Radar satellite precipitation rapid fusion algorithm based on radar quality index

Abstract

The invention discloses a radar satellite precipitation rapid fusion algorithm based on a radar quality index, and the algorithm comprises the steps: designing a radar shielding area index RSA as a piecewise function changing with a climate proportion correction factor RFC through employing the climate proportion correction factor RFC, optimizing the weight of a radar quality index analysis field through employing a radar terrain shielding index analysis field, and carrying out the calculation of the radar quality index. And rapidly fusing radar and satellite rainfall data by using the radar quality index fusion weight to generate a radar-satellite fusion background field. According to the method, the radar quality index and the terrain shielding coefficient are utilized to dynamically quantify the spatial credibility of the radar data, and the fusion weight of the radar and the satellite is optimized, so that the fusion background field can reflect the actual rainfall condition more accurately, especially in a radar coverage blind area and a site sparse area, the real rainfall condition can be reflected more accurately. And the problems of local distortion, rainfall intensity underestimation or structure fuzziness and the like are effectively reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of precipitation fusion. Specifically, it is a radar satellite precipitation rapid fusion algorithm based on radar quality index. BACKGROUND

[0002] In the multi-source precipitation fusion real-time analysis business, the high spatio-temporal resolution and complete coverage of the fusion background field is the core basis to support short-term forecast, disaster warning and model assimilation. However, due to the limited radar detection range, terrain shielding effect and satellite precipitation retrieval uncertainty and other factors, the existing fusion background field is facing severe challenges in the balance of timeliness, spatial continuity and accuracy. Especially in the radar coverage blind area (such as plateau, open sea) and sparse area, the traditional fusion method often relies on static weight distribution or simple interpolation filling, which is difficult to dynamically adapt to the quality difference of radar and satellite data, resulting in local distortion of the background field, underestimation of precipitation intensity or fuzzy structure and other problems. Research shows that if the spatial heterogeneity of radar detection efficiency is not optimized, the precipitation gradient distribution in complex terrain area and the depiction ability of typhoon heavy rain center of the fusion product will be significantly reduced, which will directly affect the accuracy and timeliness of disaster warning.

[0003] The quality difference between radar and satellite precipitation data is mainly determined by their detection mechanism and external environmental interference. Although radar precipitation data has the advantage of high temporal and spatial resolution, its detection performance is affected by multiple factors: 1) Spatial attenuation effect: with the increase of distance from the radar center, beam broadening and terrain obstruction (such as mountains, buildings) cause the attenuation of echo signal, and the detection accuracy gradually decreases; 2) Uneven coverage: the effective detection range of a single radar is limited, and the fusion noise is easily introduced due to the difference between the coverage overlap area and the blind area of the multi-radar mosaic; 3) Climate state terrain obstruction: long-term statistics show that the systematic bias of radar precipitation in fixed terrain obstacle areas (such as basins and valleys) is closely related to the seasonal distribution of heavy precipitation. Although satellite precipitation data can make up for the lack of radar coverage, its inversion algorithm is affected by cloud microphysical properties, surface radiation interference, etc., and there is significant uncertainty in the quantitative estimation of strong convective precipitation and the identification of weak precipitation. Therefore, how to dynamically quantify the spatial reliability of radar data and optimize the fusion weight of radar and satellite based on it has become the key to improving the quality of the background field. CN201910114665.8 discloses a strong convective monitoring method based on satellite and its application, which uses the daytime convective storm identification algorithm radar to make up for the blind area of the network monitoring, but it mainly targets near-shore and offshore areas, and its effect is not obvious in fixed terrain obstacle areas (such as basins and valleys). 202510174805.6 discloses a landslide surface scene deformation intelligent monitoring method and system based on precipitation prediction, which considers the terrain obstruction index, but it only triggers compensation when the index is less than 0.3, and there is no corresponding compensation measure for long-term strong obstruction areas, which easily leads to missing monitoring data, and it does not consider the complexity of the terrain in climate state obstruction areas.

[0004] The existing fusion technology mainly has two shortcomings: first, the weight allocation strategy is rigid. Traditional methods mostly use fixed thresholds (such as radar effective detection radius) or empirical coefficients to divide the contribution weights of radar and satellite, without fully considering the dynamic changes of radar quality with distance, terrain obstruction, and coverage density. For example, in the radar edge detection area, the data quality has already decreased significantly, but it is still given a weight similar to that of the center area, leading to "pseudo precipitation" or gradient distortion in the transition area of the fusion result. Second, the blind area filling technology is single. For the areas not covered by radar, most schemes directly use satellite data or climate state mean to fill, without establishing a correlation model between radar quality attenuation and satellite error characteristics, resulting in the loss of structural information of key systems such as typhoon peripheral rain bands and terrain lifting heavy precipitation. In addition, the existing methods lack a coordinated optimization mechanism for the time difference of multi-source data, making it difficult to meet the rapid update business demand, especially in extreme weather processes, which easily leads to the problem of lagging behind the real-time evolution of the fusion background field.

