Space-borne radar and ground-based millimeter wave cloud radar reflectivity factor homogeneity evaluation method

Abstract

The embodiment of the invention provides a spaceborne radar and ground-based millimeter wave cloud radar reflectivity factor uniformity evaluation method, which is applied to the technical field of atmospheric detection. The method comprises the following steps: acquiring reflectivity factor data of a spaceborne radar and a ground-based millimeter wave cloud radar; establishing reflectivity factor space-time matching data of the spaceborne radar and the ground-based millimeter wave cloud radar according to the reflectivity factor data by taking vertical direction scanning as a reference; preprocessing the time-space matching data of the reflectivity factors; and based on a preset uniformity evaluation algorithm, an evaluation result is calculated according to the preprocessed reflectivity factor space-time matching data. In this way, errors between original data can be better revealed, and a good foundation is laid for product inversion in the next step.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, in particular to the field of atmospheric detection technology, and specifically to a method for evaluating the uniformity of reflectivity factors of spaceborne radar and ground-based millimeter-wave cloud radar. Background Art

[0002] Global Precipitation Measurement Mission (GPM) is a radar system that uses electromagnetic waves for detection and is usually carried on satellites or spacecraft for remote observation. Ground based millimeter wave cloud radar (CR) is a radar system that uses millimeter wave electromagnetic waves for cloud observation. It has the characteristics of short wavelength, wide frequency band, and strong anti-interference ability.

[0003] Ground-based millimeter-wave cloud radar and space-borne radar are two different detection methods, each with its own advantages and disadvantages. At present, most cloud top height products obtained by secondary inversion of ground-based millimeter-wave cloud radar and cloud top height products obtained by space-borne radar are compared and evaluated. The errors generated may come from the errors generated in the inversion process of the products themselves, and the errors between the original data cannot be revealed, resulting in the inability to guarantee the accuracy and reliability of the data of ground-based millimeter-wave cloud radar and space-borne radar. Summary of the invention

[0004] The present invention provides a method, device, equipment and storage medium for evaluating the uniformity of reflectivity factors of spaceborne radar and ground-based millimeter-wave cloud radar.

[0005] According to a first aspect of the present disclosure, a method for evaluating the uniformity of reflectivity factors of a spaceborne radar and a ground-based millimeter-wave cloud radar is provided. The method comprises:

[0006] Obtain reflectivity factor data from spaceborne radar and ground-based millimeter-wave cloud radar;

[0007] Taking vertical scanning as a reference, establishing the reflectivity factor spatiotemporal matching data of the spaceborne radar and the ground-based millimeter-wave cloud radar according to the reflectivity factor data;

[0008] Preprocessing the reflectivity factor spatiotemporal matching data;

[0009] Based on the preset uniformity evaluation algorithm, the evaluation results are calculated according to the preprocessed reflectivity factor spatiotemporal matching data.

[0010] According to the aspects and any possible implementations described above, an implementation is further provided, wherein the reflectivity factor data is Ka or Ku band reflectivity factor data.

[0011] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein obtaining the reflectivity factor data of the spaceborne radar and the ground-based millimeter-wave cloud radar includes:

[0012] Obtain reflectivity factor data of space-borne radar and basic data of ground-based millimeter-wave cloud radar;

[0013] Taking the reflectivity factor data of the spaceborne radar as a benchmark, the reflectivity factor data of the ground-based millimeter-wave cloud radar is determined according to the base data.

[0014] According to the above aspect and any possible implementation manner, an implementation manner is further provided, wherein the preprocessing of the reflectivity factor spatiotemporal matching data comprises:

[0015] According to a preset reflectivity factor threshold, threshold limiting processing is performed on the reflectivity factor spatiotemporal matching data;

[0016] According to a preset height threshold, clutter removal processing is performed on the reflectivity factor spatiotemporal matching data.

[0017] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein establishing the reflectivity factor spatiotemporal matching data of the spaceborne radar and the ground-based millimeter-wave cloud radar according to the reflectivity factor data comprises:

[0018] The reflectivity factor data is spatially matched, temporally matched, and highly matched to establish the reflectivity factor spatiotemporal matching data of the spaceborne radar and the ground-based millimeter-wave cloud radar.

