CN112668761A - Step-by-step line aggregation rainfall data scale conversion method - Google Patents
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Abstract
The invention discloses a step-by-step line aggregation rainfall data scale conversion method, which comprises the following steps: given site control scopel(ii) a According to the second in the microwave networkiLength of a microwave linkL i To the firstiGrading the links; discretizing the link according to the grading result, equally dividing the link according to the link grade, and taking the central point of each equally divided segmented link as a virtual rainfall site; determining the position and estimation of an initial station; preliminary valuation of the unevaluated lowest-level virtual station; obtaining a link of the preliminary estimation value to carry out iterative optimization; and entering the next level of link calculation, and repeating until all the level of link calculation is completed. The concept of grading and optimizing step by step provided by the invention can effectively avoid the problem that all links fall into a local optimal solution; the progressive optimization can effectively reduce the influence of large errors in the long-chain route aggregation data on the conversion process, and ensure the convergence of the algorithmThe speed is increased and the precision of the conversion result is improved.
Description
Technical Field
The invention relates to a step-by-step line aggregation rainfall data scale conversion method, and belongs to the field of meteorological data processing.
Background
Regional rainfall monitoring by using a ground microwave communication network is an emerging technology in recent years. Compared with the traditional technology, the technology has the advantages of saving construction cost, wide coverage area, high refinement degree and the like. However, the rainfall obtained by microwave link monitoring is in a linear aggregation form, namely, the nonlinear weighted average of the rainfall intensity passing through the link covers the real point location monitoring information, so that the collected data has no practical value; on the other hand, most of the input of the existing model is point location data, and the line aggregation form has no physical meaning when modeling assumption is carried out, so that the existing model cannot be directly applied to the model theoretically. How to effectively extract effective information from line-aggregated rainfall data and convert the effective information into point location data is a key problem which hinders the microwave network rain measuring technology at present and is a decisive problem which exerts monitoring advantages of the microwave communication network.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a step-by-step line aggregation rainfall data scale conversion method, which solves the problems of line aggregation rainfall data information extraction and point rainfall acquisition in a microwave monitoring network.
The technical scheme is as follows: in order to solve the technical problem, the step-by-step line aggregation rainfall data scale conversion method comprises the following steps of:
s1, giving a station control range l;
s2, according to the length L of the xth microwave link in the microwave networkxAnd grading the x link, wherein the concrete grading mode is as follows:
wherein [ ] is a rounding function.
S3, discretizing the link according to the grading result, equally dividing the link according to the link grade, and taking the central point of each equally divided segmented link as a virtual rainfall site;
s4, determining the initial station position and estimation: taking the central point of the lowest link in all links as the position of an initial station, wherein the aggregation data of each link line of the level is a determined station estimated value, if the lowest level is 1, the value of the initial station is the final estimated value of the level station, and if the lowest level is more than 1, the initial virtual station only has the function of providing the initial value, is abandoned after confirming the initial estimated value of the virtual station of the level link, and does not participate in the calculation of links of other levels;
s5, preliminary estimation of the undermost virtual station is not estimated, and the preliminary estimation is determined by utilizing all stations with determined estimation to perform spatial interpolation and correction on the current undermost link virtual station which is not estimated;
and S6, performing iterative optimization on the site with the preliminary estimation: performing iterative optimization on the link which obtains the preliminary estimation in the S5;
and S7, entering the next level link calculation, and repeating S5 and S6 until all level link calculations are completed.
Preferably, the step S5 is specifically: let the lowest link not currently evaluated be m-level, m>Obtaining IDW (inverse distance weighting) result of m virtual stations of m-level ith link as vector theta by using obtained initial station position and estimationmi(θmi1,…,θmim) Wherein the first coordinate m of each element in the vector represents the mth level, the second subscript i represents the ith link in the m levels, the third subscript represents the 1 st to m virtual stations on the link, and if the level link is used for confirming the initial station in S4, the initial station performs IDW to obtain thetami(θmi1,θmi2,…,θmim) Is abandoned and utilized
The per-link results for that stage are modified, wherein,
corrected initial estimation vectors for m virtual stations of the ith link of m levels; rmiAggregating rainfall intensity for the line of the ith link of the m-level; bmiIf the link has only 1 link, the initial estimation value after the correction is the final result of the link, and the step is directly performed with S7;
preferably, the step S6 is specifically: for the ith link of the mth level, in the process of the tth iteration, the IDW results of the m sites of the ith link are obtained by using the results of other sites except the ith link, the results of other sites except the ith link at the same level and the results of sites determined by other levels
Then, the result is corrected by using the link line aggregation data,
and theta in S5mi(θmi1,…,θmin) With the difference thatmi(θmi1,…,θmim) Only from the initial virtual site or all low-level validated sites,
is obtained from the previous estimates of all the stations with confirmed estimates of the lower level and the stations of other links except the ith link, and when t is 1, the previous estimate is the corrected initial estimate confirmed in S5
The specific correction method comprises the following steps:
wherein the content of the first and second substances,
when t +1 iterations are carried out on rainfall estimator vectors of the t iterations of m virtual sites of the ith link of m levels
Updating the estimation value of the link to form a new input IDW in t +1 iterations; the objective function is set as
Less than a given threshold, where nmIs the number of m levels of links.
