Application of comprehensive quality index in precipitation estimation based on combined radar and automatic stations

Abstract

The present invention relates to an application method of a comprehensive quality index in joint radar and automatic station precipitation estimation, comprising the following steps: obtaining radar estimated precipitation - automatic station observed precipitation data pairs and multiple radar data quality indexes in a target area, calculating a comprehensive quality index based on the multiple radar data quality indexes, and calculating the comprehensive quality index average value; correcting the radar estimated precipitation - automatic station observed precipitation data pairs according to a first preset threshold value corresponding to the comprehensive quality index average value, and obtaining final precipitation estimation data. In the technical solution of the present invention, by applying the comprehensive quality index to the process of joint radar and automatic station estimation of precipitation, the ability of networked radars to estimate precipitation can be further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological data processing, and in particular to an application method of a comprehensive quality index in precipitation estimation by combining radar and automatic stations. Background Art

[0002] As a single-point observation data with high accuracy, the automatic station rainfall can help radar optimize the results of precipitation estimation. The automatic station rainfall data plays two roles in precipitation estimation. One is to optimize the precipitation estimation formula in conjunction with radar data before calculating the precipitation intensity, and the other is to perform spatial correction after forming the initial value field of the precipitation estimation product. Since the ZR relationship will change due to the change of the raindrop spectrum in the same precipitation process (Wu Yahao et al., 2016), the real-time optimization of the ZR relationship by combining radar and automatic station rainfall data in business can improve the accuracy of precipitation estimation. At present, some studies have verified this view: Gou Yabin (2014) fixed the b coefficient in the algorithm, based on the principle that the average value of the hourly rainfall estimated by radar and the average value of the hourly rainfall observed by the automatic station are close, and used radar and rainfall data to obtain the optimized A value in real time for precipitation estimation. This method was applied to four types of precipitation: stratiform precipitation, plum rain, squall line and typhoon, and achieved good results. Wang et al. (2012) used a similar method to estimate two heavy rainfalls in the Huaihe River Basin. The results showed that this method effectively alleviated the estimation error, reducing the root mean square error and the average relative error. Libertino et al. (2015) used the Italian C-band dual-polarization radar to calculate the ZR relationship every 60 minutes and estimate precipitation. For the estimation of stratiform cloud precipitation, the estimation bias was reduced by 80% compared with the fixed ZR relationship.

[0003] After obtaining the initial value field of precipitation estimation products, there are many methods to calibrate the initial value field using automatic station rainfall data, including the mean field deviation correction method (Wilson and Brandes, 1979), Kalman filter method (Fulton et al., 1998; Ahnert, 1986), variational method (Deng Xuejiao et al., 2000; Zhang Peichang et al., 1992), Kriging method (Huang Xiaoyu et al., 2009; Krajewski et al., 1987), and optimal interpolation method (Li Jiantong et al., 2000, 1996; Daley, 1991), etc. Studies have found that these methods can improve the accuracy of precipitation estimation of the new generation weather radar to a certain extent (Dong Gaohong et al., 2012; Guan Li et al., 2004; Zheng Yuanyuan et al., 2004). In the above studies, the methods of real-time optimization of precipitation estimation formulas using automatic station rainfall data are only applied to the ZR relationship of single-polarization or dual-polarization radars. Dual-polarization radars use more formulas to estimate precipitation, which brings great difficulties to the real-time correction of the formula. Although Zhang Yang (2019) tried to correct the precipitation estimation formula containing polarization radar parameters in real time to improve the accuracy of precipitation estimation, he did not impose specific restrictions on the data used to correct the formula, which resulted in data that was not suitable for the correction formula. At the same time, the spatial correction method used by him (Zhang Yang, 2019) cannot effectively correct precipitation in areas far away from the radar. There is currently no relevant technology to solve the above problems. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art or related art.

[0005] To this end, the purpose of the present invention is to provide an application method of a comprehensive quality index in the estimation of precipitation by combining radar and automatic station, which can further improve the ability of networked radar to estimate precipitation.