[0019] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the preset uniformity evaluation algorithm includes:

[0020]

[0021] Among them, RMSE represents the root mean square error, R represents the correlation coefficient, BIAS represents the average deviation, N represents the number of ground-based millimeter-wave cloud radar observation stations, i represents the station number, G i It represents the value of the space-time matching reflectivity factor of the spaceborne radar, and its average value is O i represents the measured value of the ground-based millimeter-wave cloud radar observation station, and its average value is

[0022] According to the above aspects and any possible implementation, an implementation is further provided, wherein the calculation of the evaluation result based on the preprocessed reflectivity factor spatiotemporal matching data based on the preset uniformity evaluation algorithm comprises:

[0023] Grouping the preprocessed reflectivity factor spatiotemporal matching data according to a preset geographical area and / or a preset altitude;

[0024] Based on the preset uniformity evaluation algorithm, the evaluation results of each group are calculated.

[0025] According to a second aspect of the present disclosure, a device for evaluating the uniformity of reflectivity factors of a spaceborne radar and a ground-based millimeter-wave cloud radar is provided. The device comprises:

[0026] An acquisition module is used to obtain reflectivity factor data of spaceborne radar and ground-based millimeter-wave cloud radar;

[0027] A matching module, used to establish the reflectivity factor spatiotemporal matching data of the spaceborne radar and the ground-based millimeter-wave cloud radar based on the reflectivity factor data, based on the vertical scanning direction;

[0028] A preprocessing module, used for preprocessing the reflectivity factor spatiotemporal matching data;

[0029] The calculation module is used to calculate the evaluation result based on the pre-processed reflectivity factor time-space matching data based on the preset uniformity evaluation algorithm.

[0030] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0031] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method described above is implemented.

[0032] The embodiment of the present application provides a method for evaluating the uniformity of reflectivity factors of satellite-borne radar and ground-based millimeter-wave cloud radar. The method can obtain the reflectivity factor data of the satellite-borne radar and the ground-based millimeter-wave cloud radar; then establish the time-space matching data of the reflectivity factors of the satellite-borne radar and the ground-based millimeter-wave cloud radar based on the reflectivity factor data based on the vertical scanning; then pre-process the time-space matching data of the reflectivity factor; then calculate the evaluation result based on the pre-processed time-space matching data of the reflectivity factor based on a preset uniformity evaluation algorithm; based on this, establish a time-space matching and comparison evaluation algorithm for the reflectivity factors of the satellite-borne radar and the ground-based millimeter-wave cloud radar, solve key technical problems such as data source quality control, time matching, space matching, and numerical matching of the reflectivity factors of the satellite-ground cloud radar, thereby studying and establishing a screening comparison method to reduce the uncertainty of the consistency evaluation of the satellite-ground cloud radar, which can better reveal the errors between the original data and lay a good foundation for the next step of inversion products.

[0033] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0035] Figure 1 A flow chart of a method for evaluating the uniformity of reflectivity factors of a spaceborne radar and a ground-based millimeter-wave cloud radar according to an embodiment of the present disclosure is shown;

[0036] Figure 2 A schematic diagram showing the evaluation results of the spatiotemporal matching data of the reflectivity factor of station A according to an embodiment of the present disclosure;

[0037] Figure 3 A schematic diagram showing the evaluation results of the spatiotemporal matching data of the reflectivity factor of Station B according to an embodiment of the present disclosure;

[0038] Figure 4 A schematic diagram showing a comparison of the difference in satellite profile data and the difference in cloud radar profile data between stations A and B according to an embodiment of the present disclosure is shown;

[0039] Figure 5 A block diagram of a device for evaluating the uniformity of reflectivity factors of a spaceborne radar and a ground-based millimeter-wave cloud radar according to an embodiment of the present disclosure is shown;

[0040] Figure 6 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0042] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0043] In the present invention, a time-space matching and comparative evaluation algorithm for the reflectivity factors of spaceborne radar and ground-based millimeter-wave cloud radar is established to solve key technical problems such as data source quality control, time matching, space matching, and numerical matching of the reflectivity factors of space-based cloud radars. In this way, a screening comparison method is studied and established to reduce the uncertainty of consistency evaluation of space-based cloud radars, which can better reveal the errors between the original data and lay a good foundation for the next step of inversion products.

[0044] Figure 1 A flow chart of a method 100 for evaluating the uniformity of reflectivity factors of a spaceborne radar and a ground-based millimeter-wave cloud radar according to an embodiment of the present disclosure is shown.

