Wind Temperature Research Articles

On the influence of solar insolation and increase of outdoor temperature on energy savings obtained in heating system with forecast control

Throughout the world there is an ongoing emphasis on the implementation of modern technologies and services to reduce the carbon footprint and increase the energy efficiency of existing and new buildings. Of particular interest are energy-efficient measures that can be easily implemented in existing building stock and at a low cost, such as a forecast controller for heating systems. However, there is a lack of detailed measured data for assessing the influence of varying outdoor conditions on energy savings from a heating system controlled by a forecast controller. The work aimed to assess the influence of variations in solar insolation and outdoor air temperature on the supply temperature, power demand, and heat consumption within thirteen buildings equipped with a forecast control system. The application employs simple correlations, utilizing an equivalent outdoor temperature derived from an online weather prediction application (API) leveraging the data from nearby meteorological stations. The algorithm utilises the weather predictions for changes in outdoor conditions, such as wind speed, temperature, and solar insolation, to optimise the conditions of the heat supplied to buildings.The field research was conducted for one year. It was found that the use of the forecast controller in four public buildings and nine residential buildings reduced heat consumption by an average of 9.0% and 11.8%, respectively, for the case when the variations of the outdoor temperature or the solar insolation were taken into account independently. Higher energy savings (16.5%) were achieved for the case when the outdoor temperature increase as well as solar insolation were taken into account at the same time in the process of forecast control of the heating system.

Modification of the turbulence properties at the bow shock: statistical results

Turbulent solar wind is known to be a main driver of the processes inside the magnetosphere, including geomagnetic storms and substorms. Experimental studies of the last decade demonstrate additional ways of interplanetary plasma transport to the magnetosphere, including small-scale processes in the magnetosphere boundary layers. This fact implies that properties of the solar wind turbulence can affect the geomagnetic activity. However, in front of the magnetosphere are a bow shock and a magnetosheath region which contribute to the changes in the properties of the solar wind turbulence and may result in destructions of the association between solar wind turbulence and the magnetosphere. The present study provides the statistics of two-point simultaneous measurements of the turbulence properties in the solar wind and the magnetosheath based on Wind and THEMIS spacecraft data. Changes in the turbulence properties are analyzed for different background conditions. Solar wind bulk speed and temperature are shown to be the main factors that influence the modification of turbulence at the quasi-perpendicular bow shock at frequencies higher than the break frequency (ion transition range). Inside the magnetosheath, significant steepening of spectra occurs with an increase in temperature anisotropy without a connection to the upstream spectrum scaling that underlines the crucial role of the instabilities in turbulence properties behind the bow shock.

Fire behavior simulation of Xintian forest fire in 2022 using WRF-fire model

IntroductionThe behavior of forest fire is a complex phenomenon, and accurate simulation of forest fire is conducive to emergency response management after ignition. In order to further understand the characteristics of forest fire spread and the applicability of WRF-Fire in China, which is a coupled fire-atmospheric wildfire model, this study simulated a high-intensity forest fire event that occurred on October 17, 2022 in Xintian County, southern Hunan Province.MethodsBased on the fire-atmosphere coupled WRF-Fire model, we used high-resolution geographic information, meteorological observation and fuel classification data to analyze the forest fire behavior. At the same time, the simulation results are compared with the fire burned area observed by satellite remote sensing forest fire monitoring data.ResultsThe study found that, the simulated wind speed, direction and temperature trends are similar to the observation results, but the simulated wind speed is overestimated, the dominant wind direction is N, and the temperature is slightly underestimated. The simulated wind field is close to the actual wind field, and the simulation results can show the spatial and temporal variation characteristics of the local wind field under complex terrain while obtaining the high-resolution wind field. The simulated fire burned area is generally overestimated, spreading to the north and southwest compared with the observed fires, but it can also capture the overall shape and spread trend of the fire well.DiscussionThe results show that the model can accurately reproduce the real spread of fire, and it is more helpful to forest fire management.

