Method for evaluating and analyzing calibration performance of stationary satellite hyperspectral infrared detector

Abstract

The invention discloses a calibration performance evaluation and analysis method for a stationary satellite hyperspectral infrared detector, and the method comprises the steps: firstly obtaining an ERA5 data set and L1-level observation data of the stationary satellite hyperspectral infrared detector, and inputting the data into a rapid transmission radiation model to obtain simulated radiation data; then comparing the simulated radiation data with L1-level observation data of a stationary satellite hyperspectral infrared detector, calculating systematic deviation and carrying out deviation analysis; and finally, performing cross matching verification on L1-level observation data of the stationary satellite hyperspectral infrared detector and observation data of the polar orbit satellite infrared atmospheric detection interferometer. On the basis of an atmospheric radiation model, more accurate and reliable calibration performance daily variation characteristics are provided to replace a calibration technology by using latest high-quality data and combining multi-source reference data, so that the defects of the prior art are effectively overcome, and the quality and the application effect of detection data are remarkably improved.

Description

Technical Field

[0001] The invention belongs to the technical field of satellite remote sensing, and in particular relates to a calibration performance evaluation and analysis method for a geostationary satellite hyperspectral infrared detector. Background Art

[0002] Liu Juanjuan and others from the Institute of Atmospheric Physics, Chinese Academy of Sciences (Liu Juanjuan, Xu Lan, Cheng Wei, et al. Analysis of bias characteristics and bias correction of the FY-4A satellite GIIRS detector for data assimilation [J]. Atmospheric Sciences, 2022, 46(02): 275-292.) conducted a comprehensive analysis of the observation bias (O-B) of the Interferometric Atmospheric Vertical Sounder (GIIRS). The team used the high-resolution regional model WRF and its assimilation system WRFDA to study the bias distribution characteristics of different channels. The study found that the bias and standard deviation of the longwave channel are generally smaller than those of the mediumwave channel, but there are contaminated channels; the daily variation of the bias is weakly related to the satellite zenith angle, but is related to the brightness temperature value and the position of the satellite scanning array, showing the characteristics of "array bias". After recalibration in 2020, the quality of GIIRS observation data has been significantly improved. Further bias correction experiments have shown that selecting the scanning array as the main factor can effectively improve the bias.

[0003] Niu et al. from the Shanghai Typhoon Institute of the China Meteorological Administration (Niu, Zeyi, et al. "Performances between the FY-4A / GIIRS and FY-4B / GIIRSlong-wave infrared (LWIR)channels under clear-sky and all-sky conditions." Quarterly Journal of theRoyal Meteorological Society 149.754(2023): 1612-1628.) The observation and background value deviation characteristics of the long-wave infrared channel of the Geostationary Interferometric Infrared Sounder (GIIRS) on the Fengyun-4A (FY-4A) and Fengyun-4B (FY-4B) satellites were compared; the results showed that under clear sky conditions, the data quality of FY-4B / GIIRS is better than that of FY-4A / GIIRS, especially in the carbon dioxide and ozone absorption bands, the standard deviation of FY-4B is less than 1K, while that of FY-4A is greater than 1K; in addition, the diurnal variation deviation of the ozone absorption band of FY-4B is smaller than that of FY-4A, while the diurnal variation deviation of the carbon dioxide absorption band shows obvious anti-phase characteristics, which may be due to the diurnal environmental field changes caused by different geostationary orbit platforms. In addition, the observations of FY-4B / GIIRS under cloudy conditions are more consistent with cloud simulations, showing a smaller root mean square error, further indicating that its observation quality in cloudy areas is better than that of FY-4A / GIIRS.

[0004] Lu Qifeng and others from the National Satellite Meteorological Center invented a satellite data quality monitoring method based on the intersection dual reference source deviation (application number: CN202111576921.9). This method combines the simulation reference source and the observation reference source to quickly distinguish between the background field problems caused by the numerical forecast model and the problems caused by satellite or instrument anomalies, thereby effectively monitoring the changes in the instrument L1 data quality, quickly locating the causes that affect the quality of the observation data, and providing the necessary information for analyzing and correcting the instrument performance. Although this invention effectively solves the data accumulation problems of the observation reference source and the accuracy problems of the simulation reference source, the polar orbit and geostationary satellite observation system methods it covers tend to be broadly statistical, ignoring the difference between the two. For geostationary satellite hyperspectral infrared detectors, special data processing methods are needed to improve the quality of statistical data, and to incorporate information from the time dimension into the statistical results to better reflect the characteristics of its high temporal resolution.

[0005] With the continuous development of China's geostationary infrared hyperspectral detection technology, the quality and resolution of detection data have been significantly improved. However, most of the existing research and technology are based on data before 2022, which are relatively old and cannot reflect the data quality after the latest technology improvement; therefore, the use of outdated data may lead to a decrease in the accuracy of research results and applications, and fail to fully utilize the advantages of the latest detectors. At the same time, many existing studies have limited data and small sample coverage, resulting in poor universality of their conclusions; too little data may not represent a wider range of climate and atmospheric conditions, thus limiting the reliability and promotion of research results in practical applications; larger-scale and more extensive data collection and analysis are important ways to improve the credibility of results. In addition, current technology usually relies on atmospheric radiation models as a reference for radiation correction; however, the atmospheric radiation model itself may have errors, especially under complex meteorological conditions; this method of over-reliance on the model may introduce systematic errors, affect the calibration accuracy, and then affect the interpretation and application of detection data; therefore, relying solely on atmospheric radiation models may not be sufficient to meet the needs of high-precision radiation calibration, and more measured data and advanced algorithms need to be combined to improve accuracy. In addition, current technology ignores the high temporal resolution of geostationary orbit infrared hyperspectral observation data. Therefore, it is necessary to incorporate information on the time dimension into the statistical results to improve the accuracy and information richness of the calibration performance evaluation, thereby providing a more valuable reference for meteorological products. Summary of the invention

