Air, land and space multi-source meteorological detection data fusion method and device and medium

Abstract

The application discloses a kind of sea-air-space multi-source meteorological detection data fusion method, device and medium, wherein method includes: satellite observation data is fed to data receiving end after quality control and format conversion;The airborne weather radar reflectivity factor, velocity, spectral width of airborne platform coordinate system is converted into geodetic coordinate system, and the data collected by the airborne platform converted into geodetic coordinate system and sounding data are fed to data receiving end after quality control and format conversion;The data detected by detection equipment is fed to data receiving end after quality control and format conversion;Meteorological sensor data is fed to data receiving end after quality control and format conversion.The application improves the use efficiency of multiple waveband detection data;At the same time, "point, line, surface, body" scanning data is converted into more valuable three-dimensional grid observation data.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of meteorological detection in the field of meteorology, more particularly to a space-air multi-source meteorological detection data fusion method, device and medium. BACKGROUND

[0002] With the rapid development of meteorological observation system, more and more observation data obtained by ground automatic weather stations, radars, satellites and the like are used, and the quality of simulation data of multiple numerical modes is continuously improved. Meanwhile, the requirements of various industries for grid-based time and space continuous meteorological data products are higher and higher. It is an effective means to obtain high-precision, high-quality, time and space continuous multi-source data fusion meteorological grid products by using data fusion and data assimilation technology to integrate multiple source observation data and multi-mode simulation data.

[0003] The U.S. NOAA Earth System Research Laboratory has developed a local analysis and prediction system (LAPS), which can obtain three-dimensional cloud fusion grid data by fusing numerical prediction products, ground, sounding, radar, geostationary meteorological satellite, GPS / MET, wind profile radar, aircraft and other multi-source observation data, and provide a better initial field for numerical prediction mode and improve the short-term prediction level of numerical prediction mode. Some other institutions have also developed similar three-dimensional cloud fusion systems, such as ARPS (Advanced Regional Prediction System) of the United States, RUC (Rapid Update Cycle), INCA (Integrated Nowcasting through Comprehensive Analysis) of Austria, etc. (Table 1). In recent years, the United States has developed a GSI-Cloud cloud analysis module by embedding ARPS and RUC in the current operational assimilation system (GSI), which realizes the function of cloud analysis and can provide an initial field containing higher precision cloud information for numerical prediction mode, which means that three-dimensional cloud fusion will become an important module of the assimilation system.

[0004] Table 1 Overview of foreign fusion numerical modes

[0005]

[0006]

[0007] In 2014, the China Meteorological Administration launched the National Meteorological Science and Technology Innovation Project "Quality Control of Meteorological Data and Multi-source Data Fusion and Reanalysis" (hereinafter referred to as "Innovation Project"), one of the research tasks of which is to develop high-quality multi-source data fusion products of land, ocean and three-dimensional cloud and related technologies. Based on the Innovation Project, the China Meteorological Administration National Meteorological Information Center has built a series of operational fusion systems, including the CMALand Data Assimilaton System (CLDAS) and the CMA Multi-source Precipitation Analysis System (CMPAS) in the Asian region, the CMA Ocean Data Analysis System-SST (CODAS-SST) and the 3D Cloud Analysis System (3DCloudAS) in the Chinese region, by introducing international advanced fusion technologies, digesting and absorbing them and conducting independent innovation. In 2017, the weather forecasting business of the China Meteorological Administration was upgraded from the original station forecast to the intelligent grid forecast, and a series of multi-source data fusion products (including temperature, precipitation, humidity, wind, total cloud cover, visibility, etc.) have been applied to the intelligent grid forecast business by optimizing the product timeliness and adjusting the grid. In addition, a series of products produced by the China Meteorological Administration Land Data Assimilation System (CLDAS), including ground meteorological elements, soil temperature and humidity, runoff, evapotranspiration, etc., are also applied to the China Meteorological Administration's smart agriculture business.

[0008] The disadvantages of the above fusion systems are:

[0009] 1. Several foreign fusion systems are only suitable for data fusion of their own national equipment, and these systems cannot identify the data format of China's meteorological detection equipment, and the internal data quality control module is also not suitable for the data quality control of China's meteorological detection equipment.

[0010] 2. At present, the domestic fusion system is only modified for the fusion of automatic weather stations, conventional weather radars and satellite data, and some new types of meteorological detection equipment such as laser wind lidar, laser fog lidar, millimeter wave cloud radar, millimeter wave wind lidar and airborne weather radar detection data cannot be identified and fused. SUMMARY

[0011] The present application is provided to solve the above problems in the prior art. Therefore, a sea-air-space multi-source meteorological detection data fusion method, device and medium are needed to solve the problem that the data of new types of meteorological detection equipment such as laser wind lidar, laser fog lidar, millimeter wave cloud radar, millimeter wave wind lidar and airborne weather radar cannot be recognized and processed by existing fusion systems.

