Statistical reconstruction method of plateau complex terrain wind field based on site contribution equalization

Abstract

The present application belongs to the technical field of terrain influence analysis method, and particularly relates to a plateau complex terrain wind field statistical reconstruction method based on site contribution equalization, determines a research area as a terrain area in Sichuan Province in the southeast edge of the Qinghai-Tibet Plateau, and a research period as a dry season of the plateau; collects hourly maximum wind and wind direction records of an encrypted automatic weather station in the research area, and slope and slope direction terrain factors extracted based on a 30-meter resolution digital elevation model; performs quality control on meteorological data, and verifies each record of extreme gale records of 10 levels and above by referring to a duty log, consulting a local meteorological station, and comparing average wind speed; defines 6 levels and above as a target gale level, and clearly defines statistical standards of a gale day, a gale hour, and an average continuous duration of a gale day; adopts a site equal weight method, eliminates influences of site density differences on terrain analysis, and performs correlation statistics of terrain factors and gale characteristics.

Description

Technical Field

[0001] This invention belongs to the technical field of dominant wind statistical methods, and particularly relates to a statistical reconstruction method for wind fields in complex plateau terrain based on station contribution equalization. Background Technology

[0002] The southeastern edge of the Qinghai-Tibet Plateau, located in Sichuan Province, is characterized by deep canyons and high mountains, with significant differences in altitude and a terrain that slopes from west to east, making it a typical complex topographic region. During the dry season, this region experiences frequent strong winds; winds of force 6 and above not only have actual destructive power but are also a key factor influencing forest fire risk. Their distribution and intensity are modulated by topography and local climate, posing a significant threat to regional ecological protection, transportation, and people's lives and livelihoods.

[0003] In statistics and data processing, equal weighting or weighted average is a common method for sample balancing, widely used in general scenarios such as routine data statistics and data integration of uniformly distributed observation networks. Examples include the aggregation of multi-source sample data in market research and the statistical analysis of meteorological elements from evenly distributed stations in plains areas. The core logic of this general equal weighting method is to assign equal contribution weights to each sample or station, avoiding excessive dominance of statistical results by high-frequency samples or densely packed stations, thereby obtaining a relatively balanced overall statistical characteristic. However, existing general equal-weighting methods have significant limitations: First, they are not designed for the special error characteristics of observation data in complex terrain areas, assuming that data errors can be corrected by conventional quality control methods, and lack adaptability to scenarios where "fundamental errors cannot be corrected"; Second, they do not establish a deep coupling mechanism with terrain factors, only achieving simple weight numerical balance, and cannot distinguish the modulation effect of different terrain units (elevation, aspect, slope) on the observation signal; Third, they do not involve the design of targeted physical signal extraction techniques, making it difficult to extract the wind field characteristics driven by the real terrain from the raw data affected by local disturbances, resulting in statistical distortion in meteorological data statistics in complex terrain areas, and failing to restore the true physical state of the wind field.

[0004] More importantly, the maximum wind data observed by automatic weather stations in the plateau region possesses a unique, inherent, and uncorrectable error characteristic: strong turbulence is prevalent in this region, and the terrain is complex and diverse. The local disturbance error contained in the instantaneous maximum wind observation value is deeply superimposed and physically coupled with the actual wind field signal driven by the terrain, making it impossible to separate using conventional data correction methods. Traditional quality control can only remove accidental outliers generated during data acquisition, but cannot isolate the local disturbance error caused by strong turbulence and complex terrain. This type of error is an inherent error closely related to the physical environment of the observed object, rather than an accidental error that can be corrected by technical means. It possesses the essential characteristic of being inherently uncorrectable, which makes the plateau high wind observation data inherently uncertain, becoming a fundamental defect restricting its statistical application.

[0005] Current research on dry-season gales in this region faces challenges beyond just statistical biases caused by uneven station distribution and insufficient quantification of the influence mechanism of topographic factors on the spatiotemporal characteristics of gales. Furthermore, it lacks specialized statistical methods adapted to the inherently uncorrectable nature of errors. Existing studies either directly apply general equal-weighting methods without addressing their coupling with topographic factors and physical signal extraction, resulting in statistical results that fail to reflect the true modulating effect of topography on gales; or they fail to employ weight balancing strategies, further amplifying the distortion caused by differences in station density. Simultaneously, a systematic analysis of the evolution patterns and driving mechanisms of dominant wind directions at different gales levels is lacking, making it difficult to accurately support the forecasting, early warning, and risk prevention of extreme gales.

[0006] There is an urgent need for a specialized statistical method that can specifically address the problem of uncorrectable fundamental errors in high-altitude wind observation, clarify the weight determination method, deeply couple topographic factors, and effectively extract physical signals. This method should balance data reliability and analytical rigor, accurately reveal the characteristics and impact mechanisms of strong winds in complex high-altitude terrain areas, and provide scientific support for extreme wind forecasting and early warning, as well as disaster risk assessment. Summary of the Invention

[0007] The purpose of this invention is to address the aforementioned technical problems by providing a statistical reconstruction method for wind fields in complex plateau terrain based on site contribution equalization.

