A method for on-orbit performance evaluation of instruments with multiple analog reference sources

Abstract

The present invention discloses an on-orbit performance evaluation method for an instrument based on multiple simulation reference sources. The method comprises the following steps: selecting multiple numerical weather analysis fields / forecast fields including ECMWF, NCEP, T639, ERA5, and GRAPES as input fields for multiple radiation transfer models, generating simulation data, and optimizing the multi-background field and multi-model results using a weighted correlation method. The improved simulation data is used as a reference source for an OMB method, which can better monitor the quality of instrument observation data. The on-orbit performance evaluation method for an instrument based on multiple simulation reference sources provides a set of optimized simulation reference sources as a basis for quality evaluation, thereby improving the monitoring accuracy of multispectral imaging / hyperspectral detection instruments, and effectively improving and supporting the degree of operational assimilation and application of satellite data in major global numerical prediction centers such as the China Meteorological Administration's Earth System Numerical Prediction Center (CEMC), the European Centre for Mesoscale Weather Forecasts (ECMWF), and the UK Meteorological Office (UK-MO).

Description

Technical Field

[0001] The present invention belongs to the field of on-orbit performance monitoring and relates to an on-orbit performance evaluation method for an instrument with multiple analog reference sources. Background Art

[0002] The Fengyun meteorological satellites are equipped with multiple multispectral imaging / detection instruments, such as the Moderate Resolution Spectral Imager (MERSI), Hyperspectral Infrared Sounder (HIRAS), Microwave Thermometer (MWTS), Microwave Hygrometer (MWHS), and Microwave Imager (MWRI) of the Fengyun-3 series. The data used are basically based on a single simulated reference source data. Due to the significant influence of different numerical forecast fields and radiation transfer models, differences often occur between the reference source data. Some of these differences are caused by differences in numerical forecast fields, while others are caused by differences in radiation transfer models. The uncertainties caused by the differences are difficult to identify and grasp. How to improve the data quality of on-orbit instruments in numerical forecasting of the Earth system is an urgent problem to be solved. Summary of the Invention

[0003] In response to the above technical problems in the related art, the present invention proposes an on-orbit performance evaluation method for an instrument with multiple analog reference sources, which can overcome the shortcomings of the above-mentioned existing technology.

[0004] A method for evaluating the on-orbit performance of an instrument with multiple analog reference sources comprises the following steps:

[0005] S1. Read the radiation transfer mode coefficient;

[0006] S2, loop reading NWP data;

[0007] S3, loop through all observation time files in sequence and read L1 data;

[0008] S4. Performing spatiotemporal matching of the L1 data and the NWP data to correspond to the observation grid, including the following steps: S41. Linearly interpolating the NWP data at two times before and after the observation time to obtain the NWP data at the observation time; S42. Using the longitude and latitude of the observation grid, resampling the NWP data near the center of the observation grid to the observation grid;

[0009] S5. Calculate forward emissivity / brightness temperature data;

[0010] S6. Perform weighted calculation on multiple numerical forecast field simulation data results to obtain an ensemble simulation reference source.

[0011] Furthermore, the radiation transfer mode coefficients include RTTOV, CRTM, and ARMS.

[0012] Furthermore, the NWP data include: numerical forecast fields (derived from ECMWF, NCEP, T639, ERA5, GRAPES), and the upper and lower layer boundaries of the atmospheric input fields of the static data part in the auxiliary data.

[0013] Furthermore, the observation grid is obtained from satellite observation data.

[0014] Furthermore, the forward emissivity / brightness temperature data are calculated.

[0015] The beneficial effects of the present invention are as follows: it solves the problem of identifying and grasping the uncertainty caused by background radiation simulation by using a multi-sample set obtained by using a variety of numerical forecast fields and a variety of radiation transfer modes, selects but is not limited to CEP, T639, ERA5, and GRAPES numerical weather analysis fields / forecast fields as input fields of a variety of radiation transfer modes, generates simulation data, and uses the weighted correlation method to optimize the multi-background field and multi-mode results. The improved simulation data is used as a reference source for the OMB method, which can better monitor the quality of instrument observation data; the on-orbit performance evaluation method of the instrument with multiple simulation reference sources of the present invention provides a set of optimized simulation reference sources as the basis for quality evaluation, improves the monitoring accuracy of multispectral imaging / hyperspectral detection instruments, and effectively improves and supports the degree of business assimilation and application of satellite data in the world's major numerical forecast centers such as the China Meteorological Administration's Earth System Numerical Prediction Center (CEMC), the European Centre for Mesoscale Weather Forecasts (ECMWF), and the UK Meteorological Office (UK-MO). BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A flowchart of a performance evaluation method.

[0018] Figure 2 A diagram of the composition of a performance evaluation method.

[0019] Figure 3 Time series monitoring results of multiple background fields.

[0020] Figure 4 Comparison of the OMB brightness temperature deviation mean before and after optimization.

