CN114706146B - Method for forecasting growth of hail embryo and hail-down stage in hail-down storm process of complex terrain - Google Patents
Method for forecasting growth of hail embryo and hail-down stage in hail-down storm process of complex terrain Download PDFInfo
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Abstract
The invention belongs to the technical field of meteorological early warning methods, and discloses a method for forecasting the growth of hail embryos and hail-down stages in the hail-down storm process of complex terrains, which comprises the steps of collecting relevant data and carrying out quality pretreatment on the collected data; carrying out water-borne particle identification on the preprocessed data by adopting a fuzzy logic particle phase state identification algorithm to obtain the quantity, the change rate with time and the change rate ratio with time of key particles, and finally obtaining the hail embryo stage prediction index of the key particles; and judging the stage of the hail embryo according to the hail embryo stage prediction index, and issuing corresponding hail storm warning. According to the method, the stage of the hail embryo can be analyzed from the microphysics process according to the hail embryo stage prediction index, whether the hail embryo is in the development and growth stage or the hail generation stage can be judged, and early warning is provided for hail storm under complex terrains.
Description
Technical Field
The invention belongs to the technical field of meteorological early warning methods, and particularly relates to a method for forecasting growth of hail embryo and hail-down stage in the hail-down storm process of complex terrain.
Background
At present, hail is a solid precipitation phenomenon under strong convection conditions, and has the characteristics of strong locality, short duration, obvious influence by terrains and high strength, and is often accompanied with weather processes such as storm, storm and the like. The complex terrains cause frequent cold and hot air intersection activities, hail disasters are frequent, and huge losses are brought to agriculture, traffic and the like.
The stage of hail embryo is important to the development of hail and the identification of hail in hail suppression operation, and many students have conducted related researches on hail embryo. Zhang Xiaojuan et al (2019) show that the convective clouds in which hail processes often occur have mixed phase cloud characteristics, with the upper layer being ice crystals and snow, the middle layer being cloud water and aragonite particles, and the lower layer being the micro-physical structure of rain water. Due to the strong upward air flow, warm liquid particles are brought from below the lower layer to above the middle layer, and supercooled cloud rainwater is formed. Studies by Zhao et al (2020) have shown that supercooled cloud rainwater is frozen due to a decrease in temperature, and that formed freeze droplets and shot particles are the major sources of hail embryos, of which about 79% are shot particles, since the rate of formation of shot particles is greater than that of freeze droplets in the development stage of the hailstorm.
The thermodynamic phase state of the water-borne particles is used as one of the cloud micro physical characteristics, and the method for identifying the water-borne particles is more commonly known as a fuzzy logic algorithm and a Boolean logic decision tree method. Studies by Liu et al (2000) have shown that fuzzy logic algorithms allow soft and overlapping boundaries, reducing the impact of calibration and measurement errors on classification. The first application of fuzzy logic to aquatic particle identification, vivekanan et al (1999), established an S-band based aquatic particle fuzzy logic identification algorithm. Thompson et al (2014) established a method of identifying precipitation particles such as laths, dendrites, polymers, rime, rain, etc., based on a T matrix and Mueller scattering patterns. Dolan et al (2009) promoted the water particle fuzzy logic identification algorithm to the X-band dual-polarization radar, constructed the water particle fuzzy logic identification algorithm based on the X-band dual-polarization radar, and compared and analyzed the results of the S-band fuzzy logic identification algorithm.
Through patent and literature retrieval, the currently disclosed hail storm early warning is mainly focused on the qualitative analysis and exploration of the macroscopic level of observation data, but the forecasting indication of the hail embryo forming stage in the hail storm process is less involved, and the quantitative characteristic forecasting index of the hail embryo in the hail embryo developing process can not be obtained accurately from the microscopic level.
Through the above analysis, the problems and defects existing in the prior art are as follows: the prior hailstorm early warning is mainly focused on the qualitative analysis and exploration of the macroscopic level of the observed data, but the forecasting indication of the hailstorm formation stage in the hailstorm process is less involved, and the quantitative characteristic forecasting index of the hailstorm stage in the hailstorm development process can not be accurately obtained from the microscopic level.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a method for forecasting the growth of hail embryo and hail-down stage in the hail-down storm process of complex terrain.
