A method for identifying supercooled water potential areas using satellite multi-parameter ensemble

Abstract

The present invention discloses a method for identifying supercooled water potential areas by using satellite multi-parameter sets, comprising: obtaining identification data of supercooled water potential areas from active remote sensing satellites as standard data, collecting parameter data from geostationary meteorological satellites, and preprocessing the standard data and parameter data; grouping the parameter data based on the distribution characteristics to obtain interval data, and selecting the parameters based on the standard data and interval data according to the cumulative frequency crossover method to obtain the influencing parameters; voting on the pixels using the influencing parameters to determine whether it is a supercooled water potential area, and calculating the probability of supercooled water to obtain the identification data; establishing a supercooled water potential area identification model based on the identification data, and optimizing the supercooled water potential area identification model using the standard data; and inputting the data to be identified into the supercooled water potential area identification model to obtain the identification result. The method significantly improves the accuracy and reliability of supercooled water potential area identification by comprehensively utilizing the multi-parameter data of satellites.

Description

Technical Field

[0001] The invention relates to the field of remote sensing technology, and in particular to a method for identifying supercooled water potential areas using satellite multi-parameter ensembles. Background Art

[0002] In nature, when the air temperature is below 0°C, some droplets in the cloud are not frozen but exist in a supercooled liquid state, which is called supercooled water. The appearance of supercooled water has an important impact on the artificial weather modification cold cloud seeding and aircraft flight safety.

[0003] Starting from the spectral range of satellite detection, the supercooled water identification method using satellite-borne passive optical remote sensing data can be divided into three types: using thermal infrared bands to identify cloud phases, using visible and near-infrared bands to identify cloud phases, and combining visible light, near-infrared and thermal infrared band data to determine cloud phases, that is, first using the relationship between brightness temperature and brightness temperature difference to distinguish ice and water, and then using the radiation transfer equation to calculate the β factor (optical depth ratio) for correction, and finally judging whether it is supercooled water by the cloud top temperature. Supercooled water can be identified by combining the cloud top temperature.

[0004] Existing supercooled water identification methods mainly rely on satellite remote sensing data, but have problems such as low identification accuracy and inability to conduct quantitative analysis. Summary of the invention

[0005] The purpose of the present invention is to provide a method for identifying supercooled water potential areas using satellite multi-parameter ensembles.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0007] The present invention comprises the following steps:

[0008] Acquire the discrimination data of the supercooled water potential area from the active remote sensing satellite as standard data, collect the parameter data from the geostationary meteorological satellite, and pre-process the standard data and the parameter data;

[0009] Based on the distribution characteristics of the parameter data, the parameter data are grouped to obtain interval data, and according to the cumulative frequency crossover method, feature selection is performed on the parameter data based on the standard data and the interval data to obtain an influencing parameter;

[0010] The influencing parameters are used to vote to determine whether it is a potential area for supercooled water, and the probability of supercooled water is calculated to obtain identification data;

[0011] Establishing a supercooled water potential zone identification model based on the identification data, and optimizing the supercooled water potential zone identification model using the standard data;

[0012] The data to be identified is input into the supercooled water potential zone identification model to obtain the identification result.

[0013] Furthermore, the method of obtaining the discrimination data of the supercooled water potential area from the active remote sensing satellite as the standard data, collecting the parameter data from the geostationary meteorological satellite, and preprocessing the standard data and the parameter data comprises:

[0014] Active remote sensing satellites carry polarized laser radar and infrared imagers. Polarized laser radar is used to identify the phase state of clouds, and infrared imagers are used to identify the temperature of clouds. Discrimination data is obtained based on the phase state and temperature of clouds. The phase state of clouds in the discrimination data includes liquid phase and ice phase. The supercooled water potential area includes the supercooled water under the ice in the ice-liquid mixed cloud in addition to the supercooled water at the top of the cloud layer. The parameter data includes the original channel value, cloud product value and sensitive channel calculation value.

[0015] Match standard data and parametric data based on geographic coordinates and timestamps.