[0008] In view of this, the present invention provides a method for statistical reconstruction of wind fields in complex plateau terrain based on site contribution equalization, comprising the following steps: Step 1: The study area is a complex terrain region in Sichuan Province on the southeastern edge of the Qinghai-Tibet Plateau. The study period is the dry season of the plateau from November to May of the following year from 2020 to 2024. The focus is on the fundamental error of the maximum wind observation principle caused by strong turbulence and complex terrain in this region. Step 2: Collect hourly maximum wind and wind direction records from 764 encrypted automatic weather stations in the study area, as well as slope and aspect topographic factors extracted based on a 30-meter resolution digital elevation model. The average elevation of the stations is 2452 meters, and the elevation range is 516-4286 meters. Step 3: Conduct multi-dimensional quality control on meteorological data, including classifying wind speed levels according to the national standard "Wind Force Scale", observing wind direction at 0-360° azimuth angles and statistically analyzing data using the eight-directional method, and verifying each record of extreme gale-force winds of level 10 and above by reviewing duty logs, consulting local meteorological stations, and comparing average wind speeds to ensure data reliability. Step 4: Define level 6 and above as the target gale level and clarify the statistical standards: the occurrence of this wind speed at any time within a single day is counted as 1 gale day, the observation of this wind speed in a certain hour is counted as 1 gale hour, and the average duration of a gale day = total number of gale hours / number of gale days; Step 5: Use the site equal weighting method to suppress errors. The specific process is as follows: For a certain terrain unit, first calculate the average number of stations in the unit, and then calculate the proportion based on the average number of stations to ensure that each station contributes with equal weight, eliminate the statistical distortion caused by uneven distribution of stations and high-frequency stations, and complete the correlation statistics between terrain factors and wind characteristics. Step Six: Conduct physical signal extraction and statistical modeling, systematically analyze the spatial distribution, diurnal variation pattern, wind direction evolution characteristics, and the influence mechanism of altitude, slope aspect, and slope on strong winds, remove local disturbance errors, enhance real terrain signals, and construct a nonlinear fitting model of wind speed extreme values ​​and terrain factors.

[0009] Preferably, the terrain factor extraction standard in step two is: the slope is divided into six levels: plain, slight slope, gentle slope, slope, steep slope, and precipitous slope. The slope aspect is divided into four levels: north slope, east slope, south slope, and west slope.

[0010] Preferably, the spatial distribution analysis in step six includes: identification of high-frequency areas of gale-force winds of level 6-7, verification of the distribution pattern of gale-force winds of level ≥8 with high frequency in the west and low frequency in the east, and analysis of the overlap characteristics between high-frequency areas of gale-force winds and centers of high duration, revealing the modulating effect of complex terrain on the spatial distribution of gale-force winds.

[0011] Preferably, the altitude impact analysis in step six adopts stratified statistics to quantify the monotonically increasing relationship between the average altitude of strong winds and the wind force level, and to clarify the dominant contribution of high-altitude areas to extreme winds. The stations in this area account for 13.9%, but contribute 53.3% of the winds of level 10 and above.

[0012] Preferably, the slope aspect and slope influence analysis in step six includes: verifying that the south slope is the area with the highest incidence of extreme winds, quantifying the dynamic lifting and acceleration effect of moderate slopes of 5°-35° on extreme winds, and this slope range contributes 61.6% of the extreme winds of level 10 and above.

[0013] Preferably, the wind direction evolution analysis in step six is ​​as follows: winds of level 6-7 are mainly southerly, and the dominant wind direction changes clockwise as the wind force increases; extreme winds of level 10 and above are mainly northwesterly, revealing the transitional characteristics of the dominant mechanism from local thermal processes to synoptic-scale dynamic forcing.

[0014] Preferably, the physical signal extraction in step six specifically involves: based on the station's equally weighted statistical results, removing the local disturbance error that cannot be effectively removed from the plateau maximum wind observation data, strengthening the modulation signal of the terrain on the frequency and intensity of the wind, and restoring the true physical state of the wind field.

[0015] Preferably, the statistical modeling in step six specifically involves: constructing a nonlinear fitting model of wind speed extreme values ​​with altitude and longitude, where wind speed and longitude are negatively correlated, and quantifying the nonlinear enhancement characteristics of the topographic dynamic forcing effect.

[0016] Preferably, the station equal weight method in step five balances the weights of each station, suppresses statistical bias caused by uneven station distribution, solves the technical problem that the fundamental error of the plateau maximum wind observation data cannot be corrected, and can still extract stable and reliable statistical patterns even under the condition that the data has inherent errors.

[0017] Preferably, the daily variation pattern analysis in step six is ​​as follows: the near-surface wind speed in the dry season of the plateau shows a single-peak daily variation, with the 10-meter average wind speed reaching its peak at 16:00 in the afternoon. The daily variation of gale intensity of level 6 and above is more significant, and the afternoon thermal forcing plays a key role in promoting the development of strong winds.