The invention is realized in such a way that a method for forecasting the growth of hail embryo and hail-down stage in the hail-down storm process of complex terrain comprises the following steps:
step one, collecting related data, including sounding data and X-band dual-polarization radar data, wherein the sounding data comprises a temperature corresponding height layer, and the X-band dual-polarization radar data comprises basic reflectivity Z H Differential reflectance Z DR Differential propagation phase shift K DP Zero delay correlation coefficient ρ HV Providing data for the next quality preprocessing;
step two, carrying out quality pretreatment on the acquired data, wherein the radar data quality pretreatment comprises differential phase shift deconvolution, filtering, differential phase shift rate calculation and attenuation correction, and the pretreated data can be used for water-borne particle identification;
step three, carrying out water particle identification on the preprocessed data by adopting a fuzzy logic particle phase state identification algorithm to obtain the quantity, the change rate with time and the change rate ratio with time of key particles, and finally obtaining a hail embryo stage prediction index of the key particles, wherein the key particles comprise high-density aragonite particles HDG, low-density aragonite particles LDG and supercooled water SWA;
and step four, judging the stage of the hail embryo according to the hail embryo stage prediction index, and issuing corresponding hail storm warning.
Further, the method for forecasting the growth of the hail embryo and the hail-down stage in the hail-down storm process of the complex terrain provided by the embodiment of the invention further comprises the following steps:
the number of the key particle libraries is combined with the number of the key particle libraries at the initial moment of the change rate, and the change rate per unit time change rate ratio can be obtained by combining the number of the key particle libraries with the number of the key particle libraries for scanning radar data once for 6 minutes; the reflectivity in the vertical direction and the maximum echo intensity in the hailstorm can obtain normalized reflectivity;
the ratio of the transformation ratios and the normalized reflectivity of the three key particles are divided into weights of 0.2, 0.3 and 0.2, and the hail embryo stage prediction index of the key particles can be obtained through a weight weighting formula;
analysis of hail storm examples in 2018 and 2019 of the region shows that 0.25 can be used as a hail embryo stage prediction index threshold of the region, the obtained hail embryo stage prediction index is compared with the hail embryo stage prediction index threshold of 0.25, if the hail embryo stage prediction index is greater than 0.25, the hail embryo is in a development stage and hail warning is issued, and if the hail embryo stage prediction index is less than or equal to 0.25, the hail embryo is in a hail stage after hail formation or hail storm.
Further, the second step specifically includes the following steps:
s21, preprocessing radar data, including differential phase shift deconvolution, filtering, differential phase shift rate calculation and attenuation correction;
s22, preliminarily screening out reflectivity Z H An instance of 45 dBZ;
s23, obtaining a specific temperature corresponding height layer from the sounding data, and obtaining the altitude H corresponding to the temperature layer at 0 ℃ through interpolation 0 -20 ℃ temperature layer corresponding to altitude H D High layer thickness H at 0 ℃ to-20 DEG C S Calculation formula H S ＝H D -H 0 Screening out H S Examples are located between 2.5 and 3.5 km.
Further, in the step S21, differential phase shift folding is processed by adopting radial continuity checking, filtering is performed by a comprehensive wavelet denoising filtering method, a least square method calculates a differential phase shift rate for the filtered differential phase shift, and adaptive attenuation correction corrects the reflectivity and the differential reflectivity.
