CN115617820A - Method for making deep learning data set for position-dependent radar quantitative precipitation estimation - Google Patents
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Abstract
The invention discloses a method for manufacturing a deep learning data set for radar quantitative precipitation estimation related to positions, which comprises the following steps: s1, calculating to obtain the observation height of a wave beam with a radar specific elevation angle above a meteorological station and the maximum DEM height of the position of the meteorological station, thereby determining the elevation angle serial number of the lowest layer non-shielding elevation angle observed by the radar corresponding to each meteorological station, and obtaining a mixed scanning strategy lookup table containing the lowest layer non-shielding elevation angle information of all the meteorological stations in the radar calibration observation range by combining the relevant data of the meteorological station and the radar; and S2, calling each record in the obtained mixed scanning strategy lookup table, generating quantitative precipitation estimation data samples related to the positions of all weather stations corresponding to the records according to the record content, and completing construction of a quantitative precipitation estimation deep learning data set related to the positions. The method provides data support for developing a position-related deep learning rainfall estimation algorithm, and effectively improves radar quantitative rainfall estimation level.
Description
Technical Field
The invention belongs to the field of deep learning data set manufacturing, and particularly relates to a deep learning data set manufacturing method for radar quantitative precipitation estimation related to positions.
Background
The quantitative rainfall estimation is one of the main purposes of the weather radar, and has important significance in the aspects of forecasting and early warning of disaster weather, artificial influence weather, researching numerical forecast modes and the like. Scientists in seriga and Bringi et al (1976) in the united states put forward the theory of dual polarization radar in the mid-70 20 th century, followed by the development of the dual polarization radar system CSU-CHILL, and succeeded in field trials. Dual polarization radars were developed in germany, japan, etc., successively in the 80's (giletetal, 1984. At the end of the 1980 s, researchers in our country have conducted two-line polarization radar research (Liu Li, et al, 1996; du Muyun, et al, 2013).
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a method for manufacturing a deep learning data set of radar quantitative precipitation estimation related to positions.
In order to achieve the above object, a method for producing a deep learning data set of a radar quantitative precipitation estimation based on a position correlation according to an embodiment of the present invention includes the steps of:
s1, establishing a hybrid scanning strategy lookup table containing the non-shielding elevation angle information of the lowest layer;
and S2, constructing a quantitative rainfall estimation deep learning data set related to the position.
Further, the step S1 establishes a hybrid scanning strategy lookup table including the lowest layer non-occlusion elevation angle information, specifically:
s11, acquiring related information and all DEM data of all M meteorological stations in a radar calibration observation range;
the related information at least comprises a weather station number, a distance between the weather station and a radar and a weather station azimuth angle;
s12, letting n =1, wherein n is a positive integer;
s13, determining an elevation angle serial number of a lowest layer of non-shielding elevation angle corresponding to radar observation of the nth weather station, and calculating to obtain the observation height of a wave beam of the radar elevation angle corresponding to the elevation angle serial number above the weather station and the maximum DEM height of the position of the weather station;
s14, writing the station number of the weather station of the nth weather station, the distance between the weather station and a radar, the azimuth angle of the weather station, the elevation sequence number, the observation height of the wave beam of the radar elevation corresponding to the elevation sequence number above the weather station and the maximum value of the DEM height of the position of the weather station as the recording data of the weather station into the mixed scanning strategy lookup table;
s15, making n = n +1, judging whether n is equal to or less than M, if so, repeatedly executing the steps S13 to S15, otherwise, executing the step S16;
and S16, obtaining a mixed scanning strategy lookup table containing the lowest layer non-shielding elevation angle information of the M meteorological station data.
