CN113960606A - Application method of comprehensive quality index in combined radar and automatic station rainfall estimation - Google Patents

Application method of comprehensive quality index in combined radar and automatic station rainfall estimation Download PDF

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CN113960606A
CN113960606A CN202111214368.4A CN202111214368A CN113960606A CN 113960606 A CN113960606 A CN 113960606A CN 202111214368 A CN202111214368 A CN 202111214368A CN 113960606 A CN113960606 A CN 113960606A
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张扬
刘黎平
吴翀
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Chinese Academy of Meteorological Sciences CAMS
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Abstract

The invention relates to an application method of a comprehensive quality index in combined radar and automatic station rainfall estimation, which comprises the following steps: acquiring radar estimated precipitation-automatic station observation precipitation data pairs and a plurality of radar data quality indexes in a target area, calculating to obtain a comprehensive quality index according to the radar data quality indexes, and calculating a comprehensive quality index average value; and correcting the radar estimated precipitation-automatic station observed precipitation data pair according to a first preset threshold corresponding to the average value of the comprehensive quality index to obtain final precipitation estimated data. According to the technical scheme, when the comprehensive quality index is applied to the process of jointly estimating rainfall by the radar and the automatic station, the capacity of estimating the rainfall by the networking radar can be further improved.

Description

Application method of comprehensive quality index in combined radar and automatic station rainfall estimation
Technical Field
The invention relates to the technical field of meteorological data processing, in particular to an application method of a comprehensive quality index in the rainfall estimation by combining radar and an automatic station.
Background
The rainfall of the automatic station is taken as single-point observation data with higher precision, and can help a radar to optimize a precipitation estimation result, and the rainfall data of the automatic station plays two roles in precipitation estimation, wherein one role is to optimize a precipitation estimation formula by combining radar data before the precipitation intensity is calculated, and the other role is to perform space correction after an initial value field of a precipitation estimation product is formed. Since the Z-R relationship changes due to the change of the raindrop spectrum during the same precipitation process (wuhao et al, 2016), optimizing the Z-R relationship in real time in association with radar and automatic station rainfall data in a business can improve the precision of precipitation estimation, which some studies have verified at present: in the algorithm of the Bin 2014, a coefficient b is fixed, and according to the principle that the average value of the hourly rainfall estimated by the radar is close to the average value of the hourly rainfall observed by the automatic station, the radar and the rainfall data are used for obtaining an optimized value A in real time to estimate the rainfall. Wang et al (2012) estimates two times of strong precipitation occurring in the river basin by a similar method, and the result shows that the method effectively relieves the estimation error and reduces the root mean square error and the average relative error. Libertino et al (2015) calculated the Z-R relationship every 60 minutes using italian C-band dual polarization radar and estimated precipitation, the estimate of lamellar cloud precipitation was reduced by 80% of the estimate bias from the fixed Z-R relationship.
After the initial value field of the rainfall estimation product is obtained, the initial value field is calibrated by using automatic station rainfall data, and the methods comprise an average field deviation correction method (Wilson and Brandes,1979), a Kalman filtering method (Fulton et al, 1998; Ahnert,1986), a variation method (Dengzhao et al, 2000; Zhang Pechang et al, 1992), a Krlijin method (Huang Jade et al, 2009; Krajewski et al, 1987), an optimal interpolation method (Li Jiantong et al, 2000,1996; Daley,1991) and the like, which are researched and found to improve the precision of radar rainfall estimation of a new generation to a certain extent (Donghong et al, 2012; Guanli et al, 2004; Zheng et al, 2004). In the above research, the method for optimizing the precipitation estimation formula in real time by combining the rainfall data of the automatic station is only applied to the Z-R relationship of a single-polarization radar or a dual-polarization radar, and the dual-polarization radar estimates precipitation using a plurality of formulas, which brings great difficulty to real-time correction of the formula, although the raise (2019) tries to correct the precipitation estimation formula containing polarization radar parameters in real time to improve the precision of precipitation estimation, no specific limitation is made on the data used for correcting the formula, so that the data which are not suitable for correcting the formula are mixed in the formula, and meanwhile, the space correction method used by the raise (2019) cannot effectively correct precipitation in a region far away from the radar, and no related technology exists at present to solve the above problems.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art or the related art.