[0012] According to a first aspect of the present application, a method for fusing multi-source meteorological detection data in space, land, air and sky is provided, and the method comprises:

[0013] After quality control of satellite observation data, format conversion is performed, and the converted data is fed to a data receiving end;

[0014] The airborne platform coordinate system of the reflectivity factor, velocity and spectral width of the airborne meteorological radar is converted into a geodetic coordinate system, and the data collected by the airborne platform converted into the geodetic coordinate system and the sounding data are subjected to quality control and format conversion and then fed to the data receiving end;

[0015] After quality control and format conversion of the data detected by the detection equipment, the converted data is fed to the data receiving end, and the detection equipment at least includes one of a ground automatic meteorological observation station, an X-band weather radar, an S-band weather radar, a C-band weather radar, a P-band wind profile radar, an L-band wind profile radar, a laser wind measuring radar, a laser fog measuring radar, a millimeter wave cloud radar and a millimeter wave wind measuring radar;

[0016] After quality control and format conversion of the meteorological sensor data, the converted data is fed to the data receiving end, and the meteorological sensor data includes data collected by meteorological sensors arranged on an automatic weather station, a buoy and / or a ship.

[0017] Further, the satellite observation data is subjected to quality control by the following method:

[0018] Topographic test: satellite observation data with land, sea ice, snow and mixed surface is removed;

[0019] Extreme value test: satellite observation data with a brightness temperature less than 70K or greater than 320K is removed;

[0020] Threshold test: satellite observation data with a brightness temperature standard deviation greater than 6K is removed, and satellite observation data with an observed brightness temperature minus a simulated brightness temperature greater than 3.5K is removed.

[0021] Further, the airborne platform coordinate system of the reflectivity factor, velocity and spectral width of the airborne meteorological radar is converted into a geodetic coordinate system by the following method:

[0022] Real-time acquisition of attitude information of the airborne platform collected by an attitude sensor, the attitude information including a pitch angle, a yaw angle and a roll angle;

[0023] In the case of shaking or attitude change during flight, the attitude change is determined according to the real-time acquired attitude information, and the coordinate system of the airborne platform is adjusted to keep the antenna stationary relative to the earth;

[0024] Real-time acquisition of the aircraft's operating parameters, including position, speed and acceleration, measured relative to the geodetic coordinate system;

[0025] According to the operating parameters of the aircraft and the attitude information of the airborne platform, the coordinates of the airborne platform are converted into the geodetic coordinate system.

[0026] Further, the data collected by the airborne platform converted into the geodetic coordinate system and the sounding data are subjected to quality control, including:

[0027] The data collected by the airborne platform converted into the geodetic coordinate system and the sounding data are subjected to the steps of ground clutter suppression, velocity ambiguity resolution, data consistency evaluation, noise and interference removal, clutter and false echo filtering, atmospheric attenuation correction, and error correction and calibration;

[0028] The step of ground clutter suppression includes:

[0029] Using space-time two-dimensional adaptive processing and / or adopting digital signal processing technology to filter or suppress fixed target clutter and slow target clutter;

[0030] The step of velocity ambiguity resolution includes:

[0031] Designing staggered double waveforms A and B, the maximum unambiguous velocities of the waveforms A and B being different and coprime, and in the case that the ambiguous velocities of the same target velocity on the A wave and the B wave are the same, the target velocity is determined to be the real target velocity;

[0032] Or in the target tracking process, according to the target multi-frame correlation, the actual displacement of the target is determined, and the displacement under multiple ambiguous velocities is matched, and the ambiguous velocity with the highest matching degree is taken as the real target velocity;

[0033] The step of data consistency evaluation includes:

[0034] Comparing the radar echo intensities at different altitudes and different time points to ensure the consistency of the data in time and space;

[0035] The step of noise and interference removal includes:

[0036] Using filters and / or smoothing algorithms to remove the effects of electromagnetic interference and random noise;

[0037] The step of clutter and false echo filtering includes:

[0038] Removing the clutter and false echoes caused by ground objects, flying birds and insects included in the radar data;

[0039] The step of atmospheric attenuation correction includes:

[0040] The atmospheric attenuation model or the measured data is used to correct the atmospheric attenuation of the radar data.

[0041] The error correction and calibration steps include:

[0042] The systematic error of the radar equipment is corrected, and the radar data is calibrated to ensure that the radar data at different times and different places has unified scale and standard; the systematic error of the radar equipment includes antenna pointing error and distance error.

[0043] Further, the meteorological sensor data is quality controlled by the following method:

[0044] Data acquisition verification includes using verification mechanisms during data acquisition, including sensor calibration, hardware device state monitoring, and data acquisition software stability testing;

[0045] After data acquisition, data preprocessing is performed, including denoising, smoothing, standardization and normalization operations;

[0046] Data integrity check includes determining whether the data record is complete, whether the time stamp is accurate, whether the data is missing and abnormal, etc.