Further, the step S13 determines an elevation angle serial number of the lowest layer of the radar observation corresponding to the nth weather station, which does not block the elevation angle, and calculates an observation height of the beam of the radar elevation angle corresponding to the elevation angle serial number above the weather station and a maximum DEM height of the position of the weather station, specifically:
s131, enabling j =0, wherein j is an integer larger than or equal to 0;
s132, calculating the observation height of the radar beam with the elevation angle of the sequence number j above the nth meteorological stationH beam ；
wherein ,H ant in order to determine the altitude of the radar antenna,H ele the absolute height of the radar beam at elevation number j above the weather station relative to the radar antenna,Lfor the radar beam propagation distance, ele is the elevation angle numbered j,r earth is the equivalent radius of the earth after standard atmospheric refraction,R resolution refers to the resolution of the range of the radar,R index the index value of the radar distance library closest to the upper part of the meteorological station is referred to;
s123, constructing a beam path triangle, and calculating the maximum DEM height of the weather station position passed by the radar beam in the process of transmitting to the nth weather stationH dem ；
S124, judgmentH beam >H dem If yes, executing step S125, otherwise, letting j = j +1, and repeatedly executing steps S122 to S124;
s125, determining j as the serial number of the unobstructed elevation angle of the nth weather station,H beam the observation height of the beam of the radar elevation angle corresponding to the elevation angle serial number j above the weather station,H dem the maximum DEM height for the weather station location.
Further, the step S123 constructs a beam path triangle, and calculates a maximum DEM height value of the weather station position passed by the radar beam in the process of transmitting to the nth weather stationH dem The method specifically comprises the following steps:
s1231, acquiring DEM matrix index values corresponding to two longitude and latitude coordinates of the radar station and the nth meteorological station, and recording the DEM matrix index values as rad _ DEM _ lon _ idx, rad _ DEM _ lat _ idx, aws _ DEM _ lon _ idx and aws _ DEM _ lat _ idx respectively;
s1232, acquiring a quadrant of the radial azimuth angle of the radar distance library corresponding to the nth meteorological station, and marking as quadrant;
s1233, respectively finding out the maximum and minimum longitude and latitude index values of four indexes of rad _ dem _ lon _ idx, rad _ dem _ lat _ idx, aws _ dem _ lon _ idx and aws _ dem _ lat _ idx, and marking as lon _ idx _ min, lon _ idx _ max, lat _ idx _ min and lat _ idx _ max to form a minimum truncated rectangle;
s1234, constructing a beam path triangle;
obtaining a tangent value through the length-width ratio of the minimum outer intercept rectangular range, and determining the angle of a beam central line; forming upper and lower boundary lines of a beam path by expanding the angle of the central line of the beam by 0.5 degrees up and down, calculating tangent values of the upper and lower boundary lines at the angle in a coordinate system taking a radar as an origin, and determining a beam path triangle by multiplying an x coordinate value by the tangent values of the upper and lower boundary lines, namely selecting a data range which is larger than the product of the lower boundary line and is smaller than the product of the upper boundary line;
s1235, finding out all indexes of the terrain height of the triangular surrounding area of the constructed wave beam path, and solving the maximum value of the indexes to obtain the maximum value of the DEM height of the position of the weather station where the elevation radar wave beam with the sequence number j passes in the process of transmitting to the weather stationH dem 。
Further, the step S2 constructs a quantitative precipitation estimation deep learning dataset related to a location, specifically:
s21, calling a first record of the established hybrid scanning strategy lookup table;
s22, generating quantitative precipitation estimation data samples related to the positions of the weather stations corresponding to the records according to the record contents;
and S23, sequentially calling all records in the mixed scanning strategy lookup table to execute the step S22, generating quantitative precipitation estimation data samples related to the positions of all M meteorological stations, and completing the construction of a quantitative precipitation estimation deep learning data set related to the positions.
Further, the step S22 generates quantitative precipitation estimation data samples related to the positions of the weather stations corresponding to the records according to the record contents, specifically:
s221, acquiring a corresponding distance library of the weather station in the record;
according to the recorded distance between the weather station and the radar and the elevation angle serial number, acquiring a distance library which is at the corresponding elevation angle of the elevation angle serial number and is closest to the weather station as a corresponding distance library;
s222, determining the acquired data selection range corresponding to the distance library;
determining distance library data of the same radial front-back and left-right adjacent radial calibration number k of the corresponding distance library as a data selection range on the corresponding elevation angle of the elevation angle serial number;
s223, generating a data sample of the meteorological station in the record;
and (6, k) matrixes consisting of the polarization quantities, the azimuth angles, the sea wave heights and the DEM of all the distance libraries in the determined data selection range and the precipitation quantity of the corresponding automatic meteorological station form quantitative precipitation estimation data samples related to the position of the meteorological station.