[0005] The construction of high-time-effect fusion background field has dual significance in meteorological business. On the one hand, it provides a benchmark precipitation field for real-time analysis products, supporting precipitation phase identification, surface rainfall estimation and flood risk assessment. On the other hand, as a rapid update assimilation field of numerical prediction model, its accuracy and timeliness directly affect the key prediction performance such as short-time heavy rain and typhoon path. However, the existing technology is insufficient in characterizing the spatial heterogeneity of radar quality, and it fails to effectively coordinate the complementary advantages of radar and satellite data, resulting in the reliability and resolution of the fusion background field in key areas (such as terrain transition zones) being difficult to meet business needs. Therefore, it is urgent to innovate the fusion algorithm, dynamically quantify the radar quality index, and optimize the multi-source data fusion mechanism, break through the limitations of static weight and single filling strategy, and realize the construction of high-time-effect and high-consistency fusion background field, providing technical support for disaster prevention and mitigation decision-making and fine meteorological service. SUMMARY

[0006] Therefore, the technical problem to be solved by the present application is to provide a radar-satellite precipitation rapid fusion algorithm based on radar quality index, which can effectively solve the problems of rigid weight allocation, single blind area information filling and lack of multi-source data timeliness coordination optimization in existing fusion technology.

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] A radar-satellite precipitation rapid fusion algorithm based on radar quality index, which uses a climate proportion correction factor RFC to design a radar shadow zone index RSA as a piecewise function varying with the climate proportion correction factor RFC, optimizes the weight of the radar quality index analysis field using a radar terrain shadow index analysis field, and then uses the radar quality index fusion weight to rapidly fuse radar and satellite precipitation data to generate a radar-satellite fusion background field.

[0009] Preferably, the above includes the following steps:

[0010] S1, a radar quality index RQI is calculated by comprehensively calculating the detection distance, coverage number and terrain height from the radar center on the radar network precipitation analysis grid;

[0011] S2, the climate proportion correction factor RFC of radar precipitation is corrected by using the INCA algorithm;

[0012] S3, the radar shadow zone index RSA is designed as a piecewise function varying with the climate proportion correction factor RFC;

[0013] S4, the weight of the radar quality index analysis field is further optimized and corrected using the radar shadow zone index analysis field, and the radar shadow zone index RSA is applied to the radar quality index RQI using a hyperbolic function to obtain the optimized RQI, as shown in formula (6):

[0014] RQI opt (x,y) = RQI(x,y) · [1 - a · tanh(b · RSA(x,y))] (6)

[0015] wherein RQI(x,y): original radar quality index (value range [0,1]);

[0016] RSA(x,y): radar shelter area index (value range [0,1], 1 represents complete shelter);

[0017] a: terrain influence intensity coefficient (0≤a≤1);

[0018] b: hyperbolic function steepness parameter (b>0);

[0019] tanh: hyperbolic tangent function, used for smooth transition sheltering effect;

[0020] S5, using radar quality index fusion weight to quickly fuse radar and satellite precipitation data to generate radar-satellite fusion background field.

[0021] Preferably, the segmented function in the above step S3 is as formula (5):

[0022]

[0023] When rfc is less than 1.0, set rsa=0.0; when rfc is greater than or equal to 1.0, rsa=1-e 0.05(rfc-1) ; when rain_rad=0, i.e. the grid point with zero long-term cumulative radar precipitation, set rsa=1.0.

[0024] Preferably, in the above step S4, the hyperbolic function is used to apply the radar shelter area index RSA to the radar quality index RQI to obtain the optimized RQI, as formula (6):

[0025] RQI opt (x,y) = RQI(x,y) · [1 - a · tanh(b · RSA(x,y))] (6)

[0026] wherein RQI(x,y): original radar quality index (value range [0,1]);

[0027] RSA(x,y): radar shelter area index (value range [0,1], 1 represents complete shelter);

[0028] a: terrain influence intensity coefficient (0≤a≤1);

[0029] b: hyperbolic function steepness parameter (b>0);

[0030] tanh: hyperbolic tangent function, used to smooth the transition of occlusion effects.