[0018] The beneficial effects of this invention are as follows: By innovatively adopting an equal-weighted statistical method for meteorological stations, it effectively avoids analytical biases caused by uneven spatial distribution of meteorological stations. Combined with high-density observation data and high-resolution topographic factors, it systematically reveals the spatiotemporal evolution of strong winds during the dry season on the Qinghai-Tibet Plateau and the characteristics of wind direction transformation with intensity. Simultaneously, it clarifies the modulation mechanism of topographic factors such as altitude, aspect, and slope on the frequency and intensity of strong winds, elucidates the dominant driving physical processes of different levels of strong winds, and achieves accurate characterization and in-depth analysis of the characteristics of strong winds in complex topographic areas.

[0019] The application of this analytical method provides a scientific basis for extreme wind forecasting and early warning, helps optimize the dynamic parameterization scheme of numerical models under complex terrain, and significantly improves the accuracy of wind disaster risk assessment. Its research results can directly serve regional ecological protection, transportation safety assurance, and disaster prevention and mitigation work for people's production and lives, providing strong support for relevant departments to formulate targeted prevention and control strategies, and have important practical guiding significance. Based on the aforementioned equal-weighting method for stations, this invention further analyzes the influence of slope aspect on the dominant wind direction of extreme winds to avoid interference from uneven station distribution. The specific process is as follows: determine the dominant wind direction based on the extreme wind records of each station; group the stations by slope aspect; and count the number of stations with different dominant wind directions within each slope aspect group, thereby ensuring that each station contributes equal weight in the statistics. Attached Figure Description

[0020] Figure 1 Topographic features and station distribution on the southeastern edge of the Qinghai-Tibet Plateau: (a) Topography and spatial distribution of stations in the study area; (b) Distribution of station elevations; Figure 2 Distribution of frequency and average duration of gale-force winds during the dry season in the study area from 2020 to 2024: (a) daily frequency of gale-force winds of level 6-7, (b) daily frequency of gale-force winds of level 8 and above, (c) daily average duration of gale-force winds of level 6-7, (d) daily average duration of gale-force winds of level 8 and above. Dashed lines indicate areas with relatively high values. Figure 3Hourly average wind speeds (Beijing time) during the dry season in the study area, 2020–2024: (a) all wind speed levels; (b) ≥ level 6; Figure 4 Distribution of extreme wind values ​​from 2020 to 2024 (a) and fitting trend of extreme wind speeds at stations with geographical factors (b): In (a), the area west of the dark blue dashed line is a relatively concentrated area with wind speeds above 30 m / s, and the area west of the red dashed line is a relatively concentrated area with wind speeds above 35 m / s; In (b), the stations are arranged in descending order of maximum wind speed, and longitude and altitude are standardized respectively. Figure 5 Distribution of strong winds during the dry season from 2020 to 2024: (ac) average wind direction at each level; (d) wind direction with maximum wind speed; Figure 6 Wind rose diagrams for different wind speeds during the dry season from 2020 to 2024. (a) 6-7 level winds (10.8~17.1 m / s); (b) 8-9 level winds (17.2~24.4 m / s); (c) ≥10 level winds (≥24.5 m / s). Figure 7 Altitude distribution characteristics of strong winds: (a) average altitude of different wind force levels; (b) frequency distribution of strong winds in different altitude ranges (equal weight of stations). Figure 8 The proportion of frequency of strong winds of different levels occurring on different slope aspects (a) and different slope gradients (b) (equal weighting for each station). Figure 9 Slope aspect and prevailing wind direction: (a) proportional distribution; (b) heat map of the number of stations. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0022] The study area is located on the southeastern edge of the Qinghai-Tibet Plateau, within Sichuan Province, China, and is a typical region with complex topography. Figure 1 a) The region's topography is dominated by deep canyons and high mountains, with significant differences in altitude. The overall terrain slopes from west to east, with the western part being a high-altitude area and the eastern part a transitional zone between the plateau and the Sichuan Basin. The study period covers the plateau's dry season from November to May of the following year, from 2020 to 2024.

[0023] The observational data used in this invention includes hourly maximum wind and wind direction records from 764 densely packed automatic weather stations in the study area, as well as topographic factors (slope, aspect) extracted based on a 30-meter resolution digital elevation model (DEM). The average station elevation is 2452 meters, and the spatial and elevation distribution of the stations is as follows: Figure 1 As shown. All wind speed data have undergone strict quality control by the Sichuan Provincial Meteorological Observation and Data Center. For extreme wind records of level 10 and above (≥24.5 m / s), each record has been verified by checking duty logs, consulting local meteorological stations, and comparing with average wind speeds to ensure the reliability of the data.

[0024] This invention selects level 6 (wind speed ≥ 10.8 m / s) and above as the research object. This level is not only a key indicator of forest fire risk but also has actual destructive power. Wind speed levels are classified according to the national standard "Wind Force Scale" (GB / T 28591-2012), which is equivalent to the WMO Beaufort scale system. Wind direction is observed from 0-360° azimuth and statistically analyzed using the octagonal method. Specific classifications are shown in Table 1. A gale event is defined as follows: if a specified level of wind speed occurs at any time within a single day at a certain station, it is recorded as one gale day, and its frequency is the percentage of gale days out of the total number of days; if a specified wind speed is observed in a certain hour, it is recorded as one gale hour; the average duration of a gale day is the ratio of the total number of gale hours to the number of gale days (unit: hours / day). Subsequent analyses are based on the above definitions.