[0047] Data outlier identification includes using statistical methods, comparative analysis methods or expert experience methods to identify outliers inconsistent with conventional meteorological data;

[0048] Data consistency analysis includes comparing data between different sensors or different observation sites to check data consistency;

[0049] In the case of identifying outliers or inconsistent data, data correction and correction are performed, wherein the correction method includes using historical data, adjacent site data or algorithm model for interpolation, smoothing or replacement, and the correction method includes hardware replacement, software upgrade or re-observation for sensor failure or operation error;

[0050] Data storage management includes storing data in a predetermined data storage format, storage medium, backup strategy and security measures;

[0051] Data quality evaluation includes determining the overall quality level of the data according to the completeness, accuracy, consistency and availability of the evaluation data.

[0052] Further, the data detected by the X-band weather radar, S-band weather radar and C-band weather radar is quality controlled by the following method:

[0053] The ground clutter suppression processing includes one or more of digital signal processing, spatial filtering, MTI filter, adaptive frequency domain filter and clutter mitigation decision processing steps, and in the case of using multiple processing steps, the results obtained by the multiple processing steps are compared and the optimal processing result is selected,

[0054] The digital signal processing step includes processing the radar echo signal based on median filtering, mean filtering and adaptive filtering to filter out or suppress fixed target clutter and slow target clutter and retain the echo of moving targets.

[0055] The spatial filtering processing step includes spatial filtering of the echo signals received by multiple antennas to suppress ground clutter from specific directions, including echoes from the sky and the ground.

[0056] The MTI filter processing step includes using an MTI filter to eliminate fixed target echoes and slow-moving clutter by subtracting the pulses in adjacent repetition periods, thereby retaining the echo of moving targets.

[0057] The adaptive frequency domain filter processing step includes using an adaptive frequency domain filter to adaptively determine the filtering position by analyzing the shape of the spectrum and to restore the filtered weather signals by a Gaussian fitting method.

[0058] The clutter mitigation decision processing step includes combining the adaptive filter and the CMD algorithm to improve the data quality of the weather radar.

[0059] In the case where the target speed observed by the radar exceeds the maximum unambiguous speed of the radar, a speed ambiguity resolution processing is performed, which includes one or more of Doppler shift compensation, phase coding and repetition frequency diversity.

[0060] The Doppler shift compensation includes measuring the Doppler shift of the target and calculating the speed of the target according to the radar system parameters to eliminate the error caused by speed ambiguity.

[0061] The phase coding includes adding a set of phase coding sequences to the transmitted signal to enable the radar to distinguish different speed echoes and solve the speed ambiguity problem.

[0062] The repetition frequency diversity includes using multiple different repetition frequencies to enable the radar to receive multiple echo signals of different speeds at the same time, thereby eliminating speed ambiguity.

[0063] In the case where the radar uses a pulse repetition frequency higher than a set value to cause range folding, random phase coding, adjusting the pulse repetition frequency, using an elevation higher than the current elevation for scanning or transforming the detection site are used.

[0064] The adjusting pulse repetition frequency comprises changing the maximum detection distance of the radar by changing the pulse repetition frequency, so as to eliminate the distance folding in the region of interest.

[0065] Further, the data detected by the P-band wind profile radar, the L-band wind profile radar and the millimeter wave radar are quality controlled by ground clutter suppression, median filtering or smoothing filtering,

[0066] The ground clutter suppression comprises: designing an IIR notch filter according to a clutter map, and then linearly interpolating between two edge points of the notch;

[0067] The median filtering comprises: determining a neighborhood of a center point of data to be processed; sorting the values of each data in the neighborhood, and taking the middle value as the new value of the center point data; and smoothing the data by using the median filtering when the window moves in time and height;

[0068] The smoothing filtering comprises: taking an observation object as Y, a corresponding independent variable of Y as X, Δx as the interval of the independent variable X, the independent variables as xi (i=1, 2, 3, …, n) which are distributed at equal intervals, obtaining an observation data sequence yi (i=1, 2, 3, …, n) according to the value of the dependent variable Y, and filtering the noise components in the observation data sequence by using the sliding smoothing, wherein the filtering the noise components in the observation data sequence by using the sliding smoothing comprises: taking the data of the i-th point and several points near the i-th point, determining a fitting straight line equation according to the least square principle, and calculating the dependent variable of the i-th point as the data value after the sliding smoothing based on the straight line equation.

[0069] Further, the data detected by the laser wind radar are quality controlled by the following method:

[0070] Processing of noise and missing values:

[0071] An M*N sliding window is selected, the data in the window is equally divided into P intervals, △d is the interval interval, and the wind speed value of the center point of the window is V ij , the P+1th interval is represented as V ij ±△d / 2;

[0072] The distribution frequency (X1, X2, X3...X P ) of the wind speed values in the M*N window in different intervals and X' are counted, X MAX is the maximum value in X1, X2, X3...X P and X', VP is taken as the middle value of the interval corresponding to X MAX .