[0031] Preferably, the quick fusion calculation formula in the above step S5 is as formula (7):

[0032] P fusion (x,y,t)=w radar (x,y,t)·P radar (x,y,t)+w sat (x,y,t)·P sat (x,y,t) (7)

[0033] Wherein, P fusion : the fused precipitation background field;

[0034] P radar , P sat : radar estimated precipitation and satellite retrieved precipitation estimates;

[0035] w radar (x,y,t), w sat (x,y,t): radar quality index fusion weight, w radar (x,y,t)+w sat (x,y,t)=1.

[0036] Preferably, in the above step S1, the calculation formula of the radar quality index RQI is as formula (1):

[0037]

[0038] Wherein, γ is the radar coverage number effect factor, H is the radar beam height factor, H1 is the critical height representing the correlation between radar detection information and ground precipitation, taking 1000 meters, H2 is the proportional factor of radar beam height change, taking 3500 meters, the closer to the center point, the lower the radar detection height, the greater the RQI index.

[0039] Preferably, the calculation formula of the above radar beam height factor H is as formula (2):

[0040] H=H rad +D*tanα+β*E (2)

[0041] Wherein, H rad is the height of the radar station, D is the distance between the radar network precipitation analysis grid and the radar center, α is the lowest layer elevation angle of radar detection, β is the terrain effect factor, E is the grid elevation (unit: m), the higher the elevation, the smaller the ROI index.

[0042] Preferably, the calculation formula of the above radar coverage number effect factor is as formula (3):

[0043]

[0044] Wherein, n is the number of radar coverage on the grid, n0 is 3, indicating that when the number of radar coverage on the grid is less than 3, it will affect the RQI of the point, the smaller the number, the smaller the ROI index.

[0045] Preferably, in the above step S2, the climate proportion correction factor RFC is calculated by using the ratio of the cumulative precipitation of the historical ground observation and the radar estimated precipitation, and the calculation formula is as formula (4):

[0046]

[0047] Wherein, P i,j is the ground observation precipitation value of the specified grid position, P Radar,i,j is the radar estimated precipitation value of the specified grid position.

[0048] Preferably, the above radar shielding area index RSA analysis field is a radar precipitation climate proportion correction factor RFC processing analysis field generated by using the ratio of the cumulative precipitation of the historical ground observation and the radar estimated precipitation in the last three months.

[0049] Preferably, the above radar and satellite precipitation data are generated by using a bias correction method to generate corrected radar and satellite precipitation data, including:

[0050] (1) Collect and preprocess the ground station observation data, radar precipitation and satellite precipitation data:

[0051] Collect the precipitation data of the ground meteorological observation station, perform quality control processing to obtain the quality controlled ground observation precipitation data processing, use a single data source control module, a multi-source data collaborative quality control module and a dynamic black list module to control the data quality, the single data source control module includes metadata inspection, characteristic value inspection, limit value inspection and dead value inspection, which can eliminate gross errors and long-time unchanged dead values in real-time precipitation data; The multi-source data collaborative quality control module is a QC algorithm developed based on the consistency of radar, weather phenomenon and various precipitation related meteorological observation data, which can accurately identify false clear sky precipitation and false 0 value data in typical precipitation area which cannot be identified by fast quality control; After the data processed by the single data source control module and the multi-source data collaborative quality control module are further processed by the dynamic black list module, the observation stations with high error proportion and long error duration are eliminated, and a flexible dynamic evaluation mechanism is adopted, and when the data quality is restored, the observation stations are removed from the black list, and the data with quality control codes of 0, 1, 3 and 4 are selected to participate in subsequent fusion analysis;

[0052] (2) Using the optimal interpolation method, the quality controlled ground observation precipitation data processing generates 1km ground grid analysis precipitation product, that is, ground precipitation grid analysis field:

[0053] 21) Establishing the grid background field of precipitation climate value;

[0054] 22) Calculating the precipitation ratio data of each station and interpolating to generate the corresponding grid point field, and the ratio data is a new element defined by means of the climate background field: precipitation ratio = observed precipitation of the station / corresponding grid precipitation climate value;

[0055] 23) Multiplying the precipitation ratio grid point field and the corresponding climate background field to generate the precipitation grid point field, wherein the interpolation method for generating the grid point field in step 22) is the optimal interpolation method, and the calculation formula (8) is as follows:

[0056]

[0057] That is, the analysis value A of the grid k is the initial value F of the point k plus the deviation of the observed value and the initial value of the point, and the deviation is obtained by weighted estimation of the deviation of n known initial values F i and observed values O i in the specified analysis range.

