CN111680259B - Cloud particle phase state identification method and system - Google Patents
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Abstract
The application discloses a cloud particle phase state identification method and a cloud particle phase state identification system, wherein the method comprises the following steps: acquiring cloud particle observation data based on various foundation remote sensing observation modes; identifying cloud particle observation data by using a cloud base height identification algorithm to obtain a cloud base height identification result; calculating to obtain a cloud top height identification result by utilizing the cloud bottom height identification result and a cloud top height identification algorithm; and identifying cloud particle observation data, cloud bottom height identification results and cloud top height identification results by utilizing a cloud particle phase identification algorithm to obtain cloud particle phase identification results. According to the application, cloud particle observation data is obtained based on various foundation remote sensing observation modes, so that the cloud particle observation data has higher detection precision, and is beneficial to the fine observation of supercooled water cloud development change in the precipitation process; and the cloud particle phase state is identified by using a cloud bottom height identification algorithm, a cloud top height identification algorithm and a cloud particle phase state identification algorithm, so that monitoring and research on the effect of the human shadow operation are facilitated, and an important basis is provided for the human shadow operation decision.
Description
Technical Field
The application relates to the field of meteorological monitoring, in particular to a cloud particle phase state identification method and system.
Background
In weather modification work, the specific effect of artificial intervention on the precipitation process is difficult to evaluate, and an important mechanism of artificial rainfall is to apply disturbance to supercooled water particles or provide condensation nuclei to promote the supercooled water particles to condense, so that cloud droplets are promoted to collide and grow to generate precipitation. The existing weather radar detects cloud areas under the limitation of the arrangement density, detection distance and scanning interval of radar stations, the farther the radar is located in the radar detection range, the lower the scanning resolution is, the more difficult to provide high-resolution fine detection data, and in areas which cannot be covered by the radar, the suitable operation area can only be estimated manually through experience, so that guidance is provided for human shadow operation; the micro-pulse laser radar can detect the sphericity of particles through the polarization channel so as to identify ice crystals and liquid drops, but because the laser radar is sensitive to aerosol on a boundary layer, how to distinguish aerosol layers from cloud layers is always a difficulty of a laser radar cloud identification algorithm, and whether the liquid drops are in a supercooled state cannot be judged, so that the method has larger uncertainty.
Disclosure of Invention
In view of the above, the embodiment of the application provides a cloud particle phase state identification method and a cloud particle phase state identification system, which solve the problem that in the prior art, weather work for developing artificial influence is influenced by radar coverage area, cloud particle phase states cannot be accurately identified, and thus people's shadow work has larger uncertainty.
In order to achieve the above purpose, the present application provides the following technical solutions:
in a first aspect, an embodiment of the present application provides a cloud particle phase identification method, including the following steps: acquiring cloud particle observation data based on various foundation remote sensing observation modes; identifying the cloud particle observation data by using a cloud base height identification algorithm to obtain a cloud base height identification result; calculating to obtain a cloud top height identification result by using the cloud bottom height identification result and a cloud top height identification algorithm; and identifying the cloud particle observation data, the cloud bottom height identification result and the cloud top height identification result by utilizing a cloud particle phase identification algorithm to obtain a cloud particle phase identification result.
In an embodiment, the acquiring cloud particle observation data based on the plurality of foundation remote sensing observation modes includes the following steps: the laser radar acquires normalized back scattering signal data and depolarization ratio data, and the microwave radiometer acquires temperature profile data and initial height value of the cloud base.
In an embodiment, the cloud particle observation data is identified by using a cloud base height identification algorithm to obtain a cloud base height identification result, and the method includes the following steps: processing the normalized back scattering signal data and the depolarization ratio data by utilizing wavelet transformation, and determining a signal change rate extreme point to obtain a cloud base suspected point height value; and identifying the initial height value of the cloud base and the suspected point height value of the cloud base by using a cloud base height identification algorithm to obtain a cloud base height identification result.
In an embodiment, the method for identifying the initial height value of the cloud base and the suspected point height value of the cloud base by using a cloud base height identification algorithm to obtain a cloud base height identification result includes the following steps: performing wavelet change on the height value of each cloud base suspected point to obtain a cloud base suspected point wavelet signal; the intensity of each cloud bottom suspected point height value in the cloud bottom suspected point wavelet signal profile is obtained, the intensity is ordered from high to low, and the intensity coefficients of the preset number of the cloud bottom suspected point height values are sequentially given; determining a distance weight coefficient by using the initial height value of the cloud base and the suspected point height value of the cloud base; determining the sum of the distance weight coefficients and the intensity coefficients as the weight coefficient of the height value of each cloud base suspected point; and determining a cloud bottom suspected point height value corresponding to the maximum value in the weight coefficient as a cloud bottom height identification result.