Therefore, the invention aims to provide an application method of the comprehensive quality index in the combined radar and automatic station rainfall estimation, which can further improve the capacity of the networking radar in the rainfall estimation.
In order to achieve the aim, the technical scheme of the invention provides an application method of a comprehensive quality index in combined radar and automatic station rainfall estimation, which comprises the following steps: acquiring 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, wherein the plurality of radar data quality indexes comprise beam shielding influence parameters, bright band or height influence parameters, signal-to-noise ratio influence parameters and non-meteorological echo influence parameters;
the expression of the plurality of radar data quality indexes is as follows:
Figure BDA0003310059610000031
Figure BDA0003310059610000032
Figure BDA0003310059610000033
Figure BDA0003310059610000034
wherein, RQIblkExpressed as the beam occlusion impact parameter, blk expressed as the radar beam occlusion rate, RQIhgtExpressed as a bright band or height-influencing parameter, haRepresenting the radar beam center height, hbDenotes the height of the bottom of the bright band of the zero-degree layer, HsfDenotes the height scale factor, hb<0 indicates that there is no bright band or that the height of the bottom of the bright band cannot be determined, RQIsnrExpressed as signal-to-noise ratio influencing parameter, snr is expressed as signal-to-noise ratio, RQIρHVExpressed as a non-meteorological echo-influencing parameter, pPVExpressed as correlation coefficient, RQIsnrAnd RQIρHVAre all represented by Gaussian functions, snr, and Δ ρHVExpressed as RQI respectivelysnrAnd RQIρHVThreshold of Gaussian function, ZHExpressed as the horizontally polarized reflectance factor, ZDRExpressed as a differential reflectivity factor, KDPExpressed as the differential propagation phase shift rate;
calculating to obtain a comprehensive quality index according to the plurality of radar data quality indexes, and calculating a mean value of the comprehensive quality index;
the expression of the comprehensive quality index is as follows:
RQI＝RQIblk×RQIhgt×RQIsnr×RQIρHV；
the expression of the average value of the comprehensive quality index is as follows:
Figure BDA0003310059610000041
in the formula, n1, n2 and n3 each represent ZH、ZDRAnd KDPThe numbers used in the estimation of precipitation, j1, j2 and j3 are numbered,
Figure BDA0003310059610000042
and
Figure BDA0003310059610000043
respectively represents ZH、ZDRAnd KDPThe quality index of (a);
and correcting the radar estimated precipitation-automatic station observed precipitation data pair according to a first preset threshold corresponding to the average value of the comprehensive quality index to obtain final precipitation estimated data.
In the above technical solution, preferably, the method for correcting the radar estimated precipitation-automatic station observed precipitation data pair according to the first preset threshold corresponding to the average value of the composite quality index to obtain the final precipitation estimated data includes the following steps:
when the first preset threshold value is ARQI not more than 0.4, correcting the precipitation data observed by the automatic station in the radar estimated precipitation-automatic station observed precipitation data pair according to a Classmann interpolation method to obtain the corrected data of the precipitation observed by the automatic station as final precipitation estimated data;
when the first preset threshold value is ARQI larger than 0.9, correcting radar estimated precipitation data in the radar estimated precipitation-automatic station observed precipitation data pair according to an optimal interpolation method to obtain radar estimated precipitation correction data serving as final precipitation estimated data;
when the first preset threshold value is more than 0.4 and less than or equal to 0.9, performing fusion calculation on the observed precipitation correction data of the automatic station and the radar estimated precipitation correction data according to the average value of the comprehensive quality index to obtain a data fusion result as final precipitation estimated data;
the expression of the fusion calculation is: rQPE-Gauge＝(1.8-2ARQI)Rc+(2ARQI-0.8)R0Wherein R isQPE-GaugeRepresenting the result of data fusion, RcIndicating data for correction of precipitation observed at an automatic station, RoIndicating radar estimated precipitation correction data.
