CN110750516A - Radar map-based rainfall analysis model construction method, construction system and analysis method - Google Patents
Radar map-based rainfall analysis model construction method, construction system and analysis method Download PDFInfo
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Abstract
The invention discloses a rainfall analysis model construction method, a rainfall analysis model construction system and a rainfall analysis model analysis method based on radar maps, belongs to the field of rainfall analysis, and aims to solve the problem of how to accurately perform rainfall analysis by combining actually measured rainfall and radar maps. The construction method comprises the following steps: carrying out geographic position registration on the radar map and the GIS map by a multipoint registration method; storing radar echo data corresponding to the fragment radar map and actual rainfall into a group; constructing a rainfall analysis model; radar echo data and actual rainfall at different times corresponding to the fragment radar chart are training samples, and a regional rainfall analysis model is obtained; and obtaining a time interval rainfall analysis model by taking radar echo data corresponding to time and actual rainfall as training samples. The system comprises a coordinate matching module, a region dividing module, a data acquisition module, a storage module, a model building module and a model training module. The regional rainfall analysis model and the time-interval rainfall analysis model are obtained through the method.
Description
Technical Field
The invention relates to the field of rainfall analysis, in particular to a radar chart-based rainfall analysis model construction method, a radar chart-based rainfall analysis model construction system and a radar chart-based rainfall analysis model analysis method.
Background
The information of the automatic rainfall stations is always the main basis for accurately reflecting the actual rainfall, however, the arrangement density of the automatic rainfall stations in China has no specific standard at present, the rationality of the arranged tens of thousands of rainfall stations is not scientifically demonstrated, and the geographical position information of many automatic rainfall stations is inaccurate after the automatic rainfall stations are moved for a plurality of times. In addition, the measurement of rainfall is greatly influenced by wind fields, and particularly when the tipping bucket type rain gauge is in heavy rain, due to the inertia of the turning of the tipping bucket, the other tipping bucket cannot turn over before being filled with water, so that the rainfall loss is caused, the measured rainfall has larger error, the recording distortion is caused, and the influence of water or silt on the tipping bucket can prevent the turning of the tipping bucket, so that the measurement error of the rainfall can be caused.
In recent years, in 2004, the state has seen a three-level quality control business system from a weather station to a ground automatic station observation data of provincial and national data departments, wherein the quality control methods at all levels are still based on traditional methods, such as: format check-extremum check-internal consistency check-time consistency check-space consistency check-human-computer interaction check. Overseas, especially in northern Europe, the standardization and technology of quality control of meteorological data are in advanced ranks in the world, and the used space quality control method mainly comprises the following steps: Madsen-Allerup method (denmark), DEC-WIM method (norway), numerical prediction mode (hirram) interpolation method (norway), Kriging statistical difference mode (finland), MESAN method (sweden), etc. However, natural rainfall has the characteristics of uneven space-time distribution and large rainfall area and intensity change, and the conventional quality control method cannot well distinguish the rainfall. The radar can detect the occurrence, development and evolution conditions of cloud and precipitation structures and systems in real time, and can rapidly provide the real-time precipitation conditions in a certain area. The advantage of reasonable spatial distribution of data measured by the radar station is widely applied to various fields of scientific research and business application.
Doppler weather radar measures rainfall generally by using Z-ARb(Z-Radar reflectance, R-rainfall intensity), which is an empirical formula derived from the results of the measured rainfall intensity and raindrop spectrum statistics. However, the parameter A, b varies widely due to the large relationship between the parameter and the type of rainfall, season, region, etc. The new generation of Doppler weather radar adopts a single Z-R relation and ignores the rainfall detail characteristics, so that the rainfall measurement value in a local area has a larger error.
Therefore, the prior art is mainly and intensively applied to the quality control of rainfall observation point data by radar information and the rainfall direction prediction by the radar information, and few researches are made on the aspect of combining the actually measured rainfall and radar information calibration parameters.
Based on the analysis, how to accurately analyze rainfall by combining actually measured rainfall and radar chart is a technical problem to be solved.