[0016] Furthermore, a method for obtaining interval data by grouping based on the distribution characteristics of the parameter data includes:

[0017] The parameter data is divided into 10c equal-width intervals, and the frequencies of liquid and ice phases in each equal-width interval are calculated. The frequency of the equal-width intervals is used to establish the data vector, and the data vector is clustered:

[0018]

[0019] Among them, τ V is the objective function, n is the number of data vectors, c is the number of clusters, is the membership of the i-th data vector to the j-th cluster, M is the fuzzy factor, is the fuzzy penalty coefficient of the jth cluster, X i is the eigenvector of data vector i, v j is the center point of cluster j, C j is the set of data vectors in cluster j, h is the bandwidth parameter, λ is the regularization parameter,

[0020] Membership function:

[0021]

[0022] Among them, d ij is the Euclidean distance between the i-th data vector and the cluster center of the j-th cluster, d max is the maximum value of the Euclidean distance between all data vectors and the cluster center of the jth cluster,

[0023] Cluster center update formula:

[0024]

[0025] in is the center point of cluster j after iteration s+1 steps,

[0026] Divide the data vector into the cluster with the largest membership and evaluate the clustering results:

[0027]

[0028] in Score the clustering results, where X a and X b are the data vectors in cluster m and cluster p respectively.

[0029] Furthermore, a method for obtaining an influencing parameter by performing feature selection on the parameter data based on the standard data and the interval data according to a cumulative frequency crossover method includes:

[0030] Based on the standard data, the parameter data is divided into ice phase and liquid phase, and the parameter data is grouped based on the interval data. For each group, the number of data points of the ice phase and liquid phase falling in it is counted to obtain the frequency; the ratio of the frequency to the total number of data points is calculated to obtain the frequency, and the frequency of each group is accumulated to obtain the cumulative frequency. The frequency of each group is accumulated to obtain the cumulative frequency.

[0031] The parameter threshold and crossover frequency for ice-water judgment are obtained based on the intersection of the ice-water cumulative frequency distribution of the parameter data, and the parameter data with a crossover frequency less than 25% or greater than 75% are used as influencing parameters for distinguishing the ice phase and water phase of clouds.

[0032] Furthermore, the method of using the influencing parameters to vote, determine whether it is a potential area for supercooled water, and calculate the probability of supercooled water to obtain the identification data includes:

[0033] When the value of CLM in the cloud product value is 0 and CTT has data, it indicates a cloud area, otherwise it is a cloudless area;

[0034] For the influencing parameters, when the cloud top temperature is lower than 0°C and the cloud top is identified as liquid phase according to the parameter threshold, it means that the cloud top is supercooled water or there is supercooled water in the cloud, and the voting value is assigned to 1, otherwise it is assigned to -1;

[0035] The voting results are accumulated to obtain the voting value. If the voting value is greater than 0, it is judged as a supercooled water potential area, otherwise it is judged as a non-supercooled water potential area;

[0036] Calculate the probability of supercooled water based on the voting value:

[0037]

[0038] Where V is the voting value, n' is the number of influencing parameters;

[0039] The identification data include the identification result of the supercooled water potential area and the supercooled water probability.

[0040] Furthermore, a method for establishing a supercooled water potential zone identification model based on the identification data includes:

[0041] The influencing parameters are divided into training set and validation set, and the identification data is used as the dependent variable of the training set to learn the mapping mode between the influencing parameters and the identification data, and establish the supercooled water potential zone identification model:

[0042] Through nonlinear mapping, the influencing parameters are mapped as independent variables into the high-dimensional feature space, and the linear regression function is:

[0043]

[0044] Where θ is the weight vector, T represents the transpose, is the mapping function of the independent variable x, c is the bias,

[0045] Quadratic programming equation:

[0046]

[0047] Where λ is the penalty coefficient, k is the index of the sample in the training set, n is the number of samples in the training set, and ν k and is the slack adjustment variable, is the dependent variable, γ is the loss function, η k is the noise term,

[0048] Lagrangian objective function:

[0049]

[0050] where δ and δ * is the solution of the Lagrangian objective function, j and g are the indices of the samples in the training set, and R j,g is the kernel function

[0051] Decision function:

[0052]

[0053] Standard data is used as the labels of the validation set to optimize the supercooled water potential zone identification model.

