CN117009427B - Assimilation method and device for wind-cloud satellite observation, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides an assimilation method, a device, electronic equipment and a storage medium for wind-cloud satellite observation, which relate to the technical field of meteorological data processing, wherein a GSI system firstly acquires the original observation data of a microwave hygrometer in a background field and a wind-cloud meteorological satellite, which are forecasted by a numerical weather forecast mode, when assimilating the wind-cloud satellite observation; then, carrying out data format conversion and data reading on the original observation data to obtain initial conversion data; performing quality control pretreatment on the initial conversion data to obtain available observation brightness temperature data; calling an RTTOV mode through a preset interface program, converting a background field into simulated bright temperature data, and obtaining observation residual error data by combining the observed bright temperature data; and then, carrying out three-dimensional variation assimilation on the background field based on the observation residual data to obtain an optimized analysis field. Therefore, the radiation transmission mode RTTOV mode is applied to the GSI system, and the direct assimilation of the wind-cloud satellite observation data in the GSI system is realized.
Description
Technical Field
The invention relates to the technical field of meteorological data processing, in particular to an assimilation method and device for wind and cloud satellite observation, electronic equipment and a storage medium.
Background
With the development of computer application technology and new detection technology, numerical weather forecast has become a support of the current weather forecast service, and accurate weather forecast is independent of the numerical forecast as a first basis. The key of improving the numerical weather forecast mode is to improve the quality of the initial field of the mode forecast, however, the conventional observation data has great limitation on the space coverage and time scale, on one hand, the conventional observation on the wide ocean surface is quite rare, on the other hand, the space coverage and time coverage of the conventional observation on the land are low, and the middle and small scale weather process which is rapidly developed and is severely changed can not be captured. Research shows that the improvement of the international numerical weather forecast capability in recent years is achieved, and a great part of contributions come from assimilation application of satellite data. Particularly, satellite observation containing atmospheric humidity information can effectively solve the problems of horizontal space limitation of conventional detection of water vapor information and low detection precision of middle and high layers, and improve analysis and forecast of high-layer water vapor distribution, temperature field and wind field in a numerical mode.
The cross-scale atmosphere forecast model (MPAS-A) has the characteristics of openness, computer program and file standardization, scientificity, advancement and the like, and can be respectively applied to global simulation and regional simulation. The global lattice statistical analysis system GSI (Gridpoint Statistical Interpolation) is an open source datse:Sup>A assimilation analysis system that provides se:Sup>A predictive initial field for MPAS-A. However, the GSI system can not realize assimilation application of the third and subsequent satellite observation data series.
Disclosure of Invention
The invention aims to provide an assimilation method, an assimilation device, electronic equipment and a storage medium for wind-cloud satellite observation, so as to realize direct assimilation of wind-cloud satellite observation data in a GSI system.
In a first aspect, an embodiment of the present invention provides an assimilation method for wind-cloud satellite observation, which is applied to a global lattice statistical analysis system GSI system, and includes:
acquiring original observation data of a microwave hygrometer in a background field and a weather meteorological satellite which are forecasted by a numerical weather forecast mode;
performing data format conversion and data reading on the original observation data to obtain initial conversion data;
performing quality control pretreatment on the initial conversion data to obtain available observation brightness temperature data;
calling an RTTOV mode through a preset interface program, converting the background field into simulated bright temperature data, and obtaining observation residual error data based on the simulated bright temperature data and the observed bright temperature data;
and carrying out three-dimensional variation assimilation on the background field based on the observation residual data to obtain an optimized analysis field.
Further, the performing data format conversion and data reading on the original observed data to obtain initial converted data includes:
Reading the original observed data in the HDF5 format based on the FORTRAN language;
converting the original observation data into BUFR format to obtain format conversion data;
reading and carrying out preliminary preprocessing on the format conversion data to obtain initial conversion data; the preliminary pretreatment comprises screening of a preset target area range, a preset target time range and an observed brightness temperature numerical range, conversion of a preset angle to radian, longitude and latitude conversion, diagnosis of earth surface type and assignment of an array.
Further, the preprocessing for performing quality control on the initial conversion data to obtain available observed brightness temperature data includes:
and performing earth surface type inspection, channel inspection, edge inspection, cloud/precipitation inspection and terrain height inspection on the initial conversion data to obtain available observation brightness temperature data.
Further, the surface type inspection includes retaining observation data for observation points with surface classification identifications less than 4; the channel inspection comprises the steps of eliminating observation data corresponding to a first detection channel and/or reserving observation data corresponding to a second detection channel, wherein the first detection channel is different from the second detection channel; the edge detection comprises the step of removing the observation data of 8 observation points at the extreme edges of each of the two ends of each scanning line; the cloud/precipitation inspection comprises the steps of removing observation data of observation points with the bright temperature difference between the detection channel 1 and the detection channel 10 being greater than 4.0 and the observation data of the observation points with the liquid water content in the cloud being greater than 0.2; the terrain elevation inspection includes removing observation data of observation points of the detection channel 15 having a surface air pressure value of less than 800 hPa.
Further, the interface program includes: according to the mode design requirement of the RTTOV mode, designating attribute configuration when the RTTOV mode is called, and corresponding parameter setting in the GSI system to the derived type of the RTTOV mode; reading an optical thickness coefficient file corresponding to satellite loads, and correspondingly setting a plurality of satellite loads and multidimensional array dimensions under a plurality of profiles; reading two/three-dimensional meteorological elements including temperature, humidity, air pressure and wind field from the background field, performing horizontal spatial interpolation and vertical spatial interpolation, and assigning each element to a derivative type required by the RTTOV mode one by one; calling the RTTOV mode built-in code, and calculating the earth surface emissivity and the reflectivity under the current assimilation condition; calling a built-in module of the RTTOV mode, and calculating the simulated bright temperature corresponding to the satellite load; and calculating an observation residual error simulating the bright temperature and the observed bright temperature, and storing the data of the observation residual error into a format required by the GSI system for outputting.
