CN112633347B - Near-surface wind characteristics of landfall typhoons and their differences from clear sky wind characteristics - Google Patents
Near-surface wind characteristics of landfall typhoons and their differences from clear sky wind characteristics Download PDFInfo
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Abstract
The invention discloses an analysis method for the wind characteristics of a landing typhoon near-stratum and the difference between the landing typhoon near-stratum wind characteristics and the clear sky wind characteristics, which comprises the steps of preprocessing landing typhoon paths and multi-source vertical gradient detection data; classifying data by adopting a typhoon moving path quadrant objective analysis method; selecting typhoon influence sample clusters by comprehensively utilizing a threshold value and a percentile method; selecting a clear sky sample cluster by using a proximity distance method and a cloud amount threshold value; acquiring paired typhoon influence and clear air sample clusters by a time-space matching method; and respectively carrying out wind profile comparison research in different influence quadrants. The method for analyzing the difference of the wind characteristics of the landing typhoon near stratum is developed based on the objective classification of the landing typhoon influence space, so that the objective classification of different influence parts of the landing typhoon is realized, the technical problem that the subjective classification cannot be popularized and applied in a large scale is solved, the problem that only the typhoon characteristics are concerned and the difference between the typhoon characteristics and good wind characteristics is ignored is solved, and the adaptability of classification analysis of the wind characteristics of the landing typhoon near stratum is integrally improved.
Description
Technical Field
The invention belongs to the technical field of meteorological data analysis and processing, relates to a method for analyzing the wind characteristics of a landing typhoon near stratum, and particularly relates to an analysis method for the wind characteristics of the landing typhoon near stratum and the difference between the wind characteristics of the landing typhoon near stratum and the wind characteristics of clear sky, which is developed based on objective classification of influence spaces.
Background
Accurate depiction of the wind characteristics of the landing typhoon near stratum is the primary task of designing the typhoon resistance of a high-rise structure, and particularly, the typhoon influence wind profile and the difference between the typhoon influence wind profile and the clear air wind profile are one of the most important factors. Landing typhoon is subjected to terrain friction, the characteristics of a near-stratum wind field are complex, and the landing typhoon has a unique asymmetric spiral structure, so that the conventional uniform wind characteristic statistical analysis method is not applicable any more. Therefore, the objective classification analysis method for landing typhoon near-stratum wind characteristics is one of the core technologies of engineering disaster resistance and disaster reduction.
The key link in the typhoon landing near-stratum wind characteristic analysis based on disaster-causing influence is to classify and classify data samples according to the asymmetric structure of landing typhoon so as to obtain representative sample clusters and carry out related wind characteristic analysis. At present, the problem of rare observation data during landing typhoon influence is solved, the previous classification analysis method is based on a wind speed change curve in a certain typhoon life cycle, a wind speed threshold is subjectively selected to classify the typhoon influence from a time dimension, individuation is strong, and large-scale popularization and application of the method are limited on one hand; on the other hand, the method does not consider the difference of the typhoon moving path in different direction spaces and the difference of the typhoon influence wind characteristics and the non-typhoon influence wind characteristics, and the asymmetric wind field characteristics of the landing typhoon are difficult to reflect really.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides an analysis method for the wind characteristics of the near-stratum of landing typhoon and the difference between the wind characteristics of the landing typhoon and the characteristics of the wind characteristics of the near-stratum of landing typhoon in clear sky, wherein the method is developed based on the objective classification of the influence space of landing typhoon and comprises the steps of preprocessing a landing typhoon path and multisource vertical gradient detection data; classifying data by adopting a typhoon moving path quadrant objective analysis method; selecting typhoon influence sample clusters by comprehensively utilizing a threshold value and a percentile method; selecting a clear sky sample cluster by using a proximity distance method and a cloud amount threshold value; acquiring paired typhoon influence and clear air sample clusters by a time-space matching method; and respectively carrying out wind profile comparison research in different influence quadrants. The method provided by the invention not only realizes objective classification of different affected parts of the landing typhoon, solves the technical problem that subjective classification cannot be popularized and applied in a large scale, but also overcomes the problem that only typhoon characteristics are concerned and the difference between the typhoon characteristics and good wind characteristics is ignored, integrally improves the adaptability of classification analysis of the wind characteristics of the landing typhoon near strata, and has strong universality.
