CN113177360A - Rain intensity self-adaptive estimation system and method based on clustering rain attenuation relation - Google Patents
Rain intensity self-adaptive estimation system and method based on clustering rain attenuation relation Download PDFInfo
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Abstract
The invention discloses a rain intensity self-adaptive estimation system and method based on a clustering rain attenuation relation, wherein the system comprises the following components in sequential connection: the system comprises a microwave communication network, a data acquisition and processing terminal, a clustering unit and a rain intensity inversion unit; the method comprises the steps that a data acquisition and processing terminal obtains the rain attenuation rate in an area to be measured and a plurality of groups of historical rainfall periods based on level signals of transmitting terminals and receiving terminals of links in a microwave communication network, and the historical rain attenuation rate is constructed into a rainfall sample set; the clustering unit divides the rainfall samples into k classes by adopting a clustering method, and automatically classifies the rainfall attenuation rate of the area to be detected according to the minimum distance principle; and the rainfall intensity inversion unit respectively establishes rainfall attenuation relations for various rainfall samples by using a least square method and performs rainfall intensity inversion on the area to be detected. The rainfall sensing field can be widely applied.
Description
Technical Field
The invention relates to the technical field of rain intensity self-adaptive estimation, in particular to a rain intensity self-adaptive estimation system and method based on a clustering rain attenuation relation.
Background
The monitoring of urban hydrological information requires acquiring rainfall data with high spatial and temporal resolution. Although weather radars can meet this requirement, they require calibration and frequent maintenance, and only sparsely deployed rain gauges are currently available for calibrating weather radars. The existing commercial communication network microwave signals can be subjected to the attenuation effect of rainfall on a path in the transmission process, and according to the basic principle, the rainfall information monitoring based on the commercial communication network can be realized. Because the network covers most of the land surface of the earth and has high density (especially in the urban area), the rainfall information acquisition requirement of near-ground, low cost and high space-time resolution in the global range can be met.
The inversion of rainfall intensity based on microwave link attenuation information generally utilizes the rain attenuation-rain intensity empirical power law relationship (ITU-R P.838-3) recommended by the international telecommunication union, but the accuracy of the relationship is influenced by the distribution of raindrop spectrums. When the microwave operating frequency is below 40GHz (the frequency band range operated by the traditional commercial communication network), the influence of the distribution of the raindrop spectrum on the relation is limited, so that the estimation error of the raininess and the accumulated rainfall is small. But when the frequency is more than 40GHz, the influence of the raindrop spectrum distribution is not negligible. With the increasing popularity of 5G communication technologies, microwave backhaul links in higher frequency bands, such as 50GHz, 60GHz, E-band (71-76GHz and 81-86GHz), and 92-95GHz, will be widely deployed worldwide. Therefore, in order that the high-frequency microwave link can also be applied to accurate quantitative rainfall estimation, the existing rain attenuation relation needs to be improved.
Disclosure of Invention
The invention aims to provide a rain intensity self-adaptive estimation system and method based on a clustering rain attenuation relation, which are used for solving the technical problems in the prior art, can be suitable for the rain attenuation relation of various rainfall samples, further realize the refined quantitative estimation of rainfall and can be widely applied to the field of meteorological information remote sensing such as rainfall information monitoring.
In order to achieve the purpose, the invention provides the following scheme: the invention provides a rain intensity self-adaptive estimation system based on a clustering rain attenuation relation, which comprises the following components in sequential connection: the system comprises a microwave communication network, a data acquisition and processing terminal, a clustering unit and a rain intensity inversion unit;
the microwave communication network comprises a plurality of links, and each link comprises a receiving end and a transmitting end;
the data acquisition and processing terminal respectively acquires the rain attenuation rate of the area to be measured and the rain attenuation rates in a plurality of groups of historical rainfall periods based on level signals of the transmitting end and the receiving end of each link in the microwave communication network, and constructs the rain attenuation rates in the plurality of groups of historical rainfall periods as a rainfall sample set;
the clustering unit takes the rain attenuation rate of each link as a characteristic quantity, and a clustering method is adopted to divide the rainfall samples in the rainfall sample set into k types; the clustering unit is also used for automatically classifying the rain attenuation rate of the region to be detected according to the minimum distance principle based on various clustering centers;
the rainfall intensity inversion unit respectively establishes a rainfall attenuation relation for various rainfall samples in the rainfall sample set by using a least square method; and the rainfall intensity inversion unit is also used for performing rainfall intensity inversion on the area to be detected by adopting a corresponding rainfall attenuation relation based on the automatic classification result of the rainfall attenuation rate of the area to be detected.
