阅读说明：本技术 一种山区暴雨监测方法 (Method for monitoring rainstorm in mountainous area ) 是由 赵现平 马仪 于辉 方明 周仿荣 文刚 赵加能 于 2020-06-09 设计创作，主要内容包括：本申请公开了一种山区暴雨监测方法,包括：获取卫星资料、地面基本站及自动站降水资料；对卫星资料进行预处理,得到卫星图像和卫星资源数据；对卫星图像进行处理,得到山区暴雨云团图像；基于卫星资源数据对山区暴雨云团图像进行处理,识别出山区暴雨云团；对山区暴雨云团进行自动追踪并进行预报；基于卫星资源数据和地面基本站及自动站降水资料建立卫星定量降水估测模型,根据卫星定量降水估测模型对预报后的山区暴雨云团进行定量降水估测。本申请可以实现对偏僻山区的暴雨监测。(The application discloses method for monitoring rainstorm in mountainous area, comprising: acquiring satellite data, ground base stations and automatic station precipitation data; preprocessing the satellite data to obtain a satellite image and satellite resource data; processing the satellite image to obtain a mountainous area rainstorm cloud cluster image; processing the image of the mountainous area rainstorm cloud cluster based on the satellite resource data to identify the mountainous area rainstorm cloud cluster; automatically tracking and forecasting the rainstorm cloud cluster in the mountainous area; and establishing a satellite quantitative precipitation estimation model based on the satellite resource data and the precipitation data of the ground basic station and the automatic station, and performing quantitative precipitation estimation on the forecasted mountainous area rainstorm cloud cluster according to the satellite quantitative precipitation estimation model. This application can realize the monitoring of the torrential rain to remote mountain area.)

1. A method for monitoring rainstorm in a mountainous area, the method comprising:

acquiring satellite data, ground base stations and automatic station precipitation data;

preprocessing the satellite data to obtain a satellite image and satellite resource data;

processing the satellite image to obtain a mountainous area rainstorm cloud cluster image;

processing the mountainous area rainstorm cloud cluster image based on the satellite resource data to identify mountainous area rainstorm cloud clusters;

automatically tracking and forecasting the rainstorm cloud cluster in the mountainous area;

and establishing a satellite quantitative precipitation estimation model based on the satellite resource data and the precipitation data of the ground basic station and the automatic station, and performing quantitative precipitation estimation on the forecasted mountainous area rainstorm cloud cluster according to the satellite quantitative precipitation estimation model.

2. The method of claim 1, wherein the pre-processing of the satellite data comprises:

splicing the satellite data, wherein the satellite data comprises: satellite orbit images;

performing geometric correction on the processed satellite data;

and converting the corrected satellite data into a preset format.

3. The monitoring method of claim 1, wherein the satellite resource data comprises: resolution spectral imager data, visible light infrared scanning radiometer data, and microwave hygrometer data.

4. The method of monitoring of claim 1, wherein said processing the satellite images comprises:

cutting the satellite image to obtain an initial image of the mountainous cloud cluster;

carrying out image binarization processing on the mountainous area cloud cluster initial image;

and carrying out filtering and denoising treatment on the processed mountainous area cloud cluster initial image.

5. The method of claim 1, wherein the processing the image of the mountainous storm cloud based on the satellite resource data to identify the mountainous storm cloud comprises:

analyzing the mountainous area rainstorm cloud cluster image by combining the satellite resource data to obtain the mountainous area rainstorm cloud cluster morphological characteristics;

identifying the mountainous area rainstorm cloud cluster based on the mountainous area rainstorm cloud cluster morphological characteristics;

wherein, mountain area rainstorm cloud form morphological characteristic includes: cloud edge, cloud skeleton, cloud center point, cloud mass center point, cloud area, cloud circularity and cloud average brightness temperature value.

6. The monitoring method according to claim 5, wherein the method for acquiring the cloud center point comprises:

wherein:

m is the number of regional pixels;

lon (i) and lat (i) are respectively the longitude and latitude of the ith pixel point.

7. The monitoring method according to claim 5, wherein the method for acquiring the cloud centroid comprises:

wherein:

m is the number of regional pixels;

sf is the sum of pixel values in the region;

f (i) is the pixel value of the ith pixel point.