Further, the radar is an S-band double-linear polarization radar, and the weather station is an automatic weather station.
Further, the radar calibration observation range is 20-100km.
Further, the calibration number k is 25.
Further, 6 in the (6,k, k) matrix refers to 6 channels.
The invention has the beneficial effects that:
1. the invention calculates the height maximum value of the elevation radar beam passing through the DEM in the process of transmitting to the automatic meteorological station by constructing the beam path triangle and finding out the maximum value of the terrain height of the area surrounded by the triangleH dem The data are converted into geometric figures, so that the calculation efficiency is effectively improved;
2. the invention calculates the observation height of the radar wave beam at the elevation above the meteorological station and the maximum height value of the DEM passing through the radar wave beam in the process of transmitting to the automatic meteorological stationH dem And comparing the sizes of the two, the method can conveniently and effectively determine the lowest layer of the radar observation corresponding to the meteorological station without shielding the elevation angleElevation angle sequence number;
3. according to the method, a set of deep learning data set related to the radar quantitative precipitation estimation oriented position is constructed by adding parameters related to the radar observation position, so that data support is provided for developing a deep learning precipitation estimation algorithm related to the position, the radar quantitative precipitation estimation precision is promoted to be further improved, and the defense capability of rainstorm and secondary disasters is improved.
Drawings
FIG. 1 is a schematic diagram of dual-linear polarization radar beam occlusion for an embodiment of a method of deep learning dataset production for location-dependent radar quantitative precipitation estimation of the present invention;
FIG. 2 is a schematic diagram of dual linear polarization radar beam elevation calculation according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the dual linear polarization radar beam horizontal broadening propagation of one embodiment of the present invention;
FIG. 4 is a schematic diagram of the construction of RCB regions on DEM data in four quadrant representations according to one embodiment of the present invention;
FIG. 5 is a schematic diagram of a sample production flow of position-dependent "dual linear polarization radar survey-automated weather station precipitation" data for one embodiment of the present invention;
FIG. 6 shows an embodiment of the present invention, data sample data (25,25)Z H A schematic diagram of (a);
FIG. 7 is a graph comparing the evaluation results of a data set using the method of the present invention and a data set using a conventional method according to an embodiment of the present invention;
FIG. 8 is a flow chart of a method of the present invention for making a deep learning dataset for location-dependent radar quantitative precipitation estimation.
In the figure:
1-radar; 2-the ground; 3-a weather station; 4-radar beam; the 5-beam path triangle RBC.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description is further provided with reference to the accompanying drawings and examples.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Through research, one important reason affecting the estimation accuracy is: the current radar precipitation estimation methods based on deep learning are all location-independent, i.e. for a given radar observation (a), (b), (c), and (d)Z H ,Z DR AndK DP ) They yield the same estimation result regardless of the observation orientation (azimuth and elevation, etc.) of the radar, but this is obviously unscientific and affects the accuracy of the estimation, depending on the radar observation characteristics. The reason is that the data set of the current training deep learning model does not contain radar observation orientation related information, and therefore, in order to improve the radar quantitative precipitation estimation level, the data set applied by the position-related deep learning estimation algorithm needs to be developed.
As shown in FIG. 8, the invention provides a method for making a deep learning data set for quantitative precipitation estimation of radar based on position correlation, which is based on dual-linear polarization radar base data and adopts original mixed scanningZ H ,Z DR AndK DP on the basis, newly-increased and radar observation position relevant parameter, establish a set of radar ration rainfall estimation oriented position relevant deep learning data set, for the relevant deep learning rainfall estimation algorithm of development position provides data support, promote the further promotion of radar ration rainfall estimation precision.