Disclosure of Invention
The technical task of the invention is to provide a rainfall analysis model construction method, a rainfall analysis model construction system and a rainfall analysis model analysis method based on radar maps, aiming at the defects, so as to solve the problem of how to accurately perform rainfall analysis by combining actual rainfall measurement and radar maps.
In a first aspect, the invention provides a rainfall analysis model construction method based on a radar map, which comprises the following steps:
acquiring radar maps at different times, performing geographic position registration on the radar maps and the GIS map by a multi-point registration method for each radar map, and matching the coordinates of each point in the radar maps with the coordinates of the GIS map after registration;
dividing each registered radar map grid into a plurality of fragment radar maps, wherein each fragment radar map is matched with at least one rainfall measuring station;
for each fragment radar map, extracting radar echo data of the fragment radar map, acquiring actual rainfall measured by all rainfall measurement stations corresponding to the fragment radar map, and respectively storing the radar echo data corresponding to the fragment radar map and the actual rainfall into a group according to a time sequence;
constructing a rainfall analysis model, wherein the rainfall analysis model is a Z-R relation model, and the expression of the Z-R relation model is as follows:
Z＝ARb
wherein Z represents radar echo data, R is rainfall, and A and b are parameters to be measured;
for each fragment radar map, taking radar echo data and actual rainfall at different times corresponding to the fragment radar map as training samples, training the rainfall analysis model and optimizing parameters A and b to obtain an area rainfall analysis model of the area;
and for each time, training the rainfall analysis model and optimizing parameters A and b by taking the radar echo data of each fragment radar graph corresponding to the time and the actual rainfall as training samples to obtain the time interval rainfall analysis model of the time interval.
Preferably, the radar map is gridded by the clip tool of ArcGIS.
Preferably, for each fragment radar map, extracting radar echo data of the fragment radar map based on the difference between the color and the radar echo intensity in the radar map, including the following steps:
carrying out picture color identification on the fragment radar image through opencv, and converting the RGB value of the pixel point into a Lab color model representation value;
calculating the difference value between the Lab color model representation value and the standard color value of the pixel point, and selecting the color of the pixel point with the minimum difference value as the standard color of the fragment radar chart;
and taking the radar echo data corresponding to the standard color as the radar echo data of the fragment radar image.
Preferably, each group of radar echo data and each group of actual rainfall are stored in a distributed storage system;
before the radar echo data and the actual rainfall are taken as training samples, preprocessing each group of radar echo data and each group of actual rainfall through a distributed storage system, wherein the preprocessing comprises the following steps:
setting a radar echo threshold range, and deleting abnormal data which exceed the radar echo threshold range in each group of radar echo data;
and setting a rainfall threshold range, and deleting abnormal data exceeding the rainfall threshold range in each group of actual rainfall.
In a second aspect, the present invention provides a rainfall analysis model building system based on radar maps, including:
the coordinate matching module is used for carrying out geographic position registration on the radar map and the GIS map by a multi-point registration method, and coordinates of each point in the radar map after registration are matched with coordinates of the GIS map;
the area division module is used for dividing each registered radar map grid into a plurality of fragment radar maps, and each fragment radar map is matched with at least one rainfall measurement station;
the data acquisition module is used for extracting radar echo data of the fragment radar map, acquiring actual rainfall measured by all rainfall measurement stations corresponding to the fragment radar map, and respectively storing the radar echo data corresponding to the fragment radar map and the actual rainfall into a group according to a time sequence;
the storage module is used for storing each group of radar echo data and each group of actual rainfall;
the model building module is used for building a rainfall analysis model, the rainfall analysis model is a Z-R relation model, and the expression of the Z-R relation model is as follows:
Z＝ARb
wherein Z represents radar echo data, R is rainfall, and A and b are parameters to be measured;
the model training module is used for training the rainfall analysis model and optimizing parameters A and b by taking radar echo data and actual rainfall at different times corresponding to the debris radar map as training samples for each debris radar map to obtain an area rainfall analysis model of the area; and for each time, training the rainfall analysis model and optimizing parameters A and b by taking the radar echo data and the actual rainfall of each fragment radar graph corresponding to the time as training samples to obtain the time interval rainfall analysis model of the time interval.
