Method and device for rainfall inversion by using GNSS PWV-assisted meteorological satellite remote sensing

Abstract

The present invention discloses a method and device for rainfall inversion assisted by GNSS PWV in meteorological satellite remote sensing. The method includes: obtaining PWV data, cloud top brightness temperature data, and brightness temperature difference data during the rainfall period; inputting the PWV data, cloud top brightness temperature data, and brightness temperature difference data during the rainfall period into a trained multi-layer neural network model to output the rainfall amount for the corresponding period. The present invention combines the atmospheric water vapor content information (PWV data) obtained by real-time calculation of GNSS observation data, supplements the deficiency of satellite remote sensing in cloud base water vapor observation, fuses GNSS observation data with meteorological satellite data, and improves the accuracy of precipitation inversion. At the same time, a trained multi-layer neural network model is introduced to automatically extract features from complex data, realizing high-time and high-precision rainfall inversion.

Description

Technical Field

[0001] The present invention relates to the field of rainfall inversion, and specifically relates to a method and device for rainfall inversion by GNSS PWV-assisted meteorological satellite remote sensing. Background Art

[0002] Currently, there are mainly three rainfall monitoring methods, including: (1) observation based on rain gauges; (2) inversion based on ground weather radars; (3) inversion based on meteorological satellites. Among them, the disadvantages of rain gauge observations are poor representativeness and limited observation range; the disadvantages of ground weather radars are small coverage, high cost, and large interference factors; the method of meteorological satellite inversion has the advantages of wide coverage and continuous observation, but the disadvantage is limited accuracy.

[0003] Meteorological satellites can obtain the brightness temperature information of rainfall clouds by observing clouds, and rainfall can be inverted through cloud top brightness temperature and brightness temperature difference. However, water vapor is an important source of rainfall. Due to the disadvantage that infrared channels are difficult to penetrate clouds, it is difficult to obtain information on atmospheric water vapor content. Summary of the Invention

[0004] To overcome the deficiencies of the above-mentioned prior art, the present invention provides a method and device for rainfall inversion by GNSS PWV-assisted meteorological satellite remote sensing, which calculates the atmospheric water vapor content (i.e., precipitable water vapor PWV) based on GNSS and assists the satellite remote sensing method to achieve high-precision real-time rainfall inversion.

[0005] According to one aspect of the specification of the present invention, there is provided a method for rainfall inversion by GNSS PWV-assisted meteorological satellite remote sensing, including:

[0006] Obtaining PWV data, cloud top brightness temperature data, and brightness temperature difference data during the rainfall period;

[0007] Inputting the PWV data, cloud top brightness temperature data, and brightness temperature difference data during the rainfall period into a trained multi-layer neural network model, and outputting the rainfall amount for the corresponding period; wherein the training of the multi-layer neural network model includes:

[0008] Obtaining long-term measured rainfall and multi-channel cloud top brightness temperature data, and calculating the brightness temperature difference between the infrared channel and the water vapor channel;

[0009] Obtaining GNSS observation data for the same period, and calculating the atmospheric precipitable water vapor PWV based on the GNSS observation data;

[0010] Constructing a sample data set according to the obtained measured rainfall, cloud top brightness temperature data, brightness temperature difference, and atmospheric precipitable water vapor PWV;

[0011] Construct a multi-layer neural network model, where the input layer of the multi-layer neural network model is the PWV sequence, the brightness temperature image sequence, and the brightness temperature difference image sequence within a time window, and the output layer is the rainfall amount for the corresponding time period;

[0012] Use the sample data set to train the constructed multi-layer neural network model to obtain a trained model.

[0013] As a further technical solution, construct a sample data set, including:

[0014] According to a preset time window, obtain the PWV sequence, the brightness temperature image sequence, the brightness temperature difference image sequence, and the rainfall amount sequence within the time window to obtain a sample data set.

[0015] As a further technical solution, the method further includes:

[0016] Randomly select a training set and a validation set from the sample data set, use the training set to train the constructed multi-layer neural network model, and use the validation set to verify the trained multi-layer neural network model.

[0017] As a further technical solution, the multi-layer neural network model is a backpropagation neural network model including three hidden layers.