[0025] Table 1. Wind force level and main wind direction; Table 2. Classification of Slope Aspect and Slope Grade;

[0026] Slope and aspect were extracted based on a 30-meter high-resolution DEM, and their classification is shown in Table 2.

[0027] Due to the uneven spatial distribution of meteorological stations, this invention employs an equal-weighting method for all statistics involving topographic factors (elevation, aspect, and slope) to eliminate the impact of station density differences on topographic analysis. The core of this method is to avoid excessively high statistical weights for certain topographic units due to station density, thereby more accurately reflecting the mechanism by which topography influences strong winds. The specific process is as follows: For a given topographic unit, firstly, the average number of stations within the unit (i.e., the ratio of total number of strong winds to the total number of stations) is calculated to characterize the average wind activity of a single station within the unit; secondly, the proportion is calculated based on the average number of stations in each unit, rather than the original frequency, ensuring that each station contributes an equal weight to the statistical results.

[0028] When analyzing the impact of slope aspect on the prevailing wind direction of extreme gales, this principle was also followed to avoid interference from high-frequency stations and the density of stations within the same slope aspect. The paper employs a method combining station-by-station statistical analysis and slope aspect classification: first, the prevailing wind direction is determined based on the extreme gale records of each station; then, stations are grouped according to their slope aspect; finally, the number of stations with different prevailing wind directions within each slope aspect group is counted. This process ensures that each station contributes only one prevailing wind direction count, achieving equal weighting in the slope aspect analysis.

[0029] During the dry season from 2020 to 2024, 764 stations recorded a total of 18.3089 million effective hourly observations. Table 3 shows the statistics for different wind speeds. Light winds of level 3 and below were the most frequent, occurring in 70.76% of cases; while gales of level 6-7, 8-9, and 10 and above occurred in 6.03%, 0.58%, and 0.04% of cases, respectively. This section will use this dataset to systematically analyze the spatiotemporal distribution patterns of gales during the dry season on the plateau and their interaction with topography.

[0030] Table 3. Frequency and proportion of different wind speeds during the dry season in the study area from 2020 to 2024;

[0031] Spatial distribution of daily frequency of strong winds during the dry season in the plateau region from 2020 to 2024 shows ( Figure 2 (ab) High-value areas with a frequency of gale-force winds (level 6-7) greater than 25% are widely distributed across most of the study area, indicating that these areas experience gale-force winds of level 6 or above at least once every four days on average. The frequency in the western part of the study area generally exceeds 50%, while the central and western parts have high-value centers accounting for more than 75% of the total. Figure 2 The area circled in black (a) became the core area where gale-force winds of level 6-7 occurred most frequently. In contrast, the distribution of gale-force winds of level 8 and above showed a pattern of decreasing from the hinterland of the plateau to the eastern edge. Figure 2 b). Regions with frequencies greater than 25% are mainly concentrated in the high-altitude areas of the western part of the study area, while high-frequency centers with frequencies greater than 33% are located in the central and western parts. Figure 2 (b) The black dashed circle reflects the key control of topography and local climate on the distribution of strong winds.

[0032] The duration of a strong wind event is an important indicator of its potential impact. For winds of force 6-7 (… Figure 2 c) The average duration varies significantly spatially: the northeastern part of the study area is a relatively low-value area (about 1-2 hours), the main part of the plateau generally exceeds 2 hours, while the high-frequency core area of ​​strong winds in the central-western and southern regions ( Figure 2 The most prominent feature is the black dotted circle (c), which can last for 4-7 hours. For winds ≥ level 8 (…), Figure 2d) The overall duration was significantly shortened, with most stations averaging less than 2 hours; very few stations exceeded 3 hours. The central and western parts of the study area were a relatively concentrated area, which highly overlapped with the high-frequency area of ​​≥8 level gale.

[0033] In summary, high-frequency areas of strong winds are often also centers of high duration, indicating that these areas not only experience frequent strong winds but also endure long periods, thus accumulating higher risks. Furthermore, the spatial distribution of strong wind elements is extremely uneven, exhibiting significant local characteristics. For example, in the central and western parts of the study area, the frequency of ≥8 level winds at adjacent stations can increase dramatically from less than 10% to over 75%. Figure 2 (b) The average duration can also jump from less than 2 hours to more than 4 hours. Figure 2 d). This huge difference highlights the significant modulating effect of complex terrain on near-surface wind fields and also confirms the necessity of high-density station observations for accurately capturing local strong wind characteristics.