In one embodiment, the intensity coefficient is calculated by the following formula:
W p ＝k*(N+1-m)
wherein W is p The method is characterized in that the method comprises the steps of representing intensity coefficients, k represents adjustment parameters, the value range of k is between 0 and 1, N represents preset numbers, and m=1, 2 and 3 … … N represent intensity ranking numbers.
In one embodiment, the distance weight coefficient is calculated by the following formula:
wherein W is H (H) Represents the distance weight coefficient, H i Representing the height value of suspected points at the bottom of the cloud, dH i And the absolute value of the difference between the suspected point height value of the cloud base and the initial height value of the cloud base is represented.
In an embodiment, the cloud particle phase state recognition algorithm is used to recognize the cloud particle observation data, the cloud bottom height recognition result and the cloud top height recognition result to obtain a cloud particle phase state recognition result, and the method includes the following steps: determining a supercooling water cloud layer identification weight coefficient by using a preset temperature range, a preset phase state discrimination threshold value and a preset pixel point range; determining a region with the supercooled water cloud layer identification weight coefficient larger than a first preset threshold value as a supercooled water region; determining a region with the supercooling water cloud layer identification weight coefficient larger than a second preset threshold value as a suspected supercooling water region, and determining the difference between the supercooling water cloud layer identification weight and the second preset threshold value as a credibility index of the suspected supercooling water; obtaining depolarization ratio data of a preset inclined path, and calculating the difference between the depolarization ratio data and the depolarization ratio data of the preset inclined path; and determining the supercooled water region or the suspected supercooled water region with the deviation ratio larger than a third preset threshold value as a large ice crystal region of the flat plate.
In an embodiment, the determining the supercooled water cloud layer identification weight coefficient by using the preset temperature range, the preset phase state discrimination threshold and the preset pixel point range includes the following steps:
calculating the temperature weight coefficient of the temperature profile data of the microwave radiometer by using the preset temperature range through the following formula:
wherein W is T0 (T) represents a temperature weight coefficient, T represents a temperature of the microwave radiometer;
and calculating cloud particle phase state discrimination weight coefficients of depolarization ratio data by using the preset phase state discrimination threshold value through the following formula:
wherein W is D (DEP) represents a cloud particle phase state discrimination weight coefficient, DEP represents a depolarization ratio, DEPc represents a water cloud discrimination threshold, and DEPi represents an ice cloud discrimination threshold;
calculating a cloud pixel weight coefficient of the laser radar relative to the back scattering signal data by using the preset pixel point range through the following formula:
wherein W is C (NRB) represents a cloud pixel weight coefficient, NRB represents a laser radar normalized back-scattered signal, NRB0 represents a clean atmosphere judgment threshold, and NRB1 represents a cloud judgment threshold;
multiplying the temperature weight coefficient, the cloud particle phase state discrimination weight coefficient and the cloud pixel weight coefficient to obtain the supercooled water cloud layer identification weight coefficient.
In a second aspect, an embodiment of the present application provides a cloud particle phase identification system, including the following modules: the acquisition module is used for acquiring cloud particle observation data based on various foundation remote sensing observation modes; the first identification module is used for identifying the cloud particle observation data by utilizing a cloud base height identification algorithm to obtain a cloud base height identification result; the second identification module is used for calculating and obtaining a cloud top height identification result by utilizing the cloud bottom height identification result and a cloud top height identification algorithm; and the third recognition module is used for recognizing the cloud particle observation data, the cloud bottom height recognition result and the cloud top height recognition result by utilizing a cloud particle phase recognition algorithm to obtain a cloud particle phase recognition result.
The embodiment of the application provides a computer readable storage medium, which stores computer instructions that when executed by a processor implement the cloud particle phase identification method according to the first aspect and any optional manner of the application.
The embodiment of the application provides electronic equipment, which comprises: the cloud particle phase identification method comprises a memory and a processor, wherein the memory and the processor are in communication connection, computer instructions are stored in the memory, and the processor executes the computer instructions, so that the cloud particle phase identification method according to the first aspect and any optional mode of the application is executed.
The technical scheme of the application has the following advantages:
1. according to the cloud particle phase state identification method and system, cloud particle observation data are acquired based on various foundation remote sensing observation modes, so that the cloud particle phase state identification method and system have higher time, spatial resolution and detection precision, and are favorable for fine observation of supercooled water cloud development change in a precipitation process; the cloud particle phase state is identified by using a cloud bottom height identification algorithm, a cloud top height identification algorithm and a cloud particle phase state identification algorithm, so that monitoring and research on the effect of the shadow operation are facilitated, the statistical rule of local supercooled water cloud occurrence can be counted, and an important basis is provided for the shadow operation decision.