[0006] To achieve the above-mentioned purpose, the technical solution of the present invention provides an application method of a comprehensive quality index in joint radar and automatic station precipitation estimation, comprising the following steps: obtaining radar estimated precipitation-automatic station observed precipitation data pairs in a target area and a plurality of radar data quality indexes corresponding to the radar estimated precipitation-automatic station observed precipitation data pairs, the plurality of radar data quality indexes including beam shielding influence parameters, bright band or height influence parameters, signal-to-noise ratio influence parameters and non-meteorological echo influence parameters;

[0007] The expressions of multiple radar data quality indices are:

[0008]

[0009]

[0010]

[0011]

[0012] Among them, RQI blk It is represented by the beam blocking effect parameter, blk represents the radar beam blocking rate, RQI hgt Expressed as a bright band or height impact parameter, h a represents the radar beam center height, h b Indicates the height of the bottom of the bright band in the zero-degree layer, H sf Represents the height scale factor, h b <0 means there is no bright band or the bottom height of the bright band cannot be determined, RQI snr It is expressed as the signal-to-noise ratio impact parameter, snr represents the signal-to-noise ratio, RQI ρHV Expressed as the non-meteorological echo impact parameter, ρ PV Expressed as correlation coefficient, RQI snr and RQI ρHV Both are represented by Gaussian functions, snr* and Δρ HV *Represents RQI snr and RQI ρHV Gaussian function threshold, Z H Expressed as the horizontal polarization reflectivity factor, Z DR Expressed as the differential reflectivity factor, K DP Expressed as the differential propagation phase shift rate;

[0013] A comprehensive quality index is calculated based on multiple radar data quality indexes, and an average value of the comprehensive quality index is calculated;

[0014] The expression of comprehensive quality index is:

[0015] RQI=RQI blk ×RQI hgt ×RQI snr ×RQI ρHV ;

[0016] The expression of the average value of the comprehensive quality index is:

[0017]

[0018] Among them, n1, n2 and n3 represent Z H , Z DR and K DP The number of times used in estimating precipitation, j1, j2 and j3 are serial numbers, and Respectively represent Z H , Z DR and K DP Quality index;

[0019] The radar estimated precipitation-automatic station observed precipitation data pair is corrected according to the first preset threshold value corresponding to the average value of the comprehensive quality index to obtain the final precipitation estimation data.

[0020] In the above technical solution, preferably, the radar estimated precipitation-automatic station observed precipitation data pair is corrected according to a first preset threshold value corresponding to the average value of the comprehensive quality index to obtain the final precipitation estimation data, including the following steps:

[0021] When the first preset threshold is ARQI≤0.4, the automatic station observed precipitation data in the radar estimated precipitation-automatic station observed precipitation data pair is corrected according to the Chrisman interpolation method to obtain the automatic station observed precipitation corrected data as the final precipitation estimated data;

[0022] When the first preset threshold is ARQI>0.9, the radar estimated precipitation data in the radar estimated precipitation-automatic station observed precipitation data pair is corrected according to the optimal interpolation method to obtain the radar estimated precipitation corrected data as the final precipitation estimated data;

[0023] When the first preset threshold is 0.4<ARQI≤0.9, the automatic station observation precipitation correction data and the radar estimated precipitation correction data are fused and calculated according to the average value of the comprehensive quality index, and the data fusion result is obtained as the final precipitation estimation data;

[0024] The expression of fusion calculation is: R QPE-Gauge =(1.8-2ARQI)R c +(2ARQI-0.8)R0, where R QPE-Gauge represents the data fusion result, R c represents the precipitation correction data observed by the automatic station, R o Represents radar estimated precipitation correction data.

