Space-borne precipitation measuring radar reflectivity factor monitoring method based on analog reference source

Abstract

The application provides a spaceborne precipitation measurement radar reflectivity factor monitoring method based on an analog reference source, and the method comprises the following steps: based on the rainfall rate data of the spaceborne precipitation measurement radar, pre-processing and quality control are performed on the data in a precipitation profile database observed by a passive microwave radiometer network, the data in the quality-controlled precipitation profile database and reflectivity factor observation data are subjected to time-space position matching and spatial resolution matching, and a matched precipitation profile database is obtained; through a radiation transmission forward operator, the data in the matched precipitation profile database are converted into reflectivity factor simulation data, and an observation simulation deviation is obtained; and the accuracy of the reflectivity factor is evaluated in combination with the observation simulation deviation of the reference radar. The precipitation data based on actual observation inversion used in the application has higher accuracy and precision compared with numerical prediction data, and the on-orbit performance of the spaceborne precipitation measurement radar can be effectively verified.

Description

Technical Field

[0001] This invention relates to the field of meteorological monitoring technology, and in particular to a method for monitoring the reflectivity factor of spaceborne precipitation measurement radar based on a simulated reference source. Background Technology

[0002] Accurate reflectivity factors of spaceborne precipitation measurement radars are crucial for the quantitative application of radar data in areas such as precipitation product inversion, numerical weather prediction assimilation, and convective forecasting and early warning. Monitoring spaceborne precipitation measurement radar observation data involves comparing signals from known targets with synchronous observations to quantitatively assess and monitor the on-orbit performance of the radar data, including accuracy and stability. This plays a vital role in the calibration and verification of the radar system and in improving the realism and accuracy of precipitation monitoring. Methods for verifying the accuracy of the reflectivity factor of spaceborne precipitation measurement radars include satellite-to-ground synchronous comparison, inter-satellite comparison, comparison with stable ocean targets, comparison with numerical weather prediction data, and comparison with data from ground precipitation observation networks.

[0003] Common star cross-comparison methods use matching data from similar radar sensors of different satellites at the same spatiotemporal location as reference sources for cross-comparison. However, this method suffers from problems such as differences in observation frequency, detection capabilities, spatiotemporal matching, and line-of-sight matching, resulting in low accuracy and precision of the data. Summary of the Invention

[0004] This invention provides a method for monitoring the reflectivity factor of spaceborne precipitation measurement radar based on a simulated reference source. Its main purpose is to monitor the reflectivity factor of spaceborne precipitation measurement radar and effectively improve the accuracy of spaceborne precipitation measurement radar observation data.

[0005] In a first aspect, embodiments of the present invention provide a method for monitoring the reflectivity factor of a spaceborne precipitation measurement radar based on a simulated reference source, comprising:

[0006] S1, based on the rainfall rate data of the spaceborne precipitation measurement radar, preprocess the data in the precipitation profile database observed by the passive microwave radiometer network to obtain the preprocessed precipitation profile database;

[0007] S2, perform quality control on the preprocessed precipitation profile database to obtain a quality-controlled precipitation profile database;

[0008] S3. Perform spatiotemporal location matching and spatial resolution matching on the data in the quality-controlled precipitation profile database and the reflectivity factor observation data of the spaceborne precipitation measurement radar to obtain the matched precipitation profile database.

[0009] S4. Using the radiative transfer forward modeling operator, the data in the matched precipitation profile database is converted into the reflectivity factor simulation data of the spaceborne precipitation measurement radar. Based on the reflectivity factor observation data and the reflectivity factor simulation data, the observation simulation bias of the spaceborne precipitation measurement radar is obtained.

[0010] S5. The accuracy of the reflectivity factor of the spaceborne precipitation measurement radar is evaluated based on the observation simulation deviation of the reference radar and the observation simulation deviation of the spaceborne precipitation measurement radar.

[0011] Secondly, embodiments of the present invention provide a reflectivity factor monitoring system for a spaceborne precipitation measurement radar based on an analog reference source, comprising:

[0012] The preprocessing module is used to preprocess the data in the precipitation profile database observed by the passive microwave radiometer network based on the rainfall rate data of the spaceborne precipitation measurement radar, so as to obtain the preprocessed precipitation profile database.

[0013] The quality control module is used to perform quality control on the preprocessed precipitation profile database to obtain a quality-controlled precipitation profile database.