[0006] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide a calibration performance evaluation and analysis method for a geostationary satellite hyperspectral infrared detector. Based on the atmospheric radiation model, a comprehensive analysis is performed on the diurnal variation of the observation data deviation, the correlation of brightness temperature deviation, and the wavelength and time variation over the year by combining multi-source data, thereby significantly improving the accuracy and reliability of the calibration technology of the daily variation characteristics of the radiation calibration performance alternative.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: The calibration performance evaluation and analysis method of the geostationary satellite hyperspectral infrared detector includes the following steps: The ERA5 data set and the L1 level observation data of the geostationary satellite hyperspectral infrared sounder are obtained and input into the rapid transmission radiation model to obtain simulated radiation data; the ERA5 data set includes the ERA5 temperature and humidity profile and the ERA5 surface temperature; the observation data of the geostationary satellite hyperspectral infrared sounder include the ground infrared emissivity, sea and land identification and digital elevation model; the rapid transmission radiation model is simulated in the land and sea areas under clear sky conditions; The simulated radiation data were compared with the L1 observation data of the geostationary satellite hyperspectral infrared sounder, and the systematic deviations were calculated and analyzed. The L1 level observation data of the geostationary satellite hyperspectral infrared sounder and the observation data of the polar-orbiting satellite infrared atmospheric sounding interferometer were cross-matched and verified.

[0008] As a preferred technical solution, the method for determining the land and sea areas under clear sky conditions is: Use the geostationary orbit radiation imager carried by the geostationary satellite to obtain cloud images; Divide the cloud image into multiple sub-image areas of x*x pixels; Determine whether each pixel in the sub-image area is covered by clouds. If not, mark the center point of the sub-image area as clear sky.

[0009] As a preferred technical solution, during the simulation of the rapid transmission radiation model, a data preprocessing operation is performed, specifically: Exclude L1-level observation data whose time difference with the ERA5 dataset exceeds m1 minutes, and exclude L1-level observation data whose satellite zenith angle exceeds 30°; The L1-level observation data whose deviation between the simulated radiation data and the L1-level observation data exceeds n times the standard deviation are regarded as outliers and are eliminated.

[0010] As a preferred technical solution, the calculation of systematic deviation is specifically as follows: Use simulated radiation data as reference data; Determine the correspondence between the reference data and the L1-level observation data through time-space matching: Based on the time-space information of the L1-level observation data, perform interpolation processing on the reference data to obtain the first fused data; during the interpolation processing, linear interpolation is used for time, and spline interpolation is used for spatial longitude and latitude; The first fusion data is grouped according to the spectral channel based on the spectral center wave number to obtain multiple groups of sub-data; each group of sub-data contains L1 observation data and reference data under the corresponding spectral channel; The overall mean value and overall standard deviation of each group of sub-data in each spectral channel are calculated, and the mean value and standard deviation of the deviation between the L1 level observation data and the reference data in each group of sub-data are calculated.

[0011] As a preferred technical solution, the deviation analysis refers to performing statistical analysis in each spectral channel; The statistical analysis includes the following four parts: Annual analysis: Taking one year as the time range, the mean and standard deviation of the deviation between the brightness temperature of L1 observation data and the brightness temperature of reference data are calculated each year; Daily analysis: Taking the day as the time range, the average value of the deviation between the brightness temperature of the daily L1 observation data and the brightness temperature of the reference data is calculated, and the change pattern of the average value with the date is analyzed; Hourly analysis: Taking the hour as the time range, the average deviation between the brightness temperature of L1 observation data and the brightness temperature of reference data is calculated every hour. The statistical deviation results at the same time on different dates are grouped together, and their average value and standard deviation are calculated. Overall deviation analysis: Taking the year as the time range, calculate the deviation between the brightness temperature of the L1-level observation data of all spectral channels and the brightness temperature of the reference data each year, make a deviation scatter plot, and calculate the correlation coefficient between the brightness temperature of the L1-level observation data of all channels and the brightness temperature of the reference data.

[0012] As a preferred technical solution, before the cross-matching verification, the L1 level observation data of the geostationary satellite hyperspectral infrared sounder and the observation data of the polar-orbiting satellite infrared atmospheric sounding interferometer are screened to select sub-satellite point crossover samples that meet the standards, specifically: Select the L1-level observation data of the geostationary satellite hyperspectral infrared sounder and the observation data of the polar-orbiting satellite infrared atmospheric sounding interferometer with a measurement time difference of less than m2 minutes and a relative difference between the zenith angles of less than a%; Use the geostationary orbit radiation imager carried by the geostationary satellite to obtain data within the central wavelength range of 10.30 to 11.30 um to select the spatially uniform observation area; The judgment standard for the spatially uniform observation area is: draw a circle in the observation area with the center of the pixel of the observation data of the geostationary satellite hyperspectral infrared detector as the center and half of the spatial resolution distance as the radius. All the geostationary orbit radiation imager observation data matched in time and space within the circle constitute a judgment data group. If the ratio of the standard deviation to the mean of the judgment data group is less than 0.01, the circle is regarded as a spatially uniform observation area.

[0013] As a preferred technical solution, when performing the cross-matching verification, the observation data of the polar-orbiting satellite infrared atmosphere sounding interferometer is processed, and the steps are: Performing inverse Fourier transform on the raw spectrum data of the polar-orbiting satellite infrared atmosphere sounding interferometer, and converting the frequency domain data into the first interferogram; The Gaussian apodization technique is applied to reduce the spectral distortion in the first interferogram, and the first interferogram is truncated to remove the high noise part in the signal and reduce the spectral resolution to obtain the second interferogram; Perform Fourier transformation on the second interference pattern and reconvert it back to frequency domain data to obtain processed spectrum data; The spectral data is smoothed using a Hamming window to reduce the sidelobe effect.