[0054] Furthermore, the method of optimizing the supercooled water potential zone identification model using the standard data includes:

[0055] The model parameters of the supercooled water potential zone identification model are optimized as particles, and the position of each particle in the search space is represented by X i=(x i1 ,x i2 ,…,x in ), where n is the dimension of the search space, x id is the position information of the particle in the dth dimension,

[0056] The force between two particles in the dth dimension at time t:

[0057]

[0058] Among them, f i (t) and f j (t) are the fitness values ​​of particles i and j respectively. The fitness value is the accuracy of the supercooled water potential zone identification model. G(t) is the gravitational constant at time t. G0 is the initial gravitational constant, α and T are the parameters that control the gravitational change, M pi (t) and M aj (t) are the masses of particle i and particle j at time t, R ij (t) is the Euclidean distance between particles i and j at time t, ε is a constant to ensure that the denominator is not zero, R ij The calculation formula of (t) is:

[0059]

[0060] Where n is the dimension of the search space, and are the positions of particles i and j in the dth dimension,

[0061] The resultant force of particle i on the dth dimension is:

[0062]

[0063] The particle's acceleration is updated:

[0064]

[0065] Where a represents acceleration, v represents velocity, η is the noise intensity, and noise(t) is a random number in the interval [-1,1].

[0066] The particle speed and position update formula is:

[0067] v d (t+1)=v d (t)+a d (t)·Δt

[0068] x d (t+1)=x d (t)+vd (t+1)·Δt

[0069] where Δt is the time step,

[0070] The formula for calculating particle mass is:

[0071]

[0072] Where w(t) is the minimum fitness value of the particles in the population, b(t) is the maximum fitness value of the particles in the population, λ is the weight of the learning mechanism, learning(t) is an adaptive function based on the historical performance of the particles, and X i (t) is the position of particle i at the current moment, is the optimal position that particle i has reached historically, β is the parameter that controls the learning speed, σ is the scaling factor,

[0073] The particles are iteratively optimized until the accuracy of the supercooled water potential zone identification model reaches 85%.

[0074] In a second aspect, an embodiment of the present application further provides an electronic device, including:

[0075] A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0076] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0077] The beneficial effects of the present invention are:

[0078] The present invention is a method for identifying supercooled water potential areas by using satellite multi-parameter ensembles. Compared with the prior art, the present invention has the following technical effects:

[0079] Real-time and automation: The operation of the algorithm is highly automated and timed, which effectively reduces the burden of manual monitoring, ensures the timeliness and continuity of data processing, and provides timely decision-making basis for artificial rainfall operations.

[0080] Improve identification accuracy: By comprehensively utilizing multi-parameter data from satellites, the present invention can more accurately identify supercooled water potential areas. This method not only takes into account the limitations of a single parameter, but also significantly improves the accuracy of identification through multi-parameter set identification.

[0081] Flight safety assurance: Accurate identification of supercooled water potential areas is of great significance to flight safety. The method of the present invention can predict in advance the presence of supercooled water and aircraft icing potential in the aircraft operation area through the changing trend of the supercooled water probability. Through air-ground communication and real-time aircraft ice accumulation monitoring, the aircraft can be directed to leave the cloud and de-ice to ensure the safety of aircraft operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 A flowchart of the steps of a method for identifying supercooled water potential areas using satellite multi-parameter ensembles according to the present invention;

[0083] Figure 2 The three types of situation diagrams for supercooled water potential areas;

[0084] Figure 3 This is the frequency and frequency distribution of ice water at 1.37μm reflectivity of FY4A satellite;

[0085] Figure 4 Flow chart for satellite multi-parameter identification of supercooled water potential areas;

[0086] Figure 5 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0087] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0088] A method for identifying supercooled water potential areas using satellite multi-parameter ensembles of the present invention comprises the following steps:

[0089] like Figure 1 As shown, in this embodiment, the following steps are included:

[0090] Acquire the discrimination data of the supercooled water potential area from the active remote sensing satellite as standard data, collect the parameter data from the geostationary meteorological satellite, and pre-process the standard data and the parameter data;

[0091] In actual assessment, the potential area of ​​supercooled water Figure 2As shown in the figure, the mixed phase is the cloud containing both liquid phase and ice phase. The active remote sensing satellite selected is the CALIPSO satellite, and the geostationary meteorological satellite is the FY4A satellite. The parameters of the FY4A satellite include: cloud products: CTH, CTT, CTP, CPD_CER, CPD_COT, CPD_LWP, CPD_IWP; original channels: R0_47, R0_65, R0_83, R1_37, R1 _61, R2_22, BT3_72a, BT3_72b, BT6_25, BT7_10, BT8_50, BT10_8, BT12_0, BT13_5; sensitive channel calculation parameters: R222b137, R161b137, R222b161, R161d137, R222d161, R222d137, BTD85_11, BTD11_12;

[0092] Based on the distribution characteristics of the parameter data, the parameter data are grouped to obtain interval data, and according to the cumulative frequency crossover method, feature selection is performed on the parameter data based on the standard data and the interval data to obtain an influencing parameter;

[0093] In the actual evaluation, the value of c is 4, and the influencing parameters, parameter thresholds, and crossover frequencies are:

[0094]

[0095]

[0096] The frequency and frequency distribution of ice water reflectivity in the 1.37μm channel of the FY-4A satellite are as follows Figure 3 As shown, the upper left corner is the frequency distribution, the upper right corner is the frequency distribution, the lower left corner is the cumulative cross frequency distribution, and the lower right corner is the cumulative cross frequency distribution. The ice phase reflectivity value is greater than the liquid phase value, and the intersection of the two cumulative frequency distributions is at a reflectivity of 0.05 and a frequency of 81.45%. This shows that when 0.05 is used as the ice water judgment threshold, 81.45% of the ice water judgments are correct;

[0097] The influencing parameters are used to vote to determine whether it is a potential area for supercooled water, and the probability of supercooled water is calculated to obtain identification data;

[0098] In practical applications, satellite multi-parameter identification of supercooled water processes Figure 4 As shown, FSCW represents the non-supercooled water potential zone, and SCW represents the supercooled water potential zone;

[0099] Establishing a supercooled water potential zone identification model based on the identification data, and optimizing the supercooled water potential zone identification model using the standard data;

[0100] The data to be identified is input into the supercooled water potential zone identification model to obtain the identification result.

[0101] In this embodiment, the method of obtaining the discrimination data of the supercooled water potential area from the active remote sensing satellite as the standard data, collecting the parameter data from the geostationary meteorological satellite, and preprocessing the standard data and the parameter data includes:

[0102] Active remote sensing satellites carry polarized laser radar and infrared imagers. Polarized laser radar is used to identify the phase state of clouds, and infrared imagers are used to identify the temperature of clouds. Discrimination data is obtained based on the phase state and temperature of clouds. The phase state of clouds in the discrimination data includes liquid phase and ice phase. The supercooled water potential area includes the supercooled water under the ice in the ice-liquid mixed cloud in addition to the supercooled water at the top of the cloud layer. The parameter data includes the original channel value, cloud product value and sensitive channel calculation value.

[0103] Match standard data and parametric data based on geographic coordinates and timestamps.

