WRFDA assimilation method, system and device based on GIIRS data

Abstract

The present application relates to assimilation processing technical field, especially to a kind of WRFDA assimilation method, system and device based on GIIRS data.The method includes obtaining GIIRS data, extracting data therefrom, merging, checking data, obtaining GIIRS observation information after checking;Using RTTOV fast radiation transfer mode, the background field data of numerical weather prediction model is corrected to obtain stable deviation corrected simulation data;After checking GIIRS observation information and deviation corrected simulation data, cloud pollution screening is carried out, and GIIRS observation information without cloud pollution is obtained;WRFDA numerical assimilation is carried out to GIIRS observation information without cloud pollution, and initial field file is obtained, and initial field file is used to adjust and optimize the output weather forecast result.The present application is more accurate by obtaining more accurate initialization file, and the simulation accuracy of typhoon precipitation area is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data assimilation processing, and particularly relates to a WRFDA assimilation method, system and device based on GIIRS data. BACKGROUND

[0002] Data assimilation is a technology of fusing multi-source meteorological observation data into a numerical model to provide a more optimal initial field for numerical weather prediction. Satellite data is generally improved by directly assimilating or indirectly assimilating the initial value of the model. The satellite data in the clear sky area is directly assimilated to reduce the error of the inversion link, which has become a trend of satellite data assimilation in various business centers. However, the cloud in the atmosphere is composed of water droplets and ice crystals, which will strongly absorb in the infrared band, causing the infrared radiation below the cloud layer to be unable to be detected, and only the atmospheric radiation above the cloud top can be measured. Moreover, the current fast radiation transfer model is not accurate enough in simulating the brightness temperature of the cloud field, resulting in a large deviation of the satellite in the cloud field brightness temperature. These data are usually discarded when assimilated. In addition, since the completely clear sky field usually accounts for only about 10%, a large amount of data will be wasted. As a hyperspectral infrared detector, GIIRS (Interferometric Atmospheric Vertical Sounder) has a large number of channels, high information redundancy and high sensitivity to clouds. The GIIRS L2 product only provides completely clear sky field points, and the utilization rate of GIIRS data is low, which needs to be processed by channel selection, cloud detection and bias correction before being effectively utilized. The current mainstream regional numerical prediction model assimilation system (WRFDA) does not contain a processing module for FY4B (Feng Yun 4B, Fengyun 4B) GIIRS data. Unlike foreign satellite data (such as the American GOES series and the European MSG series), the data format, characteristics and quality control requirements of FY4B GIIRS are special, and each processing link from the original observation data to the assimilation input needs to be independently programmed and debugged, including data decoding, format conversion, quality control, channel selection, bias correction and other key steps. In particular, cloud detection processing, as a key step in GIIRS data assimilation, directly affects the assimilation effect and prediction quality. SUMMARY

[0003] The present application aims to at least solve one of the technical problems in the related art. To this end, the present application provides a WRFDA assimilation method, system and device based on GIIRS data, which realizes more accurate initialization files and more accurate simulation precision of typhoon precipitation areas.

[0004] The present application provides a WRFDA assimilation method based on GIIRS data, comprising:

[0005] S1: obtaining GIIRS data, reading channel brightness temperature, latitude, longitude, observation time, satellite zenith angle, solar zenith angle, satellite azimuth angle and solar azimuth angle data information from the GIIRS data, and merging the data to obtain GIIRS observation information of a large area;

[0006] S2: performing quality control inspection on the GIIRS observation information to obtain GIIRS observation information after inspection;

[0007] S3: converting background field data of a numerical weather prediction model into simulated brightness temperature data using a RTTOV fast radiation transfer mode, and performing bias correction on the channel brightness temperature data to obtain stable bias-corrected simulated data;

[0008] S4: performing cloud pollution screening on the GIIRS observation information after inspection and the bias-corrected simulated data to obtain GIIRS observation information without cloud pollution;

[0009] S5: performing WRFDA numerical assimilation on the GIIRS observation information without cloud pollution to obtain an initial field file, and the initial field file is used to adjust and optimize weather prediction results.

