A method and system for evaluating the quality of a radar network combined reflectivity product

Abstract

The embodiment of the disclosure provides a radar networking combined reflectivity product quality evaluation method and system, which is applied to the technical field of weather radar observation. The method comprises the following steps: obtaining an evaluation object and an evaluation data source, the evaluation object is radar networking combined reflectivity product data, and the evaluation data source comprises satellite infrared data and ground observation temperature data; performing space-time matching processing on the evaluation object and the evaluation data source; calculating a temperature difference between the ground observation temperature data and the satellite infrared data based on the matched ground observation temperature data and the satellite infrared data; judging a state of echo in the atmosphere based on the temperature difference, and comparing the state of echo with an echo type in the radar networking combined reflectivity product data; and performing quality evaluation on the radar networking combined reflectivity product data according to a comparison result, wherein a quality evaluation index comprises a hit rate, a false alarm rate and a critical success index. In this way, accurate quantitative evaluation of networking product data quality can be realized.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of weather radar observation, in particular to a radar networking combined reflectivity product quality evaluation method and system. BACKGROUND

[0002] In the technical field of weather radar observation, reflectivity factor related product data is an important basis for analyzing weather phenomena such as precipitation, and the data quality directly affects the accuracy of meteorological prediction, disaster monitoring and other applications. However, due to the lack of direct reflectivity factor observation data provided by other observation equipment, the evaluation of the quality of weather radar reflectivity factor product data in the past mainly relies on the experience of artificial experts, and this evaluation method can only obtain qualitative results, and cannot realize the accurate quantification of data quality, it is difficult to objectively and comprehensively reflect the actual quality level of radar reflectivity factor product, and it is also difficult to provide specific and quantitative guidance for the optimization and improvement of data, to a certain extent, it limits the efficient application of radar data in meteorological business. SUMMARY

[0003] The present disclosure provides a radar networking combined reflectivity product quality evaluation method and system, which solves the technical problem of being unable to quantitatively evaluate the quality of radar networking product data.

[0004] According to a first aspect of the present disclosure, a radar networking combined reflectivity product quality evaluation method is provided. The method comprises:

[0005] Obtaining an evaluation object and an evaluation data source, the evaluation object being radar networking combined reflectivity product data, and the evaluation data source including satellite infrared data and ground observation temperature data;

[0006] Performing spatiotemporal matching processing on the evaluation object and the evaluation data source;

[0007] Based on the matched ground observation temperature data and satellite infrared data, calculating the temperature difference between the ground observation temperature data and the satellite infrared data;

[0008] Based on the temperature difference, judging the echo state in the atmosphere and comparing it with the echo type in the radar networking combined reflectivity product data;

[0009] Based on the comparison result, performing quality evaluation on the radar networking combined reflectivity product data, and the quality evaluation index includes hit rate, false alarm rate and critical success index.

[0010] As described above, the aspect and any possible implementation manner further provides an implementation manner, and the spatiotemporal matching processing on the evaluation object and the evaluation data source comprises:

[0011] Adjust the spatial range of the radar network combined reflectivity product data to the preset area, and retain the data at the integral point time;

[0012] Adjust the spatial range of the satellite infrared data to the preset area, interpolate the satellite infrared data to the grid points of the preset area according to the spatial resolution of the radar network combined reflectivity product data, and obtain the data at the integral point time;

[0013] Interpolate the ground observation temperature data to the grid points of the preset area according to the spatial resolution of the radar network combined reflectivity product data, and select the data at the integral point time.

[0014] According to the aspect and any possible implementation manner as described above, further provided is an implementation manner, after the spatio-temporal matching processing on the evaluation object and the evaluation data source, the method further comprises:

[0015] Perform consistency checking on the radar network combined reflectivity product data after spatio-temporal matching, the interpolated satellite infrared data and the interpolated ground observation temperature data, if there is an abnormality, then complete the abnormal data through interpolation and / or mark the abnormal period as an invalid period.

