阅读说明：本技术 基于bp神经网络的gnss掩星对流层参数的修正方法 (GNSS occultation troposphere parameter correction method based on BP neural network ) 是由 白伟华 邓楠 刘小煦 刘梓琰 孙越强 杜起飞 刘黎军 李伟 王先毅 蔡跃荣 夏俊 于 2021-07-13 设计创作，主要内容包括：本发明涉及大气科学研究领域,尤其涉及基于BP神经网络的GNSS掩星对流层参数的修正方法,所述方法包括：接收GNSS掩星探测仪采集反演得到的对流层顶参数产品数据；对对流层顶参数产品数据进行预处理；将预处理后的数据输入预先建立和训练好的修正模型,得到修正后的对流层顶高度和对流层顶温度；所述修正模型采用BP神经网络。本发明首次利用BP神经网络方法修正GNSS掩星对流层顶参数产品,尤其对高纬地区误差改进效果最明显,使用模型简洁高效、计算经济,并且可以有效修正GNSS掩星对流层顶产品高纬度区域参数的误差,提高了GNSS掩星对流层顶参数产品的质量。(The invention relates to the field of atmospheric science research, in particular to a GNSS occultation troposphere parameter correction method based on a BP neural network, which comprises the following steps: receiving convection layer top parameter product data acquired and inverted by a GNSS occultation detector; preprocessing the product data of the top parameter of the convection layer; inputting the preprocessed data into a pre-established and trained correction model to obtain the corrected top height and top temperature of the convection layer; the correction model adopts a BP neural network. The method for correcting the GNSS occultation convective layer top parameter product by using the BP neural network method for the first time has the advantages of most obvious effect on improving the error in high latitude areas, concise and efficient model and economic calculation, can effectively correct the error of the high latitude area parameter of the GNSS occultation convective layer top product, and improves the quality of the GNSS occultation convective layer top parameter product.)

1. A GNSS occultation troposphere parameter correction method based on a BP neural network, the method comprises the following steps:

receiving convection layer top parameter product data acquired and inverted by a GNSS occultation detector;

preprocessing the product data of the top parameter of the convection layer;

inputting the preprocessed data into a pre-established and trained correction model to obtain the corrected top height and top temperature of the convection layer;

the correction model adopts a BP neural network.

2. The method for revising tropospheric parameters of a GNSS occultation based on a BP neural network of claim 1, wherein the tropospheric top parameter product data comprises: and calculating the top temperature of the convection layer, the top height of the convection layer, the latitude and longitude of the profile and the corresponding acquisition date and time according to the GNSS occultation dry temperature profile.

3. The method for correcting tropospheric parameters of a GNSS occultation based on a BP neural network according to claim 2, wherein the preprocessing specifically comprises:

and carrying out normalization processing on the latitude and longitude of the profile and the corresponding acquisition date and time.

4. The GNSS occultation troposphere parameter correction method based on the BP neural network as claimed in claim 1, wherein the input of the correction model is preprocessed troposphere top parameter product data, and the output is corrected troposphere top height and troposphere top temperature, the adopted BP neural network comprises 5 fully-connected layers, wherein 3 layers are hidden layers, each hidden layer has 10 neurons, and each neuron adopts Relu activation function.

5. The GNSS occultation tropospheric parameter correction method based on BP neural network according to claim 4, characterized in that the method further comprises a model correction training step, specifically comprising:

selecting FY3C occultation data and temperature profile data of an ECMWF service archive library, matching according to time, longitude and latitude, and calculating the top height and the top temperature of a convection layer of the matched temperature profile through a temperature decrement rate algorithm to obtain an original sample set;

screening data of an original sample set, removing invalid data which cannot be judged on the top of a convective layer due to temperature profiles, preprocessing based on a sampling algorithm and a normalization algorithm, and randomly distributing the preprocessed sample set data to a training set and a testing set according to a certain proportion;