Has the advantages that: the step-by-step line polymerization rainfall data scale conversion method has the following advantages:
(1) the concept of grading and optimizing step by step provided by the invention can effectively avoid the problem that all links fall into a local optimal solution;
(2) the method can effectively reduce the influence of large errors in the long-chain route aggregation data on the conversion process by optimizing step by step, and improves the precision of the conversion result while ensuring the convergence speed of the algorithm;
(3) by combining the first geographic law, the invention effectively solves the problem of sparseness in the microwave link network rainfall space reconstruction, greatly reduces the requirement on the number of links and improves the calculation efficiency;
(4) the method for converting the line aggregation rainfall data into the point data is provided, and the practicability of the microwave network monitoring technology is improved.
Drawings
FIG. 1 is a schematic diagram of link hierarchy and virtual site partitioning;
FIG. 2 is a flow chart of the present invention;
fig. 3 is a flow chart of a certain level of link iterative optimization.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
The invention provides a step-by-step line aggregation rainfall data scale conversion method, which comprises the following steps:
s1, giving a station control range l;
s2, according to the length L of the xth microwave link in the microwave networkxAnd grading the x link, wherein the concrete grading mode is as follows:
wherein [ ] is a rounding function.
S3, discretizing the link according to the grading result, equally dividing the link according to the link grade, taking the central point of each segmented link after equally dividing as a virtual rainfall site, processing the link from the link with the lowest grade to the link with the high grade, storing each link from the low grade to the high grade after discretization in the actual processing process, numbering, and processing the link from the low grade to the high grade during processing;
and S4, determining the initial station position and estimation. Taking the central point of the lowest link in all links as the position of an initial station, wherein the aggregation data of each link line of the level is a determined station estimated value, if the lowest level is 1, the value of the initial station is the final estimated value of the level station, and if the lowest level is more than 1, the initial virtual station only has the function of providing the initial value, is abandoned after confirming the initial estimated value of the virtual station of the level link, and does not participate in the calculation of links of other levels;
and S5, preliminary valuation of the undervalued virtual site. The preliminary estimation is determined by spatial interpolation and correction of the current unexcited lowest link virtual station by using all the stations with determined estimation, the spatial interpolation method can select inverse distance weight method (IDW), and the current unexcited lowest link is set as m-level (m-level)>1) The IDW result of m virtual sites of the ith link of m levels is a vector thetami(θmi1,θmi2,…,θmim) Where the first coordinate m of each element in the vector represents the mth stage, the second index i represents the ith link, and the third index represents the 1 st link on that linkTo m virtual stations, if the hierarchical link is used to identify the initial station in S4, the initial station performs IDW to obtain θmi(θmi1,θmi2,…,θmim) And then discarded. By using
And correcting the result of each link of the stage. Wherein the content of the first and second substances,
corrected initial estimation vectors for m virtual stations of the ith link of m levels; rmiAggregating rainfall intensity for the line of the ith link of the m-level; bmiIs ITU rain attenuation formula parameter of the link and can be obtained by looking up a table. If the link only has 1 link, the corrected preliminary estimation value is the final result of the link, and the next optimization is not needed, and the step S7 is directly performed;
and S6, performing iterative optimization on the site with the preliminary evaluation. Performing iterative optimization on the link for obtaining the preliminary estimate in the step S5, wherein the specific method is as follows: for the ith link of the mth level, in the process of the tth iteration, the IDW results of m sites of the ith link are obtained by using the results of other sites except the ith link
Then, the result is corrected by using the link line aggregation data,
and theta in S5mi(θmi1,…,θmim) With the difference thatmi(θmi1,…,θmim) Only from the initial virtual site or all low-level validated sites,
is obtained by the previous estimation of all the sites with confirmed estimation of the lower level and the sites of other links except the ith link, when t is1, the previous estimate is the corrected preliminary estimate identified in S5
The specific correction method comprises the following steps:
wherein the content of the first and second substances,
and (4) a rainfall estimator vector of the t iteration of m virtual sites of the ith link of the m level. When t +1 times of iteration is carried out, the result of the t-th iteration is obtained
Updating the estimation value of the link to form a new input IDW in t +1 iterations; the objective function is set as
Less than a given threshold, where nmThe number of iterations may also be set as a termination condition for the number of m-level links.