[0016] According to the aspect and any possible implementation manner as described above, further provided is an implementation manner, the determining the echo state in the atmosphere based on the temperature difference comprises:

[0017] Determining the echo state in the atmosphere based on the temperature difference at each grid point, if the temperature difference is greater than a preset threshold, then there is a precipitation echo; otherwise, there is a non-precipitation echo.

[0018] According to the aspect and any possible implementation manner as described above, further provided is an implementation manner, the evaluating the radar network combined reflectivity product data according to the comparison result comprises:

[0019] Analyzing a quality evaluation index of the radar network combined reflectivity product data, obtaining the quality situation of the radar network combined reflectivity product, and comprehensively evaluating the performance of the product in terms of precipitation echo reservation, non-precipitation echo suppression and overall quality.

[0020] According to the aspect and any possible implementation manner as described above, further provided is an implementation manner, the hit rate is expressed as:

[0021]

[0022] The false alarm rate is expressed as:

[0023]

[0024] The critical success index is expressed as:

[0025]

[0026] wherein, represents the number of grid points for which the radar network combined reflectivity product data is predicted as non-precipitation echo and is actually non-precipitation echo; represents the number of grid points for which the radar network combined reflectivity product data is predicted as precipitation echo and is actually non-precipitation echo; is the number of grid points for which the radar network combined reflectivity product data is predicted as non-precipitation echo and is actually precipitation echo.

[0027] According to a second aspect of the present disclosure, a radar network combined reflectivity product quality evaluation system is provided. The system comprises:

[0028] an acquisition module configured to acquire an evaluation object and an evaluation data source, the evaluation object being radar network combined reflectivity product data, and the evaluation data source comprising satellite infrared data and ground observation temperature data;

[0029] a matching module configured to perform spatio-temporal matching processing on the evaluation object and the evaluation data source;

[0030] a calculation module configured to calculate a temperature difference between the ground observation temperature data and the satellite infrared data based on the matched ground observation temperature data and satellite infrared data;

[0031] a determination module configured to determine an echo state in the atmosphere based on the temperature difference and compare the echo state with an echo type in the radar network combined reflectivity product data;

[0032] an evaluation module configured to perform quality evaluation on the radar network combined reflectivity product data according to a comparison result, the quality evaluation index comprising a hit rate, a false alarm rate and a critical success index.

[0033] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device comprises a memory and a processor, the memory having a computer program stored thereon, and the processor implementing the method as described above when executing the program.

[0034] According to a fourth aspect of the present disclosure, a computer readable storage medium is provided, having a computer program stored thereon, the program being executed by a processor to implement the method according to the first aspect and / or the second aspect of the present disclosure.

[0035] The present disclosure takes radar networking combined reflectivity product data as an evaluation object, satellite infrared data and ground observation temperature data as evaluation data sources, performs spatio-temporal matching processing on the evaluation object and the evaluation data sources, calculates the temperature difference between the matched ground observation temperature data and satellite infrared data, then judges the echo state in the atmosphere according to the temperature difference, compares the echo type in the radar networking combined reflectivity product data, and finally calculates quality evaluation indexes such as hit rate, false alarm rate and critical success index according to the comparison result, so as to complete the quality evaluation of the radar networking combined reflectivity product data.

[0036] It should be understood that the content described in the summary section is not intended to limit or define key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0037] The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by describing in detail preferred embodiments thereof with reference to the attached drawings in which:

[0038] Figure 1 A flowchart of a radar networking combined reflectivity product quality evaluation method according to an embodiment of the present disclosure is shown;

[0039] Figure 2 An evaluation flowchart according to an embodiment of the present disclosure is shown;

[0040] Figure 3 A three-type networking combined reflectivity overall evaluation result diagram according to an embodiment of the present disclosure is shown;

[0041] Figure 4 A three-type networking combined reflectivity daily hit rate evaluation result diagram according to an embodiment of the present disclosure is shown;

[0042] Figure 5 A three-type networking combined reflectivity daily false alarm rate evaluation result diagram according to an embodiment of the present disclosure is shown;

[0043] Figure 6 A block diagram of a radar networking combined reflectivity product quality evaluation system according to an embodiment of the present disclosure is shown;

[0044] Figure 7 A block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0045] To make the purposes, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some but not all of the embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present disclosure.