Preferably, the region partitioning module performs meshing on the radar map by a clip tool of ArcGIS.
Preferably, the data acquisition module is configured to extract the radar echo data of the fragmented radar map based on a difference between a color and a radar echo intensity in the radar map, and includes the following steps:
carrying out picture color identification on the fragment radar image through opencv, and converting the RGB value of the pixel point into a Lab color model representation value;
calculating the difference value between the Lab color model representation value and the standard color value of the pixel point, and selecting the color of the pixel point with the minimum difference value as the standard color of the fragment radar chart;
and taking the radar echo data corresponding to the standard color as the radar echo data of the fragment radar image.
Preferably, the storage module is a distributed storage system;
the distributed storage system is used for preprocessing each group of radar echo data and each group of actual rainfall, and the preprocessing comprises the following steps:
setting a radar echo threshold range, and deleting abnormal data which exceed the radar echo threshold range in each group of radar echo data;
and setting a rainfall threshold range, and deleting abnormal data exceeding the rainfall threshold range in each group of actual rainfall.
In a third aspect, the invention provides a rainfall analysis method based on a radar chart, which comprises the following steps:
constructing a rainfall analysis model by the radar map-based rainfall analysis model construction method according to any one of the first aspect to obtain an area rainfall analysis model and a time-interval rainfall analysis model;
acquiring radar echo data and actual rainfall corresponding to the area rainfall analysis model as test samples based on the area rainfall analysis model, and inputting the test samples into the corresponding area rainfall analysis model to obtain rainfall analysis corresponding to the area;
and acquiring radar echo data and actual rainfall corresponding to the time period rainfall analysis model as test samples based on the time period rainfall analysis model, and inputting the test samples into the corresponding time period rainfall analysis model to obtain rainfall analysis corresponding to the time period.
The rainfall analysis model construction method, the rainfall analysis model construction system and the rainfall analysis model analysis method based on the radar map have the following advantages: the radar map is divided into a plurality of fragment radar maps through grid division, each fragment radar map corresponds to at least one rainfall measurement station, therefore, a monitoring area of the measurement station is defined through fine area segmentation fixed points, meanwhile, radar echo data corresponding to the fragment radar map and actual rainfall extracted by the corresponding rainfall measurement stations in real time can be subjected to global correlation analysis, effective rate determination of parameters of a Z-R relation model under different conditions (areas and time intervals) is achieved, quality control of actually measured rainfall data is improved, and reliability of future short-time rainfall prediction by utilizing radar data is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced 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 based on these drawings without creative efforts.
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a flow chart of a rainfall analysis model construction method based on a radar chart in example 1.
Detailed Description
The present invention is further described in the following with reference to the drawings and the specific embodiments so that those skilled in the art can better understand the present invention and can implement the present invention, but the embodiments are not to be construed as limiting the present invention, and the embodiments and the technical features of the embodiments can be combined with each other without conflict.
The embodiment of the invention provides a rainfall analysis model construction method, a rainfall analysis model construction system and a rainfall analysis model analysis method based on radar maps, which are used for solving the technical problem of how to accurately perform rainfall analysis by combining actual rainfall measurement with radar maps.
Example 1:
the invention discloses a rainfall analysis model construction method based on a radar chart, which comprises the following steps:
s100, obtaining radar maps at different times, carrying out geographic position registration on the radar maps and the GIS map by a multi-point registration method for each radar map, and matching the coordinates of each point in the radar maps after registration with the coordinates of the GIS map;
s200, dividing each registered radar map grid into a plurality of fragment radar maps, wherein each fragment radar map is matched with at least one rainfall measuring station;
s300, for each fragment radar map, extracting radar echo data of the fragment radar map, acquiring actual rainfall measured by all rainfall measurement stations corresponding to the fragment radar map, and respectively storing the radar echo data corresponding to the fragment radar map and the actual rainfall into a group according to a time sequence;
s400, constructing a rainfall analysis model, wherein the rainfall analysis model is a Z-R relation model, and the expression of the Z-R relation model is as follows:
Z＝ARb
wherein Z represents radar echo data, R is rainfall, and A and b are parameters to be measured;
s500, for each fragment radar map, training the rainfall analysis model and optimizing parameters A and b by taking radar echo data and actual rainfall at different times corresponding to the fragment radar map as training samples to obtain an area rainfall analysis model of the area;
and for each time, training the rainfall analysis model and optimizing parameters A and b by taking the radar echo data of each fragment radar graph corresponding to the time and the actual rainfall as training samples to obtain the time interval rainfall analysis model of the time interval.
