CN113095590A - High spatial-temporal resolution reconstruction analysis and short-term prediction method for microwave horizontal rainfall field - Google Patents
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Abstract
The invention provides a high spatial and temporal resolution reconstruction analysis and short-term prediction method for a microwave horizontal rainfall field, which specifically comprises the following steps: s1, building a microwave link rainfall observation network based on the distribution of the ground microwave stations, and carrying out meshing processing on a rainfall area; s2, calculating the average rain attenuation of the microwave link path, and performing reconstruction analysis of a rainfall field with high spatial-temporal resolution level according to the correlation between the rainfall intensities of all the positions in the rainfall area; and S3, based on the reconstructed high-space-time resolution continuous horizontal rainfall field, the prediction of the short-term rainfall field is realized by utilizing a deep learning method. The invention establishes a high-space-time resolution horizontal rainfall field reconstruction model and a rainfall field short-term extrapolation model considering rainfall space-time change characteristics, realizes the reconstruction and extrapolation prediction of the high-space-time resolution horizontal rainfall field by microwave link signals, and lays a technical foundation for establishing rainfall monitoring and early warning nets in a high-space-time resolution area.
Description
Technical Field
The invention relates to the field of rainfall measurement, in particular to a high spatial-temporal resolution reconstruction analysis and short-term prediction method for a microwave horizontal rainfall field.
Background
The method for measuring rainfall by using the attenuation effect of the microwave link is a new rainfall measurement technology appearing in recent years, has great potential and vital significance in aspects of monitoring natural disasters caused by strong rainfall, guaranteeing future high-technology military operations and the like, is an effective method for obtaining a high-space-time-resolution rainfall field, and has the advantages of large sampling space-time density, high resolution, wide coverage range and the like.
At present, the application of the microwave link in regional rainfall monitoring is just started, particularly, the rainfall field reconstruction analysis based on the satellite-ground microwave link is mostly realized based on an interpolation method, and no relevant scholars are internationally discussed and researched aiming at the short-term prediction of the rainfall field based on the satellite-ground microwave link. Based on the existing research, the invention establishes a horizontal rainfall field reconstruction model and a rainfall field short-term extrapolation model considering rainfall temporal-spatial variation characteristics, realizes the reconstruction analysis and extrapolation prediction of a high temporal-spatial resolution horizontal rainfall field by utilizing multi-station microwave link signals, and lays a technical foundation for establishing a rainfall monitoring early warning network in a high temporal-spatial resolution area.
Disclosure of Invention
The invention provides a high spatial-temporal resolution reconstruction analysis and short-term rainfall field prediction method for a microwave horizontal rainfall field.
In order to achieve the purpose, the invention provides the following scheme:
the high spatial-temporal resolution reconstruction analysis and short-term prediction method for the microwave horizontal rainfall field is characterized by comprising the following steps of:
s1, building a microwave link rainfall observation network composed of microwave links based on the distribution of the ground microwave stations, and carrying out gridding treatment on a rainfall area;
s2, calculating an average rainfall attenuation value of a microwave link path, and performing reconstruction analysis of a high-spatial-temporal-resolution level rainfall field according to the correlation between rainfall intensities of all positions in the rainfall region;
and S3, based on the reconstructed high-spatial-temporal-resolution horizontal rainfall field, predicting the short-term rainfall field by using a deep learning method.
Preferably, in S1, the types of the microwave link rainfall observation networks include a satellite-ground microwave link network and a ground microwave link network.
Preferably, the relevant factors composing the microwave link rainfall observation network include the number of the composing link networks, the length of the links, the density of the link networks and the spatial distribution of the link networks, and further include irregular grids based on the distribution condition and the uneven distribution of the satellites and the ground receiving stations.