In the above technical solution, preferably, the expression of the precipitation correction data observed by the automatic station is as follows:
Figure BDA0003310059610000044
wherein, R is the maximum radius of search, R is the distance between the lattice point and the automatic station, and wiWeight coefficient, G, representing the ith surrounding autonomous stationiIndicating observed rain at the ith automatic stationAn amount;
the expression of the radar estimated precipitation correction data is as follows:
Figure BDA0003310059610000051
wherein R ispEstimating rainfall data for initial quantitative precipitation at grid points, Ri gObserving the rainfall, R, for the automatic stations around the gridi pThe initial rainfall is the radar quantitative estimation rainfall corresponding to the surrounding automatic station coordinates, and P is a weight coefficient obtained in the least mean square meaning of the rainfall of the automatic station and the radar quantitative rainfall estimation rainfall.
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 corresponding to the average value of the composite quality index, the method further includes the following steps:
screening the radar estimated precipitation-automatic station observed precipitation data pair according to a second preset threshold value corresponding to the average value of the comprehensive quality index to obtain a radar estimated precipitation-automatic station observed precipitation evaluation data pair;
calculating according to the radar estimated precipitation-automatic station observation precipitation evaluation data pair to obtain an evaluation parameter;
and correcting coefficients of the radar and the precipitation formula of the automatic station according to the evaluation parameters to obtain the precipitation correction formula.
In any of the above technical solutions, preferably, the method further includes the following steps:
judging whether the radar estimated precipitation-automatic station observation precipitation data pair at the current moment is within a set time interval or not according to the radar estimated precipitation-automatic station observation precipitation data pair at the previous moment;
if not, giving the initial coefficient to the coefficients in the radar and the precipitation correction formula of the automatic station corresponding to the current moment;
if yes, the radar estimated precipitation-automatic station observation precipitation data pair is obtained again according to the precipitation correction formula.
In any of the above technical solutions, preferably, the second preset threshold is ARQI > 0.95; and/or
The expression for the evaluation parameters is:
Figure BDA0003310059610000061
wherein RA isi radarEstimating precipitation data and RA for radari gaugeFor the automatic station to observe precipitation data, bias Ratio is the Ratio of the rainfall estimated by the radar to the rainfall observed by the automatic station;
the coefficient correction expression is:
Figure BDA0003310059610000062
wherein i represents the ith precipitation intensity segment and corresponds to 0-10, 10-20, 20-30, 30-40, 40-50, 50-500(mm/h), a0The multiplier factor representing the default of the precipitation estimation formula, i.e. the initial value before correction, ξi jRepresents a correction coefficient obtained after the j-th estimation of the rainfall capacity, wherein the correction coefficient is the bias Ratio obtained by the segmented evaluationiAnd (4) calculating.
Compared with the prior art, the application method of the comprehensive quality index in the combined radar and automatic station rainfall estimation has the advantages that: when the comprehensive quality index is applied to the process of jointly estimating rainfall by the radar and the automatic station, the capacity of estimating the rainfall by the networking radar can be further improved.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 shows a block flow diagram of a method according to a first embodiment of the invention;
FIG. 2 shows a block flow diagram of a method according to a second embodiment of the invention;
FIG. 3 shows a block flow diagram of a method according to a third embodiment of the invention;
FIG. 4 shows a block flow diagram of a method according to a fourth embodiment of the invention;
FIG. 5 is a line graph illustrating the variation of the precipitation estimation coefficient with time according to an embodiment of the present invention;
fig. 6 shows a plan distribution diagram of the precipitation estimation evaluation parameter bias ratio according to an embodiment of the invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Referring to fig. 1, a method for applying a composite quality index to combined radar and automated station estimation of precipitation according to a first embodiment of the present invention includes the steps of:
s1, acquiring radar estimated precipitation (QPE) -automatic station observed precipitation (Gauge) data pairs in the target area and a plurality of radar data quality indexes corresponding to the radar estimated precipitation-automatic station observed precipitation data pairs;