[0046] In addition, the term “and / or” in this document is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the three cases of A existing alone, A and B existing simultaneously, and B existing alone. In addition, the character “ / ” in this document generally represents an “or” relationship between the front and rear associated objects.

[0047] In the present disclosure, by taking the radar networking combined reflectivity product data as the evaluation object and the satellite infrared data and the ground observation temperature data as the evaluation data source, the evaluation object and the evaluation data source are subjected to spatiotemporal matching processing, the temperature difference between the matched ground observation temperature data and the satellite infrared data is calculated, then the echo state in the atmosphere is judged according to the temperature difference, and the echo type in the radar networking combined reflectivity product data is compared, finally the hit rate, the false alarm rate, and the critical success index and other quality evaluation indexes are calculated according to the comparison result, so as to complete the quality evaluation of the radar networking combined reflectivity product data.

[0048] Figure 1 A flowchart of a radar networking combined reflectivity product quality evaluation method 100 according to an embodiment of the present disclosure is shown. As shown in Figure 1 The radar networking combined reflectivity product quality evaluation method includes:

[0049] S101, the evaluation object and the evaluation data source are obtained, the evaluation object is radar networking combined reflectivity product data, and the evaluation data source includes satellite infrared data and ground observation temperature data.

[0050] In some embodiments, since the combined reflectivity product data is the maximum reflectivity factor in the grid vertical direction, it can reflect all precipitation and non-precipitation echo information, the networking combined reflectivity product data is selected as the evaluation object, the evaluation product data types are the nationwide weather radar uncontrolled networking combined reflectivity product data, the operational networking combined reflectivity product data, and the data centralized networking combined reflectivity product data, the evaluation time is from April 1 to September 30, 2023, the spatial coverage range of the product data is 73.0°-135°E, 12.2°-53.2°N, the time resolution is 6 minutes, and the spatial resolution is 0.01°*0.01°.

[0051] The evaluation data sources are of two types: satellite infrared data and ground-based temperature observation data. The satellite infrared data is selected from the 13-channel 10.4μm infrared channel blackbody brightness temperature (TBB) product data of the Himawari-9 satellite. The spatial coverage of this product data is 80.0°–200°E and -60°–60°N, with a temporal resolution of 10 minutes and a spatial resolution of 0.02°*0.02°. The time period is from April 1, 2023 to September 30, 2023. The ground-based temperature observation data is the temperature data of automatic ground stations across the country from April 1, 2023 to September 30, 2023. Since minute-level data is limited to only some stations, hourly data is selected.

[0052] S102, perform spatiotemporal matching processing on the evaluation object and the evaluation data source.

[0053] In some embodiments, spatiotemporal matching of the evaluation object and the evaluation data source includes:

[0054] Adjust the spatial range of the radar network combined reflectivity product data to the preset area and retain the data at the top of the hour;

[0055] The spatial range of the satellite infrared data is adjusted to the preset area, and the data is interpolated to the grid points of the preset area according to the spatial resolution of the radar network combined reflectivity product data, and the data at the hour is obtained.

[0056] Ground-based temperature observation data is interpolated to grid points in a preset area according to the spatial resolution of radar network combined reflectivity product data, and data at the hour is selected.

[0057] Specifically, in order to eliminate the differences between the evaluation object and the evaluation data source in terms of spatial scope, temporal scale and spatial resolution, and to ensure that the three are analyzed under the same spatiotemporal reference, it is necessary to perform spatiotemporal resolution matching between the evaluation object and the evaluation data source.

[0058] The data of the combined reflectivity products of the national weather radar network at the hour was evaluated, and the data range was narrowed to 80.0°–135°E and 12.2°–53.2°N. Redundant data outside this area was removed, and only the data at the hour, such as 00:00 and 01:00, was retained from the time series of radar data, while the data at non-hourly times were removed.