S7, repeating S5 and S6 until all grade link calculation is completed
Example 1: the lowest level is the non-level 1 link case.
Firstly, giving a station control range l;
secondly, dividing the length L of each link by the control range L to obtain each link level by rounding off, cutting by using a circle with the radius of L as shown in fig. 1, and in fig. 1, dividing the length of 6 links by L respectively to obtain 3 level-2 links, 2 level-3 links and 1 level-6 link;
thirdly, discretizing the link according to a grading result, equally dividing the link according to the link grade, taking the central point of each equally divided segmented link as a virtual rainfall site, and as shown in fig. 1, equally dividing the 3-level link by 3 and taking the key point of the segmentation as a virtual site;
the fourth step, the initial station position,And (5) estimation determination. Since there is no level 1 link, the intermediate point of 3 level 2 links is taken as the initial virtual station, and the line aggregate rainfall intensity (R) of each link21，R22，R23) As the rainfall value of each initial virtual site, the first subscript is the level 2 link and the second subscript is the number of each link. Because all the links are of the 2-level lowest level, the initial virtual station only has the function of providing the initial value, and is abandoned after the initial estimation of the 2-level link virtual station is confirmed, and the calculation of other-level links is not participated in;
and fifthly, preliminarily evaluating the unevaluated lowest-level virtual station. Currently, the unevaluated lowest level link is level 2, and the determined initial virtual site data (R) is utilized21，R22，R23) And determining the preliminary estimation value of the virtual station by combining IDW to perform spatial interpolation on all the 2-level link virtual stations, and determining the preliminary estimation value theta of the virtual station for the ith-level-2 link2iIncluding theta2i1、θ2i2Wherein the first index number represents the 2 nd link, the second index i represents the ith link in the level, and the third index is the virtual station number of the link, and since the level link is used for identifying the initial station in S4, the initial station in the center of the 2 nd link identified in S4 obtains θ2iThen discarded, i.e. the intermediate sites are discarded, but this value is not discarded and the line aggregate rainfall intensity is used up to the end, i.e. R21，R22，R23. By using
And correcting the result of each link of the stage. Wherein the content of the first and second substances,
a new corrected preliminary estimate vector for 2 virtual sites of the ith link of level 2; r2iAggregating rainfall intensity for the line of the ith link of the level 2; b2iIs ITU rain attenuation formula parameter of the link and can be obtained by table look-up;
sixth aspect of the inventionAnd optimizing the site with the preliminary evaluation. And (3) performing iterative optimization on the 2 nd link which obtains the preliminary estimate in the fifth step, in the 1 st iteration, firstly estimating 2 sites of the 1 st 2 nd link, and only using the previous estimates of 4 virtual sites of the other two 2 nd links to perform IDW because no virtual site with lower level exists, and because the first iteration is performed, the previous estimates of the 4 virtual sites are confirmed in the fifth step
Obtaining the first IDW result of 2 virtual sites divided by the 1 st 2-level link
The subscript has the first number of link level, the second number of link number, the third number of virtual station number and the fourth number of iteration number; estimating 2 sites of the 2 nd level 2 link, and utilizing the initial virtual site values of the 1 st and 3 nd level 2 links
Performing IDW to obtain the first IDW result of 2 virtual sites divided by the 2 nd level 2 link
Estimating 2 sites of the 3 rd 2-level link, and utilizing the initial virtual site values of the 1 st and 2 nd 2-level links
Performing IDW to obtain the first IDW result of 2 virtual sites divided by the 3 rd 2-level link
Correcting the estimated value of each link by using the aggregated rainfall intensity of each link line in the 2 nd level link, taking the 1 st 2 nd link as an example, the correction formula of the 1 st iteration is：
After the first iteration, the error sum of the optimized and pre-optimized values of all 3 2-level links is calculated
If the error is smaller than the set error allowable threshold value of 0.0001, stopping iteration and entering the next-stage calculation; if the error is more than 0.0001, entering the next iteration, and utilizing the result of the first iteration
Updating the estimated value of the 2 nd-level link, performing IDW calculation, correcting, comparing errors, stopping iteration until the error is less than 0.0001, and entering the next-level calculation; in this embodiment, the next level is a level 3 link;
and seventhly, preliminarily evaluating the underestimated virtual station. The link with the lowest level not estimated currently is 3 level, and the data of 6 virtual sites of the 2 nd level 3 link determined in the sixth step is utilized
(the first subscript represents the level 2 link and the second subscript represents the second link number), all level 3 link virtual stations are spatially interpolated in conjunction with the IDW to determine their preliminary estimates. Preliminary estimate of virtual site θ for level 3 ith link3iIncluding theta3i1、θ3i2、θ3i3. By using
And correcting the result of each link of the stage.
