Dual correction method and system for observation deviation of spaceborne microwave sounder and medium

Abstract

The embodiment of the application discloses a double correction method, system and medium for observation deviation of a spaceborne microwave detector, the method introduces a ground surface parameter on the basis of the first correction of the observation deviation of the spaceborne microwave detector, accurately describes the correction residual error of the near-surface detection channel and the window channel of the spaceborne microwave detector, realizes the double correction of the observation deviation of the spaceborne microwave detector, and makes the corrected observation brightness temperature of the spaceborne microwave detector closer to the simulated brightness temperature of the spaceborne microwave detector. The application overcomes the adverse effect of the complex observation deviation characteristics of the near-surface detection channel and the window detection channel of the spaceborne microwave detector on the correction effect of the observation deviation of the spaceborne microwave detector, and effectively improves the correction effect of the observation deviation of the near-surface detection channel and the window detection channel of the spaceborne microwave detector.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the field of microwave remote sensing technology, and particularly relates to a double correction method and system for observation deviation of a spaceborne microwave detector and a medium. BACKGROUND

[0002] The spaceborne microwave detector is an important instrument for obtaining global atmospheric temperature profile, humidity profile, cloud parameters and surface parameters, and plays an important application in many fields such as atmospheric environment monitoring, numerical weather prediction and climate change research. The spaceborne microwave detector observation brightness temperature can be converted into atmospheric parameter information by using an inversion system, and can also be directly applied in a numerical weather prediction assimilation system. The physical variation algorithm is the core algorithm of the inversion system and the assimilation system, and the requirement of the spaceborne microwave detector observation brightness temperature input into the algorithm is that the observation deviation of the spaceborne microwave detector needs to meet the unbiased and high-stability as much as possible. When the observation deviation of the spaceborne microwave detector is smaller, the accuracy of the spaceborne microwave detector observation brightness temperature in inverting the atmospheric parameters will be higher, or the assimilation application effect of the spaceborne microwave detector observation brightness temperature will be better. Therefore, the spaceborne microwave detector observation brightness temperature needs to correct the observation deviation of the spaceborne microwave detector before inversion or assimilation application. The observation deviation of the spaceborne microwave detector is caused by multiple error sources, such as inaccurate data calibration of the microwave detector, unfavorable observation environment, spectral data error of the radiation transfer model, inaccurate radiation transfer model and atmospheric parameter error participating in the radiation transfer calculation and the like. The complexity of the sources of the observation deviation of the spaceborne microwave detector leads to great difficulty in determining the error sources and modeling correction from the physical point of view. When the spaceborne microwave detector detects the atmosphere, the high-altitude detection channel only detects the atmospheric parameter distribution characteristics of the high altitude, and the non-linear characteristics of the observation deviation of the high-altitude detection channel are relatively simple. The near-surface detection channel or the window channel not only detects the atmospheric parameter information of the low altitude / near-surface, but also detects the surface parameter information, and the microwave radiation of the atmospheric parameter and the surface parameter is usually mixed with the surface reflection radiation, and then the non-linear relationship of the observation deviation characteristics of the near-surface detection channel and the window channel is more complex. In the prior art, when the neural network is used to correct the observation deviation of the spaceborne microwave detector, the observation deviation correction effect of the high-altitude detection channel of the spaceborne microwave detector is significant, but the observation deviation correction effect of the near-surface detection channel and the window detection channel is poor.

[0003] The above problems need to be solved. SUMMARY

[0004] To solve the problems in the related art, the present application provides a double correction method and system for observation deviation of a spaceborne microwave detector and a medium to solve the problems mentioned in the background section.

[0005] To achieve the above object, the embodiments of the present application adopt the following technical solutions:

[0006] In a first aspect, the embodiments of the present application provide a double correction method for observation deviation of a spaceborne microwave sounder, comprising:

[0007] establishing a first correction dataset for correcting observation deviation of the spaceborne microwave sounder;

[0008] obtaining first corrected brightness temperature of the spaceborne microwave sounder based on the first correction dataset;

[0009] establishing a second correction dataset for correcting observation deviation of the spaceborne microwave sounder;

[0010] obtaining second corrected brightness temperature of the spaceborne microwave sounder based on the second correction dataset;

[0011] comparing the first corrected brightness temperature with the second corrected brightness temperature, and completing double correction of the observation deviation of the spaceborne microwave sounder according to a comparison result.

[0012] As an optional implementation, the establishing of the first correction dataset for correcting observation deviation of the spaceborne microwave sounder comprises:

[0013] calculating the observation deviation of the spaceborne microwave sounder, and establishing the first correction dataset for correcting observation deviation of the spaceborne microwave sounder.

[0014] As an optional implementation, the calculating of the observation deviation of the spaceborne microwave sounder comprises:

[0015] establishing a matching dataset of atmospheric parameter dataset and observed brightness temperature of the spaceborne microwave sounder in time and space, and calculating the observation deviation of the spaceborne microwave sounder.

[0016] As an optional implementation, the obtaining of the first corrected brightness temperature of the spaceborne microwave sounder based on the first correction dataset comprises:

[0017] establishing a first correction model of the observation deviation of the spaceborne microwave sounder based on the first correction dataset, and obtaining the first corrected brightness temperature of the spaceborne microwave sounder through the first correction model.

