CN110543971A - Satellite rainfall and actual rainfall error partition fusion correction method - Google Patents
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Abstract
the invention discloses a satellite rainfall and actual rainfall error partition fusion correction method, belongs to the technical field of satellite rainfall data precision correction, and relates to a correction method which has numerical values of each station and each moment, effectively solves the problems of excessive overestimation or underestimation, missing report in a small value region and the like of satellite rainfall monitoring, and remarkably improves the precision of the satellite rainfall and actual rainfall error partition fusion correction. The correlation between the hourly satellite rainfall data corrected by the method and the hourly actually-measured rainfall data reaches over 0.9, the requirement of flood forecasting precision is met, and in addition, the wide space coverage and the high space-time resolution of the method are added, the continuous observation on large-range rainfall can be realized, so that large-area continuous rainfall distribution is obtained, the method can better play an important role in the fields of hydrology and the like, and has better application prospects in sparse ground stations and even non-data areas.
Description
Technical Field
the invention belongs to the technical field of satellite rainfall data precision correction, and particularly relates to a satellite rainfall and actual rainfall error partition fusion correction method.
background
One of the key elements of water circulation is precipitation, which, due to its high heterogeneity in space and time, has a major impact on the spatial allocation of water resources. The high-quality rainfall data not only ensures the accuracy of flood forecasting, but also plays a crucial role in the fields of weather, hydrology, agricultural production and the like. At present, the traditional rainfall data source is observed by rainfall stations, but the rainfall stations are small in distribution density and uneven in spatial distribution, so that the accuracy of a rainfall spatial structure is difficult to guarantee. Therefore, it is necessary and important to improve the accuracy of the rainfall data.
in recent years, with the progress of remote sensing observation technology and the improvement of satellite data inversion algorithm, satellite precipitation data gradually become an important data source in hydrological research with wide space coverage and high space-time resolution. The satellite can realize continuous observation of large-scale rainfall, so that large-area continuous rainfall distribution is obtained, and the satellite can be applied to a plurality of hydrological meteorological researches.
However, the precision of the rainfall of the satellite is still not ideal at present, the satellite cannot be directly applied to the hydrologic simulation forecast work, and the problems that the rainfall value is excessively overestimated or underestimated, the rainfall value is not reported in a small value area and the like exist. The change of the terrain also has certain influence on the accuracy of satellite monitoring, and the consistency of the change of the terrain and measured rainfall shows the characteristics that the relevance of the terrain of the upstream and the downstream is better relative to the wide and flat part, the change of the terrain of the midstream is severe, and the relevance of the canyon region with larger gradient is poorer. In addition, rainfall data monitored by the satellite in an area with sparse ground sites has larger error and lower precision. The satellite rainfall monitoring data acquisition method aims to better apply the satellite rainfall monitoring data to research in the fields of hydrology and the like, and effectively improves the precision of the satellite rainfall monitoring data acquisition method, and becomes the key of the current satellite hydrology research, popularization and application.
disclosure of Invention
the purpose of the invention is as follows: the invention aims to provide a method for partitioned fusion correction of satellite rainfall and actually-measured rainfall errors, and solves the technical problems that large errors exist in existing satellite rainfall monitoring data, accuracy is poor, and the existing satellite rainfall monitoring data cannot be directly applied to research in the fields of hydrology and the like.