initializing a weight matrix of the network, and setting a summation weight of each pair of neurons which are connected in pairs;

sequentially inputting training set data into a BP (back propagation) neural network, calculating corresponding convection layer top temperature and convection layer top height according to forward propagation, taking the matched ECMWF convection layer top temperature and convection layer top height as a reference true value, calculating an MSE (mean square error) loss function according to a calculation result and the reference true value, adaptively adjusting a dynamic learning rate through Adam, and iteratively updating a weight matrix until the loss function converges and meets a preset maximum iteration number, so that a pre-trained correction model is obtained;

sequentially inputting the test set data into a pre-trained correction model, judging whether the output result meets the evaluation requirement, and re-training; if yes, the trained correction model is obtained.

6. The method for GNSS occultation tropospheric parameter modification based on BP neural network as claimed in claim 3, wherein the latitude distribution of data of the training set and the testing set is kept consistent.

7. A GNSS occultation troposphere parameter correction system based on a BP neural network is characterized by comprising: the device comprises a correction model, a receiving module, a preprocessing module and an output module; wherein the content of the first and second substances,

the receiving module is used for receiving the convective stratum top parameter product data acquired and inverted by the GNSS occultation detector;

the preprocessing module is used for preprocessing the product data of the top parameter of the convection layer;

the output module is used for inputting the preprocessed data into a pre-established and trained correction model to obtain the corrected top height and temperature of the convection layer;

the correction model adopts a BP neural network.

Technical Field

The invention relates to the field of atmospheric science research, in particular to a GNSS occultation troposphere parameter correction method based on a BP neural network.

Background

The convective roof is a hot spot in atmospheric climate research. The GNSS occultation detection technology has the characteristics of high global coverage rate, high vertical resolution and the like, the optimal detection interval is 7-25km and is matched with the height of the top of the convection layer, and therefore, a high-quality and high-global-coverage convection layer top product (convection layer top parameters obtained by GNSS occultation detection data inversion are hereinafter referred to as GNSS occultation convection layer top parameters, and the convection layer top parameters mainly comprise the convection layer top height and the convection layer top temperature) can be obtained.

The method for determining the top of the convection layer generally uses a method for determining the temperature decrease rate defined by the top of the convection layer proposed by the world weather organization WMO in 1957, that is: the lowest point meeting the requirement that the temperature is decreased with the increase of the height by more than-2K/km, and the temperature decrease rate from the point to any point in the height of 2km above the point is more than-2K/km.

At present, four-dimensional variational data provided by an ECMWF service archive library are model data with high precision, but the results of a convective stratum top product obtained by the inversion of a occultation temperature profile and the results obtained by the model profile are obviously different in partial regions.

The normal operation of the GNSS occultation detector (GNOS) in China's Fengyun model C (FY3C) has reached 7 years, and the number of the provided atmospheric temperature profiles is 400 and 500 every day. The convective layer top height obtained by inversion of the FY3C satellite GNOS occultation data has larger negative deviation in high latitude compared with the result of ECMWF four-dimensional variation data.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provides a GNSS occultation troposphere parameter correction method based on a BP neural network.

In order to achieve the above object, the present invention provides a GNSS occultation troposphere parameter correction method based on a BP neural network, including:

receiving convection layer top parameter product data acquired and inverted by a GNSS occultation detector;

preprocessing the product data of the top parameter of the convection layer;

inputting the preprocessed data into a pre-established and trained correction model to obtain the corrected top height and top temperature of the convection layer;

the correction model adopts a BP neural network.

As an improvement of the above method, the convective layer top parameter product data comprises: and calculating the top temperature of the convection layer, the top height of the convection layer, the latitude and longitude of the profile and the corresponding acquisition date and time according to the GNSS occultation dry temperature profile.

As an improvement of the above method, the pretreatment specifically comprises:

and carrying out normalization processing on the latitude and longitude of the profile and the corresponding acquisition date and time.