[0045] In block 110 , reflectivity factor data of a spaceborne radar and a ground-based millimeter-wave cloud radar are obtained.

[0046] In some embodiments, the reflectivity factor data of the spaceborne radar is obtained from the GPM_2AKa product data. Among them, the GPM_2AKa product data can use the quality-controlled GPM_2AKa product data, about 1.5 hours (a complete orbit scan) of a product file, and the specific running time trajectory can be obtained by combining the Longitude, Latitude, ScanTime, and binRealSurface data sets in the product file. The scanning mode used is NS, and the data set is ['NS']['SLV']['zFactorFinal']. The GPM inversion principle can be detailed in the "GPM / DPR Level-2 Algorithm TheoreticalBasis Document" document.

[0047] In some embodiments, the reflectivity factor data of the ground-based millimeter-wave cloud radar can be obtained from the ground-based millimeter-wave cloud radar data source. The ground-based millimeter-wave cloud radar data source all comes from the millimeter-wave cloud radar quality control post-base data product of the meteorological big data cloud platform, and the file name format is Z_RADA_I_II iii_yyyyMMddhhmmss_O_YCCR_device model_RAW_M.BIN, the file is in binary format, and the time resolution is 1 minute.

[0048] For example, ground-based site A (site A) and ground-based site B (site B) can be selected as ground-based sites of the ground-based millimeter-wave cloud radar data source, and the ground-based site information is shown in Table 1.

[0049] Table 1: Foundation site information

[0050] Station No. Station No. Station Name Station No. Station altitude Station A 54511 116.47023 39.80722 31.3 Station B 54424 117.117394 40.1693 34.7

[0051] In some embodiments, the reflectivity factor data is Ka or Ku band reflectivity factor data.

[0052] For example, based on band restrictions, the frequency range of the Ka-band is 26.5 to 40 GHz, and the cloud particles are very small. There is currently no research on the uniformity evaluation of the reflectivity factors of spaceborne radars and ground-based millimeter-wave cloud radars for this band.

[0053] In some embodiments, the above-mentioned acquisition of reflectivity factor data of spaceborne radar and ground-based millimeter-wave cloud radar specifically includes:

[0054] Obtain reflectivity factor data of space-borne radar and basic data of ground-based millimeter-wave cloud radar;

[0055] Taking the reflectivity factor data of the spaceborne radar as a benchmark, the reflectivity factor data of the ground-based millimeter-wave cloud radar is determined according to the base data.

[0056] In some embodiments, ground-based millimeter-wave cloud radar data products (dBZ CR ) and GPM / DPR precipitation satellite (dBZ GPM ) are analyzed. The evaluation objects are the basic data products (RAW) of ground-based millimeter-wave cloud radar and the reflectivity factor data of GPM satellite Ka-band radar L2 level products (version number: V07A). The evaluation element is the radar reflectivity factor (unit: dBZ).

[0057] In some embodiments, compared with the current satellite-to-ground cloud radar assessment which uses the cloud top height inverted by the ground-based cloud radar and the cloud top height of the satellite for comparative verification and evaluation, the base data reflectivity factor of the Ka or Ku band ground-based millimeter-wave cloud radar and the Ka or Ku band spaceborne radar is used for comparative verification. Comparison of the reflectivity factor data based on the base data can better reveal the errors between the original data and lay a good foundation for the next inversion product.

[0058] In block 120 , based on the vertical scanning, the reflectivity factor spatiotemporal matching data of the spaceborne radar and the ground-based millimeter-wave cloud radar are established according to the reflectivity factor data.

[0059] In some embodiments, due to the limitations of the scanning method of ground-based millimeter-wave cloud radar, vertical scanning is selected as the basis to scan images on a line and perform time-space matching.

[0060] In some embodiments, establishing the reflectivity factor spatiotemporal matching data of the spaceborne radar and the ground-based millimeter-wave cloud radar according to the reflectivity factor data includes:

[0061] The reflectivity factor data is spatially matched, temporally matched, and highly matched to establish the spatiotemporal matching data of the reflectivity factors of the spaceborne radar and the ground-based millimeter-wave cloud radar.