Further, the third step specifically includes the following steps:
s31, selecting reflectivity Z H High layer thickness H of not less than 45dBZ and 0 ℃ to-20 DEG C 0 In the example of 2.5-3.5 km, adopting a fuzzy logic particle phase state recognition algorithm to recognize the water particles in the vertical direction;
s32, identifying the hail embryo by adopting an asymmetric T-shaped function, and inputting four polarization parameters Z H 、Z DR 、K DP 、ρ HV And introducing a temperature parameter T replaced by sea level altitude;
s33, obtaining a change rate ratio of unit time according to the change rate of each particle, wherein the calculation formula is as follows:wherein M is SWA 、M LDG 、M HDG The ratio of the transformation ratios of SWA, LDG, HDG in the vertical direction; the reflectivity is normalized, and the calculation formula is +.>Wherein M is Z Z is the normalized reflectivity H For the reflectivity at each moment, MEI is the maximum echo intensity in the hailstorm process;
further, the step S32 specifically includes the following steps:
s321, when a weighted logic fuzzy algorithm is used, the method is according to the formulaAnd obtaining the number of particle libraries in the vertical direction, wherein TP (alpha) is a numerical value obtained by adopting an asymmetric T-shaped function in a fuzzy logic algorithm, and the basic form is as follows:
wherein X is 1 、X 2 、X 3 、X 4 As a function threshold, x is a variable value;
S i to correspond to Z H 、Z DR 、K DP 、ρ HV T is respectively 0.3, 0.2, 0.1 and 0.3, and specific values of the gross rain DZ, the rain RN, the low-density aragonite particles LDG, the high-density aragonite particles HDG and the distribution height of the high-density aragonite particles HDG are obtained after weighting, and DZ and RN above a height layer at 0 ℃ are called supercooled water SWA;
s322, obtaining SWA, LDG, HDG library number N in the vertical direction by fuzzy logic particle identification algorithm SWA 、N LDG And N HDG And calculates the change rate and counts the maximum intensity MEI and the reflectivity Z of the echo in the vertical direction H The unit is dBZ.
Further, the formula for calculating the change rate in step S322 is: wherein C is SWA 、C LDG And C HDG Is SWA, LDG, HDG the rate of change per unit time in the vertical direction, +.>For i time the number of banks of supercooled water in vertical direction, is->The number of the banks of supercooled water in the vertical direction after the Δt period from the time i; />For the pool number of low density aragonite particles in the vertical direction at time i,is the pool number of low density aragonite particles in the vertical direction after a period of Δt from time i; />For the number of bins of high density aragonite particles in the vertical direction at time i, +.>Is the pool number of high density aragonite particles in the vertical direction after a period of Δt from time i.
Further, the fourth step specifically includes the following steps:
s41, through a weight weighting formulaObtaining hail embryo stage prediction index, wherein M is Comprising supercooled water conversion ratio M SWA Low density aragonite ratio M LDG High density aragonite ratio M HDG Normalized reflectance M Z The weight P of each parameter is 0.2, 0.3 and 0.2;
s42, judging the stage of the hail embryo according to the value of the hail embryo stage prediction index HT, when HT is less than or equal to 0.25, the hail embryo is in the hail stage when hail is generated or hail storm is in the hail stage, and when HT is more than 0.25, the hail embryo is in the development stage and hail warning is issued.
In combination with the above technical solution and the technical problems to be solved, please analyze the following aspects to provide the following advantages and positive effects:
first, aiming at the technical problems in the prior art and the difficulty in solving the problems, the technical problems solved by the technical proposal of the invention are analyzed in detail and deeply by tightly combining the technical proposal to be protected, the results and data in the research and development process, and the like, and some technical effects brought after the problems are solved have creative technical effects. The specific description is as follows:
the invention is technically realized by identifying the water particles through a fuzzy logic algorithm, and then carrying out technical extraction on the stage where the hail embryo is located, specifically, extracting the related quantity of the dual-polarization radar data, carrying out technical extraction, setting threshold limiting, obtaining the change ratio of the low-density aragonite particles, the high-density aragonite particles and other related water particles in a specific period and the reflectivity after normalization, finally obtaining the hail embryo stage prediction index through statistical weighting, and the index prediction index can be used for roughly determining the stage where the hail embryo is located in the hail storm process and indicating whether hail can be reduced or not.