[0104] In this embodiment, the method for obtaining interval data by grouping based on the distribution characteristics of the parameter data includes:

[0105] The parameter data is divided into 10c equal-width intervals, and the frequencies of liquid and ice phases in each equal-width interval are calculated. The frequency of the equal-width intervals is used to establish the data vector, and the data vector is clustered:

[0106]

[0107] Among them, τ V is the objective function, n is the number of data vectors, c is the number of clusters, is the membership of the i-th data vector to the j-th cluster, M is the fuzzy factor, is the fuzzy penalty coefficient of the jth cluster, X i is the eigenvector of data vector i, v j is the center point of cluster j, C j is the set of data vectors in cluster j, h is the bandwidth parameter, λ is the regularization parameter,

[0108] Membership function:

[0109]

[0110] Among them, d ij is the Euclidean distance between the i-th data vector and the cluster center of the j-th cluster, d max is the maximum value of the Euclidean distance between all data vectors and the cluster center of the jth cluster,

[0111] Cluster center update formula:

[0112]

[0113] in is the center point of cluster j after iteration s+1 steps,

[0114] Divide the data vector into the cluster with the largest membership and evaluate the clustering results:

[0115]

[0116] in Score the clustering results, where X a and X b are the data vectors in cluster m and cluster p respectively.

[0117] In this embodiment, the method of obtaining the influencing parameter by performing feature selection on the parameter data based on the standard data and the interval data according to the cumulative frequency crossover method includes:

[0118] Based on the standard data, the parameter data is divided into ice phase and liquid phase, and the parameter data is grouped based on the interval data. For each group, the number of data points of the ice phase and liquid phase falling in it is counted to obtain the frequency; the ratio of the frequency to the total number of data points is calculated to obtain the frequency, and the frequency of each group is accumulated to obtain the cumulative frequency. The frequency of each group is accumulated to obtain the cumulative frequency.

[0119] The parameter threshold and crossover frequency for ice-water judgment are obtained based on the intersection of the ice-water cumulative frequency distribution of the parameter data, and the parameter data with a crossover frequency less than 25% or greater than 75% are used as influencing parameters for distinguishing the ice phase and water phase of clouds.

[0120] In this embodiment, the method of using the influencing parameters to vote, determine whether it is a supercooled water potential area, and calculate the supercooled water probability to obtain the identification data includes:

[0121] When the value of CLM in the cloud product value is 0 and CTT has data, it indicates a cloud area, otherwise it is a cloudless area;

[0122] For the influencing parameters, when the cloud top temperature is lower than 0°C and the cloud top is identified as liquid phase according to the parameter threshold, it means that the cloud top is supercooled water or there is supercooled water in the cloud, and the voting value is assigned to 1, otherwise it is assigned to -1;

[0123] The voting results are accumulated to obtain the voting value. If the voting value is greater than 0, it is judged as a supercooled water potential area, otherwise it is judged as a non-supercooled water potential area;

[0124] Calculate the probability of supercooled water based on the voting value:

[0125]

[0126] Where V is the voting value, n' is the number of influencing parameters;

[0127] The identification data include the identification result of the supercooled water potential area and the supercooled water probability.

[0128] In this embodiment, a method for establishing a supercooled water potential zone identification model based on the identification data includes:

[0129] The influencing parameters are divided into training set and validation set, and the identification data is used as the dependent variable of the training set to learn the mapping mode between the influencing parameters and the identification data, and establish the supercooled water potential zone identification model:

[0130] Through nonlinear mapping, the influencing parameters are mapped as independent variables into the high-dimensional feature space, and the linear regression function is:

[0131]

[0132] Where θ is the weight vector, T represents the transpose, is the mapping function of the independent variable x, c is the bias,

[0133] Quadratic programming equation:

[0134]

[0135] Where λ is the penalty coefficient, k is the index of the sample in the training set, n is the number of samples in the training set, and ν k and is the slack adjustment variable, is the dependent variable, γ is the loss function, η k is the noise term,

[0136] Lagrangian objective function:

[0137]

[0138] where δ and δ * is the solution of the Lagrangian objective function, j and g are the indices of the samples in the training set, and R j,g is the kernel function

[0139] Decision function:

[0140]

[0141] Standard data is used as the labels of the validation set to optimize the supercooled water potential zone identification model.

