CN117237677B - Precipitation prediction correction method for overall similarity of strong precipitation space based on deep learning - Google Patents
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Abstract
The invention discloses a precipitation prediction correction method of the overall similarity of a strong precipitation space based on deep learning, which comprises the following steps: (1) identifying precipitation attributes using YOLOv 5; (2) establishing a GAN-based rainfall forecast correction model; (3) Establishing a strong precipitation correction model O-GAN based on GAN and fused with precipitation space characteristics; (4) Substituting the numerical mode forecast data of the test period into the model O-GAN to generate a rainfall forecast after post-treatment; the correction method effectively improves the correction skill of the traditional optimization-only point-by-point error model; the end-to-end output from the precipitation picture to the precipitation rain cluster space attribute is realized, and the objective identification efficiency is improved; the problem of prediction fuzzification possibly occurring in the traditional point-by-point correction model is avoided, meanwhile, the characteristic of strong precipitation can be effectively captured, and the accuracy of precipitation prediction is improved.
Description
Technical Field
The invention relates to the technical field of weather forecast, in particular to a precipitation forecast correction method of strong precipitation space overall similarity based on deep learning.
Background
The method is limited by factors such as chaotic characteristics of the atmosphere, errors in initial field data assimilation, imperfect physical process parameterization and the like, and the original forecast output by each meteorological center numerical mode generally has systematic deviations of different degrees. Especially, the model forecasting capability is obviously insufficient for forecasting variables which are strong in nonlinearity such as strong precipitation, ambiguous in microphysics and affected by complex multiscales. Due to the extreme unbalance between the strong and weak precipitation samples, a certain correction difficulty is caused to a data-driven deep learning algorithm, and the problem of weak prediction of a strong precipitation event often occurs. In addition, because the weather forecast field has complex multi-scale space-time change characteristics, especially precipitation has the characteristics of biased distribution, non-negative data distribution, sparse local spatial distribution, temporal discontinuity and the like, the problem of spatial deviation of forecast variables should be considered and solved in the construction of the deep learning model.
The distance error of 'point-to-point' forecast is often adopted as a target loss function in the structure of the traditional deep learning mode precipitation forecast post-processing algorithm, the traditional target loss function is directly applied to the precipitation forecast regulation, the problem of 'double punishment' is usually limited, and the whole forecast value is close to the average value of a forecast object, so that the forecast distortion and fuzzification are caused. Therefore, a new method for combining the spatial structure attribute of precipitation with the deep learning technology is explored, the deep learning strong precipitation prediction model is optimized, and the method has important application value and guiding significance for improving the strong precipitation prediction level.
Disclosure of Invention
The invention aims to: the invention aims to provide a precipitation prediction correction method for the overall similarity of a strong precipitation space based on deep learning, which aims to solve the problems of insufficient strong precipitation prediction capacity and large deviation of raindrop position and shape prediction.
The technical scheme is as follows: the invention relates to a precipitation prediction correction method of the overall similarity of a strong precipitation space based on deep learning, which comprises the following steps:
(1) Identifying the precipitation attribute by utilizing YOLOv 5;
(2) Establishing a GAN-based rainfall forecast correction model;
(3) Establishing a strong precipitation correction model O-GAN based on GAN and fused with precipitation space characteristics;
(4) Substituting the numerical mode forecast data of the test period into the model O-GAN to generate a rainfall forecast after post-treatment.
Further, the step (1) includes the following steps:
(11) Preprocessing the actually measured precipitation image comprises the following steps: intercepting precipitation rain clusters, and filtering secondary precipitation rain clusters;
(12) Objective recognition of precipitation space attributes is carried out on an observed precipitation field by using a MODE method, and the extracted precipitation attributes are used as truth labels of a deep learning recognition model; wherein the precipitation spatial properties include: precipitation rain cluster, rain cluster area, rain cluster position, average rain cluster surface strength, rain cluster length ratio and rain cluster slope;
(13) And (5) establishing a mapping model of the precipitation field and the spatial attribute of the corresponding precipitation object by utilizing the YOLOv 5.