Further, after the observation residual data is obtained based on the simulated bright temperature data and the observed bright temperature data, the assimilation method of the wind-cloud satellite observation further comprises:
Performing deviation check on the observed bright temperature data based on the observed residual error data; the deviation test comprises the step of eliminating the observation data of the observation points meeting the first observation residual error requirement and/or the second observation residual error requirement, wherein the first observation residual error requirement is that the observation residual error value is larger than 15K, and the second observation residual error requirement is that the observation residual error value is larger than 3 times of the observation error standard deviation.
Further, the numerical weather forecast mode comprises an MPAS-A weather mode, the cloud weather satellite comprises se:Sup>A cloud three-star C, and the microwave hygrometer comprises se:Sup>A type II microwave hygrometer MWHS-II.
In a second aspect, an embodiment of the present invention further provides an assimilation device for wind-cloud satellite observation, which is applied to a GSI system of a global lattice statistical analysis system, and includes:
the data acquisition module is used for acquiring original observation data of a microwave hygrometer in a background field and a weather satellite which are forecasted by the numerical weather forecast mode;
the conversion reading module is used for carrying out data format conversion and data reading on the original observed data to obtain initial conversion data;
the preprocessing module is used for carrying out quality control preprocessing on the initial conversion data to obtain available observation brightness temperature data;
The residual determination module is used for calling an RTTOV mode through a preset interface program, converting the background field into simulated bright temperature data, and obtaining observation residual data based on the simulated bright temperature data and the observed bright temperature data;
and the data assimilation module is used for carrying out three-dimensional variation assimilation on the background field based on the observation residual data to obtain an optimized analysis field.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, and a processor, where the memory stores a computer program that can run on the processor, and when the processor executes the computer program, the assimilation method for wind cloud satellite observation in the first aspect is implemented.
In a fourth aspect, an embodiment of the present invention further provides a storage medium, where a computer program is stored, where the computer program when executed by a processor performs the method for assimilating wind cloud satellite observation according to the first aspect.
In the assimilation method, the assimilation device, the electronic equipment and the storage medium for the wind-cloud satellite observation provided by the embodiment of the invention, when the GSI system assimilates the wind-cloud satellite observation, the background field predicted by the numerical weather forecast mode and the original observation data of the microwave hygrometer in the wind-cloud meteorological satellite are firstly obtained; then, carrying out data format conversion and data reading on the original observation data to obtain initial conversion data; performing quality control pretreatment on the initial conversion data to obtain available observation brightness temperature data; calling an RTTOV mode through a preset interface program, converting a background field into simulated bright temperature data, and obtaining observation residual error data based on the simulated bright temperature data and the observed bright temperature data; and then, carrying out three-dimensional variation assimilation on the background field based on the observation residual data to obtain an optimized analysis field. Therefore, the radiation transmission mode RTTOV mode is applied to the GSI system, and the direct assimilation of the wind-cloud satellite observation data in the GSI system is realized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an assimilation method for wind-cloud satellite observation provided by the embodiment of the invention;
FIG. 2 is a schematic diagram of the assimilation principle of a GSI system according to an embodiment of the present invention;
FIG. 3 is a flow chart of quality control according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an assimilation device for wind-cloud satellite observation according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Since the satellite detection is generally radiation rate, and the relationship between the satellite detection and numerical mode variables such as temperature, humidity and the like is nonlinear and cannot be directly used by the mode, a related spatial mapping needs to be established between the radiation rate of the satellite and the numerical mode variables, and the radiation transmission mode is generally included besides horizontal and vertical interpolation. This mapping is applied in a numerical forecasting satellite data assimilation scheme called satellite observers.
The most widely used radiation transmission modes in the current numerical forecasting satellite data assimilation field mainly comprise two types of European rapid radiation transmission mode RTTOV (Radiative Transfer for TIROS-N Operational Vertical Sounder) and American rapid radiation transmission mode CRTM (Community Radiative Transfer Model). CRTM is a fast radiation transmission mode developed by the United states satellite data assimilation center JCSDA (Joint Center of Satellite Data Assimilation), has a better program framework structure design and a more advanced radiation transmission physical model, but has limited support for China wind cloud series satellites, and at present, CRTM only supports direct assimilation of loads of three satellites of FY-3A and FY-3B, so that CRTM cannot be used as a radiation transmission mode of wind cloud satellite III (namely FY-3C), satellite D and a follow-up transmitting satellite. RTTOV was developed by the European mid-term numerical weather forecast center ECMWF (European Centre for Medium-Range Weather Forecasts), initially a fast radiation transmission mode for ATOVS data, and later developed and perfected on an as-received basis. RTTOV possesses the ability of handling wind cloud series satellite detection data, and has released the assimilation interface to FY-3C microwave temperature and hygrometer data. Therefore, RTTOV is generally used as a rapid radiation transmission mode for assimilation of domestic meteorological satellite data in various research and business applications.
The GSI system is an open source datse:Sup>A assimilation analysis system which provides se:Sup>A predictive initial field for MPAS-A. However, the radiation transmission mode which is released and used in the official of the GSI system at present is only CRTM, and the assimilation application of the third and subsequent series of satellite data of Fengyun can not be realized. Based on the above, the method, the device, the electronic equipment and the storage medium for assimilating wind cloud satellite observation provided by the embodiment of the invention comprehensively consider the above problems, construct a scheme for directly assimilating FY-3C microwave radiometer data in a GSI system, establish an interface for reading the data by the GSI system and a data preprocessing flow, establish an interface for the RTTOV mode and the GSI system by means of the advantages of the RTTOV mode in the aspect of rapidly simulating the radiation of domestic wind cloud series satellite ultraviolet, visible light, infrared or microwave scanning radiometers, and finally realize assimilation application of the FY-3C satellite microwave hygrometer radiation rate data in the GSI system and provide a more accurate forecast initial field for a global area numerical mode MPAS.