[0062] For example, for spatial matching (nearest pixel within 5 km), pixels where the GPM satellite is close to the ground-based site can be selected; for time matching (0-10 min average), ground-based cloud radar profiles within 10 minutes before and after the GPM satellite passes the ground-based site can be selected; for altitude matching (0-15 km, with the ground as the standard), ground-based cloud radar data that is closest in altitude to the GPM satellite data can be matched.

[0063] Specifically, in order to conduct comparative studies between satellite-borne radar and ground-based cloud radar, the data must first be as consistent as possible in time and space. Both of them scan in the vertical direction. The time when the satellite passes over the radar is very short (about 1 second). The time when the satellite scans over the ground-based radar is used as the standard, and the data closest to the transit time is selected. For example, the horizontal resolution of the sub-satellite spot beam of the GPM satellite is about 5 km, the detection altitude range is 0-22 km (sea level as the standard), and the vertical resolution is 125 m, while the ground-based radar can only detect the altitude range of a fixed point, ranging from 0-15 km (ground as the standard), and the vertical resolution is 30 m. Therefore, it is necessary to calculate the time required for the cloud, precipitation and other systems to move 5 km at that time. Assuming the time is 10 minutes, the ground-based cloud radar profile within 10 minutes before and after the standard time of the satellite transit is used. The profile is obtained by time averaging, and the satellite data altitude is used as the benchmark to match the ground-based cloud radar data closest to it, and finally the profile of the two radars synchronized in time and space is formed.

[0064] At block 130 , the reflectivity factor spatiotemporal matching data is preprocessed.

[0065] In some embodiments, to further eliminate uncertainties, all dBZ CR and dBZ GPM The product is quality controlled, and then the reflectivity factor spatiotemporal matching data is preprocessed.

[0066] In some embodiments, for all dBZ CR and dBZ GPM Product quality control includes: precipitation limit, filtering out transit data with precipitation; reflectivity factor threshold limit, dBZ CR >-41dBZ,dBZ GPM>13dBZ; control the quality of satellite-borne radar data products, the precipitation type product quality and comprehensive quality are excellent, and the precipitation type is stratus precipitation; remove clutter and select matching samples with an altitude of 1km-15km.

[0067] In some embodiments, the preprocessing of the reflectivity factor spatiotemporal matching data includes:

[0068] According to the preset reflectivity factor threshold, threshold restriction processing is performed on the reflectivity factor spatiotemporal matching data;

[0069] According to the preset height threshold, the reflectivity factor spatiotemporal matching data is processed to remove clutter.

[0070] In some embodiments, the preset reflectivity factor threshold and the preset height threshold can be set according to actual needs of the user.

[0071] For example, preprocessing the reflectivity factor spatiotemporal matching data may specifically include:

[0072] (1) Reflectivity factor threshold limit, dBZ CR >-41dBZ,dBZ GPM >13dBZ;

[0073] (2) Control the quality of spaceborne radar data products, including precipitation type product quality, comprehensive quality as excellent and precipitation type as stratiform precipitation;

[0074] (3) Consider the clutter below 1 km and the matching samples with the height between 1 km and 15 km;

[0075] After the above three steps, a high-quality matching data sample is formed.

[0076] In block 140 , based on a preset uniformity evaluation algorithm, an evaluation result is calculated according to the preprocessed reflectivity factor spatiotemporal matching data.

[0077] In some embodiments, the sample data after quality control of all satellite-to-ground matching cloud radar reflectivity factors within the above-mentioned evaluation time period, that is, the pre-processed reflectivity factor spatiotemporal matching data, can be used as evaluation samples to calculate the correlation coefficient (R), root mean square error (RMSE), and average deviation (BIAS) to obtain an overall evaluation result.

[0078] In some embodiments, the preset uniformity evaluation algorithm includes:

[0079]

[0080] Among them, RMSE represents the root mean square error, R represents the correlation coefficient, BIAS represents the average deviation, N represents the number of ground-based millimeter-wave cloud radar observation stations, i represents the station number, G i It represents the value of the space-time matching reflectivity factor of the spaceborne radar, and its average value is O i represents the measured value of the ground-based millimeter-wave cloud radar observation station, and its average value is

[0081] In some embodiments, for ease of explanation, the evaluation results of Station A and Station B will be taken as an example for further explanation.

[0082] Specifically, seven ground-based millimeter-wave cloud radar data products (dBZ CR ) and GPM / DPR precipitation satellite (dBZ GPM )’s reflectivity factor data were analyzed.