Secondly, the technical scheme is regarded as a whole or from the perspective of products, and the technical scheme to be protected has the following technical effects and advantages:
the hail embryo stage prediction index is obtained through statistical weighting, the stage of the hail embryo in the hail embryo development process can be determined approximately from a microscopic level through index prediction, and whether hail indication can be made or not and whether hail storm needs to be early warned or not can be judged through the stage of the hail embryo.
Thirdly, as inventive supplementary evidence of the claims of the present invention, the following important aspects are also presented:
the technical scheme of the invention fills the technical blank in the domestic and foreign industries:
the prior hailstorm early warning is mainly focused on the qualitative analysis and exploration of the macroscopic level of the observed data, but the forecasting indication of the hailstorm formation stage in the hailstorm process is less involved, and the quantitative characteristic forecasting index aiming at the hailstorm stage in the hailstorm development process can be obtained from the microscopic level.
Drawings
Fig. 1 is a flowchart of a method for forecasting the growth of hail embryos and hail-down stages in the hail-down storm process of complex terrains according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a method for forecasting the growth of hail embryos and hail-down stages in the hail-down storm process of complex terrains according to an embodiment of the present invention.
Fig. 3 shows reflectance profiles (in dBZ) taken at times 16:44 (a) and 16:49 (b) along radial directions 25 ° and 27 °, respectively, according to an embodiment of the present invention.
Fig. 4 is a cross section of the results of identifying water-borne particles at times 16:44 (a) and 16:49 (b) along radial directions of 25 ° and 27 °, respectively, according to an embodiment of the present invention.
Fig. 5 shows reflectance profiles (in dBZ) taken at times 17:01 (a) and 17:06 (b) along radial directions of 32 ° and 33 °, respectively, according to an embodiment of the present invention.
Fig. 6 is a diagram of 17: and (3) respectively making water-borne particle identification result sections along the radial directions of 32 degrees and 33 degrees at the time (a) and the time (b) of 17:06.
Fig. 7 shows reflectivity profiles (in dBZ) taken at times 16:48 (a) and 16:54 (b) along radial directions 140 ° and 131 °, respectively, according to a second embodiment of the present invention.
Fig. 8 is a cross section of the results of identifying water-borne particles at times 16:48 (a) and 16:54 (b) according to the second embodiment of the present invention, wherein the results are 140 ° and 131 ° in the radial direction.
Fig. 9 shows reflectance profiles (in dBZ) taken along radial directions 126 ° and 121 ° at times 17:01 (a) and 17:08 (b), respectively, according to a second embodiment of the present invention.
Fig. 10 is a cross section of the results of identifying water-borne particles at the times 17:01 (a) and 17:08 (b) according to the second embodiment of the present invention, which are respectively made along the radial directions 126 ° and 121 °.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
1. The embodiments are explained. In order to fully understand how the invention may be embodied by those skilled in the art, this section is an illustrative embodiment in which the claims are presented for purposes of illustration.
As an embodiment of the present invention, as shown in fig. 1, the method for forecasting the growth and hail-down stage of the hail embryo in the hail-down storm process of the complex terrain according to the embodiment of the present invention includes:
s101, collecting related data, wherein the related data comprises sounding data and X-band dual-polarization radar data, the sounding data comprises a temperature corresponding height layer, and the X-band dual-polarization radar data comprises basic reflectivity Z H Differential reflectance Z DR Differential propagation phase shift K DP Zero delay correlation coefficient ρ HV Providing data for the next quality preprocessing;
s102: the quality preprocessing of the acquired data comprises differential phase shift deconvolution, filtering, differential phase shift rate calculation and attenuation correction, and the preprocessed data can be used for water-borne particle identification;
s103, carrying out water particle identification on the preprocessed data by adopting a fuzzy logic particle phase state identification algorithm to obtain the number of key particles, the change rate with time and the change rate ratio with time, and finally obtaining the hail embryo stage prediction index of the key particles, wherein the key particles comprise high-density aragonite particles HDG, low-density aragonite particles LDG and supercooled water SWA;
s104, judging the stage of the hail embryo according to the hail embryo stage prediction index, and issuing corresponding hail storm warning.