[0142] In this embodiment, the method of optimizing the supercooled water potential zone identification model using the standard data includes:

[0143] The model parameters of the supercooled water potential zone identification model are optimized as particles, and the position of each particle in the search space is represented by X i =(x i1 ,x i2 ,…,x in ), where n is the dimension of the search space, x id is the position information of the particle in the dth dimension,

[0144] The force between two particles in the dth dimension at time t:

[0145]

[0146] Among them, f i (t) and f j (t) are the fitness values ​​of particles i and j respectively. The fitness value is the accuracy of the supercooled water potential zone identification model. G(t) is the gravitational constant at time t. G0 is the initial gravitational constant, α and T are the parameters that control the gravitational change, M pi (t) and M aj (t) are the masses of particle i and particle j at time t, R ij (t) is the Euclidean distance between particles i and j at time t, ε is a constant to ensure that the denominator is not zero, R ij The calculation formula of (t) is:

[0147]

[0148] Where n is the dimension of the search space, and are the positions of particles i and j in the dth dimension,

[0149] The resultant force of particle i on the dth dimension is:

[0150]

[0151] The particle's acceleration is updated:

[0152]

[0153] Where a represents acceleration, v represents velocity, η is the noise intensity, and noise(t) is a random number in the interval [-1,1].

[0154] The particle speed and position update formula is:

[0155] v d (t+1)=v d (t)+a d (t)·Δt

[0156] x d (t+1)=x d (t)+v d (t+1)·Δt

[0157] where Δt is the time step,

[0158] The formula for calculating particle mass is:

[0159]

[0160] Where w(t) is the minimum fitness value of the particles in the population, b(t) is the maximum fitness value of the particles in the population, λ is the weight of the learning mechanism, learning(t) is an adaptive function based on the historical performance of the particles, and X i (t) is the position of particle i at the current moment, is the optimal position that particle i has reached historically, β is the parameter that controls the learning speed, σ is the scaling factor,

[0161] The particles are iteratively optimized until the accuracy of the supercooled water potential zone identification model reaches 85%

[0162] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0163] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0164] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0165] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a device for identifying supercooled water potential areas by using a satellite multi-parameter set at a logical level. The processor executes the program stored in the memory and is specifically used to execute any of the above-mentioned methods for identifying supercooled water potential areas by using a satellite multi-parameter set.

[0166] The above application Figure 1 The method for identifying supercooled water potential areas by satellite multi-parameter set disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capability. In the implementation process, each step of the above method can be completed by hardware integrated logic circuits or software instructions in the processor. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0167] The electronic device may also perform Figure 1 A satellite multi-parameter ensemble method for identifying supercooled water potential areas and implementing Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0168] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple application programs, any one of the aforementioned methods for identifying potential areas of supercooled water using a satellite multi-parameter set is executed.

[0169] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0170] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0171] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0173] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0174] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0175] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0176] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0177] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0178] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying supercooled water potential areas using satellite multi-parameter ensembles, characterized in that: The following steps are involved: Acquire the discrimination data of the supercooled water potential area from the active remote sensing satellite as standard data, collect the parameter data from the geostationary meteorological satellite, and pre-process the standard data and the parameter data; Based on the distribution characteristics of the parameter data, the parameter data are grouped to obtain interval data, and according to the cumulative frequency crossover method, feature selection is performed on the parameter data based on the standard data and the interval data to obtain an influencing parameter; The influencing parameters are used to vote on pixels to determine whether they are potential areas of supercooled water, and the probability of supercooled water is calculated to obtain identification data. When the value of CLM in the cloud product value is 0 and CTT has data, it indicates a cloud area, otherwise it is a cloudless area; For the influencing parameters, when the cloud top temperature is lower than 0°C and the cloud top is identified as liquid phase according to the parameter threshold, it means that the cloud top is supercooled water or there is supercooled water in the cloud, and the voting value is assigned to 1, otherwise it is assigned to -1; The voting results are accumulated to obtain the voting value. If the voting value is greater than 0, it is judged as a supercooled water potential area, otherwise it is judged as a non-supercooled water potential area; Calculate the probability of supercooled water based on the voting value: Where V is the voting value, n' is the number of influencing parameters; The identification data include the identification result of supercooled water potential area and the supercooled water probability; Establishing a supercooled water potential zone identification model based on the identification data, and optimizing the supercooled water potential zone identification model using the standard data; The data to be identified is input into the supercooled water potential zone identification model to obtain the identification result.