in the step, the data quality of the dual-polarization radar influences the accuracy of the radar quantitative precipitation estimation, the data comprehensive quality index RQI of the radar is calculated, the data quality of the radar can be effectively reflected, a reference is provided for the application of the radar data, the radar data comprehensive quality index RQI is calculated according to different factors influencing the data quality of the radar,
the plurality of radar data quality indexes comprise a wave 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 expression of the plurality of radar data quality indexes is as follows:
Figure BDA0003310059610000081
Figure BDA0003310059610000082
Figure BDA0003310059610000083
Figure BDA0003310059610000084
wherein, RQIblkExpressed as the beam occlusion impact parameter, blk expressed as the radar beam occlusion rate, RQIhgtExpressed as a bright band or height-influencing parameter, haRepresenting the radar beam center height, hbDenotes the height of the bottom of the bright band of the zero-degree layer, HsfDenotes the height scale factor, hb<0 indicates that there is no bright band or that the height of the bottom of the bright band cannot be determined, RQIsnrExpressed as signal-to-noise ratio influencing parameter, snr is expressed as signal-to-noise ratio, RQIρHVExpressed as a non-meteorological echo-influencing parameter, pPVExpressed as correlation coefficient, RQIsnrAnd RQIρHVAre all represented by Gaussian functions, snr, and Δ ρHVExpressed as RQI respectivelysnrAnd RQIρHVThreshold of Gaussian function, ZHExpressed as the horizontally polarized reflectance factor, ZDRExpressed as a differential reflectivity factor, KDPExpressed as the differential propagation phase shift rate;
s2, calculating to obtain a comprehensive quality index according to the quality indexes of the radar data, and calculating a mean value of the comprehensive quality index;
the expression of the comprehensive quality index is as follows:
RQI＝RQIblk×RQIhgt×RQIsnr×RQIρHV；
the expression of the average value of the comprehensive quality index is as follows:
Figure BDA0003310059610000091
wherein n1, n2 and n3 each represent ZH、ZDRAnd KDPThe numbers used in the estimation of precipitation, j1, j2 and j3 are numbered,
Figure BDA0003310059610000092
and
Figure BDA0003310059610000093
respectively represents ZH、ZDRAnd KDPThe quality index of (a);
in the step, the comprehensive quality index RQI obtained by synthesizing the quality indexes of the radar data is used as the quality index for evaluating radar parameters, the comprehensive quality index RQI corresponding to each radar is obtained, in order to better reflect the quality of a radar quantitative precipitation estimation product, the average value of the comprehensive quality indexes of the radars used for estimating precipitation is recorded as ARQI, and a new radar data comprehensive quality index is formed and used for reflecting the quality of the precipitation product.
And S9, correcting the radar estimated precipitation-automatic station observed precipitation data pair according to the first preset threshold corresponding to the average value of the comprehensive quality index to obtain final precipitation estimated data.
In the foregoing technical solution, as shown in fig. 2, preferably, S8, the method for correcting the radar estimated precipitation-automatic station observed precipitation data pair according to the first preset threshold corresponding to the average value of the composite quality index to obtain the final precipitation estimated data includes the following steps:
s91, when the first preset threshold value is ARQI not more than 0.4, correcting the precipitation data observed by the automatic station in the radar precipitation estimation-automatic station precipitation data pair according to a Classmann interpolation method, and obtaining the precipitation correction data observed by the automatic station as final precipitation estimation data;
s92, when the first preset threshold value is ARQI >0.9, correcting radar estimated precipitation data in the radar estimated precipitation-automatic station observed precipitation data pair according to an optimal interpolation method to obtain radar estimated precipitation correction data serving as final precipitation estimated data;
s93, when the first preset threshold value is more than 0.4 and less than or equal to ARQI and less than or equal to 0.9, performing fusion calculation on the observed precipitation correction data of the automatic station and the radar estimated precipitation correction data according to the average value of the comprehensive quality index to obtain a data fusion result as final precipitation estimation data;
the expression of the fusion calculation is: rQPE-Gauge＝(1.8-2ARQI)Rc+(2ARQI-0.8)R0Wherein R isQPE-GaugeRepresenting the result of data fusion, RcIndicating data for correction of precipitation observed at an automatic station, RoIndicating radar estimated precipitation correction data.
