Extreme Wind Speeds Research Articles

Epidemiological characteristics of tuberculosis incidence and its macro-influence factors in Chinese mainland during 2014–2021

BackgroundTuberculosis (TB) remains a pressing public health issue, posing a significant threat to individuals' well-being and lives. This study delves into the TB incidence in Chinese mainland during 2014–2021, aiming to gain deeper insights into their epidemiological characteristics and explore macro-level factors to enhance control and prevention.MethodsTB incidence data in Chinese mainland from 2014 to 2021 were sourced from the National Notifiable Disease Reporting System (NNDRS). A two-stage distributed lag nonlinear model (DLNM) was constructed to evaluate the lag and non-linearity of daily average temperature (℃, Atemp), average relative humidity (%, ARH), average wind speed (m/s, AWS), sunshine duration (h, SD) and precipitation (mm, PRE) on the TB incidence. A spatial panel data model was used to assess the impact of demographic, medical and health resource, and economic factors on TB incidence.ResultsA total of 6,587,439 TB cases were reported in Chinese mainland during 2014–2021, with an average annual incidence rate of 59.17/100,000. The TB incidence decreased from 67.05/100,000 in 2014 to 46.40/100,000 in 2021, notably declining from 2018 to 2021 (APC = -8.87%, 95% CI: -11.97, -6.85%). TB incidence rates were higher among males, farmers, and individuals aged 65 years and older. Spatiotemporal analysis revealed a significant cluster in Xinjiang, Qinghai, and Xizang from March 2017 to June 2019 (RR = 3.94, P < 0.001). From 2014 to 2021, the proportion of etiologically confirmed cases increased from 31.31% to 56.98%, and the time interval from TB onset to diagnosis shortened from 26 days (IQR: 10–56 days) to 19 days (IQR: 7–44 days). Specific meteorological conditions, including low temperature (< 16.69℃), high relative humidity (> 71.73%), low sunshine duration (< 6.18 h) increased the risk of TB incidence, while extreme low wind speed (< 2.79 m/s) decreased the risk. The spatial Durbin model showed positive associations between TB incidence rates and sex ratio (β = 1.98), number of beds in medical and health institutions per 10,000 population (β = 0.90), and total health expenses (β = 0.55). There were negative associations between TB incidence rates and population (β = -1.14), population density (β = -0.19), urbanization rate (β = -0.62), number of medical and health institutions (β = -0.23), and number of health technicians per 10,000 population (β = -0.70).ConclusionsSignificant progress has been made in TB control and prevention in China, but challenges persist among some populations and areas. Varied relationships were observed between TB incidence and factors from meteorological, demographic, medical and health resource, and economic aspects. These findings underscore the importance of ongoing efforts to strengthen TB control and implement digital/intelligent surveillance for early risk detection and comprehensive interventions.Graphical

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China.

The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue. Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence. We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)). Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.

Footprint of the air-sea momentum transfer saturation observed by ocean wave buoy network in extreme tropical cyclones

Tropical cyclones are one of the most destructive natural phenomena, causing tremendous coastal disasters worldwide. The maximum intensity of tropical cyclones is determined by momentum and heat transfer at the air-sea interface. Momentum transfer corresponds to the momentum loss of tropical cyclones and, consequently, to the underlying ocean's momentum gain causing extreme ocean waves and storm surges. Air-side observations of wind profiles and ocean-side observations of ocean subsurface currents show a slowdown of momentum transfer under high tropical cyclone wind speeds in the previous studies. However, there is still disagreement regarding the slowdown owing to lack of data. Here, we show momentum transfer under high wind speed conditions by observing ocean waves. Although ocean wave observations are highly spatially limited, we deployed a fleet of drifting ocean wave buoys covering the active area of tropical cyclones in the Western North Pacific. The buoy fleet captured extreme waves near the eye of the strongest category 5 tropical cyclone, indicating an ocean wave footprint of the momentum transfer saturation above surface wind speeds of 25 m/s. Our approach from ocean surface wave observation is a unique contribution to determination on air-sea momentum transfer slowdown under extreme wind speeds, which can compensate for the knowledge from conventional air and ocean-side observations. This finding advances tropical cyclones, extreme ocean waves, and storm surge modeling.