The specific method comprises the steps of selecting a plurality of points on a radar map, then determining an X coordinate and a Y coordinate, and matching the map coordinates through the plurality of points, wherein the more the selected points are, the higher the matching precision is.
The updating frequency of the radar map is 6 minutes, the radar maps at different times are obtained based on the frequency, and the radar map is subjected to grid division through a clip tool of ArcGIS.
In the radar map, the intensity of the echo gradually changes from blue to purple, and the color of the fragment radar map is acquired to obtain corresponding radar echo data based on the difference between the color in the radar map and the intensity of the radar echo. Based on the principle, for each fragment radar map, extracting radar echo data of the fragment radar map based on the difference between the color and the radar echo intensity in the radar map, and specifically comprising the following steps:
(1) carrying out picture color identification on the fragment radar image through opencv, and converting the RGB value of the pixel point into a Lab color model representation value;
(2) calculating the difference value between the Lab color model representation value and the standard color value of the pixel point, and selecting the color of the pixel point with the minimum difference value as the standard color of the fragment radar chart;
(3) and taking the radar echo data corresponding to the standard color as the radar echo data of the fragment radar image.
In step S300, each group of radar echo data and each group of actual rainfall are stored in the distributed storage system.
Before the radar echo data and the actual rainfall are taken as training samples, preprocessing each group of radar echo data and each group of actual rainfall through a distributed storage system, wherein the preprocessing comprises the following steps:
(1) setting a radar echo threshold range, and deleting abnormal data which exceed the radar echo threshold range in each group of radar echo data;
(2) and setting a rainfall threshold range, and deleting abnormal data exceeding the rainfall threshold range in each group of actual rainfall.
The invention discloses a rainfall analysis model construction method, which comprises the steps of dividing a radar map into a plurality of fragment radar maps through grid division, storing extracted radar echo data and actual rainfall according to time and region distribution, taking the data as training samples to obtain a regional rainfall analysis model and a time-interval rainfall analysis model, and reliably predicting future short-time rainfall through the regional rainfall analysis model and the time-interval rainfall analysis model.
Example 2:
the invention provides a rainfall analysis model building system based on a radar map, which comprises a coordinate matching module, an area dividing module, a data acquisition module, a storage module, a model building module and a model training module.
And the coordinate matching module is used for carrying out geographic position registration on the radar map and the GIS map by a multi-point registration method, and the coordinates of each point in the radar map after registration are matched with the coordinates of the GIS map.
The area division module is used for dividing each registered radar map grid into a plurality of fragment radar maps, and each fragment radar map is matched with at least one rainfall measurement station. And the region partitioning module is used for carrying out meshing on the radar map through a clip tool of ArcGIS.
In the radar map, the intensity of the echo gradually changes from blue to purple, and the color of the fragment radar map is acquired to obtain corresponding radar echo data based on the difference between the color in the radar map and the intensity of the radar echo. Based on the principle, for each fragment radar map, extracting radar echo data of the fragment radar map based on the difference between the color and the radar echo intensity in the radar map, and specifically comprising the following steps:
(1) carrying out picture color identification on the fragment radar image through opencv, and converting the RGB value of the pixel point into a Lab color model representation value;
(2) calculating the difference value between the Lab color model representation value and the standard color value of the pixel point, and selecting the color of the pixel point with the minimum difference value as the standard color of the fragment radar chart;
(3) and taking the radar echo data corresponding to the standard color as the radar echo data of the fragment radar image.
The data acquisition module is used for extracting radar echo data of the fragment radar map, acquiring actual rainfall measured by all rainfall measurement stations corresponding to the fragment radar map, and respectively storing the radar echo data corresponding to the fragment radar map and the actual rainfall into a group according to a time sequence.