The technical scheme adopted by the invention for solving the technical problem is as follows:
an analysis method of landing typhoon near-stratum wind characteristics and differences thereof from clear-sky wind characteristics, the analysis method being developed based on objective classification of landing typhoon's influence space, characterized in that the analysis method at least comprises the following steps:
SS1, pre-processing of landing typhoon moving path and multi-source vertical gradient detection data: determining a landing typhoon moving path, and primarily selecting multi-source vertical detection data within the influence range of the landing typhoon moving path according to the landing typhoon moving path;
and SS2, classifying data by adopting a typhoon moving path quadrant objective analysis method: dynamically calculating the distribution of detection stations in different influence quadrants at the central position of the typhoon hour by utilizing the typhoon moving path and station longitude and latitude information, and dividing detection data in the influence range into four influence quadrant groups of right front, right back, left front and left back according to time and space characteristics;
and SS3, comprehensively utilizing a threshold value and a percentile method to select typhoon influence sample clusters: in the four affected quadrant groups divided in the step SS2, taking a fixed detection point as a reference, and comprehensively referring to the wind speed threshold value and the percentile wind speed value of the characteristic height layer to select a strong wind sample cluster of which the detection point is affected by typhoon;
SS4, selecting a clear sky sample cluster by using a proximity distance method and a cloud amount threshold value;
SS5, acquiring paired typhoon influence and clear air sample clusters by a space-time matching method;
and SS6, respectively carrying out wind profile comparison research in different influence quadrants.
Preferably, in step SS1, the data preprocessing includes: and primarily selecting multi-source vertical detection data in the influence range of the optimal moving path of the landing typhoon, such as wind profile radar detection data, anemometer tower gradient observation data, radio sounding data, ground meteorological station timing total cloud amount observation data and the like, and rejecting stiff values and false values in the observation data.
Further, in step SS1, the vertical detection point within the typhoon influence range is initially selected according to the distance threshold method, and the stiff value and the false value in the vertical gradient observation data are removed according to the previous and subsequent time wind speed difference and the wind speed shear threshold.
Preferably, in step SS2, the probe point (x) is passeds,ys) To the current position (x) of the typhoon0,y0) Is smaller than a set threshold value to identify whether the detection point is within the typhoon influence range; respectively calculating clockwise included angles of the due north direction, the typhoon moving direction and the detection point position direction by utilizing a trigonometric function relation; and performing quadrant classification on the detection data by using the angle difference.
Preferably, in step SS3, the sample groups from the same probe point are sorted in ascending order of wind speed of the target altitude layer in the four influence quadrant groups divided in step SS2. And (4) sorting out the large wind sample cluster by a wind speed threshold value (the sample size is small) or a percentile method (the sample size is large).
Preferably, in step SS4, according to the geographical locations of the vertical detection stations and the total-cloud-volume observation weather stations, the representative-cloud-volume weather observation stations of the vertical detection stations are selected one by using a proximity distance method, the total cloud volume observed by the representative-cloud-volume weather stations is used to represent the total cloud volume of the vertical detection stations, and a clear-sky sample cluster in which each vertical detection station is not affected by the typhoon is selected according to a total-cloud-volume threshold value.
Further, in step SS4, a ground weather station closest to the detection point is selected as a cloud volume representative station corresponding to the detection point by using a distance proximity method; and marking the observation samples with the total cloud amount of the meteorological station less than 2 as clear sky samples corresponding to the detection points.