Impact Of Global Warming on Tropical Cyclones in The Northwest Pacific Ocean

Tropical cyclones are meteorological phenomena that can affect mid to low latitudes around the world. This Paper systematically summarizes and reviews the main characteristics, potential influencing factors, and influencing mechanisms of tropical cyclones in Northwestern Pacific, and summarizes and analyzes their changing trends in the context of global change. Global warming will affect the generation and development of tropical cyclones from multiple dimensions, with the most intuitive being the surface temperature of seawater. Through the analysis of global surface temperature and tropical cyclones in the Northwest Pacific, it is shown that as global climate warms, the frequency of tropical cyclones in the Northwest Pacific decreases, the extreme central pressure decreases, and the extreme maximum central wind speed increases, there is no significant trend in the average minimum pressure and average maximum wind speed of tropical cyclones. In other words, the extreme intensity of tropical cyclones strengthens; In the 2010s, this relationship was particularly prominent.

Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models

AbstractWhen different flooding drivers co‐occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections may not be very robust, as they are based on only 5 to 6 climate model simulations. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation at European tide gauges under a medium and high emissions scenario, enabled by data‐proximate cloud computing and statistical storm surge modeling. We find that the joint probability will increase in the northwest and decrease in most of the southwest of Europe. Averaged over Europe, the absolute magnitude of these changes is 36%–49% by 2080, depending on the scenario. The large‐scale changes in the joint probability of extreme storm surges and precipitation are similar to those in the joint probability of extreme wind speeds and precipitation, but locally, differences can exceed the changes themselves. Due to internal climate variability and inter‐model differences, projections based on simulations of only 5 to 6 randomly chosen CMIP6 models have a probability of higher than 10% to differ qualitatively from projections based on all CMIP6 simulations in multiple regions, especially under the medium emissions scenario and earlier in the twenty‐first century. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections.

The Expected Dynamics for the Extreme Wind and Wave Conditions at the Mouths of the Danube River in Connection with the Navigation Hazards

The entrance in the Sulina channel in the Black Sea is the target area of this study. This represents the southern gate of the seventh Pan-European transport corridor, and it is usually subjected to high navigation traffic. The main objective of the work is to provide a more comprehensive picture concerning the past and future expected dynamics of the environmental matrix in this coastal area, including especially the extreme wind and wave conditions in connection with the possible navigation risks. The methodology considered assumes analyses performed at three different levels. First, an analysis of some in situ measurements at the zero-kilometer point of the Danube is carried out for the 15-year period of 2009–2023. Together with the maximum wind speed and the maximum value of the wind gusts, the water level variation was analyzed at this point. As a second step, the analysis is based on wind speed data provided by regional climate models. Two periods, each spanning 30 years, are considered. These are the recent past (1976–2005), when comparisons with ERA5 reanalysis data were also performed, and the near future (2041–2070), when two different models and three climate scenarios were considered. The focus was on the extreme wind speed values, performing comparisons between the past and future expected extreme winds. Finally, the third analysis is related to the wave conditions. Thus, using as a forcing factor each of the wind fields that was previously analyzed, simulations employing a spectral wave model were carried out. The wave modeling system was focused using three different computational domains with increasing resolution towards the target area, and the nearshore wave conditions were evaluated. The results show that both the extreme wind and wave conditions are expected to slightly increase in the future. Especially in the wintertime, strong wind fields are often expected in this area, with wind gusts exceeding more than 70% of the hourly average wind velocity. With regard to the waves, due to the complex nearshore phenomena, considerable enhancements in terms of significant wave heights are induced, and there is also an elevated risk of the occurrence of rogue waves. This work is still ongoing, and taking into account the high navigation risks highlighted, the next step would be to elaborate the risk assessment of severe shipping conditions, particularly related to the likelihood or probability of adverse conditions with the potential of generating hazardous situations in this coastal environment.