As an embodiment of the present invention, two large links are required to build a deep learning dataset for a location-dependent radar quantitative precipitation estimate, as detailed below:
1. establishing a hybrid scanning strategy lookup table
Each individual sweep of the S-band dual-polarization weather radar in Guangdong province comprises 9 elevation angles (0.5 degrees, 1.5 degrees, 2.4 degrees, 3.3 degrees, 4.3 degrees, 6.0 degrees, 9.9 degrees, 14.6 degrees and 19.5 degrees; corresponding to elevation angle serial numbers 0,1,2,3,4,5,6,7,8 respectively), and mixed scanning data of the S-band dual-polarization weather radar is required to be obtained before quantitative precipitation estimation. The basic principle of the hybrid scanning strategy is to select the data (flare, 2019) of the lowest layer elevation angle at which the beam is not blocked, and the schematic diagram of the beam blocking of the dual-linear polarization radar is shown in fig. 1. To establish a position-dependent data set of 'dual-polarization radar observation-automatic weather station precipitation' for deep learning-oriented dual-polarization radar quantitative precipitation estimation, scanning data right above each ground automatic weather station is obtained, namely the lowest unobstructed elevation angle of radar observation data corresponding to the upper part of each ground automatic weather station is determined.
The above work is divided into the following two aspects:
on one hand, the influence of a bright band of a zero-degree layer is eliminated, the zero-degree layer is a strong echo band and a melting band, is a gas layer at the temperature of ℃ and is an important characteristic of lamellar cloud precipitation and usually appears in a place which is hundreds of meters below a zero-degree isotherm, and the bright band of the zero-degree layer reflects an obvious ice-water conversion area of the lamellar cloud precipitation. The reflection factor of the center of the bright band is 20 times larger than that of the ice crystals and snowflakes on the upper part of the bright band and is several times larger than that of the raindrops below the bright band, so that the bright band is formed. According to the scheme, observation data 20-100km away from the S-band double-linear polarization radar station is selected, the maximum height (less than about 2.8 km) of the radar lowest layer elevation wave beam which is not shielded above all automatic meteorological stations does not exceed a zero-degree layer (about 3 km), and therefore the influence of a zero-degree layer bright band on quantitative precipitation estimation does not need to be considered;
on the other hand, the lowest elevation angle of each automatic meteorological station corresponding to radar observation is determined according to a Digital Elevation Model (DEM), the used DEM data is ASTER GDEM V (advanced satellite heat carrier emission and reflection radiometer global Digital elevation model), the data is from the American space service (NASA), the data is manufactured according to a new generation earth observation satellite Terra, the spatial resolution of the data is 1 radian (about 30 meters) horizontally, 20 meters vertically, and the geographic latitude and longitude coordinates, and the data format is NETCDF (download website: https:// lpdaac. Usgs. Gov/products/astgtmv003 /).
The specific calculation idea of the invention comprises four steps:
first step of calculating the altitude of observation of a radar beam at a first elevation angle above an automated weather stationH beam ；
The second step is to solve the DEM height maximum value of the weather station position passing through the radar wave beam in the process of transmitting to the automatic weather stationH dem 。
The third step isH dem Is greater thanH beam Then, a second elevation angle is calculated in a loop from the first step untilH dem Is less than or equal toH beam The record is the record of the automatic station in the mixed scanning strategy lookup table;
and fourthly, circulating all the automatic weather stations to obtain the mixed scanning strategy lookup table records of all the automatic weather stations. Since the first step and the second step involve calculating expressions, they will be described separately below. The expressions needed are listed first:
in the above-mentioned expressions, a few of the expressions,Lin order for the radar beam to travel a distance,R resolution refers to the resolution of the range of the radar,R index the index value of the nearest radar distance library above the ground automatic weather station is referred to, ele is the elevation angle,H ant in order to determine the altitude of the radar antenna,r earth is the equivalent radius of the earth after standard atmospheric refraction,H ele the absolute height of the radar beam in elevation above the weather station relative to the radar antenna.