For the sake of understanding the present embodiment, the method for assimilating wind-cloud satellite observation disclosed in the present embodiment is first described in detail.
The embodiment of the invention provides an assimilation method for wind cloud satellite observation, which is applied to a GSI system and can be executed by electronic equipment with data processing capability. Referring to a schematic flow chart of an assimilation method of wind-cloud satellite observation shown in fig. 1, the method mainly includes the following steps S102 to S110:
Step S102, obtaining original observation data of a microwave hygrometer in a background field and a cloud meteorological satellite which are forecasted by a numerical weather forecast mode.
Optionally, the numerical weather forecast mode may include an MPAS-se:Sup>A weather mode, the cloud weather satellite may include se:Sup>A cloud No. C satellite or se:Sup>A subsequent transmitting satellite, and the microwave hygrometer may include se:Sup>A type II microwave hygrometer MWHS-II.
Step S104, converting data format and reading data of the original observed data to obtain initial conversion data.
In some possible embodiments, the raw observations in HDF5 format may be read first based on the FORTRAN language; converting the original observation data into BUFR format to obtain format conversion data; further, the format conversion data are read and subjected to preliminary preprocessing to obtain initial conversion data; the preliminary pretreatment comprises screening of a preset target area range, a target time range and an observed brightness temperature numerical range, conversion of a preset angle to radian, longitude and latitude conversion, diagnosis of earth surface type, assignment of an array and the like.
And step S106, performing quality control pretreatment on the initial conversion data to obtain available observation brightness temperature data.
In some possible embodiments, surface type verification, channel verification, edge verification, cloud/precipitation verification, and terrain elevation verification may be performed on the initial conversion data to obtain useful observed light temperature data.
In one possible implementation, the surface type inspection may include preserving the observation data of the observation points with the surface classification identity less than 4, culling the observation data of the observation points of 4, 5, 6, 7, i.e., preserving only the land, land water, on the sea surface, and culling the observations at the intersection of the hybrid subsurface to reduce errors. The channel inspection may include rejecting the observed data corresponding to the first detection channel and/or retaining the observed data corresponding to the second detection channel, the first detection channel being different from the second detection channel, e.g., the first detection channel may include detection channel 1 and detection channel 10, considering that detection channel 1 and detection channel 10 are windowed channels, and the second detection channel may be selected based on the study requirements. The edge detection can comprise removing the observation data of 8 observation points at the two ends of each scanning line so as to reduce the influence of the edge effect. It should be noted that the 8 observation points are only examples, and the embodiment of the present invention is not limited thereto, and in other embodiments, the number of the observation points may be smaller than 8 or larger than 8. Cloud/precipitation inspection may include rejecting observation data for observation points where the bright temperature difference of detection channel 1 to detection channel 10 is greater than 4.0 and for observation points where the liquid water content in the cloud is greater than 0.2. The terrain elevation test may include eliminating observation data for observation points of the detection channel 15 having a barometric pressure value less than 800hPa, where the detection channel 15 corresponds to a higher elevation.
Step S108, calling an RTTOV mode through a preset interface program, converting a background field into simulated bright temperature data, and obtaining observation residual error data based on the simulated bright temperature data and the observed bright temperature data.
In some possible embodiments, the interface program may comprise:
and designating attribute configuration when the RTTOV mode is called according to the mode design requirement of the RTTOV mode, and setting parameters in the GSI system and the derivative type of the RTTOV mode. For example, in this embodiment, by setting various options defined in the rttov_options derivative type, various aspects of the RTTOV simulation are configured; providing the atmosphere and surface variables to the RTTOV mode in the rttov_profiles derivative type; the simulated atmospheric roof radiation is stored in rttov_radiance structure, etc.
Reading an optical thickness coefficient file corresponding to satellite loads, and correspondingly setting a plurality of satellite loads and multidimensional array dimensions under a plurality of profiles;
reading two/three-dimensional meteorological elements such as temperature, humidity, air pressure, wind field and the like from a background field, performing horizontal spatial interpolation and vertical spatial interpolation, and assigning each element to a derivative type required by an RTTOV mode in a one-by-one correspondence manner;
calling an RTTOV mode built-in code, and calculating the earth surface emissivity and the reflectivity under the current assimilation condition;
Calling a built-in module of an RTTOV mode, and calculating the simulated bright temperature corresponding to the satellite load;
and calculating an observation residual error simulating the bright temperature and the observed bright temperature, and storing the data of the observation residual error into a format required by the GSI system for outputting.
Further, after the observation residual data is obtained, deviation test can be performed on the observation bright temperature data based on the observation residual data; the deviation test comprises the step of eliminating the observation data of the observation points meeting the first observation residual error requirement and/or the second observation residual error requirement, wherein the first observation residual error requirement is an observation residual error value larger than 15K, and the second observation residual error requirement is an observation error standard deviation of which the observation residual error value is larger than 3 times. After the deviation check, the observation residual data may be updated again.
And step S110, carrying out three-dimensional variation assimilation on the background field based on the observation residual data to obtain an optimized analysis field.
In the assimilation method of the wind-cloud satellite observation provided by the embodiment of the invention, when the GSI system assimilates the wind-cloud satellite observation, the GSI system firstly obtains the background field predicted by the numerical weather forecast mode and the original observation data of the microwave hygrometer in the wind-cloud meteorological satellite; then, carrying out data format conversion and data reading on the original observation data to obtain initial conversion data; performing quality control pretreatment on the initial conversion data to obtain available observation brightness temperature data; calling an RTTOV mode through a preset interface program, converting a background field into simulated bright temperature data, and obtaining observation residual error data based on the simulated bright temperature data and the observed bright temperature data; and then, carrying out three-dimensional variation assimilation on the background field based on the observation residual data to obtain an optimized analysis field. Therefore, the radiation transmission mode RTTOV mode is applied to the GSI system, and the direct assimilation of the wind-cloud satellite observation data in the GSI system is realized.