2. According to the cloud particle phase state identification method and system, the laser radar and the microwave radiometer work cooperatively, so that the supercooled water area in the cloud is monitored in a minute-level real-time manner with high space-time resolution, a more reliable cloud bottom height initial value is obtained, unreasonable wave crests are eliminated by wavelet transformation to obtain cloud bottom suspected points, and the cloud bottom suspected point height value with highest reliability is determined as the cloud bottom height value by a cloud bottom height identification algorithm; the microwave radiometer is utilized to provide cloud bottom height and temperature profile data, so that the problems that cloud layers and aerosol layers are difficult to identify by a traditional method and the problem that warm water cloud and supercooled water cloud are distinguished by lacking in temperature data are solved, the identification reliability is greatly improved, and the monitoring equipment is good in convenience and more suitable for field operation and radar blind area operation.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a specific example of a cloud particle phase identification method according to an embodiment of the present application;
FIG. 2 is a flowchart of a specific example of cloud base height identification provided by an embodiment of the present application;
fig. 3 is a flowchart of a specific example of obtaining a cloud particle phase recognition result by using a cloud particle phase recognition algorithm according to an embodiment of the present application;
fig. 4 is a schematic diagram of a cloud particle phase identification system according to an embodiment of the present application;
fig. 5 is a composition diagram of a specific example of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the application are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In addition, the technical features of the different embodiments of the present application described below may be combined with each other as long as they do not collide with each other.
Example 1
The embodiment of the application provides a cloud particle phase state identification method, which is shown in fig. 1 and comprises the following steps:
step S1: cloud particle observation data are obtained based on various foundation remote sensing observation modes.
In the embodiment of the application, acquiring cloud particle observation data based on a plurality of foundation remote sensing observation modes comprises the following steps: the laser radar acquires normalized back scattering signal data and depolarization ratio data, and the microwave radiometer acquires temperature profile data and initial height value of the cloud base. The laser radar can detect cloud particles in real time, detect the sphericity of the particles through the polarization channel, and further identify ice crystals and liquid drops by identifying normalized back scattering signal data and depolarization ratio data, and the laser radar can distinguish the liquid drops from the ice crystals, but cannot judge whether the liquid drops are in a supercooled state or not, so that a real-time air temperature profile and a cloud bottom initial height value are acquired through the microwave radiometer to assist the laser radar in judging cloud areas and cloud particle phase states.
It should be noted that, in the embodiment of the present application, the cloud particle observation data is obtained by a plurality of manners of ground-based remote sensing observation, such as a laser radar and a microwave radiometer, and in practical application, the cloud particle observation data may be obtained by other remote sensing means, which is not limited by the present application.
Step S2: and identifying the cloud particle observation data by using a cloud base height identification algorithm to obtain a cloud base height identification result.
In the embodiment of the application, cloud particle observation data are identified by using a cloud base height identification algorithm, an observation device is used for carrying out synchronous correction on signals by combining with correction parameters of a laser radar on observation results of aerosol and cloud particle backscattering signal vertical profiles on all observation sites, and further, normalized backscattering signals are recorded as NRB and depolarization ratio signal vertical profiles are calculated, a passive infrared probe of a microwave radiometer provides real-time monitoring of cloud base infrared brightness temperature, and further, a temperature profile inverted in real time by combining with a microwave radiometer is used for estimating the cloud base height.
In practical application, because the laser radar is sensitive to cloud particles and aerosol particles, in the NRB profile of the laser radar, both an aerosol layer and a cloud layer are represented as high-value areas, for example, the observation data of a certain day are taken as an example, more aerosol exists in the night residual layer on the day, and obvious multi-layer distribution is presented, so that the aerosol in the layer cloud and the aerosol in the night residual layer which are positioned at the height of about 1.0km in the morning (06-12) are difficult to distinguish effectively through an algorithm; compared with a laser radar, the infrared probe of the microwave radiometer is more sensitive to cloud particles, the inversion result is basically not influenced by an aerosol layer (except for fog), but the resolution is lower (about 500 m), so that a cloud base initial height value which is more reliable and has lower resolution can be provided for a cloud base identification algorithm of the laser radar by utilizing a cloud base height product of the microwave radiometer, and on the basis, the cloud base height can be obtained by utilizing Haar wavelet transformation to process an NRB profile of the laser radar and finding a wavelet signal peak point which is positioned near the cloud base suspected point height.