[0059] The original spatial resolution and coverage range of the Fengyun 9 satellite TBB data are different from those of the radar network combined reflectivity product data. The spatial coverage range of the Fengyun 9 satellite TBB data is adjusted to a preset area consistent with the radar network combined reflectivity product data, that is, reduced to 80.0°-135°E, 12.2°-53.2°N. Since the original spatial resolution of the Fengyun 9 satellite TBB data is 0.02°*0.02°, it needs to be interpolated to the grid points in the range of 80.0°-135°E, 12.2°-53.2°N according to the spatial resolution 0.01°*0.01° of the radar network combined reflectivity product data, to ensure that the Fengyun 9 satellite TBB data and the radar network combined reflectivity product data form a one-to-one corresponding grid matrix in space, and to extract the data at the whole point time from the time series of the Fengyun 9 satellite TBB data, such as 00:00, 01:00, etc., to ensure complete correspondence with the time nodes of the radar network combined reflectivity product data.

[0060] The ground observation temperature data come from unevenly distributed automatic stations. The original data are discrete station hour-level temperature records, which need to be converted into gridded data consistent with the radar data. According to the latitude and longitude coordinates and hourly temperature values of the ground automatic stations, the spatial gridding interpolation method is used to interpolate the discrete station data to the 0.01°*0.01° resolution grid points in the range of 80.0°-135°E, 12.2°-53.2°N, and the whole point hour data consistent with the time scale of the radar network combined reflectivity product data are selected, such as the 00:00 and 01:00 temperature records of the ground automatic stations, to ensure that the surface air temperature data are time-matched with the same period radar network combined reflectivity product data echo and Fengyun 9 satellite TBB data.

[0061] In some embodiments, the radar network combined reflectivity product data after spatio-temporal matching, the interpolated satellite infrared data, and the interpolated ground observation temperature data are subjected to consistency verification. If there is an anomaly, the abnormal data are completed by interpolation and / or the abnormal period is marked as an invalid period.

[0062] Specifically, after the above-mentioned spatio-temporal preprocessing, the radar network combined reflectivity product data, the interpolated satellite infrared data, and the interpolated ground observation temperature data need to be subjected to consistency verification, that is, by comparing the spatial range boundaries, grid point numbers, and time stamps of the three, to ensure that there is no problem of data missing, misplacement, or resolution mismatch. If an anomaly is found, the abnormal data are completed by interpolation and / or marked as an invalid period, to avoid affecting the accuracy of subsequent evaluation.

[0063] In some embodiments, after the spatio-temporal matching processing of the evaluation object and the evaluation data source is completed, the data in the radar effective detection area are selected from the matched multi-source data.

[0064] S103, calculate the temperature difference between the ground observation temperature data and the satellite infrared data based on the matched ground observation temperature data and satellite infrared data.

[0065] In some embodiments, after the spatio-temporal matching of the evaluation object and the evaluation data source is completed, the temperature difference between the matched ground observation temperature data and the satellite infrared data needs to be calculated, which will be used as the basis for determining the echo type.

[0066] In some embodiments, the ground observation temperature data and the satellite infrared data corresponding to the same spatio-temporal grid point are extracted from the matched data set.

[0067] In some embodiments, in an ideal case, if the target object detected by the satellite is the ground, the difference between the surface air temperature and the cloud top temperature detected by the satellite should be close to zero, and if the satellite detects a cloud layer, especially a high-level cloud, the difference should be greater than zero. Since the cloud classification product of the Fengyun-4 satellite has been used in the non-precipitation echo quality control during the data set quality control process, in the test evaluation, the cloud top temperature is obtained by the Japanese HImawari-9 meteorological satellite 13 infrared channel (10.4 ), and the surface air temperature is obtained by the ground automatic station observation.

[0068] Specifically, for a certain grid point in a preset area, such as longitude 90E, latitude 30N at 00:00, the ground observation temperature value of the grid point needs to be extracted, and the temperature difference between the two can be represented by the following formula:

[0069]

[0070] wherein, is the grid surface air temperature (unit: ℃) obtained by spatial interpolation of the ground observation temperature data, representing the actual temperature of the near-surface atmosphere; is the cloud top temperature (unit: ℃) of the same grid point obtained by interpolation of the satellite infrared data, which is the blackbody brightness temperature detected by the Japanese HImawari-9 meteorological satellite 13 infrared channel, reflecting the temperature at the top of the cloud layer.