For ease of understanding, as shown in fig. 2, the assimilation principle of GSI system is as follows: the background field forecasted by the numerical weather forecast mode can be converted into simulated bright temperature through the RTTOV mode; based on the simulated bright temperature and the observed bright temperature from the microwave hygrometer in the wind cloud meteorological satellite, an observation residual error can be obtained; the quality control of deviation test can be carried out on the observed bright temperature based on the observation residual error, and deviation correction can be carried out, so that new observed bright temperature is obtained, and finally the GSI system carries out three-dimensional variation assimilation treatment on the background field based on the new observed bright temperature, so that an analysis field can be obtained.
For ease of understanding, the following describes in exemplary detail the method of assimilation of the wind cloud satellite observations described above.
The complete satellite data assimilation module construction process comprises the butt joint of multiple data interfaces such as assimilation systems, forecast modes, observation operators, observation data, data processing and the like. The module constructed by the scheme adopts data processing methods such as GSI, MPAS forecast mode, RTTOV radiation transmission mode, FY-3C microwave hygrometer L1 data reading, deviation correction, quality control and the like. The whole module is written by the FORTRAN language, and can realize the initial field required by the forecast mode of directly reading in and outputting the processed data.
1. Overview of assimilation System
(1) Three-dimensional variation assimilation basic principle
According to the background field information, the observation information and the respective error characteristics, the three-dimensional variation assimilation method is used for attributing the assimilation analysis problem to a defined minimization problem of an objective function reflecting the distance between the analysis field and the background field and between the analysis field and the actual observation value, and directly solving the objective function by adopting a proper optimization algorithm (such as a conjugate gradient method) to enable the objective function to reach a minimum value, so that the optimal analysis field is obtained. The objective function is defined as:
wherein,is the analysis variable (such as temperature, humidity, wind field, etc.) to be solved, +.>Is the mode background field corresponding to the analysis variable, +.>Is the observed value,/->Is the background error covariance matrix,>is the observed error covariance matrix,>representing the value of the analysis variable in the observation space, +.>Representing the observation operator. To reduce the calculation amount of each iteration step, let +.>And assuming that the observation operator is linear, the corresponding minimization function becomes:
wherein,representing the observation increment, the gradient of the objective function at this time is:
continuously iterating to obtain an objective functionSolution at the time of minimization, finally obtained +.>And the optimal fusion analysis is realized.
(2) GSI assimilation system
The GSI assimilation system is established on the physical space in the three-dimensional coordinate system, and can directly perform three-dimensional variation assimilation analysis on the mode lattice point data.
The three-dimensional variational analysis is mainly performed on seven variables (namely, control variables in an assimilation system) in the GSI calculation process, and the seven variables comprise: flow function, unbalanced velocity potential, unbalanced virtual temperature, unbalanced ground pressure, pseudo or normalized relative humidity, ozone mix ratio, cloud coagulation mix ratio. In addition, gusts and visibility are added as analysis variables for RTMA applications.
2. Satellite observation data reading
The observation data adopted by the scheme are the data of a Fengyun third-size C star microwave hygrometer (MicroWave Humidity Souder, MWHS-II). The number of the microwave radiation rate data detection channels is 15, wherein 5 channels are positioned on a water vapor absorption line near 183GHz, 8 channels are positioned on an oxygen absorption line near 118GHz, and two window area channels are positioned on 89GHz and 150GHz, so that the temperature and humidity vertical distribution profile of the atmosphere can be comprehensively detected.
As the GSI source code issued by the authorities does not contain a reading and data preprocessing scheme of wind and cloud series satellite detection data, the scheme autonomously establishes an observation data reading interface of FY-3C MWHS-II. The observation data is L1-level global data in the HDF5 format issued by the China national satellite weather center, and each satellite load provides 14 groups of data sets every day. In the file design of the read data, the data sets in all the time windows of the current assimilation time are read one by one, and the contents comprise the scanning line, the scanning point number, the scanning channel of the satellite, the longitude, the latitude, the zenith angle of the scanning point, the emissivity of the corresponding channel and the like. Meanwhile, after information is read, the storage design is carried out according to the standard storage mode of the observation file in the GSI, so that the reading file can be directly used when other load data of the wind cloud series satellite are added later.
(1) Format conversion
Since the GSI system only recognizes observations in the BUFR format, a conversion of the data format is required before the data enters the GSI. The module is compiled using a FORTRAN or other suitable compiler, mainly by:
(1) reading FY-3C MWHS-II data (HDF 5 format) in an original format based on the FORTRAN language by using an application program interface provided by an HDF5 function library;
(2) based on the unified coding identification of NCEP on each satellite load in BUFR table, the specification requirement of BUFR table and the inherent attribute of FY-3C MWHS-II data, the relevant content of the data is newly added in the BUFR table, thus providing a foundation for the subsequent steps.
(3) Based on the first two steps, the satellite observation file in the original format is converted into the BUFR format by using an application program interface provided by the BUFR library and the newly created BUFR.
(2) Data reading
GSI lacks the reading file of this material, after accomplishing the above-mentioned format conversion, this scheme is independently newly added and read interface file read_mwhs2 of this material, realize the file reading to BUFR format, mainly include the following:
(1) reading observation data of all channels of all observation points by using an application program interface provided by a BUFR library;
(2) The range and time screening of the observation data are carried out, and only the observation data in an analog region and a time window which are arranged in an assimilation mode are reserved, so that the storage space of the observation data is reduced, and the assimilation operation efficiency is improved;
(3) performing rough inspection of the observation data, and only keeping the observation data of which the observation brightness Wen Shuzhi is between 150 and 350K;
(4) according to the system requirements of GSI, converting zenith angle/azimuth angle and other angles into radian, converting longitude/latitude, diagnosing earth surface type and the like;
(5) and assigning the read observation data to corresponding element variables of the array data_all uniformly distributed by the GSI in a standard form for subsequent program call.
3. Pretreatment of satellite detection data
The pre-processing of the observation data designed by the scheme mainly comprises quality control and deviation correction of satellite data.
(1) Quality control
Because satellite instrument detects itself and atmospheric environment etc. influence, the unavoidable error that can produce of satellite observation data, must use certain technical means to reject the great data of error before entering GSI assimilation module, guarantee the quality of the observation data that gets into assimilation module.
Referring to a quality control flow chart shown in fig. 3, the original MWHS-II data is subjected to surface type inspection, edge inspection, cloud/precipitation inspection, terrain height inspection, channel inspection and deviation inspection in sequence to obtain usable MWHS-II data.
Specifically, the quality control method for FY-3C MWHS-II data in the pretreatment stage of the scheme is as follows:
(1) surface type inspection
Because the simulation calculation of the emissivity of the complex earth surface is complex, larger errors can be generated in the assimilation process, the observation of all channels of the mixed earth surface type is eliminated, and only the observation of a single underlying surface is assimilated.
Algorithm design: and taking the earth surface classification mark output by the mode as a judgment basis, reserving the observation with the parameter smaller than 4, and rejecting the observations with the parameters of 4, 5, 6 and 7, namely reserving only the observations on land, land water and sea, and rejecting the observations at the intersection of the mixed underlying surfaces.
(2) Channel inspection
a. Window zone passage inspection
According to the FY-3C MWHS-II channel weight function distribution characteristics of the standard atmosphere profile simulation, the channel with the peak energy contribution height positioned on the surface is greatly influenced by the underlying surface, so that the window channel is not considered to be put into an assimilation module.
Algorithm design: and in the process of adding the channel screening code design, all observations of the detection channel 1 and the detection channel 10 are removed.
b. Channel verification based on research requirements
The earlier MWHS-II had 10 more channels (8 more channels were temperature sensing channels located near the 118GHz frequency) and the remaining 5 channels were vertically distributed channels located near the 183GHz water vapor absorption line for sensing humidity. The specific channels selected are based on different research requirements of users.
The algorithm design is that a setting option is added in a satellite data basic information setting file radinfo, and whether a channel is set by an assimilation mark is increased.
(3) Edge detection
The MWHS-II single scan line contains 98 scan points, but the path between the probe points at both ends thereof to the instrument channel is longer than the path between the understar point to the probe instrument channel, and the observed radiation amount is reduced, which results in the so-called edge effect.
Algorithm design: the 8 observations at the very edge are each rejected at both ends of each scan line.
(4) Cloud/precipitation inspection
The microwave detection can penetrate part of clouds, but can be subjected to scattering and absorption effects of cloud and rain particles, and the rapid radiation transmission mode has limited capabilities of absorbing, scattering and simulating radiation energy in a cloud and rain area, so that cloud detection methods are required to be used for removing cloud-affected data, and only clear sky data are assimilated.
Algorithm design:
and (3) eliminating scattering indexes of the rainfall cloud detection: if the bright temperature difference between the detection channel 1 and the detection channel 10 is greater than 4.0, eliminating the observation;
and eliminating the observation that the liquid water content in the cloud is more than 0.2.
(5) Terrain elevation inspection
Algorithm design: the culling channel 15 has an earth pressure value less than 800 hPa.
(6) Deviation checking
Absolute incremental checking: if the observation residual error between the simulated bright temperature and the observed bright temperature is larger than 15K, eliminating the observation;
relative increment inspection: and if the observation residual error between the simulated bright temperature and the observed bright temperature is greater than 3 times of the standard deviation of the observation error, eliminating the observation.
Based on the algorithm design, in the quality control module qcmod of GSI, the quality control algorithm code of FY-3C MWHS-II is newly added, and the parameters of related calling files are modified, meanwhile, in each step of quality control judgment, all the observed data are marked, the data which are removed in each step are marked correspondingly, for example, the data which do not pass the surface type test are marked as ifail_surface_qc, the data which do not pass the deviation test are marked as ifail_gross_qc. And the related information is output to a diagnosis file of the GSI, so that the later analysis and monitoring of the use of the observation data are facilitated.
(2) Deviation correction
Under the comprehensive influence of errors of the radiation transmission mode, basic spectrum data and input data thereof, errors of satellite observation data generated by the influence of instrument sensitivity, calibration, cloud and the like and errors generated by the change of response characteristics of satellite load along with time, even the radiation rate data subjected to quality control has certain systematic deviation, and a three-dimensional variation theory is established on the assumption that the observation field errors and the background field errors are subjected to Gaussian distribution. There have been a number of studies showing that effective bias ordering of these materials is essential.
Generally, there are two deviation correcting modes provided by the GSI, one is off-line angle-dependent deviation correction and air mass deviation correction in the GSI; the other is to combine deviation correction based on angle and air mass in GSI, which requires a combined input file satbias_in, and the GSI version adopted in the scheme adopts a second mode, namely, the combination of angle dependence and air mass deviation correction. Therefore, the scheme adds related parameters and processing codes of FY-3C MWHS-II in the input file.
The method for integrating angle dependence and air mass deviation correction adopts a group of predictor combinations to be related with radiance deviation, and for each channel, the deviation is calculated by using the following linear regression equation:
wherein,p i andβ i respectively represent the firstiA predictor and a deviation correction factor. The correction for deviation is calculated from a large number of samples by least squares fitting (Harris and Kelly, 2001). The predictor is then determined as a combination of the following variables: the thickness of the mode background field is 1000-300hPa, the mode surface temperature and the mode vapor total amount are included, and the scanning angle information of the detector is also included.