Step S102 in the embodiment of the present invention includes the following steps:
s21, preprocessing radar data, including differential phase shift deconvolution, filtering, differential phase shift rate calculation and attenuation correction;
s22, preliminarily screening out reflectivity Z H An instance of 45 dBZ;
s23, obtaining a specific temperature corresponding height layer from the sounding data, and obtaining the altitude H corresponding to the temperature layer at 0 ℃ through interpolation 0 -20 ℃ temperature layer corresponding to altitude H D High layer thickness H at 0 ℃ to-20 DEG C S Calculation formula H S ＝H D -H 0 Screening out H S Examples located between 2.5 and 3.5 km;
step S103 in the embodiment of the present invention includes the following steps:
s31, selecting reflectivity Z H High layer thickness H of not less than 45dBZ and 0 ℃ to-20 DEG C 0 In the example of 2.5-3.5 km, adopting a fuzzy logic particle phase state recognition algorithm to recognize the water particles in the vertical direction;
s32, identifying the hail embryo by adopting an asymmetric T-shaped function, and inputting four polarization parameters Z H 、Z DR 、K DP 、ρ HV And introducing a temperature parameter T replaced by sea level altitude;
s321, when a weighted logic fuzzy algorithm is used, the method is according to the formulaThe number of particle libraries in the vertical direction can be obtained, wherein TP (alpha) is a numerical value obtained by adopting an asymmetric T-shaped function in a fuzzy logic algorithm, and the basic form is as follows:
wherein X is 1 、X 2 、X 3 、X 4 As the function threshold value, x is a variable value, and the function threshold value is set as shown in table 1;
S i to correspond to Z H 、Z DR 、K DP 、ρ HV T is respectively 0.3, 0.2, 0.1 and 0.3, and specific values of the gross rain DZ, the rain RN, the low-density aragonite particles LDG, the high-density aragonite particles HDG and the distribution height of the high-density aragonite particles HDG are obtained after weighting, and DZ and RN above a height layer at 0 ℃ are called supercooled water SWA;
TABLE 1 setting of function thresholds
S322, obtaining SWA, LDG, HDG library number N in the vertical direction by fuzzy logic particle identification algorithm SWA 、N LDG And N HDG And calculates the change rate and counts the maximum intensity MEI and the reflectivity Z of the echo in the vertical direction H The unit is dBZ; the formula for calculating the rate of change is: wherein C is SWA 、C LDG And C HDG Is SWA, LDG, HDG the rate of change per unit time in the vertical direction, +.>For i time the number of banks of supercooled water in vertical direction, is->The number of the banks of supercooled water in the vertical direction after the Δt period from the time i; />For the pool number of low density aragonite particles in the vertical direction at time i, +.>Is the pool number of low density aragonite particles in the vertical direction after a period of Δt from time i; />For the number of bins of high density aragonite particles in the vertical direction at time i, +.>Is the pool number of high density aragonite particles in the vertical direction after a period of Δt from time i;
s33, obtaining a change rate ratio of unit time according to the change rate of each particle, wherein the calculation formula is as follows:wherein M is SWA 、M LDG 、M HDG The transformation ratios of SWA, LDG, HDG in the vertical direction are respectively C SWA 、C LDG And C HDG A rate of change per unit time in the vertical direction of SWA, LDG, HDG; the reflectivity is normalized, and the calculation formula is +.>Wherein M is Z Z is the normalized reflectivity H For the reflectivity at each moment, MEI is the maximum echo intensity in the hailstorm process;
step S104 in the embodiment of the present invention includes the following steps:
s41, through a weight weighting formulaObtaining hail embryo stage prediction index, wherein M is Comprising supercooled water conversion ratio M SWA Low density aragonite ratio M LDG High density aragonite ratio M HDG Normalized reflectance M Z The weight P of each parameter is 0.2, 0.3 and 0.2;
s42, judging the stage of the hail embryo according to the value of the hail embryo stage prediction index HT, when HT is less than or equal to 0.25, the hail embryo is in the hail stage when hail is generated or hail storm is in the hail stage, and when HT is more than 0.25, the hail embryo is in the development stage and hail warning is issued.