[0014] As a preferred technical solution, the first interference pattern is truncated to an optical path difference of 0.8 cm; The spectral resolution is reduced to the spectral resolution of a geostationary satellite hyperspectral infrared detector.

[0015] As a preferred technical solution, the cross-matching verification is specifically as follows: The observation data of the polar-orbiting satellite infrared atmospheric sounding interferometer is used as reference data; The corresponding relationship between the reference data and the L1-level observation data is determined by time-space matching: the reference data is interpolated based on the time-space information of the L1-level observation data to obtain the second fused data; during the interpolation process, linear interpolation is used for time, and spline interpolation is used for spatial longitude and latitude; The second fusion data is grouped according to the spectral channel based on the spectral center wave number to obtain multiple groups of channel data; each group of channel data includes L1 observation data and reference data under the corresponding spectral channel; Calculate the overall mean and overall standard deviation of each group of channel data in each spectral channel, and calculate the mean and standard deviation of the deviation between the L1 observed data and the reference data in each group of channel data.

[0016] On the other hand, it provides a calibration performance evaluation and analysis system for geostationary satellite hyperspectral infrared detectors, including a radiation data simulation module, a deviation calculation and analysis module, and a cross-matching verification module; The radiation data simulation module is used to obtain the ERA5 data set and the L1 level observation data of the geostationary satellite hyperspectral infrared sounder, and input them into the rapid transmission radiation model to obtain simulated radiation data; the ERA5 data set includes the ERA5 temperature and humidity profile and the ERA5 surface temperature; the observation data of the geostationary satellite hyperspectral infrared sounder includes the infrared channel observation brightness temperature, observation angle, ground infrared emissivity, sea and land identification and digital elevation model; the rapid transmission radiation model is simulated in the land and sea areas under clear sky conditions; The deviation calculation and analysis module is used to compare the simulated radiation data with the L1 level observation data of the geostationary satellite hyperspectral infrared sounder, calculate the systematic deviation and perform deviation analysis; The cross-matching verification module is used to cross-match and verify the L1 level observation data of the geostationary satellite hyperspectral infrared sounder with the observation data of the polar-orbiting satellite infrared atmospheric sounding interferometer.

[0017] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. Based on the atmospheric radiation model, the present invention additionally refers to the observation data of the same type of instrument IASI carried on the polar-orbiting satellite. By combining multi-source data, a comprehensive analysis is conducted on the diurnal variation of the observation data deviation, the correlation of brightness temperature deviation, and the variation of wavelength and time over the year. The existing technology usually relies on only a single atmospheric radiation model, which is prone to introduce model errors. The multi-source data reference method of the present invention significantly improves the accuracy and reliability of the radiation calibration performance daily variation characteristic replacement calibration technology.

[0018] 2. The present invention describes the characteristics of the observation data in more detail by comprehensively analyzing the deviation changes of the observation data within a year, especially the performance under different wavelengths and time conditions. Compared with the existing technology, the technical implementation means are more sophisticated and comprehensive, avoiding the errors that may be introduced by over-reliance on the atmospheric radiation model.

[0019] 3. The existing technology mainly relies on relatively old data and a single atmospheric radiation model for radiation calibration, ignoring the unique characteristics of geostationary satellite infrared hyperspectral instruments compared to other instruments (low cloud area detection accuracy, high temporal resolution, multiple channels, high spectral resolution, etc.), resulting in insufficient timeliness and universality of the data, and may introduce systematic errors. In contrast, the present invention provides a more accurate and reliable calibration performance daily variation feature replacement calibration technology by using the latest high-quality data and combining multi-source reference data, thereby effectively overcoming the shortcomings of the existing technology and significantly improving the quality of detection data and application effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 It is an overall flow chart of the calibration performance evaluation and analysis method of the geostationary satellite hyperspectral infrared detector in an embodiment of the present invention.

[0022] Figure 2 Schematic diagram of the process of comparison and deviation analysis in an embodiment of the present invention.

[0023] Figure 3 The figure is a schematic diagram of the process of performing cross-matching verification in an embodiment of the present invention.

[0024] Figure 4 Schematic diagram of the annual average statistical deviation of each channel in an embodiment of the present invention.

[0025] Figure 5 It is a statistical schematic diagram of daily variation characteristics of the calibration performance of the stationary hyperspectral infrared detector in an embodiment of the present invention.

[0026] Figure 6 It is a statistical schematic diagram of the time-varying characteristics of the calibration performance of the stationary hyperspectral infrared detector in an embodiment of the present invention.

[0027] Figure 7 This is a statistical diagram of the scatter points of each sample deviation of the calibration performance of the stationary hyperspectral infrared detector in an embodiment of the present invention.

[0028] Figure 8 It is a structural diagram of the calibration performance evaluation and analysis system of the geostationary satellite hyperspectral infrared detector in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0030] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0031] The technical terms used in this application are defined below. Other undefined terms should be understood according to the common technical knowledge of those skilled in the art: Geostationary satellite: A satellite that is located at an orbital altitude of about 36,000 kilometers above the Earth's equator and can keep pace with the Earth's rotation, thereby maintaining a fixed position relative to the ground.

[0032] Polar-orbiting satellite: A near-geosynchronous satellite that uses a near-polar sun-synchronous orbit and maintains a fixed angle between the satellite orbit plane and the sun's rays. It can provide effective observation data in medium-term numerical weather forecasting, climate diagnosis and prediction, natural disasters and environmental monitoring.