[0058] (3) PDF deviation correction:

[0059] 31) Consistency matching analysis is performed on the ground observed precipitation and the radar estimated precipitation, including: (1) data preparation and time alignment, (2) consistency index calculation, (3) determination of the optimal lag time; the difference relationship between the 10-minute cumulative precipitation of the ground automatic station observation in the precipitation process and the radar QPE at the lag 0-minute time is analyzed, and the correlation coefficient, root mean square error and relative deviation index are used to determine the optimal lag time;

[0060] 32) Constructing a correction model, adjusting the space-time matching window of the ground and radar precipitation PDF sample, and setting the space-time matching window parameters to 1 hour and 35 km, and the minimum effective sample logarithm participating in the PDF matching is 120;

[0061] 33) Using the ground precipitation grid analysis field to perform PDF deviation correction on the radar network estimated precipitation data.

[0062] The technical scheme of the present application has the following beneficial technical effects:

[0063] 1、The application breaks through the rigid limitation of the weight distribution strategy in the traditional method by using radar quality index and terrain shielding coefficient to dynamically quantify the spatial reliability of radar data and optimizing the fusion weight of radar and satellite according to the radar quality index. The innovation makes the fusion background field more accurately reflect the actual precipitation, especially in the radar coverage blind area and sparse area, effectively reduces the problems of local distortion, low estimation of precipitation intensity or fuzzy structure, and significantly improves the reliability and business applicability of the fusion product.

[0064] 2、The high-time-efficiency fusion background field constructed by the application not only provides a high-precision reference precipitation field for live analysis products, supports precipitation phase identification, surface rainfall estimation and flood risk assessment, but also serves as a rapid update assimilation field of numerical prediction model, improving the key prediction performance of short-time heavy rain and typhoon path. The technical scheme effectively coordinates the complementary advantages of radar and satellite data, meets the demand of meteorological business for rapid update and high consistency of fusion background field, and provides a solid technical support for disaster prevention and decision-making and fine meteorological service. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 The function curve of the radar precipitation terrain shielding factor (RSA) on the radar quality index (RQI) of the application. DETAILED DESCRIPTION

[0066] The radar satellite precipitation rapid fusion algorithm based on radar quality index of the embodiment includes:

[0067] S1, the radar quality index RQI is calculated by using the detection distance from the radar center, the number of coverages and the terrain height on the radar network precipitation analysis grid, and the specific formula is as formula (1)-(3):

[0068]

[0069] H=H rad +D*tanα+β*E(2)

[0070]

[0071] Wherein, γ is the radar coverage number action factor, H is the radar beam height factor, H1 is the critical height representing the relationship between radar detection information and ground precipitation, and the value is 1000 meters, H2 is the proportional factor of the change of radar beam height, and the value is 3500 meters, the closer to the center point, the lower the radar detection height, and the larger the RQI index; H radwhere D is the distance between the radar and the grid point, α is the lowest elevation angle of radar detection, β is the terrain factor, E is the height of the grid point, n is the number of radar coverage, and n0 is 3, which means that the RQI is affected when the number of radar coverage is less than 3.

[0072] S2, the radar precipitation climatological scaling factor RFC (Climatological Radar scaling Factor, RFC) is corrected by using the INCA algorithm, and the analysis grid climatological scaling factor RFC is calculated by using the ratio of the cumulative precipitation of the recent three months of historical ground observation and radar estimated precipitation, and the calculation formula is as formula (4):

[0073]

[0074] where P i,j is the ground observation precipitation value at the specified grid point position, and P Radar,i,j is the radar estimated precipitation value at the specified grid point position.

[0075] S3, the radar shaded area index RSA (Radar Shaded Area, RSA) is designed as a piecewise function varying with the climatological scaling factor RFC, as formula (5):

[0076]

[0077] For the case where rfc is less than 1.0, it is assumed that there is no shielding effect, and rsa is set to 0.0. For the case where rfc is greater than 1.0, it is assumed that there is a certain degree of shielding, and the greater the rfc, the more serious the shielding, and rsa = 1-e 0.05(rfc-1) , for example, when rfc is greater than 15, rsa is greater than 0.5, corresponding to a more serious shielding. In addition, for the grid point with zero long-term cumulative radar precipitation, it is considered as absolute shielding, and rsa is set to 1.0.

[0078] S4, the radar terrain shielding index analysis field is used to further optimize the weight of the radar quality index analysis field, and the radar shielding area index RSA is used to the radar quality index RQI by using the hyperbolic function, to obtain the optimized radar quality index RQI, as formula (6):

[0079] RQI opt (x,y) = RQI(x,y)·[1-α·tanh(β·RSA(x,y))] (6)

[0080] where RQI(x,y): original radar quality index (value range [0, 1]).