With fig. 2 as a schematic model, briefly describing the calculation process of the first step as follows:
(a) And finding a Radar bin index (Radar bin index) corresponding to the space above the ground automatic weather station, and calculating the value of L according to the expression (3).
(b) Calculating according to expression (2)H ele The value of (c).
(c) According to the expression (1), theH ant AndH ele are added to obtainH beam The value of (c).
Taking fig. 3 as a schematic model, briefly describing the calculation process of the second step as follows:
(a) DEM data with the resolution of 30m in Guangdong province is downloaded and read.
(b) A beam path triangular RBC as in fig. 3 was constructed and the maximum DME value was calculated. The specific process is as follows:
(1.) finding DEM matrix index values corresponding to two longitude and latitude coordinates of the radar station and the automatic weather station respectively, and marking the DEM matrix index values as rad _ DEM _ lon _ idx, rad _ DEM _ lat _ idx, aws _ DEM _ lon _ idx, aws _ DEM _ lat _ idx and quadrants to which radial azimuth angles of a radar distance library corresponding to the automatic weather station belong as quadrant;
(2.) find out the maximum and minimum longitude and latitude index values of four indexes of rad _ dem _ lon _ idx, rad _ dem _ lat _ idx, aws _ dem _ lon _ idx and aws _ dem _ lat _ idx, which are marked as lon _ idx _ min, lon _ idx _ max, lat _ idx _ min and lat _ idx _ max, respectively, and form the minimum truncated rectangle;
(3.) boundary calculation and data selection: obtaining a tangent (tan) value through an aspect ratio of a rectangular range, determining an angle of a beam centerline, expanding the beam centerline angle up and down by 0.5 degrees to form upper and lower boundary lines of a beam path, calculating an upper and lower boundary line tan value at an angle in a coordinate system with radar as an origin, and determining a beam path triangle through a product of an x-coordinate value and the upper and lower boundary tangent values, namely selecting a data range which is larger than the lower boundary product and smaller than the upper boundary product value (as shown in fig. 4);
(4.) find out all indexes of the terrain height of the triangle-enclosed area, which are recorded as dem _ idxs, and calculate the maximum value, which is recorded as dem _ idxsH dem 。
(c) ComparisonH beam AndH dem the size of (1) whenH beam Is greater thanH dem Then, it is the station of the automatic weather stationShielding the elevation angle; when the temperature is higher than the set temperatureH beam Is less than or equal toH dem And then, raising an elevation angle, and circularly calculating until finding the non-shielding elevation angle of the automatic weather station.
(d) And traversing all the automatic weather stations to form a look-up table of the mixed scanning strategy of the automatic weather stations for later use.
2. Constructing a location-dependent quantitative precipitation estimate deep learning dataset
Firstly, establishing a data sample of 'dual linear polarization radar observation-automatic weather station precipitation' of 1 position-related automatic weather station, and then constructing a data set through multiple cycles, wherein the specific steps are as follows (see figure 5):
(a) Selecting an automatic weather station: in order to avoid the influence of melting layers and ground clutter, an automatic meteorological station which is 20-100 kilometers away from a radar and has a precipitation observation value of more than 0.1mm/6min is selected for precipitation estimation.
(b) Sweeping a mixture: and (3) obtaining elevation angle data to be adopted by precipitation estimation of each automatic meteorological station by referring to the automatic meteorological station mixed scanning strategy lookup table manufactured by the step 1 (establishing the mixed scanning strategy lookup table).
(c) Position correspondence: and selecting the distance library which is closest to the automatic meteorological station on the corresponding elevation angle as a corresponding distance library according to the distance from the automatic meteorological station to the radar.
(d) Selecting the range of the distance library: at the selected elevation angle, distance library data corresponding to a specific number k (tested k =25 is the best effect, for example, 25 distance library ranges refer to 12 distance library ranges in front and back and 12 radial distance library ranges in left and right) of the same radial front and back and left and right adjacent distance library is selected, and the matrix is formed as k x k.