[0014] The matching module is used to perform spatiotemporal location matching and spatial resolution matching between the data in the quality-controlled precipitation profile database and the reflectivity factor observation data of the spaceborne precipitation measurement radar, so as to obtain the matched precipitation profile database.

[0015] The deviation module is used to convert the data in the matched precipitation profile database into the reflectivity factor simulation data of the spaceborne precipitation measurement radar through the radiative transfer forward modeling operator, and to obtain the observation simulation deviation of the spaceborne precipitation measurement radar based on the reflectivity factor observation data and the reflectivity factor simulation data.

[0016] The monitoring module is used to evaluate the accuracy of the reflectivity factor of the spaceborne precipitation measurement radar based on the observation simulation deviation of the reference radar and the observation simulation deviation of the spaceborne precipitation measurement radar.

[0017] Thirdly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described method for monitoring the reflectivity factor of a spaceborne precipitation measurement radar based on an analog reference source.

[0018] Fourthly, embodiments of the present invention provide a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for monitoring the reflectivity factor of a spaceborne precipitation measurement radar based on an analog reference source.

[0019] This invention proposes a method for monitoring reflectivity factors of spaceborne precipitation measurement radar based on simulated reference sources. It utilizes a global precipitation profile database obtained through passive microwave radiometer network observations and inversion. This database undergoes preprocessing, quality control, spatiotemporal location matching, spatial resolution matching, and radiative transfer calculation to convert it into simulated reflectivity factor data for spaceborne precipitation measurement radar. Double-difference analysis is then used to evaluate the accuracy of the reflectivity factor data for spaceborne precipitation measurement radar. Compared to traditional star cross-reference methods concentrated in high-latitude regions, this method has a wider spatial distribution, with samples distributed across various underlying surfaces such as oceans and land. It eliminates the need for stable target selection, enabling global sample collection and covering a broader range of atmospheric conditions. Furthermore, the larger sample size better reflects the nonlinear characteristics of data errors, allowing for radar reflectivity factor comparisons at different altitudes in the vertical dimension. This invention avoids uncertainties caused by differences in frequency and observation capabilities, as well as space-time matching between satellite and ground. Compared with traditional simulated reference source monitoring methods, the precipitation data retrieved based on actual observations has higher accuracy and precision than numerical forecast data, and can effectively verify the on-orbit performance of the spaceborne precipitation measurement radar. Attached Figure Description

[0020] Figure 1 The flowchart is a method for monitoring the reflectivity factor of spaceborne precipitation measurement radar based on a simulated reference source, as proposed in an embodiment of the present invention.

[0021] Figure 2 The flowchart is a method for monitoring the reflectivity factor of a spaceborne precipitation measurement radar based on a simulated reference source, according to an embodiment of the present invention.

[0022] Figure 3 A schematic diagram of the reflectivity factor monitoring system of a spaceborne precipitation measurement radar with a simulated reference source provided in an embodiment of the present invention;

[0023] Figure 4 This invention provides a schematic diagram of the structure of a computer device.

[0024] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0025] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0026] To enable those skilled in the art to better understand the solutions of this application, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0027] In the embodiments of this application, "at least one" refers to one or more; "multiple" refers to two or more. In the description of this application, terms such as "first," "second," and "third" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or order. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0028] References such as “one embodiment” or “some embodiments” as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the terms “comprising,” “including,” “having,” and variations thereof, as used in this specification, mean “including, but not limited to,” unless otherwise specifically emphasized.

[0029] In traditional methods, the space-to-ground synchronous comparison method uses ground-based radar observations in the same frequency band as a reference source to verify and compare the observation data of spaceborne radar. However, direct comparison between ground-based radar and spaceborne radar has problems such as differences in observation frequency, differences in detection capabilities, volume mismatch, and uncertainty in attenuation correction methods. It can only be carried out on the land surface, and the land terrain will block and attenuate the signal of ground-based radar. At the same time, strict quality control of data is required, including accurate spatiotemporal matching and the removal of abnormal data caused by ground clutter and radar failure.

[0030] The star cross-comparison method uses matching data from similar radar sensors of different satellites at the same spatiotemporal location as a reference source for cross-comparison. However, it also has problems such as differences in observation frequency, detection capability, spatiotemporal matching, and line-of-sight matching.