Preferably, the data acquisition and processing terminal comprises a data acquisition unit, a link attenuation acquisition unit and a rain attenuation rate acquisition unit which are connected in sequence; the data acquisition unit is respectively connected with a receiving end and a transmitting end of each link in the microwave communication network, and the rain attenuation unit is connected with the clustering unit;
the data acquisition unit is used for acquiring the transmitting level and the receiving level of each link in the microwave communication network;
the link attenuation obtaining unit calculates the total attenuation of each link based on the transmitting level and the receiving level of each link;
the rain attenuation rate obtaining unit extracts the average rain attenuation rate of each link based on the total attenuation of each link.
Preferably, the number of links in the microwave communication network includes, but is not limited to, 1.
The invention also provides a rain intensity self-adaptive estimation method based on the clustering rain attenuation relation, which is characterized by comprising the following steps of:
s1, acquiring a plurality of sets of level signals of a link receiving end and a transmitting end of the microwave communication network in the historical rainfall period by using the data acquisition processing terminal, and extracting the rain attenuation rate based on the level signals of the link receiving end and the transmitting end of the microwave communication network;
s2, taking the rain attenuation rate of each link as a characteristic quantity, and dividing the rainfall samples in the rainfall sample set into k types by adopting a clustering method;
s3, respectively establishing rain attenuation relations for various rainfall samples in the rainfall sample set by using a least square method;
and S4, acquiring the rain attenuation rate of the area to be detected, automatically classifying the rain attenuation rate of the area to be detected according to the minimum distance principle based on various clustering centers in the step S2, and performing rainfall intensity inversion on the area to be detected by adopting a corresponding rain attenuation relation based on a classification result.
Preferably, the step S1 specifically includes the following steps:
s1.1, selecting n links in a microwave communication network in an area, wherein the microwave frequencies transmitted by the n links are f1，f2，…，fnIn GHz units and polarization mode of alpha1，α2，…，αn(ii) a At the same time, the transmitting levels of the n links are tx1，tx2，…，txnIn dB, the received levels are rx after passing through the rainfall space1，rx2，…，rxnIn dB;
s1.2, calculating total attenuation A of each link based on the transmitting level and the receiving level of each linkiThe unit dB is shown as follows:
Ai＝txi-rxi(i＝1,…,n)；
s1.3, extracting the average rain attenuation rate gamma of each link based on the total attenuation of each linkiAs shown in the following formula:
γi＝(Ai-Aref,i)/Li(i＝1,...,n)
wherein A isref,iRepresents the reference attenuation value of the ith link in dB and LiIndicating the length of the ith link.
Preferably, the polarization mode includes horizontal polarization and vertical polarization.
Preferably, in step S2, the clustering method includes, but is not limited to, a fuzzy C-means clustering method.
Preferably, in step S3, the rain attenuation relationship includes, but is not limited to, a single-link power law relationship.
Preferably, in step S3, the method for respectively establishing a rain attenuation relationship for each type of rainfall sample in the rainfall sample set by using a least square method is as follows:
wherein, alpha and beta are rain attenuation coefficient, Rinverse,tAnd Rreal,t(mm h-1) Respectively is the rainfall intensity estimated value and the true value of the t-th rainfall sample in a certain type of rainfall sample, and s is the number of the certain type of rainfall sample.