The technical scheme is as follows: in order to achieve the purpose, the invention provides the following technical scheme:
a satellite rainfall and actual rainfall error partition fusion correction method comprises the following steps:
1) collecting rainfall data: collecting actual measurement field rainfall data of a download site and rainfall data of a satellite monitoring hour;
2) Rainfall data processing: carrying out time interpolation processing on the actually measured rainfall data of the initial station to enable the actually measured rainfall data to be data with equal time intervals; and (3) performing time interpolation processing on the actually measured rainfall data of the initial station by adopting a rainfall accumulation time sequence piecewise linear interpolation method to enable the actually measured rainfall data to be data with equal time intervals. The method comprises the steps of firstly calculating a time sequence of accumulated rainfall, then determining the accumulated rainfall value at the starting and ending moments of each time period by using a linear interpolation method according to time steps required by research application, wherein the difference value of the accumulated rainfall value and the accumulated rainfall value is the rainfall of the step length of the time period;
3) rainfall grid interpolation: obtaining the rainfall on the proper grid points by adopting a space interpolation method such as an inverse distance weight method;
4) Calculating a statistical value: calculating an error statistic value of each grid point at each moment by taking the ratio of the measured rainfall data to the satellite rainfall data as a statistic value;
5) and (3) primary partition fusion correction: selecting an initial error correction step length, partitioning error statistic values, and multiplying the original hourly satellite rainfall data in each interval by the median value of the statistic values in the interval so as to finish initial correction;
6) and (3) calculating the rainfall of the drainage basin surface: respectively calculating the river basin surface rainfall of the corrected hourly satellite rainfall data and the hourly actually-measured rainfall data;
7) Calculating a correlation coefficient: calculating a correlation coefficient of the corrected hourly satellite rainfall and the hourly actually-measured rainfall according to the acquired rainfall of the drainage basin surface, and counting the correlation;
8) determining a suitable step size: regularly reducing the error correction step length, repeating the steps 5) -7) to obtain correlation coefficients of two groups of rainfall data under different step lengths, and when the value of the correlation coefficients is more than 0.9, considering the step length to be proper, and stopping circulation;
9) And (5) obtaining a correction result: and under the step length determined in the last step, the corrected hourly satellite rainfall data is the final result.
Further, in the step 1), the collected measured rainfall data of the field and the rainfall data of the satellite monitoring hours are consistent in space and time ranges.
further, in the step 2), the initial rainfall data is processed, that is, the rainfall data difference value of the non-equal time interval is equal time interval, and the time interval between the satellite rainfall and the actually measured rainfall should be the same.
further, in the step 4), if the hourly satellite rainfall original data is 0, directly taking hourly actually-measured rainfall data as corrected satellite rainfall data, and avoiding an error caused by missed reporting of rainfall events by the satellite; if the initial data of the hourly satellite rainfall data is not 0, then calculating the statistical value of the hourly satellite rainfall data, wherein the statistical value ai, j is calculated according to the following formula (I):
pi and j are specific values of the satellite precipitation data at the jth station at the moment i; mi, j is the concrete value of the j site when the rainfall is actually measured at the site at the time i; i is a time count; j is the station count.
further, in the step 5), when the statistical value is greater than a larger value, the monitoring error of the satellite rainfall data at the original hour of the site is larger, so that the partition is defined as an extremum area, and the actually measured rainfall data is used as corrected satellite rainfall data.
further, in the step 6), whether all or part of each grid is located in the drainage basin range is judged, the rainfall of the drainage basin actual measurement surface and the rainfall of the satellite monitoring surface are calculated by adopting a grid area weighting method according to the following formulas (II) and (III):
Wherein Pi is the rainfall of the actually measured surface at the moment i; monitoring rainfall of a rainfall surface by a satellite with Mi as the moment i; wi and j are the weights of the jth station at the moment i; and n is the total number of stations.
further, in the step 7), a surface rainfall correlation coefficient is used as a judgment basis for the melting correction precision, and the correlation coefficient is calculated according to the following formula (IV):
Wherein is the average of Pi; is the average value of Mi; m is the total number of times.
Has the advantages that: compared with the prior art, the satellite rainfall and actual rainfall error partition fusion correction method has the advantages that the correction method is customized to the numerical value of each station and each moment, the problems of excessive overestimation or underestimation, missing report in a small value partition and the like existing in satellite rainfall monitoring are effectively solved, and the precision is remarkably improved. The correlation between the hourly satellite rainfall data corrected by the method and the hourly actually-measured rainfall data reaches over 0.9, the requirement of flood forecasting precision is met, and in addition, the wide space coverage and the high space-time resolution of the method are added, the continuous observation on large-range rainfall can be realized, so that large-area continuous rainfall distribution is obtained, the method can better play an important role in the fields of hydrology and the like, and has better application prospects in sparse ground stations and even non-data areas.
Drawings
FIG. 1 is a flow chart of a method for zonal fusion correction of satellite rainfall and actual rainfall errors;
FIG. 2 is a graph relating pre-satellite rainfall correction data to measured rainfall data for a certain watershed hour;
FIG. 3 is a graph of satellite rainfall corrected data and measured rainfall data over a watershed hour.