[0018] As an optional implementation, the establishing of the second correction dataset for correcting observation deviation of the spaceborne microwave sounder comprises:

[0019] calculating a correction residual of the spaceborne microwave sounder, and establishing the second correction dataset for correcting observation deviation of the spaceborne microwave sounder based on the correction residual of the spaceborne microwave sounder.

[0020] As an optional implementation, the establishing the second correction data set for correcting the observation bias of the spaceborne microwave sounder further comprises:

[0021] The second correction model of the observation bias of the spaceborne microwave sounder is established based on the second correction data set.

[0022] As an optional implementation, the second correction brightness temperature of the spaceborne microwave sounder is obtained based on the second correction data set, and specifically comprises:

[0023] The second correction model of the observation bias of the spaceborne microwave sounder is established based on the second correction data set, and the second correction brightness temperature of the spaceborne microwave sounder is obtained according to the second correction model.

[0024] As an optional implementation, the double correction of the observation bias of the spaceborne microwave sounder is completed according to the comparison result, and specifically comprises:

[0025] The double correction of the observation bias of the spaceborne microwave sounder is completed according to the comparison result, and the correction brightness temperature with the highest accuracy is obtained.

[0026] In a second aspect, an embodiment of the present application provides a double correction system for the observation bias of a spaceborne microwave sounder, which adopts the double correction method for the observation bias of the spaceborne microwave sounder provided in the first aspect, and comprises:

[0027] A first correction data set establishing module is configured to establish a first correction data set for correcting the observation bias of the spaceborne microwave sounder.

[0028] A first correction brightness temperature obtaining module is configured to obtain a first correction brightness temperature of the spaceborne microwave sounder based on the first correction data set.

[0029] A second correction data set establishing module is configured to establish a second correction data set for correcting the observation bias of the spaceborne microwave sounder.

[0030] A second correction brightness temperature obtaining module is configured to obtain a second correction brightness temperature of the spaceborne microwave sounder based on the second correction data set.

[0031] A double correction module is configured to compare the first correction brightness temperature with the second correction brightness temperature, and complete the double correction of the observation bias of the spaceborne microwave sounder according to the comparison result.

[0032] In a third aspect, an embodiment of the present application provides a computer readable storage medium, which stores computer execution instructions, and the computer execution instructions are executed by a processor to implement the double correction method for the observation bias of the spaceborne microwave sounder provided in the first aspect.

[0033] The technical scheme provided by the embodiment of the present application overcomes the adverse effect of the complex observation deviation characteristics of the near-surface detection channel and the window zone detection channel of the spaceborne microwave detector on the observation deviation correction effect of the spaceborne microwave detector, introduces the ground surface parameters on the basis of the first correction of the observation deviation of the spaceborne microwave detector, accurately describes the correction residual of the near-surface detection channel and the window zone channel of the spaceborne microwave detector, realizes the double correction of the observation deviation of the spaceborne microwave detector, and makes the corrected observation brightness temperature of the spaceborne microwave detector closer to the simulated brightness temperature of the spaceborne microwave detector. The technical scheme provided by the embodiment of the present application effectively improves the observation deviation correction effect of the near-surface detection channel and the window zone detection channel of the spaceborne microwave detector, is simple and easy to operate, and is suitable for popularization and application. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate and understand the technical scheme in the embodiments of the present application, the drawings needed to be used in the background of the present application and the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the contents of the embodiments of the present application and the drawings.

[0035] Figure 1 The flow chart of the double correction method of the observation deviation of the spaceborne microwave detector provided by the first embodiment of the present application is shown in the figure.

[0036] Figure 2 The display figure of the root mean square error between the first corrected brightness temperature and the observation brightness temperature of the MWTS-II of the Fengyun No. 3E satellite and the simulated brightness temperature of the MWTS-II provided by the first embodiment of the present application is shown in the figure.

[0037] Figure 3 The display figure of the root mean square error comparison between the second corrected brightness temperature and the first corrected brightness temperature of the MWTS-II of the Fengyun No. 3E satellite and the simulated brightness temperature of the MWTS-II provided by the first embodiment of the present application is shown in the figure.

[0038] Figure 4 The principle schematic diagram of the double correction system of the observation deviation of the spaceborne microwave detector provided by the second embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0039] In order to make the technical problems solved by the present application, the technical scheme adopted and the technical effects achieved more clear, the technical scheme of the embodiments of the present application will be further described in detail below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0040] Embodiment One

[0041] Reference is made to Figure 1 Said, Figure 1 A flowchart of a double correction method for observation bias of a spaceborne microwave sounder according to Embodiment One of the present application is provided.

[0042] As shown in the figure, the double correction method 100 for observation bias of a spaceborne microwave sounder according to the present embodiment includes:

[0043] S101. Establishing a first correction dataset for correcting observation bias of a spaceborne microwave sounder;

[0044] S102. Obtaining a first corrected brightness temperature of a spaceborne microwave sounder based on the first correction dataset;

[0045] S103. Establishing a second correction dataset for correcting observation bias of a spaceborne microwave sounder;

[0046] S104. Obtaining a second corrected brightness temperature of a spaceborne microwave sounder based on the second correction dataset;

[0047] S105. Comparing the first corrected brightness temperature with the second corrected brightness temperature, and completing double correction of the observation bias of the spaceborne microwave sounder according to the comparison result.

[0048] Illustratively, the establishing of the first correction dataset for correcting observation bias of a spaceborne microwave sounder includes:

[0049] Calculating observation bias of a spaceborne microwave sounder, and establishing a first correction dataset for correcting observation bias of a spaceborne microwave sounder.