[0033] Geosynchronous Interferometric Infrared Sounder (GIIRS) is the world's first precision remote sensing instrument that detects the three-dimensional vertical structure of the atmosphere in a geostationary orbit by using infrared hyperspectral interferometry. It can detect and record the radiation characteristics of target objects with high spectral resolution in the infrared band, observe infrared radiation in different spectral bands by using Michelson interferometry, obtain the vertical distribution of atmospheric temperature and humidity, and provide large-scale, continuous, fast and accurate remote sensing information for weather forecasting. It is used for the detection of vertical profiles of atmospheric temperature and humidity and the assimilation of numerical forecast data.

[0034] Infrared Atmospheric Sounding Interferometer / IASI (Infrared Atmospheric Sounding Interferometer): A new generation of ultra-high spectrum infrared atmospheric sounding instrument carried on the European polar-orbiting meteorological satellite. It uses interferometric spectroscopy technology. Its spectral measurement range covers multiple absorption bands and can be used to invert the atmosphere, ocean, clouds and atmospheric components, providing rich remote sensing data for Earth's atmospheric remote sensing, meteorological services and scientific research.

[0035] Advanced Geostationary Radiation Imager / AGRI (Advanced Geostationary Radiation Imager): One of the main payloads of the Fengyun-4 geostationary meteorological satellite, it realizes accurate and flexible two-dimensional pointing through a sophisticated dual-scanning mirror mechanism, and can achieve rapid regional scanning at the minute level; it adopts an off-axis three-reflector main optical system to obtain earth cloud images in more than 14 bands at high frequency, and uses the on-board blackbody for high-frequency infrared calibration to ensure the accuracy of the observation data; it is mainly responsible for the task of obtaining cloud images.

[0036] Calibration: Adjustment of a measuring instrument using a known standard or reference object to ensure the accuracy and consistency of its output.

[0037] Rapid Transfer for TOVS (RTTOV): A fast radiative transfer model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) to simulate infrared and microwave radiation from Earth observations by meteorological satellite sensors.

[0038] ERA5: A global atmospheric reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), providing high-resolution atmospheric, land and ocean meteorological data, widely used as input for radiative transfer models.

[0039] This embodiment takes the hyperspectral infrared detector (i.e., GIIRS) carried by Fengyun-4B (FY-4B) as an example to elaborate on the calibration performance evaluation and analysis method of the geostationary satellite hyperspectral infrared detector proposed in this application. Figure 1 As shown, the following steps are included: S1. Obtain the ERA5 dataset and the L1 observation data of FY-4B / GIIRS, and input them into the Rapid Transmission Radiation Model (RTTOV) to obtain simulated radiation data. Among them, the ERA5 dataset includes the ERA5 temperature and humidity profile and the ERA5 surface temperature; the observation data of the geostationary satellite hyperspectral infrared sounder include infrared channel observation brightness temperature, observation angle, ground infrared emissivity, sea and land identification and digital elevation model (DEM). RTTOV simulates the land and sea areas under clear sky conditions to obtain simulated radiation data.

[0040] S2. Compare the simulated radiation data with the L1 observation data of the geostationary satellite hyperspectral infrared sounder, calculate the systematic deviation and perform deviation analysis.

[0041] S3. Cross-match and verify the L1 observation data of the geostationary satellite hyperspectral infrared sounder with the observation data of the polar-orbiting satellite infrared atmospheric sounding interferometer.

[0042] Furthermore, to ensure the accuracy of the simulation, this application focuses on the radiation transfer simulation of land and sea areas under clear sky conditions, because the presence of clouds will introduce significant uncertainty; under clear sky conditions, the accuracy of RTTOV simulation is usually high, and the simulation error of land areas is less than 0.01K. For the combined land and sea areas under clear sky, the average deviation between RTTOV and line-by-line simulation is also less than 0.01K. Therefore, the land and sea areas under clear sky conditions are selected for simulation, and the determination method of the land and sea areas under clear sky conditions is: The cloud map is obtained using AGRI carried by FY4B; then the cloud map is divided into multiple sub-image areas of x*x pixels; it is determined whether each sub-image area is covered by clouds. If every pixel in the sub-image area is not covered by clouds, the center point of the sub-image area is marked as clear sky. This method ensures that the radiation transfer characteristics of the selected area meet the clear sky conditions, thereby minimizing the impact of clouds on deviation analysis.

[0043] In this embodiment, the cloud image is divided into a plurality of 5*5 pixel sub-image areas. If 25 pixels in a sub-image area are not covered by clouds, the center point of the sub-image area is marked as clear sky, which meets the clear sky condition.

[0044] Furthermore, in order to improve the data quality, data preprocessing operations are performed during the simulation of the rapid transmission radiation model to ensure the reliability of the analysis results. First, the L1-level observation data with a time difference of more than m1 minutes between the FY-4B / GIIRSL1-level observation data and the ERA5 data set are excluded; secondly, the L1-level observation data with a satellite zenith angle of more than 30° in the observation data are excluded, which helps to reduce the uncertainty caused by the observation geometry angle; in addition, the L1-level observation data with a deviation between the simulated radiation data and the L1-level observation data exceeding n times the standard deviation are regarded as outliers and eliminated; the above operations ensure the high quality of data during deviation analysis.

[0045] In this embodiment, observation data with a time difference of more than 10 minutes are excluded, and L1-level observation data with a deviation exceeding 3 times the standard deviation are regarded as outliers and removed.

[0046] In this embodiment, RTTOV uses the TOVS 13.1 version of the radiative transfer model. Although the RTTOV model has uncertainties (especially in terms of input profiles), the RTTOV model can simulate GIIRS observations throughout the day, thus providing a large sample size; this advantage allows for a more detailed analysis, including the diurnal variation of the bias, the correlation of brightness temperature bias, and the variation of the bias with wavelength and time over a year; this advantage of a large sample size makes up for the shortcomings of the limited cross-matching data.