8. The method of monitoring of claim 1, wherein said automatically tracking and forecasting said mountainous storm cloud comprises:

storing the rainstorm cloud cluster recognition results of all time periods, and constructing a dynamic four-dimensional space storage structure of the rainstorm cloud cluster;

acquiring a time sequence of the mountainous area rainstorm cloud cluster based on the four-dimensional space storage structure, and tracking the mountainous area rainstorm cloud cluster according to the time sequence;

carrying out extrapolation forecast on the tracked rainstorm cloud cluster in the mountainous area, wherein the forecast indexes comprise: cloud location, cloud area, and cloud intensity.

9. The method of claim 1, wherein the modeling a quantitative satellite precipitation estimate based on the satellite asset data and the ground base station and automatic station precipitation data comprises:

sampling the satellite resource data to obtain sample training data; carrying out normalization processing on the sample training data to obtain normalized sample data;

processing the satellite resource data based on a correlation analysis method to obtain cloud cluster characteristic parameters;

processing the normalized sample data, the cloud cluster characteristic parameters, the ground basic station and the automatic station rainfall data based on a multiple regression analysis method to obtain a satellite quantitative rainfall estimation correction equation;

solving the satellite quantitative precipitation estimation correction equation based on the scimit-leann machine learning algorithm to obtain a satellite quantitative precipitation estimation model.

Technical Field

The application relates to the technical field of atmospheric monitoring, in particular to a mountainous area rainstorm monitoring method.

Background

Rainstorms can cause natural disasters such as floods, landslides, debris flows and the like, so that a series of natural disasters can be avoided or alleviated only by accurately forecasting the rainstorms and preventing in advance. The position and the range of the rainstorm can be further predicted by accurately identifying the position and the range of the rainstorm cloud cluster, so that the monitoring is realized.

Disclosure of Invention

The application provides a method for monitoring rainstorm in a mountainous area, which aims to solve the problem that the prior art cannot monitor the rainstorm in a remote mountainous area.

In order to solve the technical problem, the embodiment of the application discloses the following technical scheme:

the application provides a method for monitoring rainstorm in a mountainous area, which comprises the following steps:

acquiring satellite data, ground base stations and automatic station precipitation data;

preprocessing the satellite data to obtain a satellite image and satellite resource data;

processing the satellite image to obtain a mountainous area rainstorm cloud cluster image;

processing the mountainous area rainstorm cloud cluster image based on the satellite resource data to identify mountainous area rainstorm cloud clusters;

automatically tracking and forecasting the rainstorm cloud cluster in the mountainous area;

and establishing a satellite quantitative precipitation estimation model based on the satellite resource data and the precipitation data of the ground basic station and the automatic station, and performing quantitative precipitation estimation on the forecasted mountainous area rainstorm cloud cluster according to the satellite quantitative precipitation estimation model.

Optionally, the preprocessing the satellite data includes:

splicing the satellite data, wherein the satellite data comprises: satellite orbit images;

performing geometric correction on the processed satellite data;

and converting the corrected satellite data into a preset format.

Optionally, the satellite resource data includes: resolution spectral imager data, visible light infrared scanning radiometer data, and microwave hygrometer data.

Optionally, the processing the satellite image data includes:

cutting the satellite image to obtain an initial image of the mountainous cloud cluster;

carrying out image binarization processing on the mountainous area cloud cluster initial image;

and carrying out filtering and denoising treatment on the processed mountainous area cloud cluster initial image.

Optionally, the processing the image of the mountainous area rainstorm cloud based on the satellite resource data to identify the mountainous area rainstorm cloud includes:

analyzing the mountainous area rainstorm cloud cluster image by combining the satellite resource data to obtain the mountainous area rainstorm cloud cluster morphological characteristics;

identifying the mountainous area rainstorm cloud cluster based on the mountainous area rainstorm cloud cluster morphological characteristics;

wherein, mountain area rainstorm cloud form morphological characteristic includes: cloud edge, cloud skeleton, cloud center point, cloud mass center point, cloud area, cloud circularity and cloud average brightness temperature value.

Optionally, the method for acquiring the cloud center point includes:

wherein:

m is the number of regional pixels;

lon (i) and lat (i) are respectively the longitude and latitude of the ith pixel point.