[0034] The diurnal variation of near-surface wind fields is significant in plateau regions. For example... Figure 3 As shown in Figure a, the 10-meter average wind speed exhibits a typical single-peak diurnal variation, starting to rise continuously from 09:00 Beijing time, reaching a peak of 7.4 m / s at 16:00, then gradually weakening, reaching a trough of 2.7 m / s at 08:00 the following day. Periods with average wind speeds above 4 m / s are mainly concentrated between 12:00 and 23:00. The average wind speed of gale-force winds (≥6) also exhibits a single-peak diurnal variation. Figure 3 (b) However, its diurnal variation amplitude (approximately 0.8 m / s) is much smaller than the diurnal variation amplitude of all wind speeds (4.7 m / s), indicating that the high wind speed background maintaining this type of strong wind may be dominated by a relatively stable synoptic-scale forcing, with diurnal thermodynamic processes playing a modulating role. The wind speed of this type of strong wind reaches its peak around 16:00, then drops significantly between 16:00 and 20:00, continuing to decrease from night to the next morning (20:00 to 09:00), reaching its minimum value (12.8 m / s) at 09:00.

[0035] This unimodal diurnal variation is consistent with typical thermodynamic cycles in plateau regions. During the day, enhanced solar radiation reduces boundary layer stability and strengthens turbulent exchange, which facilitates the downward transfer of momentum from the upper atmosphere, driving wind speeds to their peak during the afternoon when radiation is strongest (approximately 14:00–16:00). At night, surface radiation cools to form a stable boundary layer, inhibiting vertical momentum exchange and resulting in a significant reduction in near-surface wind speeds.

[0036] The average maximum wind speed at the stations in the study area during the dry season from 2020 to 2024 was 23.5 m / s, and the spatial distribution generally showed a macroscopic pattern of higher wind speeds in the west and lower wind speeds in the east. Figure 4a). Most stations recorded extreme wind speeds between 19 and 27 m / s. Areas with wind speeds exceeding 30 m / s were concentrated in the high-altitude regions of the central and western parts of the study area. Figure 4 West of the blue-black dashed line, the average elevation of stations in this area generally exceeds 3000 meters. Figure 1 a). It is worth noting that a more extreme extreme value center appeared in the northwest of the study area, with wind speeds exceeding 35 m / s ( Figure 4 (West of the red dashed line), the average elevation of its stations is above 3500 meters. The formation of this extreme center indicates that the dynamic lifting and funneling effects generated by complex terrain have a significant enhancing effect on local wind speeds.

[0037] Analysis based on nonparametric fitting and smoothing techniques ( Figure 4 (b) This study further reveals the intrinsic relationship between extreme wind speeds and geographical factors. It found that wind speed increases significantly and non-linearly with increasing altitude, and the increase widens with increasing altitude, indicating a clear non-linear enhancement of the topographic dynamic forcing effect. Simultaneously, wind speed shows a negative correlation with longitude (R²=0.67, p<0.01), clearly depicting the east-west spatial difference in wind speed decreasing from the hinterland of the plateau to the eastern Sichuan Basin. This pattern reflects that the high and open terrain in the west is more susceptible to the influence of the large-scale westerly winds, while the significant topographic shielding effect in the east leads to weakened wind speeds. These findings provide important observational evidence for optimizing dynamic parameterization schemes for complex terrain in numerical models and improving the accuracy of extreme wind forecasts.

[0038] To reveal the prevailing wind direction characteristics of strong winds during the dry season on the southeastern edge of the Qinghai-Tibet Plateau, this invention statistically analyzes the average wind direction at various stations, categorized by four directions (Northeast: 0~90°, Southeast: 90~180°, Southwest: 180~270°, Northwest: 270~360°), and distinguishes their spatial distribution by color. Figure 5 The results showed that southerly winds (southwest and southeast) dominated for winds of force 6-7, accounting for 90.5% of all winds. Southwesterly winds were the most frequent (397 stations), followed by southeasterly winds (288 stations), while northwesterly and northeasterly winds were less common. Northerly winds were mainly distributed in areas north of 31°N latitude. For winds of force 8-9, the proportion of southerly winds decreased to 78.7%, while the proportion of northerly winds significantly increased to 21.3%, and their affected area shifted southward accordingly. For extreme winds of force 10 and above, and for extreme wind speeds at various stations, southerly winds still predominated, but the proportion of northerly winds further increased. Among extreme wind directions, the proportions of southwesterly and northwesterly winds were similar, reflecting a significant increase in the contribution of northerly winds to extreme winds.

[0039] Further wind rose diagrams were drawn based on the eight directions ( Figure 6The analysis revealed the systematic evolution of the dominant wind direction as the wind force increased. For gales of force 6–7, southerly winds (S) were dominant (18.5%), along with southwesterly winds (SW) and westerly winds (W), accounting for a total of 50.4%. As the wind force increased to force 8–9, the dominant wind direction shifted to southwesterly winds (SW, 17.7%), with the main wind direction combination still being SW, S, and W, accounting for a total of 51.1%. For extreme gales of force 10 and above, the dominant direction further shifted to northwesterly winds (NW, 22.6%), while westerly winds (W, 19.3%), southerly winds (S, 14.4%), and northerly winds (N, 13.6%) also occupied significant proportions.

[0040] The clockwise evolution of the dominant wind direction (southeast, southwest, northwest) as its intensity increases indicates that the key physical mechanisms driving strong winds may shift with wind force levels. Low-level strong winds (Force 6–7) are dominated by southerly winds, consistent with the controlling role of local circulation (e.g., valley winds or upslope winds) driven by intense daytime thermal effects over the plateau. As wind force increases, the proportion of northerly winds (especially northwesterly winds) rises significantly, typically associated with large-scale weather systems (such as cold fronts and upper-level troughs) and their accompanying downward momentum transfer of upper-level westerlies. Overall, the clockwise evolution of wind direction reveals a transition from local thermal processes to synoptic-scale dynamic forcing as the dominant mechanism.