[0031] The traditional method for comparing stable ocean targets, numerical weather prediction data, and surface precipitation observation network data is based on simulated reference sources. However, stable ocean target comparison uses background values ​​over the ocean as a reference, numerical weather prediction model comparison uses numerically simulated precipitation field data to simulate radar observations, thereby verifying the accuracy of spaceborne radar. Surface precipitation observation network data comparison also uses simulated ground echoes from spaceborne precipitation radar based on surface precipitation observation data for verification. However, stable ocean target comparison has high requirements for the selection of stable targets and is easily affected by marine environmental conditions such as sea winds. The accuracy of the precipitation field location and intensity of numerically predicted precipitation field simulation data is poor, and surface precipitation observation data is limited to ground echo comparison, which has limitations in application.

[0032] To address the shortcomings and deficiencies of existing methods for monitoring the reflectivity factor of spaceborne precipitation measurement radar, a new method based on a simulated reference source is proposed. This method utilizes a global precipitation profile database obtained through passive microwave radiometer network observation and inversion. The global precipitation profile database is preprocessed, quality controlled, and subjected to spatiotemporal location matching, spatial resolution matching, and radiative transfer calculation to convert it into simulated reflectivity factor data for spaceborne precipitation measurement radar. The accuracy of the reflectivity factor of the spaceborne precipitation measurement radar is then evaluated through double-difference analysis.

[0033] Figure 1 This is a flowchart of a satellite-borne precipitation measurement radar reflectivity factor monitoring method based on a simulated reference source, as proposed in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0034] S1, based on the rainfall rate data of the spaceborne precipitation measurement radar, preprocess the data in the precipitation profile database observed by the passive microwave radiometer network to obtain the preprocessed precipitation profile database;

[0035] Step S1 can be achieved through the following two steps:

[0036] S11, acquire the rainfall rate data and the precipitation profile database;

[0037] First, precipitation rate data from the spaceborne precipitation measurement radar and a precipitation profile database observed by the passive microwave radiometer network were acquired. The spaceborne precipitation measurement radar (PMR) was the radar to be monitored, and precipitation rate data from the second-order near-surface of the PMR were extracted. The GPROF (Goddard Profiling Algorithm) satellite precipitation profile database consisted of data from nine passive microwave radiometers. These nine instruments included four microwave imagers, three SSMI (Special Sensor Microwave Imager / Sounders), and five microwave sounders. The four microwave imagers were of the AMSR2 (Advanced Microwave Scanning Radiometer 2) type, and the five microwave sounders included one microwave ATMS and four MHS (Microwave Humidity Sounders). A global precipitation profile database was obtained based on observations and inversion from the passive microwave radiometer network. This database was generated by using the Goddard precipitation profile inversion algorithm to retrieve observations from the global radiometer constellation.

[0038] S12, perform time matching and spatial resolution matching on the rainfall rate data and the data in the precipitation profile database to obtain a preprocessed precipitation profile database.

[0039] Based on the near-surface precipitation rate data from the spaceborne precipitation measurement radar, the nearest neighbor matching method was used to perform temporal and spatial matching with the near-surface precipitation rate data from all microwave radiometer GPROF data.

[0040] Spatial resolution matching is performed on the matched dataset. Specifically, the observations of 3*3 PMR pixels around the matched PMR pixel are selected as the center and the arithmetic mean is performed to obtain the resampled PMR observations, thus obtaining the preprocessed precipitation profile database.

[0041] S2, perform quality control on the preprocessed precipitation profile database to obtain a quality-controlled precipitation profile database;

[0042] Step S2 can be achieved through the following steps:

[0043] S21, calculate the correlation coefficient error, normalization bias error, normalization root mean square error, and HSS exponent error between the rainfall rate data and the rainfall rate data in the precipitation profile database, respectively.

[0044] The correlation coefficient error between the rainfall rate data and the rainfall rate data in the precipitation profile database is calculated as shown in formula (1):

[0045]

[0046] k = 1, ..., 9, representing 9 instruments.

[0047] Where, x i For PMR rainfall rate, y i To match the GPROF rainfall rate, r k (x i ,y i Cov(x) is the correlation coefficient between the k-th GPROF satellite and the PMR rainfall rate. i ,y i ) is x i With y i The covariance, Var[x i ] is x i The variance, Var[y i ] is y i The variance.