Optionally, the method for obtaining the cloud centroid point includes:

wherein:

m is the number of regional pixels;

sf is the sum of pixel values in the region;

f (i) is the pixel value of the ith pixel point.

Optionally, the automatically tracking and forecasting the rainstorm cloud cluster in the mountainous area includes:

storing the rainstorm cloud cluster recognition results of all time periods, and constructing a dynamic four-dimensional space storage structure of the rainstorm cloud cluster;

acquiring a time sequence of the mountainous area rainstorm cloud cluster based on the four-dimensional space storage structure, and tracking the mountainous area rainstorm cloud cluster according to the time sequence;

carrying out extrapolation forecast on the tracked rainstorm cloud cluster in the mountainous area, wherein the forecast indexes comprise: cloud location, cloud area, and cloud intensity.

Optionally, the building a satellite quantitative precipitation estimation model based on the satellite resource data, the ground base station and the automatic station precipitation data includes:

sampling the satellite resource data to obtain sample training data; carrying out normalization processing on the sample training data to obtain normalized sample data;

processing the satellite resource data based on a correlation analysis method to obtain cloud cluster characteristic parameters;

processing the normalized sample data, the cloud cluster characteristic parameters, the ground basic station and the automatic station rainfall data based on a multiple regression analysis method to obtain a satellite quantitative rainfall estimation correction equation;

solving the satellite quantitative precipitation estimation correction equation based on the scimit-leann machine learning algorithm to obtain a satellite quantitative precipitation estimation model.

Compared with the prior art, the beneficial effect of this application is:

the application provides a method for monitoring rainstorm in a mountainous area, which comprises the following steps: acquiring satellite data, ground base stations and automatic station precipitation data; preprocessing the satellite data to obtain a satellite image and satellite resource data; processing the satellite image to obtain a mountainous area rainstorm cloud cluster image; processing the image of the mountainous area rainstorm cloud cluster based on the satellite resource data to identify the mountainous area rainstorm cloud cluster; automatically tracking and forecasting the rainstorm cloud cluster in the mountainous area; and establishing a satellite quantitative precipitation estimation model based on the satellite resource data and the precipitation data of the ground basic station and the automatic station, and performing quantitative precipitation estimation on the forecasted mountainous area rainstorm cloud cluster according to the satellite quantitative precipitation estimation model. This application utilizes the satellite to monitor the torrential rain, and the coverage rate is high, combines data such as infrared, visible light and the steam resource of satellite to carry out continuous monitoring and carry out the analysis to weather simultaneously, can obtain the evolution information of torrential rain cloud cluster, and the rate of accuracy is high to whether can take place the torrential rain to remote mountain area and realized accurate monitoring.

Drawings

In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any creative effort.

Fig. 1 is a schematic flow chart of a method for monitoring rainstorm in a mountainous area according to an embodiment of the present application.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

Referring to fig. 1, a schematic flow chart of a method for monitoring rainstorm in a mountainous area according to an embodiment of the present application is provided. As can be seen with reference to fig. 1, the method comprises the following steps:

s1, acquiring satellite data, ground base station and automatic station precipitation data;

s2, preprocessing the satellite data to obtain satellite images and satellite resource data;

s3, processing the satellite images to obtain mountainous area rainstorm cloud images;

s4, processing the mountainous area rainstorm cloud cluster image based on the satellite resource data, and identifying mountainous area rainstorm cloud clusters;

s5, automatically tracking and forecasting the rainstorm cloud cluster in the mountainous area;

and S6, establishing a satellite quantitative precipitation estimation model based on the satellite resource data and the ground basic station and automatic station precipitation data, and performing quantitative precipitation estimation on the forecasted mountainous area rainstorm cloud cluster according to the satellite quantitative precipitation estimation model.

The respective steps will be described in detail below.

In step S1, satellite data, ground base station data, and automatic station precipitation data are obtained.

In the embodiment of the application, the FY3-C/D satellite is used for monitoring the rainstorm. The FY3-C/D satellite belongs to a polar orbit satellite, and an orbit file is generated every 5 minutes as satellite data.

Specifically, the satellite data is a satellite orbit image.

In step S2, the satellite data is preprocessed to obtain satellite images and satellite resource data.