[0047] Furthermore, after obtaining the simulated radiation data, it is compared with the L1 observation data of FY-4B / GIIRS to calculate the systematic deviation, specifically: The simulated radiation data is used as the reference data; first, the correspondence between the reference data and the L1-level observation data is determined by time-space matching: the reference data is interpolated based on the time-space information of the L1-level observation data to obtain the first fused data; during the interpolation process, linear interpolation is used for time, and spline interpolation is used for spatial longitude and latitude. Then, the first fused data is grouped according to the spectral channel based on the spectral center wave number, and the data of each spectral channel is independently divided into a group to obtain multiple groups of sub-data, each of which contains two data sets under the corresponding spectral channel: L1-level observation data and reference data, and each element in these two data sets is consistent in time-space information. Finally, for each group of sub-data, the overall mean and overall standard deviation of each group of sub-data in each spectral channel are calculated, and the mean and standard deviation of the deviation between the L1-level observation data and the reference data in each group of sub-data are calculated, thereby providing a basis for the analysis of systematic deviations.

[0048] Furthermore, the geostationary satellite hyperspectral infrared detector is divided into several spectral channels according to the central wave number of the spectrum, and statistical analysis is performed independently in each spectral channel. The statistical analysis mainly includes the following four parts: Annual analysis: Taking one year as the time range, the mean and standard deviation of the deviation between the brightness temperature of L1 observation data and the brightness temperature of reference data are calculated each year.

[0049] Daily analysis: Taking the day as the time range, the average value of the deviation between the brightness temperature of the daily L1-level observation data and the brightness temperature of the reference data is calculated, and the change pattern of the average value with the date is analyzed.

[0050] Hourly analysis: Taking hours as the time range, the average deviation between the brightness temperature of L1 observation data and the brightness temperature of reference data is calculated every hour. The statistical deviation results at the same time on different dates are grouped together, and their average value and standard deviation are calculated.

[0051] Overall deviation analysis: Taking the year as the time range, calculate the deviation between the brightness temperature of the L1-level observation data points of all spectral channels each year and the brightness temperature of the reference data, make a deviation scatter plot, and calculate the correlation coefficient between the brightness temperature of the L1-level observation data of all spectral channels and the brightness temperature of the reference data.

[0052] Specifically, the correlation coefficient calculation formula is: , in, X and Y are vectors composed of observed data and reference data respectively, Cov ( X , Y )for X and Y The covariance of Var ( X )and Var ( Y ) are X and Y The variance of .

[0053] By analyzing the GIIRS-RTTOV data (i.e., L1-level observation data and simulated radiation data), the diurnal variation of the bias can be observed, which is crucial to understanding the performance of FY-4B / GIIRS, because the radiation characteristics of the atmosphere and surface may differ significantly between day and night; these analysis results will provide a scientific basis for future calibration and data applications. In addition, this application also analyzes the variation of the bias with wavelength and time. This analysis helps to identify systematic biases in different spectral bands and different time periods, and further understand the long-term stability of FY-4B / GIIRS. These findings are crucial to improving the quality and reliability of satellite data.

[0054] Furthermore, in order to more comprehensively evaluate the performance of FY-4B / GIIRS, this application also compares the observation data with that of IASI carried by the same type of polar-orbiting satellite. In order to ensure that the observations between GIIRS and IASI are carried out simultaneously, the sub-satellite point intersection (SNO) samples that meet strict standards are selected, specifically: The L1 observation data of GIIRS and the observation data of IASI were selected with a measurement time difference of less than m2 minutes and a relative difference between the zenith angles of the two instruments of less than a%. These conditions ensured that the two instruments collected data under similar observation conditions, making the comparison results more reliable. Due to the difference in field of view between GIIRS and IASI, the data in the central wavelength range of 10.30~11.30um acquired by AGRI carried by FY-4B were used to select a spatially uniform observation area. This selection helps to reduce the impact of field of view differences on the comparison results and make the analysis more accurate.

[0055] More specifically, the criteria for determining a spatially uniform observation area are as follows: a circle is drawn in the observation area with the center of the pixel of the geostationary satellite hyperspectral infrared detector observation data as the center and half of the spatial resolution distance as the radius. All the geostationary orbit radiation imager observation data that are matched in time and space within the circle constitute a determination data group. If the ratio of the standard deviation to the mean of the determination data group is less than 0.01, then the circle is considered to be a spatially uniform observation area.

[0056] In this embodiment, the measurement time difference is less than 10 minutes, and the relative difference between the zenith angles is less than 1%.

[0057] Furthermore, in the GIIRS-IASI comparison process, this application uses a series of data processing to process the IASI observation data to ensure the accuracy and consistency of the results, such as Figure 3 As shown, the steps include: Inverse Fourier transform: First, the raw spectral data of IASI are inversely Fourier transformed to convert the frequency domain data back to the first interferogram.

[0058] De-Gaussian apodization: In the first interferogram, de-Gaussian apodization is applied to reduce the spectral distortion caused by the spectrometer system response function.

[0059] Truncated interferogram: The first interferogram is truncated to remove the high noise part in the signal, retain the main signal information, and reduce the spectral resolution to obtain the second interferogram. In this embodiment, the first interferogram is truncated to 0.8 cm optical path difference; the spectral resolution is reduced to the spectral resolution of the geostationary satellite hyperspectral infrared detector.

[0060] Fourier transform: Then, Fourier transform is performed to convert the second interferogram back into frequency domain data to obtain processed spectral data.

[0061] Hamming window apodization: Finally, the spectral data is further smoothed using Hamming window apodization to reduce sidelobe effects.

[0062] Furthermore, the processed IASI observation data and GIIRS observation data are cross-matched and verified, specifically: The processed IASI observation data with higher spectral resolution is used as reference data, and compared with the observation data of FY-4B / GIIRS. The systematic deviation is calculated and the deviation analysis is performed after obtaining the systematic deviation. This process is consistent with the comparison process of the above-mentioned GIIRS-RTTOV data. The difference is that the reference data set is replaced, so it will not be repeated here.