[0010] According to the WRFDA assimilation method based on GIIRS data provided by the application, step S1 comprises:

[0011] S11: integrating the channel brightness temperature, latitude, longitude, observation time, satellite zenith angle, solar zenith angle, satellite azimuth angle and solar azimuth angle data of the same time period to obtain an HDF file;

[0012] S12: merging the HDF file in a longitude-increasing manner to obtain observation information of a latitude band;

[0013] S13: merging the observation information of the latitude band of different time periods in a latitude-increasing manner to obtain the GIIRS observation information of the large area.

[0014] According to the WRFDA assimilation method based on GIIRS data provided by the application, the quality control inspection comprises removing mixed ground observation, removing observation with a satellite zenith angle greater than 60°, removing grid points with an absolute value of observed brightness temperature minus simulated brightness temperature greater than 15K, and removing grid points with an absolute value of observed brightness temperature minus simulated brightness temperature greater than 3 times the standard deviation of observation error.

[0015] According to the WRFDA assimilation method based on GIIRS data provided by the application, the quality control inspection process comprises:

[0016] S21: setting a system reference state through variable definition and parameter initialization;

[0017] S22: performing basic inspection, sequentially carrying out composite check of sensor saturation value, abnormal radiation value, sea surface temperature consistency, solar pollution, cloud cover and geographical boundary constraint, and eliminating invalid observation;

[0018] S23: the data of inspection enters a statistical processing module, gives final QC determination result, marks good and bad observation, and adjusts error;

[0019] S24: writing qualified data into output file according to standardized format.

[0020] According to the WRFDA assimilation method based on GIIRS data provided by the application, the cost function of bias correction is :

[0021]

[0022] Wherein, is an atmospheric state vector, is a bias correction coefficient, is an atmospheric state vector of a background field, is a bias correction coefficient of a background field, is an observation result, is an error covariance matrix of , is an error covariance matrix of , is an error covariance matrix of , is a bias-corrected observation operator, is a matrix transpose;

[0023]

[0024] Wherein, is an original observation operator, is a coefficient of the th prediction factor, is the th prediction factor, is the serial number of the prediction factor, is the total number of prediction factors, .

[0025] According to the WRFDA assimilation method based on GIIRS data provided by the application, step S4 comprises:

[0026] S41: by comparing the brightness temperature of the mode bias correction coefficient file and the deviation of the brightness temperature of the GIIRS observation information after inspection, the influence of the cloud is identified, the channel polluted by the cloud is recorded as the cloudy channel, and the channel not polluted by the cloud is recorded as the clear sky channel;

[0027] S42: the emissivity of each wave band in the infrared spectrum is different, wave band division is carried out, the channel height is defined, the deviation after the brightness temperature of the GIIRS observation information after correction is sorted, the noise equivalent radiation difference is used to describe the measurement accuracy of the instrument, and the channel with an abnormally large noise equivalent radiation difference value is removed;

[0028] S43: the radiation values of all channels are calculated, the radiation of the GIIRS observation information after correction is subtracted from the clear sky radiation simulated by the bias correction coefficient, and the deviation results of each spectral band are obtained;

[0029] S44: the GIIRS observation data and the cloud detection product of AGRI are matched in longitude and latitude, the cloud detection results of AGRI in the corresponding field of view of GIIRS are obtained, and the radiance deviation threshold for judging whether the field of view is polluted by clouds is preliminarily obtained;

[0030] S45: channel selection and integration are performed, and the GIIRS observation information without cloud pollution is obtained according to the channel in the information file.

[0031] According to the WRFDA assimilation method based on GIIRS data provided by the application, step S5 comprises:

[0032] S51: the channel information of the GIIRS is output by using the verification mode of assimilation, the observation information file needs to be offline bias corrected and statistically, and the bias content is corrected;

[0033] S52: based on the WRFDA assimilation system, the GIIRS high-resolution data and the GTS conventional observation data are assimilated, the boundary conditions are updated, the initial field of the fusion observation data is obtained, the variational assimilation method is used, and the analysis field file is generated;

[0034] S53: by using the bias correction technology, the quality control module of the WRFDA system is used to perform systematic bias correction on the analysis field file, and the initial field file is obtained.