Specifically, the pretreatment process comprises:

s201, splicing the satellite data.

According to the embodiment of the application, the images are possibly separately located on two adjacent files when the satellite passes through the monitoring research area, so that the satellite orbit images need to be spliced.

S202, geometric correction is carried out on the processed satellite data.

The FY3-C/D satellite L1 level product data format is HDF5, the channel grid data set, the longitude and latitude data set, the calibration coefficient and other attribute information are separated, the longitude and latitude lines are irregular curves, the calibration coefficient of each channel is different due to the difference of spectral response, and geometric correction is needed to obtain the channel reflectivity and radiation brightness temperature data with geographic positioning for further analysis and application.

Specifically, the embodiment of the application directly performs geometric correction on the FY3 image by using a GLT geometric correction method. Because the FY3 data contains Latitude and Longitude information for each pixel, the Latitude data for the pixel is stored in the "Latitude" dataset and the Longitude data is stored in the "Longituude" dataset.

The GLT geometric correction method (GLT) is a conventional technique, and generates a geographic location lookup table file according to input longitude and latitude data, where the geographic location lookup table file includes actual geographic location information of an initial image pixel in a corrected image. The geographic location look-up table file is a two-dimensional image file, and two wave bands contained in the file respectively represent rows and columns of the corrected image. The gray value corresponding to the file represents the geographical position coordinate information corresponding to each pixel of the original image, and the geographical position coordinate information is stored by using signed integer. When the symbol is positive, the picture element uses the real picture element position value; a negative sign indicates that the position value of an adjacent picture element is used, and a value of 0 indicates that there is no position value of an adjacent picture element in the surrounding 7 picture elements. The GLT file contains the geographic positioning information of each pixel in the initial image, the processing of low-resolution image data by using a quadratic polynomial geometric correction method through ground control points is avoided, and the correction precision is high.

S203, converting the corrected satellite data into a preset format.

Specifically, format conversion is performed on the spliced and corrected satellite data, and other data formats such as TIF, MICAPS4 and the like are output as required.

The embodiment of the application can obtain the following satellite resource data: resolution Spectral Imager (MERSI) data, visible-infrared scanning radiometer (VIRR) data, and microwave hygrometer (MWHS) data.

Wherein: the FY3 satellite-mounted visible light infrared scanning radiometer VIRR (VisibleandInfrared radiometer) has 10 observation channels with resolution of 1 km, wherein the observation channels comprise a visible light channel with high sensitivity and three infrared atmospheric window area channels. The visible light infrared scanning radiometer can be mainly used for monitoring global cloud cover, judging cloud height, cloud type and phase state, detecting earth surface temperature and ocean water color, monitoring vegetation growth condition, monitoring high-temperature thermal power, identifying earth surface coverage type and the like. The thermal infrared channel of the VIRR is sensitive to radiation change of the underlying surface and is mainly used for extracting surface characteristic parameters. The medium resolution spectral imager MERSI (MediumResolutionSpectraImager) has the characteristics of multiple spectra and high resolution, has the detection capability of 20 channels from visible light to thermal infrared, and can detect visible light reflection radiation of atmosphere, land and sea and radiation emitted by thermal infrared. The MERSI can realize global remote sensing monitoring on terrestrial surface characteristics such as vegetation, ecology, earth surface coverage types and the like. Microwave hygrometer (MWHS) data is used to detect global atmospheric humidity profile and heavy rainfall.

In step S3, a mountainous area rainstorm cloud image is acquired. The method specifically comprises the following steps:

s301, cutting the satellite image to obtain an initial image of the mountainous cloud cluster.

The original image comprises a large-area region, in order to reduce the calculation amount and strengthen the pertinence of calculation analysis, the original image is firstly cut, and a target mountain area cloud picture is extracted to serve as an initial image.

And S302, carrying out image binarization processing on the mountainous area cloud cluster initial image.

Specifically, image binarization is a process of converting a multi-gray-scale image into an image with only two gray-scale levels by a certain processing method in the prior art. The image binary segmentation algorithm is a highly researched field, and has many different algorithms. A threshold binarization method is used herein.