[0081] RSA(x, y): Radar Shielding Area Index (value range [0, 1], 1 represents complete shielding);

[0082] a: terrain influence intensity coefficient (0≤a≤1);

[0083] b: hyperbolic function steepness parameter (b>0);

[0084] tanh: hyperbolic tangent function, used for smooth transition shielding effect.

[0085] S5, radar, satellite precipitation rapid fusion: using radar quality index RQI fusion weight to rapidly fuse radar, satellite precipitation data, generate radar-satellite fusion background field, the calculation formula is as formula (7):

[0086] P fusion (x, y, t) = w radar (x, y, t)·P radar (x, y, t) + w sat (x, y, t)·P sat (x, y, t) (7)

[0087] Wherein, P fusion : the fusion precipitation background field;

[0088] P radar , P sat : radar estimated precipitation and satellite inversion precipitation estimated value;

[0089] w radar (x, y, t), w sat (x, y, t): radar quality index fusion weight, w radar (x, y, t) + w sat (x, y, t) = 1.

[0090] The present application is embodied by the following information.

[0091] In China, the RQI value of most areas east of 105°E and south of 42°N is basically 1, while the RQI value of the northern, western, Qinghai-Tibet Plateau and surrounding areas in the southeast, eastern and southern offshore areas, and other areas far from the radar center, with fewer covering radars and complex terrain, is relatively low.

[0092] In this embodiment, the radar, satellite precipitation data can be used to generate corrected radar, satellite precipitation data by using the bias correction method, specifically:

[0093] (1) Collect and pretreat ground station observation data, radar precipitation and satellite precipitation data:

[0094] The ground meteorological observation station precipitation data is collected and quality controlled to obtain the quality controlled ground observation precipitation data processing. The single data source control module, multi-source data collaborative quality control module and dynamic black list module are used for data quality control. The single data source control module includes metadata inspection, characteristic value inspection, limit value inspection and rigid value inspection, which can eliminate gross errors in real-time precipitation data and rigid values that do not change for a long time. The multi-source data collaborative quality control module is a QC algorithm developed based on the consistency of radar, weather phenomenon and various precipitation related meteorological observation data, which can accurately identify false clear sky precipitation and false 0 value data in typical precipitation areas that cannot be identified by rapid quality control. The data processed by the single data source control module and the multi-source data collaborative quality control module is further processed by the dynamic black list module to eliminate observation stations with high error proportion and long error duration. At the same time, a flexible dynamic evaluation mechanism is adopted, and when the data quality is restored, the data with quality control codes of 0, 1, 3 and 4 are selected for subsequent fusion analysis.

[0095] (2) The quality controlled ground observation precipitation data processing is generated by using the optimal interpolation method to generate 1km ground grid analysis precipitation product, i.e. ground precipitation grid analysis field:

[0096] 21) Establish a grid background field of precipitation climate value;

[0097] 22) Calculate the precipitation ratio data of each station and interpolate to generate the corresponding grid point field. The ratio data is a new element defined by means of the climate background field: precipitation ratio = observed precipitation of the station / corresponding grid precipitation climate value;

[0098] 23) The precipitation grid point field is generated by multiplying the precipitation ratio grid point field and the corresponding climate background field. In step 22), the interpolation method for generating the grid point field is the optimal interpolation method, and the calculation formula (8) is as follows:

[0099]

[0100] That is, the analysis value A of the grid k is the initial value F of the point k plus the deviation of the observation value and the initial value, and the deviation is estimated by weighting the deviation of n known initial values F i and observation values O i in the specified analysis range.

[0101] (3) PDF deviation correction:

[0102] 31) Consistency matching analysis of ground observation precipitation and radar estimated precipitation, including: (1) data preparation and time alignment, (2) consistency index calculation, (3) optimal lag time determination; analyze the difference relationship between the 10-minute cumulative precipitation observed by the ground automatic station in the precipitation process and the radar QPE at the lag 0-minute time, and determine the optimal lag time by using the correlation coefficient, root mean square error, and relative deviation index;

[0103] 32) Construct a revised model to adjust the spatiotemporal matching window of ground and radar precipitation PDF samples, set the spatiotemporal matching window parameter to 1 hour and 35 km, and the minimum effective sample pair number for PDF matching to 120;

[0104] 33) Use the ground precipitation grid analysis field to correct the PDF bias of radar network estimated precipitation data.