[0035] According to the WRFDA assimilation method based on GIIRS data provided by the application, the steps of the variational assimilation method are:

[0036] S521: the conjugate gradient method is used to minimize the cost function when analyzing the control variable , and the atmospheric state is estimated:

[0037]

[0038] wherein, is the atmospheric state vector, is the atmospheric state vector of the background field, is an error covariance matrix of the state vector, is an error covariance matrix of the state vector, is an observation operator, is an observation result, is a matrix transpose;

[0039] S522: solving the gradient of the objective function:

[0040]

[0041] wherein, is a tangent linear operator, is an observation simulation value, is an optimal atmospheric state;

[0042] S523: substituting the increment into the objective function to solve the equation:

[0043]

[0044] wherein, is an increment, is an observation difference, is a minimized cost function with the increment as an independent variable;

[0045]

[0046]

[0047] a first derivative of the objective function is:

[0048] .

[0049] The application also provides a WRFDA assimilation system based on GIIRS data, comprising:

[0050] a data preprocessing module: used for acquiring GIIRS data, extracting channel brightness temperature, longitude and latitude, observation time, satellite zenith angle, sun zenith angle, satellite azimuth angle and sun azimuth angle data therefrom, and merging the data to obtain GIIRS observation information of a large area;

[0051] a data checking module: used for performing quality control checking on the GIIRS observation information to obtain GIIRS observation information after checking;

[0052] a bias correction module: used for converting background field data of a numerical weather prediction model into simulated brightness temperature data using a RTTOV fast radiation transfer model, and performing offline correction using channel brightness temperature data to obtain simulated data after stable bias correction; ​

[0053] Data screening module: used for cloud pollution screening of the post-inspection GIIRS observation information and bias-corrected simulation data, to obtain cloud-pollution-free GIIRS observation information;

[0054] Data assimilation module: used for WRFDA numerical assimilation of the cloud-pollution-free GIIRS observation information, to obtain an initial field file, which is used for adjusting and optimizing the output weather prediction result.

[0055] The application further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the steps of the WRFDA assimilation method based on GIIRS data according to any one of the above embodiments when executing the program.

[0056] The one or more technical solutions described above in the embodiments of the application have at least one of the following technical effects:

[0057] The WRFDA assimilation method, system and device based on GIIRS data provided by the application can improve the typhoon precipitation prediction effect through systematic verification and evaluation and quantitative analysis of the improvement degree of the typhoon precipitation prediction effect, and provide a scientific basis for business application.

[0058] Additional aspects and advantages of the application will be described in the following description and become apparent from the following description or will be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0060] Figure 1 is a flowchart of the WRFDA assimilation method based on GIIRS data provided by the application.

[0061] Figure 2 is a structural block diagram of the WRFDA assimilation device based on GIIRS data provided by the application.

[0062] Figure 3 is a structural schematic diagram of the electronic device provided by the application.

[0063] Reference signs:

[0064] 100, data preprocessing module; 200, data checking module; 300, bias correction module; 400, data screening module; 500, data assimilation module; 810, processor; 820, communication interface; 830, memory; 840, communication bus. DETAILED DESCRIPTION

[0065] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application. The following embodiments are used to illustrate the present application, but cannot be used to limit the scope of the present application.

[0066] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in combination with the embodiment or example are contained in at least one embodiment or example of the embodiments of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, the skilled in the art can combine and combine the different embodiments or examples described in the present application and the features of the different embodiments or examples without contradiction.

[0067] The present application will be described below in combination with Figures 1 to 3 The present application.

[0068] As Figure 1 shown, Figure 1 The flowchart of the WRFDA assimilation method based on GIIRS data provided by the present application includes the following steps:

[0069] S1: obtaining GIIRS data, reading channel brightness temperature, latitude and longitude, observation time, satellite zenith angle, solar zenith angle, satellite azimuth angle and solar azimuth angle data information from the GIIRS data, and merging the data to obtain GIIRS observation information of a large area;

[0070] S2: performing quality control inspection on the GIIRS observation information to obtain GIIRS observation information after inspection;

[0071] S3: converting the background field data of the numerical weather prediction model into simulated brightness temperature data using the RTTOV fast radiation transfer mode, and performing bias correction using the channel brightness temperature data to obtain stable bias-corrected simulated data;

[0072] S4: cloud pollution screening is performed on the post-inspection GIIRS observation information and the bias-corrected simulation data to obtain cloud-pollution-free GIIRS observation information;

[0073] S5: the cloud-pollution-free GIIRS observation information is subjected to WRFDA numerical assimilation to obtain an initial field file, which is used to adjust and optimize weather prediction results.

[0074] Specifically, step S1 includes:

[0075] S11: the channel brightness temperature, latitude, longitude, observation time, satellite zenith angle, sun zenith angle, satellite azimuth angle and sun azimuth angle data of the same time period are integrated to obtain an HDF file;

[0076] S12: the HDF file is combined in an increasing latitude manner to obtain observation information of a latitude band;

[0077] S13: the observation information of the latitude band of different time periods is combined in an increasing latitude manner to obtain the GIIRS observation information of the large area.

[0078] During the data merging process, the system retains all attribute information of the original data set, which includes metadata such as data unit, range, quality mark, calibration parameter, etc. At the same time, in order to improve the storage and access efficiency, the system dynamically adjusts the block parameters according to the shape of the merged data. After the data merging is completed, the next stage is to extract key information and perform time marking processing. The system first extracts important dimension information from the original file, including the number of scanning points, the long-wave data dimension and the medium-wave data dimension. Then, the system extracts time information such as year, month, day, hour, minute, etc. from the file name. Since the GIIRS file name follows a fixed format, these time information can be obtained by extracting characters at specific positions. Based on the extracted time information, the system constructs a unified time array to associate each data point with an accurate timestamp, facilitating subsequent time series analysis.

[0079] In some specific cases, further screening and adjustment of the merged data is required. During processing, additional data sets such as "Datetime" (containing time information of five dimensions: year, month, day, hour, and minute) and "Dim" (containing main data dimension size information) are created in the new file. These additional data sets provide a structured index for the original data, facilitating subsequent analysis programs to quickly locate and understand the data organization method. Through this series of processing, the original scattered and unstructured observation data is transformed into a unified data set with clear structure, continuous time and consistent space

[0080] Since the WRFDA assimilation system itself does not contain the data reading interface of the GIIRS instrument, it is necessary to add an interface for reading the HDF format of the GIIRS data in the WRFDA assimilation system, so that it can read the observation data and perform subsequent quality control processes. Mainly including writing and debugging the main program and other necessary programs for reading GIIRS data, the main program needs to adapt to the file format of GIIRS data, the variable name and variable attribute information contained in the data, the platform number, instrument number and other information of FY-4B satellite and GIIRS instrument in WRFDA data assimilation system, assimilation time and other information, so as to ensure that the main program can correctly read the HDF format of GIIRS observation data and extract valid variable information for calling by other modules of WRFDA system.

[0081] Specifically, the quality control check in step S2 is to remove mixed ground observations, remove observations with satellite zenith angle greater than 60°; Remove the grid points with the absolute value of the difference between the observed brightness temperature and the simulated brightness temperature greater than 15K; Remove the grid points with the absolute value of the difference between the observed brightness temperature and the simulated brightness temperature greater than 3 times the standard deviation of the observation error, and the specific process is as follows:

[0082] S21: Set the system reference state through variable definition and parameter initialization;

[0083] S22: Perform basic inspection, and sequentially carry out composite check of sensor saturation value, abnormal radiation value, sea surface temperature consistency, solar pollution, cloud cover and geographical boundary constraint, and remove invalid observations;

[0084] S23: The data of the inspection enters the statistical processing module, gives the final QC judgment result, marks good and bad observations, and adjusts the error;

[0085] S24: Write the qualified data into the output file according to the standardized format.

[0086] Some constants and local variables are set, and various counters are initialized, such as ngood, nrej series arrays, for counting the number of different types of observation data acceptance or rejection. Set the hypothesis standard qc_good. Carry out basic QC checks, including surface type related checks, according to the surface flag isflg, combined with the emissivity Jacobian matrix, to remove channel data of land surface, sea ice, snow and mixed surface types. Edge observation removal, according to the scan position scanpos, removes edge observation data. Respectively, from the absolute and relative angle, check the difference between the observation and the background, decide whether to reject the observation data, and adjust the error. According to the cloud flag, the channel selection information in the information file, give the final QC judgment result, mark good and bad observation, and adjust the error. Through the inter-processor channel information, the various statistical data are summarized, and the quality control statistical information is written into the file according to the process. Modify the interface of data reading called in da_radiance.f90 and other related programs, and add the information of GIIRS. Mainly add the variable information of GIIRS instrument in the program, and call the file of GIIRS data reading, so that the assimilation module can execute the reading interface of GIIRS.

[0087] Specifically, step S3 comprises: the cost function of the bias correction is:

[0088]

[0089] wherein, is an atmospheric state vector, is a bias correction coefficient, is an atmospheric state vector of a background field, is a bias correction coefficient of a background field, is an observation result, is an error covariance matrix of an error covariance matrix of an error covariance matrix of an error covariance matrix of an observation operator after bias correction, is a matrix transpose;

[0090]

[0091] wherein, is an original observation operator, is a coefficient of the th prediction factor, is the th prediction factor, is the serial number of the prediction factor, is the total number of prediction factors,​ .

[0092] Specifically, step S4 comprises:

[0093] S41: the influence of the cloud is identified by comparing the brightness temperature of the mode deviation correction coefficient file and the brightness temperature of the checked GIIRS observation information, the channel contaminated by the cloud is recorded as the cloudy channel, and the channel not contaminated by the cloud is recorded as the clear sky channel;

[0094] S42: the emissivity of each wave band in the infrared spectrum is used for wave band division, the channel height is defined, the deviation obtained after the brightness temperature of the checked GIIRS observation information is subtracted from the brightness temperature of the deviation correction coefficient file is sorted, the noise equivalent radiation difference is used to describe the measurement accuracy of the instrument, and the channel with an abnormally large noise equivalent radiation difference value is removed;

[0095] S43: the radiation value of all channels is calculated, the radiation of the checked GIIRS observation information is subtracted from the clear sky radiation simulated by the deviation correction coefficient, and the deviation result of each spectral band is obtained;

[0096] S44: the GIIRS observation data and the cloud detection product of AGRI are matched in longitude and latitude, the cloud detection result of AGRI in the corresponding field of view of GIIRS is obtained, and the radiance deviation threshold for judging whether the field of view is contaminated by the cloud is preliminarily obtained; wherein the equivalent radiation difference value threshold in the daytime is 8 mW / (m 2 sr cm -1 ), and the equivalent radiation difference value threshold at night is 3 mW / (m 2 sr cm -1 ).

[0097] S45: channel selection and integration are performed, and the GIIRS observation information without cloud contamination is obtained according to the marking of the channels in the information file.

[0098] Specifically, the step S4 of the present application is mainly realized by relying on the info file, the quality control and the cloud detection file.

[0099] Specifically, step S5 comprises:

[0100] S51: the channel information of GIIRS is output by using the assimilation verification mode, the observation information file needs to be offline deviation corrected and counted, and the deviation content is corrected;

[0101] S52: based on the WRFDA assimilation system, the GIIRS high-resolution data and the GTS conventional observation data are assimilated, the boundary condition is updated, the initial field of the fused observation data is obtained, the variational assimilation method is used, and the analysis field file is generated;

[0102] S53: The bias correction technology is adopted by the quality control module of the WRFDA system to perform systematic bias correction on the analysis field file to obtain the initial field file.

[0103] The WRFDA system adopts a linear bias model by default. In each iteration, the bias value of each observation is calculated based on the background field, and the bias is removed from the original information increment to obtain the bias-corrected information increment. VARBC inputs these bias correction coefficients as increment control variables into the increment assimilation framework together with the increment of the physical field. In the increment equation set, the background error covariance of the bias coefficient is also set (usually assuming that the coefficients are independent of each other and the variance is constant) to limit the variation range. In each inner iteration, the system adjusts the field state increment and the bias coefficient increment simultaneously to minimize the objective function. After all iterations are completed, VARBC outputs the bias correction coefficients obtained through the assimilation calculation and writes them into the specified output file. Next time the assimilation is run, only the latest bias coefficients need to be read from the file to be used as the new background bias, realizing continuous tracking and dynamic correction of the bias.

[0104] When the VARBC bias correction method is introduced into the FY-4B GIIRS observation, statistical analysis of the initial bias correction coefficients of each channel is first carried out in an offline manner. Since the WRFDA system does not include special bias correction parameters for GIIRS observations by default, reference is made to the built-in bias correction parameters of other high-spectral instruments and the bias correction research results of previous Fengyun-4 satellites to set reasonable initial values for each channel. Specifically, in the VARBC.in file, the pred_use variable is assigned to specify the forecast factor required for bias correction, and the preset coefficients are filled in the corresponding parameter positions.

[0105] After the bias correction switch is turned on, the data assimilation program is run, and da_varbc_init.inc automatically reads all the key information (such as satellite number, sensor type, and selected forecast field factor) in VARBC.in and initializes the GIIRS-specific bias correction variables accordingly. During the assimilation process, the WRFDA system not only outputs the updated input field file wrfvar_output, but also generates a VARBC.out file containing the bias correction coefficients obtained in this iteration. Since a single time point of coefficients cannot reflect the long-term bias characteristics, the example is repeatedly executed with a fixed interval (e.g., every 3 hours) within October 2024, and the corresponding VARBC.out is exported each time. After collecting and statistically summarizing the coefficients extracted from these files, and combining the bias correction results of previous Fengyun-4 satellites, the final long-term stable bias correction coefficient file used for assimilation experiments is obtained.

[0106] Specifically, the steps of the variational assimilation method are:

[0107] S521: minimizing the cost function with the conjugate gradient method when analyzing the control variable , estimate the atmospheric state:

[0108]

[0109] wherein, is the atmospheric state vector, is the atmospheric state vector of the background field, is the error covariance matrix of , is the error covariance matrix of , is the observation operator, is the observation result, is the matrix transpose; S522: solve the gradient of

[0110]

[0111]

[0112] wherein, is the tangent linear operator, is the observation simulation value, is the optimal atmospheric state;

[0113] S523: substitute the increment into the objective function to solve the equation:

[0114]

[0115] wherein, is the increment, is the observation difference, is the minimized cost function with the increment as the independent variable;

[0116]

[0117]

[0118] the first derivative of is:

[0119] .

[0120] As shown in Figure 2 , the application also provides a WRFDA assimilation system of GIIRS data, comprising the following modules:

[0121] ​​​The data preprocessing module 100 is configured to acquire GIIRS data, extract channel brightness temperature, latitude, longitude, observation time, satellite zenith angle, sun zenith angle, satellite azimuth angle and sun azimuth angle data from the GIIRS data, and combine the data to obtain GIIRS observation information of a large area.

[0122] The data checking module 200 is configured to perform quality control checking on the GIIRS observation information to obtain GIIRS observation information after checking.

[0123] The bias correction module 300 is configured to convert background field data of a numerical weather prediction model into simulated brightness temperature data using an RTTOV fast radiative transfer model, and perform bias correction on the channel brightness temperature data offline to obtain simulated data after stable bias correction.

[0124] The data screening module 400 is configured to perform cloud pollution screening on the GIIRS observation information after checking and the simulated data after bias correction to obtain GIIRS observation information without cloud pollution.

[0125] The data assimilation module 500 is configured to perform WRFDA numerical assimilation on the GIIRS observation information without cloud pollution to obtain an initial field file, and the initial field file is used to adjust and optimize output weather prediction results.

[0126] Figure 3 An example of an entity structure diagram of an electronic device is shown in Figure 3 The electronic device can include a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 can communicate with each other through the communication bus 840. The processor 810 can invoke a logical instruction in the memory 830 to execute a WRFDA assimilation method based on GIIRS data, and the method includes:

[0127] S1: acquiring GIIRS data, extracting channel brightness temperature, latitude, longitude, observation time, satellite zenith angle, sun zenith angle, satellite azimuth angle and sun azimuth angle data from the GIIRS data, and combining the data to obtain GIIRS observation information of a large area;

[0128] S2: performing quality control checking on the GIIRS observation information to obtain GIIRS observation information after checking.

[0129] S3: converting background field data of a numerical weather prediction model into simulated brightness temperature data using an RTTOV fast radiative transfer model, and performing bias correction on the channel brightness temperature data to obtain a stable bias correction coefficient file.

[0130] S4: cloud pollution screening is performed on the GIIRS observation information after the checking and the bias correction coefficient file, to obtain GIIRS observation information without cloud pollution;

[0131] S5: the GIIRS observation information without cloud pollution is subjected to WRFDA numerical assimilation, to obtain an initial field file, which is used to adjust and optimize the output weather prediction result.

[0132] In addition, the logical instructions in the memory 830 described above can be implemented in the form of a software function unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0133] The device embodiments described above are only schematic, wherein the units illustrated as separate components can or can not be physically separated, and the components illustrated as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the present embodiment according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0134] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the method described in each embodiment or some part of the embodiment.

[0135] Finally, it should be noted that the above embodiments are merely used to illustrate the technical solutions of the present application, instead of limiting the present application; even though the present application has been described in detail with reference to the foregoing embodiments, those ordinarily skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or equivalently replace some of the technical features thereof; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

[0136] It should be noted that embodiments of the present disclosure can be realized by hardware, software, or a combination of software and hardware. The hardware part can be realized by special logic, and the software part can be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or a specially designed hardware. Those skilled in the art can understand that the above-mentioned devices and methods can be realized by computer executable instructions and / or included in processor control codes, for example, such codes are provided on a programmable memory or a data carrier such as an optical or electronic signal carrier.

[0137] In addition, although the operations of the methods of the present disclosure are described in a particular order in the drawings, this does not require or imply that the operations must be performed in that particular order, or that all of the illustrated operations must be performed to achieve the desired result. On the contrary, the steps depicted in the flowcharts can change the order of execution. Additionally or alternatively, some steps can be omitted, combined into one step, and / or divided into multiple steps. It should also be noted that the features and functions of two or more devices according to the present disclosure can be embodied in one device. Conversely, the features and functions of one device described above can be further divided into devices embodied by multiple devices.

[0138] Although the present disclosure has been described with reference to several specific embodiments, it should be understood that the present disclosure is not limited to the disclosed specific embodiments. The present disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A WRFDA assimilation method based on GIIRS data, characterized in that, The method comprises the following steps: S1: acquiring GIIRS data, obtaining channel brightness temperature, latitude and longitude, observation time, satellite zenith angle, solar zenith angle, satellite azimuth angle and solar azimuth angle data information from the GIIRS data, and merging the data to obtain GIIRS observation information of a large area; S2: performing quality control inspection on the GIIRS observation information to obtain GIIRS observation information after inspection; S3: converting background field data of a numerical weather prediction model into simulated brightness temperature data by using a RTTOV fast radiation transfer mode, and obtaining stable deviation-corrected simulated data by using channel brightness temperature data for deviation correction; S4: performing cloud pollution screening on the GIIRS observation information after inspection and the deviation-corrected simulated data to obtain GIIRS observation information without cloud pollution, comprising: S41: identifying the influence of clouds by comparing the deviation between the brightness temperature of a mode deviation correction coefficient file and the brightness temperature of the GIIRS observation information after inspection, recording channels polluted by clouds as cloudy channels, and recording channels not polluted by clouds as clear sky channels; S42: dividing wave bands by using the different emissivities of infrared wave bands, defining channel height, sorting the deviation obtained by subtracting the brightness temperature of the deviation correction coefficient file from the brightness temperature of the GIIRS observation information after inspection, and removing channels with abnormally large noise equivalent radiance difference values by using the noise equivalent radiance difference to describe the measurement accuracy of an instrument; S43: calculating the radiation values of all channels, and obtaining the deviation results of each wave band by subtracting the clear sky radiation simulated by the deviation correction coefficient from the radiation of the GIIRS observation information after inspection; S44: matching the GIIRS observation data with the cloud detection product of AGRI in terms of latitude and longitude to obtain the cloud detection results of AGRI in the corresponding field of view of GIIRS, and preliminarily obtaining a radiance deviation threshold for judging whether the field of view is polluted by clouds; S45: performing channel selection and integration, and marking the channels according to the information file to obtain GIIRS observation information without cloud pollution; S5: performing WRFDA numerical assimilation on the GIIRS observation information without cloud pollution to obtain an initial field file, wherein the initial field file is used to adjust and optimize weather prediction results, comprising: S51: using a verification mode of assimilation to output an observation information file, and performing offline deviation correction statistics on the observation information file to correct the deviation content; S52: based on the WRFDA assimilation system, assimilating GIIRS high-resolution data and GTS conventional observation data, updating boundary conditions, obtaining an initial field fused with observation data, and generating an analysis field file by using a variational assimilation method; S53: using a bias correction technique to perform systematic bias correction on the analysis field file by using a quality control module of the WRFDA system, and obtaining an initial field file. 2.The WRFDA assimilation method based on GIIRS data according to claim 1, characterized in that, Step S1 comprises: S11: integrating the channel brightness temperature, latitude and longitude, observation time, satellite zenith angle, solar zenith angle, satellite azimuth angle and solar azimuth angle data of the same time period to obtain an HDF file; S12: merging the HDF file in a latitude-increasing manner to obtain observation information of a latitude band. S13: merging the observation information of the latitude bands of different time periods according to the increasing latitude to obtain the GIIRS observation information of the large area. 3.The WRFDA assimilation method based on GIIRS data according to claim 1, characterized in that, The quality control check includes eliminating mixed ground surface observations, eliminating observations with a satellite zenith angle greater than 60°, eliminating grid points with an absolute value of observed brightness temperature minus simulated brightness temperature greater than 15K, and eliminating grid points with an absolute value of observed brightness temperature minus simulated brightness temperature greater than 3 times the standard deviation of the observation error. 4.The WRFDA assimilation method based on GIIRS data according to claim 2, characterized in that, The quality control check process is as follows: S21: setting the system reference state through variable definition and parameter initialization; S22: performing basic inspection, and sequentially carrying out composite verification of sensor saturation value, abnormal radiation value, sea surface temperature consistency, solar pollution, cloud coverage and geographical boundary constraint to eliminate invalid observations; S23: the data subjected to inspection enters a statistical processing module to give a final QC determination result, mark good and bad observations, and adjust the error; S24: writing the qualified data into an output file in a standardized format.