(e) Generating a data sample: the matrix (called data matrix in the form of (6, k)) composed of all polarization, azimuth, sea wave height and DEM of all range bins in the radar observation range and the corresponding automatic weather station precipitation (called label) form a sample, wherein 6 refers toZ H 、Z DR 、 K DP 6 channels, azimuth, altitude, and DEM, tested k =25The best estimated effect.
(f) And (e) repeating the steps (a) - (e) until the preparation of all the automatic weather station samples is completed, namely the preparation of the whole data set is completed.
3. Examples are:
the following description is given by taking the Guangzhou radar and the automatic weather station G590 as examples, in which 'a deep learning data set for quantitative rainfall estimation of the Guangzhou S-band dual-polarization radar is prepared for landing typhoon rainfall events':
(1) The method comprises the following steps of establishing a hybrid scanning strategy lookup table of the Guangzhou automatic weather station, and specifically comprising the following steps of:
(a) Preparing data: selecting all automatic weather stations within 20-100km of the observation range of the Guangzhou radar, and downloading DEM data of the observation range of the Guangzhou radar;
(b) Calculating the observed altitude of the radar beam at the first elevation angle above the automated weather station G590H beam ，R resolution =250m，R index =396，ele=0.5°，H ant =179m，r earth =6370km*1000=6370000m
L=250m*（396+1）=99250m;
H ele =99250m*sin(0.5*(pi/180))+（99250m） 2 /(6370000m*2)=579.900m
H beam =179m+579.900m=758.900m
(c) According to the algorithms described in fig. 3 and 4:
(d) Determining the lowest elevation angle for non-occlusion: here, theH dem <H beam Therefore, the lowest elevation angle of the automatic weather station G590 that is not blocked is 0.5 degrees, i.e. the elevation angle index is 0, and the hybrid scanning strategy look-up table is shown in table 1 below (bold).
(2) Taking typhoon "mangosteen" rainfall event as an example, explaining the step of constructing a deep learning data set for quantitative rainfall estimation of the Guangzhou S-band dual-polarization radar;
(a) Selecting an automatic station and mixed scanning elevation angle data thereof, selecting an automatic weather station G590 as an example within a range of 20-100km away from the radar, and establishing a mixed scanning strategy lookup table by reference to obtain an elevation angle (the serial number of the elevation angle is 0) of the station for rainfall estimation, wherein the elevation angle is 0.5 degrees.
(b) Position correspondence: according to the specific location of the automatic weather station G590, an elevation angle of 0.5 ° (elevation angle with serial number 0) is selected, the corresponding range bin for this station is azimuth 10 °, range bin serial number 396.
(c) Selecting the range of the distance library: for G590, i.e., at 0.5 ° elevation, 10 ° azimuth, 25 × 25 matrices of 25 distance library data in front of and behind and left and right of the same radial with distance library 396.
(d) Generating a data sample: for G590, the data (data) is formed as a (6,25,25) matrix (where FIG. 6 isZ H Schematic of (b) corresponding to the amount of precipitation it observes (label), a sample is formed.
(e) And circularly making all data samples to complete the construction of the whole data set.
The obtained data set is used as a test data set, the rainfall of the automatic weather station is used as a standard, radar data and terrain at intervals of 6 minutes are input into a network, the output is accumulated to be the hour rainfall, and the estimation accuracy is comprehensively measured by five indexes (as a calculation formula below) of a Correlation Coefficient (CC), a Root Mean Square Error (RMSE), a normalized relative deviation (NB), a normalized absolute error (NE) and a deviation ratio (as bias) of the radar estimation and the rainfall of the automatic station, and compared with the traditional method for analysis.
wherein ,RAis the accumulated rainfall obtained by radar (superscript rad) or automatic station (superscript gauge), the upper line indicates the average value,nis thatRA i gauge –R i radar The number of the pairs is such that,RMSEin the unit of a millimeter,NEandNBare all the percentage by weight of the total weight of the composition,bias ratiogreater (or less) than 1 indicates overestimation (or underestimation).