In the embodiment of the present application, the threshold standard selection for image binarization is the same as the brightness temperature value of the medium-scale convection cloud cluster identification standard, that is: the A standard is-32 ℃, the B standard is-52 ℃ and is used as a brightness temperature threshold, pixel points lower than the threshold are marked by 1, and other pixel points are marked by 0.

And S303, carrying out filtering and denoising treatment on the processed mountainous area cloud cluster initial image.

It should be noted that the obtained initial binary image has many problems, such as noise points and burrs, small holes in large cloud blocks, and many fine cloud blocks, which bring inconvenience to subsequent feature extraction, so the embodiment of the present application performs denoising processing to extract a plurality of main cloud blocks.

Specifically, the filtering processing in the embodiment of the present application adopts methods such as mean filtering and median filtering to eliminate spike noise interference, and processes the image so as to facilitate subsequent target identification and feature extraction.

And (3) mean filtering method:

for the (m, n) point, the pixel value of the (m, n) point is replaced by the middle value of the average value of the pixels in the sub-window with the size of k × k around the (m, n) point.

The median filtering method comprises the following steps:

for the (m, n) point, the pixel value of the (m, n) point is replaced with the median of all pixel values within a sub-window of k × k size around the (m, n) point.

In step S4, the mountainous area rainstorm cloud image is processed based on the satellite resource data, and the mountainous area rainstorm cloud is identified.

Specifically, the method comprises the following steps:

and analyzing the image of the mountainous area rainstorm cloud cluster by combining the satellite resource data to obtain the morphological characteristics of the mountainous area rainstorm cloud cluster. And identifying the mountainous area rainstorm cloud cluster based on the mountainous area rainstorm cloud cluster morphological characteristics.

The cloud morphological characteristics comprise: cloud cluster type, cloud cluster edge, cloud cluster skeleton, cloud cluster center point, cloud cluster area, cloud cluster eccentricity and other morphological characteristics.

In the embodiment of the application, the selected morphological characteristics of the rainstorm cloud cluster in the mountainous area comprise: cloud edge, cloud skeleton, cloud center point, cloud mass center point, cloud area, cloud circularity and cloud average brightness temperature value.

Wherein:

acquiring cloud edge:

specifically, the edge of the image is an important feature of the image, and the position, the shape, the area and other features of the image can be determined by using a small amount of information.

In the embodiment of the application, the cloud cluster to be detected is set as a set A, the boundary of the set A is B (A), the A is corroded by the B, and the corroded part is subtracted from the A to obtain the edge of the cloud cluster.

Obtaining a cloud cluster framework:

specifically, the skeleton of the image is one of the main features describing the geometric shape and topological properties of the image, and the process of finding the skeleton from the image is called thinning. In the embodiment of the application, the edges of the image are extracted layer by layer until only one pixel is left, and the remaining line is the required cloud cluster skeleton.

Acquiring a cloud center point:

it should be noted that, in the embodiment of the present application, each target is identified on the basis of a binary image, and then features such as a center point, a centroid point, an area, a circularity, an average brightness temperature value, and the like of each target are extracted. Removing objects with an area of less than 1 kilo square kilometer or a circularity of less than 0.5, the remaining objects being possible clouds of storms

Specifically, the latitude and longitude of the geometric center point are as follows:

wherein:

m is the number of regional pixels;

lon (i) and lat (i) are respectively the longitude and latitude of the ith pixel point.

Obtaining cloud mass center points:

the latitude and longitude of the area pixel value quality center point (i.e. the centroid point) are as follows:

wherein:

m is the number of regional pixels;

sf is the sum of pixel values in the region;

f (i) is the pixel value of the ith pixel point.

The centroid point and the geometric center point can identify the position of the rainstorm cloud cluster, and the deviation of the centroid point and the geometric center point can be used for judging the mass deviation direction of the rainstorm cloud cluster. For example, the centroid is located to the right of the geometric center, indicating that the cold zone of the rainstorm cloud is shifted to the east, i.e., the east is cooler than the west.

Acquiring the cloud cluster area:

the calculation of the spherical area of the cloud area is obtained by calculating according to a spherical polygon formed by boundary points of the cloud area, and the range and the strength of the cloud area are measured by the characteristics.