A Prediction Method for the Average Winding Temperature of a Transformer Based on the Fully Connected Neural Network

The average winding temperature of a transformer (AWTT), serving as a key indicator for assessing the running state of the transformer, is of utmost importance in determining a transformer’s electrical properties and the insulation longevity of the transformer. An accurate prediction of AWTT is essential for ensuring the safe operation of the transformer. A novel method for predicting AWTT is introduced based on the analysis of field monitoring data. Firstly, the thermal characteristics and operational mechanisms of oil-immersed transformers are examined. Secondly, a factor analysis model is developed to streamline the network structure, accounting for the strong correlations among ambient temperature, load current, and top oil temperature. Thirdly, the independent temperature factor and load factor are extracted as pivotal features, and then input into the fully connected neural network to predict AWTT. Through a case study involving a 110 kV/10 kV oil-immersed transformer, the results show that the proposed method reduces redundant correlation information compared to traditional methods and improves the prediction accuracy of AWTT, establishing a foundation for further transformer state assessments.

Airborne particulate matter integral assessment in Magdalena department, Colombia: Patterns, health impact, and policy management

The relevance of atmospheric particulate matter (PM) to health and the environment is widely known. Long-term studies are necessary for understanding current and future trends in air quality management. This study aimed to assess the long-term PM concentration in the Magdalena department (Colombia). It focused on the following aspects: i) spatiotemporal patterns, ii) correlation with meteorology, iii) compliance with standards, iv) temporal trends over time, v) impact on health, and vi) impact of policy management. Fifteen stations from 2003 to 2021 were analyzed. Spearman-Rho and Mann-Kendall methods were used to correlate concentration with meteorology. The temporal and five-year moving trends were determined, and the trend magnitude was calculated using Teil-Sen. Acute respiratory infection odd ratios and risk of cancer associated with PM concentration were used to assess the impact on health. The study found that the maximum PM10 concentration was 194.5 μg/m3, and the minimum was 3 μg/m3. In all stations, a negative correlation was observed between PM10 and atmospheric water content, while the wind speed and temperature showed a positive correlation. The global trends indicated an increasing value, with five fluctuations in five-year moving trends, consistent with PM sources and socio-economic behavior. PM concentrations were found to comply with national standard; however, the results showed a potential impact on population health. The management regulation had a limited impact on increasing concentration. Considering that national regulations tend to converge towards WHO standards, the study area must create a management program to ensure compliance.

Simulation Study on Nondestructive Testing of Winding Hot Spot Temperature Based on Ultrasonic Emission

Simulation Study on Nondestructive Testing of Winding Hot Spot Temperature Based on Ultrasonic Emission

Magnetohydrodynamic Modeling of Background Solar Wind near Mars: Comparison with MAVEN and Tianwen-1

Combined with data assimilation methods, a three-dimensional magnetohydrodynamic (MHD) numerical model is an effective tool to explore the mechanism of space weather. As a driver of space weather, the dynamic development of stream interaction regions (SIRs) near the orbit of Mars is an area of active research. In this study, we use the interplanetary total variation diminishing (TVD) MHD model to simulate solar wind parameters and model SIRs near Mars from 2021 November 15 to 2021 December 31. In this model, the MHD equations are solved by the conservation TVD Lax–Friedrichs scheme in a rotating spherical coordinate system with six component meshes used on the spherical shell. Solar wind velocity, density, temperature, and magnetic field strength are given at the inner boundary due to the characteristic waves propagating outward. We compared modeled results with observations from Mars Atmospheric Volatile EvolutioN (MAVEN) and Tianwen-1 (China’s first Mars exploration mission). Statistical analysis shows that the simulated results can capture SIRs and are in good agreement with observations; moreover, the assimilated results based on the Kalman filter improve the accuracy of numerical prediction compared with simulated results. This paper is the first attempt to simulate SIR events combined with MAVEN and Tianwen-1 in situ observations. Our work demonstrates that using the MHD model with the Kalman filter to reconstruct solar wind parameters can help us study the characteristics of SIRs near Mars, improve the capabilities of space weather forecasting, and understand the background solar wind environment.

Transformer partial discharge location technology based on gradient oil temperature

IntroductionThe traditional partial discharge localization improvement strategy mainly starts from the intelligent algorithm, but fails to consider the influence of core winding and oil temperature on partial discharge positioning.MethodsThis paper also considers the influence of the iron core winding and oil temperature. Through finite element simulation, a transformer model was established to analyze the propagation characteristics of ultrasonic signals generated by partial discharge under the interference of gradient oil temperature and winding. The chaotic firefly-particle swarm hybrid algorithm is proposed, and through the calculation of Shubert’s multi-peak function. Finally, a partial discharge defect platform based on gradient oil temperature was built to verify the chaotic firefly-particle swarm hybrid localization algorithm.ResultsThe ultrasonic velocity generated by partial discharge in transformers cannot be fixed, and it is suggested that ultrasonic sensors should be installed near the center of the top of the transformer. The proposed algorithm can be better optimized in the case of multiple local extreme points. Under gradient oil temperature experiments, the algorithm achieves positioning errors less than 100 and 55 mm for cases with and without winding obstruction, respectively, with average positioning errors of 74.2 and 35.2 mm.DiscussionThe positioning method in this paper can provide a technical reference for the partial discharge positioning of transformers in actual operation.

Determination of Fine Particulate Matter Episode Periods in Jakarta by Incorporating Meteorological Conditions

The analysis of observed fine particulate matter (PM2.5) in Jakarta, Indonesia, was conducted from January 2021 to October 2023 using hourly monitoring data from AirNow, managed by the U.S. Environmental Protection Agency. Data were collected from two air quality monitoring stations in Central and South Jakarta. Meteorological data, including wind speed, temperature, humidity, and rainfall, were obtained from NASA's global forecasting database. The objective was to understand the correlation between PM2.5 formation and meteorological conditions to identify favorable meteorology during high air pollution episodes. During the dry season (May to August), there was a notable increase in PM2.5, linked to higher temperatures, lower humidity, slower wind speeds, and minimal rainfall. The results showed that meteorological conditions significantly affected PM2.5 concentrations, influenced by seasonal changes. In contrast, the rainy season had lower PM2.5 concentrations due to higher rainfall, humidity, and wind speed, and lower temperatures. Further modeling studies are required to assess the combined impacts of emission build-up and meteorology on recurring PM2.5 episodes in Jakarta.

The application of the analysis framework for compound extreme event dependencies in China

AbstractThe escalation of compound extreme events has resulted in noteworthy economic and property losses. Recognizing the intricate interconnections among these events has become imperative. To tackle this challenge, we have formulated a comprehensive framework for the systematic analysis of their dependencies. This framework consists of three steps. (1) Define extreme events using Mahalanobis distance thresholds. (2) Represent dependencies among multiple extreme events through a point process‐based method. (3) Verify dependencies with residual tail coefficients, determining the final dependency structure. Applying this framework to assess the extreme dependence of precipitation on wind speed and temperature in China, revealed four distinct dependency structures. In northern, Jianghuai, and southern China, precipitation heavily relies on wind speed, while temperatures maintain relative independence. In northeastern and northwestern China, precipitation exhibits relative independence, yet a notable dependence exists between temperatures and wind speed. In southwestern China, precipitation strongly depends on temperature, while wind speed remains relatively independent. The Qinghai–Tibet Plateau region displays a significant dependence relationship among precipitation, wind speed, and temperature, with weaker dependence between extreme wind speed and temperature. This framework is instrumental for analyzing dependencies among extreme values in compound events.

Research on Microclimate Optimization of Traditional Residential Buildings in Central Anhui Based on Humid and Hot Climate Characteristics and Regional Architectural Features

This study examines how building space, materials, and structure affect the microclimate of Liu Tong Xinglongzhuang in central Anhui, known for its hot, humid climate and distinct architecture. Using qualitative and quantitative methods, including field monitoring, PHOENICS simulation, and Ladybug Tools, the following conclusions were drawn: (1) Building materials: wood reduces indoor temperature; a glass roof over the patio lowers temperature and improves wind speed; and reducing tile area decreases solar radiation. (2) Building space: optimal comfort in stairwells is achieved with a length-to-width ratio of 1.5:1 and height-to-width ratio of 2:1; courtyards are most comfortable with a length-to-width ratio of 2.5:1 and height-to-width ratio of 1.5:1; walk-through halls are optimal with a length-to-width ratio of 2:1 and height-to-width ratio of 1.5:1; and wings achieve the highest comfort with a length-to-width ratio of 2.5:1 and height-to-width ratio of 1.5:1. (3) Building structure: optimal wind speed and temperature are achieved with specific window height-to-width ratios of 1:1 for exterior and 1.5:1 for interior windows; the lowest temperatures occur at 2:1 ratios; hexagonal exterior and circular interior windows maximize wind speed and temperature reduction; and smaller exterior door openings lower indoor temperatures and are best achieved with an interior door ratio of 2:1. These findings offer valuable data and methods for optimizing the microclimate of traditional houses, with significant practical implications.

Fast dynamic prediction of consequences of heavy gas leakage accidents based on machine learning

The field of emergency risk management in chemical parks has been characterized by a lack of fast, precise and dynamic prediction methods. The application of computational fluid dynamics (CFD) models, which offer the potential for dynamic and precise prediction, has been hindered by high computational costs. Therefore, taking liquid benzene as a case study, this paper combined machine learning (ML) algorithms with a CFD-based precise prediction model, to develop an ML model for fast dynamic prediction of heavy gas leakage consequences in chemical parks. Employing the CFD data as the input, the prediction models were developed using ML algorithms, refined with Bayesian optimization for parameter tuning. This study utilized PHOENICS software to establish a dynamic prediction model for the diffusion of liquid benzene leakage, validated by Burro nine experiment data. Comparative analyses of models based on five ML algorithms were conducted to evaluate the reliability of their predictions using both CFD standard and noisy data. The results indicated that temperature had the most significant effect on the consequences of the leakage accidents among four key factors (wind speed, temperature, leakage aperture and atmospheric stability), followed by wind speed. These factors served as input variables for ML model training. Among the models evaluated, the eXtreme Gradient Boosting (XGBoost) model showed superior performance, irrespective of the presence of noise in the data. An XGBoost-based fast prediction model was ultimately developed for predicting the consequences of liquid benzene leakage. A case analysis was conducted to validate the feasibility of the model prediction. The relative errors between the predicted and actual values of the model for acute exposure guideline level-1 (AEGL-1), AEGL-2, and AEGL-3 distances were 2.70%, 2.58%, and 0.23%, respectively. Furthermore, the XGBoost model completed the prediction in only 0.218 s, a stark contrast to the hours necessitated by the CFD model, thus offering substantial computational time savings while maintaining high accuracy levels. This paper introduces an ML model for fast dynamic prediction of heavy gas leakage, enabling chemical parks to make more timely and accurate decisions in emergency risk management.

Inversion Method for Transformer Winding Hot Spot Temperature Based on Gated Recurrent Unit and Self-Attention and Temperature Lag.

The hot spot temperature of transformer windings is an important indicator for measuring insulation performance, and its accurate inversion is crucial to ensure the timely and accurate fault prediction of transformers. However, existing studies mostly directly input obtained experimental or operational data into networks to construct data-driven models, without considering the lag between temperatures, which may lead to the insufficient accuracy of the inversion model. In this paper, a method for inverting the hot spot temperature of transformer windings based on the SA-GRU model is proposed. Firstly, temperature rise experiments are designed to collect the temperatures of the entire side and top of the transformer tank, top oil temperature, ambient temperature, the cooling inlet and outlet temperatures, and winding hot spot temperature. Secondly, experimental data are integrated, considering the lag of the data, to obtain candidate input feature parameters. Then, a feature selection algorithm based on mutual information (MI) is used to analyze the correlation of the data and construct the optimal feature subset to ensure the maximum information gain. Finally, Self-Attention (SA) is applied to optimize the Gate Recurrent Unit (GRU) network, establishing the GRU-SA model to perceive the potential patterns between output feature parameters and input feature parameters, achieving the precise inversion of the hot spot temperature of the transformer windings. The experimental results show that considering the lag of the data can more accurately invert the hot spot temperature of the windings. The inversion method proposed in this paper can reduce redundant input features, lower the complexity of the model, accurately invert the changing trend of the hot spot temperature, and achieve higher inversion accuracy than other classical models, thereby obtaining better inversion results.

Study on Thermal Characteristics of Winding under Inter-Turn Fault of Oil-Immersed Transformer

The inter-turn fault is a common fault of oil-immersed transformers. Due to the deterioration of inter-turn insulation, its resistance decreases, which leads to an increase in the winding coil current and loss, causing the winding temperature to rise. Therefore, analyzing the electrothermal characteristics of inter-turn short circuit faults in oil-immersed transformers can provide a theoretical basis for the diagnosis of inter-turn faults in transformers. This paper studies the equivalent circuit under inter-turn faults and establishes a field–circuit coupling model to calculate the current characteristics under different inter-turn short circuit faults of transformers. The distribution of coil losses under faults is calculated as thermal source excitations, and a thermal–fluid-coupled calculation model for oil-immersed transformers is established. After the experimental verification, it is found that both the errors between the winding and tank surface temperatures do not exceed the acceptable limits, validating the effectiveness of this model. Using this model to calculate the temperature distribution for various inter-turn short circuit faults in transformers reveals their hot-spot characteristics. The results indicate that under inter-turn fault conditions, the temperature at faulty turns will gradually increase towards a limit, eventually leading to coil damage. This research provides theoretical support for enhancing transformer detection and diagnostic capabilities.

The similitude of indoor airflow in natural ventilation for a reduced-scale model: Investigation of nonisothermal flow fields by RANS simulation

Reduced-scale experiments and simulations are important approaches in natural ventilation research, and the similarity requirement is fundamental for generalising the flow characteristics obtained from reduced-to full-scale conditions. However, the similarity requirement of a nonisothermal natural ventilation flow in a reduced-scale model poses additional challenges because of the reduced approaching flow, which can potentially result in Reynolds dependence issues. This study investigated the Reynolds number (Re) independence of indoor airflow in natural ventilation under isothermal and nonisothermal conditions using computational fluid dynamics (CFD) with Reynolds-averaged Navier–Stokes. A wind tunnel experiment was first conducted to validate the accuracy of the CFD using a reduced-scale model. Indoor airflow fields characterised by the same Archimedes number (Ar) but with varying approaching wind velocities and temperatures were compared between the full-scale and 1/10 reduced-scale simulations. The dimensionless ventilation rate showed the least dependence on the Re number, while the temperature field was very sensitive to the Re number, especially in the near-wall region. However, the temperature field on the ventilation pathway is much less dependent on the Re number, the deviation of which is less than 10 % compared to the full-scale simulation. The temperature distribution in the reduced-scale simulation exhibits a thermal stratification pattern similar to that in the full-scale simulation.

The associations of Tibetan Plateau spring snow cover with East Asian summer monsoon rainfall before and after 1990

In recent decades, the existence of a relationship between snow cover on the Tibetan Plateau (TP) and East Asian summer monsoon (EASM) rainfall has been emphasized. According to recently published studies this snow-monsoon relationship experienced a shift after 1990. Although the changing snow-monsoon relationship has been studied, the causes of the interdecadal changes remain unclear. This study assesses the associations of TP spring snow cover with EASM rainfall before and after 1990 and explores what possible mechanisms could be responsible for the interdecadal changes. Correlation and composite analyses were used to assess the strength of the relationship between TP spring snow cover and EASM rainfall and to analyze the atmospheric and land surface patterns associated with high snow cover. The outcomes suggest that the relationship between TP spring snow cover and EASM rainfall changes from partially negative to positive over all regions of the TP from 1968–1990 (P1) to 1991–2019 (P2), implying that more snow cover is associated with less (more) EASM rainfall during P1 (P2). In P1, years with high snow cover are associated with an anomalous cyclone southwest of the TP (positioned over Iran and Pakistan) in spring, which persists into the following summer, partly due to the underlying snow cover. The anomalous cyclone is accompanied by downstream anomalies over East Asia, which form a strong east-west oriented wave pattern and induce a northerly inflow of dry air over East Asia, reducing rainfall over the northern EASM domain. In P2, high snow cover years are associated with an anomalous cyclone over the western TP, which weakens and loses its significance in May-June and summer, partly due to a decline in snow forcing. Southeastward propagation of wave energy in May-June initiates the formation of an anomalous anticyclone over southeastern China and the western North Pacific. Concurrently, a meridional circulation develops over East Asia that enhances the southwesterly moisture inflow, resulting in increased EASM rainfall. The changing snow-monsoon relationship can be linked to different wave train patterns resulting from changes in the background zonal wind and meridional temperature gradients. This research contributes to a better understanding of the changing snow-monsoon relationship.

Active Wildland Fires in Central Chile and Local Winds (Puelche)

Central Chile (CC, latitudes 32–40°S) experienced very active fires in 2017 and 2023. These fires burned large areas and killed many people. These unprecedented fires for CC presented a need for more defined fire weather conditions on the synoptic scale. In this paper, fire weather conditions were analyzed using various satellite-derived fire data (hotspots, HSs), wind streamlines, distribution maps of wind flow and temperature, and various synoptic-scale weather maps. Results showed that local winds, known as Puelche, blew on the peak fire days (26 January 2017 and 3 February 2023). The number of HSs on these days was 2676 and 2746, respectively, about 90 times the average (30). The occurrence of Puelche winds was confirmed by streamlines from high-pressure systems offshore of Argentina to the study area in CC. The formation of strong winds and high-temperature areas associated with Puelche winds were identified on the Earth survey satellite maps. Strong winds of about 38 km h−1 and high temperatures above 32 °C with low relative humidity below 33% were actually observed at the weather station near the fire-prone areas. Lastly, some indications for Puelche winds outbreaks are summarized. This paper’s results will be used to prevent future active fire occurrences in the CC.