[0041] In summary, the analysis in this section shows that westerly, southerly, and southwesterly winds dominate gale-force winds of level 6 and above, while the proportion of northwesterly and northerly winds increases significantly with increasing wind force. Overall, gale-force winds of level 6 and above are predominantly located in the southwest quadrant, a distribution characteristic closely related to the background field controlled by the southern branch of westerly circulation during the dry season on the plateau and the strong thermal effects during the day. Notably, the dominant wind direction for extreme gale-force winds of level 10 and above shifts to northwesterly, further supporting the inference that strong cold air activity and the downward momentum transfer of upper-level westerly winds play a crucial role in extreme events.

[0042] Altitude profoundly influences the distribution characteristics of strong winds by altering air pressure, friction layer thickness, and relative position to weather systems. To quantify the impact of altitude on the frequency of strong winds, this invention employs a site-weighted method for stratified statistical analysis. The results show that the average altitude of strong wind events exhibits a clear monotonically increasing trend with increasing wind force (…). Figure 7 a). The average altitude is lowest for winds of force 3 and below (≤5.4 m / s), at 2431 meters. As wind force increases, the average altitude gradually rises, with the first jump at force 6, reaching 2697 meters, and the second rapid increase at force 10, reaching 3120 meters. The average altitude for winds of force 12 and above is close to 3500 meters. (Based on the altitude distribution of the frequency of strong winds...) Figure 7(b) Significant differences exist in the contribution of different altitude ranges to different levels of gale intensity: high altitude ranges (>3500 meters) dominate the contribution of gale intensity for all levels, and their contribution to gale frequency increases sharply with increasing wind force. Although this region has the lowest proportion of stations (13.9%), its contribution jumps from 31.7% for gale intensity 6-7 to 42.9% for gale intensity 8-9, and reaches 53.3% for gale intensity 10 and above, occupying an absolute dominant position. High altitude areas are the core areas for gale intensity, especially extreme gale intensity, with both frequency and intensity increasing significantly with altitude. This characteristic is closely related to the stronger pressure gradient force, reduced surface friction, and more direct downward momentum transfer of upper-level westerly winds in high altitude areas.

[0043] Slope aspect, as a key topographic factor, significantly influences the distribution of local strong winds through differentiated thermal and dynamic processes. Figure 8 A shows the frequency distribution of different wind levels across different slope directions. Overall, winds of force 6-7 are relatively evenly distributed across different slope directions. As the wind force increases, the differences between slope directions become more prominent. Winds of force 8-9 occur most frequently on the south slope (13.9%). For extreme winds of force 10 and above, slope direction selectivity is more significant, with the south slope accounting for as much as 26.8%, far higher than the west slope (12.3%), east slope (8.0%), and north slope (7.4%), making it the area with the highest incidence of extreme winds.

[0044] To further reveal the intrinsic relationship between slope aspect and prevailing wind direction, this paper conducts a detailed correlation analysis of all extreme wind events. A combination of station-by-station statistical analysis and slope aspect classification was used (ensuring that each station contributes only one prevailing wind direction) to statistically analyze the number and proportion of stations with different prevailing wind directions within each slope aspect group. Figure 9 The analysis results show a systematic distribution pattern. Figure 9 a): Westerly winds (W+NW+SW) are the absolute dominant wind directions on all slope aspects, accounting for as high as 68.5% on the north slope and 57.6% on the south slope, reflecting the background control effect of large-scale westerly circulation. Northerly winds are the second dominant wind direction on all slope aspects except the south slope; while the south slope is the only slope aspect where southerly winds (S+SW+SE) are the second dominant wind direction, accounting for 40.4%, revealing the significant contribution of local thermal circulation (such as valley winds / uphill winds) in this region. (Heat map showing the distribution of dominant wind directions on each slope aspect...) Figure 9 (b) It is evident that although the number of stations with northwesterly winds (NW) across different slopes is small, combined with the conclusion in Section 3.1.4 that northwesterly winds are the dominant wind direction for extreme gales of force 10 and above, these stations are high-incidence points for extreme gales. This confirms the strong correlation between northwesterly winds and extreme gales, and also indicates that although the spatial distribution of stations with northwesterly winds as the dominant wind direction is limited, the frequency of local extreme gales is extremely high.

[0045] In summary, the dominant physical processes differ across slope aspects: the north and west slopes primarily exhibit westerly wind forcing characteristics; the south slope is dominated by both background westerly winds and local thermal effects; and the east slope, as a transitional type, shows a more complex wind direction distribution. This finding indicates that during the dry season on the plateau, the background westerly wind field provides the basic conditions for strong winds, while slope aspect modulates the final manifestation of the local wind field through thermal and dynamic processes.