Preferably, in step SS5, in the four influence quadrant groups divided in step SS2, a pair of clear sky samples that are consistent with the detection point to which the strong wind sample cluster belongs at the time point is selected from the clear sky sample clusters completed in step SS4 with the strong wind sample cluster selected in step SS3 as a reference, and a typhoon influence and a clear sky sample cluster that are paired one by one are obtained.
Preferably, in step SS6, in the four influence quadrant groups divided in step SS2, the paired data obtained in step SS5 are used to perform analysis of the wind profiles in different influence quadrants and in different underlay surface roughness environments and the differences between the wind profiles and the clear sky wind profile.
Preferably, in the steps SS5 and SS6, in the four affected quadrants divided in the step SS2, with the detection point (subsurface mat type) as a reference, samples marked as clear sky at the same time are sequentially selected for one-to-one matching in a period of time near the time when the gale sample belongs (with consistent background climate characteristics), and wind profile comparison analysis is performed.
Compared with the prior art, the invention has the beneficial effects that: the method for analyzing the difference of the typhoon-influenced wind characteristics is developed based on the objective classification of the landing typhoon-influenced space, so that the objective classification of different influenced parts of the landing typhoon is realized, the technical problem that the subjective classification cannot be popularized and applied in a large scale is solved, the problem that only typhoon characteristics are concerned but the difference between the typhoon characteristics and good wind characteristics is ignored is solved, and the adaptability of the classification analysis of the typhoon-near-stratum wind characteristics is integrally improved. According to the method, the objective classification of the detection samples can be realized only by analyzing the optimal path of the typhoon and the longitude and latitude information of the vertical detection points, so that the high dependence of the traditional method on the detection data time sequence is reduced, the vertical detection points in the typhoon influence range can be accurately and quickly identified, and the method has strong universality.
Drawings
FIG. 1 is a flow chart of a method for performing a difference analysis of typhoon-influenced wind characteristics based on objective classification of landing typhoon-influenced spaces according to the present invention.
FIG. 2 is a diagram illustrating vertical detection data after preprocessing.
FIG. 3 is a diagram of objective analysis of the quadrant of the typhoon moving path.
Fig. 4 is a schematic diagram of vertical probe points and four-quadrant classification scatter points within a typhoon path influence range, wherein (a) is a schematic diagram of vertical probe points within a "liqima" typhoon path influence range, and (b) is a schematic diagram of four-quadrant classification scatter points.
FIG. 5 is a schematic diagram of a high wind sample point within the "Liqima" typhoon path influence range.
Fig. 6 is a schematic diagram of the comparison between the typhoon-influenced wind profile (solid line) and the clear-air wind profile (dotted line) in the right quadrant of "liqima", wherein (a) is the wind profile detected by the 1663# wind measuring tower, and (b) is the wind profile detected by the 6602# wind measuring tower.
Fig. 7 is a schematic diagram comparing the wind profile (solid line) influenced by typhoon in the front left quadrant of "liqima" with the wind profile (dotted line) in clear sky, wherein (a) is the wind profile detected by the 58760# wind profile radar, (b) is the wind profile detected by the 58557# wind profile radar, (c) is the wind profile detected by the 1663# wind measuring tower, and (d) is the wind profile detected by the 6602# wind measuring tower.
Fig. 8 is a schematic diagram of a comparison between a typhoon-influenced wind profile (solid line) and a clear-air wind profile (dotted line) detected by a 1663# anemometer tower in a right quadrant of the "liqima".
Fig. 9 is a schematic diagram of the comparison between the typhoon-influenced wind profile (solid line) and the clear-sky wind profile (dotted line) detected by the 58760# wind profile radar in the left rear quadrant of the "liqima".
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. The described embodiments are only some, but not all embodiments of the invention. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the method for analyzing characteristics of landing typhoon and differences between them and characteristics of clear sky wind based on objective classification of influence space of the present invention at least includes the following steps:
SS1, preprocessing landing typhoon path and multi-source vertical gradient detection data;
SS2, adopting a typhoon moving path quadrant objective analysis method to classify data;
SS3, comprehensively utilizing a threshold value and a percentile method to select typhoon influence sample clusters;
SS4, selecting a clear sky sample cluster by using a proximity distance method and a cloud amount threshold value;
SS5, acquiring paired typhoon influence and clear air sample clusters by a space-time matching method;
and SS6, respectively carrying out wind profile comparison research in different influence quadrants.