[0025] In the above technical solution, preferably, the expression of the precipitation correction data observed by the automatic station is:

[0026] Among them, R is the maximum radius of the search, r represents the distance between the grid point and the automatic station, and w i represents the weight coefficient of the surrounding i-th automatic station, G i represents the observed rainfall at the ith automatic station;

[0027] The expression of radar estimated precipitation correction data is:

[0028] Among them, R p is the initial quantitative precipitation estimation rainfall data at the grid point, R ig is the rainfall observed by the automatic stations around the grid point, R i p is the initial rainfall estimated by radar corresponding to the coordinates of the surrounding automatic stations, and P is the weight coefficient obtained by minimizing the root mean square of the rainfall at the automatic station and the rainfall estimated by radar.

[0029] In any of the above technical solutions, preferably, before correcting the radar estimated precipitation-automatic station observed precipitation data pair according to the first preset threshold value corresponding to the average value of the comprehensive quality index, the following steps are also included:

[0030] The radar estimated precipitation-automatic station observed precipitation data pairs are screened according to a second preset threshold value corresponding to the average value of the comprehensive quality index to obtain radar estimated precipitation-automatic station observed precipitation evaluation data pairs;

[0031] The evaluation parameters are obtained by calculating the radar estimated precipitation-automatic station observed precipitation evaluation data pair;

[0032] The coefficients of the precipitation formulas of radar and automatic stations are corrected according to the evaluation parameters to obtain the precipitation correction formula.

[0033] In any of the above technical solutions, preferably, the following steps are also included:

[0034] According to the radar estimated precipitation-automatic station observed precipitation data pair at the previous moment, it is judged whether the radar estimated precipitation-automatic station observed precipitation data pair at the current moment is within the set time interval;

[0035] If not, assign the coefficients in the precipitation correction formula corresponding to the radar and automatic station at the current moment to the initial coefficients;

[0036] If so, the radar estimated precipitation-automatic station observed precipitation data pair is re-obtained according to the precipitation correction formula.

[0037] In any of the above technical solutions, preferably, the second preset threshold is ARQI>0.95; and / or

[0038] The expressions for evaluating the parameters are:

[0039] Among them, RA i radar Precipitation data and RA for radar estimation i gauge is the precipitation data observed by the automatic station, and the bias ratio is the ratio of the radar estimated rainfall to the rainfall observed by the automatic station;

[0040] The coefficient correction expression is:

[0041]

[0042] Among them, i represents the i-th precipitation intensity segment, corresponding to 0-10, 10-20, 20-30, 30-40, 40-50, 50-500 (mm / h), a 0 represents the default multiplier coefficient of the precipitation estimation formula, that is, the initial value before correction, ξ i j It represents the correction coefficient obtained after the jth estimation of hourly rainfall. The correction coefficient is obtained by segmented evaluation. i Calculated.

[0043] The advantage of the method for applying the comprehensive quality index provided by the present invention to the joint radar and automatic station estimation of precipitation compared with the prior art is that by applying the comprehensive quality index to the process of joint radar and automatic station estimation of precipitation, the ability of the networked radar to estimate precipitation can be further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0045] Figure 1 A flowchart of a method according to a first embodiment of the present invention is shown;

[0046] Figure 2 A flowchart of a method according to a second embodiment of the present invention is shown;

[0047] Figure 3 A flowchart of a method according to a third embodiment of the present invention is shown;

[0048] Figure 4 A flowchart of a method according to a fourth embodiment of the present invention is shown;

[0049] Figure 5 A line graph showing changes in precipitation estimation coefficients over time according to an embodiment of the present invention is shown;

[0050] Figure 6 The planar distribution diagram of the precipitation estimation evaluation parameter bias ratio involved in the embodiment of the present invention is shown. DETAILED DESCRIPTION

[0051] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0053] like Figure 1 As shown, the method for applying the comprehensive quality index to the precipitation estimation of the combined radar and automatic station according to the first embodiment of the present invention includes the following steps:

[0054] S1, obtaining radar estimated precipitation (QPE)-automatic station observed precipitation (Gauge) data pairs in the target area and multiple radar data quality indexes corresponding to the radar estimated precipitation-automatic station observed precipitation data pairs;

[0055] In this step, the quality of dual-polarization radar data affects the accuracy of radar quantitative precipitation estimation. Calculating the radar data comprehensive quality index RQI can effectively reflect the radar data quality and provide a reference for the application of radar data. The radar data comprehensive quality index RQI is based on different factors that affect the radar data quality.