[0048] The normalized bias error and normalized root mean square error between the rainfall rate data and the rainfall rate data in the precipitation profile database are shown in Equations (2) and (3), respectively:

[0049]

[0050]

[0051] Among them, nBias k and nRmse k Let n be the standardized bias and root mean square error of the rainfall rate between the k-th GPROF satellite and the PMR, and n be the matched sample size. This represents the average PMR rainfall rate.

[0052] The HSS exponential error between the rainfall rate data and the rainfall rate data in the precipitation profile database is calculated as shown in formula (4):

[0053]

[0054] HSS stands for Hederek technical score, and ad represents hit rate, false alarm rate, false negative rate, and correct subsamples, respectively.

[0055] S22, Based on the correlation coefficient error, the normalized deviation error, the normalized root mean square error, and the HSS exponent error, and combined with the correlation threshold, deviation threshold, root mean square threshold, and exponent threshold, a quality-controlled precipitation profile database is obtained.

[0056]

[0057] Statistical analysis was performed on the data matched between nine passive microwave radiometers (GPROF) and PMR. Available satellite data were screened based on quality control thresholds to obtain a quality-controlled precipitation profile database.

[0058] S3. Perform spatiotemporal location matching and spatial resolution matching on the data in the quality-controlled precipitation profile database and the reflectivity factor observation data of the spaceborne precipitation measurement radar to obtain the matched precipitation profile database.

[0059] Step S3 can be obtained through the following steps:

[0060] S31. Based on the pixel center position and pixel time information observed in the quality-controlled precipitation profile database, and based on the pixel center position and pixel time information observed by the spaceborne precipitation measurement radar, the passive observation position of the quality-controlled precipitation profile database is matched to the active observation position of the spaceborne precipitation measurement radar using the nearest neighbor method.

[0061] In the nearest neighbor method, the spatial interval is less than 5km between pixel centers and the time interval is less than 15 minutes.

[0062] S32, using linear interpolation or quadratic interpolation, interpolates the three-dimensional spatial distribution information of the atmospheric background field in the numerical forecast database after quality control to the observation time and location of the spaceborne precipitation measurement radar.

[0063] S33, the nearest neighbor matching method is used to interpolate the spatial vertical direction, and the measurement data of the spaceborne precipitation measurement radar is interpolated to the vertical height layer position in the quality-controlled precipitation profile database.

[0064] S34. Spatial resolution matching is performed on the radar observation pixels in the precipitation profile database obtained in step S33 to obtain resampled radar observation pixels, and then the matched precipitation profile database is obtained.

[0065] In the spatial resolution matching process, the matching radar observation pixel is used as the center, and the observations of the surrounding 3*3 radar observation pixels are selected for arithmetic averaging to obtain the resampled radar observations, which in turn yields the matched precipitation profile database.

[0066] S4. Using the radiative transfer forward modeling operator, the data in the matched precipitation profile database is converted into the reflectivity factor simulation data of the spaceborne precipitation measurement radar. Based on the reflectivity factor observation data and the reflectivity factor simulation data, the observation simulation bias of the spaceborne precipitation measurement radar is obtained.

[0067] The results are obtained through the following formulas (6) to (9):

[0068]

[0069]

[0070]

[0071]

[0072] Among them, Z e This represents the simulated reflectivity factor data, where λ represents the wavelength, and σ... b,i Let k represent the single-particle backscattering coefficient of the i-th hydrogel species. ext,i Let represent the single-particle backward attenuation coefficient for the i-th hydrogel species. This represents the total backscattering coefficient. Let n(D) represent the total attenuation coefficient, n(D) represent the particle size distribution function, and D represent the particle diameter. (Perm) water Let represent the dielectric constant of water, i be a positive integer, r represent the distance between the radar and the target, nspec represent the total number of water-soluble particles, Dmin represent the minimum particle diameter, and Dmax represent the maximum particle diameter.

[0073] It should be noted that n(D) is the particle size distribution function, and its value is related to the liquid water content / ice water content of the condensate. The types of condensates include rain, snow, cloud water, and cloud ice, which are mainly determined by the type of condensate in the GPROF precipitation profile data.

[0074] S5. The accuracy of the reflectivity factor of the spaceborne precipitation measurement radar is evaluated based on the observation simulation deviation of the reference radar and the observation simulation deviation of the spaceborne precipitation measurement radar.