[0063] Figure 4 The results of the average noise equivalent temperature difference (GIIRS NEdT) of each FY-4B / GIIRS channel in the first year after the launch of FY-4B (July 2022-June 2023), the statistical mean deviation based on the IASI observation data (GIIRS-IASIBias), the statistical standard deviation based on the IASI observation data (GIIRS-IASI STD), the statistical mean deviation based on the simulated radiation data (GIIRS-RT Bias), and the statistical standard deviation based on the simulated radiation data (GIIRS-RT STD) are shown, where the ordinate represents the deviation / standard deviation of the brightness temperature (unit: K), the abscissa represents the spectral center wavenumber of each group of spectral channels, "Bias" represents the deviation, "STD" represents the standard deviation, "Wave Length" represents the wavelength, "Wave Number" represents the wavenumber, and "NedT" represents the equivalent noise temperature difference.

[0064] Figure 5 The following figure shows the time series of the daily average deviation between the observed brightness temperature (BT) and the simulated brightness temperature (OB) in the first year after the launch of FY-4B, as well as the performance of the corresponding fitting line under different channels: Figure 5 Middle (a) 703.125 cm -1 , (b) 775.0cm -1 , (c) 1807.5 cm -1 , and (d) 2022.5 cm -1 aisle, Figure 5(e) shows the slope and intercept of the bias-time fitting line for all FY-4B / GIIRS channels. These linear fitting results provide a systematic bias correction formula for each channel. Figure 5 In (a), (b), (c), and (d), the horizontal axis represents the number of days, and the vertical axis represents the daily average deviation; Figure 5 In (e), the horizontal axis represents the central wavenumber of the spectrum of each group of channels, and the vertical axis represents the deviation slope and deviation intercept.

[0065] Figure 6 An error bar graph is shown, where the horizontal axis represents the hour of the day and the vertical axis represents the bias, illustrating the daytime GIIRS observation and background bias. In the four selected spectral channels, the average bias at 1807.5 cm⁻¹ ranges from -0.41 to 0.75K ( Figure 6 (c)), while the range at 2022.5 cm⁻¹ is -0.57 to 0.63 K ( Figure 6 (d)), indicating that the daily variation of the deviation is relatively small. However, in the two spectral channels at 703.125 cm⁻¹ and 775.0 cm⁻¹ ( Figure 6 In (a) and (b), the average deviations range from -1.48 to 0.77 K and -1.19 to -0.11 K, respectively, indicating that the deviations or uncertainties of these two spectral channels are relatively large. In addition, the diurnal variation characteristics of the deviations in the two long-wave infrared (LWIR) sample channels of FY-4B / GIIRS are not significant; on the contrary, in the two mid-wave infrared (MWIR) channels, the average deviations show a slow diurnal variation characteristic, gradually increasing from 18:00 to 05:00 UTC, and then gradually decreasing from 06:00 to 17:00 UTC.

[0066] Figure 7 The relationship between the GIIRS bias and the observed brightness temperature of four selected spectral channels and the correlation coefficient are shown. Specifically, the distribution of the GIIRS-RTTOV bias and the observed brightness temperature in the range of 0.1K and 0.5K is analyzed, covering Figure 7 There are four spectral channels: (a) 703.125 cm⁻¹, (b) 775.0 cm⁻¹, (c) 1807.5 cm⁻¹, and (d) 2022.5 cm⁻¹. In addition, Figure 7(e) shows the correlation coefficients between the GIIRS bias and the observed brightness temperature for all channels of FY-4B / GIIRS. The results show that the GIIRS observed bias remains relatively stable over the dynamic range from -4 to 4 K across different spectral channels. In addition, the correlation coefficient between the BT and the bias of FY-4B / GIIRS is lower than 0.83. Except for the spectral channels in the wavenumber range of 691.25 cm⁻¹ to 708.125 cm⁻¹ and 2228.125 cm⁻¹ to 2250 cm⁻¹, the calibration stability of FY-4B / GIIRS is less dependent on the observed brightness temperature.

[0067] In actual operation, the L1-level observation data can be linearly compensated according to these statistical results, thereby realizing the calibration of the FY-4B / GIIRS observation data. Specifically, by linearly compensating the deviations of each channel, the systematic errors caused by changes in detector performance, environmental factors, etc. can be effectively reduced. This method is not only simple and efficient, but also can significantly improve the accuracy of the data, making it more suitable for subsequent quantitative analysis and applied research. Combined with the latest high-quality data and multi-source reference information, the method of the present invention ensures the accuracy and reliability of the calibration process, and provides a scientific basis for further optimizing the performance of the detector.

[0068] The present invention uses the GIIRS-RTTOV and GIIRS-IASI datasets (where GIIRS-RTTOV represents the statistics of GIIRS observation data errors with reference to simulated radiation data, and GIIRS-IASI represents the statistics of GIIRS observation data errors with reference to IASI observation data) to analyze the bias characteristics of FY-4B / GIIRS in the first year; the GIIRS-IASI dataset is mainly used to analyze the variation of bias with wavelength and time. However, the availability of IASI data is limited, with only 1-2 tracks intersecting GIIRS every day, resulting in insufficient sample size for analyzing BT-Bias (brightness temperature-bias) relationships and diurnal variations. This limitation makes it difficult to conduct a comprehensive analysis relying solely on IASI data. The present invention comprehensively evaluates the bias characteristics of FY-4B / GIIRS by comparing L1-level observation data with reference data (reference data includes RTTOV simulated radiation data and IASI observation data); at the same time, the use of samples under clear sky conditions ensures the high accuracy of RT simulation, and through strict data quality control measures, the reliability of the analysis results is guaranteed. Despite the limited availability of IASI data, the large sample size provided by the RTTOV simulation enables a more detailed analysis, including the diurnal variation of the biases and the temporal variation of the wavelength. These results provide important scientific support for the on-orbit radiation calibration performance of FY-4B / GIIRS and lay the foundation for future quantitative data applications.