[0083] A total of 827 satellite orbits were obtained through time-space matching, with about 172 matched orbits per year, and the satellites swept over the ground-based radar approximately every 2 days. In order to verify the consistency of the two types of products under precipitation conditions, the hourly rainfall data of the ground rain gauge co-located with the ground-based cloud radar were correlated, and the transit data with precipitation were screened out, totaling 69 orbits and 280 profiles.

[0084] To further eliminate uncertainties, all dBZ CR and dBZ GPM The product quality control was carried out, and a total of 36 tracks and 76 profiles were screened. At the same time, the data samples of bright bands monitored by the satellite were screened, and a total of 8 tracks and 14 profiles were screened. Further screening was carried out to find 5 tracks that passed 2-3 stations at the same time, and 11 profiles.

[0085] The 76 and 14 profiles mentioned above were quantitatively analyzed, i.e., homogeneity evaluated. The correlation coefficients (R) were 0.40 and 0.67, the root mean square errors (RMSE) were 23.78 and 22.88 dBZ, and the average deviations (BIAS) were -22.24 and -22.09 dBZ, respectively. The results show that the consistency of the two types of data is better under the echo bright band condition. For further intuitive explanation, please refer to Figure 2 , Figure 3 As shown, the blue curve is dBZ GPM Outline, red curve is dBZ CRThe black solid line is the top height of the bright band, and the black dotted line is the bottom height of the bright band. Among the 14 selected profiles, Station A matched 3 profiles, and the trends of the two types of data were basically consistent, with a correlation coefficient (R) of 0.78, a root mean square error (RMSE) of 28.57dBZ, and an average bias (BIAS) of -27.91dBZ; Station B matched 2 profiles, and the trends of the two types of data were basically consistent, with a correlation coefficient (R) of 0.76, a root mean square error (RMSE) of 22.07dBZ, and an average bias (BIAS) of -21.5dBZ.

[0086] Therefore, 14 profiles were selected for analysis and research, and the following characteristics were obtained:

[0087] (1)dBZ GPM The detected echo intensity is generally higher than dBZ CR The detected echo is much stronger;

[0088] (2)dBZ GPM The echo intensity value range is 13~38dBZ; and dBZ CR The echo intensity range is -30~20dBZ;

[0089] (3) The change trends of the two types of products are generally consistent, and the height of the intensity mutation is relatively consistent, and most of them reach the peak at the center of the bright band;

[0090] (4) From the evaluation results of each station individually, the correlation coefficient (R) is greater than 0.6, indicating good consistency, but the root mean square error (RMSE) is higher than 20 dBZ, indicating a large error.

[0091] Furthermore, when GPM passes through multiple ground cloud radars in the same orbit, the difference between the satellite profile data and the cloud radar profile between different stations is calculated, and the difference between the two differences is compared and analyzed. According to the above, there are 5 orbits passing through 2-3 stations at the same time, with 11 profiles. Below the bright band, the difference between the satellite profile data and the cloud radar profile data between the two stations is compared. The vertical distribution change characteristics are relatively consistent, and the difference is below 5dBZ. The difference is obvious above the bright band. For further intuitive explanation, please refer to Figure 4 As shown, GPM orbit number is 043015, passing through station B on 9 / 24 / 2021 02:36:06 and station A on 9 / 24 / 2021 02:35:57. The GPM profile and cloud radar profile data of the two stations were obtained by satellite-ground matching. Below the bright band (3750m), the difference between the satellite profile data and the cloud radar profile data between Pinggu and Nanjiao stations was compared. The vertical distribution change characteristics were relatively consistent, with the difference below 5dBZ, and the difference above the bright band was obvious.

[0092] In summary, the effective observation data of GPM / Ka precipitation satellite and ground-based millimeter-wave cloud radar were screened by using the time-space matching method. Considering factors such as precipitation, altitude, satellite data quality and echo bright band, 14 profiles were finally obtained for error source and result analysis. The conclusions are as follows:

[0093] (1) Due to the characteristics of GPM transit and the limited number of large-area precipitation echoes, the effective data is very limited, and the evaluation results are easily affected by the characteristics of individual cases. In the next step, more cases can be added for comparison and further research;

[0094] (2) This assessment did not consider the elimination of oversaturated data, the influence of water film on ground-based radar antenna covers, and did not perform attenuation correction on the two types of data. In the next step, the quality control of the data can be strengthened;