The invention discloses the following technical effects:
the invention provides a rain intensity self-adaptive estimation system and method based on a clustering rain attenuation relation, wherein the average rain attenuation rate is extracted as characteristic quantity by collecting level signals of a link receiving end and a link transmitting end of a microwave communication network; according to the basic idea of 'clustering by things', clustering rainfall samples by adopting a clustering method, and fitting a clustering rain attenuation relation by utilizing a least square method; according to the rainfall sensing method, the rainfall attenuation relation suitable for various rainfall samples is established in a clustering mode, further refined quantitative estimation of rainfall is achieved, and the rainfall sensing method can be widely applied to the field of meteorological information remote sensing such as rainfall information monitoring. Meanwhile, in the application process of the method provided by the invention, the rainfall samples can be automatically classified only by inputting the rainfall attenuation characteristic quantity of each link, and then the rainfall intensity is inverted by adopting the corresponding rainfall attenuation relation, so that the uncertainty of the traditional single-link power law rainfall attenuation relation is greatly reduced. The method can be applied to actual services as a novel microwave link quantitative precipitation inversion method.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a rain intensity adaptive estimation system based on a clustering rain attenuation relation according to the present invention;
FIG. 2 is a flow chart of the present invention for determining a refined rain attenuation relationship based on a clustering method;
FIG. 3 is a flow chart of the adaptive estimation of rain intensity based on the relation of rain attenuation in clusters according to the present invention;
fig. 4 is a schematic diagram of the adaptive estimation of rain intensity based on the clustering rain attenuation relationship (when k is 3);
FIG. 5(a) is a result of adaptive evaluation of rain intensity based on clustering rain attenuation relationship in an example of the present invention; fig. 5(b) shows the evaluation result of the cumulative rainfall based on the clustering rain attenuation relation in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Referring to fig. 1, the embodiment provides a rain intensity adaptive estimation system based on a clustering rain attenuation relationship, including sequentially connected: the system comprises a microwave communication network, a data acquisition and processing terminal, a clustering unit and a rain intensity inversion unit;
the microwave communication network comprises a plurality of links, and each link comprises a receiving end and a transmitting end; the number of links in the microwave communication network includes, but is not limited to, 1.
The data acquisition and processing terminal respectively acquires the rain attenuation rate of the area to be measured and the rain attenuation rates in a plurality of groups of historical rainfall periods based on level signals of the transmitting end and the receiving end of each link in the microwave communication network, and constructs the rain attenuation rates in the plurality of groups of historical rainfall periods as a rainfall sample set.
The data acquisition and processing terminal comprises a data acquisition unit, a link attenuation acquisition unit and a rain attenuation rate acquisition unit which are sequentially connected; the data acquisition unit is respectively connected with a receiving end and a transmitting end of each link in the microwave communication network, and the rain attenuation unit is connected with the clustering unit;
the data acquisition unit is used for acquiring the transmitting level and the receiving level of each link in the microwave communication network; selecting n links in a microwave communication network, wherein the microwave frequencies transmitted by the n links are f1，f2，…，fn(unit: GHz), polarization modes are respectively alpha1，α2，…，αn(typically horizontally or vertically polarized). At the same time, the transmitting levels of the n links are tx1，tx2，…，txn(unit: dB), after passing through the rainfall space, the receiving levels are rx respectively1，rx2，…，rxn(unit: dB).
The link attenuation obtaining unit calculates the total attenuation of each link based on the transmitting level and the receiving level of each link; total attenuation a of each linkiAs shown in the following formula:
Ai＝txi-rxi(i＝1,…,n)；
the rain attenuation rate obtaining unit extracts the average rain attenuation rate of each link based on the total attenuation of each link. Average rain attenuation rate gamma of each linkiSuch asRepresented by the formula:
γi＝(Ai-Aref,i)/Li(i＝1,...,n)
wherein A isref,iRepresents the reference attenuation value of the ith link in dB and LiIndicating the length of the ith link.
The clustering unit takes the rain attenuation rate of each link as a characteristic quantity, and a clustering method is adopted to divide the rainfall samples in the rainfall sample set into k types; the clustering unit is also used for automatically classifying the rain attenuation rate of the region to be detected according to the minimum distance principle based on various clustering centers; wherein the clustering method includes, but is not limited to, fuzzy C-means clustering method.