The eighth step, toSites with preliminary estimates are optimized. And (3) performing iterative optimization on the 3 rd-level link which obtains the preliminary evaluation in the seventh step, wherein in the 1 st iteration, 3 sites of the 1 st 3-level link are estimated firstly, and all the determined 3 2-level links and 6 virtual sites are utilized because the values of the lower-level virtual sites are determined
And carrying out IDW on 3 virtual station previous estimation values of the other 1 level-3 links, wherein the 3 virtual station previous estimation values of the other 1 links are confirmed in the seventh step due to the first iteration
Obtaining the first IDW result of 3 virtual sites divided by the 1 st 3-level link
Estimating 3 sites of the 2 nd 3-level link, and utilizing 6 virtual sites of all determined 3 2-level links
And initial virtual site value of 1 st level 3 link
Performing IDW to obtain the first IDW result of 3 virtual sites divided by the 2 nd 3-level link
Correcting the estimated value of each link by using the aggregated rainfall intensity of each link line in the 3 rd level link, taking the 1 st 3 th level link as an example, the 1 st iteration correction formula is as follows:
after the first iteration, the error sum of the optimized and pre-optimized values of all 2 3-level links is calculated
If the error is smaller than the set error allowable threshold value of 0.0001, stopping iteration and entering the next-stage calculation; if the error is more than 0.0001, entering the next iteration, and utilizing the result of the first iteration
Updating the estimated value of the 3 rd-level link, performing IDW calculation, correcting, comparing errors, stopping iteration until the error is less than 0.0001, and entering the next-level calculation; in this embodiment, the next level is a level 6 link;
and step nine, preliminary valuation of the undervalued virtual station. The link with the lowest level currently not estimated is 6 levels, and the data of 6 virtual sites of the 2 nd level 3 links determined in the sixth step are utilized
And 6 virtual site data for level 3, 2 links
All level 6 link virtual stations are spatially interpolated in conjunction with IDW to determine their preliminary estimates. Preliminary estimate of virtual site θ for level 6, level 1 link61Including theta611、θ612、θ613、θ614、θ615、θ616. By using
The stage 1 link result is corrected. Since level 6 has only 1 link, then
The conversion of this example was completed.
Example 2: the lowest level is the level 1 link case.
