storms are natural disasters that have a serious impact on various aspects of human life and physical infrastructure, particularly in urban areas causing health risks, reducing visibility, impairing the transportation sector, and interfering with communication systems. The ability to predict the movement patterns of dust storms is crucial for effective disaster prevention and management. By understanding how these phenomena travel, it is possible to identify the areas that are most at risk and take appropriate measures to mitigate their impact on urban environments. Deep learning methods have been demonstrated to be efficient tools for predicting moving processes while considering multiple geographic information sources. By developing a convolutional neural network (CNN) method, this study aimed to predict the pathway of dust storms that occur in arid regions in central and southern Asia. A total of 54 dust-storm events were extracted from the modern-era retrospective analysis for research and applications, version 2 (MERRA-2) product to train the CNN model and evaluate the prediction results. In addition to dust-storm data (aerosol optical depth (AOD) data), geographic context information including relative humidity, surface air temperature, surface wind direction, surface skin temperature, and surface wind speed was considered. These features were chosen using the random forest feature importance method and had feature importance values of 0.2, 0.1, 0.06, 0.03, and 0.02, respectively. The results show that the CNN model can promisingly predict the dust-transport pathway, such that for the 6, 12, 18, and 24-h time steps, the overall accuracy values were 0.9746, 0.975, 0.9751, and 0.9699, respectively; the F1 score values were 0.7497, 0.7525, 0.7476, and 0.6769, respectively; and the values of the kappa coefficient were 0.7369, 0.74, 0.7351, and 0.6625, respectively.