As an improvement of the method, the input of the correction model is preprocessed convection layer top parameter product data, the output is corrected convection layer top height and convection layer top temperature, the adopted BP neural network comprises 5 full-connection layers, wherein 3 layers are hidden layers, each hidden layer comprises 10 neurons, and each neuron adopts a Relu activation function.

As an improvement of the above method, the method further includes a training step of modifying the model, specifically including:

selecting FY3C occultation data and temperature profile data of an ECMWF service archive library, matching according to time, longitude and latitude, and calculating the top height and the top temperature of a convection layer of the matched temperature profile through a temperature decrement rate algorithm to obtain an original sample set;

screening data of an original sample set, removing invalid data which cannot be judged on the top of a convective layer due to temperature profiles, preprocessing based on a sampling algorithm and a normalization algorithm, and randomly distributing the preprocessed sample set data to a training set and a testing set according to a certain proportion;

initializing a weight matrix of the network, and setting a summation weight of each pair of neurons which are connected in pairs;

sequentially inputting training set data into a BP (back propagation) neural network, calculating corresponding convection layer top temperature and convection layer top height according to forward propagation, taking the matched ECMWF convection layer top temperature and convection layer top height as a reference true value, calculating an MSE (mean square error) loss function according to a calculation result and the reference true value, adaptively adjusting a dynamic learning rate through Adam, and iteratively updating a weight matrix until the loss function converges and meets a preset maximum iteration number, so that a pre-trained correction model is obtained;

sequentially inputting the test set data into a pre-trained correction model, judging whether the output result meets the evaluation requirement, and re-training; if yes, the trained correction model is obtained.

As an improvement of the above method, the latitude distribution of the data of the training set and the test set is kept consistent.

A GNSS occultation tropospheric parameter correction system based on a BP neural network, the system comprising: the device comprises a correction model, a receiving module, a preprocessing module and an output module; wherein the content of the first and second substances,

the receiving module is used for receiving the convective stratum top parameter product data acquired and inverted by the GNSS occultation detector;

the preprocessing module is used for preprocessing the product data of the top parameter of the convection layer;

the output module is used for inputting the preprocessed data into a pre-established and trained correction model to obtain the corrected top height and temperature of the convection layer;

the correction model adopts a BP neural network.

Compared with the prior art, the invention has the advantages that:

the method for correcting the GNSS occultation convective layer top parameter product by using the BP neural network method for the first time has the advantages of most obvious effect on improving the error in high latitude areas, concise and efficient model and economic calculation, can effectively correct the error of the high latitude area parameter of the GNSS occultation convective layer top product, and improves the quality of the GNSS occultation convective layer top parameter product.

Drawings

FIG. 1 is a general flow chart of the correction method of troposphere parameters of GNSS occultation based on BP neural network of the present invention;

FIG. 2 is a schematic diagram of a BP neural network training process according to the present invention;

FIG. 3 is an example of training set and test set data, including latitude and seasonal distributions;

FIG. 4 is a graph showing the effect of correction by the method of the present invention, wherein FIG. 4(a) shows the effect of correction of the top height of the convection layer and FIG. 4(b) shows the effect of correction of the top temperature of the convection layer.

Detailed Description

Through investigation, large deviation exists in troposphere parameters obtained by inversion of satellite GNSS occultation data such as FY3C and the like, and particularly, the convective stratum top height obtained by inversion of the GNSS occultation data has large negative deviation in a high-altitude area compared with an ECMWF four-dimensional variation data result, so that the negative deviation in the high-altitude area of a GNSS convective stratum top parameter product is corrected in an attempt to achieve the purpose of improving the product precision.

As most of the convection layer top products are lattice point average or latitude average, the result is a statistical result. And in this way, selecting a BP neural network method to correct the GNSS occultation convective stratum top parameter product.