Detailed Description
The invention will be further described with reference to the following drawings and specific embodiments.
As shown in fig. 1, a method for partitioned fusion correction of satellite rainfall and actually measured rainfall errors includes the following steps:
1) collecting rainfall data: collecting actual measurement field rainfall data of a download site and rainfall data of a satellite monitoring hour;
2) rainfall data processing: carrying out time interpolation processing on the actually measured rainfall data of the initial station to enable the actually measured rainfall data to be data with equal time intervals; and (3) performing time interpolation processing on the actually measured rainfall data of the initial station by adopting a rainfall accumulation time sequence piecewise linear interpolation method to enable the actually measured rainfall data to be data with equal time intervals. The method comprises the steps of firstly calculating a time sequence of accumulated rainfall, then determining the accumulated rainfall value at the starting and ending moments of each time period by using a linear interpolation method according to time steps required by research application, wherein the difference value of the accumulated rainfall value and the accumulated rainfall value is the rainfall of the step length of the time period;
3) rainfall grid interpolation: obtaining the rainfall on the proper grid points by adopting a space interpolation method such as an inverse distance weight method;
4) calculating a statistical value: calculating an error statistic value of each grid point at each moment by taking the ratio of the measured rainfall data to the satellite rainfall data as a statistic value;
5) And (3) primary partition fusion correction: selecting an initial error correction step length, partitioning error statistic values, and multiplying the original hourly satellite rainfall data in each interval by the median value of the statistic values in the interval so as to finish initial correction;
6) and (3) calculating the rainfall of the drainage basin surface: respectively calculating the river basin surface rainfall of the corrected hourly satellite rainfall data and the hourly actually-measured rainfall data;
7) Calculating a correlation coefficient: calculating a correlation coefficient of the corrected hourly satellite rainfall and the hourly actually-measured rainfall according to the acquired rainfall of the drainage basin surface, and counting the correlation;
8) determining a suitable step size: regularly reducing the error correction step length, repeating the steps 5) -7) to obtain correlation coefficients of two groups of rainfall data under different step lengths, and when the value of the correlation coefficients is more than 0.9, considering the step length to be proper, and stopping circulation;
9) And (5) obtaining a correction result: under the step length determined in the last step, the corrected hourly satellite rainfall data is the final result;
The actual measurement field rainfall data collected in the step 1) and the satellite monitoring hour rainfall data need to be kept consistent in space and time ranges;
processing initial rainfall data in the step 2), namely, setting the rainfall data difference value of non-equal time intervals as equal time intervals, wherein the time intervals of satellite rainfall and actually-measured rainfall are the same;
in the step 4), if the hourly satellite rainfall original data is 0, directly taking hourly actually-measured rainfall data as corrected satellite rainfall data to avoid n errors caused by missed reports of rainfall events by satellites; if the initial data of the hourly satellite rainfall data is not 0, then calculating the statistical value of the hourly satellite rainfall data, wherein the statistical value ai, j is calculated according to the following formula (I):
Pi and j are specific values of the satellite precipitation data at the jth station at the moment i; mi, j is the concrete value of the j site when the rainfall is actually measured at the site at the time i; i is a time count; j is the station count.
In the step 5), when the statistical value is larger than a larger value, the monitoring error of the satellite rainfall data of the station at the original hour is larger, so that the subarea is defined as an extremum area, and the actually measured rainfall data is used as corrected satellite rainfall data;
In step 6), judging whether all or part of each grid is positioned in the drainage basin range, calculating rainfall of the drainage basin actual measurement surface and rainfall of the satellite monitoring surface by adopting a grid area weighting method, and calculating according to the following formulas (II) and (III):
Wherein Pi is the rainfall of the actually measured surface at the moment i; monitoring rainfall of a rainfall surface by a satellite with Mi as the moment i; wi and j are the weights of the jth station at the moment i; and n is the total number of stations.
And 7), adopting the surface rainfall correlation coefficient as a judgment basis of the melting correction precision. The correlation coefficient is calculated by the following formula (IV):
Wherein is the average of Pi; is the average value of Mi; m is the total number of times.