1. In the step SS1, typhoon landing path and multi-source vertical gradient detection data preprocessing
And primarily selecting multi-source vertical detection data in the influence range of the optimal moving path of the landing typhoon according to the optimal moving path of the landing typhoon, such as wind profile radar detection data, anemometer tower gradient observation data, radio sounding data, ground meteorological station timing total cloud amount observation data and the like. The collected multi-source vertical detection data has a plurality of stiff values or false values caused by faults, sensor failures and the like, namely, the detection values of a plurality of continuous times are fixed or the wind speed shear of adjacent height layers exceeds a reasonable threshold value. For stiffness values, by a certain height level ut+1-utScreening and eliminating (0), wherein ut+1Represents the wind speed, u, at time t +1tRepresenting the wind speed at time t; for ghost values, the wind shear of the adjacent height layers is calculated by the following formula: α ═ ln (u)2/u1)/ln(z2/z1) Wherein u is1Wind speed, u, of the 1 st altitude layer2Representing the wind speed, z, of the 2 nd altitude layer1Indicates the altitude, z, of the 1 st altitude layer2The altitude of the 2 nd altitude layer is represented, and further false detection values exceeding a wind shear threshold value are removed, so that the validity of vertical detection data is guaranteed, as shown in fig. 2, in the diagram, TY1, TY2 and TY3 respectively represent three wind profiles at typhoon-influenced moments, and NON1, NON2 and NON3 respectively represent three clear air wind profiles during NON-typhoon-influenced periods.
2. In the above step SS2, data classification is performed by using a typhoon moving path quadrant objective analysis method
The distribution of detection stations in different influence quadrants at the optimal central position of the typhoon is dynamically calculated hour by utilizing the typhoon moving path and the station longitude and latitude information, and station detection data in the influence range is divided into four quadrant groups of right front, right back, left front and left back according to time and space characteristics, more specifically:
the quadrant objective analysis method of the typhoon moving path is shown in FIG. 3, in which the quadrangle star (x)s,ys) The lower right dot (x) at the position of a certain detection point S0,y0) The upper left dot (x) is the position of the typhoon at the current moment1,y1) And for the position of the typhoon at the next moment, identifying the typhoon influence quadrant to which the detection data of the detection point S at the current moment belong by calculating the theta angle. The whole identification process can be divided into the following steps:
I. calculating a probe point (x) using the typhoon path and the probe point position informations,ys) To the current position (x) of the typhoon0,y0) If R is smaller than a set threshold value, the detection point is in a typhoon influence range, otherwise, the detection point does not belong to the typhoon influence point, wherein the distance R from the detection point S to the current position of the typhoon is calculated by the following formula:
R＝6371×acos[cos(ys)×cos(y0)×cos(xs-x0)+sin(ys)×sin(y0)]
II, respectively calculating clockwise included angles theta of the due north direction, the typhoon moving direction and the detection point position direction by utilizing the trigonometric function relationsAnd theta1。
thetai＝atan((yi-y0)/(xi-x0))×180/3.14159
When y isi-y0＜0，xi-x0When < 0, thetai＝270-abs(thetai)；
When y isi-y0≥0，xi-x0When not less than 0, thetai＝90-abs(thetai)；
When y isi-y0＜0，xi-x0When not less than 0, thetai＝90+abs(thetai)；
When y isi-y0≥0，xi-x0When < 0, thetai＝270+abs(thetai)。
Theta is calculated and quadrant classification is performed.
theta＝thetas-thetaiWhen theta is less than 0, theta +360
When theta is less than or equal to 90, the quadrant is marked as the front right quadrant;
when theta is more than 90 and less than or equal to 180, marking as a right rear quadrant;
when theta is more than 180 and less than or equal to 270, marking as a left rear quadrant;
when 270 < theta ≦ 360, it is labeled as the "right front" quadrant.