In general, the user may download the standard file for correcting the GDAS deviation from the home network by himself, but the coefficient does not have a regional characteristic from the viewpoint of service application, and has a large influence on the correction result. The scheme realizes the dynamic update of the deviation correction coefficient file, designs codes, and outputs and stores the coefficient file assimilated in the previous time as the input file assimilated in the next time, thereby dynamically updating. When in business application, two weeks of data are generally selected for cyclic assimilation in advance, so that the coefficient file is updated.
4. Summary of radiation transfer modes (RTTOV)
(1) Principle of calculation of radiation transmission mode
The atmospheric radiation transmission mode can calculate the radiation intensity received by each channel of the satellite microwave detector along the observation direction, and the simulated radiation is generally calculated by dividing the simulated radiation into two parts under the influence of clear sky and clouds, so that the atmospheric radiation transmission equation can be approximately expressed as:
wherein,vin order to be a frequency of the light,θin order to scan the angle of the beam,Nis used for the purposes of the cloud cover,L Clr (v,θ)the top of the atmosphere in a clear sky state radiates upwards,L Cld (v,θ)is the top radiation of the atmosphere in the cloud state.
If the air condition is clear, the formula only leaves the first term, and the atmosphere in the clear air condition radiates upwardsL Clr (v,θ)Can be represented by the following formula:
in the above-mentioned method, the step of,Tin order to be able to achieve an average temperature,Tsin order to be the surface temperature,τ s (v,θ)is the transmittance of the earth surface to the space,ε s (v,θ)is the surface emissivity, which is usually determined to be constant on land because of the complex surface type and difficult calculation, and the emissivity of the water body can be calculated by adopting a fast surface emission mode FASTEM-2 (DeBlonde, 2000).B(v,T)As a planck function, one can express:
c 1 andc 2 respectively, is a constant of the planck function,aandband (5) correcting coefficients for the wave bands.
In cloudy conditions, assuming an opaque cloud, then cloud upward radiation on a single layer can be expressed as:
Wherein,is the transmittance of the surface to the space under the cloud state, +.>Is the surface temperature in the cloud state.
Generally, for the infrared band, the cloud top emissivity may be set to a fixed value, but for the microwave band, the cloud top emissivity may be ignored.
(2) RTTOV mode introduction
Given the atmospheric temperature profile, the atmospheric humidity profile, and the atmospheric ozone content profile (i.e., given the background field), the RTTOV mode can calculate the corresponding radiation values through coefficients such as weight height functions of different detector channels, and the mode layering is generally 54 layers or 101 layers, and the vertical height ranges from 1050.00hPa to 0.005hPa.
RTTOV mode not only requires calculation of forward radiation transmission, but also the gradient of the corresponding variable of the input profile in the state space. Assuming a state variable ofxRadiation quantityyCan be calculated by the following formula:
wherein,Hi.e. radiation transmission mode. For an assumed atmospheric state variablex 0 By Jacobin matrixHI.e. calculate the change of the state variableRadiation change amount +.>：
There are 4 application modules in the calculation of RTTOV: RTTOV module, is used for calculating H (x) directly; RTTOVK module for calculating gradient H%x 0 ) The method comprises the steps of carrying out a first treatment on the surface of the RTTOVTL module, is used for the tangent line to calculate; RTTOVAD module for companion calculation.
(3) GSI-RTTOV interface design
In the source code of GSI, CRTM is selected as its radiation transmission mode by default, thus requiring the user to install and configure RTTOV mode himself and to establish an interface between GSI and RTTOV. In the scheme, an interface program rttov_interface is newly added and used for calling an RTTOV mode, calculating the simulated brightness and observation residual error, and therefore satellite data is enabled to enter a GSI assimilation system.
The main contents of the interface program rttov_interface are as follows:
(1) according to the mode design requirement of RTTOV, designating attribute configuration when RTTOV is called, and corresponding parameter setting in GSI with the fixed derivative type of RTTOV;
(2) reading an optical thickness coefficient file corresponding to satellite loads, wherein the emphasis is on the corresponding arrangement of the multidimensional array dimensions when a plurality of satellite loads and a plurality of profiles are carried out;
(3) reading two/three-dimensional meteorological elements such as temperature, humidity, air pressure, wind field and the like from a background field file, and firstly performing horizontal spatial interpolation (adopting a bilinear interpolation method) and vertical spatial interpolation (adopting a logarithmic interpolation method); secondly, assigning each element to an inherent derivative type rttov_profiles required by the RTTOV mode in a one-by-one correspondence mode;
(4) calling RTTOV built-in codes to calculate the surface emissivity and the reflectivity under the current assimilation condition;
(5) And calling an RTTOV built-in module, and calculating the simulated bright temperature corresponding to the satellite load. RTTOV forward calculation is achieved through calling the rttov_direct module, RTTOV gradient mode calculation is achieved through calling the rttov_k module, RTTOV tangential mode calculation is achieved through calling the rttov_tl module, and RTTOV accompanying mode calculation is achieved through calling the rttov_ad module.
(6) And calculating an observation residual error of the simulated bright temperature and the observed bright temperature, and storing the result as a format output required by GSI.
Compared with the prior art, the embodiment of the invention has at least the following advantages:
aiming at the defect that a GSI system is not suitable for the data assimilation of a domestic wind cloud satellite three-dimensional microwave hygrometer, algorithms such as FY-3C microwave hygrometer data reading, pretreatment such as quality control and deviation correction, and the like, and interfaces of RTTOV and GSI are designed, and a complete flow scheme for directly assimilating FY-3C microwave hygrometer data by the GSI is constructed.
Corresponding to the assimilation method of the wind-cloud satellite observation, the embodiment of the invention also provides an assimilation device of the wind-cloud satellite observation, which is applied to a GSI system. Referring to fig. 4, a schematic structural diagram of an assimilation device for wind-cloud satellite observation according to an embodiment of the present invention is provided, where the device includes:
The data acquisition module 401 is used for acquiring original observation data of a microwave hygrometer in a background field and a weather satellite of a numerical weather forecast mode;
the conversion reading module 402 is configured to perform data format conversion and data reading on the original observation data to obtain initial conversion data;
a preprocessing module 403, configured to perform quality control preprocessing on the initial conversion data, so as to obtain available observed brightness temperature data;
the residual determination module 404 is configured to invoke an RTTOV mode through a preset interface program, convert a background field into simulated bright temperature data, and obtain observed residual data based on the simulated bright temperature data and the observed bright temperature data;
the data assimilation module 405 is configured to assimilate the three-dimensional variation of the background field based on the observation residual data, so as to obtain an optimized analysis field.
Further, the conversion reading module 402 is specifically configured to: reading the original observed data in the HDF5 format based on the FORTRAN language; converting the original observation data into BUFR format to obtain format conversion data; reading and carrying out preliminary preprocessing on the format conversion data to obtain initial conversion data; the preliminary pretreatment comprises screening of a preset target area range, a target time range and an observed brightness temperature numerical range, conversion of a preset angle to radian, longitude and latitude conversion, diagnosis of earth surface type and assignment of an array.
Further, the preprocessing module 403 is specifically configured to: and (3) performing earth surface type inspection, channel inspection, edge inspection, cloud/precipitation inspection and terrain height inspection on the initial conversion data to obtain available observation brightness temperature data.
Further, the surface type inspection comprises the step of reserving observation data of observation points with surface classification identifiers smaller than 4; the channel inspection comprises the steps of eliminating the observation data corresponding to the first detection channel and/or reserving the observation data corresponding to the second detection channel, wherein the first detection channel is different from the second detection channel; the edge detection comprises the steps of removing the observation data of 8 observation points at the extreme edges of each of the two ends of each scanning line; the cloud/precipitation inspection comprises the steps of eliminating the observation data of the observation points with the bright temperature difference between the detection channel 1 and the detection channel 10 being more than 4.0 and the observation data of the observation points with the liquid water content in the cloud being more than 0.2; the terrain elevation test includes removing the observation data of the observation points of the detection channel 15 having the earth pressure value less than 800 hPa.
Further, the interface program includes: according to the mode design requirement of the RTTOV mode, designating attribute configuration when the RTTOV mode is called, and corresponding parameter setting in the GSI system to the derived type of the RTTOV mode; reading an optical thickness coefficient file corresponding to satellite loads, and correspondingly setting a plurality of satellite loads and multidimensional array dimensions under a plurality of profiles; reading two/three-dimensional meteorological elements including temperature, humidity, air pressure and wind field from a background field, performing horizontal spatial interpolation and vertical spatial interpolation, and assigning each element to a derivative type required by an RTTOV mode one by one; calling an RTTOV mode built-in code, and calculating the earth surface emissivity and the reflectivity under the current assimilation condition; calling a built-in module of an RTTOV mode, and calculating the simulated bright temperature corresponding to the satellite load; and calculating an observation residual error simulating the bright temperature and the observed bright temperature, and storing the data of the observation residual error into a format required by the GSI system for outputting.
Further, the apparatus further includes:
the deviation checking module is used for performing deviation checking on the observed bright temperature data based on the observed residual error data; the deviation test comprises the step of eliminating the observation data of the observation points meeting the first observation residual error requirement and/or the second observation residual error requirement, wherein the first observation residual error requirement is an observation residual error value larger than 15K, and the second observation residual error requirement is an observation error standard deviation of which the observation residual error value is larger than 3 times.
Further, the numerical weather forecast mode comprises an MPAS-A weather mode, the cloud weather satellite comprises se:Sup>A cloud third C star, and the microwave hygrometer comprises se:Sup>A type II microwave hygrometer MWHS-II.
The implementation principle and the generated technical effects of the assimilation device for wind-cloud satellite observation provided in this embodiment are the same as those of the assimilation method embodiment for wind-cloud satellite observation, and for the sake of brief description, reference may be made to corresponding contents in the assimilation method embodiment for wind-cloud satellite observation where the assimilation device embodiment for wind-cloud satellite observation is not mentioned.
As shown in fig. 5, an electronic device 500 according to an embodiment of the present invention includes: the system comprises a processor 501, a memory 502 and a bus, wherein the memory 502 stores a computer program capable of running on the processor 501, and when the electronic device 500 runs, the processor 501 and the memory 502 communicate through the bus, and the processor 501 executes the computer program to realize the assimilation method of the wind cloud satellite observation.
Specifically, the memory 502 and the processor 501 can be general-purpose memories and processors, which are not particularly limited herein.
The embodiment of the invention also provides a storage medium, and a computer program is stored on the storage medium, and the computer program is executed by a processor to execute the assimilation method of the wind cloud satellite observation in the previous method embodiment. The storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a RAM, a magnetic disk, or an optical disk, etc., which can store program codes.