[0056] Multiple radar data quality indices include beam obstruction impact parameters, bright band or height impact parameters, signal-to-noise ratio impact parameters, and non-meteorological echo impact parameters;

[0057] The expressions of multiple radar data quality indices are:

[0058]

[0059]

[0060]

[0061]

[0062] Among them, RQI blk It is represented by the beam blocking effect parameter, blk represents the radar beam blocking rate, RQI hgt Expressed as a bright band or height impact parameter, h a represents the radar beam center height, h b Indicates the height of the bottom of the bright band in the zero-degree layer, H sf Represents the height scale factor, h b <0 means there is no bright band or the bottom height of the bright band cannot be determined, RQI snr It is expressed as the signal-to-noise ratio impact parameter, snr represents the signal-to-noise ratio, RQI ρHV Expressed as the non-meteorological echo impact parameter, ρ PV Expressed as correlation coefficient, RQI snr and RQIρHV Both are represented by Gaussian functions, snr* and Δρ HV *Represents RQI snr and RQI ρHV Gaussian function threshold, Z H Expressed as the horizontal polarization reflectivity factor, Z DR Expressed as the differential reflectivity factor, K DP Expressed as the differential propagation phase shift rate;

[0063] S2, obtaining a comprehensive quality index according to the multiple radar data quality indexes, and calculating an average value of the comprehensive quality index;

[0064] The expression of comprehensive quality index is:

[0065] RQI=RQI blk ×RQI hgt ×RQI snr ×RQI ρHV ;

[0066] The expression of the average value of the comprehensive quality index is:

[0067] Among them, n1, n2 and n3 represent Z H , Z DR and K DP The number of times used in estimating precipitation, j1, j2 and j3 are serial numbers, and Respectively represent Z H , Z DR and K DP Quality index;

[0068] In this step, the comprehensive quality index RQI obtained by integrating the quality index of each radar data is used as the quality index for evaluating radar parameters, and the comprehensive quality index RQI corresponding to each radar is obtained. In order to better reflect the quality of radar quantitative precipitation estimation products, the average value of the comprehensive quality index of the radar used to estimate precipitation is recorded as ARQI, forming a new radar data comprehensive quality index, which is used to reflect the quality of precipitation products.

[0069] S9, correcting the radar estimated precipitation-automatic station observed precipitation data pair according to a first preset threshold value corresponding to the average value of the comprehensive quality index to obtain final precipitation estimation data.

[0070] In the above technical solution, preferably, Figure 2 As shown, S8, correcting the radar estimated precipitation-automatic station observed precipitation data pair according to the first preset threshold value corresponding to the average value of the comprehensive quality index to obtain the final precipitation estimation data, including the following steps:

[0071] S91, when the first preset threshold is ARQI≤0.4, correcting the automatic station observed precipitation data in the radar estimated precipitation-automatic station observed precipitation data pair according to the Chrisman interpolation method, and obtaining the automatic station observed precipitation corrected data as the final precipitation estimated data;

[0072] S92, when the first preset threshold is ARQI>0.9, correcting the radar estimated precipitation data in the radar estimated precipitation-automatic station observed precipitation data pair according to the optimal interpolation method, and obtaining the radar estimated precipitation corrected data as the final precipitation estimated data;

[0073] S93, when the first preset threshold is 0.4<ARQI≤0.9, fusing the automatic station observed precipitation correction data and the radar estimated precipitation correction data according to the average value of the comprehensive quality index to obtain a data fusion result as the final precipitation estimation data;

[0074] The expression of fusion calculation is: R QPE-Gauge =(1.8-2ARQI)R c +(2ARQI-0.8)R0, where R QPE-Gauge represents the data fusion result, R c represents the precipitation correction data observed by the automatic station, R o Represents radar estimated precipitation correction data.