Acquiring cloud cluster circularity:

the circularity is calculated on the basis of the area and the perimeter and is used for measuring the complexity of the shape of the cloud area. Namely:

where e is the circularity, s is the area, and l is the perimeter. For a circle, the radius is r and the area is π r²。

The circumference is 2 pi r and the circularity is 1. The more complex the shape, the less its circularity.

Acquiring the average cloud brightness temperature value:

the cloud area average brightness temperature value is the average value of all pixel values in the cloud cluster and is used for measuring the intensity of the rainstorm cloud cluster. Namely:

in step S5, the mountainous area rainstorm cloud is automatically tracked and forecasted.

The method comprises the following steps:

s501, storing the rainstorm cloud cluster recognition results in all time periods, and constructing a dynamic four-dimensional space storage structure of the rainstorm cloud cluster.

Specifically, in order to perform time series tracking on the rainstorm cloud cluster, the identification result of the rainstorm cloud cluster at each time step needs to be saved. Each time step may contain a plurality of rainstorm clouds, and the number of rainstorm clouds contained in different time steps may be different, so that the storage of the rainstorm clouds is a dynamic four-dimensional space structure.

S502, acquiring a time sequence of the mountainous area rainstorm cloud cluster based on the four-dimensional space storage structure, and tracking the mountainous area rainstorm cloud cluster according to the time sequence.

Specifically, the purpose of automatic tracking is to form a time series of each target and further calculate the position movement of the moving target and the change of characteristics such as area and intensity. And extrapolating the movement and intensity change of the early warning target for hours in the future according to the tracked time sequence. The basis for realizing automatic tracking is the four-dimensional space storage structure. By constructing a time series of individual objects, pointers to the same object in the next time step are formed. The specific implementation of automatic tracking is the comparison of targets in adjacent time steps. This comparison is made purely in terms of the features that describe the object, where the key comparisons include:

when the position difference does not exceed 50 m/s (the maximum moving distance in 1 hour does not exceed 180 km), the position difference of the mass center is calculated.

When the area difference does not exceed 5 square kilometers per second (the maximum area difference of 1 hour does not exceed 18000 square kilometers), the calculation is carried out through the area difference.

When the intensity difference does not exceed 0.001 degree/sec (1 hour maximum intensity difference does not exceed 3.6 degrees), it is calculated by the average intensity difference.

If two targets in adjacent time steps satisfy the above relationship, the same target is considered. In the storage structure, the target time pointer for the previous time step points to the same target for the next time step.

After tracking, two special cases may occur, namely that a plurality of targets in the previous time step point to one target in the next time step, and that a target in the previous time step points to a plurality of targets in the next time step. The first case is merging of targets and the second case is splitting of targets.

S503, carrying out extrapolation forecast on the tracked rainstorm cloud cluster in the mountainous area, wherein the forecast indexes comprise: cloud location, cloud area, and cloud intensity.

In the embodiment of the application, the extrapolating and forecasting of the rainstorm cloud cluster in the mountainous area specifically comprises the following steps:

extrapolation forecast index and aging:

the extrapolation prediction is to comprehensively predict the positions and the intensities of the moving target of a plurality of times in the future according to the change conditions of indexes such as the positions, the areas and the intensities of the moving target of a plurality of times in the past. In the experimental process, the extrapolated forecast aging is 1 hour, and the forecast time interval is 30 minutes. The forecast indexes include position, area and intensity.

An extrapolation forecasting method comprises the following steps:

when no other meteorological data are referred to, the extrapolation prediction is to perform linear fitting by adopting a least square method according to the feature quantity of a target cloud cluster in past times, and predict the feature change of 2 times of the target in the future time interval of 30 minutes according to a regression line obtained by fitting and the feature of the target in the last time. And finally, acquiring a time sequence of a certain target at the time step according to the time chain, and carrying out reverse linear extrapolation according to the characteristic change of the target to obtain the change within 1 hour in the future.

In step S6, a satellite quantitative precipitation estimation model is established based on the satellite resource data and the ground base station and automatic station precipitation data, and quantitative precipitation estimation is performed on the forecasted mountainous area rainstorm cloud according to the satellite quantitative precipitation estimation model.

Specifically, the method for acquiring the satellite quantitative precipitation estimation model comprises the following steps:

s6011, sampling the satellite resource data to obtain sample training data; and carrying out normalization processing on the sample training data to obtain normalized sample data.