The invention aims to provide a GNSS occultation convective layer top parameter correction method based on a BP neural network, which has the advantages of high calculation efficiency and good regression correction effect, and can effectively improve the quality of GNSS occultation convective layer top parameter products, particularly the quality of parameter products in high latitude areas with negative deviation.

In order to improve the accuracy of the GNSS occultation convective stratum top parameter product in high latitude areas by using a BP neural network algorithm, the technical scheme mainly comprises the following five steps, as shown in fig. 1:

the first step is as follows: and constructing a raw data sample set. Selecting a large amount of GNSS occultation temperature profile data and ECMWF four-dimensional variation model temperature profile data for space-time matching, and calculating the respective convection layer top height and temperature of space-time matched GNSS occultation products and ECMWF data to obtain an original sample;

the second step is that: a training set and a test set are generated. Firstly, screening original sample data, and eliminating invalid values caused by the fact that the temperature profile cannot judge the top of a convection layer; after screening, the problems of uneven distribution and inconsistent dimension of the data set are solved based on a sampling algorithm and a normalization algorithm, and the preprocessed sample set is divided into a training set and a testing set according to the ratio of 6.5: 3.5.

The third step: and constructing a BP neural network model. Firstly, determining the number of neurons of an input layer and an output layer of a model according to input characteristics and output characteristics, and then respectively determining the number of neurons of a hidden layer and the number of neurons of the hidden layer of a neural network according to continuous tests, thereby constructing a BP neural network model which takes the top height and the temperature of a GNSS occultation convective layer and normalized time, date, longitude and latitude as input parameters and takes the top height and the temperature of the convective layer after correction as output parameters.

The fourth step: and (5) training a BP neural network model. The specific flow is shown in fig. 2. Firstly, optimizing a model structure, and testing, wherein the neural network uses 5 layers of fully-connected neural networks, wherein 3 hidden layers are provided, and each hidden layer is provided with 10 neurons. Activation functions for each neuron are then assigned, and the Relu function is used in this model. And then initializing a weight matrix of the network, and giving a summation weight of each pair of the connected neurons. The matched ECMWF convective layer top temperature and the matched convective layer top height are used as reference true values, a loss function is set to be MSE, an error back propagation iterator algorithm is Adam, the algorithm uses a self-adaptive dynamic learning rate, the learning rate is gradually increased from a smaller learning rate eta, and the training precision is guaranteed. Subsequently, the number of epochs for training is determined from the validation set error. And training and updating the weight matrix for multiple times until the loss function converges or the maximum iteration number is met, wherein the iteration number is 25000 times.

The fifth step: and correcting the top parameters of the convection layer. And calculating the corrected convective layer top height and temperature parameters by using the trained BP neural network model, and evaluating the correction effect.

The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and examples.

Example 1

The embodiment 1 of the invention provides a GNSS occultation troposphere parameter correction method based on a BP neural network.

By using the occultation observation data of a GNSS occultation detector (GNOS for short) on an FY3C satellite, the invention corrects the data of the fluidized bed top parameter in the high-latitude area of the FY3C satellite GNSS occultation by using the BP neural network method. The FY3C satellite, which was launched in 2013 at 9 months, is a sun-synchronous orbital satellite with an orbital inclination of 98.8 degrees, an average altitude of 836km and an orbital period of 101.5 minutes. The loaded GNSS masker receiver GNOS can be compatible with the signal of a Beidou navigation satellite system (BDS) and the signal of a Global Positioning System (GPS) at the same time. The number of atmospheric temperature profiles provided during normal service operation of FY3CGNOS is 400-500 per day.

First-step raw data sample construction: the FY3C masker data was space-time matched to the ECMWF data. In the example, the FY3C occultation data of 6-2018 (JJA) and the temperature profile data of the ECMWF service archive are matched according to time, longitude and latitude, and the top height and the temperature of the convection layer of the matched temperature profile are calculated through a temperature decrement rate algorithm to obtain an original sample set. Wherein 20187 and 10798 groups of data are recorded in 2018.6-2018.8 time periods.