Any particular values in all examples shown and described herein are to be construed as merely illustrative and not a limitation, and thus other examples of exemplary embodiments may have different values.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (9)
1. The assimilation method of the wind cloud satellite observation is characterized by being applied to a global lattice point statistical analysis system GSI system, and comprises the following steps:
acquiring original observation data of a microwave hygrometer in a background field and a weather meteorological satellite which are forecasted by a numerical weather forecast mode;
performing data format conversion and data reading on the original observation data to obtain initial conversion data;
performing quality control pretreatment on the initial conversion data to obtain available observation brightness temperature data;
calling an RTTOV mode through a preset interface program, converting the background field into simulated bright temperature data, and obtaining observation residual error data based on the simulated bright temperature data and the observed bright temperature data;
Performing three-dimensional variation assimilation on the background field based on the observation residual data to obtain an optimized analysis field;
wherein the interface program includes: according to the mode design requirement of the RTTOV mode, designating attribute configuration when the RTTOV mode is called, and corresponding parameter setting in the GSI system to the derived type of the RTTOV mode; reading an optical thickness coefficient file corresponding to satellite loads, and correspondingly setting a plurality of satellite loads and multidimensional array dimensions under a plurality of profiles; reading two/three-dimensional meteorological elements including temperature, humidity, air pressure and wind field from the background field, performing horizontal spatial interpolation and vertical spatial interpolation, and assigning each element to a derivative type required by the RTTOV mode one by one; calling the RTTOV mode built-in code, and calculating the earth surface emissivity and the reflectivity under the current assimilation condition; calling a built-in module of the RTTOV mode, and calculating the simulated bright temperature corresponding to the satellite load; and calculating an observation residual error simulating the bright temperature and the observed bright temperature, and storing the data of the observation residual error into a format required by the GSI system for outputting.
2. The method of assimilating wind-cloud satellite observation according to claim 1, wherein the performing data format conversion and data reading on the raw observation data to obtain initial conversion data comprises:
Reading the original observed data in the HDF5 format based on the FORTRAN language;
converting the original observation data into BUFR format to obtain format conversion data;
reading and carrying out preliminary preprocessing on the format conversion data to obtain initial conversion data; the preliminary pretreatment comprises screening of a preset target area range, a preset target time range and an observed brightness temperature numerical range, conversion of a preset angle to radian, longitude and latitude conversion, diagnosis of earth surface type and assignment of an array.
3. The method for assimilating wind-cloud satellite observation according to claim 1, wherein the preprocessing of the initial conversion data for quality control to obtain available observed bright temperature data comprises:
and performing earth surface type inspection, channel inspection, edge inspection, cloud/precipitation inspection and terrain height inspection on the initial conversion data to obtain available observation brightness temperature data.
4. A method of assimilating wind cloud satellite observations according to claim 3 wherein the earth type test comprises retaining observation data for observation points with earth classification identifications less than 4; the channel inspection comprises the steps of eliminating observation data corresponding to a first detection channel and/or reserving observation data corresponding to a second detection channel, wherein the first detection channel is different from the second detection channel; the edge detection comprises the step of removing the observation data of 8 observation points at the extreme edges of each of the two ends of each scanning line; the cloud/precipitation inspection comprises the steps of removing observation data of observation points with the bright temperature difference between the detection channel 1 and the detection channel 10 being greater than 4.0 and the observation data of the observation points with the liquid water content in the cloud being greater than 0.2; the terrain elevation inspection includes removing observation data of observation points of the detection channel 15 having a surface air pressure value of less than 800 hPa.
5. The method of assimilating wind-cloud satellite observation according to claim 1, wherein after obtaining observation residual data based on the simulated bright temperature data and the observed bright temperature data, the method of assimilating wind-cloud satellite observation further comprises:
performing deviation check on the observed bright temperature data based on the observed residual error data; the deviation test comprises the step of eliminating the observation data of the observation points meeting the first observation residual error requirement and/or the second observation residual error requirement, wherein the first observation residual error requirement is that the observation residual error value is larger than 15K, and the second observation residual error requirement is that the observation residual error value is larger than 3 times of the observation error standard deviation.
6. The method of assimilation of wind cloud satellite observation according to claim 1, wherein said numerical weather forecast pattern comprises an MPAS-se:Sup>A weather pattern, said wind cloud weather satellite comprises se:Sup>A wind cloud No. C star, and said microwave hygrometer comprises se:Sup>A type II microwave hygrometer MWHS-II.
7. The assimilation device for wind cloud satellite observation is characterized by being applied to a global lattice point statistical analysis system GSI system, and comprising:
the data acquisition module is used for acquiring original observation data of a microwave hygrometer in a background field and a weather satellite which are forecasted by the numerical weather forecast mode;
The conversion reading module is used for carrying out data format conversion and data reading on the original observed data to obtain initial conversion data;
the preprocessing module is used for carrying out quality control preprocessing on the initial conversion data to obtain available observation brightness temperature data;
the residual determination module is used for calling an RTTOV mode through a preset interface program, converting the background field into simulated bright temperature data, and obtaining observation residual data based on the simulated bright temperature data and the observed bright temperature data;
the data assimilation module is used for carrying out three-dimensional variation assimilation on the background field based on the observation residual data to obtain an optimized analysis field;
wherein the interface program includes: according to the mode design requirement of the RTTOV mode, designating attribute configuration when the RTTOV mode is called, and corresponding parameter setting in the GSI system to the derived type of the RTTOV mode; reading an optical thickness coefficient file corresponding to satellite loads, and correspondingly setting a plurality of satellite loads and multidimensional array dimensions under a plurality of profiles; reading two/three-dimensional meteorological elements including temperature, humidity, air pressure and wind field from the background field, performing horizontal spatial interpolation and vertical spatial interpolation, and assigning each element to a derivative type required by the RTTOV mode one by one; calling the RTTOV mode built-in code, and calculating the earth surface emissivity and the reflectivity under the current assimilation condition; calling a built-in module of the RTTOV mode, and calculating the simulated bright temperature corresponding to the satellite load; and calculating an observation residual error simulating the bright temperature and the observed bright temperature, and storing the data of the observation residual error into a format required by the GSI system for outputting.
8. An electronic device comprising a memory, a processor, the memory having stored thereon a computer program executable on the processor, wherein the processor, when executing the computer program, implements the method of assimilating wind cloud satellite observation according to any of claims 1-6.
9. A storage medium having stored thereon a computer program, which when executed by a processor performs the method of assimilation of wind cloud satellite observation according to any of claims 1-6.
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