[0018] According to one aspect of the specification of the present invention, provide a rainfall inversion device for GNSS PWV-assisted meteorological satellite remote sensing, including:

[0019] A data acquisition module for acquiring PWV data, cloud top brightness temperature data, and brightness temperature difference data during a rainfall period;

[0020] A rainfall inversion module for inputting the PWV data, cloud top brightness temperature data, and brightness temperature difference data during the rainfall period into the trained multi-layer neural network model and outputting the rainfall amount for the corresponding time period; wherein, the training of the multi-layer neural network model includes:

[0021] Obtain long-term measured rainfall and multi-channel cloud top brightness temperature data, and calculate the brightness temperature difference between the infrared channel and the water vapor channel;

[0022] Obtain GNSS observation data for the same time period, and calculate the precipitable water vapor PWV based on the GNSS observation data;

[0023] Construct a sample data set according to the obtained measured rainfall, cloud top brightness temperature data, brightness temperature difference, and precipitable water vapor PWV;

[0024] Construct a multi-layer neural network model, where the input layer of the multi-layer neural network model is the PWV sequence, the brightness temperature image sequence, and the brightness temperature difference image sequence within a time window, and the output layer is the rainfall amount for the corresponding time period;

[0025] Train the constructed multi - layer neural network model using the sample data set to obtain a trained model.

[0026] According to one aspect of the specification of the present invention, there is provided a computing device, including a processor and a memory. The memory stores program instructions executed by the processor, and the processor invokes the program instructions to perform the steps of the rainfall inversion method for GNSS PWV - assisted meteorological satellite remote sensing.

[0027] According to one aspect of the specification of the present invention, there is provided a non - transitory computer - readable storage medium. The non - transitory computer - readable storage medium stores computer instructions, and the computer instructions cause the computer to perform the steps of the rainfall inversion method for GNSS PWV - assisted meteorological satellite remote sensing.

[0028] According to one aspect of the specification of the present invention, there is provided a computer program product, including computer program instructions. The computer program instructions cause the computer to perform the steps of the rainfall inversion method for GNSS PWV - assisted meteorological satellite remote sensing.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] (1) The present invention combines the information of atmospheric water vapor content that can be solved in real time by GNSS, compensates for the deficiency of satellite remote sensing in observing water vapor at the cloud base, and fuses GNSS and meteorological satellite data, which can improve the accuracy of precipitation inversion.

[0031] (2) By introducing deep - learning technology, the present invention can automatically extract features from complex data, optimize the precipitation estimation process, realize real - time high - precision rainfall inversion, and meet the requirements of rainfall monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings used in the description of the embodiments or the prior art. Obviously, the following - described drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0033] Figure 1 It is a schematic flow chart of the rainfall inversion method for GNSS PWV - assisted meteorological satellite remote sensing provided by the embodiment of the present invention.

[0034] Figure 2 It is a schematic flow chart of the training of the multi - layer neural network model provided by the embodiment of the present invention.

[0035] Figure 3 This is a schematic structural diagram of a rainfall inversion device for GNSS PWV-assisted meteorological satellite remote sensing provided by an embodiment of the present invention. Detailed implementation manners

[0036] Aiming at the problem that it is difficult to obtain the atmospheric water vapor content when inverting rainfall based on meteorological satellites in the prior art, resulting in limited accuracy of the inverted rainfall, the present invention designs a method for inverting rainfall by using the atmospheric water vapor content to assist meteorological satellite remote sensing data. By introducing GNSS to calculate the atmospheric water vapor content (i.e., the precipitable water vapor PWV), it assists the satellite remote sensing method to achieve high-precision real-time rainfall inversion.

[0037] It should be noted that: the technologies mentioned in the specification of the present invention are all well-known technologies unless otherwise specified.

[0038] The terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0039] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention. In addition, the technical features in each embodiment or a single embodiment provided by the present invention can be arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the order of steps and / or the structural composition mode, but must be based on the fact that those of ordinary skill in the art can implement it. When the combination of technical solutions conflicts with each other or cannot be implemented, it should be considered that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0040] An embodiment of the present invention provides a method for inverting rainfall by GNSS PWV-assisted meteorological satellite remote sensing, as Figure 1 shown, including: obtaining PWV data, cloud top brightness temperature data, and brightness temperature difference data during a rainfall period; inputting the PWV data, cloud top brightness temperature data, and brightness temperature difference data during the rainfall period into a trained multi-layer neural network model, and outputting the rainfall amount corresponding to the period.