[0075] Specifically, the observation simulation bias of the reference radar is obtained according to steps S1 to S4. Figure 2 A flowchart of a satellite-borne precipitation measurement radar reflectivity factor monitoring method based on a simulated reference source, provided in an embodiment of the present invention, is shown below. Figure 2 The method for obtaining the observation simulation bias of the reference radar is consistent with the method for obtaining the observation simulation bias of the target radar. The reference radar is a high-precision foreign instrument, such as the DPR (Dual-frequency Precipitation Radar) of the GPM (Global Precipitation Measurement) satellite. The onboard precipitation measurement radar is the radar device to be monitored, such as the PMR precipitation measurement radar of the Fengyun satellite.

[0076] The observation simulation bias refers to the OB residual, which is the difference between the observed brightness temperature in the reflectivity factor observation data and the simulated observed brightness temperature in the reflectivity factor simulation data, as shown in formula (10):

[0077]

[0078] Where j is the altitude layer number, n is the channel number, k is the total number of matched observations in a day, and y n For reflectivity factor observation data, H is the forward modeling operator, i.e., formula (6), x i For matching precipitation profiles.

[0079] OB residual statistics and analysis involves selecting quality control conditions and using statistical algorithms to monitor the spatiotemporal distribution modes of OB residuals. Filtering conditions include latitude bands (within ±60° latitude), precipitation identification, and clutter removal. Statistical indicators include mean, standard deviation, and root mean square error. Deviation characteristics include variations with latitude, observation angle, orbital elevation, day-night cycle, geographical distribution, and long-term series.

[0080] Then, statistical monitoring is performed based on the double-difference method for observables (OB). When the center wavelengths of the channels are similar but the observation modes differ, parallel evaluation of observations can be performed using double-difference between different instruments. Double-difference results can reduce the influence of frequency differences, background profiles, and forward operators; the obtained double difference is only related to instrument errors (including calibration bias and differences in channel responses). The double-difference calculation is defined as follows:

[0081] D j-m =δB j (n)-δB m (n′), (11)

[0082] Where j and m are instrument numbers, in this case the reference radar and the spaceborne precipitation measurement radar, n is the channel number, and δB is the OB residual. Quality control conditions are selected to statistically monitor the deviation characteristics of the double difference, so as to evaluate and monitor the accuracy and stability of the reflectivity factor observation data based on the monitoring results, ensuring the stability and accuracy of the observation data.

[0083] This invention proposes a method for monitoring reflectivity factors of spaceborne precipitation measurement radar based on simulated reference sources. It utilizes a global precipitation profile database obtained through passive microwave radiometer network observations and inversion. This database undergoes preprocessing, quality control, spatiotemporal location matching, spatial resolution matching, and radiative transfer calculation to convert it into simulated reflectivity factor data for spaceborne precipitation measurement radar. Double-difference analysis is then used to evaluate the accuracy of the reflectivity factor data for spaceborne precipitation measurement radar. Compared to traditional star cross-reference methods concentrated in high-latitude regions, this method has a wider spatial distribution, with samples distributed across various underlying surfaces such as oceans and land. It eliminates the need for stable target selection, enabling global sample collection and covering a broader range of atmospheric conditions. Furthermore, the larger sample size better reflects the nonlinear characteristics of data errors, allowing for radar reflectivity factor comparisons at different altitudes in the vertical dimension. This invention avoids uncertainties caused by differences in frequency and observation capabilities, as well as space-time matching between satellite and ground. Compared with traditional simulated reference source monitoring methods, the precipitation data retrieved based on actual observations has higher accuracy and precision than numerical forecast data, and can effectively verify the on-orbit performance of the spaceborne precipitation measurement radar.

[0084] Figure 3 A schematic diagram of the reflectivity factor monitoring system of a spaceborne precipitation measurement radar with a simulated reference source provided in an embodiment of the present invention is shown below. Figure 3 As shown, the system includes a preprocessing module 210, a quality control module 220, a matching module 230, a deviation module 240, and a monitoring module 250, wherein:

[0085] The preprocessing module 210 is used to preprocess the data in the precipitation profile database observed by the passive microwave radiometer network based on the rainfall rate data of the spaceborne precipitation measurement radar, so as to obtain the preprocessed precipitation profile database.

[0086] The quality control module 220 is used to perform quality control on the preprocessed precipitation profile database to obtain a quality-controlled precipitation profile database.