The storage module is a distributed database and is used for storing each group of radar echo data and each group of actual rainfall. The storage module is used for preprocessing each group of radar echo data and each group of actual rainfall, and the preprocessing comprises the following steps:
(1) setting a radar echo threshold range, and deleting abnormal data which exceed the radar echo threshold range in each group of radar echo data;
(2) and setting a rainfall threshold range, and deleting abnormal data exceeding the rainfall threshold range in each group of actual rainfall.
The model construction module is used for constructing a rainfall analysis model, the rainfall analysis model is a Z-R relation model, and the expression of the Z-R relation model is as follows:
Z＝ARb
wherein Z represents radar echo data, R is rainfall, and A and b are parameters to be measured;
the model training module is used for training a rainfall analysis model and optimizing parameters A and b to obtain an area rainfall analysis model of each fragment radar map by taking radar echo data and actual rainfall at different times corresponding to the fragment radar map as training samples; and for each time, training the rainfall analysis model and optimizing parameters A and b by taking the radar echo data and the actual rainfall of each fragment radar graph corresponding to the time as training samples to obtain the rainfall analysis model in the time period.
The system can realize the rainfall analysis model construction method based on the radar chart disclosed in the embodiment 1.
Example 3:
the rainfall analysis method based on the radar map comprises the following steps:
(1) a rainfall analysis model is constructed by the radar chart-based rainfall analysis model construction method disclosed in the embodiment 1, and an area rainfall analysis model and a time-interval rainfall analysis model are obtained;
(2) based on a region rainfall analysis model, acquiring radar echo data and actual rainfall corresponding to the region rainfall analysis model as test samples, and inputting the test samples into the corresponding region rainfall analysis model to obtain rainfall analysis corresponding to the region;
and based on the time-interval rainfall analysis model, acquiring radar echo data and actual rainfall corresponding to the time-interval rainfall analysis model as test samples, and inputting the test samples into the corresponding time-interval rainfall analysis model to obtain rainfall analysis corresponding to the time interval.
The method for acquiring the radar echo data is the same as the method disclosed in embodiment 1.
In the radar map, the intensity of the echo gradually changes from blue to purple, and the color of the fragment radar map is acquired to obtain corresponding radar echo data based on the difference between the color in the radar map and the intensity of the radar echo. Based on the principle, for each fragment radar map, extracting radar echo data of the fragment radar map based on the difference between the color and the radar echo intensity in the radar map, and specifically comprising the following steps:
(1) carrying out picture color identification on the fragment radar image through opencv, and converting the RGB value of the pixel point into a Lab color model representation value;
(2) calculating the difference value between the Lab color model representation value and the standard color value of the pixel point, and selecting the color of the pixel point with the minimum difference value as the standard color of the fragment radar chart;
(3) and taking the radar echo data corresponding to the standard color as the radar echo data of the fragment radar image.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (9)
1. A rainfall analysis model construction method based on radar maps is characterized by comprising the following steps:
acquiring radar maps at different times, performing geographic position registration on the radar maps and the GIS map by a multi-point registration method for each radar map, and matching the coordinates of each point in the radar maps with the coordinates of the GIS map after registration;
dividing each registered radar map grid into a plurality of fragment radar maps, wherein each fragment radar map is matched with at least one rainfall measuring station;
for each fragment radar map, extracting radar echo data of the fragment radar map, acquiring actual rainfall measured by all rainfall measurement stations corresponding to the fragment radar map, and respectively storing the radar echo data corresponding to the fragment radar map and the actual rainfall into a group according to a time sequence;
constructing a rainfall analysis model, wherein the rainfall analysis model is a Z-R relation model, and the expression of the Z-R relation model is as follows:
Z＝ARb
wherein Z represents radar echo data, R is rainfall, and A and b are parameters to be measured;
for each fragment radar map, taking radar echo data and actual rainfall at different times corresponding to the fragment radar map as training samples, training the rainfall analysis model and optimizing parameters A and b to obtain an area rainfall analysis model of the area;
and for each time, training the rainfall analysis model and optimizing parameters A and b by taking the radar echo data of each fragment radar graph corresponding to the time and the actual rainfall as training samples to obtain the time interval rainfall analysis model of the time interval.