In the above technical solution, preferably, the automatic station observation precipitation correction data R calculated by the klismann interpolation methodcThe expression of (a) is:
Figure BDA0003310059610000101
wherein, R is the maximum radius of search, R is the distance between the lattice point and the automatic station, and wiWeight coefficient, G, representing the ith surrounding autonomous stationiIndicating the observed rainfall of the ith automatic station;
radar estimated precipitation correction data R calculated by adopting optimal interpolation method0The expression of (a) is:
Figure BDA0003310059610000102
wherein R ispEstimating rainfall data for initial quantitative precipitation at grid points, Ri gObserving the rainfall, R, for the automatic stations around the gridi pThe initial rainfall is the radar quantitative estimation rainfall corresponding to the surrounding automatic station coordinates, and P is a weight coefficient obtained in the least mean square meaning of the rainfall of the automatic station and the radar quantitative rainfall estimation rainfall.
In any of the above technical solutions, as shown in fig. 3, preferably, before correcting the radar estimated precipitation-automatic station observed precipitation data pair according to the first preset threshold corresponding to the average value of the composite quality index at S9, the method further includes the following steps:
s3, screening the radar estimated precipitation-automatic station observed precipitation data pair according to a second preset threshold value corresponding to the average value of the comprehensive quality index to obtain a radar estimated precipitation-automatic station observed precipitation evaluation data pair;
s4, calculating according to the radar estimated precipitation-automatic station observation precipitation evaluation data pair to obtain evaluation parameters;
and S5, correcting coefficients of the radar and the precipitation formula of the automatic station according to the evaluation parameters to obtain the precipitation correction formula.
In any of the above technical solutions, preferably, as shown in fig. 4, the method further includes the following steps:
s6, judging whether the radar estimated precipitation-automatic station observation precipitation data pair at the current moment is in a set time interval or not according to the radar estimated precipitation-automatic station observation precipitation data pair at the previous moment;
if not, S7, giving the coefficients in the precipitation correction formula corresponding to the radar and the automatic station at the current moment to the initial coefficients;
if yes, S8, the radar estimated precipitation-automatic station observation precipitation data pair is obtained again according to the precipitation correction formula.
In any of the above technical solutions, preferably, the second preset threshold is ARQI > 0.95; and/or
The expression for the evaluation parameters is:
Figure BDA0003310059610000111
wherein RA isi radarEstimating precipitation data and RA for radari gaugeFor the automatic station to observe precipitation data, bias Ratio is the Ratio of the rainfall estimated by the radar to the rainfall observed by the automatic station;
the coefficient correction expression is:
Figure BDA0003310059610000112
wherein i represents the ith precipitation intensityDegree segmentation, which respectively corresponds to 0-10, 10-20, 20-30, 30-40, 40-50, 50-500(mm/h), a0The multiplier factor representing the default of the precipitation estimation formula, i.e. the initial value before correction, ξi jRepresents a correction coefficient obtained after the j-th estimation of the rainfall capacity, wherein the correction coefficient is the bias Ratio obtained by the segmented evaluationiAnd (4) calculating.
Compared with the prior art, the application method of the comprehensive quality index in the combined radar and automatic station rainfall estimation has the advantages that: when the comprehensive quality index is applied to the process of jointly estimating rainfall by the radar and the automatic station, the capacity of estimating the rainfall by the networking radar can be further improved.
Comparative test
In order to verify the effect of the technical scheme of the invention, a plurality of groups of tests are set, and the specific method is as follows:
TABLE 1 several groups of comparative test methods tabulated
Figure BDA0003310059610000121
Test set 1 only uses radar to estimate precipitation; in the 2 nd and 3 rd groups of tests, the automatic station data are used for correcting the precipitation estimation formula in real time, no space correction is carried out, but the formula coefficient is adjusted after all data are evaluated in the 2 nd group, the formula coefficient is only evaluated and adjusted in the 3 rd group of tests on data with better quality (ARQI >0.9), and the function of the ARQI in the process of correcting the formula is verified through the comparison of the two groups of tests; the tests of groups 4 and 5 are all based on the test of group 3, and the spatial correction is carried out, but the optimal interpolation method is used in the 4 th group, and the correction is carried out according to ARQI size segmentation in the 5 th group, namely, the new method is provided, and the comparison of the two groups of tests verifies the function of ARQI in the spatial correction.