[0071] In some embodiments, in meteorology, the formation of precipitation is closely related to the vertical development of cloud layer: when the cloud layer develops vigorously (such as cumulonimbus), the cloud top height is high, the temperature is low, and the surface air temperature is relatively high, and the temperature difference T is large, at this time, it is easy to form precipitation; on the contrary, when the cloud layer is shallow or there is no cloud, the cloud top temperature is close to the surface air temperature, and the temperature difference T is small, generally no precipitation.

[0072] In some embodiments, when the calculation is completed, the temperature difference T needs to be reasonably checked, and obviously abnormal values such as negative values or values exceeding the conventional physical range due to data errors are removed to ensure the reliability of subsequent echo type determination.

[0073] In some embodiments, in the determination process, if the temperature difference T of a certain grid point is an abnormal value due to data errors, such as T being much larger than the reasonable range or being negative, interpolation correction needs to be performed in combination with the determination results of the surrounding grid points, or it is directly marked as an “invalid grid point” and removed in subsequent evaluation.

[0074] S104, determining the echo state in the atmosphere based on the temperature difference, and comparing with the echo type in the radar network combined reflectivity product data.

[0075] In some embodiments, the echo state in the atmosphere is determined based on the temperature difference at each grid point. If the temperature difference is greater than a preset threshold, there is a precipitation echo; otherwise, there is a non-precipitation echo.

[0076] Specifically, for each spatio-temporal grid point after preprocessing, the difference between the ground temperature and the cloud top temperature is calculated, T≥20℃ is set as the precipitation echo determination threshold, if the temperature difference T of the grid point is T≥20℃, the cloud top temperature is much lower than the near-surface temperature, indicating that the cloud layer is developing vigorously (such as cumulonimbus), then the grid point is determined as a precipitation echo; if the temperature difference T of the grid point is T<20℃, the difference between the cloud top and the near-surface temperature is small, the cloud layer is shallow or there is no cloud, which does not meet the precipitation condition, then the grid point is determined as a non-precipitation echo.

[0077] For the radar network combined reflectivity product data, the echo type of each grid point is identified. If the grid reflectivity factor Z is within a set invalid value range, it indicates that the radar network combined reflectivity product data has a precipitation echo, otherwise it indicates that the radar network combined reflectivity product data has a non-precipitation echo.

[0078] In some embodiments, for each grid point, the real echo state is compared with the echo type of the radar network combined reflectivity product data to obtain a comparison result.

[0079] S105, quality evaluation of the radar network combined reflectivity product data according to the determination result, the quality evaluation indexes including hit rate, false alarm rate and critical success index.

[0080] In some embodiments, the hit rate is represented as:

[0081]

[0082] The false alarm rate is represented as:

[0083]

[0084] The critical success index is expressed as:

[0085]

[0086] Wherein, represents the number of grid points that are predicted by the radar network combined reflectivity product data as non-precipitation echoes and are actually non-precipitation echoes; represents the number of grid points that are predicted by the radar network combined reflectivity product data as precipitation echoes and are actually non-precipitation echoes; is the number of grid points that are predicted by the radar network combined reflectivity product data as non-precipitation echoes and are actually precipitation echoes.

[0087] In some embodiments, the quality evaluation indicators of the radar network combined reflectivity product data are analyzed to obtain the quality of the radar network combined reflectivity product, and the performance of the product in terms of precipitation echo retention, non-precipitation echo suppression, and overall quality is comprehensively evaluated.

[0088] In some embodiments, the above indicators are calculated respectively for the evaluation objects in the same preset area and time range to obtain the hit rate, false alarm rate, and critical success index of the product, and the optimal product is selected by comparing the differences between the indicators.

[0089] In some embodiments, if there is a conflict in a single indicator, a comprehensive judgment needs to be made in combination with business requirements.