The rainfall intensity inversion unit respectively establishes a rainfall attenuation relation for various rainfall samples in the rainfall sample set by using a least square method; the rainfall intensity inversion unit is also used for carrying out rainfall intensity inversion on the area to be detected by adopting a corresponding rainfall attenuation relation based on the automatic classification result of the rainfall attenuation rate of the area to be detected; wherein the rain fade relationship includes, but is not limited to, a single link power law relationship.
The embodiment shown in fig. 2 to 4 further provides a method for adaptively estimating the rain intensity based on the clustering rain attenuation relationship, which includes the following steps:
s1, acquiring a plurality of sets of level signals of a link receiving end and a transmitting end of the microwave communication network in the historical rainfall period by using the data acquisition and processing terminal, extracting the rain attenuation rate based on the level signals of the link receiving end and the transmitting end of the microwave communication network, and constructing the rain attenuation rate into a rainfall sample set; the method specifically comprises the following steps:
s1.1, selecting n links in a microwave communication network in an area, wherein the microwave frequencies transmitted by the n links are f1，f2，…，fn(GHz) polarization modes are respectively alpha1，α2，…，αn(typically horizontally or vertically polarized). At the same time, the transmitting levels of the n links are tx1，tx2，…，txn(dB), after passing through the rainfall space, the receiving levels are rx respectively1，rx2，…，rxn(dB)；
S1.2, calculating total attenuation A of each link based on the transmitting level and the receiving level of each linkiThe unit dB is shown as follows:
Ai＝txi-rxi(i＝1,…,n)；
s1.3, extracting the average rain attenuation rate gamma of each link based on the total attenuation of each linkiAs shown in the following formula:
γi＝(Ai-Aref,i)/Li(i＝1,...,n)
wherein A isref,iRepresents the reference attenuation value of the ith link in dB and LiIndicating the length of the ith link.
In this embodiment, 2 links are selected in a microwave communication network in an area to be measured, the transmitted microwave frequencies are 15GHz and 81GHz respectively, the polarization modes are horizontal polarizations, the link lengths are 1Km, and the transmission levels of the 2 links are tx respectively at the same time1And tx2(dB), after passing through the rainfall space, the receiving levels are rx respectively1And rx2(dB)；
Based on the transmit and receive levels of the 2 links, the total attenuation (dB) for each link is calculated individually:
Ai＝txi-rxi(i＝1,2)；
extracting the average rain attenuation rate (dB/Km) of 2 links:
γi＝(Ai-Aref,i)/Li(i＝1,2)
wherein A isref,iCan be determined from the median of the average link total attenuation over the hour of the week;
s2, taking the rain attenuation rate of each link as a characteristic quantity, and dividing the rainfall samples in the rainfall sample set into k types by adopting a clustering method;
wherein the characteristic amount Xj＝[γ1,γ2,…,γn]，j∈[1,m]And m is the number of rainfall samples in the rainfall sample set, and the clustering method comprises but is not limited to a fuzzy C-means clustering method.
Fuzzy c-means clustering fuses the essence of fuzzy theory. Fuzzy c-means clustering provides a more flexible clustering result than hard clustering of k-means. Since in most cases objects in a data set cannot be divided into clearly separated clusters, assigning an object to a particular cluster is somewhat rigid and may also be subject to error. Therefore, a weight is assigned to each object and each cluster, indicating the extent to which the object belongs to the cluster. Of course, probability-based methods can also give such weights, but sometimes it is difficult to determine a suitable statistical model, so using a fuzzy c-means with natural, non-probabilistic characteristics is a good choice.
The fuzzy C-means clustering method is realized by defining an objective function J (u, C):
ujp＝||Xj-cp||
wherein d isjp＝||Xj-cp||，cpAs the cluster center of the p-th cluster, ujpMembership to the pth cluster for the jth rainfall sample
b is a weighting parameter (b.gtoreq.1). Then, calculating and replacing each sample membership according to Lagrange number multiplication, as shown in the following formula:
wherein λ is a constant. On this basis, the cluster centers are calculated and replaced according to the following formula:
the sample membership degree and the clustering center value are alternately updated through the formula until the maximum iteration times is reached or the algorithm is converged (the objective function value is reduced by less than 10 when the discrimination condition is continuously carried out twice)-6) And finishing the clustering of the rainfall samples.