[0021] Figure 5 Comparison of OMB brightness temperature variance before and after.

[0022] Figure 6Comparison of the NCEP simulated brightness temperature data and the NCEP observed-simulated brightness temperature difference (OMB) data before and after optimization. Test data: ATMS data collected on July 18, 2021, from the Channel 10 descending orbit.

[0023] Figure 7 Comparison of ERA5 simulated brightness temperature data and ERA5 observed-simulated brightness temperature difference (OMB) data before and after optimization. Test data: ATMS data collected on July 18, 2021, during the Channel 10 descending orbit. DETAILED DESCRIPTION

[0024] The following will be combined with the appended Figure 1-7 , the technical solution of the present invention is clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of the present invention.

[0025] Example 1 discloses a method for evaluating the on-orbit performance of an instrument with multiple simulated reference sources. It uses two types of benchmark data, a simulated reference source and an observation reference source, as the basis for tracing the source. The simulated reference source mainly uses multiple numerical weather forecast field data as background data. The ideal instrument observation background radiance (brightness temperature) under corresponding weather conditions is obtained by simulating the radiation transfer process, and data that meets the above ideal observation characteristics (such as clear sky weather conditions) is selected from the observation data. The background field after optimizing the multi-mode results of the multiple numerical forecast fields using the weighted correlation method is an embodiment of the collective simulated reference source, which includes the following steps:

[0026] S1. Environment settings, including:

[0027] Name settings, including satellite names FY-3A / B / C / D and FY-4A, and instrument names MWTS, MWHS, MWRI, IRAS, HIRAS, GIIRS, MERSI, and AGRI. Satellite and instrument settings are passed to the main program by modifying the SAT_NAME and SEN_NAME variables in the namelist. For example, setting SAT_NAME = "FY3D" and SEN_NAME = "MWTS" in the namelist means simulating the microwave thermometer (MWTS) on the Fengyun-3D satellite (FY-3D).

[0028] Mode type parameter settings include: RTTOV, ARMS or CRTM, which are achieved by setting the variable RTMNAME in the makefile. If you need to use RTTOV mode, you can set RTMNAME=RTTOV in the command line or directly modify the variable RTMNAME=RTTOV in the makefile. After selecting a specific mode, the makefile can compile and link the static library of the corresponding mode;

[0029] Other auxiliary parameter settings include: surface emissivity or model-calculated surface emissivity. Taking the RTTOV mode as an example, by setting opts%calcemis=False or True in the main program, you can decide whether to use the user-provided surface emissivity or the model-calculated surface emissivity. When opts%calcemis=True, you can choose which emissivity model to use by selecting opts%fastem_version=0,1,2,3,4,5,6, where fastem_version=0 is TESSEM2 and fastem_version=1,2,3,4,5,6 is the 1-6 version of the FASTEM model.

[0030] The required daily data storage space is set up. The storage space scale of the microwave detector ATMS for one day is:

[0031] 3E MWTS data is about 140MB per day;

[0032] 3E MWHS has about 245MB of data per day;

[0033] 3E MERSI generates about 2.8GB of data per day;

[0034] 3E MERSI uses about 24GB of data per day.

[0035] S2, read in the static data part of satellite, numerical prediction field, auxiliary data and the upper and lower boundaries of the atmospheric input field;

[0036] S3. Reading radiation transfer mode coefficients according to parameters set by the satellite and the instrument;

[0037] S4. Cycle through the NWP data of ECMWF, NECP, T639, and GRAPES and read the corresponding NWP data;

[0038] S5, select L1 data of all times in turn to execute the loop of observation files;

[0039] First, obtain the observation grid from satellite observation data and match the NWP data to the observation grid according to the following time and space requirements:

[0040] S51. Obtain the time parameters of the satellite observations, obtain numerical forecast data for three adjacent time periods based on the time parameters, read the atmospheric profile data for these three time periods, and perform unit and format conversion. The converted data are then subjected to linear time interpolation, inverse range interpolation in horizontal space, and vertical interpolation to the satellite observation location and time.

[0041] S52. Using the longitude and latitude of the observation grid, resample the NWP data at the center of the observation grid to the observation grid.

[0042] S6. Calculate forward emissivity / brightness temperature data;

[0043] That is, the atmosphere is assumed to be a parallel plane atmosphere with local thermal and dynamic balance. On the isobaric surface, without considering scattering, the approximate form of the clear sky atmospheric radiation transfer equation can be expressed as:

[0044]

[0045] Here, B(ν,T) represents the corrected mean Planck function of the spectral width of coverage channel i when the ambient temperature is T. Substituting NWP data and radiative transfer model coefficients into the above formula, the radiation / brightness temperature simulation data on the observation grid can be calculated;

[0046] S7, save the data results;

[0047] S8. After calculating the observation simulation results for all times, the relevant weighted calculation is started as follows:

[0048] The results obtained from multiple simulation reference sources are F i , let its weight be ω i ≥0, where i=1, 2, ...n, and weighted calculation is performed using formula (1):

[0049]

[0050] and

[0051] Calculate the loss function L according to formula (2):

[0052] By optimizing, we can get the minimum L (i=1,2,…n), is the optimized weight result, then the optimized O is obtained by weighted calculation according to formula (3) min ,

[0053] It is a collective simulation reference source.