After 5043 estimation data are compared, and the data set manufactured by the method is used for researching the deep learning-based dual-polarization radar quantitative precipitation, the average error of the radar quantitative precipitation estimation is reduced to 3.67 from 4.36%, the value of the bias ratio is improved to 0.93 from 0.9, the better estimation accuracy is shown, and 5043 estimation results show a better convergence state as shown in figure 7.
Alternatively, in this embodiment, a person skilled in the art may understand that all or part of the steps in the methods of the foregoing embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
The present invention and its embodiments have been described above, and the description is not intended to be limiting, and the drawings are only one embodiment of the present invention, and the actual structure is not limited thereto. In summary, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for making a deep learning data set for position-dependent radar quantitative precipitation estimation is characterized by comprising the following steps:
s1, establishing a hybrid scanning strategy lookup table containing the non-shielding elevation angle information of the lowest layer;
and S2, constructing a quantitative rainfall estimation deep learning data set related to the position.
2. The method for producing a deep-learning dataset for radar quantitative precipitation estimation according to claim 1, wherein step S1 comprises establishing a hybrid scanning strategy lookup table containing non-occlusion elevation information at the lowest level, specifically:
s11, acquiring related information and all DEM data of all M meteorological stations in a radar calibration observation range;
the related information at least comprises a weather station number, a distance between the weather station and a radar and a weather station azimuth angle;
s12, letting n =1, wherein n is a positive integer;
s13, determining an elevation angle serial number of a lowest layer of non-shielding elevation angle corresponding to radar observation of the nth weather station, and calculating to obtain the observation height of a wave beam of the radar elevation angle corresponding to the elevation angle serial number above the weather station and the maximum DEM height of the position of the weather station;
s14, writing the station number of the weather station of the nth weather station, the distance between the weather station and a radar, the azimuth angle of the weather station, the elevation sequence number, the observation height of a beam of a radar elevation angle corresponding to the elevation sequence number above the weather station and the maximum value of the DEM height of the position of the weather station as the recording data of the weather station into the mixed scanning strategy lookup table;
s15, making n = n +1, judging whether n is equal to or less than M, if so, repeatedly executing the steps S13 to S15, otherwise, executing the step S16;
and S16, obtaining a mixed scanning strategy lookup table of the M meteorological station data, wherein the mixed scanning strategy lookup table comprises lowest layer non-shielding elevation angle information.
3. The method for producing a deep learning dataset for radar quantitative precipitation estimation according to claim 2, wherein the step S13 is to determine an elevation sequence number of the lowest unobstructed elevation angle of the radar observation corresponding to the nth weather station, and calculate the observation height of the beam of the radar elevation angle corresponding to the elevation sequence number above the weather station and the maximum DEM height of the weather station position, specifically:
s131, letting j =0, where j is an integer greater than or equal to 0;
s132, calculating radar beam observation height of elevation angle with serial number j above the nth weather stationH beam ；
wherein ,H ant in order to determine the altitude of the radar antenna,H ele the absolute height of the radar beam at elevation number j above the weather station relative to the radar antenna,Lfor the radar beam propagation distance, ele is the elevation angle numbered j,r earth is the equivalent radius of the earth after standard atmospheric refraction,R resolution refers to the resolution of the range of the radar,R index the index value of the radar distance library closest to the sky above the meteorological station is indicated;
s123, constructing a beam path triangle, and calculating the maximum DEM height of the weather station position passed by the radar beam in the process of transmitting to the nth weather stationH dem ；
S124, judgmentH beam >H dem If yes, executing step S125, otherwise, letting j = j +1, and repeatedly executing steps S122 to S124;
s125, determining j as the serial number of the unobstructed elevation angle of the nth weather station,H beam elevation angle of radar corresponding to elevation sequence number jThe observation height of the beam above the weather station,H dem is the maximum DEM height at the meteorological station location.