[0073] Let the new value of the window center point be V ij The noise elimination and missing data filling are realized by the following formula:

[0074]

[0075] In the formula, K1 is the discrimination threshold of whether to eliminate noise, and K2 is the discrimination threshold of whether to fill in missing data. When the original value V ij of the window center point is not 0, it represents that this time is an effective echo point. When X'<= threshold K1, the point is eliminated. When X'> threshold K1, the corresponding point is retained. When the original value V ij of the window is equal to 0, it is an invalid point. If X MAX >= threshold K2, filling is performed, and the center point VP of the corresponding interval of XMAX is assigned to V ij . If X MAX < threshold K2, no filling is performed.

[0076] The data detected by the laser fog detection radar is quality controlled by the following method:

[0077] Dead time correction, and the calculation formula is:

[0078]

[0079] Wherein, N represents the true count rate, M represents the measured count rate, and t represents the time resolution.

[0080] Geometric overlap factor correction, and the calculation formula is:

[0081]

[0082] Wherein, P(R, λ) is the original echo signal; P'(R, λ) is the signal after geometric overlap factor correction; R is the height from the radar; λ is the laser emission wavelength, and O(R) is the geometric overlap factor.

[0083] Background noise deduction, and the calculation formula is:

[0084] P''(R, λ) = P(R, λ) - P bg (R, λ)

[0085] Wherein, P(R, λ) is the original echo signal; P''(R, λ) is the signal after background noise deduction, and P bg (R, λ) is the signal of background noise.

[0086] The echo signal is smoothed.

[0087] Cloud and aerosol classification: the slope of the corrected signal curve is calculated, and according to the positive or negative change of the slope value, the cloud layer or aerosol layer is determined;

[0088] Data quality check: according to the score of the RCS curve, the signal-to-noise ratio of the original echo signal, and the consistency with the atmospheric molecular signal fitting, the original echo data is checked for quality, and corresponding quality control identification code and quality control type code are given. The consistency with the atmospheric molecular signal fitting refers to that, in the region above the boundary layer, the aerosol content in the atmosphere is below the set threshold, in the case that the set echo signal is only related to the density of molecules, the molecular backscattering signal calculated according to the laser radar equation is consistent with the atmospheric molecular signal within the set error after taking log and distance square correction, and then it is determined to have consistency.

[0089] According to the second aspect of the present application, a sea-air-space multi-source weather detection data fusion device is provided, and the device comprises:

[0090] The first quality control module is configured to feed the satellite observation data after quality control and format conversion to the data receiving end;

[0091] The second quality control module is configured to convert the airborne weather radar reflectivity factor, speed and spectral width of the airborne platform coordinate system into the geodetic coordinate system, and perform quality control and format conversion on the data collected by the airborne platform converted into the geodetic coordinate system and the sounding data, and then feed the data to the data receiving end;

[0092] The third quality control module is configured to feed the data detected by the detection equipment to the data receiving end after quality control and format conversion, wherein the detection equipment at least includes one of a ground automatic weather observation station, an X-band weather radar, an S-band weather radar, a C-band weather radar, a P-band wind profile radar, an L-band wind profile radar, a laser wind measuring radar, a laser fog measuring radar, a millimeter wave cloud radar, and a millimeter wave wind measuring radar;

[0093] The fourth quality control module is configured to feed the meteorological sensor data to the data receiving end after quality control and format conversion, wherein the meteorological sensor data includes data collected by meteorological sensors arranged on an automatic weather station, a buoy and / or a ship.

[0094] According to the third aspect of the present application, a readable storage medium is provided, and the readable storage medium stores one or more programs which can be executed by one or more processors to implement the method as described above.

[0095] The present application has at least the following beneficial effects:

[0096] 1. Added the reading interface, data conversion program, and quality control program for the laser wind radar radial velocity Vr and signal-to-noise ratio SNR data, so that the laser wind radar radial velocity Vr data can be imported into the LAPS numerical model.

[0097] 2. Add the reading interface, data conversion program and quality control program for the visibility VIS data of the laser fog radar, so that the two types of data of the laser fog radar can be imported into the small and medium-scale forecast numerical model.

[0098] 3. Add the reading interface, data conversion program, and quality control program of millimeter-wave cloud radar reflectivity factor data so that millimeter-wave cloud radar reflectivity factor data can be imported into small and medium-scale forecast numerical models.

[0099] 4. Add the millimeter-wave wind radar velocity data reading interface, data conversion program, and quality control program so that the millimeter-wave wind radar velocity data can be imported into the small and medium-scale forecast numerical model.

[0100] 5. Add the reading interface, data conversion program and quality control program of airborne meteorological radar reflectivity factor and radial velocity data, so that the airborne meteorological radar reflectivity factor and radial velocity data can be imported into the small and medium-scale forecast numerical model.

[0101] 6. Overall, the present invention realizes the numerical fusion of observation data from various meteorological detection equipment on land, sea, air and space rather than a simple "optimal selection" of multi-source observation data; improves the efficiency of using detection data of multiple bands (X, S, C, L, P, millimeter wave, laser); at the same time, it converts "point, line, surface, and volume" scanning data into more valuable three-dimensional grid observation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 A schematic diagram showing the structure and working principle of a multi-source meteorological detection data fusion system for land, sea, air and space according to an embodiment of the present invention is shown.

[0103] Figure 2 The figure shows an overall flow chart of a method for fusing multi-source meteorological detection data from land, sea, air and space according to an embodiment of the present invention.

[0104] Figure 3 A flow chart of quality control of satellite observation data according to an embodiment of the present invention is shown;

[0105] Figure 4 A flow chart showing the conversion of airborne platform coordinates into geodetic coordinates according to an embodiment of the present invention is shown;

[0106] Figure 5 A flow chart of airborne weather radar data quality control according to an embodiment of the present invention is shown;

[0107] Figure 6 A flow chart of automatic weather station / buoy / boat weather sensor data quality control according to an embodiment of the application is shown;

[0108] Figure 7 A flow chart of X / S / C wave band weather radar data quality control according to an embodiment of the application is shown;

[0109] Figure 8 A flow chart of L / P wave band wind profile radar / millimeter wave wind radar data quality control according to an embodiment of the application is shown;

[0110] Figure 9 A flow chart of laser fog radar data quality control according to an embodiment of the application is shown;

[0111] Figure 10 A structural diagram of a sea-air-space multi-source meteorological detection data fusion device according to an embodiment of the application is shown. DETAILED DESCRIPTION

[0112] To make the skilled in the art better understand the technical solutions of the present application, the present application will be described in detail below in combination with the drawings and specific embodiments. The embodiments of the present application will be further described in detail below in combination with the drawings and specific embodiments, but not as a limitation on the present application. The order in which each step is described herein as an example should not be considered as a limitation, and those skilled in the art should know that the order can be adjusted, as long as the logic between them is not destroyed and the whole process cannot be realized.

[0113] The embodiment of the present application provides a sea-air-space multi-source meteorological detection data fusion system, the structure of which is shown in Figure 1 The system can collect, quality control and fuse the detection data of marine meteorological and hydrological instruments, marine buoys, marine weather radars, land X wave band weather radars, S wave band weather radars, cloud radars, three-dimensional laser wind radars, automatic weather stations, air-borne weather radar detection data, sounding data, and data from Fengyun satellites and HJ-1 satellites, to obtain three-dimensional wind analysis field, three-dimensional cloud analysis field, three-dimensional water vapor analysis field, three-dimensional temperature analysis field and two-dimensional ground analysis field, and other meteorological element information, and truly realize the fusion of data from various different wave bands and different detection principles of meteorological equipment.

[0114] The embodiment of the present application also provides a sea-air-space multi-source meteorological detection data fusion method, which can be realized based on the fusion system shown in Figure 1 Further as shown in Figure 2As shown in the figure, it is a flow chart of the method for fusing multi-source meteorological detection data in sea-air-land-space, which comprises the following steps S100-S400, and the details are as follows:

[0115] Step S100, after quality control of satellite observation data, format conversion is performed and then the data is fed to a data receiving end.

[0116] Step S200, the reflectivity factor, speed and spectral width of the airborne meteorological radar in the airborne platform coordinate system are converted into the geodetic coordinate system, the data collected by the airborne platform converted into the geodetic coordinate system and the sounding data are subjected to quality control and format conversion and then fed to the data receiving end.

[0117] Step S300, after quality control and format conversion of the data detected by the detection equipment, the data is fed to the data receiving end, wherein the detection equipment at least comprises one of a ground automatic meteorological observation station, an X-band weather radar, an S-band weather radar, a C-band weather radar, a P-band wind profile radar, an L-band wind profile radar, a laser wind detection radar, a laser fog detection radar, a millimeter wave cloud radar and a millimeter wave wind detection radar.

[0118] Step S400, after quality control and format conversion of the meteorological sensor data, the data is fed to the data receiving end, wherein the meteorological sensor data comprises the data collected by the meteorological sensor arranged on the automatic weather station, the buoy and / or the ship.

[0119] It should be noted that the data receiving end mentioned above can be a small-scale numerical model LAPS / STMAS or ARPS, and steps S100-S400 are parallel steps in implementation, i.e., the sequence of the steps is only one of the implementation sequences, and in other implementation manners, the execution sequence of steps S100-S400 can be arbitrary, and each step is used for quality control and format conversion of different data, i.e., the observation data of various meteorological detection equipment in sea-air-land-space is subjected to quality control and format conversion and then fed into the small-scale numerical model LAPS / STMAS or ARPS, and then three-dimensional wind field, three-dimensional temperature field, three-dimensional cloud analysis field, three-dimensional water vapor analysis field and two-dimensional ground analysis field are obtained, i.e., the acquisition of basic meteorological elements from multi-source detection equipment observation data is completed, and the conversion of "point, line and surface" observation data into three-dimensional observation data is completed.

[0120] The embodiment of the present application will be described in detail below.

[0121] As shown in the figure, the quality control of satellite observation data is realized by the following steps: Figure 3

[0122] Step S301, terrain verification: the observation data of the land surface being land, sea ice, snow and mixed type are removed.​

[0123] Step S302, Extreme Value Test: Remove data with brightness temperature less than 70K or greater than 320K.

[0124] Step S303, Threshold Test: Remove data with brightness temperature standard deviation greater than 6K, and remove data with observed brightness temperature minus simulated brightness temperature greater than 3.5K.

[0125] Step S304, System Bias Correction: These errors are often comparable to the typical errors of the numerical model's short-term forecast of the atmospheric temperature field, so corresponding bias correction must be performed before the satellite observation data is assimilated using the variational method. Bias correction can be divided into two categories: one is the scanning bias correction due to the satellite itself, and the other is the air mass bias correction due to the change of atmospheric transmittance itself.

[0126] As shown in Figure 4 , the conversion of airborne platform coordinates to geodetic coordinates mainly relies on the attitude sensors and navigation equipment in the airborne device. The conversion steps are as follows:

[0127] Step S401, the airborne platform first obtains its attitude information such as pitch angle, yaw angle and roll angle through its embedded attitude sensors. These information describes the direction and attitude of the aircraft relative to the geodetic coordinate system.

[0128] Step S402, when the aircraft shakes or changes its attitude during flight, the attitude sensor will monitor these changes in real time and adjust the coordinate system of the airborne platform to ensure that the antenna can remain stationary relative to the earth.

[0129] Step S403, the navigation equipment on the aircraft monitors the aircraft's operating parameters such as position, speed and acceleration in real time. These parameters are measured relative to the geodetic coordinate system.

[0130] Step S404, the navigation equipment converts the coordinates of the airborne platform according to the aircraft's operating parameters and the data of the attitude sensor. The purpose of the conversion is to convert the coordinates of the airborne platform to the coordinates in the geodetic coordinate system, so as to align and compare with the ground or other reference system.

[0131] It should be noted that the accuracy and precision of coordinate conversion are affected by many factors such as sensor accuracy, navigation equipment performance and data processing algorithms. Therefore, when performing coordinate conversion, appropriate sensors and navigation equipment should be selected, and appropriate algorithms and techniques should be used to ensure the accuracy and reliability of the conversion results.

[0132] As shown in Figure 5 , the steps of airborne weather radar data quality control are as follows:

[0133] Step S501, ground clutter suppression:

[0134] 1) One method is to use space-time two-dimensional adaptive processing (STAP). Adaptive processing can achieve effective matching with complex external environment, and can compensate for the influence of system error to a certain extent, thereby greatly improving the performance of the system. However, adaptive processing often requires the system to be flexible to form multiple beams and to calculate adaptive weights in real time, which involves a large amount of computation.

[0135] In order to reduce the amount of computation, digital beamforming (DBF) technology and very large scale integrated circuits (VLSI) can be used. The development of these technologies provides a guarantee for precise control of space-time adaptive weights and faster processing speed, thereby providing favorable conditions for the practical application of space-time two-dimensional adaptive processing.

[0136] 2) The suppression of clutter can also use digital signal processing technology to filter out or suppress fixed target clutter and slow target clutter, thereby retaining the echo of moving targets. This helps to improve the ability of the radar to detect targets in a strong clutter background.

[0137] Step S502, velocity ambiguity resolution.

[0138] There are two methods for velocity ambiguity resolution of airborne weather radar:

[0139] 1) Design staggered double waveforms A and B, the maximum unambiguous velocities of the two waveforms are different and prime to each other, then the ambiguous velocities of the same target velocity on A wave and B wave are different, and the same is the real target velocity. This method can be solved by using the residue theorem.

[0140] 2) When detecting, do not do velocity ambiguity resolution, but put it into target tracking. According to the correlation of multiple frames of target, determine the actual displacement of the target, and match the displacement under multiple ambiguous velocities, and the highest matching degree is the real target velocity.

[0141] Step S503, data consistency evaluation.

[0142] Data consistency evaluation mainly checks whether the radar data from different sources or different time points is contradictory or inconsistent. This requires comparative analysis of the data, such as comparing the radar echo intensity at different altitudes and different time points, to ensure the consistency of the data in time and space.

[0143] Step S504, noise and interference removal.

[0144] There are often various noises and interferences in radar data, such as electromagnetic interference, random noise, etc. These noises and interferences will affect the accuracy and reliability of the radar data. Therefore, it is necessary to remove or reduce the influence of these noises and interferences through filters, smoothing algorithms, etc.

[0145] Step S505, clutter and false echo filtering.

[0146] Radar data may contain clutter and false echoes caused by ground objects, birds, insects, etc. These clutter and false echoes can interfere with the detection and identification of weather targets by radar. Therefore, it is necessary to filter out these clutter and false echoes through appropriate algorithms and techniques to improve the quality of radar data.

[0147] Step S506, atmospheric attenuation correction.

[0148] Radar signals can be attenuated by the atmosphere during transmission, resulting in a decrease in radar echo intensity. Therefore, atmospheric attenuation correction is needed for radar data to restore the true intensity of radar echoes. This usually requires correction through atmospheric attenuation models or measured data.

[0149] Step S507, error correction and calibration.

[0150] Error correction and calibration is the last step of radar data quality control. This includes correcting systematic errors of radar equipment, such as antenna pointing error, distance error, etc., and calibrating radar data to ensure uniform scale and standard of radar data at different times and different places. Through error correction and calibration, the accuracy and reliability of radar data can be further improved.

[0151] As shown in Figure 6 , automatic weather station / buoy / boat weather sensor data quality control includes the following steps:

[0152] Step S601, data collection verification.

[0153] Data collection is the primary task of automatic weather stations / buoys / boats weather sensors, and ensuring the accuracy of collected data is the basis of data quality control. In the data collection process, verification mechanisms include sensor calibration, hardware device state monitoring, data collection software stability testing, etc., to ensure the reliability of observation data at the source.

[0154] Step S602, data preprocessing.

[0155] After data collection, data preprocessing is necessary. Preprocessing includes denoising, smoothing, standardization and normalization, etc. to eliminate or reduce non-weather factor interference in data, such as sensor noise, electromagnetic interference, etc.

[0156] Step S603, data integrity check.

[0157] Data integrity check is a systematic and comprehensive check on observation data, including whether the data records are complete, whether the timestamps are accurate, data missing and abnormal situations. If the data is found to be incomplete, it needs to be supplemented or marked in time.

[0158] Step S604, data outlier identification.

[0159] Outlier identification is to identify outliers that do not conform to normal meteorological data through statistical methods, comparative analysis methods or expert experience. These outliers may be caused by sensor failure, environmental factor interference or human operation error, etc.

[0160] Step S605, data consistency analysis.

[0161] Data consistency analysis is to check whether the data is consistent by comparing the data between different sensors or different observation sites. This analysis helps to find inconsistencies and potential errors in the data.

[0162] Step S606, data correction and modification.

[0163] After identifying outliers or inconsistent data, data correction and modification are needed. Correction methods include using historical data, neighboring site data or algorithm models for interpolation, smoothing or replacement. Modification is to replace the hardware, upgrade the software or re-observe for sensor failure or operation error.

[0164] Step S607, data storage management.

[0165] Data storage management involves data storage format, storage medium, backup strategy and security measures, etc. Efficient and stable data storage solutions should be selected to ensure long-term preservation and easy retrieval of data. At the same time, data should be backed up regularly to prevent data loss or damage.

[0166] Step S608, data quality assessment.

[0167] Data quality assessment is a summary and feedback of the entire data quality control process. Through the assessment of data integrity, accuracy, consistency and usability, etc., the overall quality level of the data is understood. At the same time, according to the evaluation results, the data quality control method is continuously optimized and improved to improve the reliability and accuracy of the data.

[0168] In summary, automatic weather station / buoy / ship weather sensor data quality control is a systematic and comprehensive work involving multiple aspects and links. Through the implementation of effective quality control measures, the quality of observation data can be improved to provide more accurate and reliable data support for meteorological forecasting, climate research and other fields.

[0169] X / S / C band weather radar data quality control is an important step to ensure the accuracy and reliability of radar data. As shown in Figure 7 X / S / C band weather radar data quality control includes the following steps:

[0170] Step S701, ground clutter suppression.

[0171] Digital signal processing technology: Through digital signal processing algorithms such as median filtering, mean filtering, adaptive filtering, etc., the radar echo signal is processed to filter out or suppress fixed target clutter and slow target clutter, and to retain the echo of moving targets. This method can effectively reduce the interference of ground clutter in the signal processing stage.

[0172] Spatial filtering technology: This technology uses the echo signals received by multiple antennas to perform spatial filtering to suppress ground clutter from a specific direction. This method can effectively distinguish between sky and ground echoes, further improving the target detection accuracy of the radar.

[0173] MTI filter: MTI filter, i.e. moving target indication filter, is a radar clutter suppression method. It subtracts the pulses in adjacent repeated periods to eliminate fixed target echoes and slow-moving clutter, thus retaining the echo of moving targets. MTI filter can be divided into single delay line canceller, double delay line canceller and multiple delay line canceller, with performance improving in turn, but the structure complexity also increases accordingly.

[0174] Adaptive frequency domain filter: Adaptive frequency domain filter analyzes the shape of the spectrum to adaptively determine where to filter, and restores the filtered weather signal through Gaussian fitting and other methods. This method can filter out clutter while preserving useful weather information to the greatest extent.

[0175] Clutter mitigation decision algorithm (CMD): CMD algorithm is an algorithm for identifying ground clutter, which can effectively identify and suppress ground clutter. By combining adaptive filter and CMD algorithm, the data quality of X-band weather radar can be further improved.

[0176] Step S702, velocity dealiasing.

[0177] When the target velocity observed by the radar exceeds the maximum unambiguous velocity of the radar, velocity aliasing occurs, causing the radar to be unable to accurately measure the target velocity.

[0178] The basic principle of velocity dealiasing technology is to process the radar echo signal to eliminate the error caused by velocity aliasing. The specific implementation method can use Doppler shift compensation, phase coding, repetition frequency diversity and other technical means.