Further, the step (12) includes the steps of:
(121) Identifying a study object of interest by spatial smoothing and precipitation threshold control; spatially convolving an original precipitation field with a radiusSmoothing, namely convolution processing is carried out, and the formula is as follows:
；
wherein, for the original data field +.>For convolved fields +.>Is a filtering function; />And->Is the grid point coordinate;
(122) Threshold control is carried out on the convolution field to obtain a mask fieldI.e. the intensity of precipitation in the convolution field is greater than or equal to the threshold valueIs defined as follows:
；
assigning all the grid points in the continuous area of M (x, y) =1 to the corresponding grid points in the original precipitation field to obtain a reconstructed fieldThe formula is as follows:
；
(123) Calculating spatial attributes of the identified precipitation object, namely the centroid position, the area size and the aspect ratio; the shaft angle is used as a truth value label corresponding to original precipitation observation; wherein the aspect ratio comprises: the ratio of the short axis to the long axis, the aspect ratio of the circular object is 1.0, otherwise <1; the axis angle is the degree by which the spindle rotates counterclockwise from the x-axis.
Further, the step (13) specifically comprises the following steps: error between the rainfall object attribute true value obtained by MODE calculation and the predicted value obtained by YOLO-V5 recognition; using Euclidean distance as a loss function based on a YOLO-V5 precipitation attribute identification model; training the model by an optimization method based on a deep learning model to obtain a deep learning model with high accuracy for identifying a precipitation object and attributes thereof from a precipitation field.
Further, the step (2) includes the following steps:
(21) Establishing a multi-element weather forecast factor and a corresponding precipitation actual measurement data set;
(22) The meteorological element data preprocessing comprises the steps of taking logarithm of precipitation related variables, carrying out maximum and minimum normalization and carrying out missing value processing;
(23) Establishing a model for numerical mode release between a plurality of weather predictors and precipitation variables by using a U-Net model, wherein the corresponding loss function isThe formula is as follows:
;
wherein,is a rainfall forecast field obtained by a U-Net model, < >>Is a corresponding precipitation observation field.
(24) Taking the established U-Net as a generator in a numerical mode release model based on GAN, and selecting a convolution network as a discriminator to obtain corresponding rainfall forecast and corresponding loss functionThe formula is as follows:
；
wherein,is->Weight constant coefficient of (2); />The loss function representing GAN is given by:
。
further, the step (3) includes the following steps:
(31) Respectively extracting the precipitation prediction of the GAN generator and the precipitation attribute corresponding to the actually measured precipitation in the step (2) by using the training stable YOLO-v5 precipitation attribute identification model in the step (1), and establishing a loss function based on a precipitation objectThe formula is as follows:
；
wherein,the training YOLO-v5 model identifies and predicts the rainfall object attribute of the rainfall field; />Representing precipitation attributes corresponding to the measured precipitation;
(32) And constructing a mixed loss function of the point-to-point error, the precipitation data distribution error and the overall similarity of the strong precipitation spatial characteristics, and constructing a deep learning prediction model integrating the precipitation spatial characteristics.
Further, the formula of the mixing loss function in the step (32) is as follows:
；
wherein,and->Respectively->And->Is a weight constant coefficient of (a).
The invention relates to a precipitation prediction correction system based on deep learning and with strong precipitation space overall similarity, which comprises the following components:
precipitation attribute identification module: for identifying precipitation attributes using YOLOv 5;
and a GAN correction module: the method is used for establishing a GAN-based rainfall forecast correction model;
an O-GAN correction module: the method comprises the steps of establishing a strong precipitation correction model O-GAN based on GAN and fusing precipitation space characteristics;
precipitation forecasting module: and substituting the numerical mode forecast data of the test period into the model O-GAN to generate a rainfall forecast after post-treatment.
The device comprises a memory, a processor and a program stored on the memory and capable of running on the processor, wherein the processor realizes the steps in any one of the precipitation prediction correction methods based on the overall similarity of the deep learning strong precipitation space when executing the program.
The storage medium of the present invention stores a computer program designed to implement, when running, the steps in any one of the precipitation prediction correction methods based on the deep learning strong precipitation space overall similarity.