Concurrent and dynamical interdependency of compound precipitation and wind speed extremes over India

Compound precipitation and wind speed extremes have the potential to cause significant socio-economic losses. Therefore, this study aims to investigate the association of compound precipitation and wind speed extremes in observed period (1981–2020) and future periods (2021–2060 (F1) and 2061–2100 (F2)) over India. In this study, we have used IMD precipitation and ERA5 windspeed gridded (0.250 × 0.250) data for the observed period. For future analysis, we have used CMIP6-based EC-Earth3 GCM (SSP2–4.5 and SSP5–8.5) and CORDEX CMIP5 RCM (RCP-4.5 and RCP-8.5) climate models for precipitation and wind speed outputs. We have analyzed the characteristics of precipitation and wind speed extremes using three approaches: empirical method, tail dependence analysis, and event coincidence analysis. The empirical analysis revealed that the proportion of the area with >240 Compound Precipitation and Wind speed Extremes (CPWE) on the same day is 2.8% in observed period. However, it increased to 22.22% in future period F2, particularly in the SSP5–8.5 scenario of the CMIP6 case, and this increase is observed in regions like Am, eastern parts of Aw and Cwa, Bwk, and Dsb. The tail dependence analysis revealed that the area with tail dependence >0.5 constitutes approximately 0.72% in the observed period across the study area. However, under climate change scenarios, this percentage varies between 2.45% to 3.66% and notably, the increase is observed in regions such as Am, Bwk, and Dsb. The results of precursor coincidence rate (pcr) shows a high value of 3.52% in the entire study area for the observed period. The future analysis reveals an increase of areas in between 3.18% to 5.14% for different climate change scenarios. Consequently, the trigger coincidence rate (tcr) results also revealed the occurrence of high tcr in the range of 0.52% to 5.79% across the study area within the temporal window. However, the tcr range increases to 2.17% to 23.32% during the monsoon season during the observed period with respect to the days in the temporal window. The overall results showed that the high values of tail dependence, pcr and tcr are concentrated in the Am, coastal areas of Aw and Cwa, as well as in the Bwk and Dsb regions. This will lead to increase the extreme precipitation driven by extreme wind speed in those regions consequently, the risk of flash floods, landslides and natural hazards may rise in the future. Overall, this study provides valuable insights into the spatial distribution of compound precipitation and wind speed extremes across the study area.

Metocean conditions at two Norwegian sites for development of offshore wind farms

This paper examines metocean data from NORA3, a state-of-the-art wind and wave hindcast dataset for Northern Europe. Two offshore Norwegian areas, Utsira Nord (UN) and Sørlige Nordsjø II (SN2), are investigated. Both areas offer significant potential for the offshore wind sector. UN is situated in deep-sea water, suitable for floating offshore wind turbines. In contrast, SN2 lies in intermediate waters and ranks among the North Sea’s most promising regions allocated for offshore wind. Data from NORA3, originally on a 3-km resolution grid, are resampled into unstructured grids spanning from 1982 to 2022. This refined dataset offers a climatology time scale with superior spatial and temporal resolution compared to most other hindcast and reanalysis databases. The study examines mean wind speed and direction across seven levels, ranging from 10 m to 750 m above the surface. Analyses of extreme wind and wave conditions have been conducted. Results reveal that UN experiences higher extreme wave heights than SN2 while the extreme wind speeds may be substantially larger at SN2 than UN. Moreover, this study establishes joint distribution models that encompass several parameters, including mean wind speed, significant wave height, wave spectral peak period, and direction difference between wind and waves. Thus, this metocean data is valuable for designing and analyzing floating wind farms over their lifecycles.

Global tropical cyclone extreme wave height climatology

A global study of extreme value (1 in 100-year return period) tropical cyclone generated waves is conducted across all tropical cyclone basins. The study uses a 1000 year tropical cyclone synthetic track database to force a validated parametric wave model. The resulting distributions of extreme significant wave height show that values in the North Atlantic and Western Pacific basins are the largest globally. This is partly due to the relative intensities and frequencies of occurrence of storms in these basins but also because the typical velocities of forward movement of storms are larger and hence can sustain the generation of larger waves. These larger values of velocity of forward movement tend to occur at higher latitudes. As a result, in both of these basins the largest extreme waves occur at higher latitudes than the maximum tropical cyclone winds. In all other tropical cyclone basins, storms tend to propagate more east–west and hence the maximum values of extreme significant wave height and wind speed occur at comparable latitudes.