[0050] Illustratively, the calculating of observation bias of a spaceborne microwave sounder includes:

[0051] Establishing a matching dataset of atmospheric parameter dataset and spaceborne microwave sounder observation brightness temperature in time and space, and calculating observation bias of a spaceborne microwave sounder.

[0052] Specifically, in the embodiment, the temperature profile, the humidity profile, the cloud water profile, the surface temperature, the surface humidity, the surface pressure, and the 10m wind speed are selected from the historical climate data set to establish an atmospheric parameter data set. The atmospheric parameter data set is matched with the brightness temperature observed by the spaceborne microwave sounder in time and space to establish a matching data set. The atmospheric parameters in the matching data set and the observation angle of the brightness temperature observed by the spaceborne microwave sounder are input into a radiation transfer model to calculate the simulated brightness temperature of the spaceborne microwave sounder. The spaceborne microwave sounder observation bias can be calculated according to the simulated brightness temperature of the spaceborne microwave sounder and the observed brightness temperature of the spaceborne microwave sounder. The spaceborne microwave sounder observation bias is:

[0053] B i = O i - S i

[0054] wherein B represents the spaceborne microwave sounder observation bias, B i represents the observation bias of the i th channel of the spaceborne microwave sounder, i = 1, 2, 3…, n represents n channels of the spaceborne microwave sounder; O represents the observed brightness temperature of the spaceborne microwave sounder, O i represents the observed brightness temperature of the i th channel of the spaceborne microwave sounder; S represents the simulated brightness temperature of the spaceborne microwave sounder, S i represents the simulated brightness temperature of the i th channel of the spaceborne microwave sounder.

[0055] By calculating the spaceborne microwave sounder observation bias, the matching data set contains the atmospheric parameter data set, the observed brightness temperature of the spaceborne microwave sounder, the simulated brightness temperature of the spaceborne microwave sounder, and the spaceborne microwave sounder observation bias. The matching data set is used as the first correction data set for the first correction of the spaceborne microwave sounder observation bias.

[0056] For example, based on the first correction data set, the first corrected brightness temperature of the spaceborne microwave sounder is obtained, including:

[0057] Based on the first correction data set, a first correction model of the spaceborne microwave sounder observation bias is established, and the first corrected brightness temperature of the spaceborne microwave sounder is obtained through the first correction model.

[0058] Specifically, in the present embodiment, firstly, 80% of the matched data in the first correction data set is randomly selected as the training data set of the first correction data, and the remaining 20% of the matched data in the first correction data set is selected as the validation data set of the first correction data set. Secondly, a three-layer neural network structure (one input layer, one hidden layer, and one output layer) is established, with the observed brightness temperature of the spaceborne microwave sounder in the training data set of the first correction data set as the input and the matched observed bias of the spaceborne microwave sounder as the output. The three-layer neural network structure is trained, and different predicted values of the observed bias of the spaceborne microwave sounder can be obtained by adjusting the number of hidden layer neurons. Then, the root mean square error between the predicted values of the observed bias of the spaceborne microwave sounder and the corresponding observed bias of the spaceborne microwave sounder is calculated, and a corresponding relationship between the root mean square error and the number of hidden layer neurons is established. The neural network structure corresponding to the number of hidden layer neurons with the minimum root mean square error is taken as the optimal neural network, i.e., the first correction model of the observed bias of the spaceborne microwave sounder. Finally, the observed brightness temperature of the spaceborne microwave sounder in the validation data set of the first correction data set is input into the first correction model of the observed bias of the spaceborne microwave sounder to obtain the predicted value B' of the observed bias of the spaceborne microwave sounder, and further obtain the first corrected brightness temperature C' of the spaceborne microwave sounder, and the first corrected brightness temperature C' of the i-th channel of the spaceborne microwave sounder. i is represented as:

[0059] C' i = O i -B' i

[0060] wherein B' i represents the predicted value of the observed bias of the i-th channel of the spaceborne microwave sounder.

[0061] Exemplarily, the establishing the second correction data set for correcting the observed bias of the spaceborne microwave sounder comprises:

[0062] calculating the correction residual of the spaceborne microwave sounder, and establishing the second correction data set for correcting the observed bias of the spaceborne microwave sounder based on the correction residual of the spaceborne microwave sounder.

[0063] Exemplarily, after the establishing the second correction data set for correcting the observed bias of the spaceborne microwave sounder, the method further comprises:

[0064] establishing the second correction model of the observed bias of the spaceborne microwave sounder based on the second correction data set.