[0046] Slope, as an important topographic factor, significantly influences the occurrence of localized strong winds by affecting the ascent, acceleration, and circulation processes of airflow. It should be noted that the slope distribution of the stations within the study area is extremely uneven. Sloping (5°-15°) and steep (15°-35°) slopes are the main station distribution ranges, accounting for over 70% combined, while the proportion of stations on gentle slopes (0.5°-2°) and plains is relatively low. Figure 8 (b) This further highlights the necessity of using the site equal weighting method in the analysis.

[0047] Figure 8 b shows the frequency distribution of different wind intensities across various slope gradients. Statistical results reveal a clear selectivity of wind intensity based on slope: winds of force 6-7 occur most frequently on gentle slopes (20.7%), then gradually decrease with increasing slope, dropping to 10.8% on steep slopes of 35°-55°, indicating that moderate-intensity winds are more likely to occur on relatively flat terrain; winds of force 8-9 are relatively evenly distributed across slope gradients, with the highest frequency on steep slopes (20.3%); winds of force 10 and above exhibit the most pronounced slope distribution characteristics, peaking at 33.3% on slopes, followed by steep slopes at 28.3%, together contributing 61.6% of all extreme wind events. In stark contrast, gentle slopes account for only 3.3%. This non-monotonic distribution pattern suggests that moderate slopes of 5° to 35° provide the most favorable dynamic conditions for the occurrence of extreme winds. Within this range, the slope can effectively lift and accelerate the airflow, while avoiding insufficient dynamic forcing due to an excessively gentle slope, or airflow separation and frictional dissipation caused by an excessively steep slope. Therefore, a moderate slope is one of the key parameters constituting the optimal terrain configuration for extreme winds.

[0048] The specific calculation scheme used in the statistical method of this invention is further explained as follows: To eliminate the impact of uneven spatial distribution of stations on topographic analysis, this invention adopts an equal-weighted station method in the statistics involving topographic factors (elevation, aspect, and slope). The core of this method is to calculate the standardized station ratio and standardized frequency to ensure that each station contributes equally to the statistical results, thereby truly revealing the relationship between topography and wind distribution.

[0049] For a specific terrain category i (such as a certain elevation range, aspect, or slope grade), the statistical process consists of two steps: Calculate the station frequency ratio: the average gale activity of a single station within this category.

[0050] ; In the formula, E i S represents the total number of strong wind events observed within category i. i This represents the total number of sites that fall within category i.

[0051] Calculate the standardized frequency: the relative frequency of occurrence of this category of gale events.

[0052] ; In the formula, n is the total number of terrain categories, and ∑R is the sum of the station frequency ratios for all categories. i This is the frequency (%) after standardization by station density, used to objectively compare the relative contributions of different terrain types to strong wind events.

[0053] The analyses in this invention are all based on the above framework, specifically as follows: Altitude analysis: Terrain category i represents different altitude intervals. The standardized frequency P for each altitude interval is calculated. elev This study analyzes the distribution of strong wind frequency with altitude. Slope aspect analysis: Topographic category i represents different slope aspects (e.g., north, east, south, west slopes). The standardized frequency P for each slope aspect is calculated. aspect To identify slopes with high wind incidence. Slope analysis: Terrain category ii represents different slope grades. Calculate the standardized frequency P for each slope grade. slope To determine the favorable slope range for strong winds.

[0054] To investigate the relationship between slope aspect and prevailing wind direction during extreme strong winds, and adhering to the principle of equal weighting of stations, this invention employs a station counting method based on prevailing wind direction for specific statistical analysis. The specific process is as follows: ① Determine the prevailing wind direction for a single station: For each station, determine the prevailing wind direction that occurs most frequently based on all its extreme wind (≥ level 10) records.

[0055] ② Grouping by slope aspect: Group all stations according to their slope aspect.

[0056] ③Statistics and Calculations: Within each slope group, count the number of stations where the prevailing wind direction is a specific orientation (e.g., northwest or south). aspect,winddir The proportion of this wind direction on this slope, F winddir,aspect The calculation formula is: ; In the formula, S aspectThis represents the total number of stations within the slope group. This method ensures that each station contributes only one wind direction count, effectively avoiding the influence of station distribution density on wind direction statistics.

[0057] In summary, this invention, through the aforementioned systematic site-weighted statistical framework, eliminates the bias caused by uneven observation networks, making the frequency distribution and correlation derived from factors such as altitude, aspect, and slope more reflective of the true terrain modulation effect, and significantly improving the reliability and comparability of the analysis results.