4. The method of claim 1, wherein the step S123 of constructing a beam path triangle calculates a DEM height maximum of a weather station position through which the radar beam propagates to the nth weather stationH dem The method specifically comprises the following steps:
s1231, acquiring DEM matrix index values corresponding to two longitude and latitude coordinates of the radar station and the nth meteorological station, and recording the DEM matrix index values as rad _ DEM _ lon _ idx, rad _ DEM _ lat _ idx, aws _ DEM _ lon _ idx and aws _ DEM _ lat _ idx respectively;
s1232, acquiring a quadrant to which a radial azimuth angle of the radar distance library corresponding to the nth meteorological station belongs, and marking as quadrant;
s1233, respectively finding out the maximum and minimum longitude and latitude index values of the four indexes of rad _ dem _ lon _ idx, rad _ dem _ lat _ idx, aws _ dem _ lon _ idx and aws _ dem _ lat _ idx, and recording the maximum and minimum longitude and latitude index values as lon _ idx _ min, lon _ idx _ max, lat _ idx _ min and lat _ idx _ max to form a minimum outer truncated rectangle;
s1234, constructing a beam path triangle;
obtaining a tangent value through the length-width ratio of the minimum outer intercept rectangular range, and determining the angle of a beam central line; forming upper and lower boundary lines of a beam path by expanding the angle of the central line of the beam by 0.5 degrees up and down, calculating tangent values of the upper and lower boundary lines at the angle in a coordinate system taking a radar as an origin, and determining a beam path triangle by multiplying an x coordinate value by the tangent values of the upper and lower boundary lines, namely selecting a data range which is larger than the product of the lower boundary line and is smaller than the product of the upper boundary line;
s1235, finding out all indexes of the terrain height of the triangular surrounding area of the constructed wave beam path, and solving the maximum value of the indexes to obtain the maximum value of the DEM height of the weather station position passed by the elevation radar wave beam with the sequence number of j in the process of transmitting to the weather stationH dem 。
5. The method for producing a deep learning dataset of radar quantitative precipitation estimates related to a location according to claim 2, wherein the step S2 is to construct a deep learning dataset of quantitative precipitation estimates related to a location, specifically:
s21, calling a first record of the established hybrid scanning strategy lookup table;
s22, generating quantitative precipitation estimation data samples related to the positions of the weather stations corresponding to the records according to the record contents;
and S23, sequentially calling all records in the mixed scanning strategy lookup table to execute the step S22, generating quantitative precipitation estimation data samples related to the positions of all M meteorological stations, and completing the construction of a quantitative precipitation estimation deep learning data set related to the positions.
6. The method as claimed in claim 5, wherein the step S22 is to generate the data samples of the position-related quantitative precipitation estimates of the weather station according to the record content, specifically:
s221, acquiring a corresponding distance library of the weather station in the record;
according to the recorded distance between the weather station and the radar and the elevation angle serial number, acquiring a distance library which is at the corresponding elevation angle of the elevation angle serial number and is closest to the weather station as a corresponding distance library;
s222, determining the acquired data selection range corresponding to the distance library;
determining distance library data of the same radial front-back and left-right adjacent radial calibration number k of the corresponding distance library as a data selection range on the corresponding elevation angle of the elevation angle serial number;
s223, generating data samples of the meteorological station in the record;
and (6, k) matrixes consisting of the polarization quantities, the azimuth angles, the sea wave heights and the DEM of all the distance libraries in the determined data selection range and the precipitation quantity of the corresponding automatic meteorological station form quantitative precipitation estimation data samples related to the position of the meteorological station.
7. The method of claim 1, wherein the radar is an S-band dual polarization radar and the weather station is an automated weather station.
8. The method of making a deep learning data set of location-dependent radar quantitative precipitation estimates of claim 1, wherein the radar calibration observation range is 20-100km.
9. The method of claim 6, wherein the number k of calibrations is 25.
10. The method of claim 6, wherein 6 of the (6, k) matrices refer to 6 channels.
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