The beneficial effects are that: compared with the prior art, the invention has the following remarkable advantages:
1. according to the method, the actual requirements of the rainfall spatial properties, such as the rainfall center, the rainfall cluster shape and the like, in the rainfall forecast are fully considered, the spatial properties of the rainfall are used as one of targets for training the deep learning model, and the correction skill of the traditional only-optimized point-by-point error model is effectively improved.
2. According to the invention, by utilizing the recognition model YOLO-v5 in computer vision and combining the rainfall and rain cluster space attribute characteristics extracted by utilizing the meteorological objective recognition model MODE, the end-to-end output from the rainfall image to the rainfall and rain cluster space attribute is realized, the objective recognition efficiency is improved, and the rainfall and rain cluster space attribute is effectively fused in a deep learning model frame.
3. According to the method, the generated network GAN is further introduced into the rainfall forecast correction task, the problem of forecast fuzzification possibly occurring in the traditional point-by-point correction model is avoided, meanwhile, the strong rainfall characteristic can be effectively captured, and the rainfall forecast accuracy is improved.
Drawings
FIG. 1 is a general flow chart of the present invention;
FIG. 2 is a schematic diagram of a model for identifying the spatial attribute of precipitation based on YOLO-v 5;
FIG. 3 is a schematic diagram of a rainfall forecast correction model based on U-Net in the invention;
FIG. 4 is a schematic diagram of a correction model for a GAN-based precipitation forecast according to the present invention;
FIG. 5 is a schematic diagram of a correction model for rainfall forecast based on O-GAN.
Description of the embodiments
The technical scheme of the invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the embodiment of the invention provides a precipitation prediction correction method of the overall similarity of a strong precipitation space based on deep learning, which comprises the following steps:
(1) Identifying the precipitation attribute by utilizing YOLOv 5; as shown in fig. 2, the method comprises the following steps:
(11) Preprocessing the actually measured precipitation image comprises the following steps: intercepting precipitation rain clusters, and filtering secondary precipitation rain clusters;
(12) Objective recognition of precipitation space attributes is carried out on an observed precipitation field by using a MODE method, and the extracted precipitation attributes are used as truth labels of a deep learning recognition model; wherein the precipitation spatial properties include: precipitation rain cluster, rain cluster area, rain cluster position, average rain cluster surface strength, rain cluster length ratio and rain cluster slope; the method comprises the following steps:
(121) Identifying a study object of interest by spatial smoothing and precipitation threshold control; spatially convolving an original precipitation field with a radiusSmoothing, namely convolution processing is carried out, and the formula is as follows:
；
wherein, for the original data field +.>For convolved fields +.>Is a filtering function; />And->Is the grid point coordinate;
(122) Threshold control is carried out on the convolution field to obtain a mask fieldI.e. the intensity of precipitation in the convolution field is greater than or equal to the threshold valueIs defined as follows:
；
assigning all the grid points in the continuous area of M (x, y) =1 to the corresponding grid points in the original precipitation field to obtain a reconstructed fieldThe formula is as follows:
；
(123) Calculating spatial attributes of the identified precipitation object, namely the centroid position, the area size and the aspect ratio; the shaft angle is used as a truth value label corresponding to original precipitation observation; wherein the aspect ratio comprises: the ratio of the short axis to the long axis, the aspect ratio of the circular object is 1.0, otherwise <1; the axis angle is the degree by which the spindle rotates counterclockwise from the x-axis.
(13) And (5) establishing a mapping model of the precipitation field and the spatial attribute of the corresponding precipitation object by utilizing the YOLOv 5. The method comprises the following steps: error between the rainfall object attribute true value obtained by MODE calculation and the predicted value obtained by YOLO-V5 recognition; using Euclidean distance as a loss function based on a YOLO-V5 precipitation attribute identification model; training the model, such as Adam, by an optimization method based on a deep learning model, and obtaining the deep learning model for identifying the precipitation object and the attribute thereof from the precipitation field with high accuracy.