Evaluation method
After the rainfall estimation result is obtained through radar data calculation, the rainfall estimation result needs to be evaluated, rainfall accumulated by automatic stations for one hour is selected as a true value during evaluation, and each automatic station corresponds to 9 radar distance libraries: directly above the automatic stationAnd 8 distance bins around the distance bin, wherein the average value of the rainfall per hour of the 9 distance bins is used as an estimated value of the radar to be evaluated so as to eliminate the drift of raindrops caused by horizontal wind. In addition, considering that the average error value is lowered when the 0.1mm automatic station observes a large amount of rainfall for 1 hour, only the rainfall value larger than 0.1mm is used for the evaluation. By RAi radar(mm) represents the cumulative 1 hour estimated rainfall for the radar, using RAi gauge(mm) represents the cumulative observed rainfall of the automatic station for 1 hour, and the estimated value RA is finally obtainedi radarAnd the observed value RAi gaugeIs used for QPE evaluation.
Evaluating the estimation result by using the Correlation Coefficient (CC), the Root Mean Square Error (RMSE), the normalized relative error (NB), the normalized absolute error (NE) and the Ratio (bias Ratio) of the radar estimated rainfall to the automatic station observed rainfall, wherein the expression of each evaluation parameter is as follows:
Figure BDA0003310059610000131
Figure BDA0003310059610000132
Figure BDA0003310059610000133
Figure BDA0003310059610000134
Figure BDA0003310059610000135
CC represents the degree of correlation between the estimated measurement and the true value, the closer the value to 1, the higher the correlation between the estimated measurement and the true value, the better the estimation effect, the positive value of NB (%) can be positive or negative, the positive value represents overestimation, the negative value represents underestimation, the closer to 0, the smaller the error is, the RMSE (mm) and NE (%) are both non-negative values, the smaller the value is, the smaller the error is, but the RMSE is influenced by the intensity of precipitation, and the NE is not influenced by the error. The bias Ratio can evaluate the estimation effect at a certain automatic station, the value of which is more than 1 represents overestimation and less than 1 represents underestimation, and the bias Ratio is mainly used for analyzing the plane distribution of the estimation effect.
Effects of the embodiment
Strong precipitation occurs in the Guangdong area of 12.6.6.2019 (UTC) and is estimated according to a set comparison test, the precipitation is estimated within the range of ARQI >0.9, and the precipitation estimation result is shown in a curve chart of the precipitation estimation coefficients (a) NB (b) RMSE (c) NE (d) CC along with the time in 12.6.2019 (UTC) in FIG. 5. Different colors represent different test methods, purple: run 1, green: run 2, yellow: run 3, red: experiment 4 compares experiments 1 and 2, when the formula is fixed by using all data, NB, NE and RMSE increase, CC decreases, overestimation situation is more prominent, error becomes larger, and effect becomes worse, because all data contain radar data with poor quality, the fixed formula coefficient cannot normally reflect the change of the raindrop spectrum. And after the test 3 is added with the restriction of ARQI >0.95, the influence of poor radar data on the precipitation estimation result is greatly eliminated, so that the corrected formula coefficient is more reasonable, therefore, NB is closer to 0, the overestimation condition is improved, NE is reduced, and the error is reduced. On the basis of the test 3, the test 4 adds spatial correction to the precipitation estimation result, NB is further close to 0, NE and RMSE are further reduced, CC is increased, and the estimation effect is further improved. In addition, since the evaluation is performed in the range of ARQI >0.9, the data processing methods of the tests 4 and 5 are consistent with each other in the range, and the results are not shown.
FIG. 6 shows the planar distribution of the precipitation estimation evaluation parameter bias ratio in 5 trials, 6, 12, 6, 24, 00(UTC) in 2019, indicated by colored circles, with color indicating the parameter value, the size of the circle indicating the average hourly rainfall for the automatic station, and (a) - (e) indicating the evaluation results of trials 1-5, respectively; (f) the plane distribution of ARQI is shown, similar to the situation shown in fig. 2, in experiment 2, the overestimation situation in experiment 1 is more serious than that in the region with larger ARQI, the overestimation situation in experiment 3 is relieved, and in experiment 4, the obvious regional overestimation is hardly seen, and particularly, it needs to be noted that in experiment 5, the situation that the estimation effect of the whole region is improved because the ARQI segments are used for correction and fusion is obviously improved compared with the situation that the ARQI segment in the periphery of experiment 4 is obviously underestimated, so that the estimation effect of the whole region is improved.