[0069] It should be noted that, for the sake of convenience, the aforementioned method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present invention is not limited to the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously.

[0070] Based on the same idea as the calibration performance evaluation and analysis method of the geostationary satellite hyperspectral infrared detector in the above-mentioned embodiment, the present invention also provides a calibration performance evaluation and analysis system for a geostationary satellite hyperspectral infrared detector, which can be used to execute the calibration performance evaluation and analysis method of the geostationary satellite hyperspectral infrared detector. For ease of explanation, the structural schematic diagram of the embodiment of the calibration performance evaluation and analysis system for the geostationary satellite hyperspectral infrared detector only shows the parts related to the embodiment of the present invention. Those skilled in the art can understand that the illustrated structure does not constitute a limitation on the device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0071] like Figure 8 As shown, another embodiment of the present invention provides a calibration performance evaluation and analysis system for a geostationary satellite hyperspectral infrared detector, including a radiation data simulation module, a deviation calculation and analysis module, and a cross-matching verification module; Among them, the radiation data simulation module is used to obtain the ERA5 data set and the L1 level observation data of the geostationary satellite hyperspectral infrared sounder, and input them into the rapid transmission radiation model to obtain simulated radiation data; the ERA5 data set includes the ERA5 temperature and humidity profiles and the ERA5 surface temperature; the observation data of the geostationary satellite hyperspectral infrared sounder include ground infrared emissivity, sea and land identification and digital elevation model; RTTOV simulates the land and sea areas under clear sky conditions to obtain simulated radiation data.

[0072] The deviation calculation and analysis module is used to compare the simulated radiation data with the L1-level observation data of the geostationary satellite hyperspectral infrared sounder, calculate the systematic deviation and perform deviation analysis; The cross-matching verification module is used to cross-match and verify the L1 level observation data of the geostationary satellite hyperspectral infrared sounder with the observation data of the polar-orbiting satellite infrared atmospheric sounding interferometer.

[0073] It should be noted that the calibration performance evaluation and analysis system of the geostationary satellite hyperspectral infrared detector of the present invention corresponds one to one with the calibration performance evaluation and analysis method of the geostationary satellite hyperspectral infrared detector of the present invention. The technical features and beneficial effects described in the embodiment of the calibration performance evaluation and analysis method of the geostationary satellite hyperspectral infrared detector mentioned above are applicable to the embodiment of the calibration performance evaluation and analysis system of the geostationary satellite hyperspectral infrared detector. For specific contents, please refer to the description in the embodiment of the method of the present invention, which will not be repeated here. This is hereby declared.

[0074] In addition, in the implementation of the calibration performance evaluation and analysis system of the geostationary satellite hyperspectral infrared detector in the above-mentioned embodiment, the logical division of each program module is only an example. In actual applications, the above-mentioned functions can be assigned to different program modules as needed, for example, for the configuration requirements of the corresponding hardware or the convenience of software implementation. That is, the internal structure of the calibration performance evaluation and analysis system of the geostationary satellite hyperspectral infrared detector is divided into different program modules to complete all or part of the functions described above.

[0075] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A calibration performance evaluation and analysis method for a geostationary satellite hyperspectral infrared detector, characterized in that: The steps include: The ERA5 data set and the L1-level observation data of the geostationary satellite hyperspectral infrared sounder are obtained and input into the rapid transmission radiation model to obtain simulated radiation data; the ERA5 data set includes the ERA5 temperature and humidity profile and the ERA5 surface temperature; the observation data of the geostationary satellite hyperspectral infrared sounder include infrared channel observation brightness temperature, observation angle, ground infrared emissivity, sea and land identification and digital elevation model; the rapid transmission radiation model is simulated in the land and sea areas under clear sky conditions; The simulated radiation data were compared with the L1 observation data of the geostationary satellite hyperspectral infrared sounder, and the systematic deviations were calculated and analyzed. The L1 level observation data of the geostationary satellite hyperspectral infrared sounder and the observation data of the polar-orbiting satellite infrared atmospheric sounding interferometer were cross-matched and verified.

2. The calibration performance evaluation and analysis method of the geostationary satellite hyperspectral infrared detector according to claim 1 is characterized in that: The method for determining the land and sea areas under the clear sky conditions is as follows: Use the geostationary orbit radiation imager carried by the geostationary satellite to obtain cloud images; Divide the cloud image into multiple sub-image areas of x*x pixels; Determine whether each pixel in the sub-image area is covered by clouds. If not, mark the center point of the sub-image area as clear sky.

3. The calibration performance evaluation and analysis method of the geostationary satellite hyperspectral infrared detector according to claim 1 is characterized in that: During the simulation of the rapid transmission radiation model, data preprocessing operations are performed, specifically: Exclude L1-level observation data whose time difference with the ERA5 dataset exceeds m1 minutes, and exclude L1-level observation data whose satellite zenith angle exceeds 30°; The L1-level observation data whose deviation between the simulated radiation data and the L1-level observation data exceeds n times the standard deviation are regarded as outliers and are eliminated.

4. The calibration performance evaluation and analysis method of the geostationary satellite hyperspectral infrared detector according to claim 1 is characterized in that: The calculation of systematic deviation is specifically: Use simulated radiation data as reference data; Determine the correspondence between the reference data and the L1-level observation data through time-space matching: Based on the time-space information of the L1-level observation data, perform interpolation processing on the reference data to obtain the first fused data; during the interpolation processing, linear interpolation is used for time, and spline interpolation is used for spatial longitude and latitude; The first fusion data is grouped according to the spectral channel based on the spectral center wave number to obtain multiple groups of sub-data; each group of sub-data contains L1 observation data and reference data under the corresponding spectral channel; The overall mean value and overall standard deviation of each group of sub-data in each spectral channel are calculated, and the mean value and standard deviation of the deviation between the L1 level observation data and the reference data in each group of sub-data are calculated.