Step S3: and calculating to obtain a cloud top height identification result by using the cloud bottom height identification result and a cloud top height identification algorithm.
In the embodiment of the application, the cloud top height identification result is calculated by utilizing the cloud bottom height identification result and the cloud top height identification algorithm, and for the laser radar, signals are difficult to penetrate through a cloud layer, so that noise signals are represented in the signals above a cloud zone, and on the basis of the cloud bottom height, the cloud top height is defined as the position where NRB signals first appear negative above cloud bottom height CB, namely the position where signals are lost, and the fact that the cloud top height is not an actual cloud top, but is the top of a cloud zone which can be effectively detected by the laser radar is needed.
Step S4: and identifying cloud particle observation data, cloud bottom height identification results and cloud top height identification results by utilizing a cloud particle phase identification algorithm to obtain cloud particle phase identification results.
In the embodiment of the application, the depolarization ratio data of the laser radar can identify the sphericity of cloud particles, a first preset threshold value is given in a cloud particle phase state identification algorithm, and the cloud with the depolarization ratio lower than the first preset threshold value is considered to be in a liquid-water phase state; giving a second preset threshold, and considering that clouds with depolarization ratio higher than the second preset threshold are ice clouds, wherein the depolarization ratio between the first preset threshold and the second preset threshold is an uncertain phase state; meanwhile, in the embodiment of the application, the microwave radiometer can give out an air temperature vertical profile of 58 layers of 0-10km at a certain time interval (for example, 2 minutes), the 0-degree isotherm height H0 is calculated by using the profile, the-4-degree isotherm height H4 is calculated, the liquid water cloud above the 0-degree isotherm can be regarded as supercooled water cloud according to supercooled water cloud definition, in actual human shadow operation, the supercooled water cloud in a temperature region above-4 degrees is regarded as being most suitable for operation, and therefore, the supercooled water cloud region can be obtained by inversion through identifying the liquid water cloud region between H0 and H4.
In practical application, a cloud particle phase recognition result can be obtained through a cloud particle phase recognition algorithm: a water cloud zone, a supercooled water cloud zone, a suspected supercooled water cloud zone, an ice cloud zone, and a flat large ice crystal zone.
According to the cloud particle phase state identification method, cloud particle observation data are acquired based on various foundation remote sensing observation modes, so that the cloud particle phase state identification method has higher time, spatial resolution and detection precision, and is beneficial to the fine observation of supercooled water cloud development change in the precipitation process; the cloud particle phase state is identified by using a cloud bottom height identification algorithm, a cloud top height identification algorithm and a cloud particle phase state identification algorithm, so that monitoring and research on the effect of the shadow operation are facilitated, the statistical rule of local supercooled water cloud occurrence can be counted, and an important basis is provided for the shadow operation decision.
In a specific embodiment, the cloud particle observation data is identified by using a cloud base height identification algorithm to obtain a cloud base height identification result, which comprises the following steps:
step S21: and processing the normalized back scattering signal data and the depolarization ratio data by utilizing wavelet transformation, and determining the extreme point of the signal change rate to obtain the height value of the suspected point of the cloud bottom.
In the embodiment of the application, the main characteristic points of the wind profile are identified by adopting Haar wavelet transformation, the wavelet transformation is used for calculating the accumulated value W of the product of the profile and the function from the lower height limit zb to the upper height limit zt in the range of the height limit zt, wherein W is actually calculated by taking the point with the height b as the center and taking the gradient of the average value of the width parts of the upper and lower a/2 as the center, and the extremum of the signal gradient is expressed as the peak (valley) value of the wavelet signal. The accumulated value W of each height point is calculated by the formula (1):
wherein W (b) represents an accumulated value of each height point, a represents a width of the wavelet signal, b represents a center of each height point, z represents a height of the wavelet signal, zb represents a lower height limit, and zt represents an upper height limit.
The constructed wavelet function h can be represented by formula (2):
where h (x) represents the wavelet function and x represents the signal point.
By reasonably adjusting the width a, the wavelet transformation can effectively filter out high-frequency and low-frequency fluctuation of signals, so that only the fluctuation part of a specific wavelength interval with the half width of a wave crest near the a is reserved, wherein the main characteristic point of the cloud bottom height is the maximum value point of NRB (non-return coefficient) variability, namely the main wave crest position in the whole wavelet profile, and the wave crest and all wave troughs which have small fluctuation and are relatively unreasonable are eliminated. Through wavelet transformation, the wave crest and the wave trough of the NRB signal are close to 0 value in the wavelet signal, and the extreme value position of the signal transformation rate is expressed as the wave crest and the wave trough in the wavelet signal, so that the position of the extreme value of the signal transformation rate, namely the suspected point of the cloud base height, can be determined, and the suspected point height value of the cloud base is calculated.