2. The method for constructing a rainfall analysis model based on radar map according to claim 1, wherein the radar map is gridded by clip tool of ArcGIS.
3. The method for building a rainfall analysis model based on radar maps according to claim 1, wherein for each debris radar map, the radar echo data of the debris radar map are extracted based on the difference between the colors in the radar map and the radar echo intensities, and the method comprises the following steps:
carrying out picture color identification on the fragment radar image through opencv, and converting the RGB value of the pixel point into a Lab color model representation value;
calculating the difference value between the Lab color model representation value and the standard color value of the pixel point, and selecting the color of the pixel point with the minimum difference value as the standard color of the fragment radar chart;
and taking the radar echo data corresponding to the standard color as the radar echo data of the fragment radar image.
4. The method of claim 1, wherein each set of radar echo data and each set of actual rainfall are stored in a distributed storage system;
before the radar echo data and the actual rainfall are taken as training samples, preprocessing each group of radar echo data and each group of actual rainfall through a distributed storage system, wherein the preprocessing comprises the following steps:
setting a radar echo threshold range, and deleting abnormal data which exceed the radar echo threshold range in each group of radar echo data;
and setting a rainfall threshold range, and deleting abnormal data exceeding the rainfall threshold range in each group of actual rainfall.
5. Rainfall analysis model construction system based on radar map is characterized by comprising:
the coordinate matching module is used for carrying out geographic position registration on the radar map and the GIS map by a multi-point registration method, and coordinates of each point in the radar map after registration are matched with coordinates of the GIS map;
the area division module is used for dividing each registered radar map grid into a plurality of fragment radar maps, and each fragment radar map is matched with at least one rainfall measurement station;
the data acquisition module is used for extracting radar echo data of the fragment radar map, acquiring actual rainfall measured by all rainfall measurement stations corresponding to the fragment radar map, and respectively storing the radar echo data corresponding to the fragment radar map and the actual rainfall into a group according to a time sequence;
the storage module is used for storing each group of radar echo data and each group of actual rainfall;
the model building module is used for building a rainfall analysis model, the rainfall analysis model is a Z-R relation model, and the expression of the Z-R relation model is as follows:
Z＝ARb
wherein Z represents radar echo data, R is rainfall, and A and b are parameters to be measured;
the model training module is used for training the rainfall analysis model and optimizing parameters A and b by taking radar echo data and actual rainfall at different times corresponding to the debris radar map as training samples for each debris radar map to obtain an area rainfall analysis model of the area; and for each time, training the rainfall analysis model and optimizing parameters A and b by taking the radar echo data and the actual rainfall of each fragment radar graph corresponding to the time as training samples to obtain the time interval rainfall analysis model of the time interval.
6. The system according to claim 5, wherein the region partitioning module is used for meshing the radar map through a clip tool of ArcGIS.
7. The rainfall analysis model building system based on radar map of claim 5, wherein the data acquisition module is used for extracting radar echo data of the debris radar map based on the difference between the colors in the radar map and the radar echo intensities, and comprises the following steps:
carrying out picture color identification on the fragment radar image through opencv, and converting the RGB value of the pixel point into a Lab color model representation value;
calculating the difference value between the Lab color model representation value and the standard color value of the pixel point, and selecting the color of the pixel point with the minimum difference value as the standard color of the fragment radar chart;
and taking the radar echo data corresponding to the standard color as the radar echo data of the fragment radar image.
8. The radar map-based rainfall analysis model building system of claim 5, wherein said storage module is a distributed storage system;
the distributed storage system is used for preprocessing each group of radar echo data and each group of actual rainfall, and the preprocessing comprises the following steps:
setting a radar echo threshold range, and deleting abnormal data which exceed the radar echo threshold range in each group of radar echo data;
and setting a rainfall threshold range, and deleting abnormal data exceeding the rainfall threshold range in each group of actual rainfall.