In the present invention, the terms "first", "second", and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance; the term "plurality" means two or more unless expressly limited otherwise. The terms "mounted," "connected," "fixed," and the like are to be construed broadly, and for example, "connected" may be a fixed connection, a removable connection, or an integral connection; "coupled" may be direct or indirect through an intermediary. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "left", "right", "front", "rear", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply that the referred device or unit must have a specific direction, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
In the description herein, the description of the terms "one embodiment," "some embodiments," "specific embodiments," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. An application method of a comprehensive quality index in combined radar and automatic station rainfall estimation is characterized by comprising the following steps:
acquiring 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 comprise 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 expression of the plurality of radar data quality indexes is as follows:
Figure FDA0003310059600000011
Figure FDA0003310059600000012
Figure FDA0003310059600000013
Figure FDA0003310059600000014
wherein, RQIblkExpressed as the beam occlusion impact parameter, blk expressed as the radar beam occlusion rate, RQIhgtExpressed as a bright band or height-influencing parameter, haRepresenting the radar beam center height, hbDenotes the height of the bottom of the bright band of the zero-degree layer, HsfRepresenting height scale factor，hb<0 indicates that there is no bright band or that the height of the bottom of the bright band cannot be determined, RQIsnrExpressed as signal-to-noise ratio influencing parameters, snr represents the signal-to-noise ratio,
Figure FDA0003310059600000015
expressed as a non-meteorological echo-influencing parameter, pPVExpressed as correlation coefficient, RQIsnrAnd
Figure FDA0003310059600000016
are all represented by Gaussian functions, snr, and Δ ρHVExpressed as RQI respectivelysnrAnd
Figure FDA0003310059600000024
threshold of Gaussian function, ZHExpressed as the horizontally polarized reflectance factor, ZDRExpressed as a differential reflectivity factor, KDPExpressed as the differential propagation phase shift rate;
calculating to obtain the comprehensive quality index according to the plurality of radar data quality indexes and calculating the average value of the comprehensive quality index;
the expression of the comprehensive quality index is as follows:
Figure FDA0003310059600000025
the expression of the average value of the comprehensive quality index is as follows:
Figure FDA0003310059600000021
wherein n1, n2 and n3 each represent ZH、ZDRAnd KDPThe numbers used in the estimation of precipitation, j1, j2 and j3 are numbered,
Figure FDA0003310059600000022
and
Figure FDA0003310059600000023
respectively represents ZH、ZDRAnd KDPThe quality index of (a);
and correcting the radar estimated precipitation-automatic station observed precipitation data pair according to a first preset threshold corresponding to the average value of the comprehensive quality index to obtain final precipitation estimated data.
2. The method of claim 1, wherein the radar-estimated precipitation-automatic station-observed precipitation data pair is corrected according to a first predetermined threshold corresponding to the average value of the composite quality index to obtain final precipitation estimation data, and the method comprises the steps of:
when the first preset threshold value is ARQI not more than 0.4, correcting the observed precipitation data of the automatic station in the radar precipitation estimation-automatic station observed precipitation data pair according to a Classmann interpolation method to obtain the observed precipitation correction data of the automatic station as the final precipitation estimation data;
when the first preset threshold value is ARQI larger than 0.9, correcting the radar estimated precipitation data in the radar estimated precipitation-automatic station observed precipitation data pair according to an optimal interpolation method to obtain radar estimated precipitation correction data serving as the final precipitation estimated data;
when the first preset threshold value is more than 0.4 and less than or equal to 0.9, performing fusion calculation on 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;
the expression of the fusion calculation is: rQPE-Gauge＝(1.8-2ARQI)Rc+(2ARQI-0.8)R0Wherein R isQPE-GaugeRepresenting the result of data fusion, RcIndicating data for correction of precipitation observed at an automatic station, RoRepresenting the radar estimated precipitation correction data.