[0090] Next, a radar network combined reflectivity product quality evaluation method 100 provided by an embodiment of the present disclosure will be described in detail in combination with a specific embodiment, Figure 2 A radar network combined reflectivity product quality evaluation flow framework diagram according to an embodiment of the present disclosure is shown, and is specifically as follows:

[0091] The evaluation object and the evaluation data source are obtained, the evaluation object is radar network combined reflectivity product data, and the radar network combined reflectivity product data is cropped to the range of 80.0°-135°E and 12.2°-53.2°N according to the principle of “range reduction and time sparseness”, the data at the whole point time is selected, and a data set to be evaluated is obtained.

[0092] The evaluation data sources include blackbody brightness temperature (TBB) product data of 13-channel 10.4 μm infrared channel of Fengyun 9 satellite and ground observation temperature data. The TBB product data of Fengyun 9 satellite is cropped to the range of 80.0°-135°E and 12.2°-53.2°N, interpolated to the same grid according to the resolution difference of radar network combined reflectivity product data, and screened for data at integral points. The ground observation temperature data is interpolated to the grid in the range of 80.0°-135°E and 12.2°-53.2°N according to the spatial resolution of radar network combined reflectivity product data, and data at 14:00 is selected.

[0093] The data validity is checked point by point and hour by hour, and the grid points meeting the conditions are marked and reserved. The effective detection area is screened, that is, the grid points where the TBB product data of Fengyun 9 satellite and the ground temperature data are valid, and the radar network combined reflectivity product data exist echo are reserved.

[0094] For each grid point and each hour in the effective detection area, the ground temperature and the satellite cloud top temperature are calculated, and the difference is expressed by the formula:

[0095]

[0096] If , it is determined that the grid point is a precipitation echo, and if , it is determined that the grid point is a non-precipitation echo.

[0097] Taking grid point A at 117.32E and 36.68N and grid point B at 118.15E and 39.92N as examples, the echo state in the atmosphere and the echo state identified by the radar network combined reflectivity product data are judged.

[0098] In grid point A is 27.8℃, is 5.4℃, and the temperature difference is ≥20℃, indicating that there is a precipitation echo in the atmosphere; in grid point B is 18.7℃, is 17.9℃, and the temperature difference is <20℃, indicating that there is a non-precipitation echo in the atmosphere.

[0099] For the radar network combined reflectivity product data, the echo types of grid point A and grid point B are identified. If the reflectivity factor at grid point A is within the set invalid value range, the radar network combined reflectivity product data is identified as a precipitation echo; if the reflectivity factor at grid point B is not within the set invalid value range, the radar network combined reflectivity product data is identified as a non-precipitation echo.

[0100] All grid points are processed according to the above steps to obtain the large-area echo state of each grid point and the echo state identified by the radar network combined reflectivity product data. The large-area echo state of each grid point is compared with the echo state identified by the radar network combined reflectivity product data to obtain a comparison result.

[0101] Based on the comparison result, the hit rate, false alarm rate and critical success index of the unqualified network combined reflectivity product data, the operational network combined reflectivity product data and the data set network combined reflectivity product data are calculated, and the optimal radar product is selected according to the quality evaluation index.

[0102] Figure 3 The three types of network combined reflectivity overall evaluation result schematic diagram according to the embodiments of the present disclosure is shown. The evaluation results of the three types of network combined reflectivity product data from April to September 2023 show that the hit rate of the data set network combined reflectivity product reaches 0.93, which is improved by 0.10 compared with the operational network combined reflectivity hit rate and by 0.51 compared with the unqualified network combined reflectivity hit rate. The false alarm rate of the data set network combined reflectivity product is 0.31, which is slightly lower than the false alarm rates of the unqualified network combined reflectivity and the operational network combined reflectivity, which are 0.33. The critical success index of the data set network combined reflectivity product is 0.66, which is improved by 0.07 compared with the critical success index of the operational network combined reflectivity and by 0.31 compared with the critical success index of the unqualified network combined reflectivity. The false alarm rate of the unqualified network combined reflectivity has reached 0.33, indicating that the precipitation echo observed by the radar is much smaller than the satellite observation data. The main reason for this phenomenon is that the satellite observes clouds, but the clouds may not be precipitation. Therefore, on the basis of ensuring correct identification of precipitation, the non-precipitation existing in the data set network combined reflectivity is far smaller than the original observation and the operational product, and the data quality is higher than that of the two types of products.