[0041] The atmospheric water vapor content information (PWV data) calculated in real time by the embodiments of the present invention in combination with GNSS observation data makes up for the deficiency of satellite remote sensing in observing water vapor at the cloud base, fuses GNSS observation data with meteorological satellite data, and improves the accuracy of precipitation inversion. At the same time, a trained multi-layer neural network model is introduced to automatically extract features from complex data, realizing high-time and high-precision precipitation inversion.

[0042] In the embodiments of the present invention, the training of the multi-layer neural network model is as Figure 2 shown and includes:

[0043] Obtain long-time series measured precipitation and multi-channel cloud top brightness temperature data, and calculate the brightness temperature difference between the infrared channel and the water vapor channel;

[0044] Obtain GNSS observation data in the same period, and calculate the precipitable water vapor PWV based on the GNSS observation data.

[0045] Construct a sample data set according to the obtained measured precipitation, cloud top brightness temperature data, brightness temperature difference, and precipitable water vapor PWV;

[0046] Construct a multi-layer neural network model, where the input layer of the multi-layer neural network model is the PWV sequence, brightness temperature image sequence, and brightness temperature difference image sequence within the time window, and the output layer is the precipitation in the corresponding time period;

[0047] Use the sample data set to train the constructed multi-layer neural network model to obtain a trained model.

[0048] As a preferred embodiment, the embodiments of the present invention introduce an embodiment of using GNSS PWV to assist meteorological satellite remote sensing data for precipitation inversion based on a backpropagation neural network, including the following steps:

[0049] (1) Download precipitation data in Guangzhou from June to September 2023 and real-time products of multi-channel cloud top temperature observed by FY4B satellite, and calculate the brightness temperature difference between the infrared channel and the water vapor channel.

[0050] (2) Obtain GNSS observation data in the same period, and calculate the precipitable water vapor (PWV).

[0051] (3) Preprocess the data: Take a 6-hour time window to obtain the PWV sequence, brightness temperature image sequence, brightness temperature difference image sequence, and Guangzhou precipitation sequence within the time window to obtain a sample data set.

[0052] (4) Randomly select a training set and a validation set from the processed PWV, brightness temperature, brightness temperature difference, and precipitation data sets (here, the data set ratio is set to 8:2).

[0053] (5) Construct a backpropagation neural network (BPNN) model with three hidden layers. The input layer is the PWV, cloud top brightness temperature, and brightness temperature difference image sequences, and the output layer is the rainfall amount for the corresponding time period.

[0054] (6) Input the training set and validation set data into the BPNN model, train the model until convergence, and save the model parameters.

[0055] (7) Invert the rainfall amount based on the trained model. First, obtain the GNSS PWV during the rainfall period and the multi-channel cloud top brightness temperature and brightness temperature difference observed by satellite remote sensing, and substitute them into the trained neural network model to obtain the rainfall amount for that period.

[0056] The implementation basis of each embodiment of the present invention is achieved through programmed processing by a device with processor functions. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention are encapsulated into various modules. Based on this actual situation, on the basis of the above embodiments, an embodiment of the present invention provides a rainfall inversion device for GNSS PWV-assisted meteorological satellite remote sensing, which is used to execute the GNSS PWV-assisted meteorological satellite remote sensing rainfall inversion method in the above method embodiments.