Extreme Wind Speed Estimation for Wind-Resistance Design of a Transmission Line Situated in a Typhoon-Prone and Hilly Area

The southeastern coast of China is annually threatened by typhoons originating from the northwestern Pacific Ocean, posing a risk of severe damage to coastal transmission lines. This study employs the random forest-based typhoon full-track simulation method and YM wind field to assess the wind-resistance reliability of transmission lines. The obtained extreme wind speeds of a transmission line site in Wenzhou are 33.7m/s and 35.8m/s under the 50-year and 100-year return periods, respectively. The effects of micro-topography on the extreme typhoon wind speed are further analyzed, and the extreme wind speed distribution maps of Wenzhou City and Yueqing City are plotted, respectively. The results indicate a decreasing trend in typhoon wind speed within inland regions, while micro-topography displays a substantial effect on enhancing the typhoon wind speed.

Instantaneous 2D extreme wind speed prediction using the novel Wind Gust Prediction Net based on purely convolutional neural mechanism

Accurate prediction of spatial–temporal extreme wind gust is vital for the wind farm dynamic regulation, the floating wind turbine deployment and its early warning. Deep-learning approaches have been applied for wind prediction to alleviate the computational challenges of traditional numerical models. Yet, most previous studies emphasized the prediction accuracy only employing location-specific dataset, such methodologies are site-specific and ignore the importance of spatial–temporal fidelity. Furthermore, the Recurrent Neural Networks (RNN)-based approach previous employed exhibit low efficiency in terms of model convergence and on the aspect of practical engineering purposes. This study firstly proposed the wind gust prediction net (WGPNet), using residual learning with attention modulations to predict the instantaneous spatial–temporal wind gust in the West Pacific region with great potential wind-energy. And a public reanalysis dataset with very high resolution (0.25° x 0.25°) was employed to verify the proposed method under different criteria. The overall RMSE of predicted gust fields obtained by the proposed method dropped to 0.18 m/s. Comprehensive discussions with both temporal and spatial perspective, revealing that the proposed model can offer an accurate 2D wind gust prediction along timeline (the PCC equals to 0.98).

Characteristics and distribution model of wind and wave in Shengsi mussel culture area under the impacts of tropical cyclones

Abstract The primary industry in Shengsi County, Zhejiang Province, China, is the raft-based cultivation of Mytilus coruscus, which is highly sensitive to wind and wave disturbances. This study aims to understand the characteristics of tropical cyclones along Zhejiang's coast, their relation to climate change, and the impact of wind and waves on Shengsi mussel farming during cyclones. Analyzing data from 2000 to 2018, including tropical cyclone paths, wind and wave data, and mussel farming statistics, the research identifies four cyclone path types, with the peak prevalence from July to September. Annual cyclone energy fluctuates, peaking in August. In the cyclone impact zone, the average wind speed is 24.59 m/s. Type D cyclones have the most significant impact. A linear relationship exists between cyclone center distance and wind speed/wave height. Extreme wind speed and wave height follow the Gumbel joint probability distribution. Since 2000, the Shengsi mussel farming area experienced an 11.20-year return period for wind and waves from the strongest cyclone, Muifa. Given the recurrence period of Muifa intensity cyclones in 2020 and the following years, the study recommends disaster preparedness for the Shengsi mussel farming area and emphasizes the role of mutual aid insurance.