[0103] Figure 4 The three types of network combined reflectivity daily hit rate evaluation result schematic diagram according to the embodiments of the present disclosure is shown. The daily hit rate evaluation results of the three types of network combined reflectivity product data from April to September 2023 show that the hit rate of the data set network combined reflectivity product is basically above 0.9, and individual times are below 0.8; in the entire time sequence, the hit rate is significantly higher than that of the operational network combined reflectivity and the unqualified network combined reflectivity. The hit rates of the operational network combined reflectivity and the unqualified network combined reflectivity product are below 0.8 and 0.4, respectively, which mainly occur at night due to the unqualified quality control of the majority of clear air echoes, and the data quality is still seriously affected by the clutter.

[0104] Figure 5A three-type networking combined reflectivity daily false alarm rate evaluation result schematic diagram according to an embodiment of the present disclosure is shown. The daily false alarm rate evaluation result of the three-type networking combined reflectivity product data from April to September in 2023 shows that, in the whole time sequence, the false alarm rate of the networking combined reflectivity product in the data set is slightly lower than that of the business networking combined reflectivity and the un-qualified networking combined reflectivity, and the precipitation echo is better reserved.

[0105] According to an embodiment of the present disclosure, the quality evaluation of the radar networking combined reflectivity product data is realized, reliable support is provided for radar product optimization and business application, and the meteorological radar data quality evaluation is more scientific and practical.

[0106] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited by the action sequence described, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know 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.

[0107] The above is the introduction of the method embodiment, and the scheme described in the present disclosure will be further described through the device embodiment.

[0108] Figure 6 A block diagram of a radar networking combined reflectivity product quality evaluation system 600 according to an embodiment of the present disclosure is shown. As shown in Figure 6 The system 600 includes:

[0109] An acquisition module 601 is configured to acquire an evaluation object and an evaluation data source. The evaluation object is radar networking combined reflectivity product data, and the evaluation data source includes satellite infrared data and ground observation temperature data.

[0110] A matching module 602 is configured to perform time-space matching processing on the evaluation object and the evaluation data source.

[0111] A calculation module 603 is configured to calculate a temperature difference between the ground observation temperature data and the satellite infrared data based on the matched ground observation temperature data and satellite infrared data.

[0112] A determination module 604 is configured to determine an echo state in the atmosphere based on the temperature difference, and compare the echo state with an echo type in the radar networking combined reflectivity product data.

[0113] An evaluation module 605 is configured to perform quality evaluation on the radar networking combined reflectivity product data according to a comparison result. The quality evaluation index includes a hit rate, a false alarm rate and a critical success index.

[0114] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described modules can refer to the corresponding process in the foregoing method embodiments, which will not be described here again. According to the embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0115] Figure 7 A schematic block diagram of an electronic device 700 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0116] The electronic device 700 includes a computing unit 701 that can perform various appropriate actions and processes in accordance with a computer program stored in a ROM 702 or a computer program loaded from the storage unit 708 into a RAM 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An I / O interface 705 is also connected to the bus 704.

[0117] Various components in the electronic device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, a speaker, etc.; a storage unit 708, such as a magnetic disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the electronic device 700 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0118] The computing unit 701 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded onto the RAM 703 and executed by the computing unit 701, one or more steps of the method 100 described above can be performed. Alternatively, in other embodiments, the computing unit 701 can be configured to perform the method 100 by any other suitable means, such as by means of firmware.

[0119] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0120] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0121] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0122] To provide for interaction with a user, the systems and techniques described here 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 a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, 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, speech, or tactile input.

[0123] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can 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.

[0124] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0125] It should be understood that the various forms of flow shown above can be used to reorder, add, or remove steps. For example, the steps recited in the present disclosure can be performed in parallel, in series, or in a different order, as long as the desired results of the technology disclosed in the present disclosure are achieved, which is not limited herein.