[0095] (3)dBZ GPM and dBZ CR The detected precipitation echo intensity changes with height in a generally consistent trend, and the height of the intensity mutation is relatively consistent. Most of them reach the peak at the center of the bright band. However, at the same height, dBZ GPM The detected echo intensity is significantly greater than dBZ CR The detected echo strength;

[0096] (4)dBZ GPM The spatial resolution of the product is about 5km, and it is necessary to calculate the time it takes for clouds, precipitation and other systems to move 5km. This report only assumes that it takes 10 minutes. In the next step, the wind speed information at different altitudes can be combined to accurately calculate the matching time window.

[0097] (5) Before screening for bright echo bands, the correlation coefficient (R) was 0.4, the root mean square error (RMSE) was 23.78 dBZ, and the average deviation (BIAS) was -22.24 dBZ. After screening, the correlation coefficient (R) was 0.67, the root mean square error (RMSE) was 22.88 dBZ, and the average deviation (BIAS) was -22.09 dBZ. The correlation was better after screening for bright echo bands.

[0098] (6) Using the GPM precipitation satellite as a comparison standard for ground-based millimeter-wave cloud radar can improve the accuracy and reliability of ground-based millimeter-wave cloud radar products, and also provide some reference for the future joint observation application of spaceborne radar and ground-based multi-band radar.

[0099] In some embodiments, the above-mentioned calculation of the evaluation result based on the preset uniformity evaluation algorithm according to the preprocessed reflectivity factor spatiotemporal matching data includes:

[0100] Grouping the preprocessed reflectivity factor spatiotemporal matching data according to a preset geographical area and / or a preset altitude;

[0101] Based on the preset uniformity evaluation algorithm, the evaluation results of each group are calculated.

[0102] In some embodiments, the preset geographical area and the preset altitude can be set according to the actual needs of the user.

[0103] For example, assessments can be conducted by region, province, or altitude.

[0104] Specifically, regional evaluation: all satellite data samples are divided into 7 different sample groups according to the regions where the ground-based stations belong, namely Southwest, Northwest, Southeast, Northeast, South China, North China, and Central China, and the regional evaluation results are calculated according to the evaluation formula; provincial evaluation: all satellite data samples are formed into different sample groups according to the provinces to which the ground-based stations belong, and the provincial evaluation results are calculated according to the evaluation formula; altitude evaluation: all satellite data samples are divided into multiple different sample groups according to the different geographical altitudes (accurate to meters) of the ground-based stations, and the altitude evaluation results are calculated according to the evaluation formula.

[0105] According to the embodiments of the present disclosure, the following technical effects are achieved:

[0106] It can obtain the reflectivity factor data of spaceborne radar and ground-based millimeter-wave cloud radar; then, based on the vertical scanning, establish the reflectivity factor time-space matching data of spaceborne radar and ground-based millimeter-wave cloud radar according to the reflectivity factor data; then preprocess the reflectivity factor time-space matching data; then, based on the preset uniformity evaluation algorithm, calculate the evaluation result according to the preprocessed reflectivity factor time-space matching data; based on this, establish the time-space matching and comparison evaluation algorithm of the reflectivity factors of spaceborne radar and ground-based millimeter-wave cloud radar, that is, establish a ground-based cloud radar uniformity evaluation algorithm based on spaceborne radar, and establish a uniformity evaluation plan and process for spaceborne and ground-based cloud radars with correlation coefficient, root mean square error and average deviation as indicators, solve the key technical problems such as data source quality control, time matching, space matching and numerical matching of satellite-to-ground cloud radar reflectivity factors, so as to study and establish a screening comparison method, reduce the uncertainty of consistency evaluation of satellite-to-ground cloud radars, better reveal the errors between the original data, and lay a good foundation for the next inversion product.

[0107] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0108] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0109] Figure 5 FIG. 5 is a block diagram of a device 500 for evaluating the uniformity of reflectivity factors of a spaceborne radar and a ground-based millimeter-wave cloud radar according to an embodiment of the present disclosure. Figure 5 As shown, the device 500 includes:

[0110] An acquisition module 510 is used to acquire reflectivity factor data of spaceborne radar and ground-based millimeter-wave cloud radar;

[0111] A matching module 520 is used to establish the reflectivity factor spatiotemporal matching data of the spaceborne radar and the ground-based millimeter-wave cloud radar based on the reflectivity factor data, based on the vertical scanning direction;

[0112] A preprocessing module 530 is used to preprocess the reflectivity factor spatiotemporal matching data;

[0113] The calculation module 540 is used to calculate the evaluation result according to the pre-processed reflectivity factor spatiotemporal matching data based on a preset uniformity evaluation algorithm.