Ensemble Bayesian Model Averaging Projections of Wind‐Speed Extremes for Wind Energy Applications Over China Under Climate Change

AbstractWind energy has grown rapidly in recent years as a measure to control carbon emissions and mitigate climate change. Extreme wind can damage wind turbines, cause losses to wind power plants, limit economic benefits of wind energy facilities, and disrupt regional grid balance. Therefore, an accurate assessment of extreme wind speeds at wind turbine hub height and their spatiotemporal variation under climate change is critical for the planning of wind energy and for guaranteeing regional energy security. In this study, the 100‐m extreme wind speeds in China are estimated using an empirical downscaling and Bayesian model averaging ensemble method with the latest ERA5 reanalysis and 20 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). Two shared socioeconomic pathways (SSP), that is, SSP2‐4.5 and SSP5‐8.5, are considered to account for the uncertainty in anthropogenic emissions. According to the results, the highest extreme wind speeds are primarily found in Inner Mongolia, northeast China, western Tibet, and the eastern coastal region. Extreme wind speeds in central and southeastern China are projected to increase by approximately 2% in the middle (2031–2060) and the end (2071–2100) of the 21st century relative to the baseline period (1985–2014). Summer extreme wind speeds in northwestern Tibet are expected to increase by more than 9% at the end of the century. The findings of this study indicate that it is important to take the present and projected changes in local wind extremes into account when choosing locations for wind power plants and wind energy installations.

Grid‐based assessment of hydrogen leakages for an offshore process to improve the design and human performance

AbstractHydrogen is gaining global recognition as a sustainable energy source, but its combustible nature raises concerns, especially in congested offshore settings. Steam methane reforming (SMR) remains the predominant hydrogen production method; however, offshore SMR facilities exposed to harsh weather could potentially compromise safety because of leakages. This study uses the fire dynamics simulator (FDS) to carry out the first‐of‐its‐kind CFD modeling of hydrogen leakage and its wind‐influenced dispersion on an offshore SMR platform. It also provides the spatial risk that accounts for the probabilities of human errors and wind speeds. The study uses a grid‐based approach with 120 monitor points (MPs) to measure locally dispersed gas concentration. At 2 m/s wind speed, only nine grids contain explosive concentrations while the rest remain safe. At 5 m/s, the flammable zones increase by 133%, affecting 21 grids. Extreme wind speeds of 12.5 m/s have limited impact, but SMR1 exhibits higher stoichiometric concentrations. MPs 43–48 record flammable concentrations at all wind speeds; however, at 12.5 m/s the explosion risk is well below the threshold of 1 × 10−4 due to the low wind occurrence probability. Overall, this research contributes to addressing the safety concerns associated with hydrogen in offshore settings and provides a foundation for future risk assessments.

Copula-based estimation of directional extreme wind speeds: Application for wind-resistant structural design

Accurate probabilistic modelling of wind loads has important implications to the wind-resistant design of structures. Whereas, most previous studies merely concerned the statistical analysis of wind speed, while the effect of wind direction was usually neglected in the wind load modelling. This paper thereby proposes a novel copula-based approach to construct the joint distribution of wind speed and direction for determining the directional extreme wind speed. Based on the wind records at six typical sites in Hong Kong, a series of parametric and nonparametric copula models are applied to model the joint probability density function (JPDF) of wind speed and direction. The goodness-of-fit test indicates that the Bernstein copula is the best model to describe the correlation between variables. Furthermore, by applying the superior copula model, directional extreme wind speeds with a 10-year return period at the six selected sites are determined on basis of the conditional probability theory. The results show that the estimated extreme wind speeds vary obviously among different directions. And, the estimated directional extreme wind speed may be unreliable in some directions when ignoring the joint effect of wind speed and direction in wind load modelling. Notably, the presented method is an effective and practical tool to estimate the directional extreme wind speed with a certain return period. This paper aims to provide useful reference for the wind-resistant design in tropical cyclone-prone regions.

Extreme wind speeds in the Atlantic sector of the Arctic: statistics and circulation models

Extreme wind speeds in the Atlantic sector of the Arctic: statistics and circulation models

Research on Operation and Maintenance Strategy of New Energy Photovoltaic Power Generation System Considering Extreme Weather Conditions