2. Application example. In order to prove the inventive and technical value of the technical solution of the present invention, this section is an application example on specific products or related technologies of the claim technical solution.
After radar data of 30 days of 3 months and 18 days of 5 months in 2018 are preprocessed, reflectivity and height layer thickness of 0 ℃ to-20 ℃ in the example are verified, relevant key particle change rate and change rate ratio are obtained after the identification of the aquatic substance particles, and the hail embryo stage prediction index can be obtained by combining the normalized reflectivity, so that the stage of the hail embryo is judged.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
3. Evidence of the effect of the examples. The embodiment of the invention has a great advantage in the research and development or use process, and has the following description in combination with data, charts and the like of the test process.
The hail storm process of experimental example one occurs on 30 days 3 months 2018, the duration of the whole process is 34 minutes, the strongest echo is 61.6dBZ, hail cloud monomer generation Yu Weining county is above northeast, 16: a large area of greater than 55dBZ occurs in the monomer at time 55 and hail is reduced at 17: weining was removed at time 29.
As shown in fig. 3 a, it is 16 in experimental example one: time 44, 16: as shown in fig. 2 b at time 49, the echo top height increases rapidly from 5km to 6km or more, and the maximum reflectance reaches 54.2dBZ. After interpolation of the corresponding height layer of the temperature layer in the exploration data at 20 days, the height layer with the temperature of 0 ℃ is 4.1km, the height layer with the temperature of-20 ℃ is 7.3km, namely the height layer thickness H with the temperature of 0 ℃ to-20 ℃ can be obtained S Is 3.2km, thereby meeting the selection requirement of the embodiment.
As shown in fig. 4, the ratio of supercooled water to low-density aragonite, high-density aragonite and normalized reflectivity of 0.34, 0.03, 0.25 and 0.88 at a certain moment of hail storm is selected, and the obtained ht=0.328 is calculated through weighted summation, which belongs to the range of HT >0.25, and hail embryo is in the development stage and hail warning is issued.
As shown in fig. 5 and 6, the supercooled water transformation ratio, the low-density aragonite transformation ratio, the high-density aragonite transformation ratio and the normalized reflectivity of 0.03, -0.07, -0.11 and 0.9 are selected at a certain moment of the hailstorm, and the obtained ht=0.132 is calculated through weighted summation, so that the ht=0.132 is within the range of HT less than or equal to 0.25, and the hailstorm is in the hailstorm stage.
The hailstorm process of experimental example two occurs on 18 days 5 months 2018, the whole process lasts for 60 minutes, the strongest echo is 67.2dBZ, the line system is generated in Weining double dragon, one of the monomer researches is selected, and 16: a large value region of greater than 55dBZ occurs within the cell at time 48, 17: hail reduction starts at 03, at 17: the dissipation phase is entered at time 41.
As shown in fig. 7 a, the experimental example two is 16:48, 16: as shown in fig. 7 b at time 54, the echo top height rapidly increases from 9km to 10km or more, and the maximum reflectance reaches 61.8dBZ. After interpolation of the corresponding height layer of the temperature layer in the exploration data at 20 days, the height layer with the temperature of 0 ℃ is 5.2km, the height layer with the temperature of-20 ℃ is 8.3km, namely the height layer thickness H with the temperature of 0 ℃ to-20 ℃ can be obtained S Is 3.1km, thereby meeting the selection requirement of the embodiment.
As shown in fig. 8, the ratio of supercooled water to low-density aragonite, high-density aragonite and normalized reflectivity of 0.23, 0.12, 0.08 and 0.92 at a certain moment of hail storm is selected, and the obtained ht=0.29 is calculated through weighted summation, which belongs to the range of HT >0.25, and hail embryo is in the development stage and hail warning is issued.