Taking "liqima" typhoon as an example, the detection data of the wind profile radar (shown by pentagon in fig. 4 (a)) and the anemometer tower (shown by triangle in fig. 4 (a)) within the influence range (within 200 km) are divided into four influence quadrant groups, as shown in fig. 4 (b).
3. In the step SS3, selecting a strong wind sample cluster influenced by typhoon by comprehensively utilizing a threshold value and a percentile method
And in the four affected quadrant groups divided in the step SS2, taking a fixed detection point as a reference, and comprehensively referring to the wind speed threshold value and the percentile wind speed value of the characteristic height layer to select a strong wind sample cluster of which the detection point is affected by typhoon. Specifically, in the four affected quadrant groups of FIG. 4(b), the sample groups from the same probe point are sorted in ascending order of wind speed for the target elevation layer, i.e., u1＜u2…＜un，u1、u2、…、unRespectively the wind speeds of the target height layer of the detection point at different moments. The large wind sample cluster is sorted out by wind speed threshold (small sample size) or percentile method (large sample size), as shown in fig. 5.
4. In the above step SS4, selecting the cloud amount representative station and the clear sky sample cluster
According to the geographical positions of the vertical detection stations and the total cloud amount observation meteorological stations, representative cloud amount meteorological stations of the vertical detection stations are selected one by using a proximity distance method, the total cloud amount observed by the representative cloud amount meteorological stations is used for representing the total cloud amount of the vertical detection stations, and clear air sample clusters of all the vertical detection points which are not affected by typhoons are selected according to a total cloud amount threshold value. Specifically, vertical detection is calculated using the ground weather station and the detection point location informationPoint S (x)s,ys) To ground weather station (x)i,yi) Distance R ofiTaking RiThe meteorological station where the minimum value is located is a cloud volume representative station corresponding to the detection point, wherein the distance R from the detection point S to the ground meteorological stationiCalculated by the following formula:
Ri＝6371×acos[cos(ys)×cos(yi)×cos(xs-xi)+sin(ys)×sin(yi)]
and marking the observation samples with the total cloud amount of the meteorological station less than 2 as clear sky samples corresponding to the detection points.
5. In the above step SS5, paired typhoon influence and clear sky sample clusters are obtained by a space-time matching method
And in the four influence quadrant groups divided in the step SS2, selecting a clear sky sample pair consistent with the detection point of the moment of the strong wind sample cluster from the clear sky sample cluster finished in the step SS4 by taking the strong wind sample cluster selected in the step SS3 as a reference, and acquiring the typhoon influence and the clear sky sample cluster in one-to-one pairing mode. Specifically, in the four influence quadrant groups shown in fig. 4(b), with a detection point (subsurface mat type) as a reference, samples marked as clear sky at the same time are sequentially selected for one-to-one matching in a time period near the time when the strong wind sample belongs to (the background climate characteristics are consistent), for example, when the strong wind sample is 8 month and 8 day 08, samples marked as clear sky at 08 times can be selected for pairing in 8 month and 1 day to 8 month and 15 day (the time period range is adjustable), and finally, a one-to-one matching typhoon influence and clear sky sample pair is obtained.
6. In the step SS6, the typhoon-influenced wind profiles are respectively analyzed in different influence quadrants
And respectively carrying out the wind profiles in different influence quadrants and different analysis between the wind profiles and the clear air wind profiles in different underlay roughness environments by using the pairing data obtained in the step SS5 in the four influence quadrant groups divided in the step SS2. Specifically, as shown in fig. 6 to 9, profile analysis is performed on the screened and matched samples in four influence quadrants of the front right, the front left, the rear right and the rear left according to different underlying surface characteristics, so as to obtain a refined description of the landing typhoon characteristics.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
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