In this embodiment, the rain attenuation rate of 2 links is used as the eigenvector Xj＝[γ1,γ2]According to the fuzzy C-means clustering method, the number k of clustering clusters is set to be 10, and m historical rainfall samples are divided into 10 types.
S3, respectively establishing rain attenuation relations for various rainfall samples in the rainfall sample set by using a least square method; wherein the rain fade relationship includes, but is not limited to, a single link power law relationship.
In this embodiment, the least square method is used to determine the rain attenuation relation R corresponding to each type of rainfall sampleinverse＝aγβ(81 GHz rain attenuation was chosen here):
wherein alpha and beta are rain attenuation coefficients related to frequency, polarization mode and raindrop spectrum, and R isinverse,tAnd Rreal,t(mm h-1) Respectively is the rainfall intensity estimated value and the true value of the t-th rainfall sample in a certain type of rainfall sample, and s is the number of the certain type of rainfall sample.
And S4, acquiring the rain attenuation rate of the area to be detected, automatically classifying the rain attenuation rate of the area to be detected according to the minimum distance principle based on various clustering centers in the step S2, and performing rainfall intensity inversion on the area to be detected by adopting a corresponding rain attenuation relation based on a classification result.
In the practical application process, by extracting the rain attenuation characteristics of a new rainfall sample, one close to the new rainfall sample is searched in 10 clustering centers, and the type is used as the type of the rainfall sample. And then, the 81GHz rain attenuation relation corresponding to the class is used for inverting the rain intensity value. Fig. 5(a) and 5(b) are adaptive evaluation results of rain intensity and accumulated rainfall based on the clustering rain attenuation relationship.
The invention has the following technical effects:
the invention provides a rain intensity self-adaptive estimation method based on a clustering rain attenuation relation, which extracts an average rain attenuation rate as a characteristic quantity by acquiring level signals of a link receiving end and a transmitting end of a microwave communication network; according to the basic idea of 'clustering by things', clustering rainfall samples by adopting a clustering method, and fitting a clustering rain attenuation relation by utilizing a least square method; according to the rainfall sensing method, the rainfall attenuation relation suitable for various rainfall samples is established in a clustering mode, further refined quantitative estimation of rainfall is achieved, and the rainfall sensing method can be widely applied to the field of meteorological information remote sensing such as rainfall information monitoring. Meanwhile, in the application process of the method provided by the invention, the rainfall samples can be automatically classified only by inputting the rainfall attenuation characteristic quantity of each link, and then the rainfall intensity is inverted by adopting the corresponding rainfall attenuation relation, so that the uncertainty of the traditional single-link power law rainfall attenuation relation is greatly reduced. The method can be applied to actual services as a novel microwave link quantitative precipitation inversion method.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (9)
1. A rain intensity self-adaptive estimation system based on clustering rain attenuation relation is characterized by comprising the following components in sequential connection: the system comprises a microwave communication network, a data acquisition and processing terminal, a clustering unit and a rain intensity inversion unit;
the microwave communication network comprises a plurality of links, and each link comprises a receiving end and a transmitting end;
the data acquisition and processing terminal respectively acquires the rain attenuation rate of the area to be measured and the rain attenuation rates in a plurality of groups of historical rainfall periods based on level signals of the transmitting end and the receiving end of each link in the microwave communication network, and constructs the rain attenuation rates in the plurality of groups of historical rainfall periods as a rainfall sample set;
the clustering unit takes the rain attenuation rate of each link as a characteristic quantity, and a clustering method is adopted to divide the rainfall samples in the rainfall sample set into k types; the clustering unit is also used for automatically classifying the rain attenuation rate of the region to be detected according to the minimum distance principle based on various clustering centers;
the rainfall intensity inversion unit respectively establishes a rainfall attenuation relation for various rainfall samples in the rainfall sample set by using a least square method; and the rainfall intensity inversion unit is also used for performing rainfall intensity inversion on the area to be detected by adopting a corresponding rainfall attenuation relation based on the automatic classification result of the rainfall attenuation rate of the area to be detected.