[0087] The matching module 230 is used to perform spatiotemporal location matching and spatial resolution matching between the data in the quality-controlled precipitation profile database and the reflectivity factor observation data of the spaceborne precipitation measurement radar, so as to obtain the matched precipitation profile database.

[0088] The deviation module 240 is used to convert the data in the matched precipitation profile database into the reflectivity factor simulation data of the spaceborne precipitation measurement radar through the radiative transfer forward modeling operator, and to obtain the observation simulation deviation of the spaceborne precipitation measurement radar based on the reflectivity factor observation data and the reflectivity factor simulation data.

[0089] The monitoring module 250 is used to evaluate the accuracy of the reflectivity factor of the spaceborne precipitation measurement radar based on the observation simulation deviation of the reference radar and the observation simulation deviation of the spaceborne precipitation measurement radar.

[0090] This embodiment is a system embodiment corresponding to the above method. Its specific implementation process is the same as the above method. For details, please refer to the above method embodiment. This system embodiment will not repeat the details.

[0091] The various modules in the reflectivity factor monitoring system of the aforementioned simulated reference source spaceborne precipitation measurement radar can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware within or independently of the processor in a computer device, or stored in software within the memory of the computer device, so that the processor can call and execute the corresponding operations of each module.

[0092] Figure 4 This invention provides a schematic diagram of the structure of a computer device, which may be a server, and its internal structure diagram may be as follows. Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes a computer storage medium and internal memory. The computer storage medium stores the operating system, computer programs, and the database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the computer storage medium. The database stores data generated or acquired during the execution of a reflectivity factor monitoring method for a spaceborne precipitation measurement radar based on an analog reference source. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a reflectivity factor monitoring method for a spaceborne precipitation measurement radar based on an analog reference source.

[0093] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a reflectivity factor monitoring method for a spaceborne precipitation measurement radar based on an analog reference source as described in the above embodiment. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in this embodiment of a reflectivity factor monitoring system for a spaceborne precipitation measurement radar based on an analog reference source.

[0094] In one embodiment, a computer storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the steps of the reflectivity factor monitoring method for a spaceborne precipitation measurement radar based on an analog reference source as described in the above embodiment. Alternatively, when executed by a processor, the computer program implements the functions of each module / unit in the above embodiment of the reflectivity factor monitoring system for a spaceborne precipitation measurement radar based on an analog reference source.

[0095] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0096] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0097] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for monitoring the reflectivity factor of spaceborne precipitation measurement radar based on a simulated reference source, characterized in that, include: S1, based on the rainfall rate data of the spaceborne precipitation measurement radar, preprocess the data in the precipitation profile database observed by the passive microwave radiometer network to obtain the preprocessed precipitation profile database; S2, perform quality control on the preprocessed precipitation profile database to obtain a quality-controlled precipitation profile database; S3. Perform spatiotemporal location matching and spatial resolution matching on the data in the quality-controlled precipitation profile database and the reflectivity factor observation data of the spaceborne precipitation measurement radar to obtain the matched precipitation profile database. S4. Using the radiative transfer forward modeling operator, the data in the matched precipitation profile database is converted into the reflectivity factor simulation data of the spaceborne precipitation measurement radar. Based on the reflectivity factor observation data and the reflectivity factor simulation data, the observation simulation bias of the spaceborne precipitation measurement radar is obtained. S5. The accuracy of the reflectivity factor of the spaceborne precipitation measurement radar is evaluated based on the observation simulation deviation of the reference radar and the observation simulation deviation of the spaceborne precipitation measurement radar.

2. The method for monitoring the reflectivity factor of spaceborne precipitation measurement radar based on a simulated reference source according to claim 1, characterized in that, Step S1 includes: S11, acquire the rainfall rate data and the precipitation profile database; S12, perform time matching and spatial resolution matching on the rainfall rate data and the data in the precipitation profile database to obtain a preprocessed precipitation profile database.

3. The method for monitoring the reflectivity factor of spaceborne precipitation measurement radar based on a simulated reference source according to claim 1, characterized in that, Step S2 includes: S21, calculate the correlation coefficient error, normalization bias error, normalization root mean square error, and HSS exponent error between the rainfall rate data and the rainfall rate data in the precipitation profile database, respectively; S22, Based on the correlation coefficient error, the normalized deviation error, the normalized root mean square error, and the HSS exponent error, and combined with the correlation threshold, deviation threshold, root mean square threshold, and exponent threshold, a quality-controlled precipitation profile database is obtained.