[0126] The specific implementation described above does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A radar network combined reflectivity product quality evaluation method, comprising: obtaining an evaluation object and an evaluation data source, the evaluation object being radar network combined reflectivity product data, and the evaluation data source including satellite infrared data and ground observation temperature data; performing spatiotemporal matching processing on the evaluation object and the evaluation data source; which comprises: adjusting the spatial range of the radar network combined reflectivity product data to a preset area and retaining data at integral point times; adjusting the spatial range of the satellite infrared data to the preset area, interpolating the satellite infrared data to grid points in the preset area according to the spatial resolution of the radar network combined reflectivity product data, and obtaining data at integral point times; interpolating the ground observation temperature data to grid points in the preset area according to the spatial resolution of the radar network combined reflectivity product data, and selecting data at integral point times; performing consistency checking on the radar network combined reflectivity product data after spatiotemporal matching, the interpolated satellite infrared data, and the interpolated ground observation temperature data, and if there is an abnormality, completing the abnormal data by interpolation and / or marking the abnormal period as an invalid period; calculating a temperature difference between the ground observation temperature data and the satellite infrared data based on the matched ground observation temperature data and the satellite infrared data; judging a state of echoes in the atmosphere based on the temperature difference and comparing the state of echoes in the atmosphere with echo types in the radar network combined reflectivity product data; performing quality evaluation on the radar network combined reflectivity product data according to the comparison result, the quality evaluation indexes including a hit rate, a false alarm rate, and a critical success index.

2. The method of claim 1, wherein, The judgment of the state of echoes in the atmosphere based on the temperature difference comprises: judging the state of echoes in the atmosphere based on the temperature difference at each grid point, and if the temperature difference is greater than a preset threshold, there is a precipitation echo; otherwise, there is a non-precipitation echo.

3. The method of claim 1, wherein, The quality evaluation on the radar network combined reflectivity product data according to the comparison result comprises: analyzing the quality evaluation indexes of the radar network combined reflectivity product data to obtain the quality of the radar network combined reflectivity product, and comprehensively evaluating the performance of the product in terms of precipitation echo retention, non-precipitation echo suppression, and overall quality.

4. The method of claim 1, wherein, The hit rate is expressed as: The false alarm rate is expressed as: The critical success index is expressed as: wherein, Z1 represents the number of grid points for which the radar network combined reflectivity product data predicts non-precipitation echoes and the actual is non-precipitation echoes; Z2 represents the number of grid points for which the radar network combined reflectivity product data predicts precipitation echoes and the actual is non-precipitation echoes; Z3 is the number of grid points for which the radar network combined reflectivity product data predicts non-precipitation echoes and the actual is precipitation echoes. 5.A radar network combined reflectivity product quality evaluation system, comprising: an obtaining module configured to obtain an evaluation object and an evaluation data source, the evaluation object being radar network combined reflectivity product data, and the evaluation data source including satellite infrared data and ground observation temperature data; a matching module configured to perform spatiotemporal matching processing on the evaluation object and the evaluation data source; which comprises: adjusting the spatial range of the radar network combined reflectivity product data to a preset area and retaining data at integral point times; adjusting the spatial range of the satellite infrared data to the preset area, interpolating the satellite infrared data to grid points in the preset area according to the spatial resolution of the radar network combined reflectivity product data, and obtaining data at integral point times; interpolating the ground observation temperature data to grid points in the preset area according to the spatial resolution of the radar network combined reflectivity product data, and selecting data at integral point times; The radar network combined reflectivity product data matched in space and time, the interpolated satellite infrared data and the interpolated ground observation temperature data are subjected to consistency check, if there is an abnormality, the abnormal data is completed by interpolation and / or the abnormal period is marked as invalid period; The calculation module is configured to calculate a temperature difference between the ground observation temperature data and the satellite infrared data based on the matched ground observation temperature data and the satellite infrared data; The determination module is configured to determine a state of echo in the atmosphere based on the temperature difference and compare the state of echo with a type of echo in the radar network combined reflectivity product data; The evaluation module is configured to evaluate a quality of the radar network combined reflectivity product data according to a comparison result, and the quality evaluation index includes a hit rate, a false alarm rate and a critical success index. 6.An electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable 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 1-4.

7. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method of any one of claims 1-4.

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