2. The system according to claim 1, wherein the data acquisition and processing terminal comprises a data acquisition unit, a link attenuation acquisition unit, and a rain attenuation rate acquisition unit, which are connected in sequence; the data acquisition unit is respectively connected with a receiving end and a transmitting end of each link in the microwave communication network, and the rain attenuation unit is connected with the clustering unit;
the data acquisition unit is used for acquiring microwave frequency transmitting level and receiving level of each link in a microwave communication network;
the link attenuation obtaining unit calculates the total attenuation of each link based on the transmitting level and the receiving level of each link;
the rain attenuation rate obtaining unit extracts the average rain attenuation rate of each link based on the total attenuation of each link.
3. A system for adaptive estimation of rain intensity based on clustered rain fade relationship according to claim 1, wherein the number of links in the microwave communication network includes but is not limited to 1.
4. A method for self-adaptive estimation of rain intensity based on clustering rain decay relation according to any one of claims 1-3, characterized by comprising the following steps:
s1, acquiring a plurality of sets of level signals of a link receiving end and a transmitting end of the microwave communication network in the historical rainfall period by using the data acquisition processing terminal, and extracting the rain attenuation rate based on the level signals of the link receiving end and the transmitting end of the microwave communication network;
s2, taking the rain attenuation rate of each link as a characteristic quantity, and dividing the rainfall samples in the rainfall sample set into k types by adopting a clustering method;
s3, respectively establishing rain attenuation relations for various rainfall samples in the rainfall sample set by using a least square method;
and S4, acquiring the rain attenuation rate of the area to be detected, automatically classifying the rain attenuation rate of the area to be detected according to the minimum distance principle based on various clustering centers in the step S2, and performing rainfall intensity inversion on the area to be detected by adopting a corresponding rain attenuation relation based on a classification result.
5. The method according to claim 4, wherein the step S1 specifically comprises the following steps:
s1.1, selecting n links in a microwave communication network in an area, wherein the microwave frequencies transmitted by the n links are f1，f2，…，fnIn GHz units and polarization mode of alpha1，α2，…，αn(ii) a At the same time, the transmitting levels of the n links are tx1，tx2，…，txnIn dB, the received levels are rx after passing through the rainfall space1，rx2，…，rxnIn dB;
s1.2, calculating total attenuation A of each link based on the transmitting level and the receiving level of each linkiThe unit dB is shown as follows:
Ai＝txi-rxi(i＝1,…,n)；
s1.3, extracting the average rain attenuation rate gamma of each link based on the total attenuation of each linkiAs shown in the following formula:
γi＝(Ai-Aref,i)/Li(i＝1,...,n)
wherein A isref,iRepresents the reference attenuation value of the ith link in dB and LiIndicating the length of the ith link.
6. The method according to claim 5, wherein the polarization mode comprises horizontal polarization and vertical polarization.
7. The method for adaptive estimation of raininess based on clustered raininess relations as claimed in claim 4, wherein said step S2, said clustering method includes but is not limited to fuzzy C-means clustering method.
8. The method for adaptive estimation of rain intensity based on clustered rain attenuation relationship according to claim 4, wherein the rain attenuation relationship includes but is not limited to single-link power law relationship at step S3.
9. The method for adaptively estimating the rainfall intensity based on the clustering rainfall attenuation relationship of claim 5, wherein in the step S3, the method for respectively establishing the rainfall attenuation relationship for each type of rainfall samples in the rainfall sample set by using the least square method is as follows:
wherein, alpha and beta are rain attenuation coefficient, Rinverse,tAnd Rreal,t(mm h-1) Respectively is the rainfall intensity estimated value and the true value of the t-th rainfall sample in a certain type of rainfall sample, and s is the number of the certain type of rainfall sample.
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| CN116068559A (en) * | 2022-12-14 | 2023-05-05 | 广东省气象台(南海海洋气象预报中心、珠江流域气象台) | Combined raindrop spectrum and networking dual-polarization radar quantitative precipitation estimation method and device |
| CN116068559B (en) * | 2022-12-14 | 2023-09-12 | 广东省气象台(南海海洋气象预报中心、珠江流域气象台) | Combined raindrop spectrum and networking dual-polarization radar quantitative precipitation estimation method and device |
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