Preferably, in S2, the step of reconstructing and analyzing the rainfall field with high spatial-temporal resolution level includes:
calculating the average rainfall condition of the microwave link path to obtain a path rainfall attenuation value;
obtaining the average rainfall intensity of all links through a power model;
and reconstructing the horizontal rainfall field with high space-time resolution by a kriging interpolation method or a short-distance weighting method based on the correlation between the rainfall intensities of all the positions in the rainfall region by using the obtained average rainfall intensity of all the links.
Preferably, in S3, the step of predicting the short rainfall field by using the deep learning method includes:
s3.1, constructing a deep learning network with an input and output structure for the high spatial and temporal resolution area rainfall field extrapolation;
s3.2, establishing a relation training set of the rainfall intensity in each grid and the rainfall intensities at n adjacent moments in the horizontal rainfall field through historical data;
s3.3, training the deep learning network by using an LSTM method and combining with the distribution characteristics of the rainfall intensity in the rainfall field actually measured in the training set, and establishing a mapping relation for predicting the rainfall intensity in the rainfall field at the next moment;
s3.4, according to the error between the rainfall intensity of the rainfall field and the actually measured rainfall field predicted at the next moment, adjusting the parameters of the LSTM model in an iteration mode until the loss function meets the termination condition or reaches the maximum iteration times;
and S3.5, predicting the rainfall intensity of the actually measured rainfall field according to the deep learning network model to obtain the predicted short-term rainfall field.
Preferably, the deep learning network comprises a sequence input layer, a convolutional layer, a pooling layer, an LSTM layer, a fully-connected layer, a regression layer, and an output layer.
Preferably, said S3.3 specifically comprises:
the sequence input layer receives rainfall field samples from the training set, the rainfall intensity characteristics in each rainfall field are extracted through the convolutional layer, the characteristic vectors generated after passing through the pooling layer are sent to the LSTM layer, output quantities are generated and enter the full-connection layer, and the rainfall intensity mapping relation in the rainfall field at the next moment is established through the full-connection layer.
Preferably, in S3.4, the termination condition is that the loss function loss is less than 0.01, and the maximum number of iterations is 500.
The invention has the beneficial effects that:
according to the high spatial and temporal resolution reconstruction analysis and short-term prediction method for the microwave horizontal rainfall field, a high spatial and temporal resolution horizontal rainfall field reconstruction model and a rainfall field short-term extrapolation model considering rainfall temporal and spatial variation characteristics are established, reconstruction and extrapolation prediction of the high spatial and temporal resolution horizontal rainfall field by microwave link signals are realized, and a technical foundation is laid for establishing rainfall monitoring and early warning networks in a high spatial and temporal resolution area.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a link network detection diagram of the present invention;
FIG. 3 is a schematic diagram of the prediction of a short rainfall field based on a deep learning network according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The invention utilizes the attenuation information of satellite-ground link or ground microwave link signals in the propagation of a rainfall region, carries out reconstruction analysis of a horizontal rainfall field with high space-time resolution through microwave link networking based on multiple stations, and carries out prediction of a short-term rainfall field based on deep learning, as shown in the attached figure 1. The method for high space-time resolution reconstruction analysis and short-term prediction of a microwave horizontal rainfall field by taking a satellite-ground link network as an embodiment mainly comprises the following steps:
the method comprises the following steps: based on the position of a ground satellite receiving station, a satellite-ground link network composed of k satellite-ground links is designed and built, the specific structure distribution of the satellite-ground link network is shown in fig. 2, wherein 40 ground stations receive signals of 5 broadcasting satellites, the satellite-ground link network is composed of 93 links, the received satellite signals are all vertically polarized, and the working frequency is 11.56 GHz-12.59 GHz.
As shown in fig. 2, the horizontal rainfall region is gridded, and the whole rainfall field is divided into 35 × 35 grids, and the resolution of each grid is 1 × 1km2The distribution of rain intensity within each grid is assumed to be uniform and the variation of rain intensity over vertical height is not taken into account.