[0057] See Figure 3 , the device includes: a data acquisition module, which is used to acquire PWV data, cloud top brightness temperature data, and brightness temperature difference data during the rainfall period; a rainfall inversion module, which is used to input the PWV data, cloud top brightness temperature data, and brightness temperature difference data during the rainfall period into the trained multi-layer neural network model and output the rainfall amount for the corresponding period; wherein, the training of the multi-layer neural network model includes: obtaining the measured rainfall amount and multi-channel cloud top brightness temperature data of a long time series, and calculating the brightness temperature difference between the infrared channel and the water vapor channel; obtaining GNSS observation data of the same period, and calculating the precipitable water vapor PWV based on the GNSS observation data; constructing a sample data set according to the obtained measured rainfall amount, cloud top brightness temperature data, brightness temperature difference, and precipitable water vapor PWV; constructing a multi-layer neural network model, the input layer of the multi-layer neural network model is the PWV sequence, brightness temperature image sequence, and brightness temperature difference image sequence within the time window, and the output layer is the rainfall amount for the corresponding time period; using the sample data set to train the constructed multi-layer neural network model to obtain a trained model.

[0058] In view of the problem that it is difficult to obtain the atmospheric water vapor content when inverting rainfall based on meteorological satellites in the prior art, resulting in limited accuracy of the inverted rainfall amount, the present invention adopts Figure 3 several modules in

[0059] It should be noted that the device embodiments provided by the present invention are used not only to implement the methods in the above method embodiments, but also to implement the methods in other method embodiments provided by the present invention. The difference is only in setting corresponding functional modules, and its principle is basically the same as that of the above device embodiments provided by the present invention. As long as those skilled in the art, based on the above device embodiments, refer to the specific technical solutions in other method embodiments, obtain corresponding technical means by combining technical features, and the technical solutions composed of these technical means, and on the premise of ensuring the practicability of the technical solutions, improve the modules in the above device embodiments to obtain corresponding device-type embodiments for implementing the methods in other method-type embodiments. For example:

[0060] Based on the content of the above device embodiments, as a preferred embodiment, in a rainfall inversion device for GNSS PWV-assisted meteorological satellite remote sensing provided in an embodiment of the present invention, constructing a sample data set further includes:

[0061] According to a preset time window, obtain the PWV sequence, brightness temperature image sequence, brightness temperature difference image sequence, and rainfall sequence within the time window to obtain a sample data set.

[0062] Based on the content of the above device embodiments, as a preferred embodiment, in a rainfall inversion device for GNSS PWV-assisted meteorological satellite remote sensing provided in an embodiment of the present invention, constructing a sample data set further includes:

[0063] Randomly select a training set and a validation set from the sample data set, use the training set to train the constructed multi-layer neural network model, and use the validation set to verify the trained multi-layer neural network model.

[0064] Based on the content of the above device embodiments, as a preferred embodiment, in a rainfall inversion device for GNSS PWV-assisted meteorological satellite remote sensing provided in an embodiment of the present invention, the multi-layer neural network model is a backpropagation neural network model including three hidden layers.

[0065] The method of the embodiment of the present invention is implemented relying on a computing device. Therefore, it is necessary to introduce the relevant computing device. For this purpose, an embodiment of the present invention provides a computing device, including a processor and a memory. The memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the rainfall inversion method for GNSS PWV-assisted meteorological satellite remote sensing, specifically:

[0066] Obtain PWV data, cloud top brightness temperature data, and brightness temperature difference data during the rainfall period;

[0067] Input the PWV data, cloud top brightness temperature data, and brightness temperature difference data during the rainfall period into the trained multi-layer neural network model to output the rainfall amount during the corresponding period. Among them, the training of the multi-layer neural network model includes:

[0068] Obtain the measured rainfall amounts and multi-channel cloud top brightness temperature data with a long time series, and calculate the brightness temperature difference between the infrared channel and the water vapor channel;

[0069] Obtain the GNSS observation data during the same period, and calculate the precipitable water vapor PWV based on the GNSS observation data;

[0070] Construct a sample data set according to the obtained measured rainfall amounts, cloud top brightness temperature data, brightness temperature difference, and precipitable water vapor PWV;

[0071] Construct a multi-layer neural network model. The input layer of the multi-layer neural network model is the PWV sequence, brightness temperature image sequence, and brightness temperature difference image sequence within a time window, and the output layer is the rainfall amount during the corresponding period;

[0072] Use the sample data set to train the constructed multi-layer neural network model to obtain a trained model.

[0073] It should be noted that the processor in the embodiments of the present invention may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiments may be completed by the integrated logic circuit in the hardware of the processor or the instructions in the form of software. The above processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention may be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium 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. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0074] It can be understood that the memory in the embodiments of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but not be limited to, these and any other suitable types of memory.