As shown in fig. 9 and 10, the supercooled water transformation ratio, the low-density aragonite transformation ratio, the high-density aragonite transformation ratio and the normalized reflectivity of 0.003, -0.04, -0.01 and 0.92 are selected at a certain moment of the hailstorm, and the obtained ht= 0.1696 is calculated through weighted summation and belongs to the range of HT less than or equal to 0.25, wherein the hailstorm is in the hailstorm-generated stage or the hailstorm is in the hailstorm-down stage.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (7)
1. A method for forecasting the growth and hail-down stage of a hail embryo in the hail-down storm process of a complex terrain, which is characterized in that the method for forecasting the growth and hail-down stage of the hail embryo in the hail-down storm process of the complex terrain comprises the following steps:
step one, collecting related data, including sounding data and X-band dual-polarization radar data, wherein the sounding data comprises a temperature corresponding height layer, and the X-band dual-polarization radar data comprises basic reflectivity Z H Differential reflectance Z DR Differential propagation phase shift K DP Zero delay correlation coefficient ρ HV Providing data for the next quality preprocessing;
step two, carrying out quality pretreatment on the acquired data, wherein the radar data quality pretreatment comprises differential phase shift deconvolution, filtering, differential phase shift rate calculation and attenuation correction, and the pretreated data can be used for water-borne particle identification;
step three, carrying out water particle identification on the preprocessed data by adopting a fuzzy logic particle phase state identification algorithm to obtain the quantity, the change rate with time and the change rate ratio with time of key particles, and finally obtaining a hail embryo stage prediction index of the key particles, wherein the key particles comprise high-density aragonite particles HDG, low-density aragonite particles LDG and supercooled water SWA;
judging the stage of the hail embryo according to the hail embryo stage prediction index, and issuing corresponding hail storm warning;
adopting radial continuity checking to process differential phase shift folding, filtering by a comprehensive wavelet denoising filtering method, calculating differential phase shift rate for the filtered differential phase shift by a least square method, and correcting reflectivity and differential reflectivity by self-adaptive attenuation correction;
the third step specifically comprises the following steps:
selecting the reflectivity Z H In the example that the thickness H0 of the height layer is 2.5-3.5 km and is more than or equal to 45dBZ and is between 0 ℃ and-20 ℃, adopting a fuzzy logic particle phase state identification algorithm to identify the aquatic particles in the vertical direction;
identifying hail embryo by adopting asymmetric T-shaped function, and inputting four polarization parameters Z H 、Z DR 、K DP 、ρ HV And introducing a temperature parameter T replaced by sea level altitude;
the change rate ratio per unit time can be obtained from the change rate of each particle, and the calculation formula is as follows:
wherein M is SWA 、M LDG 、M HDG The ratio of the transformation ratios of SWA, LDG, HDG in the vertical direction; the reflectivity is normalized, and the calculation formula is +.>Wherein M is Z Z is the normalized reflectivity H For the reflectivity at each moment, MEI is the maximum echo intensity in the hailstorm process;
identifying hail embryo by adopting asymmetric T-shaped function, and inputting four polarization parameters Z H 、Z DR 、K DP 、ρ HV And introducing a temperature parameter T replaced by sea level altitude comprises in particular the following steps:
when using a weighted logic fuzzy algorithm, the formula is followedAnd obtaining the number of particle libraries in the vertical direction, wherein TP (alpha) is a numerical value obtained by adopting an asymmetric T-shaped function in a fuzzy logic algorithm, and the basic form is as follows:
wherein X is 1 、X 2 、X 3 、X 4 As a function threshold, x is a variable value;
si is corresponding to Z H 、Z DR 、K DP 、ρ HV The weights of T are respectively 0.3, 0.2, 0.1 and 0.3, and specific numerical values of the hair rain DZ, the rain RN, the low-density aragonite particles LDG and the high-density aragonite particles HDG and the distribution heights of the specific numerical values are obtained after weighting;
the number N of the libraries occupied by SWA, LDG, HDG in the vertical direction is obtained by a fuzzy logic particle identification algorithm SWA 、N LDG And N HDG And calculate the variationThe conversion rate and the maximum intensity MEI and the reflectivity Z of the echo in the vertical direction are counted H The unit is dBZ;
the number N of the libraries occupied by SWA, LDG, HDG in the vertical direction is obtained by a fuzzy logic particle identification algorithm SWA 、N LDG And N HDG The formula for calculating the rate of change is:
wherein C is SWA 、C LDG And C HDG Is SWA, LDG, HDG the rate of change per unit time in the vertical direction, +.>The number of vertically oriented banks of supercooled water at time i,the number of the banks of supercooled water in the vertical direction after the Δt period from the time i; />For the pool number of low density aragonite particles in the vertical direction at time i, +.>Is the pool number of low density aragonite particles in the vertical direction after a period of Δt from time i;for the number of bins of high density aragonite particles in the vertical direction at time i, +.>Is the pool number of high density aragonite particles in the vertical direction after a period of Δt from time i.