[0065] Specifically, in the embodiment, first, the first corrected brightness temperature of the spaceborne microwave sounder is added to the verification dataset of the first corrected dataset, the spaceborne microwave sounder correction residual R is calculated in the verification dataset of the first corrected dataset, and the spaceborne microwave sounder correction residual is added to the verification dataset of the first corrected dataset. The correction residual R of the i th channel of the spaceborne microwave sounder is expressed as: i is expressed as:

[0066] R i = C i -S i

[0067] Secondly, 80% of the matching datasets in the verification dataset of the first corrected dataset are randomly selected as the training dataset of the second corrected dataset, the remaining 20% of the matching datasets in the verification dataset of the first corrected dataset are used as the verification dataset of the second corrected dataset, then, a three-layer neural network structure (one input layer, one hidden layer, and one output layer) is established, the first corrected brightness temperature of the spaceborne microwave sounder, the surface temperature, the surface humidity, the surface pressure, and the 10m wind speed in the training dataset of the second corrected dataset are used as the input, and the matched spaceborne microwave sounder correction residual is used as the output, the three-layer neural network structure is trained, different prediction values of the spaceborne microwave sounder correction residual can be obtained by adjusting the number of hidden layer neurons. Finally, the root mean square error between the prediction values of different spaceborne microwave sounder correction residuals and the corresponding spaceborne microwave sounder correction residuals is calculated, and the corresponding relationship between the root mean square error and the number of hidden layer neurons is established. The neural network structure corresponding to the number of hidden layer neurons with the minimum root mean square error is used as the optimal neural network, that is, the second corrected model of the spaceborne microwave sounder observation bias.

[0068] For example, based on the second corrected dataset, the second corrected brightness temperature of the spaceborne microwave sounder is obtained, specifically including:

[0069] Based on the second corrected dataset, a second corrected model of the spaceborne microwave sounder observation bias is established, and the second corrected brightness temperature of the spaceborne microwave sounder is obtained according to the second corrected model.

[0070] Specifically, in the embodiment, first, the first corrected brightness temperature of the spaceborne microwave sounder, the surface temperature, the surface humidity, the surface pressure, and the 10m wind speed in the verification dataset of the second corrected dataset are input into the second corrected model of the spaceborne microwave sounder observation bias, and the prediction value R' of the spaceborne microwave sounder correction residual is obtained. i Then, the second corrected brightness temperature C'' of the spaceborne microwave sounder is calculated, and the second corrected brightness temperature C i '' of the i th channel of the spaceborne microwave sounder is expressed as:

[0071] C i ”=C i '-R i '

[0072] wherein, R' i represents the predicted value of the corrected residual error of the i-th channel of the spaceborne microwave sounder.

[0073] Then, in the validation data set of the second correction data set, the root mean square error between the first corrected brightness temperature of the spaceborne microwave sounder and the simulated brightness temperature of the spaceborne microwave sounder is calculated for each detection channel of the spaceborne microwave sounder as the first corrected root mean square error, and the root mean square error between the second corrected brightness temperature of the spaceborne microwave sounder and the simulated brightness temperature of the spaceborne microwave sounder is calculated as the second corrected root mean square error. Finally, for each detection channel of the spaceborne microwave sounder, the first corrected root mean square error and the second corrected root mean square error are compared, the corrected brightness temperature corresponding to the larger root mean square error is discarded, and the corrected brightness temperature corresponding to the smaller root mean square error is taken as the corrected brightness temperature of the detection channel. According to the comparison results of the first corrected root mean square error and the second corrected root mean square error of the spaceborne microwave sounder, the double corrected brightness temperature of the spaceborne microwave sounder is obtained, and the observation bias of the spaceborne microwave sounder is double corrected. It is worth mentioning that the larger root mean square error and the smaller root mean square error can be flexibly set according to actual conditions, and are not limited to a certain amount.

[0074] In order to facilitate the understanding of the double correction method of the observation bias of the spaceborne microwave sounder proposed in this embodiment, examples are given for detailed description. In this embodiment, the spaceborne microwave sounder selected is the Microwave Temperature Sounder II (MWTS-II) carried on the Fengyun-3E satellite, and the ERA5 reanalysis data set of the European Centre for Medium-Range Weather Forecasts is selected as the historical climate data set to establish the atmospheric parameter data set required by the present application. This embodiment carries out double correction on the observation bias of the MWTS-II.

[0075] The geographical range and time range of the MWTS-II observed brightness temperature and the ERA5 reanalysis dataset used for selection are the same, the geographical range is (15°N-35°N, 160°E-210°E), and the time range is from January 2023 to December 2023. From the ERA5 reanalysis dataset, the atmospheric parameter dataset is established by selecting temperature profile, humidity profile, cloud water profile, surface temperature, surface humidity, surface pressure, and 10m wind speed. The atmospheric parameter dataset is matched with the MWTS-II observed brightness temperature in time and space to establish a matching dataset, wherein the matching rule is that the time error of the atmospheric parameter and the MWTS-II observed brightness temperature is less than 20 minutes, and the longitude and latitude error is less than 0.1°, and 635287 pairs of matching data can be obtained.

[0076] The observation angle of the atmospheric parameter and the MWTS-II observed brightness temperature in the matching dataset is input into the radiation transfer model to calculate the MWTS-II simulated brightness temperature, and the MWTS-II observation bias can be calculated according to the MWTS-II simulated brightness temperature and the MWTS-II observed brightness temperature. The MWTS-II observation bias is:

[0077] B i =O i -S i

[0078] Wherein, B represents the MWTS-II observation bias, B i represents the observation bias of the i th channel of MWTS-II, i = 1, 2, 3…, 15 represents 15 channels of MWTS-II; O represents the MWTS-II observed brightness temperature, O i represents the observed brightness temperature of the i th channel of MWTS-II; S represents the MWTS-II simulated brightness temperature, S i represents the simulated brightness temperature of the i th channel of MWTS-II.

[0079] By calculating the MWTS-II observation bias, the matching dataset contains the atmospheric parameter dataset, the MWTS-II observed brightness temperature, the MWTS-II simulated brightness temperature, and the MWTS-II observation bias. This matching dataset is used as the first correction dataset for the first correction of the MWTS-II observation bias.