(2) Establishing a GAN-based rainfall forecast correction model; the method comprises the following steps:
(21) Establishing a multi-element weather forecast factor and a corresponding precipitation actual measurement data set;
(22) The meteorological element data preprocessing comprises the steps of taking logarithm of precipitation related variables, carrying out maximum and minimum normalization and carrying out missing value processing;
(23) A numerical model release model between a plurality of weather predictors and precipitation variables is established by using a U-Net model, and as shown in figure 3, the U-Net mainly comprises four parts: the method comprises a convolution layer, a pooling layer, an up-sampling layer and a jump connection layer, wherein the whole network structure is like a letter U, the left half part is a down-sampling process, namely an encoding process, the spatial resolution is reduced along with the depth, the right half part is an up-sampling process, namely a decoding process, and the spatial resolution is gradually improved. The convolution layers extract data space features, and under the cooperation of other parts of the model, different convolution layers extract feature information of space fields with different scales, so that multi-scale features of a weather system can be considered. The skip connection layer retains and transfers the high resolution information extracted in the down-sampling process to the corresponding up-sampling layer, while avoiding the problem of pixel distortion during up-sampling. U-Net typically uses a loss function based on point-by-point errorThe optimization model is given by the following formula:
；
wherein,is a rainfall forecast field obtained by a U-Net model, < >>Is a corresponding precipitation observation field.
As shown in FIG. 4, (24) the established U-Net is used as a generator in the model for numerical mode release based on GAN, and a convolution network is used as a discriminator to obtain corresponding rainfall forecast and corresponding loss functionThe formula is as follows:
；
wherein,is->Weight constant coefficient of (2); />The loss function representing GAN is given by:
。
(3) Establishing a strong precipitation correction model O-GAN based on GAN and fused with precipitation space characteristics; as shown in fig. 5, the method comprises the following steps:
(31) Respectively extracting the precipitation prediction of the GAN generator and the precipitation attribute corresponding to the actually measured precipitation in the step (2) by using the training stable YOLO-v5 precipitation attribute identification model in the step (1), and establishing a loss function based on a precipitation objectThe formula is as follows:
；
wherein,the training YOLO-v5 model identifies and predicts the rainfall object attribute of the rainfall field; />Representing precipitation attributes corresponding to the measured precipitation;
(32) And constructing a mixed loss function of the point-to-point error, the precipitation data distribution error and the overall similarity of the strong precipitation spatial characteristics, and constructing a deep learning prediction model integrating the precipitation spatial characteristics.
The formula of the mixing loss function is as follows:
；
wherein,and->Respectively->And->Is a weight constant coefficient of (a).
(4) Substituting the numerical mode forecast data of the test period into the model O-GAN to generate a rainfall forecast after post-treatment.
The embodiment of the invention also provides a precipitation prediction correction system based on the deep learning and with the overall similarity of the strong precipitation space, which comprises the following steps:
precipitation attribute identification module: for identifying precipitation attributes using YOLOv 5;
and a GAN correction module: the method is used for establishing a GAN-based rainfall forecast correction model;
an O-GAN correction module: the method comprises the steps of establishing a strong precipitation correction model O-GAN based on GAN and fusing precipitation space characteristics;
precipitation forecasting module: and substituting the numerical mode forecast data of the test period into the model O-GAN to generate a rainfall forecast after post-treatment.
The embodiment of the invention also provides equipment, which comprises a memory, a processor and a program stored on the memory and capable of running on the processor, wherein the processor realizes the steps in the precipitation forecast correction method based on the deep learning strong precipitation space overall similarity when executing the program.
The embodiment of the invention also provides a storage medium, which stores a computer program designed to realize the steps in the precipitation prediction correction method based on the overall similarity of the deep learning strong precipitation space when running.