Examples
selecting a river basin, and correcting satellite rainfall data (TRMM 3B42RT) by adopting the method for partitioned fusion correction of satellite rainfall and actually measured rainfall errors provided by the invention, specifically comprising the following steps:
(1) According to data sources such as hydrological annual book, actual rainfall data of the watershed in the flood period between 2007 and 2014 and rainfall data of TRMM 3B42RT satellites in the flood period between 2007 and 2014 are selected, time span is the same, and spatial resolution is 0.25 degrees multiplied by 0.25 degrees.
(2) since the time interval of TRMM 3B42RT satellite rainfall data is 1 hour, and the initial measured rainfall data is not at equal time intervals, the measured rainfall is interpolated by the above described rainfall accumulation time series piecewise linear interpolation method, and is converted into 1 hour equal time interval data.
(3) And (3) interpolating the actually measured rainfall of the station into the spatial resolution of the satellite rainfall data by adopting a spatial interpolation method such as reverse distance weight and the like, namely, 0.25 degrees multiplied by 0.25 degrees.
(4) under the conditions that the spatial resolution is 0.25 degrees multiplied by 0.25 degrees and the time interval is 1 hour, if the satellite rainfall data of the network points is 0, the original TRMM 3B42RT satellite rainfall data is replaced by the measured rainfall data, and the ratio of the measured rainfall data to the TRMM 3B42RT satellite rainfall data is calculated by the rest network points to serve as a statistic value.
(5) selecting 2 as an initial step length, partitioning satellite rainfall data according to statistical values, dividing the satellite rainfall data into intervals of [0,2 ], [2,4 ], [4,6 ], … and [18,20), and multiplying original TRMM 3B42RT satellite rainfall data in the intervals by interval median values, namely multiplying the satellite rainfall data by 1, 3, 5, … and 19 respectively. Define the [20, + ∞) interval as the extremum interval during which the measured rainfall replaces the original satellite rainfall data.
(6) After the preliminary correction is finished, the surface rainfall value of the measured rainfall and the corrected satellite rainfall at each moment in the drainage basin is calculated.
(7) and calculating a correlation coefficient CC between the measured rainfall and the corrected rainfall of the TRMM 3B42RT satellite rainfall data plane by using the formula 1.
(8) changing the step size to 1.9 and repeating the steps 5) -7) because the value of the correlation coefficient CC is not more than 0.9; if the correlation number is still less than 0.9 when the step size is 1.9, the step size is continuously changed until the correlation coefficient is greater than 0.9.
(9) the finally determined step length of the watershed is 1.2, and the correlation coefficient of the corrected TRMM 3B42RT satellite rainfall data and the measured rainfall data is 0.904 under the step length, so that the precision requirement required by flood forecasting is met. The correlation graphs of the satellite rainfall data and the actually measured rainfall data before and after correction are shown in fig. 2-3, and it can be known from the graphs that the precision of the satellite rainfall data after correction is obviously improved, the correlation coefficient with the actually measured rainfall is obviously improved, and the deviation is greatly reduced. The corrected TRMM satellite rainfall data is improved on the problem of excessively underestimating the rainfall data, so that the precision of the TRMM satellite rainfall data is obviously improved.