[0080] For the first correction of MWTS-II observation bias, firstly, 80% of the matched data in the first correction data set is randomly selected as the training data set of the first correction data, and the remaining 20% of the matched data in the first correction data set is the validation data set of the first correction data set; secondly, a three-layer neural network structure (one input layer, one hidden layer and one output layer) is established, the MWTS-II observed brightness temperature in the training data set of the first correction data set is taken as the input, and the matched MWTS-II observation bias is taken as the output, the three-layer neural network structure is trained, different MWTS-II observation bias prediction values can be obtained by adjusting the number of hidden layer neurons; then, the root mean square error between the different MWTS-II observation bias prediction values and the corresponding MWTS-II observation bias is calculated, the corresponding relationship between the root mean square error and the number of hidden layer neurons is established, the neural network structure corresponding to the number of hidden layer neurons with the minimum root mean square error is taken as the optimal neural network, that is, the first correction model of MWTS-II observation bias; finally, the MWTS-II observed brightness temperature in the validation data set of the first correction data set is input into the first correction model of MWTS-II observation bias, the MWTS-II observation bias prediction value B' is obtained, and then the first correction brightness temperature C' of MWTS-II is obtained, and the first correction brightness temperature C' of the i th channel of MWTS-II i is represented as:

[0081] C' i = O i -B' i

[0082] wherein B' i represents the prediction value of the observation bias of the i th channel of MWTS-II.

[0083] The first correction brightness temperature of MWTS-II is added to the validation data set of the first correction data set, the MWTS-II correction residual R is calculated in the validation data set of the first correction data set, and the MWTS-II correction residual is added to the validation data set of the first correction data set, and the correction residual R of the i th channel of MWTS-II i is represented as:

[0084] R i =C i '-S i

[0085] In the validation dataset of the first calibration dataset, 80% of the matching datasets are randomly selected as the training dataset for the second calibration dataset, and the remaining 20% ​​of the matching data in the validation dataset of the first calibration dataset are used as the validation dataset for the second calibration dataset. A three-layer neural network structure (one input layer, one hidden layer, and one output layer) is established. The first calibration brightness temperature, surface temperature, surface humidity, surface pressure, and 10m wind speed of MWTS-II in the training dataset of the second calibration dataset are used as inputs, and the matching MWTS-II calibration residuals are used as outputs. The three-layer neural network structure is trained, and different predicted values ​​of MWTS-II calibration residuals can be obtained by adjusting the number of neurons in the hidden layer. Finally, the root mean square error between the predicted values ​​of different MWTS-II calibration residuals and the corresponding MWTS-II calibration residuals is calculated, and the correspondence between the root mean square error and the number of neurons in the hidden layer is established. The neural network structure corresponding to the number of hidden layer neurons with the minimum root mean square error is taken as the optimal neural network, that is, the second calibration model for MWTS-II observation bias.

[0086] The first-correction brightness temperature, surface temperature, surface humidity, surface pressure, and 10m wind speed of MWTS-II from the validation dataset of the second correction dataset are input into the second correction model for MWTS-II observation bias to obtain the predicted value R of the MWTS-II correction residual. i Then, calculate the second corrected brightness temperature C″ of MWTS-II, and the second corrected brightness temperature C of the i-th channel of MWTS-II. i " is represented as:

[0087] C i =C i '-R i '

[0088] Among them, R iThe prediction value of the correction residual of the i-th channel of MWTS-II. In the validation data set of the second correction data set, the root mean square error between the first correction brightness temperature of MWTS-II and the simulated brightness temperature of MWTS-II is calculated for each detection channel of MWTS-II as the root mean square error after the first correction, and the root mean square error between the second correction brightness temperature of MWTS-II and the simulated brightness temperature of MWTS-II is calculated as the root mean square error after the second correction; finally, for each detection channel of MWTS-II, the root mean square error after the first correction is compared with the root mean square error after the second correction, the correction brightness temperature corresponding to the larger root mean square error is discarded, and the correction brightness temperature corresponding to the smaller root mean square error is taken as the correction brightness temperature of the detection channel, and then according to the comparison result of the root mean square error after the first correction and the root mean square error after the second correction, the double correction brightness temperature of MWTS-II is obtained, and the observation bias of MWTS-II is double corrected.

[0089] The root mean square error between the first correction brightness temperature of MWTS-II in the validation data set of the first correction data set and the observation brightness temperature (i.e. uncorrected brightness temperature) and the simulated brightness temperature of MWTS-II is calculated as shown in Figure 2 The root mean square error between the second correction brightness temperature of MWTS-II in the validation data set of the second correction data set and the simulated brightness temperature of MWTS-II is calculated, and the comparison with the root mean square error between the first correction brightness temperature of MWTS-II in the validation data set of the first correction data set and the simulated brightness temperature of MWTS-II is shown in Figure 3 .

[0090] As can be seen from Figure 2 , compared with the uncorrected brightness temperature of MWTS-II, the first observation bias correction can effectively correct the observation bias in all 13 channels of MWTS-II. However, the correction residual in the near-surface detection channels 1, 2, 3 and 4 of MWTS-II is still large, which is 4.5K, 2.7K, 1.4K and 0.7K respectively. When the observation bias of MWTS-II is corrected for the second time, it can be seen from Figure 3 that the correction residual in channels 1, 2, 3, 4 and 5 of MWTS-II is further reduced by 1.1K, 0.95K, 0.5K, 0.24K and 0.16K respectively. The correction residual in detection channels 6-13 of MWTS-II remains unchanged. It can be seen that the double correction method of observation bias of the satellite-borne microwave detector proposed by the present application can effectively improve the correction effect of the observation bias in the near-surface detection channels 1-5 of MWTS-II.