And a second step of generating a training set and a test set: and screening the original sample set, removing invalid data which cannot be judged on the top of the convective layer due to the temperature profile, randomly selecting 65% as a training set, and using the rest 35% as a test set, so as to ensure that the latitudes of the data in the test set are consistent, wherein the specific latitudes are distributed as shown in figure 3.

Thirdly, building a BP neural network: a5-layer BP neural network is selected to be used, the 3-layer hidden layer is included, the MSE function is selected as the loss function, and the Adam algorithm is used for error back propagation. And then selecting the evaluation index of the model as root mean square error RMSE, and building a BP neural network model by taking the top parameter of the masked convective layer of FY3C before correction as input and the top parameter of the masked convective layer of FY3C after correction as output.

The method specifically comprises the following steps:

the model was first constructed using a 5-layer fully-connected neural network with 3 hidden layers of 10 neurons each. The input data are FY3C convection layer top height, temperature and normalized time, date, longitude, latitude. The output data is the corrected convection layer top height and temperature.

And (3) activating by a Relu function, adaptively adjusting the dynamic learning rate by Adam until the model obtains an output value, and calculating the error between the output value and a target value, namely a loss function. And updating the network parameters according to the errors, and circulating until a specified circulation number is reached or the loss function meets the requirements. Successfully training the model and saving the parameters.

Fourthly, training a BP neural network model: initializing a weight matrix, inputting the related parameters of the top of the convective layer of the FY3C occultation of the training set into the network, and taking the height and the temperature of the top of the convective layer of the corresponding ECMWF of the training set as target parameters. And updating the weight matrix through iterative training to ensure that the MSE approaches to the minimum and the iteration times is 25000.

The training set and the testing set have the same latitude distribution and are both 80 degrees N-80 degrees S. The time is summer (2018.6-2018.8). The input data is the convection layer top temperature, the convection layer top height, the profile longitude and latitude, the date and the time which are obtained by calculating the FY3C satellite GNSS occultation dry temperature profile, and the target data is the convection layer top height and the convection layer top temperature which are obtained by calculating the ECMWF four-dimensional variation temperature profile matched with the FY3C satellite GNSS occultation dry temperature profile. Before the training data is sent to the machine learning model, the longitude and latitude, date and time data are normalized.

Fifthly, correcting the parameters of the top of the convection layer: inputting the data to be measured into a BP neural network convective layer top parameter correction model to obtain the corrected convective layer top height and temperature parameters, and evaluating the correction condition of the model on the FY3C occultation convective layer top parameter. Overall, the method significantly reduces the deviation of the top of convective layer parameters from ECMWF results as masked by FY3C, as shown in fig. 4.

Fig. 4 shows the absolute value of the error reduction (the grid of the inverted triangle represents the error increase) of the FY3C GNSS masker data product and ECMWF mode data before and after correction in each geographic grid, where fig. 4(a) shows the effect of correcting the top height of the convection layer and fig. 4(b) shows the effect of correcting the top temperature of the convection layer. In summer (6-8 months), the neural network has a very obvious effect of correcting parameters of the convective layer top of a GNSS occultation in a northern hemisphere high latitude area FY3C satellite, and only individual grid points are marked.

Example 2

The embodiment 2 of the invention provides a GNSS occultation troposphere parameter correction system based on a BP neural network, which is realized based on the method of the embodiment 1, and the system comprises: the device comprises a correction model, a receiving module, a preprocessing module and an output module; wherein the content of the first and second substances,

the receiving module is used for receiving the convective stratum top parameter product data acquired and inverted by the GNSS occultation detector;

the preprocessing module is used for preprocessing the product data of the top parameter of the convection layer;

the output module is used for inputting the preprocessed data into a pre-established and trained correction model to obtain the corrected top height and temperature of the convection layer;

the correction model adopts a BP neural network.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and are not limited. Although the present invention has been described in detail with reference to the embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims.