Two methods for separating the magnetospheric solar wind charge exchange soft X-ray emission from the diffuse X-ray background

Solar wind charge exchange(SWCX)is the process of solar wind high-valence ions exchanging charges with neutral components and generating soft X-rays.Recently,detecting the SWCX emission from the magnetosphere is proposed as a new technique to study the magnetosphere using panoramic soft X-ray imaging.To better prepare for the data analysis of upcoming magnetospheric soft X-ray imaging missions,this paper compares the magnetospheric SWCX emission obtained by two methods in an XMM-Newton observation,during which the solar wind changed dramatically.The two methods differ in the data used to fit the diffuse X-ray background(DXB)parameters in spectral analysis.The method adding data from the ROSAT All-Sky Survey(RASS)is called the RASS method.The method using the quiet observation data is called the Quiet method,where quiet observations usually refer to observations made by the same satellite with the same target but under weaker solar wind conditions.Results show that the spectral compositions of magnetospheric SWCX emission obtained by the two methods are very similar,and the changes in intensity over time are highly consistent,although the intensity obtained by the RASS method is about 2.68±0.56 keV cm^(-2)s^(-1)sr^(-1)higher than that obtained by the Quiet method.Since the DXB intensity obtained by the RASS method is about 2.84±0.74 keV cm^(-2)s^(-1)sr^(-1)lower than that obtained by the Quiet method,and the linear correlation coefficient between the difference of SWCX and DXB obtained by the two methods in diffe rent energy band is close to-1,the diffe rences in magnetospheric SWCX can be fully attributed to the diffe rences in the fitted DXB.The difference between the two methods is most significant when the energy is less than 0.7 keV,which is also the main energy band of SWCX emission.In addition,the difference between the two methods is not related to the SWCX intensity and,to some extent,to solar wind conditions,because SWCX intensity typically va ries with the solar wind.In summary,both methods are robust and reliable,and should be considered based on the best available options.

Solar wind ion charge state distributions and compound cross sections for solar wind charge exchange X-ray emission

Solar Wind Charge eXchange X-ray(SWCX) emission in the heliosphere and Ea rth’s exosphere is a hard to avoid signal in soft Xray obse rvations of astrophysical targets.On the other hand,the X-ray imaging possibilities offered by the SWCX process has led to an increasing number of future dedicated space missions for investigating the solar wind-terrestrial inte ractions and magnetospheric interfaces.In both cases,accurate modelling of the SWCX emission is key to correctly interpret its signal,and remove it from obse rvations,when needed.In this paper,we compile solar wind abundance measurements from ACE for different solar wind types,and atomic data from literature,including charge exchange cross-sections and emission probabilities,used fo r calculating the compound cross-section a for the SWCX X-ray emission.We calculate a values for charge-exchange with H and He,relevant to soft X-ray energy bands(0.1-2.0 keV)for various solar wind types and solar cycle conditions.

U-Net Models for Representing Wind Stress Anomalies over the Tropical Pacific and Their Integrations with an Intermediate Coupled Model for ENSO Studies

El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.

Near-surface wind field characteristics of the desert-oasis transition zone in Dunhuang,China

The desert-oasis transition zone(DOTZ)serves as a buffer area between the desert and oasis.Understanding its wind field characteristics is of great significance for the prevention and control of aeolian disasters in the oasis.In this study,we used meteorological data during 2013–2019 from the portable meteorological stations at five sites(site A on the edge of the oasis,sites B,C,and D in the DOTZ,and site O in the desert)in Dunhuang,China to analyze the near-surface wind field characteristics and their causes,as well as to reveal the key role of the DOTZ in oasis protection.The results showed that the mean wind speed,frequency of sand-driving wind,and directional variability of wind decreased from west to east within the DOTZ,and wind speed was significantly affected by air temperature.The terrain influenced the prevailing winds in the region,mainly from northeast and southwest.Only some areas adjacent to the oasis were controlled by southeasterly wind.This indicated that the near-surface wind field characteristics of the DOTZ were caused by the combined effects of local terrain and surface hydrothermal difference.At site D,the annual drift potential(DP)was 24.95 vector units(VU),indicating a low wind energy environment,and the resultant drift direction(RDD)showed obvious seasonal differences.Additionally,the DOTZ played an important buffering role between the desert and oasis.Compared with the desert,the mean wind speed in the oasis decreased by 64.98%,and the prevailing wind direction was more concentrated.The results of this study will be useful in interpreting the aeolian activity of the DOTZ in Dunhuang.

Effects of wind speed,underlying surface,and seed morphological traits on the secondary seed dispersal in the Tengger Desert,China

The maintenance of sand-fixing vegetation is important for the stability of artificial sand-fixing systems in which seed dispersal plays a key role.Based on field wind tunnel experiments using 11 common plant species on the southeastern edge of the Tengger Desert,China,we studied the secondary seed dispersal in the fixed and semi-fixed sand dunes as well as in the mobile dunes in order to understand the limitations of vegetation regeneration and the maintenance of its stability.Our results indicated that there were significant variations among the selected 11 plant species in the threshold of wind speed(TWS).The TWS of Caragana korshinskii was the highest among the 11 plant species,whereas that of Echinops gmelinii was the lowest.Seed morphological traits and underlying surface could generally explain the TWS.During the secondary seed dispersal processes,the proportions of seeds that did not disperse(no dispersal)and only dispersed over short distance(short-distance dispersal within the wind tunnel test section)were significantly higher than those of seeds that were buried(including lost seeds)and dispersed over long distance(long-distance dispersal beyond the wind tunnel test section).Compared with other habitats,the mobile dunes were the most difficult places for secondary seed dispersal.Buried seeds were the easiest to be found in the semi-fixed sand dunes,whereas fixed sand dunes were the best sites for seeds that dispersed over long distance.The results of linear mixed models showed that after controlling the dispersal distance,smaller and rounder seeds dispersed farther.Shape index and wind speed were the two significant influencing factors on the burial of seeds.The explanatory power of wind speed,underlying surface,and seed morphological traits on the seeds that did not disperse and dispersed over short distance was far greater than that on the seeds that were buried and dispersed over long distance,implying that the processes and mechanisms of burial and long-distance dispersal are more complex.In summary,most seeds in the study area either did not move,were buried,or dispersed over short distance,promoting local vegetation regeneration.

A nonlinear wake model of a wind turbine considering the yaw wake steering

Duo to fluctuations in atmospheric turbulence and yaw control strategies,wind turbines are often in a yaw state.To predict the far wake velocity field of wind turbines quickly and accurately,a wake velocity model was derived based on the method of momentum conservation considering the wake steering of the wind turbine under yaw conditions.To consider the shear effect of the vertical incoming wind direction,a two-dimensional Gaussian distribution function was introduced to model the velocity loss at different axial positions in the far wake region based on the assumption of nonlinear wake expansion.This work also developed a“prediction-correction”method to solve the wake velocity field,and the accuracy of the model results was verified in wake experiments on the Garrad Hassan wind turbine.Moreover,a 33-kW two-blade horizontal axis wind turbine was simulated using this method,and the results were compared with the classical wake model under the same parameters and the computational fluid dynamics(CFD)simulation results.The results show that the nonlinear wake model well reflected the influence of incoming flow shear and yaw wake steering in the wake velocity field.Finally,computation of the wake flow for the Horns Rev offshore wind farm with 80 wind turbines showed an error within 8%compared to the experimental values.The established wake model is less computationally intensive than other methods,has a faster calculation speed,and can be used for engineering calculations of the wake velocity in the far wakefield of wind turbines.

Comparison and Verification of Coherent Doppler Wind Lidar and Radiosonde Data in the Beijing Urban Area

As a new type of wind field detection equipment, coherent Doppler wind lidar(CDWL) still needs more relevant observation experiments to compare and verify whether it can achieve the accuracy and precision of traditional observation equipment in urban areas. In this experiment, a self-developed CDWL provided four months of observations in the southern Beijing area. After the data acquisition time and height match, the wind profile data obtained based on a Doppler beam swinging(DBS) five-beam inversion algorithm were compared with radiosonde data released from the same location. The standard deviation(SD) of wind speed is 0.8 m s^(–1), and the coefficient of determination R~2 is 0.95. The SD of the wind direction is 17.7° with an R~2 of 0.96. Below the height of the roughness sublayer(about 400 m), the error in wind speed and wind direction is significantly greater than the error above the height of the boundary layer(about 1500 m). For the case of wind speeds less than 4 m s^(–1), the error of wind direction is more significant and is affected by the distribution of surrounding buildings. Averaging at different height levels using suitable time windows can effectively reduce the effects of turbulence and thus reduce the error caused by the different measurement methods of the two devices.

Variational Reconstruction and Simulation Experiments of Sea Surface Wind Field for Ocean Data Buoy

The sea surface wind field is an important physical parameter in oceanography and meteorology.With the continuous refinement of numerical weather prediction,air-sea interface materials,energy exchange,and other studies,three-dimensional(3D)wind field distribution at local locations on the sea surface must be measured accurately.The current in-situ observation of sea surface wind parameters is mainly achieved through the installation of wind sensors on ocean data buoys.However,the results obtained from this single-point measurement method cannot reflect wind field distribution in a vertical direction above the sea surface.Thus,the present paper proposes a theoretical framework for the optimal inversion of the 3D wind field structure variation in the area where the buoy is located.The variation analysis method is first used to reconstruct the wind field distribution at different heights of the buoy,after which theoretical analysis verification and numerical simulation experiments are conducted.The results indicate that the use of variational methods to reconstruct 3D wind fields is significantly effective in eliminating disturbance errors in observations,which also verifies the correctness of the theoretical analysis of this method.The findings of this article can provide a reference for the layout optimization design of wind measuring instruments in buoy observation systems and also provide theoretical guidance for the design of new observation buoys in the future.

A call for enhanced data-driven insights into wind energy flow physics

With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations,machine learning(ML)models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays,the generated wakes and their interactions,and wind energy harvesting.However,the majority of the existing ML models for predicting wind turbine wakes merely recreate Computational fluid dynamics(CFD)simulated data with analogous accuracy but reduced computational costs,thus providing surrogate models rather than enhanced data-enabled physics insights.Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models,using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue.In this letter,we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations,along with new promising research strategies.

Research on Leading Edge Erosion and Aerodynamic Characteristics of Wind Turbine Blade Airfoil

The effects of the erosion present on the leading edge of a wind turbine airfoil(DU 96-W-180)on its aerodynamic performances have been investigated numerically in the framework of a SST k–ωturbulence model based on the Reynolds Averaged Navier-Stokes equations(RANS).The results indicate that when sand-induced holes and small pits are involved as leading edge wear features,they have a minimal influence on the lift and drag coefficients of the airfoil.However,if delamination occurs in the same airfoil region,it significantly impacts the lift and resistance characteristics of the airfoil.Specifically,as the angle of attack grows,there is a significant decrease in the lift coefficient accompanied by a sharp increase in the drag coefficient.As wear intensifies,these effects gradually increase.Moreover,the leading edge wear can exacerbate flow separation near the trailing edge suction surface of the airfoil and cause forward displacement of the separation point.

Protective benefits of HDPE board sand fences in an environment with variable wind directions on Gobi surfaces:wind tunnel study

The Golmud-Korla Railway in the Gobi area faces operational challenges due to sand hazards,caused by strong and variable winds.This study addresses these challenges by conducting wind tunnel tests to evaluate the protective benefits of High Density Polyethylene(HDPE)board sand fences,focusing on their orientation relative to various wind directions(referred to as'wind angle').This study found that the size of the low-velocity zone on the leeward side of the sand fences(LSF)expanded with an increase in the wind angle(WA).At 1H(the height of the sand fence)and 2H positions on the LSF,the wind speed profiles(WSP)exhibited a segmented logarithmic growth,constrained by Z=H at varying WAs.The efficacy of the sand fence in obstructing airflow escalated as WA increased.The size of the WA has a significant impact on the protective efficiency of HDPE board sand fences.Furthermore,compared to typical sandy surfaces,the rate of sand transport across the Gobi surface diminishes more slowly with height,attributed to the gravel's rebound effect.This phenomenon allows some sand particles to bypass the fences,rendering them less effective at blocking wind and trapping sand than in sandy environments.This paper offers scientific evidence supporting the practical use and enhancement of HDPE board sand fences in varied wind conditions.

Solar and wind energy potential assessment for Razavi Khorasan Province in Iran

This study aims to evaluate the solar and wind energy potential across Razavi Khorasan Province,Iran,with a specific focus on the Khaf region.A preliminary assessment of mean solar radiation,mean wind speeds,and Weibull distribution parameters was conducted for different towns and zones within the province.The findings showed that Khaf has favorable characteristics for further analysis.The solar and wind energy metrics examined include global horizontal irradiance,clearness index,wind rose patterns,and turbulence intensity.At a height of 40 m,Khaf’s wind power density reached 1650 W/m^(2),indicating exceptional wind energy generation potential.Additionally,Khaf received an average annual solar radiation of 2046 kW·h/m^(2),representing significant solar energy potential.Harnessing these substantial renewable resources in Khaf could allow Razavi Khorasan Province to reduce reliance on fossil fuels,improve energy sustainability,and mitigate climate change impacts.This research contributes an in-depth assessment of Razavi Khorasan's solar and wind energy potential,particularly for the promising Khaf region.Further work may examine optimal sites for renewable energy projects and grid integration strategies to leverage these resources.

A Task Offloading Strategy Based on Multi-Agent Deep Reinforcement Learning for Offshore Wind Farm Scenarios

This research is the first application of Unmanned Aerial Vehicles(UAVs)equipped with Multi-access Edge Computing(MEC)servers to offshore wind farms,providing a new task offloading solution to address the challenge of scarce edge servers in offshore wind farms.The proposed strategy is to offload the computational tasks in this scenario to other MEC servers and compute them proportionally,which effectively reduces the computational pressure on local MEC servers when wind turbine data are abnormal.Finally,the task offloading problem is modeled as a multi-intelligent deep reinforcement learning problem,and a task offloading model based on MultiAgent Deep Reinforcement Learning(MADRL)is established.The Adaptive Genetic Algorithm(AGA)is used to explore the action space of the Deep Deterministic Policy Gradient(DDPG),which effectively solves the problem of slow convergence of the DDPG algorithm in the high-dimensional action space.The simulation results show that the proposed algorithm,AGA-DDPG,saves approximately 61.8%,55%,21%,and 33%of the overall overhead compared to local MEC,random offloading,TD3,and DDPG,respectively.The proposed strategy is potentially important for improving real-time monitoring,big data analysis,and predictive maintenance of offshore wind farm operation and maintenance systems.

Understanding Simulated Causes of Damaging Surface Winds in a Derecho-Producing Mesoscale Convective System near the East China Coast Based on Convection-Permitting Simulations

A mesoscale convective system(MCS) occurred over the East China coastal provinces and the East China Sea on 30April 2021, producing damaging surface winds near the coastal city Nantong with observed speeds reaching 45 m s^(–1). A simulation using the Weather Research and Forecasting model with a 1.5-km grid spacing generally reproduces the development and subsequent organization of this convective system into an MCS, with an eastward protruding bow segment over the sea. In the simulation, an east-west-oriented high wind swath is generated behind the gust front of the MCS. Descending dry rear-to-front inflows behind the bow and trailing gust front are found to feed the downdrafts in the main precipitation regions. The inflows help to establish spreading cold outflows and enhance the downdrafts through evaporative cooling. Meanwhile, front-to-rear inflows from the south are present, associated with severely rearward-tilted updrafts initially forming over the gust front. Such inflows descend behind(north of) the gust front, significantly enhancing downdrafts and near-surface winds within the cold pool. Consistently, calculated trajectories show that these parcels that contribute to the derecho originate primarily from the region ahead(south) of the east-west-oriented gust front, and dry southwesterly flows in the low-to-middle levels contribute to strong downdrafts within the MCS. Moreover, momentum budget analyses reveal that a large westward-directed horizontal pressure gradient force within the simulated cold pool produced rapid flow acceleration towards Nantong. The analyses enrich the understanding of damaging wind characteristics over coastal East China and will prove helpful to operational forecasters.

Differences in Precipitation and Related Wind Dynamics and Moisture and Heat Features in Separate Areas of the South China Sea before and after Summer Monsoon Onset

Using surface and balloon-sounding measurements, satellite retrievals, and ERA5 reanalysis during 2011–20, this study compares the precipitation and related wind dynamics, moisture and heat features in different areas of the South China Sea(SCS) before and after SCS summer monsoon onset(SCSSMO). The rainy sea around Dongsha(hereafter simply referred to as Dongsha) near the north coast, and the rainless sea around Xisha(hereafter simply referred to as Xisha) in the western SCS, are selected as two typical research subregions. It is found that Dongsha, rather than Xisha, has an earlier and greater increase in precipitation after SCSSMO under the combined effect of strong low-level southwesterly winds, coastal terrain blocking and lifting, and northern cold air. When the 950-h Pa southwesterly winds enhance and advance northward, accompanied by strengthened moisture flux, there is a strong convergence of wind and moisture in Dongsha due to a sudden deceleration and rear-end collision of wind by coastal terrain blocking. Moist and warm advection over Dongsha enhances early and deepens up to 200 h Pa in association with the strengthened upward motion after SCSSMO, thereby providing ample moisture and heat to form strong precipitation. However, when the 950-h Pa southwesterly winds weaken and retreat southward, Xisha is located in a wind-break area where strong convergence and upward motion centers move in. The vertical moistening and heating by advection in Xisha enhance later and appear far weaker compared to that in Dongsha, consistent with later and weaker precipitation.

A positive trend in the stability of global offshore wind energy

The recognition on the trend of wind energy stability is still extremely rare,although it is closely related to acquisition efficiency,grid connection,equipment lifetime,and costs of wind energy utilization.Using the 40-year(1979–2018)ERA-Interim data from the European Center for Medium-Range Weather Forecasts,this study presented the spatial-temporal distribution and climatic trend of the stability of global offshore wind energy as well as the abrupt phenomenon of wind energy stability in key regions over the past 40 years with the climatic analysis method and Mann-Kendall(M-K)test.The results show the following 5 points.(1)According to the coefficient of variation(C_(v))of the wind power density,there are six permanent stable zones of global offshore wind energy:the southeast and northeast trade wind zones in the Indian,Pacific and Atlantic oceans,the Southern Hemisphere westerly,and a semi-permanent stable zone(North Indian Ocean).(2)There are six lowvalue zones for both seasonal variability index(S_(v))and monthly variability index(M_(v))globally,with a similar spatial distribution as that of the six permanent stable zones.M_(v) and S_(v) in the Arabian Sea are the highest in the world.(3)After C_(v),M_(v) and S_(v) are comprehensively considered,the six permanent stable zones have an obvious advantage in the stability of wind energy over other sea areas,with C_(v) below 0.8,M_(v) within 1.0,and S_(v) within 0.7 all the year round.(4)The global stability of offshore wind energy shows a positive climatic trend for the past four decades.C_(v),M_(v) and S_(v) have not changed significantly or decreased in most of the global ocean during 1979 to2018.That is,wind energy is flat or more stable,while the monthly and seasonal variabilities tend to shrink/smooth,which is beneficial for wind energy utilization.(5)C_(v) in the low-latitude Pacific and M_(v) and S_(v) in both the North Indian Ocean and the low-latitude Pacific have an obvious abrupt phenomenon at the end of the20th century.

Wind farms increase land surface temperature and reduce vegetation productivity in the Inner Mongolia

Wind power has been developing rapidly as a key measure to mitigate human-driven global warming.The under-standing of the development and impacts of wind farms on local climate and vegetation is of great importance for their rational use but is still limited.In this study,we combined remote sensing and on-site investigations to identify wind farm locations in Inner Mongolia and performed landscape pattern analyses using Fragstats.We explored the impacts of wind farms on land surface temperature(LST)and vegetation net primary productivity(NPP)between 1990 and 2020 by contrasting these metrics in wind farms with those in non-wind farm areas.The results showed that the area of wind farms increased rapidly from 1.2 km2 in 1990 to 10,755 km2 in 2020.Spatially,wind farms are mainly clustered in three aggregation areas in the center.Further,wind farms increased nighttime LST,with a mean value of 0.23℃,but had minor impacts on the daytime LST.Moreover,wind farms caused a decline in NPP,especially over forest areas,with an average reduction of 12.37 GC/m^(2).Given the impact of wind farms on LST and NPP,we suggest that the development of wind farms should fully consider their direct and potential impacts.This study provides scientific guidance on the spatial pattern of future wind farms.

Semi-supervised surface defect detection of wind turbine blades with YOLOv4

Timely inspection of defects on the surfaces of wind turbine blades can effectively prevent unpredictable accidents.To this end,this study proposes a semi-supervised object-detection network based on You Only Looking Once version 4(YOLOv4).A semi-supervised structure comprising a generative adversarial network(GAN)was designed to overcome the difficulty in obtaining sufficient samples and sample labeling.In a GAN,the generator is realized by an encoder-decoder network,where the backbone of the encoder is YOLOv4 and the decoder comprises inverse convolutional layers.Partial features from the generator are passed to the defect detection network.Deploying several unlabeled images can significantly improve the generalization and recognition capabilities of defect-detection models.The small-scale object detection capacity of the network can be improved by enhancing essential features in the feature map by adding the concurrent spatial and channel squeeze and excitation(scSE)attention module to the three parts of the YOLOv4 network.A balancing improvement was made to the loss function of YOLOv4 to overcome the imbalance problem of the defective species.The results for both the single-and multi-category defect datasets show that the improved model can make good use of the features of the unlabeled images.The accuracy of wind turbine blade defect detection also has a significant advantage over classical object detection algorithms,including faster R-CNN and DETR.

Decoupling of temporal/spatial broadening effects in Doppler wind LiDAR by 2D spectral analysis

Pulse echo accumulation is commonly employed in coherent Doppler wind LiDAR(light detection and ranging)under the assumption of steady wind.Here,the measured spectral data are analyzed in the time dimension and frequency dimension to cope with the temporal wind shear and achieve the optimal accumulation time.A hardware-efficient algorithm combining the interpolation and cross-correlation is used to enhance the wind retrieval accuracy by reducing the frequency sampling interval and then reduce the spectral width calculation error.Moreover,the temporal broadening effect and spatial broadening effect are decoupled according to the strategy we developed.

Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.