[0091] In Figure 3In the embodiment, for each detection channel of the MWTS-II, the root mean square error after the first correction is compared with the root mean square error after the second correction, the correction brightness temperature corresponding to the larger root mean square error is discarded, the correction brightness temperature corresponding to the smaller root mean square error is taken as the correction brightness temperature of the detection channel, and then the second correction brightness temperature of the MWTS-II is the double correction brightness temperature of the MWTS-II, so that the double correction of the observation bias of the MWTS-II is realized.

[0092] The double correction method 100 for the observation bias of the satellite-borne microwave detector provided in the embodiment overcomes the adverse effect of the complex observation bias characteristics of the near-surface detection channel and the window detection channel of the satellite-borne microwave detector on the correction effect of the observation bias of the satellite-borne microwave detector, introduces the ground surface parameter on the basis of the first correction of the observation bias of the satellite-borne microwave detector, accurately describes the correction residual error of the near-surface detection channel and the window detection channel of the satellite-borne microwave detector, realizes the double correction of the observation bias of the satellite-borne microwave detector, and makes the corrected observation brightness temperature of the satellite-borne microwave detector closer to the simulated brightness temperature of the satellite-borne microwave detector. The technical scheme provided in the embodiment effectively improves the correction effect of the observation bias of the near-surface detection channel and the window detection channel of the satellite-borne microwave detector, and is simple and easy to operate.

[0093] Embodiment two

[0094] Please refer to Figure 4 The, Figure 4 The double correction system for the observation bias of the satellite-borne microwave detector provided in the embodiment two is shown in Fig. 2. As shown in the figure, the double correction system 400 for the observation bias of the satellite-borne microwave detector in the embodiment adopts the double correction method 100 for the observation bias of the satellite-borne microwave detector provided in the embodiment one, and includes:

[0095] The first correction data set establishing module 401 is configured to establish a first correction data set for correcting the observation bias of the satellite-borne microwave detector.

[0096] The first correction brightness temperature obtaining module 402 is configured to obtain the first correction brightness temperature of the satellite-borne microwave detector based on the first correction data set.

[0097] The second correction data set establishing module 403 is configured to establish a second correction data set for correcting the observation bias of the satellite-borne microwave detector.

[0098] The second correction brightness temperature obtaining module 404 is configured to obtain the second correction brightness temperature of the satellite-borne microwave detector based on the second correction data set.

[0099] The double correction module 405 is configured to compare the first correction brightness temperature with the second correction brightness temperature, and complete the double correction of the observation bias of the satellite-borne microwave detector according to the comparison result.

[0100] Specifically, the first correction dataset establishing module 401 is specifically configured to:

[0101] The atmospheric parameter dataset is established by selecting temperature profiles, humidity profiles, cloud water profiles, surface temperature, surface humidity, surface pressure, 10m wind speed, and other atmospheric parameters from the historical climate dataset, matching the atmospheric parameter dataset with the satellite-borne microwave detector observed brightness temperature in time and space to establish a matching dataset. The atmospheric parameters and the observation angle of the satellite-borne microwave detector observed brightness temperature in the matching dataset are input into a radiation transfer model to calculate the simulated brightness temperature of the satellite-borne microwave detector, and the satellite-borne microwave detector observation bias can be calculated according to the simulated brightness temperature of the satellite-borne microwave detector and the satellite-borne microwave detector observed brightness temperature. The satellite-borne microwave detector observation bias is:

[0102] B = O - S i i i

[0103] Wherein, B represents the satellite-borne microwave detector observation bias, B i represents the observation bias of the i-th channel of the satellite-borne microwave detector, i = 1, 2, 3…, n represents n channels of the satellite-borne microwave detector; O represents the satellite-borne microwave detector observed brightness temperature, O i represents the observed brightness temperature of the i-th channel of the satellite-borne microwave detector; S represents the simulated brightness temperature of the satellite-borne microwave detector, S i represents the simulated brightness temperature of the i-th channel of the satellite-borne microwave detector.

[0104] By calculating the satellite-borne microwave detector observation bias, the matching dataset contains the atmospheric parameter dataset, the satellite-borne microwave detector observed brightness temperature, the satellite-borne microwave detector simulated brightness temperature, and the satellite-borne microwave detector observation bias. This matching dataset is used as the first correction dataset for the first correction of the satellite-borne microwave detector observation bias.