4. The method for monitoring the reflectivity factor of spaceborne precipitation measurement radar based on a simulated reference source according to claim 1, characterized in that, Step S3 includes: S31. Based on the pixel center position and pixel time information observed in the quality-controlled precipitation profile database, and based on the pixel center position and pixel time information observed by the spaceborne precipitation measurement radar, the passive observation position of the quality-controlled precipitation profile database is matched to the active observation position of the spaceborne precipitation measurement radar using the nearest neighbor method. S32, using linear interpolation or quadratic interpolation, interpolates the three-dimensional spatial distribution information of the atmospheric background field in the numerical forecast database after quality control to the observation time and location of the spaceborne precipitation measurement radar. S33, the nearest neighbor matching method is used to interpolate the spatial vertical direction, and the measurement data of the spaceborne precipitation measurement radar is interpolated to the vertical height layer position in the quality-controlled precipitation profile database. S34. Spatial resolution matching is performed on the radar observation pixels in the precipitation profile database obtained in step S33 to obtain resampled radar observation pixels, and then the matched precipitation profile database is obtained.

5. The method for monitoring the reflectivity factor of spaceborne precipitation measurement radar based on a simulated reference source according to claim 1, characterized in that, In step S4, the data in the matched precipitation profile database is converted into the reflectivity factor simulation data of the spaceborne precipitation measurement radar using the radiative transfer forward modeling operator, which is obtained through the following formula: Among them, Z e This represents the simulated reflectivity factor data, where λ represents the wavelength, and σ... b,i Let k represent the single-particle backscattering coefficient of the i-th hydrogel species. ext,i Let σ represent the single-particle backward attenuation coefficient for the i-th hydrogel species. b k represents the total backscattering coefficient. ext Let n(D) represent the total attenuation coefficient, n(D) represent the particle size distribution function, and D represent the particle diameter. (Perm) water Let represent the dielectric constant of water, i be a positive integer, r represent the distance between the radar and the target, nspec represent the total number of water-soluble particles, Dmin represent the minimum particle diameter, and Dmax represent the maximum particle diameter.

6. The method for monitoring the reflectivity factor of spaceborne precipitation measurement radar based on a simulated reference source according to claim 1, characterized in that, The observation simulation bias is the OB residual.

7. The method for monitoring the reflectivity factor of spaceborne precipitation measurement radar based on a simulated reference source according to claim 6, characterized in that, Step S5 includes: Based on the observation simulation bias of the reference radar and the observation simulation bias of the spaceborne precipitation measurement radar, the accuracy of the reflectivity factor of the spaceborne precipitation measurement radar is evaluated using the double-difference method.

8. A reflectivity factor monitoring system for a spaceborne precipitation measurement radar based on an analog reference source, characterized in that, include: The preprocessing module is used to preprocess the data in the precipitation profile database observed by the passive microwave radiometer network based on the rainfall rate data of the spaceborne precipitation measurement radar, so as to obtain the preprocessed precipitation profile database. The quality control module is used to perform quality control on the preprocessed precipitation profile database to obtain a quality-controlled precipitation profile database. The matching module is used to perform spatiotemporal location matching and spatial resolution matching between the data in the quality-controlled precipitation profile database and the reflectivity factor observation data of the spaceborne precipitation measurement radar, so as to obtain the matched precipitation profile database. The deviation module is used to convert the data in the matched precipitation profile database into the reflectivity factor simulation data of the spaceborne precipitation measurement radar through the radiative transfer forward modeling operator, and to obtain the observation simulation deviation of the spaceborne precipitation measurement radar based on the reflectivity factor observation data and the reflectivity factor simulation data. The monitoring module is used to evaluate the accuracy of the reflectivity factor of the spaceborne precipitation measurement radar based on the observation simulation deviation of the reference radar and the observation simulation deviation of the spaceborne precipitation measurement radar.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the satellite-borne precipitation measurement radar reflectivity factor monitoring method based on a simulated reference source as described in any one of claims 1 to 7.

10. A computer storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the satellite-borne precipitation measurement radar reflectivity factor monitoring method based on a simulated reference source as described in any one of claims 1 to 7.

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