[0075] In the above technical solution, preferably, the automatic station observation precipitation correction data R calculated by the Klissmann interpolation method is c The expression is:

[0076]

[0077] Among them, R is the maximum radius of the search, r represents the distance between the grid point and the automatic station, and w i represents the weight coefficient of the surrounding i-th automatic station, G i represents the observed rainfall at the ith automatic station;

[0078] The expression of radar estimated precipitation correction data R0 calculated by the optimal interpolation method is:

[0079]

[0080] Among them, R p is the initial quantitative precipitation estimation rainfall data at the grid point, R i g is the rainfall observed by the automatic stations around the grid point, R i pis the initial rainfall estimated by radar corresponding to the coordinates of the surrounding automatic stations, and P is the weight coefficient obtained by minimizing the root mean square of the rainfall at the automatic station and the rainfall estimated by radar.

[0081] In any of the above technical solutions, preferably, Figure 3 As shown, S9, before correcting the radar estimated precipitation-automatic station observed precipitation data pair according to the first preset threshold value corresponding to the average value of the comprehensive quality index, the following steps are also included:

[0082] S3, screening the radar estimated precipitation-automatic station observed precipitation data pairs according to a second preset threshold value corresponding to the average value of the comprehensive quality index to obtain radar estimated precipitation-automatic station observed precipitation evaluation data pairs;

[0083] S4, calculating the radar estimated precipitation-automatic station observed precipitation evaluation data pair to obtain evaluation parameters;

[0084] S5, correct the coefficients of the precipitation formulas of the radar and automatic stations according to the evaluation parameters to obtain the precipitation correction formula.

[0085] In any of the above technical solutions, preferably, Figure 4 As shown, the following steps are also included:

[0086] S6, judging whether the radar estimated precipitation-automatic station observed precipitation data pair at the current moment is within a set time interval according to the radar estimated precipitation-automatic station observed precipitation data pair at the previous moment;

[0087] If not, S7, assign the coefficients in the precipitation correction formula corresponding to the radar and automatic station at the current moment to the initial coefficients;

[0088] If so, S8, re-obtain the radar estimated precipitation-automatic station observed precipitation data pair according to the precipitation correction formula.

[0089] In any of the above technical solutions, preferably, the second preset threshold is ARQI>0.95; and / or

[0090] The expressions for evaluating the parameters are: Among them, RA i radar Precipitation data and RA for radar estimation i gauge is the precipitation data observed by the automatic station, and the bias ratio is the ratio of the radar estimated rainfall to the rainfall observed by the automatic station;

[0091] The coefficient correction expression is:

[0092]

[0093] Among them, i represents the i-th precipitation intensity segment, corresponding to 0-10, 10-20, 20-30, 30-40, 40-50, 50-500 (mm / h), a 0 represents the default multiplier coefficient of the precipitation estimation formula, that is, the initial value before correction, ξ i j It represents the correction coefficient obtained after the jth estimation of hourly rainfall. The correction coefficient is obtained by segmented evaluation. i Calculated.

[0094] The advantage of the method for applying the comprehensive quality index provided by the present invention to the joint radar and automatic station estimation of precipitation compared with the prior art is that by applying the comprehensive quality index to the process of joint radar and automatic station estimation of precipitation, the ability of the networked radar to estimate precipitation can be further improved.