[0105] ​​Specifically, the first corrected brightness temperature obtaining module 402 is specifically configured to: randomly select 80% of the matching data in the first correction data set as the training data set of the first correction data, and the remaining 20% of the matching data in the first correction data set as the verification data set of the first correction data set. A three-layer neural network structure (one input layer, one hidden layer, and one output layer) is established, the observed brightness temperature of the spaceborne microwave detector in the training data set of the first correction data set is taken as the input, the matched spaceborne microwave detector observation bias is taken as the output, the three-layer neural network structure is trained, and different prediction values of the spaceborne microwave detector observation bias can be obtained by adjusting the number of hidden layer neurons. The root mean square error between the prediction value of the different spaceborne microwave detector observation bias and the corresponding spaceborne microwave detector observation bias is calculated, and a corresponding relationship between the root mean square error and the number of hidden layer neurons is established. The neural network structure corresponding to the number of hidden layer neurons with the minimum root mean square error is taken as the optimal neural network, that is, the first correction model of the spaceborne microwave detector observation bias. Finally, the observed brightness temperature of the spaceborne microwave detector in the verification data set of the first correction data set is input into the first correction model of the spaceborne microwave detector observation bias, the prediction value B' of the spaceborne microwave detector observation bias is obtained, and then the first correction brightness temperature C' of the spaceborne microwave detector is obtained. The first correction brightness temperature C' of the i th channel of the spaceborne microwave detector is represented as: i

[0106] C' i = O i -B' i

[0107] B' i represents the prediction value of the observation bias of the i th channel of the spaceborne microwave detector.

[0108] In the embodiment, the second correction data set establishing module 403, the second correction brightness temperature obtaining module 404, and the double correction module 405 are specifically configured to: add the first correction brightness temperature of the spaceborne microwave detector to the verification data set of the first correction data set, calculate the correction residual R of the spaceborne microwave detector in the verification data set of the first correction data set, and add the correction residual of the spaceborne microwave detector to the verification data set of the first correction data set. The correction residual R of the i th channel of the spaceborne microwave detector is represented as: i

[0109] R i =C i -S i

[0110] ​​In the validation data set of the first correction data set, 80% of the matching data sets are randomly selected as the training data set of the second correction data set, and the remaining 20% of the matching data in the validation data set of the first correction data set is used as the validation data set of the second correction data set. A three-layer neural network structure (one input layer, one hidden layer, and one output layer) is established, the first corrected brightness temperature of the spaceborne microwave detector, the ground temperature, the ground humidity, the ground pressure, and the 10m wind speed in the training data set of the second correction data set are used as inputs, and the matched spaceborne microwave detector correction residual is used as output. The three-layer neural network structure is trained, and by adjusting the number of hidden layer neurons, different prediction values of the spaceborne microwave detector correction residual can be obtained. The root mean square error between the prediction values of different spaceborne microwave detector correction residuals and the corresponding spaceborne microwave detector correction residuals is calculated, and the corresponding relationship between the root mean square error and the number of hidden layer neurons is established. The neural network structure corresponding to the number of hidden layer neurons with the minimum root mean square error is used as the optimal neural network, i.e., the second correction model of the spaceborne microwave detector observation bias. The first corrected brightness temperature of the spaceborne microwave detector, the ground temperature, the ground humidity, the ground pressure, and the 10m wind speed in the validation data set of the second correction data set are input into the second correction model of the spaceborne microwave detector observation bias, and the prediction value R of the spaceborne microwave detector correction residual is obtained.

[0111] The prediction value R of the spaceborne microwave detector correction residual is calculated as follows: i Then, the second corrected brightness temperature C" of the spaceborne microwave detector is calculated, and the second corrected brightness temperature C i ” of the i-th channel of the spaceborne microwave detector is represented as:

[0112] C i ”=C i '-R i '

[0113] wherein R iThe predicted value of the corrected residual error of the i-th channel of the spaceborne microwave detector. In the validation data set of the second correction data set, the root mean square error between the first corrected brightness temperature of the spaceborne microwave detector and the simulated brightness temperature of the spaceborne microwave detector is calculated for each detection channel of the spaceborne microwave detector as the first corrected root mean square error, and the root mean square error between the second corrected brightness temperature of the spaceborne microwave detector and the simulated brightness temperature of the spaceborne microwave detector is calculated as the second corrected root mean square error. For each detection channel of the spaceborne microwave detector, the first corrected root mean square error and the second corrected root mean square error are compared, the corrected brightness temperature corresponding to the larger root mean square error is discarded, and the corrected brightness temperature corresponding to the smaller root mean square error is taken as the corrected brightness temperature of the detection channel. According to the comparison result of the first corrected root mean square error and the second corrected root mean square error of the spaceborne microwave detector, the double corrected brightness temperature of the spaceborne microwave detector is obtained, and the observation bias of the spaceborne microwave detector is double corrected. It is worth mentioning that the larger root mean square error and the smaller root mean square error can be flexibly set according to actual conditions, and are not limited to a certain amount.

[0114] The double correction system 400 for the observation bias of the spaceborne microwave detector provided in the embodiment overcomes the adverse effects of the complex observation bias characteristics of the near-surface detection channel and the window detection channel of the spaceborne microwave detector on the correction effect of the observation bias of the spaceborne microwave detector, introduces the ground surface parameter on the basis of the first correction of the observation bias of the spaceborne microwave detector, accurately describes the corrected residual error of the near-surface detection channel and the window channel of the spaceborne microwave detector, realizes the double correction of the observation bias of the spaceborne microwave detector, and makes the corrected observation brightness temperature of the spaceborne microwave detector closer to the simulated brightness temperature of the spaceborne microwave detector. The technical scheme provided in the embodiment of the application effectively improves the correction effect of the observation bias of the near-surface detection channel and the window detection channel of the spaceborne microwave detector, and is simple and easy to operate.

[0115] Embodiment three

[0116] The embodiment provides a computer readable storage medium, and the computer readable storage medium stores computer execution instructions. When the computer execution instructions are executed by a processor, the computer execution instructions are used to implement the double correction method 100 for the observation bias of the spaceborne microwave detector provided in the above embodiment one.