Claims (4)
1. The precipitation prediction correction method for the strong precipitation space overall similarity based on deep learning is characterized by comprising the following steps of:
(1) Identifying the precipitation attribute by utilizing YOLOv 5; the method comprises the following steps:
(11) Preprocessing the actually measured precipitation image comprises the following steps: intercepting precipitation rain clusters, and filtering secondary precipitation rain clusters;
(12) Objective recognition of precipitation space attributes is carried out on an observed precipitation field by using a MODE method, and the extracted precipitation attributes are used as truth labels of a deep learning recognition model; wherein the precipitation spatial properties include: precipitation rain cluster, rain cluster area, rain cluster position, average rain cluster surface strength, rain cluster length ratio and rain cluster slope; the method comprises the following steps:
(121) Identifying a study object of interest by spatial smoothing and precipitation threshold control; spatially convolving an original precipitation field with a radiusSmoothing, namely convolution processing is carried out, and the formula is as follows:
；
wherein, for the original data field +.>For convolved fields +.>Is a filtering function; />And->Is the grid point coordinate;
(122) Threshold control is carried out on the convolution field to obtain a mask fieldI.e. the intensity of precipitation in the convolution field is greater than or equal to the threshold value +.>Is defined as follows:
；
assigning all the grid points in the continuous area of M (x, y) =1 to the corresponding grid points in the original precipitation field to obtain a reconstructed fieldThe formula is as follows:
；
(123) Calculating spatial attributes of the identified precipitation object, namely the centroid position, the area size and the aspect ratio; the shaft angle is used as a truth value label corresponding to original precipitation observation; wherein the aspect ratio comprises: the ratio of the short axis to the long axis, the aspect ratio of the circular object is 1.0, otherwise <1; the shaft angle is the degree of counterclockwise rotation of the spindle from the x-axis;
(13) Utilizing YOLOv5 to establish a mapping model of a precipitation field and the spatial attribute of a corresponding precipitation object; the method comprises the following steps: error between the rainfall object attribute true value obtained by MODE calculation and the predicted value obtained by YOLO-V5 recognition; using Euclidean distance as a loss function based on a YOLO-V5 precipitation attribute identification model; training the model by an optimization method based on a deep learning model to obtain a deep learning model with high accuracy for identifying a precipitation object and attributes thereof from a precipitation field;
(2) Establishing a GAN-based rainfall forecast correction model; the method comprises the following steps:
(21) Establishing a multi-element weather forecast factor and a corresponding precipitation actual measurement data set;
(22) The meteorological element data preprocessing comprises the steps of taking logarithm of precipitation related variables, carrying out maximum and minimum normalization and carrying out missing value processing;
(23) Establishing a model for numerical mode release between a plurality of weather predictors and precipitation variables by using a U-Net model, wherein the corresponding loss function isThe formula is as follows:
；
wherein,is a rainfall forecast field obtained by a U-Net model, < >>Is a corresponding precipitation observation field;
(24) Taking the established U-Net as a generator in a numerical mode release model based on GAN, and selecting a convolution network as a discriminator to obtain corresponding rainfall forecast and corresponding loss functionThe formula is as follows:
；
wherein,is->Weight constant coefficient of (2); />The loss function representing GAN is given by:
；
(3) Establishing a strong precipitation correction model O-GAN based on GAN and fused with precipitation space characteristics; the method comprises the following steps:
(31) Respectively extracting the precipitation prediction of the GAN generator and the precipitation attribute corresponding to the actually measured precipitation in the step (2) by using the training stable YOLO-v5 precipitation attribute identification model in the step (1), and establishing a loss function based on a precipitation objectThe formula is as follows:
；
wherein,the training YOLO-v5 model identifies and predicts the rainfall object attribute of the rainfall field; />Representing precipitation attributes corresponding to the measured precipitation;
(32) Constructing a mixed loss function of the point-to-point error, precipitation data distribution error and overall similarity of the strong precipitation space characteristics, and constructing a deep learning prediction model fused with the precipitation space characteristics; the formula of the mixing loss function is as follows:
；
wherein,and->Respectively->And->Weight constant coefficient of (2);
(4) Substituting the numerical mode forecast data of the test period into the model O-GAN to generate a rainfall forecast after post-treatment.