[0095] Comparative test

[0096] In order to verify the effect of the technical solution of the present invention, several groups of experiments are set up, and the specific methods are as follows:

[0097] Table 1. List of several comparative test methods

[0098]

[0099] The first group of experiments used radar to estimate precipitation only. The second and third groups of experiments used automatic station data to revise the precipitation estimation formula in real time without performing spatial correction. However, in the second group, the formula coefficients were adjusted after evaluating all the data, while in the third group, only the data with better quality (ARQI>0.9) were evaluated and adjusted. The role of ARQI in correcting the formula was verified by comparing these two groups of experiments. The fourth and fifth groups of experiments performed spatial corrections on the basis of the third group of experiments. However, in the fourth group, the optimal interpolation method was used, while in the fifth group, the correction was performed in sections according to the size of the ARQI, which is the new method proposed in this paper. The role of ARQI in spatial correction was verified by comparing these two groups of experiments.

[0100] Evaluation Methodology

[0101] After obtaining the precipitation estimation result through radar data calculation, it needs to be evaluated. During the evaluation, the accumulated rainfall of the automatic station for one hour is selected as the "true value". Each automatic station corresponds to 9 radar distance libraries: the distance library directly above the automatic station and the 8 distance libraries around it. The hourly rainfall average corresponding to these 9 distance libraries is used as the radar estimation value for evaluation to eliminate the drift of raindrops caused by horizontal wind. In addition, considering that the average error value will be reduced when the 1-hour rainfall observation of the automatic station of 0.1 mm is large, only rainfall values ​​greater than 0.1 mm are used for evaluation. Use RA iradar (mm) represents the radar 1-hour cumulative estimated rainfall, expressed as RA i gauge (mm) represents the accumulated rainfall observed in one hour at the automatic station, and the estimated value RA is finally obtained. i radar and the observed value RA i gauge The data pairs are used for QPE evaluation.

[0102] The estimation results are evaluated using the correlation coefficient (CC), root mean square error (RMSE), normalized relative error (NB), normalized absolute error (NE) and the ratio of radar estimated rainfall to automatic station observed rainfall (bias Ratio). The expressions of the evaluation parameters are as follows:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] CC indicates the degree of correlation between the estimated value and the true value. The closer its value is to 1, the higher the correlation between the two is, and the better the estimation effect is. The value of NB(%) can be positive or negative. A positive value indicates overestimation, and a negative value indicates underestimation. The closer it is to 0, the smaller the error is. Both RMSE(mm) and NE(%) are non-negative values. The smaller the value, the smaller the error is. However, RMSE is affected by the intensity of precipitation, while NE is not affected by it. Bias Ratio can evaluate the estimation effect at a certain automatic station. A value greater than 1 indicates overestimation, and a value less than 1 indicates underestimation. It is mainly used to analyze the plane distribution of the estimation effect.

[0109] Individual effect

[0110] Heavy rainfall occurred in Guangdong from 10:00 to 24:00 (UTC) on June 12, 2019. The rainfall was estimated according to the set comparative test and evaluated within the range of ARQI>0.9. The rainfall evaluation results are as follows: Figure 5The following is a line chart showing the changes in the precipitation estimation evaluation coefficients (a) NB (b) RMSE (c) NE (d) CC over time from 10:00 to 24:00 (UTC) on June 12, 2019. Different colors represent different test methods, purple: test 1, green: test 2, yellow: test 3, red: test 4. Compared with tests 1 and 2, when all data are used to correct the formula, NB, NE, and RMSE increase, CC decreases, the overestimation becomes more prominent, the error becomes larger, and the effect becomes worse. This is because all data contain poor quality radar data, which makes the corrected formula coefficients unable to properly reflect the changes in the raindrop spectrum. After adding the restriction of ARQI>0.95 to test 3, the influence of poor radar data on the precipitation estimation results is greatly eliminated, making the corrected formula coefficients more reasonable, so NB is closer to 0, the overestimation is improved, NE is reduced, and the error is reduced. Based on Experiment 3, Experiment 4 adds spatial correction of precipitation estimation results, NB is closer to 0, NE and RMSE are further reduced, CC is increased, and the estimation effect is further improved. In addition, since this is an evaluation of the range of ARQI>0.9, Experiments 4 and 5 have the same data processing methods and results in this range, so they are not shown.