[0117] It is noted that the above-mentioned storage media can be realized by any type of volatile or non-volatile storage devices or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk. The storage media can be any available media that can be accessed by a general or special purpose computer.

[0118] It is noted that the above-mentioned storage media can be realized by any type of volatile or non-volatile storage devices or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk. The storage media can be any available media that can be accessed by a general or special purpose computer. It is noted that the above-mentioned storage media can be realized by any type of volatile or non-volatile storage devices or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk. The storage media can be any available media that can be accessed by a general or special purpose computer. It is noted that the above-mentioned storage media can be realized by any type of volatile or non-volatile storage devices or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk. The storage media can be any available media that can be accessed by a general or special purpose computer.

Claims

1. A dual correction method for observation bias of a spaceborne microwave detector, characterized in that, include: Establish the first calibration dataset for correcting observation biases of the spaceborne microwave detector; Based on the first calibration dataset, the first calibration brightness temperature of the spaceborne microwave detector is obtained; Calculate the correction residual between the first corrected brightness temperature and the simulated brightness temperature of the spaceborne microwave detector; Based on the aforementioned correction residuals and surface parameters, a second correction dataset is established to correct the observation bias of the spaceborne microwave detector; the surface parameters include at least surface temperature, surface humidity, surface pressure, and surface wind speed. Based on the second calibration dataset, the second calibration brightness temperature of the spaceborne microwave detector is obtained; By comparing the first and second brightness temperature corrections, and based on the comparison results, a dual correction of the observation deviation of the spaceborne microwave detector is completed.

2. The dual correction method for observation deviation of a spaceborne microwave detector according to claim 1, characterized in that, The establishment of the first calibration dataset for correcting observation biases of the spaceborne microwave detector includes: Calculate the observation bias of the spaceborne microwave detector and establish the first calibration dataset for correcting the observation bias of the spaceborne microwave detector.

3. The dual correction method for observation deviation of a spaceborne microwave detector according to claim 2, characterized in that, The calculation of the observation bias of the spaceborne microwave detector includes: Establish a dataset of atmospheric parameters and a dataset of temporal and spatial matching of brightness temperature observed by a spaceborne microwave detector, and calculate the observation bias of the spaceborne microwave detector.

4. The dual correction method for observation deviation of a spaceborne microwave detector according to claim 3, characterized in that, The step of obtaining the first calibration brightness temperature of the spaceborne microwave detector based on the first calibration dataset includes: Based on the first calibration dataset, a first calibration model for the observation bias of the spaceborne microwave detector is established, and the first calibration brightness temperature of the spaceborne microwave detector is obtained through the first calibration model.

5. The dual correction method for observation deviation of a spaceborne microwave detector according to claim 1, characterized in that, The establishment of the second calibration dataset for correcting observation biases of the spaceborne microwave detector includes: Calculate the correction residuals of the spaceborne microwave detector, and based on the correction residuals of the spaceborne microwave detector, establish a second correction dataset for correcting the observation bias of the spaceborne microwave detector.

6. The dual correction method for observation deviation of a spaceborne microwave detector according to claim 5, characterized in that, The process of establishing a second calibration dataset for correcting observation biases of the spaceborne microwave detector further includes: Based on the second calibration dataset, a second calibration model for the observation bias of the spaceborne microwave detector is established.

7. The dual correction method for observation bias of a spaceborne microwave detector according to claim 6, characterized in that, The process of obtaining the second calibration brightness temperature of the spaceborne microwave detector based on the second calibration dataset specifically includes: Based on the second calibration dataset, a second calibration model for the observation bias of the spaceborne microwave detector is established, and the second calibration brightness temperature of the spaceborne microwave detector is obtained according to the second calibration model.

8. The dual correction method for observation deviation of a spaceborne microwave detector according to claim 7, characterized in that, The process of performing dual correction of the observation bias of the spaceborne microwave detector based on the comparison results includes: Based on the comparison results, the highest accuracy correction brightness temperature is obtained, thus completing the dual correction of the observation deviation of the spaceborne microwave detector.

9. A dual correction system for observation bias of a spaceborne microwave detector, characterized in that, The system employs the dual correction method for observation bias of the spaceborne microwave detector as described in claim 1, comprising: The first calibration dataset creation module is used to create the first calibration dataset for correcting the observation bias of the spaceborne microwave detector. The first calibration brightness temperature acquisition module is used to acquire the first calibration brightness temperature of the spaceborne microwave detector based on the first calibration dataset. The second calibration dataset establishment module is used to calculate the calibration residual between the first calibration brightness temperature and the simulated brightness temperature of the spaceborne microwave detector; and based on the calibration residual and surface parameters, to establish a second calibration dataset for correcting the observation bias of the spaceborne microwave detector; the surface parameters include at least surface temperature, surface humidity, surface pressure and surface wind speed. The second calibration brightness temperature acquisition module is used to acquire the second calibration brightness temperature of the spaceborne microwave detector based on the second calibration dataset. The dual correction module is used to compare the first correction brightness temperature with the second correction brightness temperature, and based on the comparison result, to complete the dual correction of the observation deviation of the spaceborne microwave detector.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that, When the computer execution instructions are executed by the processor, they are used to implement the dual correction method for observation deviation of the spaceborne microwave detector as described in claim 1.

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