[0105] The spatial distribution of 24-hour cumulative precipitation of radar precipitation with 1 km and 10-minute bias correction on July 31, 2021, and the spatial distribution of 24-hour cumulative precipitation of satellite precipitation with 1 km and 10-minute bias correction on July 31, 2021. The RQI is used as the fusion weight coefficient of radar precipitation, the FY4 satellite data is introduced by linear weighting, and the areas not covered by radar are filled in to optimize the background field in the sparse area of the station. This not only forms a complete radar-satellite fusion background field covering the whole region of China, but also effectively utilizes satellite data to improve the monitoring capability of the typhoon precipitation system in the offshore area. In addition, the weight fusion method based on RQI greatly reduces the calculation time of the BMA radar-satellite background field fusion method used in the three-source fusion scheme of the minute product, and is more suitable for the time requirement of real-time product development.

[0106] Based on the 10-minute cumulative precipitation data of more than 2400 national automatic stations from July 20 to 31, 2021, the optimization effect of introducing minute-level FY4 satellite precipitation data on the precipitation background field is independently evaluated, and the results are shown in Table 1.

[0107] Table 1 Error statistics of 1 km and 10-minute precipitation test products from July 20 to 31, 2021

[0108]

[0109] Overall, although the accuracy of FY4 precipitation is much lower than that of radar precipitation products, the quality of radar joint background field is still improved to some extent compared with single-source radar precipitation products, such as correlation coefficient (CC) increase, root mean square error (RMSE) decrease.

[0110] The radar shaded area (RSA) index is set based on the radar climate scaling factor (RFC). The RSA index is designed as a piecewise function that varies with the climate scaling factor RFC. For the case where rfc is less than 1.0, it is assumed that there is no shielding effect, and rsa is set to 0.0. For the case where rfc is greater than 1.0, it is assumed that there is a certain degree of shielding, and the greater the rfc, the more serious the shielding, for example, when rfc is greater than 15, rsa is greater than 0.5, corresponding to more serious shielding. In addition, for the grid points with a long-term cumulative radar precipitation of zero, it is considered to be absolutely shielded, and rsa is set to 1.0. The radar precipitation terrain shielding condition obtained in this way can be seen that the areas with serious radar terrain shielding are mainly located in the western and northeastern regions, among which the radar shielding in Gansu, Tibet, Yunnan, Sichuan, Xinjiang and other regions is more serious, and the areas with absolute shielding calculated based on RSA are also basically located in these regions.

[0111] The hyperbolic function is used to apply the radar shaded area index (RSA) to the radar quality index (RQI) to obtain the optimized RQI (Figure 5). When the shielding factor is low, the radar quality index changes little, and when the shielding factor is greater than 0.7, the radar quality index decreases rapidly, and when the shielding factor tends to 1, the radar quality index tends to 0. It can be seen that after the introduction of the terrain shielding factor, the RQI index in the northeast, Xinjiang, the eastern part of the Qinghai-Tibet Plateau and its surrounding areas, and the southwest region decreases significantly, which makes the radar quality index more scientific and reasonable.

[0112] The quality of the background field before and after the introduction of the radar shaded area index (RSA) into the radar quality index (RQI) weight is independently evaluated using the site observation precipitation, and the results are shown in Table 2.

[0113] Table 2 Influence of the radar quality index (RQI) weight on the background field after the introduction of the radar shaded area index (RSA)

[0114]

[0115] From the overall effect, the fusion of radar and satellite precipitation data using the radar quality index can significantly improve the quality of the background field, and the quality of the radar-satellite background field fused using the optimized radar quality index is higher, and the relative deviation is improved from -2.6% to -0.2%.

[0116] In summary, the present application uses radar quality index (RQI) to weight the radar bias-corrected precipitation and satellite bias-corrected precipitation, quickly generates a higher-quality fused background field, and on this basis, introduces radar shadow area index RSA (terrain shielding coefficient) to optimize the radar quality index, and compares and evaluates the fusion effects before and after optimization. From the overall evaluation results of the country, it can be seen that the weight fusion method based on RQI and the method after optimization can improve the quality of the fused background field to a certain extent, but the effect of the optimized method is more significant. Although the RQI weight fusion method can use satellite data to compensate for the lack of radar coverage and improve the quality of the background field, there are still some deviations in the complex terrain and radar coverage edge area. The RQI weight fusion method after optimization of RSA, by dynamically quantifying the spatial heterogeneity of radar quality and considering the influence of terrain shielding on radar detection efficiency, significantly improves the accuracy of the fused background field in key areas such as terrain transition zone and radar coverage blind area. Through the preservation of more than 2400 national automatic station precipitation observation data to independently test the fusion results, from the statistical analysis results, in the area with low radar quality and complex terrain, the optimized fusion method shows better improvement effect. In the area with low radar quality, the relative deviation of the optimized fused background field is improved from-2.6% to-0.2%. From the spatial distribution of the fused precipitation and the statistical analysis of the evaluation, the optimized radar quality index (RQI) is used to quickly weight the radar bias-corrected precipitation and satellite bias-corrected precipitation, compensate for the lack of radar coverage, significantly improve the quality of the background field, and be more consistent with the station observation, thereby enhancing the reliability and business applicability of the fused background field.