5. The WRFDA assimilation method based on GIIRS data according to claim 1, characterized in that, Step S3 comprises: a cost function of the bias correction is: wherein, is the atmospheric state vector, is the bias correction coefficient, is the atmospheric state vector of the background field, is the bias correction coefficient of the background field, is the observation, is is the error covariance matrix of is is the error covariance matrix of is is the error covariance matrix of is the bias-corrected observation operator, is the matrix transpose; wherein is the original observation operator, is the coefficient of the th predictor, is the th predictor, is the ordinal number of the predictor, is the total number of predictors, . 6.The WRFDA assimilation method based on GIIRS data according to claim 1, characterized in that, The steps of the variational assimilation method are as follows: S521: Minimizing the cost function using the conjugate gradient method while analyzing the control variables Estimating the atmospheric state: wherein is the atmospheric state vector, is the atmospheric state vector of the background field, is the error covariance matrix of is the error covariance matrix of is the error covariance matrix of is the observation operator, is the observation result, is the observation result, is the matrix transpose; S522: solving the gradient of: wherein is a tangent linear operator, is an observed simulation value, is the best atmospheric state; S523: substituting the increment into the equation of the objective function: wherein, is an increment, is an observation difference, is a minimized cost function with the increment as the argument; first derivative is: 。 7. A WRFDA assimilation system based on GIIRS data, configured to perform the WRFDA assimilation method based on GIIRS data according to any one of claims 1 to 6, characterized in that, including: A data preprocessing module is used to obtain GIIRS data, extract channel brightness temperature, longitude and latitude, observation time, satellite zenith angle, solar zenith angle, satellite azimuth angle and solar azimuth angle data therefrom, and merge the data to obtain GIIRS observation information of a large area; A data inspection module is used to perform quality control inspection on the GIIRS observation information to obtain GIIRS observation information after inspection; A bias correction module is used to convert background field data of a numerical weather prediction model into simulated brightness temperature data using a RTTOV fast radiation transfer model, and obtain stable simulated data after bias correction using offline correction of channel brightness temperature data; A data screening module is used to perform cloud pollution screening on the GIIRS observation information after inspection and the simulated data after bias correction to obtain GIIRS observation information without cloud pollution; A data assimilation module is used to perform WRFDA numerical assimilation on the GIIRS observation information without cloud pollution to obtain an initial field file, which is used to adjust and optimize the output weather prediction result.

8. An electronic device comprising a processor, a communication interface, a memory and a communication bus, characterized in that, The processor implements the steps of the WRFDA assimilation method based on GIIRS data according to any one of claims 1 to 6 when executing the computer program.

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