Step two: and calculating the average rainfall condition of the satellite-ground link path. For the kth satellite-ground link in the rainfall area, the rainfall attenuation of the path is AR(k) Can be expressed as:
in the formula, h0Height of rain zone, theta elevation angle of satellite antenna, gammaiFor the attenuation of the ith lattice point, N represents the number of grids traversed by the kth link, L represents the length of the horizontal projection of the link on the ground, diRepresenting the projected length of the link in the ith mesh.
And obtaining the average rainfall intensity R (k) of all links by utilizing a power-law model according to the obtained rainfall attenuation value:
R(k)＝[AR(k)sinθ/h0α]β-1
wherein, alpha and beta are power coefficient, h0Is the height of the rain zone, and theta is the satellite antenna elevation angle.
Reconstructing a horizontal rainfall field with high space-time resolution by using the obtained average rainfall intensity of all links and a kriging interpolation method according to the correlation between the rainfall intensities of all positions in the rainfall area:
in the formula, R (x)i,yi) Is the satellite-to-ground link at position R (x)t,yt) Inverted rainfall intensity, n denotes the number of satellite-to-ground links, λiRepresents R (x) as a weight coefficienti,yi) And R (x)t,yt) Is related toAnd (4) degree.
Step three: according to a continuous rainfall field reconstructed by a satellite-ground link network, a learning network for region rainfall field extrapolation with high space-time resolution is designed based on a deep learning mode, so that the short-term rainfall approaching process is predicted, and the method specifically comprises the following steps:
(1) a deep learning network with an Input/Output structure for high-spatial-resolution regional rainfall field extrapolation is designed, and as shown in fig. 3, the deep learning network provided by the present invention for realizing short-term rainfall field prediction includes 7 different functional layers, which are a Sequence Input Layer (Sequence Input Layer), a Convolutional Layer (Convolutional Layer), a Pooling Layer (Pooling Layer), a long-short time neural network Layer (LSTM Layer), a full connection Layer (full Connected), a Regression Layer (Regression Layer), and an Output Layer (Output Layer).
(2) Constructing a deep learning network with an input and output structure for the high spatial and temporal resolution area rainfall field extrapolation; establishing a relation training set of the raininess of each grid point and the raininess of n adjacent moments in the horizontal rainfall field by using historical data; and training the deep learning network by using an LSTM method and combining with the distribution characteristics of the rainfall intensity in the rainfall field actually measured in the training set. Specifically, a sequence input layer receives rainfall field samples from a training set, a two-dimensional convolution layer extracts rainfall intensity characteristics in each rainfall field through a convolution kernel of 3 x 3, a two-dimensional pooling layer is used for reducing redundant characteristic quantities and reflecting rainfall intensity spatial distribution information, the generated characteristic quantities enter an LSTM unit composed of a forgetting gate, an updating gate and an output gate, the generated output quantities enter a full-connection layer, and a mapping relation for predicting the rainfall intensity in the rainfall field at the next moment is established through the full-connection layer.
(3) And (3) predicting the error between the rainfall intensity of the rainfall field and the actually measured rainfall field according to the next moment, and adjusting the parameters of the LSTM model in an iteration mode until the loss function meets the termination condition or reaches the maximum iteration times. In the deep learning network, the termination condition may be that the loss function loss is less than 0.01, and the maximum iteration number is 500.
(4) And predicting the rainfall intensity of the actually measured rainfall field according to the determined deep learning network model to obtain the predicted short rainfall field.