[0111] Figure 6 The plane distribution of the biasratio of the precipitation estimation evaluation parameter in five experiments from 10:00 to 24:00 (UTC) on June 12, 2019 is shown, represented by colored circles. The color represents the parameter value, and the size of the circle represents the average hourly rainfall at the automatic station. (a)-(e) represent the evaluation results of experiments 1-5, respectively; (f) represents the plane distribution of ARQI, which is similar to Figure 2 The results are similar. In Experiment 2, the overestimation in the area with larger ARQI is more serious than that in Experiment 1, while in Experiment 3, the overestimation is alleviated. In Experiment 4, there is almost no obvious regional overestimation. It is particularly noteworthy that in Experiment 5, due to the use of ARQI segmentation for correction and fusion, the obvious underestimation in the outer area with smaller ARQI is significantly improved compared with Experiment 4, which improves the estimation effect of the entire region.

[0112] In the present invention, the terms "first", "second", and "third" are used for descriptive purposes only and should not be understood as indicating or implying relative importance; the term "plurality" refers to two or more, unless otherwise clearly defined. The terms "installed", "connected", "connected", "fixed", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; "connected" can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0113] In the description of the present invention, it is necessary to understand that the directions or positional relationships indicated by terms such as “upper”, “lower”, “left”, “right”, “front” and “back” are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or unit referred to must have a specific direction, be constructed and operated in a specific orientation, and therefore, cannot be understood as a limitation on the present invention.

[0114] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0115] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for applying a comprehensive quality index to precipitation estimation using a combined radar and automatic station, characterized in that: The following steps are involved: Acquire a radar-estimated precipitation-automatic station observed precipitation data pair in a target area and a plurality of radar data quality indexes corresponding to the radar-estimated precipitation-automatic station observed precipitation data pair, wherein the plurality of radar data quality indexes include a beam shielding influence parameter, a bright band or height influence parameter, a signal-to-noise ratio influence parameter, and a non-meteorological echo influence parameter; The expressions of multiple radar data quality indices are: Among them, RQI blk It is represented by the beam blocking effect parameter, blk represents the radar beam blocking rate, RQI hgt Expressed as a bright band or height impact parameter, h a represents the radar beam center height, h b Indicates the height of the bottom of the bright band in the zero-degree layer, H sf Represents the height scale factor, h b <0 means there is no bright band or the bottom height of the bright band cannot be determined, RQI snr It is expressed as the signal-to-noise ratio influencing parameter, snr represents the signal-to-noise ratio, Expressed as the non-meteorological echo impact parameter, ρ HV Expressed as correlation coefficient, RQI snr and Both are represented by Gaussian functions, snr* and Δρ HV * indicates RQI snr and Gaussian function threshold, Z H Expressed as the horizontal polarization reflectivity factor, Z DR Expressed as the differential reflectivity factor, K DP Expressed as the differential propagation phase shift rate; Calculating the comprehensive quality index according to the plurality of radar data quality indexes, and calculating an average value of the comprehensive quality index; The expression of the comprehensive quality index is: The expression of the average value of the comprehensive quality index is: Among them, n1, n2 and n3 represent Z H , Z DR and K DP The number of times used in estimating precipitation, j1, j2 and j3 are serial numbers, and Respectively represent Z H , Z DR and K DP Quality index; The radar estimated precipitation-automatic station observed precipitation data pair is corrected according to a first preset threshold value corresponding to the average value of the comprehensive quality index to obtain final precipitation estimation data.