[0114] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0115] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0116] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0117] Figure 6A block diagram of an exemplary electronic device 600 capable of implementing an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0118] The electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a ROM 602 or a computer program loaded from a storage unit 608 into a RAM 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An I / O interface 605 is also connected to the bus 604.

[0119] Multiple components in the electronic device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the electronic device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0120] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608.

[0121] In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute the method 100 in any other appropriate manner (e.g., by means of firmware).

[0122] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0124] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0126] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0127] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0128] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0129] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for evaluating the uniformity of reflectivity factors of spaceborne radar and ground-based millimeter-wave cloud radar, characterized in that: include: Obtain reflectivity factor data from spaceborne radar and ground-based millimeter-wave cloud radar; Taking vertical scanning as a reference, establishing the reflectivity factor spatiotemporal matching data of the spaceborne radar and the ground-based millimeter-wave cloud radar according to the reflectivity factor data; Preprocessing the reflectivity factor spatiotemporal matching data; Based on the preset uniformity evaluation algorithm, the evaluation results are calculated according to the preprocessed reflectivity factor spatiotemporal matching data.

2. The method according to claim 1, characterized in that The reflectivity factor data is Ka or Ku band reflectivity factor data.

3. The method according to claim 1, characterized in that The obtaining of reflectivity factor data of spaceborne radar and ground-based millimeter-wave cloud radar includes: Obtain reflectivity factor data of spaceborne radar and basic data of ground-based millimeter-wave cloud radar; Taking the reflectivity factor data of the spaceborne radar as a benchmark, the reflectivity factor data of the ground-based millimeter-wave cloud radar is determined according to the base data.

4. The method according to claim 1, characterized in that The preprocessing of the reflectivity factor spatiotemporal matching data comprises: According to a preset reflectivity factor threshold, threshold limiting processing is performed on the reflectivity factor spatiotemporal matching data; According to a preset height threshold, clutter removal processing is performed on the reflectivity factor spatiotemporal matching data.

5. The method according to claim 1, characterized in that The step of establishing the reflectivity factor spatiotemporal matching data of the spaceborne radar and the ground-based millimeter-wave cloud radar according to the reflectivity factor data includes: The reflectivity factor data is spatially matched, temporally matched, and highly matched to establish the reflectivity factor spatiotemporal matching data of the spaceborne radar and the ground-based millimeter-wave cloud radar.

6. The method according to claim 1, characterized in that The preset uniformity evaluation algorithm includes: Among them, RMSE represents the root mean square error, R represents the correlation coefficient, BIAS represents the average deviation, N represents the number of ground-based millimeter-wave cloud radar observation stations, i represents the station number, G i It represents the value of the space-time matching reflectivity factor of the spaceborne radar, and its average value is O i represents the measured value of the ground-based millimeter-wave cloud radar observation station, and its average value is 7. The method according to any one of claims 1 to 6, characterized in that: The calculation of the evaluation result based on the preset uniformity evaluation algorithm and the pre-processed reflectivity factor spatiotemporal matching data includes: Grouping the preprocessed reflectivity factor spatiotemporal matching data according to a preset geographical area and / or a preset altitude; Based on the preset uniformity evaluation algorithm, the evaluation results of each group are calculated.

8. A device for evaluating the uniformity of reflectivity factors of spaceborne radar and ground-based millimeter-wave cloud radar, characterized in that: include: An acquisition module is used to obtain reflectivity factor data of spaceborne radar and ground-based millimeter-wave cloud radar; A matching module, used to establish the reflectivity factor spatiotemporal matching data of the spaceborne radar and the ground-based millimeter-wave cloud radar based on the reflectivity factor data, based on the vertical scanning direction; A preprocessing module, used for preprocessing the reflectivity factor spatiotemporal matching data; The calculation module is used to calculate the evaluation result based on the pre-processed reflectivity factor time-space matching data based on the preset uniformity evaluation algorithm.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

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