Step S22: and identifying the initial height value of the cloud base and the suspected point height value of the cloud base by using a cloud base height identification algorithm to obtain a cloud base height identification result.
In the embodiment of the application, the obtained cloud bottom suspected point height value has some problems, such as existence of an aerosol layer under the cloud, small fluctuation still exists in the NRB signal, and the amplitude of the wavelet signal is too small at the moment and belongs to unimportant fluctuation; yun Zhongli sub-distribution may have strong irregular fluctuations in the NRB signal, where the amplitude of the wavelet signal is large, but unlike the NRB signal at the cloud base, which increases approximately monotonically with height, there are a large number of peak-troughs and thus appear as a large number of intersections (zero points) with the 0-value line in the wavelet profile. Therefore, a filtering algorithm of unreasonable points is required to be added to highlight main cloud base height fluctuation information, a cloud base initial height value and a cloud base suspected point height value are identified by using a cloud base height identification algorithm, and a cloud base suspected point height position with highest reliability is obtained, so that a cloud base height identification result can be obtained.
In a specific embodiment, as shown in fig. 2, a cloud base initial height value and a cloud base suspected point height value are identified by using a cloud base height identification algorithm, so as to obtain a cloud base height identification result, which includes the following steps:
step S221: and carrying out wavelet change on the height values of the suspected points of the cloud base to obtain wavelet signals of the suspected points of the cloud base.
Step S222: and (3) acquiring the intensity of each cloud bottom suspected point height value in the wavelet signal profile of the cloud bottom suspected point, sequencing the intensity from high to low, and sequentially endowing the preset number of intensity coefficients of the cloud bottom suspected point height values.
In the embodiment of the application, the intensities of the height values of each cloud bottom suspected point in the wavelet signal profile of the cloud bottom suspected point are obtained, the intensities in the wavelet signal profile of each cloud bottom suspected point are ordered, the intensity coefficients are sequentially assigned to the cloud bottom suspected points with the intensities arranged in the front preset number (for example, the front 5) and can be calculated by the formula (3):
W p ＝k*(N+1-m) (3)
wherein W is p The method is characterized in that the method comprises the steps of representing intensity coefficients, k represents adjustment parameters, the value range of k is between 0 and 1, N represents preset numbers, and m=1, 2 and 3 … … N represent intensity ranking numbers. It should be noted that the adjustment parameters are set to adapt to different observation conditions and geographic conditions of different observation regions, and may be adjusted according to actual situations, which is not limited by the present application.
Step S223: and determining a distance weight coefficient by using the initial height value of the cloud base and the suspected point height value of the cloud base.
In the embodiment of the application, the distance weight coefficient is determined by using the initial height value of the cloud base and the suspected point height value of the cloud base through a formula (4):
wherein W is H (H) Represents the distance weight coefficient, H i Representing the height value of suspected points at the bottom of the cloud, dH i And the absolute value of the difference between the suspected point height value of the cloud base and the initial height value of the cloud base is represented.
Step S224: and determining the sum of the distance weight coefficients and the intensity coefficients as the weight coefficient of the height value of each cloud bottom suspected point.
In the embodiment of the application, the weight coefficient is calculated by the formula (5):
W CB ＝W p +W H (5)
wherein W is CB Weight coefficient representing height value of suspected point of cloud bottom, W p Representing the intensity coefficient, W H Representing the distance weight coefficient.
Step S225: and determining a suspected cloud bottom point height value corresponding to the maximum value in the weight coefficient as a cloud bottom height identification result.
In a specific embodiment, as shown in fig. 3, a cloud particle phase recognition algorithm is used to recognize cloud particle observation data, a cloud bottom height recognition result and a cloud top height recognition result, so as to obtain a cloud particle phase recognition result, which includes the following steps:
step S41: and determining a supercooled water cloud layer identification weight coefficient by using a preset temperature range, a preset phase state discrimination threshold value and a preset pixel point range.
In the embodiment of the application, the temperature weight coefficient of the temperature profile data of the microwave radiometer is calculated by using a preset temperature range through a formula (6):
wherein W is T0 (T) represents a temperature weight coefficient, and T represents a temperature of the microwave radiometer.
Calculating cloud particle phase state discrimination weight coefficients of depolarization ratio data through a formula (7) by using a preset phase state discrimination threshold value:
wherein W is D (DEP) represents a cloud particle phase state discrimination weight coefficient, DEP represents a depolarization ratio, DEPc represents a water cloud discrimination threshold, and DEPi represents an ice cloud discrimination threshold.