2. According to claim 1, a method for identifying supercooled water potential areas using satellite multi-parameter ensembles is characterized in that: The method of obtaining the discrimination data of the supercooled water potential area from the active remote sensing satellite as the standard data, collecting the parameter data from the geostationary meteorological satellite, and preprocessing the standard data and the parameter data comprises: Active remote sensing satellites carry polarized laser radar and infrared imagers. Polarized laser radar is used to identify the phase state of clouds, and infrared imagers are used to identify the temperature of clouds. Discrimination data is obtained based on the phase state and temperature of clouds. The phase state of clouds in the discrimination data includes liquid phase and ice phase. The supercooled water potential area includes the supercooled water under the ice in the ice-liquid mixed cloud in addition to the supercooled water at the top of the cloud layer. The parameter data includes the original channel value, cloud product value and sensitive channel calculation value. Match standard data and parametric data based on geographic coordinates and timestamps.

3. The method for identifying supercooled water potential areas using satellite multi-parameter ensemble according to claim 1, characterized in that: The method of obtaining the influencing parameter by performing feature selection on the parameter data based on the standard data and the interval data according to the cumulative frequency crossover method comprises: Based on the standard data, the parameter data is divided into ice phase and liquid phase, and the parameter data is grouped based on the interval data. For each group, the number of data points of the ice phase and liquid phase falling in it is counted to obtain the frequency; the ratio of the frequency to the total number of data points is calculated to obtain the frequency, and the frequency of each group is accumulated to obtain the cumulative frequency. The frequency of each group is accumulated to obtain the cumulative frequency. The parameter threshold and crossover frequency for ice-water judgment are obtained based on the intersection of the ice-water cumulative frequency distribution of the parameter data, and the parameter data with a crossover frequency less than 25% or greater than 75% are used as influencing parameters for distinguishing the ice phase and water phase of clouds.

4. The method for identifying supercooled water potential areas using satellite multi-parameter ensemble according to claim 1, characterized in that: The method of optimizing the supercooled water potential zone identification model using the standard data comprises: The model parameters of the supercooled water potential zone identification model are optimized as particles, and the position of each particle in the search space is represented by X i =(x i1 ,x i2 ,…,x in ), where n is the dimension of the search space, x id is the position information of the particle in the dth dimension, The force between two particles in the dth dimension at time t: Among them, f i (t) and f j (t) are the fitness values ​​of particles i and j respectively. The fitness value is the accuracy of the supercooled water potential zone identification model. G(t) is the gravitational constant at time t. G0 is the initial gravitational constant, α and T are the parameters that control the gravitational change, M pi (t) and M aj (t) are the masses of particle i and particle j at time t, R ij (t) is the Euclidean distance between particles i and j at time t, ε is a constant to ensure that the denominator is not zero, R ij The calculation formula of (t) is: Where n is the dimension of the search space, and are the positions of particles i and j in the dth dimension, The resultant force of particle i on the dth dimension is: The particle's acceleration is updated: Where a represents acceleration, v represents velocity, η is the noise intensity, and noise(t) is a random number in the interval [-1,1]. The particle speed and position update formula is: v d (t+1)=v d (t)+a d (t)·Δt x d (t+1)=x d (t)+v d (t+1)·Δt where Δt is the time step, The formula for calculating particle mass is: Where w(t) is the minimum fitness value of the particles in the population, b(t) is the maximum fitness value of the particles in the population, λ is the weight of the learning mechanism, learning(t) is an adaptive function based on the historical performance of the particles, and X i (t) is the position of particle i at the current moment, is the optimal position that particle i has reached historically, β is the parameter that controls the learning speed, σ is the scaling factor, The particles are iteratively optimized until the accuracy of the supercooled water potential zone identification model reaches 85%.

5. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, which when executed cause the processor to perform the method according to any one of claims 1 to 4.

6. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute the method according to any one of claims 1 to 4.

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