Hydrometeorological conditions drive long-term changes in the spatial distribution of Potamogeton crispus in a subtropical lake.

Globally, anthropogenic disturbance and climate change caused a rapid decline of submerged macrophytes in lake ecosystems. Potamogeton crispus (P. crispus), a species that germinates in winter, explosively expanded throughout many Chinese lakes, yet the underlying mechanism remained unclear. Here, this study examined the long-term changes in the distribution patterns of P. crispus in Lake Gaoyou by combining remote sensing images and hydrometeorological data from 1984 to 2022 and water quality data from 2009 to 2022. It aims to unravel the relationships between the distribution patterns of P. crispus and hydrometeorological and water quality factors. The results showed that the area of P. crispus in Lake Gaoyou showed a slight increase from 1984 to 2009, a marked increase from 2010 to 2019, followed by a decline after 2020. Spatially, P. crispus was primarily distributed in the western and northern parts of Lake Gaoyou, with less distribution in the central and southeastern parts of the lake. Wind speed (WS), temperature (Temp), water level (WL), ammonia nitrogen (NH3-N), and Secchi depth (SD) were identified as the key factors regulating the variation in the P. crispus area in Lake Gaoyou. We found that the P. crispus area showed an increasing trend with increasing Temp, WL, and SD and decreasing WS and NH3-N. The influence of environmental factors on the area of P. crispus in Lake Gaoyou varied among seasons. The results indicated that hydrometeorology (WS, Temp, and WL) may override water quality (NH3-N and SD) in driving the succession of P. crispus distribution. The findings of this study offer valuable insights into the recent widespread expansion of P. crispus in shallow lakes across Eastern China.

Severe compound events of low wind and cold temperature for the British power system

AbstractBritain's power system has shifted towards a major contribution from wind energy. However, wind is highly variable, and exceptionally low wind events can simultaneously occur with cold conditions, which increase demand. These conditions can pose a threat for the security of energy supply. Here we use bias‐corrected wind supply data and the estimated temperature‐related part of demand to analyse events of potential weather‐related energy shortfall based on the historic meteorological record. We conduct sensitivity studies with varying scenarios of Britain's total wind energy capacity and the temperature sensitivity of national demand. These scenarios are estimates for present‐day conditions as well as potential future changes of the power system. We apply a new methodology to estimate the potential severity of an event for the power system, and analyse the atmospheric conditions associated with the most severe events. We find that events of potentially severe shortfall are relatively rare and short‐lived, and often occur with an atmospheric pattern broadly resembling a negative North Atlantic Oscillation. This broad tendency emerges from a wide range of individual daily weather patterns that cause cold and still conditions. With an increase in wind capacity, it is likely that severe events will become rarer, although the most severe days of the record are relatively insensitive to changes in wind supply and temperature sensitivity of demand under our assumptions.

Simulations on Levitation and Spatial Distribution of Charged Dust on the Moon Surface

The levitation of charged dust, which may cause serious hazards to astronauts and lunar rovers, has been one of the most significant challenges in lunar exploration. Here we simulate lunar sheath potentials in different solar wind conditions and solar zenith angles (SZAs) on the lunar surface by the particle-in-cell method. The simulated potentials exhibit two types of distributions as a function of height, depending on the SZAs. For SZA ∼ 0°–70°, the nonmonotonic distribution with positive surface potential dominates in the photoelectron sheath. For SZA >∼81°, the monotonic distribution with negative surface potential is observed in the plasma sheath. With the calculated potentials and the assumption that the dust radius distribution exponentially decreases, we further investigate spatial distributions of the dust levitated above the surface. It is found that number density of the levitating lunar dust is enhanced at the terminator (SZA ∼ 81°) in the plasma sheath. In the photoelectron sheath it gradually decreases as the SZA increases from 0° to 70°. Further calculations of the potential and the derived electrostatic field suggest that the dust spatial distributions can be influenced by the bulk velocity, number density, and temperature of the solar wind. Those findings deepen our understanding of lunar surface charging and the mechanism of lunar dust levitation, which can provide technical support for lunar explorations.