Claims (7)
1. A satellite rainfall and actual rainfall error partition fusion correction method is characterized by comprising the following steps: the method comprises the following steps:
1) collecting rainfall data: collecting actual measurement field rainfall data of a download site and rainfall data of a satellite monitoring hour;
2) Rainfall data processing: carrying out time interpolation processing on the actually measured rainfall data of the initial station to enable the actually measured rainfall data to be data with equal time intervals; and (3) performing time interpolation processing on the actually measured rainfall data of the initial station by adopting a rainfall accumulation time sequence piecewise linear interpolation method to enable the actually measured rainfall data to be data with equal time intervals. The method comprises the steps of firstly calculating a time sequence of accumulated rainfall, then determining the accumulated rainfall value at the starting and ending moments of each time period by using a linear interpolation method according to time steps required by research application, wherein the difference value of the accumulated rainfall value and the accumulated rainfall value is the rainfall of the step length of the time period;
3) Rainfall grid interpolation: obtaining the rainfall on the proper grid points by adopting a space interpolation method such as an inverse distance weight method;
4) Calculating a statistical value: calculating an error statistic value of each grid point at each moment by taking the ratio of the measured rainfall data to the satellite rainfall data as a statistic value;
5) And (3) primary partition fusion correction: selecting an initial error correction step length, partitioning error statistic values, and multiplying the original hourly satellite rainfall data in each interval by the median value of the statistic values in the interval so as to finish initial correction;
6) And (3) calculating the rainfall of the drainage basin surface: respectively calculating the river basin surface rainfall of the corrected hourly satellite rainfall data and the hourly actually-measured rainfall data;
7) Calculating a correlation coefficient: calculating a correlation coefficient of the corrected hourly satellite rainfall and the hourly actually-measured rainfall according to the acquired rainfall of the drainage basin surface, and counting the correlation;
8) Determining a suitable step size: regularly reducing the error correction step length, repeating the steps 5) -7) to obtain correlation coefficients of two groups of rainfall data under different step lengths, and when the value of the correlation coefficients is more than 0.9, considering the step length to be proper, and stopping circulation;
9) And (5) obtaining a correction result: and under the step length determined in the last step, the corrected hourly satellite rainfall data is the final result.
2. the method for partitioned fusion correction of satellite rainfall and actually measured rainfall errors according to claim 1, wherein: in the step 1), the collected actual rainfall data of the field and the rainfall data of the satellite monitoring hours are kept consistent in space and time ranges.
3. the method for partitioned fusion correction of satellite rainfall and actually measured rainfall errors according to claim 1, wherein: in the step 2), the initial rainfall data is processed, that is, the rainfall data difference value of the non-equal time interval is equal time interval, and the time interval between the satellite rainfall and the actually measured rainfall should be the same.
4. the method for partitioned fusion correction of satellite rainfall and actually measured rainfall errors according to claim 1, wherein: in the step 4), if the initial data of the hourly satellite rainfall is 0, directly taking the hourly actually-measured rainfall data as corrected satellite rainfall data to avoid errors caused by missed reports of rainfall events by the satellite; if the initial data of the hourly satellite rainfall data is not 0, then calculating the statistical value of the hourly satellite rainfall data, wherein the statistical value ai, j is calculated according to the following formula (I):
Pi and j are specific values of the satellite precipitation data at the jth station at the moment i; mi, j is the concrete value of the j site when the rainfall is actually measured at the site at the time i; i is a time count; j is the station count.
5. the method for partitioned fusion correction of satellite rainfall and actually measured rainfall errors according to claim 1, wherein: in the step 5), when the statistical value is greater than a larger value, the monitoring error of the satellite rainfall data at the original hour of the site is larger, so that the partition is defined as an extremum area, and the actually measured rainfall data is used as corrected satellite rainfall data.
6. the method for partitioned fusion correction of satellite rainfall and actually measured rainfall errors according to claim 4, wherein: in the step 6), whether all or part of each grid is located in the drainage basin range is judged, the rainfall of the drainage basin actual measurement surface and the rainfall of the satellite monitoring surface are calculated by adopting a grid area weighting method according to the following formulas (II) and (III):
wherein Pi is the rainfall of the actually measured surface at the moment i; monitoring rainfall of a rainfall surface by a satellite with Mi as the moment i; wi and j are the weights of the jth station at the moment i; and n is the total number of stations.