[0117] The present application applies radar quality index analysis field and radar terrain shielding index analysis field, dynamically quantifies the spatial heterogeneity of radar quality, optimizes radar quality index (RQI) by introducing radar shadow area index (RSA), realizes dynamic adjustment of radar and satellite fusion weight, breaks through the limitations of static weight and single filling strategy, and on the other hand, the optimized fusion method is more suitable for the time requirement of minute-level real-time product development, can quickly respond to extreme weather processes, provides high timeliness and high consistency of the fused background field for disaster prevention and reduction decision and fine meteorological service, and promotes the overall improvement of meteorological business in short-term forecast, disaster warning and model assimilation.

[0118] Obviously, the above embodiments are only examples for clearly illustrating, rather than limiting the embodiments. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments are not required to be exhausted. The obvious changes or variations derived therefrom are still within the protection scope of the patent application claims.

Claims

1. A rapid fusion algorithm for radar satellite precipitation based on radar quality index, characterized in that, Using the climate proportionality correction factor RFC, the radar obstruction area index RSA is designed as a piecewise function that varies with the climate proportionality correction factor RFC. The radar terrain obstruction index analysis field is used to optimize the radar quality index analysis field weights. Then, the radar quality index fusion weights are used to quickly fuse radar and satellite precipitation data to generate a radar-satellite fused background field.

2. The radar satellite precipitation fast fusion algorithm based on radar quality index according to claim 1, characterized in that, Includes the following steps: S1. The radar quality index (RQI) is calculated by combining the detection distance from the radar center, the number of coverage points, and the terrain height at the radar network precipitation analysis grid points. S2. Correction is performed by calculating the climate proportion correction factor (RFC) of radar precipitation using the INCA algorithm; S3. The radar obstruction zone index RSA is designed as a piecewise function that varies with the climate proportional correction factor RFC. S4. The weights of the radar quality index analysis field are further optimized and corrected using the radar obstruction zone index analysis field. The radar obstruction zone index RSA is applied to the radar quality index RQI using a hyperbolic function to obtain the optimized RQI, as shown in equation (6): RQI opt (x,y)=RQI(x,y)·[1-α·tanh(β·RSA(x,y))] (6) Wherein, RQI(x,y): the original radar quality index (value range [0,1]); RSA(x,y): Radar Obstruction Index (value range [0,1], 1 indicates complete obstruction); α: Intensity coefficient of topographic influence (0≤α≤1); β: Steepness parameter of hyperbolic function (β>0); tanh: Hyperbolic tangent function, used to smoothly transition occlusion effects; S5. Utilize radar quality index fusion weights to quickly fuse radar and satellite precipitation data to generate a radar-satellite fused background field.

3. The radar-satellite precipitation fast fusion algorithm based on radar quality index according to claim 1, characterized in that, The piecewise function in step S3 is as shown in equation (5): When RFC is less than 1.0, let RSA = 0.0; when RFC is greater than or equal to 1.0, let RSA = 1 - e 0.05(rfc-1) When rain_rad = 0, that is, the grid point where the long-term cumulative radar precipitation is zero, let rsa = 1.

0.

4. The radar-satellite precipitation fast fusion algorithm based on radar quality index according to claim 2, characterized in that, The rapid fusion calculation formula in step S5 is as shown in equation (7): P fusion (x,y,t)=w radar (x,y,t)·P radar (x,y,t)+w sat (x,y,t)·P sat (x,y,t) (7) Among them, P fusion : The merged precipitation background field; P radar P sat Radar-estimated precipitation and satellite-retrieved precipitation estimates; w radar (x,y,t),w sat (x,y,t): Radar quality index fusion weights, w radar (x,y,t)+w sat (x,y,t)=1.

5. The radar-satellite precipitation fast fusion algorithm based on radar quality index according to claim 2, characterized in that, In step S1, the radar quality index (RQI) is calculated using the formula (1): Wherein, γ is the radar coverage factor, H is the radar beam height factor, H1 is the critical height characterizing the correlation between radar detection information and ground precipitation, with a value of 1000 meters, and H2 is the proportional factor of radar beam height change, with a value of 3500 meters. The closer to the center point, the lower the radar detection height, and the larger the RQI index.