Calculating a cloud pixel weight coefficient of the laser radar relative to the back scattering signal data through a formula (8) by using a preset pixel point range:
wherein W is C (NRB) represents a cloud pixel weight coefficient, NRB represents a laser radar normalized back scattering signal, NRB0 represents a clean atmosphere judgment threshold, NRB1 represents a cloud judgment threshold, and the temperature weight coefficient, the cloud particle phase judgment weight coefficient and the cloud pixel weight coefficient are multiplied to obtain a supercooled water cloud layer identification weight coefficient. It should be noted that, the preset threshold and the judgment threshold in the embodiment of the present application are determined according to actual experience or existing theory, and the present application is not limited thereto.
Step S42: and determining a region with the supercooled water cloud layer identification weight coefficient larger than a first preset threshold value as a supercooled water region.
Step S43: and determining a region with the supercooled water cloud layer identification weight coefficient larger than a second preset threshold value as a suspected supercooled water region, and determining the difference between the supercooled water cloud layer identification weight and the second preset threshold value as a credibility index of the suspected supercooled water.
It should be noted that, in the embodiment of the present application, the first preset threshold and the second preset threshold are set according to actual experience and actual requirements, and the present application is not limited thereto.
Step S44: and obtaining the depolarization ratio data of the preset inclined path, and calculating the depolarization ratio difference between the depolarization ratio data and the depolarization ratio data of the preset inclined path.
In the embodiment of the application, in the ice crystal cloud, a type of vertically-oriented flat large ice crystal is arranged, and when the laser radar performs vertical zenith observation, the flat large ice crystal has a very low depolarization ratio, so that the flat large ice crystal is often misjudged to be supercooled water cloud, and in order to solve the problem, a preset oblique path (for example, an oblique path with a zenith angle of 5 degrees) is added to the laser radar for observation, and the flat large ice crystal can be observed to have a remarkably high depolarization ratio. Thus, the difference in the off-set ratio between the vertical path and the diagonal path at the same level is calculated.
Step S45: and determining the supercooled water region or the suspected supercooled water region with the deviation ratio difference larger than the third preset threshold value as a plate large ice crystal region.
According to the cloud particle phase state identification method, the laser radar and the microwave radiometer work cooperatively to realize the minute-level real-time monitoring of high space-time resolution of a supercooled water area in the cloud, so that a more reliable cloud bottom height initial value is obtained, unreasonable wave crests are eliminated by wavelet transformation to obtain cloud bottom suspected points, and the cloud bottom suspected point height value with highest reliability is determined as a cloud bottom height value by a cloud bottom height identification algorithm; the microwave radiometer is utilized to provide cloud bottom height and temperature profile data, so that the problems that cloud layers and aerosol layers are difficult to identify by a traditional method and the problem that warm water cloud and supercooled water cloud are distinguished by lacking in temperature data are solved, the identification reliability is greatly improved, and the monitoring equipment is good in convenience and more suitable for field operation and radar blind area operation.
Example 2
An embodiment of the present application provides a cloud particle phase identification system, as shown in fig. 4, including:
the acquisition module 1 is used for acquiring cloud particle observation data based on various foundation remote sensing observation modes; this module performs the method described in step S1 in embodiment 1, and will not be described here again.
The first identification module 2 is used for identifying cloud particle observation data by utilizing a cloud base height identification algorithm to obtain a cloud base height identification result; this module performs the method described in step S2 in embodiment 1, and will not be described here.
The second recognition module 3 is used for calculating and obtaining a cloud top height recognition result by utilizing the cloud bottom height recognition result and a cloud top height recognition algorithm; this module performs the method described in step S3 in embodiment 1, and will not be described here.
The third recognition module 4 is configured to recognize the cloud particle observation data, the cloud bottom height recognition result, and the cloud top height recognition result by using a cloud particle phase recognition algorithm, so as to obtain a cloud particle phase recognition result; this module performs the method described in step S4 in embodiment 1, and will not be described here.
The cloud particle phase state identification system provided by the application acquires cloud particle observation data based on various foundation remote sensing observation modes, has higher time, spatial resolution and detection precision, and is beneficial to the fine observation of supercooled water cloud development change in the precipitation process; the cloud particle phase state is identified by using a cloud bottom height identification algorithm, a cloud top height identification algorithm and a cloud particle phase state identification algorithm, so that monitoring and research on the effect of the human shadow operation are facilitated, the statistical rule of local supercooled water cloud occurrence can be counted, and an important basis is provided for the human shadow operation decision; the method has the advantages that the minute-level real-time monitoring with high space-time resolution is carried out on the supercooled water area in the cloud, the problem that cloud layers and aerosol layers are difficult to identify by the traditional method and the problem that warm water cloud and supercooled water cloud are distinguished by lacking temperature data are solved, the identification reliability is greatly improved, the monitoring equipment is good in convenience, and the method is more suitable for field operation and radar blind area operation.