2. Precipitation forecast correction system of strong precipitation space overall similarity based on degree of depth study, characterized by comprising:
precipitation attribute identification module: for identifying precipitation attributes using YOLOv 5; comprising the following steps:
(11) Preprocessing the actually measured precipitation image comprises the following steps: intercepting precipitation rain clusters, and filtering secondary precipitation rain clusters;
(12) Objective recognition of precipitation space attributes is carried out on an observed precipitation field by using a MODE method, and the extracted precipitation attributes are used as truth labels of a deep learning recognition model; wherein the precipitation spatial properties include: precipitation rain cluster, rain cluster area, rain cluster position, average rain cluster surface strength, rain cluster length ratio and rain cluster slope; the method comprises the following steps:
(121) Identifying a study object of interest by spatial smoothing and precipitation threshold control; spatially convolving an original precipitation field with a radiusSmoothing, namely convolution processing is carried out, and the formula is as follows:
；
wherein, for the original data field +.>For convolved fields +.>Is a filtering function; />And->Is the grid point coordinate;
(122) Threshold control is carried out on the convolution field to obtain a mask fieldI.e. the intensity of precipitation in the convolution field is greater than or equal to the threshold value +.>Is defined as follows:
；
assigning all the grid points in the continuous area of M (x, y) =1 to the corresponding grid points in the original precipitation field to obtain a reconstructed fieldThe formula is as follows:
；
(123) Calculating spatial attributes of the identified precipitation object, namely the centroid position, the area size and the aspect ratio; the shaft angle is used as a truth value label corresponding to original precipitation observation; wherein the aspect ratio comprises: the ratio of the short axis to the long axis, the aspect ratio of the circular object is 1.0, otherwise <1; the shaft angle is the degree of counterclockwise rotation of the spindle from the x-axis;
(13) Utilizing YOLOv5 to establish a mapping model of a precipitation field and the spatial attribute of a corresponding precipitation object; the method comprises the following steps: error between the rainfall object attribute true value obtained by MODE calculation and the predicted value obtained by YOLO-V5 recognition; using Euclidean distance as a loss function based on a YOLO-V5 precipitation attribute identification model; training the model by an optimization method based on a deep learning model to obtain a deep learning model with high accuracy for identifying a precipitation object and attributes thereof from a precipitation field;
and a GAN correction module: the method is used for establishing a GAN-based rainfall forecast correction model; comprising the following steps:
(21) Establishing a multi-element weather forecast factor and a corresponding precipitation actual measurement data set;
(22) The meteorological element data preprocessing comprises the steps of taking logarithm of precipitation related variables, carrying out maximum and minimum normalization and carrying out missing value processing;
(23) Establishing a model for numerical mode release between a plurality of weather predictors and precipitation variables by using a U-Net model, wherein the corresponding loss function isThe formula is as follows:
；
wherein,is a rainfall forecast field obtained by a U-Net model, < >>Is a corresponding precipitation observation field;
(24) Taking the established U-Net as a generator in a numerical mode release model based on GAN, and selecting a convolution network as a discriminator to obtain corresponding rainfall forecast and corresponding loss functionThe formula is as follows:
；
wherein,is->Weight constant coefficient of (2); />The loss function representing GAN is given by:
；
an O-GAN correction module: the method comprises the steps of establishing a strong precipitation correction model O-GAN based on GAN and fusing precipitation space characteristics; comprising the following steps:
(31) Respectively extracting the precipitation prediction of the GAN generator and the precipitation attribute corresponding to the actually measured precipitation in the step (2) by using the training stable YOLO-v5 precipitation attribute identification model in the step (1), and establishing a loss function based on a precipitation objectThe formula is as follows:
；
wherein,the training YOLO-v5 model identifies and predicts the rainfall object attribute of the rainfall field; />Representing precipitation attributes corresponding to the measured precipitation;
(32) Constructing a mixed loss function of the point-to-point error, precipitation data distribution error and overall similarity of the strong precipitation space characteristics, and constructing a deep learning prediction model fused with the precipitation space characteristics; the formula of the mixing loss function is as follows:
；
wherein,and->Respectively->And->Weight constant coefficient of (2);
precipitation forecasting module: and substituting the numerical mode forecast data of the test period into the model O-GAN to generate a rainfall forecast after post-treatment.
3. An apparatus comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor performs the steps of a method for correcting precipitation predictions based on overall similarity of strong precipitation spaces for deep learning as claimed in any one of claims 1.
4. A storage medium storing a computer program, characterized in that the computer program is designed to, when run, implement the steps in a method for correcting precipitation forecast of strong precipitation space overall similarity based on deep learning according to any of the claims 1.
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