If there are level 1 links that meet the number requirement, then:
firstly, giving a station control range l;
secondly, dividing the length L of each link by the control range L to round off to obtain each link grade;
discretizing the link according to a grading result, equally dividing the link according to the link grade, and taking the central point of each equally divided segmented link as a virtual rainfall site;
and fourthly, determining the position and estimation of the initial station. If the lowest level is level 1, taking the central points of all level 1 links as initial station positions, wherein the aggregated data of all link lines of the level is a determined station estimated value, and the value of the initial station is the final estimated value of the level station;
and fifthly, preliminarily evaluating the unevaluated lowest-level virtual station. In the fourth step, the estimated value of the 1 st level link is confirmed, so in the conversion of the lowest level link after the 1 st level, the estimated value of the 1 st level link is confirmed by utilizing all estimated values of the 1 st level link to carry out spatial interpolation and correction on the virtual station of the level link; when other unestimated lowest link is converted, all the stations with determined estimation are used to make space interpolation and correction on the virtual station of current unestimated lowest link to determine its initial estimation, and the space interpolation method can use inverse distance weighted method (IDW), and the current unestimated lowest link is defined as m-level (m-level)>1) The IDW result of m virtual sites of the ith link of m levels is a vector thetami(θmi1,θmi2,…,θmin) Wherein, the first coordinate m of each element in the vector represents the mth level, the second subscript i represents the ith link, and the third subscript represents the 1 st to m virtual stations on the link. By using
And correcting the result of each link of the stage. Wherein the content of the first and second substances,
corrected initial estimation vectors for m virtual stations of the ith link of m levels; rmiAggregating rainfall intensity for the line of the ith link of the m-level; bmiIs ITU rain attenuation formula parameter of the link and can be obtained by looking up a table. If the link only has 1 link, the corrected preliminary estimation value is the final result of the link, and the seventh step is directly carried out without carrying out the next optimization;
and sixthly, optimizing the sites with the preliminary evaluation. And performing iterative optimization on the link for obtaining the preliminary evaluation value in the fifth step in a specific mode that: for the ith link of the mth level, in the process of the tth iteration, the IDW results of m sites of the ith link are obtained by using the results of other sites (all confirmed evaluation sites and m levels of sites except the ith link) except the ith link
Then, the result is corrected by using the link line aggregation data,
theta with respect to the fifthmi(θmi1,…,θmin) With the difference thatmi(θmi1,…,θmin) Only from the initial virtual site or all low-level validated sites,
the estimated values of all the stations with confirmed estimated values of the lower level and the stations of other links of the current level are obtained by previous estimated values, and when t is 1, the previous estimated value is a corrected preliminary estimated value confirmed in the fifth level
The specific correction method comprises the following steps:
wherein the content of the first and second substances,
and (4) a rainfall estimator vector of the t iteration of m virtual sites of the ith link of the m level. When t +1 times of iteration is carried out, the result of the t-th iteration is obtained
Updating the estimation value of the link to form a new input IDW in t +1 iterations; the objective function is set as
Less than a given threshold, where nmThe number of iterations may also be set as a termination condition for the number of m-level links.
And seventhly, repeating the fifth step and the sixth step until all the grade links are calculated.
By adopting the method, the problem that all links fall into the local optimal solution can be effectively avoided; the influence of large errors in the long-chain route aggregation data on the conversion process can be effectively reduced through gradual optimization, the convergence rate of the algorithm is guaranteed, and the precision of the conversion result is improved; by combining the first geographic law, the problem of sparseness in the microwave link network rainfall space reconstruction is effectively solved, the requirement on the number of links is greatly reduced, and the calculation efficiency is improved; the method for converting the line aggregation rainfall data into the point data is provided, and the practicability of the microwave network monitoring technology is improved.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (3)
1. A stepwise line aggregation rainfall data scale conversion method is characterized by comprising the following steps:
s1, setting a station control range l and a threshold value;
s2, according to the length L of the xth microwave link in the microwave networkxAnd grading the x link, wherein the concrete grading mode is as follows:
wherein [ ] is a rounding function;
s3, discretizing the link according to the grading result, equally dividing the link according to the link grade, and taking the central point of each equally divided segmented link as a virtual rainfall site;
s4, determining the initial station position and estimation: taking the central point of the lowest link in all links as the initial site position, and the aggregate data of all link lines of the level is the determined site valuation;
s5, preliminary estimation of the undermost virtual station is not estimated, and the preliminary estimation is determined by utilizing all stations with determined estimation to perform spatial interpolation and correction on the current undermost link virtual station which is not estimated;
and S6, performing iterative optimization on the site with the preliminary estimation: performing iterative optimization on the link which obtains the preliminary estimate in the S5, and entering S7 after the optimization is finished;
and S7, entering the next level link calculation, and repeating S5 and S6 until all level link calculations are completed.