[0075] The embodiments of the present invention also provide a computer program product, which includes: computer program instructions, when the computer program instructions run on a computer, enabling the computer to execute each step or process executed in any of the above method embodiments.

[0076] The embodiments of the present invention also provide a computer-readable storage medium, which stores program code, when the program code runs on a computer, enabling the computer to execute each step or process executed in any of the above method embodiments.

[0077] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. The GNSS PWV-assisted meteorological satellite remote sensing rainfall inversion method is characterized by: include: Obtain PWV data, cloud top brightness temperature data and brightness temperature difference data during the rainfall period; The PWV data, cloud top brightness temperature data and brightness temperature difference data of the rainfall period are input into the trained multi-layer neural network model, and the rainfall amount of the corresponding period is output; wherein the training of the multi-layer neural network model includes: Obtain long-term series of measured rainfall and multi-channel cloud top brightness temperature data, and calculate the brightness temperature difference between the infrared channel and the water vapor channel; Obtain GNSS observation data for the same period, and calculate atmospheric precipitable water (PWV) based on the GNSS observation data; A sample data set was constructed based on the measured rainfall, cloud top brightness temperature data, brightness temperature difference and atmospheric precipitable water volume (PWV). Constructing a multi-layer neural network model, wherein the input layer of the multi-layer neural network model is a PWV sequence, a brightness temperature image sequence, and a brightness temperature difference image sequence within a time window, and the output layer is the rainfall in the corresponding time period; The constructed multi-layer neural network model is trained using the sample data set to obtain a trained model.

2. The GNSS PWV-assisted meteorological satellite remote sensing rainfall inversion method according to claim 1, characterized in that: Construct a sample dataset, including: According to the preset time window, the PWV sequence, brightness temperature image sequence, brightness temperature difference image sequence and rainfall sequence within the time window are obtained to obtain the sample data set.

3. The GNSS PWV-assisted meteorological satellite remote sensing rainfall inversion method according to claim 2, characterized in that: The method further comprises: A training set and a validation set are randomly selected from the sample data set, the training set is used to train the constructed multi-layer neural network model, and the validation set is used to validate the trained multi-layer neural network model.

4. The GNSS PWV-assisted meteorological satellite remote sensing rainfall inversion method according to claim 1, characterized in that: The multi-layer neural network model is a back-propagation neural network model including three hidden layers.

5. GNSS PWV-assisted meteorological satellite remote sensing rainfall inversion device, characterized in that: include: A data acquisition module is used to obtain PWV data, cloud top brightness temperature data and brightness temperature difference data during the rainfall period; The rainfall inversion module is used to input the PWV data, cloud top brightness temperature data and brightness temperature difference data of the rainfall period into the trained multi-layer neural network model, and output the rainfall of the corresponding period; wherein the training of the multi-layer neural network model includes: Obtain long-term series of measured rainfall and multi-channel cloud top brightness temperature data, and calculate the brightness temperature difference between the infrared channel and the water vapor channel; Obtain GNSS observation data for the same period, and calculate atmospheric precipitable water (PWV) based on the GNSS observation data; A sample data set was constructed based on the measured rainfall, cloud top brightness temperature data, brightness temperature difference and atmospheric precipitable water volume (PWV). Constructing a multi-layer neural network model, wherein the input layer of the multi-layer neural network model is a PWV sequence, a brightness temperature image sequence, and a brightness temperature difference image sequence within a time window, and the output layer is the rainfall in the corresponding time period; The constructed multi-layer neural network model is trained using the sample data set to obtain a trained model.

6. A computing device, characterized in that It comprises a processor and a memory, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the GNSS PWV-assisted meteorological satellite remote sensing rainfall inversion method as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the steps of the GNSSPWV-assisted meteorological satellite remote sensing rainfall inversion method as described in any one of claims 1 to 4.

8. A computer program product, characterized in that The method comprises computer program instructions, wherein the computer program instructions enable the computer to execute the steps of the GNSS PWV-assisted meteorological satellite remote sensing rainfall inversion method as claimed in any one of claims 1 to 4.

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