[0054] S9, result statistics output; time series results of multiple background fields (ECMWF, NECP, T639, GRAPES and ensemble simulation reference sources) are shown in Figure 3 The long time series statistics are shown in Figure 4 ;

[0055] Figure 3 There are time series results of ECMWF, NECP, T639, GRAPES and ensemble simulation reference sources. The time series results of ECMWF, NECP, T639 and GRAPES are expressed as time series of individual background fields. The time series basically hovers between zero and negative 2 on the coordinate axis. The ensemble simulation reference source is obtained by weighted optimization of the above simulation reference sources. It can be seen that the ensemble simulation reference source has fewer mutations and its brightness temperature deviation is closer to 0, reflecting the good performance of the ATMS instrument.

[0056] Figure 4 The long-term statistical results of the mean brightness temperature deviation of OMB show that the mean brightness temperature deviation graph of the ensemble method in the dotted line is closer to zero, indicating that its representation effect is the best;

[0057] Figure 5 The long-term statistical results of OMB brightness temperature variance show that the brightness temperature variance graph of the ensemble method is closer to zero, indicating that its characterization effect is the best;

[0058] Figure 6 This is a comparison of the NCEP simulated brightness temperature data of the Microwave Sounder and the NCEP observed and simulated brightness temperature difference (OMB) data of the Microwave Sounder before and after optimization. The same test data was used: the channel 10 down-orbit data collected by the ATMS instrument on July 18, 2021. The clarity of the optimized image is significantly higher than that of the image before optimization.

[0059] Figure 7 This is a comparison of the microwave sounder ERA5 simulated brightness temperature data and the microwave sounder ERA5 observed and simulated brightness temperature difference (OMB) data before and after optimization. The same test data was used: the channel 10 down-orbit data collected by the ATMS instrument on July 18, 2021. The clarity of the optimized image is significantly higher than that of the image before optimization.

[0060] The present invention is not limited to the above-mentioned optimal implementation mode. Anyone can derive other forms of products under the inspiration of the present invention. However, no matter what changes are made in the method, any technical solution that is the same or similar to that of the present application falls within the scope of protection of the present invention.

Claims

1. A method for on-orbit performance evaluation of an instrument with multiple analog reference sources, characterized in that: The following steps are involved: S1. Read the radiation transfer mode coefficient; S2, loop reading NWP data; S3, loop through all observation time files in sequence and read L1 data; S4. Temporally and spatially match the L1 data and NWP data to the observation grid, including the following steps: S41. Linearly interpolate the NWP data of the two times before and after the observation time to obtain the NWP data at the observation time; S42, using the latitude and longitude of the observation grid, resampling the NWP data at the center of the observation grid to the observation grid; S5. Calculate forward emissivity / brightness temperature data; S6. Weighted calculation of multiple numerical forecast field simulation data results to obtain an ensemble simulation reference source. The algorithm is as follows: The resulting emissivity / brightness temperature data obtained from multiple simulated reference sources is F i , let its weight be ω i ≥0, where i=1, 2, ...n, using the formula The weighted calculation is 0, and Press Calculate the loss function L and obtain the coefficient that minimizes L through optimization (i=1,2,…n), the smallest coefficient Substitute into the formula Calculate the optimized emissivity / brightness temperature data O min , O min That is to generate an ensemble simulated reference source; at this time, the ensemble simulated reference source has fewer mutations and its brightness temperature deviation is closer to 0, and the ATMS instrument performance is good.

2. The on-orbit performance evaluation method for an instrument with multiple analog reference sources according to claim 1, characterized in that: The radiation transmission modes include RTTOV, CRTM, and ARMS.

3. The on-orbit performance evaluation method for an instrument with multiple analog reference sources according to claim 1, characterized in that: The NWP data includes: numerical forecast fields and upper and lower boundary layers of the atmospheric input fields of the static data part in the auxiliary data.

4. The on-orbit performance evaluation method for an instrument with multiple analog reference sources according to claim 1, characterized in that: The observation grid is obtained from satellite observation data.

5. The on-orbit performance evaluation method for an instrument with multiple analog reference sources according to claim 1, characterized in that: The algorithm for calculating forward radiation / brightness temperature data is as follows: Assuming the atmosphere to be a parallel plane of local thermal and dynamic balance, on the isobaric surface, B(ν,T) represents the corrected mean Planck function of the spectral width of coverage channel i when the ambient temperature is T. Substituting the NWP data and the radiative transfer model coefficient into the above formula, the forward emissivity / brightness temperature simulation data on the observation grid can be calculated.

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