The inventor of the application finds that a large number of linear precipitation forecast researches show that the brightness and the temperature have a close relation with precipitation. In view of this, the sample training data is normalized by using the brightness and temperature data of the infrared 1, the infrared 2, the water vapor and the visible light channels. The processing formula is as follows:

t＝(t-t_min)/(t_max-t_min)

wherein:

t_minand t_maxRespectively the maximum and minimum values of the characteristic quantity in the training sample data set;

and t is an input factor obtained after normalization processing.

From this normalized sample data for statistical analysis is obtained.

S6012, processing the satellite resource data based on a correlation analysis method to obtain cloud cluster characteristic parameters.

Specifically, the correlation analysis method is a statistical analysis method for studying the correlation between two or more equally positioned random variables.

S6013, processing the normalized sample data, the cloud cluster characteristic parameters, the ground basic station and the automatic station rainfall data based on a multiple regression analysis method to obtain a satellite quantitative rainfall estimation correction equation.

Specifically, the multivariate regression analysis method is a regression analysis method for studying the relationship between a plurality of variables, and can be divided into regression analysis of one dependent variable to a plurality of independent variables (simply referred to as "one-to-many" regression analysis) and regression analysis of a plurality of dependent variables to a plurality of independent variables (simply referred to as "many-to-many" regression analysis) according to the number corresponding relationship between the dependent variable and the independent variable, and can be divided into linear regression analysis and nonlinear regression analysis according to the type of the regression model.

S6014, solving the satellite quantitative precipitation estimation correction equation based on the scimit-learn machine learning algorithm to obtain a satellite quantitative precipitation estimation model.

Specifically, a scimit-spare (abbreviated as skspare) machine learning algorithm is the prior art, is a machine learning Python library, and is based on a BSD open source license. The basic function of scimit-spare is mainly divided into six parts: classification, regression, clustering, data dimensionality reduction, model selection and data preprocessing. Commonly used classifiers include SVM, KNN, Bayes, linear regression, logistic regression, decision trees, random forest, xgboost, GBDT, boosting, neural network NN. Common dimension reduction methods include TF-IDF, topic model LDA, principal component analysis PCA, and the like.

In the embodiment of the application, statistical analysis is performed by means of a scimit-learn machine learning algorithm, correlation and significance test is calculated, quantitative rainfall estimation correlation factors are selected, the satellite quantitative rainfall estimation correction equation is solved, and a satellite quantitative rainfall estimation regression model is obtained.

And S602, carrying out quantitative precipitation estimation on the forecasted rainstorm cloud cluster in the mountainous area according to the satellite quantitative precipitation estimation model.

The embodiment of the application adopts a satellite quantitative precipitation estimation model to estimate quantitative precipitation according to FY3-C/D satellite real-time observation data, and concretely, the FY3-C/D real-time observation data comprise total water-reducible quantity, ground water-reducible rate, atmospheric temperature profile, water vapor profile, surface soil moisture and the like.

The embodiment of the application utilizes the rainfall data of the ground basic station and the automatic station, increases the size of the sample space, and increases the stability and the forecast precision of the quantitative rainfall estimation model. The objectivity and the business of quantitative precipitation estimation are realized.

In summary, compared with the prior art, the method has the following beneficial effects:

the embodiment of the application utilizes the satellite to monitor, the uninterrupted operation of the satellite can continuously acquire data, and the data can cover the whole country, so that a remote mountain area can be monitored, meanwhile, the development and evolution information of a mountain area rainstorm cloud cluster can be acquired by combining the continuous monitoring of infrared, visible light and water vapor channels of geostationary satellites such as FY-3C/D/E and the like and the matching analysis of a weather system, and the accurate monitoring of the mountain area rainstorm is realized.

Since the above embodiments are all described by referring to and combining with other embodiments, the same portions are provided between different embodiments, and the same and similar portions between the various embodiments in this specification may be referred to each other. And will not be described in detail herein.

It is noted that, in this specification, relational terms such as "first" and "second," and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such circuit structure, article, or apparatus. The term "comprising" a defined element does not, without further limitation, exclude the presence of other like elements in a circuit structure, article, or device that comprises the element.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims. The above-described embodiments of the present application do not limit the scope of the present application.