Example 3
An embodiment of the present application provides an electronic device, as shown in fig. 5, including: at least one processor 401, such as a CPU (Central Processing Unit ), at least one communication interface 403, a memory 404, at least one communication bus 402. Wherein communication bus 402 is used to enable connected communications between these components. The communication interface 403 may include a Display screen (Display) and a Keyboard (Keyboard), and the optional communication interface 403 may further include a standard wired interface and a wireless interface. The memory 404 may be a high-speed RAM memory (Ramdom Access Memory, volatile random access memory) or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 404 may also optionally be at least one storage device located remotely from the aforementioned processor 401. Wherein the processor 401 may perform the cloud particle phase identification method of embodiment 1. A set of program codes is stored in the memory 404, and the processor 401 calls the program codes stored in the memory 404 for executing the cloud particle phase identification method of embodiment 1.
The communication bus 402 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. Communication bus 402 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one line is shown in fig. 5, but not only one bus or one type of bus.
Wherein the memory 404 may include volatile memory (English) such as random-access memory (RAM); the memory may also include a nonvolatile memory (english: non-volatile memory), such as a flash memory (english: flash memory), a hard disk (english: hard disk drive, abbreviated as HDD) or a solid-state drive (english: SSD); memory 404 may also include a combination of the above types of memory.
The processor 401 may be a central processor (English: central processing unit, abbreviated: CPU), a network processor (English: network processor, abbreviated: NP) or a combination of CPU and NP.
Wherein the processor 401 may further comprise a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof (English: programmable logic device). The PLD may be a complex programmable logic device (English: complex programmable logic device, abbreviated: CPLD), a field programmable gate array (English: field-programmable gate array, abbreviated: FPGA), a general-purpose array logic (English: generic array logic, abbreviated: GAL), or any combination thereof.
Optionally, the memory 404 is also used for storing program instructions. The processor 401 may call program instructions to implement the cloud particle phase identification method according to embodiment 1 of the present application.
The embodiment of the application also provides a computer readable storage medium, and computer executable instructions are stored on the computer readable storage medium, and the computer executable instructions can execute the cloud particle phase identification method of the embodiment 1. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), or a Solid-State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present application.
Claims (8)
1. The cloud particle phase state identification method is characterized by comprising the following steps of:
cloud particle observation data is obtained based on a plurality of foundation remote sensing observation modes, and the cloud particle observation data comprises: the laser radar acquires normalized back scattering signal data and depolarization ratio data, and the microwave radiometer acquires temperature profile data and a cloud base initial height value;
and identifying the cloud particle observation data by using a cloud base height identification algorithm to obtain a cloud base height identification result, wherein the cloud base height identification method comprises the following steps of:
processing the normalized back scattering signal data and the depolarization ratio data by utilizing wavelet transformation, and determining a signal change rate extreme point to obtain a cloud base suspected point height value;
performing wavelet change on the height value of each cloud base suspected point to obtain a cloud base suspected point wavelet signal;
the intensity of each cloud bottom suspected point height value in the cloud bottom suspected point wavelet signal profile is obtained, the intensity is ordered from high to low, and the intensity coefficients of the preset number of the cloud bottom suspected point height values are sequentially given;
determining a distance weight coefficient by using the initial height value of the cloud base and the suspected point height value of the cloud base;
determining the sum of the distance weight coefficients and the intensity coefficients as the weight coefficient of the height value of each cloud base suspected point;
determining a cloud bottom suspected point height value corresponding to the maximum value in the weight coefficient as a cloud bottom height identification result;
calculating to obtain a cloud top height identification result by using the cloud bottom height identification result and a cloud top height identification algorithm;
and identifying the cloud particle observation data, the cloud bottom height identification result and the cloud top height identification result by utilizing a cloud particle phase identification algorithm to obtain a cloud particle phase identification result.
2. The cloud particle phase identification method according to claim 1, wherein the intensity coefficient is calculated by the following formula:
W p ＝k*(N+1-m)，
wherein W is p The method is characterized in that the method comprises the steps of representing intensity coefficients, k represents adjustment parameters, the value range of k is between 0 and 1, N represents preset numbers, and m=1, 2 and 3 … … N represent intensity ranking numbers.