2. The method of claim 1, wherein the method comprises the steps of: the step S5 specifically includes: let the lowest link not currently evaluated be m-level, m>Obtaining IDW results of m virtual sites of m levels of ith links as a vector theta by using the obtained initial site positions and estimation valuesmi(θmi1,…,θmim) Wherein the first coordinate m of each element in the vector represents the mth level, the second subscript i represents the ith link in the m levels, the third subscript represents the 1 st to m virtual stations on the link, and if the level link is used for confirming the initial station in S4, the initial station performs IDW to obtain thetami(θmi1,θmi2,…,θmim) Is abandoned and utilized
The per-link results for that stage are modified, wherein,
corrected initial estimation vectors for m virtual stations of the ith link of m levels; rmiAggregating rainfall intensity for the line of the ith link of the m-level; bmiIf the link has only 1 link, the preliminary estimation value after the correction is the final result of the link, and the step is directly performed with S7.
3. The method of claim 1, wherein the method comprises the steps of: the step S6 specifically includes: for the ith link of the mth level, in the process of the tth iteration, the IDW results of m sites of the ith link are obtained by using the results of other sites except the ith link
Then, the result is corrected by using the link line aggregation data,
is obtained by the previous estimation of all the stations with confirmed estimation of the lower level and the stations of other links of the same level except the ith link, when t is 1, the previous estimation is the corrected initial estimation confirmed in S5
The specific correction method comprises the following steps:
wherein the content of the first and second substances,
when t +1 iterations are carried out on rainfall estimator vectors of the t iterations of m virtual sites of the ith link of m levels
Updating the estimation value of the link to form a new input IDW in t +1 iterations; the objective function is set as
Less than a given threshold, where nmIs the number of m levels of links.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104656163A (en) * | 2015-02-04 | 2015-05-27 | 中国人民解放军理工大学 | Rainfall distribution and dynamic measurement method based on big-data mobile communication network |
| CN106324709A (en) * | 2016-10-21 | 2017-01-11 | 中国人民解放军理工大学 | Rainfall field reconstruction method by integrating microwave link, disdrometer, rain gauge and weather radar |
| US20170336533A1 (en) * | 2016-05-20 | 2017-11-23 | The Climate Corporation | Radar based precipitation estimates using spatiotemporal interpolation |
| CN108154193A (en) * | 2018-01-16 | 2018-06-12 | 黄河水利委员会黄河水利科学研究院 | A kind of long-term sequence precipitation data NO emissions reduction method |
| CN110533233A (en) * | 2019-08-20 | 2019-12-03 | 河海大学 | Wireless microwave based on fitness optimization surveys rain link planing method |
| CN111414974A (en) * | 2020-03-30 | 2020-07-14 | 中国人民解放军国防科技大学 | Microwave link rain measurement network topological structure optimization method based on communication base station |
-
2020
- 2020-12-17 CN CN202011493548.6A patent/CN112668761B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104656163A (en) * | 2015-02-04 | 2015-05-27 | 中国人民解放军理工大学 | Rainfall distribution and dynamic measurement method based on big-data mobile communication network |
| US20170336533A1 (en) * | 2016-05-20 | 2017-11-23 | The Climate Corporation | Radar based precipitation estimates using spatiotemporal interpolation |
| CN106324709A (en) * | 2016-10-21 | 2017-01-11 | 中国人民解放军理工大学 | Rainfall field reconstruction method by integrating microwave link, disdrometer, rain gauge and weather radar |
| CN108154193A (en) * | 2018-01-16 | 2018-06-12 | 黄河水利委员会黄河水利科学研究院 | A kind of long-term sequence precipitation data NO emissions reduction method |
| CN110533233A (en) * | 2019-08-20 | 2019-12-03 | 河海大学 | Wireless microwave based on fitness optimization surveys rain link planing method |
| CN111414974A (en) * | 2020-03-30 | 2020-07-14 | 中国人民解放军国防科技大学 | Microwave link rain measurement network topological structure optimization method based on communication base station |
Non-Patent Citations (5)
| Title |
|---|
| QIUMING KUANG: "Spatiotemporal Modeling and Implementation for Radar-Based Rainfall Estimation", 《IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 》 * |
| 徐海飞 等: "高分辨率遥感降水对强降雨的监测能力研究", 《科学技术与工程》 * |
| 解恒燕: "降水量空间插值方法在小样本区域的比较研究", 《水土保持研究》 * |
| 郑鑫: "缺资料地区日降雨空间插值方法研究", 《中国农村水利水电》 * |
| 高清泉: "微波通信链路监测降水试验及可行性探究", 《成都信息工程大学学报》 * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113553705A (en) * | 2021-07-19 | 2021-10-26 | 河海大学 | Spatial interpolation method suitable for microwave link monitoring network |
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