7. the method for partitioned fusion correction of satellite rainfall and actually measured rainfall errors according to claim 5, wherein: in the step 7), a surface rainfall correlation coefficient is used as a judgment basis for the melting correction precision, and the correlation coefficient is calculated according to the following formula (IV):
Wherein is the average of Pi; is the average value of Mi; m is the total number of times.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112381337A (en) * | 2021-01-14 | 2021-02-19 | 四川大汇大数据服务有限公司 | Multi-source meteorological data fusion processing method, system, terminal and medium |
| CN113158139A (en) * | 2021-02-26 | 2021-07-23 | 河海大学 | Downscale product error calculation method for satellite observation rainfall data |
| US20220045509A1 (en) * | 2020-08-05 | 2022-02-10 | Wuhan University | Method and system of predicting electric system load based on wavelet noise reduction and emd-arima |
| CN114781713A (en) * | 2022-04-13 | 2022-07-22 | 中国电建集团成都勘测设计研究院有限公司 | Rainfall station network optimization method and device based on satellite inversion precipitation product |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5854609A (en) * | 1996-12-02 | 1998-12-29 | Electronics And Telecommunications Research Institute | Satellite tracking method for vehicle-mounted antenna system |
| US20090216860A1 (en) * | 2008-02-25 | 2009-08-27 | Georgetown University | System and method for detecting, collecting, analyzing, and communicating event related information |
| CN104820754A (en) * | 2015-05-13 | 2015-08-05 | 南京信息工程大学 | Space statistical downscaling rainfall estimation method based on geographical difference analysis method |
| CN105069295A (en) * | 2015-08-10 | 2015-11-18 | 河海大学 | Assimilation method for satellite and ground rainfall measured values based on Kalman filtering |
| CN106776481A (en) * | 2016-11-29 | 2017-05-31 | 河海大学 | A kind of NO emissions reduction bearing calibration for acting on satellite precipitation data |
| CN110059745A (en) * | 2019-04-17 | 2019-07-26 | 武汉大学 | A kind of Basin Rainfall product correction method based on star merged and system |
-
2019
- 2019-08-02 CN CN201910710647.6A patent/CN110543971B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5854609A (en) * | 1996-12-02 | 1998-12-29 | Electronics And Telecommunications Research Institute | Satellite tracking method for vehicle-mounted antenna system |
| US20090216860A1 (en) * | 2008-02-25 | 2009-08-27 | Georgetown University | System and method for detecting, collecting, analyzing, and communicating event related information |
| CN104820754A (en) * | 2015-05-13 | 2015-08-05 | 南京信息工程大学 | Space statistical downscaling rainfall estimation method based on geographical difference analysis method |
| CN105069295A (en) * | 2015-08-10 | 2015-11-18 | 河海大学 | Assimilation method for satellite and ground rainfall measured values based on Kalman filtering |
| CN106776481A (en) * | 2016-11-29 | 2017-05-31 | 河海大学 | A kind of NO emissions reduction bearing calibration for acting on satellite precipitation data |
| CN110059745A (en) * | 2019-04-17 | 2019-07-26 | 武汉大学 | A kind of Basin Rainfall product correction method based on star merged and system |
Non-Patent Citations (3)
| Title |
|---|
| 嵇涛等: "多源遥感数据的降水空间降尺度研究——以川渝地区为例", 《地理信息科学》 * |
| 张亚萍等: "天气雷达定量降水估测不同校准方法的比较与应用", 《气象》 * |
| 杜迎燕: "基于网格的面雨量实时计算方法研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220045509A1 (en) * | 2020-08-05 | 2022-02-10 | Wuhan University | Method and system of predicting electric system load based on wavelet noise reduction and emd-arima |
| US11888316B2 (en) * | 2020-08-05 | 2024-01-30 | Wuhan University | Method and system of predicting electric system load based on wavelet noise reduction and EMD-ARIMA |
| CN112381337A (en) * | 2021-01-14 | 2021-02-19 | 四川大汇大数据服务有限公司 | Multi-source meteorological data fusion processing method, system, terminal and medium |
| CN112381337B (en) * | 2021-01-14 | 2021-04-23 | 国能大渡河大数据服务有限公司 | Multi-source meteorological data fusion processing method, system, terminal and medium |
| CN113158139A (en) * | 2021-02-26 | 2021-07-23 | 河海大学 | Downscale product error calculation method for satellite observation rainfall data |
| CN113158139B (en) * | 2021-02-26 | 2021-10-08 | 河海大学 | Downscale product error calculation method for satellite observation rainfall data |
| CN114781713A (en) * | 2022-04-13 | 2022-07-22 | 中国电建集团成都勘测设计研究院有限公司 | Rainfall station network optimization method and device based on satellite inversion precipitation product |
| CN114781713B (en) * | 2022-04-13 | 2023-04-07 | 中国电建集团成都勘测设计研究院有限公司 | Rainfall station network optimization method and device based on satellite inversion precipitation product |
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