The invention has the following beneficial effects:
the invention establishes a high-space-time resolution horizontal rainfall field reconstruction model and a rainfall field short-term extrapolation model considering rainfall space-time change characteristics, realizes the reconstruction and extrapolation prediction of the high-space-time resolution horizontal rainfall field by microwave link signals, and lays a technical foundation for establishing rainfall monitoring and early warning nets in a high-space-time resolution area.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (8)
1. The high spatial-temporal resolution reconstruction analysis and short-term prediction method for the microwave horizontal rainfall field is characterized by comprising the following steps of:
s1, building a microwave link rainfall observation network composed of microwave links based on the distribution of the ground microwave stations, and carrying out gridding treatment on a rainfall area;
s2, calculating an average rainfall attenuation value of a microwave link path, and performing reconstruction analysis of a high-spatial-temporal-resolution level rainfall field according to the correlation between rainfall intensities of all positions in the rainfall region;
and S3, based on the reconstructed high-spatial-temporal-resolution horizontal rainfall field, predicting the short-term rainfall field by using a deep learning method.
2. The method for high spatial-temporal resolution reconstruction analysis and short-term prediction of a microwave horizontal rainfall field according to claim 1, wherein in S1, the types of the microwave link rainfall observation networks include a satellite-ground microwave link network and a ground microwave link network.
3. The method for high spatial-temporal resolution reconstruction analysis and short-term prediction of microwave horizontal rainfall field according to claim 2, wherein the relevant factors composing the microwave link rainfall observation network include the number of the links composing the link network, the length of the links, the density of the link network, the spatial distribution of the link network, and irregular grids based on the distribution condition and the uneven distribution of the satellite and the ground receiving station.
4. The method for high spatial and temporal resolution reconstruction analysis and shorthand prediction of a microwave horizontal rainfall field according to claim 1, wherein in S2, the step of high spatial and temporal resolution reconstruction analysis of a horizontal rainfall field comprises:
calculating the average rainfall condition of the microwave link path to obtain a path rainfall attenuation value;
obtaining the average rainfall intensity of all links through a power model;
and reconstructing the horizontal rainfall field with high space-time resolution by a kriging interpolation method or a short-distance weighting method based on the correlation between the rainfall intensities of all the positions in the rainfall region by using the obtained average rainfall intensity of all the links.
5. The method for high spatial and temporal resolution reconstruction analysis and short-term prediction of a microwave horizontal rainfall field according to claim 1, wherein the step of predicting the short-term rainfall field by using a deep learning method in S3 comprises:
s3.1, constructing a deep learning network with an input and output structure for the high spatial and temporal resolution area rainfall field extrapolation;
s3.2, establishing a relation training set of the rainfall intensity in each grid and the rainfall intensities at n adjacent moments in the horizontal rainfall field through historical data;
s3.3, training the deep learning network by using an LSTM method and combining with the distribution characteristics of the rainfall intensity in the rainfall field actually measured in the training set, and establishing a mapping relation for predicting the rainfall intensity in the rainfall field at the next moment;
s3.4, according to the error between the rainfall intensity of the rainfall field and the actually measured rainfall field predicted at the next moment, adjusting the parameters of the LSTM model in an iteration mode until the loss function meets the termination condition or reaches the maximum iteration times;
and S3.5, predicting the rainfall intensity of the actually measured rainfall field according to the deep learning network model to obtain the predicted short-term rainfall field.
6. The method of claim 5, wherein the deep learning network comprises a sequence input layer, a convolutional layer, a pooling layer, an LSTM layer, a fully-connected layer, a regression layer, and an output layer.
7. The microwave horizontal rainfall field high spatial-temporal resolution reconstruction analysis and shorthand prediction method according to claim 5, wherein the S3.3 specifically comprises:
the sequence input layer receives rainfall field samples from the training set, the rainfall intensity characteristics in each rainfall field are extracted through the convolutional layer, the characteristic vectors generated after passing through the pooling layer are sent to the LSTM layer, output quantities are generated and enter the full-connection layer, and the rainfall intensity mapping relation in the rainfall field at the next moment is established through the full-connection layer.
8. The method for high spatial and temporal resolution reconstruction analysis and short-term prediction of a microwave-level rainfall field according to claim 5, wherein in S3.4, the termination condition is that the loss function loss is less than 0.01, and the maximum iteration number is 500.
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