2. The method for forecasting the growth and hail stage of a hail embryo during a hail storm in a complex terrain as defined in claim 1, wherein said method for forecasting the growth and hail stage of a hail embryo during a hail storm in a complex terrain further comprises:
the number of the key particle libraries is combined with the number of the key particle libraries at the initial moment of the change rate, and the change rate per unit time change rate ratio can be obtained by combining the number of the key particle libraries with the number of the key particle libraries for scanning radar data once for 6 minutes; the reflectivity in the vertical direction and the maximum echo intensity in the hailstorm can obtain normalized reflectivity;
the ratio of the transformation ratios and the normalized reflectivity of the three key particles are divided into weights of 0.2, 0.3 and 0.2, and the hail embryo stage prediction index of the key particles can be obtained through a weight weighting formula;
analysis of hail storm examples in 2018 and 2019 shows that 0.25 can be used as a hail embryo stage prediction index threshold value in the region, the obtained hail embryo stage prediction index is compared with the hail embryo stage prediction index threshold value of 0.25, if the hail embryo stage prediction index is greater than 0.25, the hail embryo is in a development stage and hail warning is issued, and if the hail embryo stage prediction index is less than or equal to 0.25, the hail embryo is in a hail stage after hail formation or hail storm.
3. The method for forecasting the growth and hail-down stage of a hail embryo in the hail-down storm process of a complex terrain according to claim 1, wherein the step two specifically comprises the following steps:
preprocessing radar data, including differential phase shift deconvolution, filtering, calculating differential phase shift rate and attenuation correction;
preliminary screening out reflectivity Z H An instance of 45 dBZ;
the specific temperature corresponding height layer obtained from the sounding data can be interpolated to obtain the altitude height H0 corresponding to the temperature layer at 0 ℃ and the altitude height HD corresponding to the temperature layer at-20 ℃ and the height layer thickness HS at 0 ℃ to-20 ℃, the calculation formula HS=HD-H0, and the example of the HS at 2.5 km to 3.5km is screened out.
4. The method for forecasting the growth and hail-down stage of a hail embryo during hail-down storm in complex terrain as defined in claim 1, wherein said step four specifically comprises the steps of:
by weighting formula of weightsObtaining hail embryo stage prediction index, wherein M is Comprising supercooled water conversion ratio M SWA Low density aragonite ratio M LDG High density aragonite ratio M HDG Normalized reflectance M Z The weight P of each parameter is 0.2, 0.3 and 0.2;
and judging the stage of the hail embryo according to the value of the hail embryo stage prediction index HT, when HT is less than or equal to 0.25, the hail embryo is in the hail stage of generated hail or hail storm, and when HT is more than 0.25, the hail embryo is in the development stage and hail warning is issued.
5. A computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the method of forecasting the growth and hail-down stage of a hail embryo during a hail-down storm in a complex terrain as claimed in any one of claims 1 to 4.
6. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the method of forecasting the growth and hail stage of a hail embryo in a hail storm process in a complex terrain as claimed in any one of claims 1 to 4.
7. An information data processing terminal, wherein the information data processing terminal is used for realizing the method for forecasting the growth and hail-down stage of the hail embryo in the hail-down storm process of the complex terrain according to any one of claims 1 to 4.
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