5. The calibration performance evaluation and analysis method for the geostationary satellite hyperspectral infrared detector according to claim 4 is characterized in that: The deviation analysis refers to performing statistical analysis in each spectral channel; The statistical analysis includes the following four parts: Annual analysis: Taking one year as the time range, the mean and standard deviation of the deviation between the brightness temperature of L1 observation data and the brightness temperature of reference data are calculated each year; Daily analysis: Taking the day as the time range, the average value of the deviation between the brightness temperature of the daily L1 observation data and the brightness temperature of the reference data is calculated, and the change pattern of the average value with the date is analyzed; Hourly analysis: Taking the hour as the time range, the average deviation between the brightness temperature of L1 observation data and the brightness temperature of reference data is calculated every hour. The statistical deviation results at the same time on different dates are grouped together, and their average value and standard deviation are calculated. Overall deviation analysis: Taking the year as the time range, calculate the deviation between the brightness temperature of the L1-level observation data of all spectral channels and the brightness temperature of the reference data each year, make a deviation scatter plot, and calculate the correlation coefficient between the brightness temperature of the L1-level observation data of all channels and the brightness temperature of the reference data.

6. The calibration performance evaluation and analysis method for a geostationary satellite hyperspectral infrared detector according to claim 1 is characterized in that: Before the cross-matching verification, the L1 level observation data of the geostationary satellite hyperspectral infrared sounder and the observation data of the polar-orbiting satellite infrared atmospheric sounding interferometer are screened to select sub-satellite point crossover samples that meet the standards, specifically: Select the L1-level observation data of the geostationary satellite hyperspectral infrared sounder and the observation data of the polar-orbiting satellite infrared atmospheric sounding interferometer with a measurement time difference of less than m2 minutes and a relative difference between the zenith angles of less than a%; Use the geostationary orbit radiation imager carried by the geostationary satellite to obtain data within the central wavelength range of 10.30 to 11.30 um to select the spatially uniform observation area; The judgment standard for the spatially uniform observation area is: draw a circle in the observation area with the center of the pixel of the observation data of the geostationary satellite hyperspectral infrared detector as the center and half of the spatial resolution distance as the radius. All the geostationary orbit radiation imager observation data matched in time and space within the circle constitute a judgment data group. If the ratio of the standard deviation to the mean of the judgment data group is less than 0.01, the circle is regarded as a spatially uniform observation area.

7. The calibration performance evaluation and analysis method for a geostationary satellite hyperspectral infrared detector according to claim 1 is characterized in that: When performing the cross-matching verification, the observation data of the polar-orbiting satellite infrared atmosphere sounding interferometer is processed, and the steps are: Performing inverse Fourier transform on the raw spectrum data of the polar-orbiting satellite infrared atmosphere sounding interferometer, and converting the frequency domain data into the first interferogram; The Gaussian apodization technique is applied to reduce the spectral distortion in the first interferogram, and the first interferogram is truncated to remove the high noise part in the signal and reduce the spectral resolution to obtain the second interferogram; Perform Fourier transformation on the second interference pattern and reconvert it back to frequency domain data to obtain processed spectrum data; The spectral data is smoothed using a Hamming window to reduce the sidelobe effect.

8. The calibration performance evaluation and analysis method for a geostationary satellite hyperspectral infrared detector according to claim 7 is characterized in that: The first interference pattern is truncated to an optical path difference of 0.8 cm; The spectral resolution is reduced to the spectral resolution of a geostationary satellite hyperspectral infrared detector.

9. The calibration performance evaluation and analysis method for a geostationary satellite hyperspectral infrared detector according to claim 1 is characterized in that: The cross-matching verification is specifically as follows: The observation data of the polar-orbiting satellite infrared atmospheric sounding interferometer is used as reference data; The corresponding relationship between the reference data and the L1-level observation data is determined by time-space matching: the reference data is interpolated based on the time-space information of the L1-level observation data to obtain the second fused data; during the interpolation process, linear interpolation is used for time, and spline interpolation is used for spatial longitude and latitude; The second fusion data is grouped according to the spectral channel based on the spectral center wave number to obtain multiple groups of channel data; each group of channel data includes L1 observation data and reference data under the corresponding spectral channel; Calculate the overall mean and overall standard deviation of each group of channel data in each spectral channel, and calculate the mean and standard deviation of the deviation between the L1 observed data and the reference data in each group of channel data.

10. The calibration performance evaluation and analysis system of the geostationary satellite hyperspectral infrared detector is characterized in that: It includes radiation data simulation module, deviation calculation and analysis module and cross-matching verification module; The radiation data simulation module is used to obtain the ERA5 data set and the L1 level observation data of the geostationary satellite hyperspectral infrared sounder, and input them into the rapid transmission radiation model to obtain simulated radiation data; the ERA5 data set includes the ERA5 temperature and humidity profile and the ERA5 surface temperature; the observation data of the geostationary satellite hyperspectral infrared sounder includes the infrared channel observation brightness temperature, observation angle, ground infrared emissivity, sea and land identification and digital elevation model; the rapid transmission radiation model is simulated in the land and sea areas under clear sky conditions; The deviation calculation and analysis module is used to compare the simulated radiation data with the L1 level observation data of the geostationary satellite hyperspectral infrared sounder, calculate the systematic deviation and perform deviation analysis; The cross-matching verification module is used to cross-match and verify the L1 level observation data of the geostationary satellite hyperspectral infrared sounder with the observation data of the polar-orbiting satellite infrared atmospheric sounding interferometer.

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