Abstract This paper employs the gray correlation analysis method to compute the correlation between irradiance, ambient temperature, wind speed, and photovoltaic power generation efficiency. This method relies on screened data for data normalization, and after processing, it determines the degree of similarity between the factors. We propose a dynamic combined maintenance strategy for PV power generation systems that considers reliability, overhaul resources, and overhaul time constraints. We propose a two-layer optimization strategy based on extreme weather factors and failure-related characteristics, and we use empirical analysis to investigate the maintenance strategy’s effectiveness. The analysis reveals a positive correlation between irradiance and PV power generation efficiency, with a multiplicative increase in the range of 1-3. In the correlation analysis of ambient temperature and wind speed variables, the power generation curve is not consistent with its trend, which is related to the influence of extreme temperature and extreme wind speed, and the meteorological factors affecting the PV power generation efficiency change at different moments of the day, where the correlation coefficient at 6:45 is 0.912. When the failure correlation strength increases to a value of 0.1, the number of repairs rises to 8. When the weather accessibility ratio is 0.2, the reduction in system availability is up to 62%. The two-layer optimization strategy, which considers extreme weather factors and failure-related characteristics, is both economical and universal.

Grey parameter estimation method for extreme value distribution of short-term wind speed data

&lt;abstract&gt; &lt;p&gt;Accurate parameter estimation of extreme wind speed distribution is of great importance for the safe utilization and assessment of wind resources. This paper emphatically establishes a novel grey generalized extreme value method for parameter estimation of annual wind speed extremum distribution (AWSED). Considering the uncertainty and frequency characteristics of the parent wind speed, the generalized extreme value distribution (GEVD) is selected as the probability distribution, and the Weibull distribution is utilized as the first-order accumulation generating operator. Then, the GEVD differential equation is derived, and it is transformed into the grey GEVD model using the differential information principle. The least squares method is used to estimate the grey GEVD model parameters, and then a novel estimation method is proposed through grey parameters. A hybrid particle swarm optimization algorithm is used to optimize distribution parameters. The novel method is stable under different sample sizes according to Monte Carlo comparison simulation results, and the suitability for the novel method is confirmed by instance analysis in Wujiaba, Yunnan Province. The new method performs with high accuracy in various indicators, the hypothesis test results are above 95%, and the statistical errors such as MAPE and Wasserstein distance yield the lowest, which are 3.33% and 0.2556, respectively.&lt;/p&gt; &lt;/abstract&gt;

Methodology for obtaining a new map of basic wind speeds

Within the methods for obtaining extreme wind speeds for the development of structures, conditioning techniques have emerged, such as the differentiation of winds according to their generating mechanisms. This has proven to be effective in places with mixed climates, such as Cuba. Another way to reduce the uncertainties in calculating the wind load is by verifying the conditions of the meteorological stations and their speed records. The World Meteorological Organization establishes specific criteria to guarantee that the records of a station are reliable, among which are the characteristics of the surrounding terrain. The present work contributes to the improvement of the current Cuban wind standard where these techniques were not considered, and aims to obtain a new wind speed map that applies the method of independent storms. In addition, the wind speed values were corrected from the terrain roughness data of the stations studied by using two methods. The results showed concordance between both methods of obtaining the roughness of the terrain and differences with the values of wind speeds of the current Cuban standard.

Metric Reliability Analysis of Autonomous Marine LiDAR Systems under Extreme Wind Loads

As the key route detection device, the performance of marine LiDAR in harsh environments is of great importance. In this paper, a metric reliability analysis method for marine LiDAR systems under extreme wind loads is proposed. First, a static measurement accuracy evaluation model for the LiDAR system is proposed, targeting the problem that the LiDAR measurement tail reduces the measurement accuracy. Second, the distribution of extreme wind speeds in the Pacific Northwest is investigated, and a wind load probability model is developed. Finally, the impact of hull fluctuations on LiDAR measurement accuracy is analyzed by performing hull fluctuation simulations based on the wind load probability model, and the relationship curve between the metric reliability and measurement accuracy of marine LiDAR systems under extreme wind loads is addressed using the Monte-Carlo method. Experimental results show that the proposed LiDAR static measurement accuracy evaluation model can improve the measurement accuracy by more than 30%. Meanwhile, the solved curve of the LiDAR metric reliability versus the measurement allowable error indicates that the metric reliability can reach above 0.89 when the allowable error is 60 mm, which is instructive for the reliable measurement of marine LiDAR systems during ship navigation.