9. The rainfall analysis method based on the radar map is characterized by comprising the following steps:
constructing a rainfall analysis model by the radar chart-based rainfall analysis model construction method according to any one of claims 1 to 4, and obtaining an area rainfall analysis model and a period rainfall analysis model;
acquiring radar echo data and actual rainfall corresponding to the area rainfall analysis model as test samples based on the area rainfall analysis model, and inputting the test samples into the corresponding area rainfall analysis model to obtain rainfall analysis corresponding to the area;
and acquiring radar echo data and actual rainfall corresponding to the time period rainfall analysis model as test samples based on the time period rainfall analysis model, and inputting the test samples into the corresponding time period rainfall analysis model to obtain rainfall analysis corresponding to the time period.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111397686A (en) * | 2020-02-24 | 2020-07-10 | 广州风雨雷科技有限公司 | Urban waterlogging early warning method |
| CN111650572A (en) * | 2020-07-23 | 2020-09-11 | 浪潮云信息技术股份公司 | Method and system for reducing short-time rainfall estimation deviation |
| CN112364029A (en) * | 2021-01-12 | 2021-02-12 | 中国气象局气象探测中心 | Satellite-ground raindrop spectrum parameter matching and evaluating method and system |
| CN112731403A (en) * | 2021-03-17 | 2021-04-30 | 北京墨迹风云科技股份有限公司 | Precipitation prediction method and device |
| CN114488351A (en) * | 2022-02-10 | 2022-05-13 | 中国民用航空华东地区空中交通管理局 | System and method for monitoring and early warning of field intensity echo |
| CN116468737A (en) * | 2023-04-10 | 2023-07-21 | 华能澜沧江水电股份有限公司 | Rainfall information acquisition method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101533095A (en) * | 2009-04-16 | 2009-09-16 | 福州四创软件开发有限公司 | Early radar warning method based on GIS |
| JP2010197185A (en) * | 2009-02-25 | 2010-09-09 | National Research Institute For Earth Science & Disaster Provention | Estimating system of rainfall distribution, and estimation method of rainfall distribution |
| US20170357029A1 (en) * | 2016-06-14 | 2017-12-14 | The Climate Corporation | Supervised neural network to predict unlabeled rain rates |
-
2019
- 2019-09-29 CN CN201910931683.5A patent/CN110750516B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010197185A (en) * | 2009-02-25 | 2010-09-09 | National Research Institute For Earth Science & Disaster Provention | Estimating system of rainfall distribution, and estimation method of rainfall distribution |
| CN101533095A (en) * | 2009-04-16 | 2009-09-16 | 福州四创软件开发有限公司 | Early radar warning method based on GIS |
| US20170357029A1 (en) * | 2016-06-14 | 2017-12-14 | The Climate Corporation | Supervised neural network to predict unlabeled rain rates |
Non-Patent Citations (1)
| Title |
|---|
| 何健;王春林;毛夏;孙向明;: "利用雷达回波与GIS技术反演面雨量研究" * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111397686A (en) * | 2020-02-24 | 2020-07-10 | 广州风雨雷科技有限公司 | Urban waterlogging early warning method |
| CN111650572A (en) * | 2020-07-23 | 2020-09-11 | 浪潮云信息技术股份公司 | Method and system for reducing short-time rainfall estimation deviation |
| CN111650572B (en) * | 2020-07-23 | 2023-02-24 | 浪潮云信息技术股份公司 | Method and system for reducing short-time precipitation estimation deviation |
| CN112364029A (en) * | 2021-01-12 | 2021-02-12 | 中国气象局气象探测中心 | Satellite-ground raindrop spectrum parameter matching and evaluating method and system |
| CN112731403A (en) * | 2021-03-17 | 2021-04-30 | 北京墨迹风云科技股份有限公司 | Precipitation prediction method and device |
| CN114488351A (en) * | 2022-02-10 | 2022-05-13 | 中国民用航空华东地区空中交通管理局 | System and method for monitoring and early warning of field intensity echo |
| CN116468737A (en) * | 2023-04-10 | 2023-07-21 | 华能澜沧江水电股份有限公司 | Rainfall information acquisition method and device |
| CN116468737B (en) * | 2023-04-10 | 2024-07-26 | 华能澜沧江水电股份有限公司 | Rainfall information acquisition method and device |
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