3. The method of using the composite quality index of claim 2 in the combined radar and automated station estimation of precipitation, wherein:
the expression of the automatic station observation precipitation correction data is as follows: (i.e., R)cOr the expression of Kreismann interpolation
Figure FDA0003310059600000031
Figure FDA0003310059600000032
Wherein, R is the maximum radius of search, R is the distance between the lattice point and the automatic station, and wiWeight coefficient, G, representing the ith surrounding autonomous stationiIndicating the observed rainfall of the ith automatic station;
the radar estimated precipitation correction data is expressed as (i.e., R)0Or an expression for optimal interpolation):
Figure FDA0003310059600000033
wherein R ispEstimating rainfall data for initial quantitative precipitation at grid points, Ri gObserving the rainfall, R, for the automatic stations around the gridi pThe initial rainfall is the radar quantitative estimation rainfall corresponding to the surrounding automatic station coordinates, and P is a weight coefficient obtained in the least mean square meaning of the rainfall of the automatic station and the radar quantitative rainfall estimation rainfall.
4. The method of any one of claims 1 to 3, wherein the method of applying the composite quality index to combine radar and automatic station estimated precipitation further comprises the steps of, before correcting the radar estimated precipitation-automatic station observed precipitation data pair according to a first predetermined threshold corresponding to the average value of the composite quality index:
screening the radar estimated precipitation-automatic station observed precipitation data pair according to a second preset threshold corresponding to the average value of the comprehensive quality index to obtain a radar estimated precipitation-automatic station observed precipitation evaluation data pair;
calculating according to the radar estimated precipitation-automatic station observation precipitation evaluation data pair to obtain an evaluation parameter;
and correcting coefficients of the radar and the precipitation formula of the automatic station according to the evaluation parameters to obtain the precipitation correction formula.
5. The method of using the composite quality index for combined radar and automated station estimation of precipitation according to claim 4, further comprising the steps of:
judging whether the radar estimated precipitation-automatic station observed precipitation data pair at the current moment is within a set time interval or not according to the radar estimated precipitation-automatic station observed precipitation data pair at the previous moment;
if not, giving an initial coefficient to the coefficients in the precipitation correction formulas of the radar and the automatic station corresponding to the current moment;
if yes, the radar estimated precipitation-automatic station observation precipitation data pair is obtained again according to the precipitation correction formula.
6. The method of claim 3, wherein the second predetermined threshold is ARQI > 0.95; and/or
The expression of the evaluation parameter is as follows:
Figure FDA0003310059600000041
wherein RA isi radarEstimating precipitation data and RA for radari gaugeFor the automatic station to observe precipitation data, bias Ratio is the Ratio of the rainfall estimated by the radar to the rainfall observed by the automatic station;
the formula of the coefficient correction is (how to perform correction or the formula involved in the correction process needs to be supplemented):
Figure FDA0003310059600000042
Figure FDA0003310059600000043
wherein i represents the ith precipitation intensity segment and corresponds to 0-10, 10-20, 20-30, 30-40, 40-50, 50-500(mm/h), a0The multiplier factor representing the default of the precipitation estimation formula, i.e. the initial value before correction, ξi jRepresents a correction coefficient obtained after the j-th estimation of the rainfall capacity, wherein the correction coefficient is the bias Ratio obtained by the segmented evaluationiAnd (4) calculating.
CN202111214368.4A 2021-10-19 2021-10-19 Application method of comprehensive quality index in combined radar and automatic station rainfall estimation Pending CN113960606A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
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CN114114198A (en) * 2022-01-27 2022-03-01 江西省气象信息中心(江西省气象培训中心、江西省农村经济信息中心) Precipitation data quality control method and device, storage medium and equipment
CN114417264A (en) * 2022-03-28 2022-04-29 中国气象科学研究院 Raindrop spectrum inversion method and device
CN114565330A (en) * 2022-04-30 2022-05-31 江西省气象信息中心(江西省气象培训中心、江西省农村经济信息中心) Health degree evaluation method, system, equipment and storage medium of precipitation observation equipment

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114114198A (en) * 2022-01-27 2022-03-01 江西省气象信息中心(江西省气象培训中心、江西省农村经济信息中心) Precipitation data quality control method and device, storage medium and equipment
CN114114198B (en) * 2022-01-27 2022-05-03 江西省气象信息中心(江西省气象培训中心、江西省农村经济信息中心) Precipitation data quality control method and device, storage medium and equipment
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CN114417264B (en) * 2022-03-28 2022-07-08 中国气象科学研究院 Raindrop spectrum inversion method and device
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