6. The radar-satellite precipitation fast fusion algorithm based on radar quality index according to claim 5, characterized in that, The formula for calculating the radar beam height factor H is as shown in equation (2): H=H rad +D*tanα+β*E (2) Among them, H rad α is the radar station height, D is the distance from the radar center to the radar network precipitation analysis grid point, α is the elevation angle of the lowest layer detected by the radar, β is the terrain effect factor, and E is the grid elevation (unit: m). The higher the elevation, the smaller the ROI index.

7. The radar-satellite precipitation fast fusion algorithm based on radar quality index according to claim 5, characterized in that, The formula for calculating the radar coverage factor is as shown in equation (3): Where n is the number of radars covering the grid, and n0 takes the value 3, which means that when the number of radars covering the grid is less than 3, it will affect the RQI of that point. The fewer the number, the smaller the ROI index.

8. The radar-satellite precipitation fast fusion algorithm based on radar quality index according to claim 2, characterized in that, In step S2, the climate proportion correction factor RFC is calculated using the ratio of historical ground observations and radar-estimated cumulative precipitation, as shown in equation (4): Among them, P i,j For ground-observed precipitation values ​​at specified grid locations, P Radar,i,j Estimated precipitation values ​​for radar at specified grid locations.

9. The radar-satellite precipitation fast fusion algorithm based on radar quality index according to claim 2, characterized in that, The radar obstruction zone index (RSA) analysis field is generated by processing the climate proportion correction factor (RFC) of radar precipitation calculated using the ratio of historical ground observations and radar-estimated cumulative precipitation over the past three months.

10. The radar-satellite precipitation fast fusion algorithm based on radar quality index according to claim 1 or 2, characterized in that, The radar and satellite precipitation data are used to generate corrected radar and satellite precipitation data using a bias correction method, including: (1) Collection and preprocessing of ground station observation data, radar precipitation data, and satellite precipitation data: Precipitation data from ground meteorological observation stations is collected and processed under quality control to obtain the quality-controlled ground observation precipitation data. The quality control is achieved using a single data source control module, a multi-source data collaborative quality control module, and a dynamic blacklist module. The single data source control module includes metadata checks, feature value checks, boundary value checks, and dead value checks, which can eliminate gross errors and long-term unchanging dead values ​​in real-time precipitation data. The multi-source data collaborative quality control module uses a QC algorithm developed based on the consistency of meteorological observation data related to radar and various weather phenomena, which can accurately identify false clear-sky precipitation and false zero-value data in typical precipitation areas that are difficult to identify with rapid quality control. The data, after being quality controlled by the single data source control module and the multi-source data collaborative quality control module, is further processed by the dynamic blacklist module to remove observation stations with high error rates and long error durations. A flexible dynamic evaluation mechanism is also used; data is removed from the blacklist only after its quality is restored. Data with quality control codes of 0, 1, 3, and 4 are selected for subsequent fusion analysis. (2) Using the optimal interpolation method, the quality-controlled ground observation precipitation data are processed to generate a 1km ground grid analysis precipitation product, i.e., the ground precipitation grid analysis field: 21) Establish a grid background field for precipitation climate values; 22) Calculate the precipitation ratio data of each station and interpolate to generate the corresponding grid field. The ratio data is a new element defined with the help of the climate background field: Precipitation ratio = Station observed precipitation / Corresponding grid precipitation climate value; 23) The precipitation grid field is generated by multiplying the precipitation ratio grid field with the corresponding climate background field. The interpolation method used in step 22) to generate the grid field is the optimal interpolation method, and the calculation formula (8) is as follows: That is, the analysis value A of the grid. k The initial estimate F at that point k Including the deviation between the observed value and the initial estimate at that point, the deviation is calculated from n known initial estimates F within the specified analysis range. i With the observed value O i The bias weighted estimate is obtained. (3) PDF deviation correction: 31) Perform consistency matching analysis between ground-observed precipitation and radar-estimated precipitation, including: (1) data preparation and time alignment, (2) consistency index calculation, and (3) determination of optimal lag time; analyze the difference between the cumulative precipitation observed by the ground automatic station in the first 10 minutes and the radar QPE at the lag time of 0 minutes during the precipitation process, and determine the optimal lag time using correlation coefficient, root mean square error, and relative deviation index; 32) Construct a correction model and adjust the spatiotemporal matching window of the ground and radar precipitation PDF samples. The spatiotemporal matching window parameters are set to 1 hour and 35 km, and the minimum number of valid sample pairs participating in PDF matching is 120. 33) Use the ground precipitation grid analysis field to perform PDF bias correction on radar network-estimated precipitation data.

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