[0058] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A statistical reconstruction method for wind fields in complex plateau terrain based on station contribution equalization, characterized by: Includes the following steps: Step 1: The study area is a complex terrain region in Sichuan Province on the southeastern edge of the Qinghai-Tibet Plateau. The study period is the dry season of the plateau from November to May of the following year from 2020 to 2024. The focus is on the fundamental error of the maximum wind observation principle caused by strong turbulence and complex terrain in this region. Step 2: Collect hourly maximum wind and wind direction records from 764 encrypted automatic weather stations in the study area, as well as slope and aspect topographic factors extracted based on a 30-meter resolution digital elevation model. The average elevation of the stations is 2452 meters, and the elevation range is 516-4286 meters. Step 3: Conduct multi-dimensional quality control on meteorological data, including classifying wind speed levels according to the national standard "Wind Force Scale", observing wind direction at 0-360° azimuth angles and statistically analyzing data using the eight-directional method, and verifying each record of extreme gale-force winds of level 10 and above by reviewing duty logs, consulting local meteorological stations, and comparing average wind speeds to ensure data reliability. Step 4: Define level 6 and above as the target gale level and clarify the statistical standards: the occurrence of this wind speed at any time within a single day is counted as 1 gale day, the observation of this wind speed in a certain hour is counted as 1 gale hour, and the average duration of a gale day = total number of gale hours / number of gale days; Step 5: Use the site equal weighting method to suppress errors. The specific process is as follows: For a certain terrain unit, first calculate the average number of stations in the unit, and then calculate the proportion based on the average number of stations to ensure that each station contributes with equal weight, eliminate the statistical distortion caused by uneven distribution of stations and high-frequency stations, and complete the correlation statistics between terrain factors and wind characteristics. Step Six: Conduct physical signal extraction and statistical modeling, systematically analyze the spatial distribution, diurnal variation pattern, wind direction evolution characteristics, and the influence mechanism of altitude, slope aspect, and slope on strong winds, remove local disturbance errors, enhance real terrain signals, and construct a nonlinear fitting model of wind speed extreme values ​​and terrain factors.

2. The method for statistical reconstruction of wind fields in complex plateau terrain based on station contribution equalization as described in claim 1, characterized in that: The criteria for extracting terrain factors in step two are as follows: slope is divided into six levels: plain, slight slope, gentle slope, slope, steep slope, and precipitous slope. The slope aspect is divided into four levels: north slope, east slope, south slope, and west slope.

3. The method for statistical reconstruction of wind fields in complex plateau terrain based on station contribution equalization according to claim 1, characterized in that: Step six, spatial distribution analysis, includes: The identification of high-frequency areas of gale-force winds of level 6-7, the verification of the west-high and east-low distribution pattern of gale-force winds of level ≥8, and the analysis of the overlap characteristics between high-frequency areas of gale-force winds and centers of high duration reveal the modulating effect of complex terrain on the spatial distribution of gale-force winds.

4. The method for statistical reconstruction of wind fields in complex plateau terrain based on station contribution equalization according to claim 1, characterized in that: In step six, the altitude impact analysis adopts stratified statistics to quantify the monotonically increasing relationship between the average altitude of strong winds and the wind force level, and to clarify the dominant contribution of high-altitude areas to extreme winds. The stations in this area account for 13.9%, but contribute 53.3% of the winds of level 10 and above.

5. The method for statistical reconstruction of wind fields in complex plateau terrain based on station contribution equalization according to claim 1, characterized in that: Step six, the analysis of the influence of slope aspect and slope gradient, includes: The study verified that the southern slope is the area with the highest incidence of extreme winds, and quantified the dynamic lifting and acceleration effect of moderate slopes of 5°-35° on extreme winds. This slope range contributed 61.6% of the extreme winds of level 10 and above.

6. The method for statistical reconstruction of wind fields in complex plateau terrain based on station contribution equalization according to claim 1, characterized in that: The wind direction evolution analysis in step six is ​​as follows: The winds of force 6-7 were mainly southerly, and the prevailing wind direction changed clockwise as the wind force increased. The extreme winds of force 10 and above were mainly northwesterly, revealing the transitional characteristics of the dominant mechanism from local thermal processes to synoptic-scale dynamic forcing.

7. The method for statistical reconstruction of wind fields in complex plateau terrain based on station contribution equalization according to claim 1, characterized in that: Step six, physical signal extraction, specifically involves: Based on the station's equally weighted statistical results, local disturbance errors that cannot be effectively removed from the plateau maximum wind observation data are eliminated, the modulation signal of topography on the frequency and intensity of strong winds is enhanced, and the true physical state of the wind field is restored.

8. The method for statistical reconstruction of wind fields in complex plateau terrain based on station contribution equalization according to claim 1, characterized in that: The statistical modeling in step six specifically involves: A nonlinear fitting model is constructed for wind speed extrema with altitude and longitude, where wind speed and longitude are negatively correlated, quantifying the nonlinear enhancement characteristics of topographic dynamic forcing effect.

9. The method for statistical reconstruction of wind fields in complex plateau terrain based on station contribution equalization according to claim 1, characterized in that: In step five, the site equal weighting method balances the weights of each site, thus suppressing statistical bias caused by uneven site distribution.

10. The method for statistical reconstruction of wind fields in complex plateau terrain based on station contribution equalization according to claim 1, characterized in that: The daily variation pattern analysis in step six is ​​as follows: During the dry season on the plateau, the near-surface wind speed exhibits a single-peak diurnal variation, with the 10-meter average wind speed reaching its peak at 16:00 in the afternoon. The diurnal variation amplitude of gale force 6 and above is much smaller than the diurnal variation amplitude of the average wind speed, indicating that the high wind speed background that sustains such strong winds may be dominated by a relatively stable synoptic scale forcing, with diurnal thermodynamic processes playing a modulating role.

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