2. The method for applying the comprehensive quality index to precipitation estimation by combining radar and automatic station according to claim 1, characterized in that: The radar estimated precipitation-automatic station observed precipitation data pair is corrected according to a first preset threshold value corresponding to the average value of the comprehensive quality index to obtain final precipitation estimation data, including the following steps: When the first preset threshold is ARQI≤0.4, correcting the automatic station observed precipitation data in the radar estimated precipitation-automatic station observed precipitation data pair according to the Chrisman interpolation method to obtain the automatic station observed precipitation corrected data as the final precipitation estimated data; When the first preset threshold is ARQI>0.9, the radar estimated precipitation data in the radar estimated precipitation-automatic station observed precipitation data pair is corrected according to the optimal interpolation method to obtain the radar estimated precipitation corrected data as the final precipitation estimated data; When the first preset threshold is 0.4<ARQI≤0.9, the automatic station observed precipitation correction data and the radar estimated precipitation correction data are fused and calculated according to the average value of the comprehensive quality index to obtain a data fusion result as the final precipitation estimation data; The expression of fusion calculation is: R QPE-Gauge =(1.8-2ARQI)R c +(2ARQI-0.8)R0, where R QPE-Gauge represents the data fusion result, R c represents the precipitation correction data observed by the automatic station, R o represents the radar estimated precipitation correction data.

3. The method for applying the comprehensive quality index to precipitation estimation by combining radar and automatic station according to claim 2 is characterized by: The expression of the precipitation correction data observed by the automatic station is R c Or the expression of Klissmann interpolation is: Among them, R is the maximum radius of the search, r represents the distance between the grid point and the automatic station, and w i represents the weight coefficient of the surrounding i-th automatic station, G i represents the observed rainfall at the ith automatic station; The expression of radar estimated precipitation correction data, that is, the expression of R0 or optimal interpolation method is: Among them, R p is the initial quantitative precipitation estimation rainfall data at the grid point, R i g is the rainfall observed by the automatic stations around the grid point, R i p is the initial rainfall estimated by radar corresponding to the coordinates of the surrounding automatic stations, and P is the weight coefficient obtained by minimizing the root mean square of the rainfall at the automatic station and the rainfall estimated by radar.

4. The method for applying the comprehensive quality index to precipitation estimation by combining radar and automatic station according to any one of claims 1 to 3, characterized in that: Before correcting the radar estimated precipitation-automatic station observed precipitation data pair according to the first preset threshold value corresponding to the average value of the comprehensive quality index, the following steps are also included: The radar estimated precipitation-automatic station observed precipitation data pair is screened according to a second preset threshold value corresponding to the average value of the comprehensive quality index to obtain the radar estimated precipitation-automatic station observed precipitation evaluation data pair; Calculating based on the radar estimated precipitation-automatic station observed precipitation evaluation data pair to obtain evaluation parameters; The coefficients of the precipitation formulas of the radar and the automatic station are corrected according to the evaluation parameters to obtain a precipitation correction formula.

5. The method for applying the comprehensive quality index to precipitation estimation by combining radar and automatic station according to claim 4 is characterized in that: The following steps are also included: Determine whether the radar estimated precipitation-automatic station observed precipitation data pair at the current moment is within a set time interval based on the radar estimated precipitation-automatic station observed precipitation data pair at the previous moment; If not, assigning the coefficients in the precipitation correction formula corresponding to the radar and the automatic station at the current moment to the initial coefficients; If so, the radar estimated precipitation-automatic station observed precipitation data pair is re-obtained according to the precipitation correction formula.

6. The method for applying the comprehensive quality index to precipitation estimation by combining radar and automatic station according to claim 4, characterized in that: The second preset threshold is ARQI>0.95; and / or The expression of the evaluation parameter is: Among them, RA i radar Precipitation data and RA for radar estimation i gauge is the precipitation data observed by the automatic station, and the bias ratio is the ratio of the radar estimated rainfall to the rainfall observed by the automatic station; The coefficient correction expression is: x i min=0.5,ξ i max=1.5; Among them, i represents the i-th precipitation intensity segment, corresponding to 0-10, 10-20, 20-30, 30-40, 40-50, 50-500 mm / h, a 0 represents the default multiplier coefficient of the precipitation estimation formula, that is, the initial value before correction, It represents the correction coefficient obtained after the jth estimation of hourly rainfall. The correction coefficient is obtained by segmented evaluation. i Calculated.

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