3. The cloud particle phase identification method according to claim 1, wherein the distance weight coefficient is calculated by the following formula:
wherein W is H (H) Represents the distance weight coefficient, H i Representing the height value of suspected points at the bottom of the cloud, dH i And the absolute value of the difference between the suspected point height value of the cloud base and the initial height value of the cloud base is represented.
4. The cloud particle phase identification method according to claim 1, wherein the identifying the cloud particle observation data, the cloud bottom height identification result, and the cloud top height identification result by using a cloud particle phase identification algorithm to obtain a cloud particle phase identification result comprises:
determining a supercooling water cloud layer identification weight coefficient by using a preset temperature range, a preset phase state discrimination threshold value and a preset pixel point range;
determining a region with the supercooled water cloud layer identification weight coefficient larger than a first preset threshold value as a supercooled water region;
determining a region with the supercooling water cloud layer identification weight coefficient larger than a second preset threshold value as a suspected supercooling water region, and determining the difference between the supercooling water cloud layer identification weight and the second preset threshold value as a credibility index of the suspected supercooling water;
obtaining depolarization ratio data of a preset inclined path, and calculating the difference between the depolarization ratio data and the depolarization ratio data of the preset inclined path;
and determining the supercooled water region or the suspected supercooled water region with the deviation ratio larger than a third preset threshold value as a large ice crystal region of the flat plate.
5. The cloud particle phase identification method of claim 4, wherein determining the supercooled water cloud layer identification weight coefficient using a preset temperature range, a preset phase discrimination threshold, and a preset pixel range comprises:
calculating the temperature weight coefficient of the temperature profile data of the microwave radiometer by using the preset temperature range through the following formula:
wherein W is T0 (T) represents a temperature weight coefficient, T represents a temperature of the microwave radiometer;
and calculating cloud particle phase state discrimination weight coefficients of depolarization ratio data by using the preset phase state discrimination threshold value through the following formula:
wherein W is D (DEP) represents a cloud particle phase state discrimination weight coefficient, DEP represents a depolarization ratio, DEPc represents a water cloud discrimination threshold, and DEPi represents an ice cloud discrimination threshold;
calculating a cloud pixel weight coefficient of the laser radar relative to the back scattering signal data by using the preset pixel point range through the following formula:
wherein W is C (NRB) represents a cloud pixel weight coefficient, NRB represents a laser radar normalized back-scattered signal, NRB0 represents a clean atmosphere judgment threshold, and NRB1 represents a cloud judgment threshold;
multiplying the temperature weight coefficient, the cloud particle phase state discrimination weight coefficient and the cloud pixel weight coefficient to obtain the supercooled water cloud layer identification weight coefficient.
6. A cloud particle phase identification system, comprising:
the acquisition module is used for acquiring cloud particle observation data based on a plurality of foundation remote sensing observation modes and comprises the following steps: the laser radar acquires normalized back scattering signal data and depolarization ratio data, and the microwave radiometer acquires temperature profile data and a cloud base initial height value;
the first identification module is used for identifying the cloud particle observation data by using a cloud base height identification algorithm to obtain a cloud base height identification result, and comprises the following steps:
processing the normalized back scattering signal data and the depolarization ratio data by utilizing wavelet transformation, and determining a signal change rate extreme point to obtain a cloud base suspected point height value;
performing wavelet change on the height value of each cloud base suspected point to obtain a cloud base suspected point wavelet signal;
the intensity of each cloud bottom suspected point height value in the cloud bottom suspected point wavelet signal profile is obtained, the intensity is ordered from high to low, and the intensity coefficients of the preset number of the cloud bottom suspected point height values are sequentially given;
determining a distance weight coefficient by using the initial height value of the cloud base and the suspected point height value of the cloud base;
determining the sum of the distance weight coefficients and the intensity coefficients as the weight coefficient of the height value of each cloud base suspected point;
determining a cloud bottom suspected point height value corresponding to the maximum value in the weight coefficient as a cloud bottom height identification result;
the second identification module is used for calculating and obtaining a cloud top height identification result by utilizing the cloud bottom height identification result and a cloud top height identification algorithm;
and the third recognition module is used for recognizing the cloud particle observation data, the cloud bottom height recognition result and the cloud top height recognition result by utilizing a cloud particle phase recognition algorithm to obtain a cloud particle phase recognition result.
7. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the cloud particle phase identification method of any of claims 1-5.
8. An electronic device, comprising:
a memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the cloud particle phase identification method of any of claims 1-5.
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| CN113589273A (en) * | 2021-08-11 | 2021-11-02 | 中国科学院大气物理研究所 | Millimeter wave/infrared active and passive imaging detection device and method |
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