[0179] Doppler shift compensation is a method of resolving velocity ambiguity. It measures the amount of Doppler shift of the target and calculates the velocity of the target based on the radar system parameters, thereby eliminating the error caused by velocity ambiguity. Phase coding technology adds a specific phase coding sequence to the transmitted signal, allowing the radar to distinguish different velocity echoes and thus solve the velocity ambiguity problem. Repetition frequency diversity technology uses multiple different repetition frequencies to allow the radar to receive multiple echo signals of different velocities simultaneously, thereby eliminating velocity ambiguity.

[0180] Step S703, range folding resolution.

[0181] Meteorological radar range folding resolution is the process of eliminating range folding by the radar signal processor. When the radar uses a high pulse repetition frequency (PRF), its maximum detection range will be shortened. In this case, the radar may receive a strong echo of a target outside its maximum detection range and mistakenly identify it as being within its maximum detection range, resulting in range folding.

[0182] To eliminate this range folding phenomenon, the radar signal processor uses a series of technical means, such as random phase coding technology, adjusting the pulse repetition frequency (PRF), selecting a higher elevation scan, or changing the detection location. The purpose of these methods is to change the maximum detection range (Rmax) of the radar, thereby eliminating the range folding phenomenon in the area of interest.

[0183] Among them, adjusting the pulse repetition frequency (PRF) can change the maximum detection range (Rmax) of the radar by changing the PRF, thereby eliminating range folding in the area of interest. In addition, selecting a higher elevation scan can also help overcome the range folding problem, because a higher elevation will result in a shorter detection range for the radar, thereby reducing the possibility of range folding.

[0184] It should be noted that the implementation of the range folding resolution technique needs to consider various factors such as radar system parameters, target characteristics, and environmental conditions. Therefore, in actual application, the appropriate range folding resolution method needs to be selected according to the specific situation, and appropriate optimization and adjustment need to be made to achieve the best effect.

[0185] The data quality control methods for L-band wind profile radar, P-band wind profile radar, and millimeter wave wind measurement radar (vertical profile scanning mode) are the same. As shown in FIG. 1, the data quality control for L / P-band wind profile radar / millimeter wave wind measurement radar includes the following steps: Figure 8

[0186] Step S801, ground clutter suppression.

[0187] ​According to the clutter map, the IIR notch filter is designed, and then linear interpolation is performed between the two edge points of the notch, which can compensate for the loss of the atmospheric echo signal spectrum caused by the removal of spectral components. However, in some cases, when the spectrum of the atmospheric echo signal is very narrow and close to the spectrum of the ground clutter, selecting a wide filter will still attenuate some atmospheric echo signals. And sometimes, the atmospheric signal is completely covered by the ground clutter interference, and the atmospheric signal will be lost by using this method.

[0188] Step S802, median filtering.

[0189] Median filtering is a kind of nonlinear smoothing filtering method. It is a kind of neighborhood operation, similar to convolution, but not weighted sum calculation. Its main principle is: first, determine a neighborhood around a certain center point of the data to be processed; then sort the values of each data in the neighborhood, and take the middle value as the new value of the center point data, where the neighborhood is usually referred to as a window; when the window moves along time and height, the data can be well smoothed by using the median filtering algorithm. In this paper, the detection data at the same height is first filtered by median filtering along time, and then the detection data at the same time is filtered along height.

[0190] This algorithm checks the spatial and temporal continuity of the data, and it is effective based on the assumption that the ratio of outliers is small, so its disadvantage is that if there are a large number of continuous outliers in the data, this algorithm will consider these outliers as valid.

[0191] Step S803, smoothing filtering.

[0192] Sliding average filtering is a kind of linear sliding smoothing method. Let the observation object be Y, and the corresponding independent variable be X, with Δx as the interval of the independent variable X, and the independent variables x1, x2, …, xi, …, xn are distributed at equal intervals. The observation data sequence obtained by observing the dependent variable Y at these values is y1, y2, …, yi, …, yn. The noise component in yi (i = 1, 2, 3, …, n) can be filtered out by applying sliding smoothing. The principle is: take the data of the ith point and its nearby points, determine a fitting straight line equation according to the least square principle, and then calculate the dependent variable of the ith point as the data value after sliding smoothing. Generally speaking, linear sliding smoothing has three-point linear sliding smoothing, five-point linear sliding smoothing, seven-point linear sliding smoothing and m-point linear sliding smoothing. Generally speaking, the larger the value of m, the stronger the suppression of high-frequency noise.

[0193] The data quality control of the laser wind measurement radar is realized by the following scheme a or scheme b:

[0194] a. Processing of missing values

[0195] There are noise and missing data in radar-based data. Common noise includes isolated points, singular points, and radial strip clutter, which are mostly caused by pulse noise and Gaussian noise from aircraft, bird flocks, and radio interference. In addition, it also includes clutter that has not been completely removed by the radar signal processor. Missing values include missing points, missing small blocks, and missing radial. These missing and noise will affect the subsequent radar algorithm to some extent, so it is necessary to process them first. Common noise removal and missing data filling methods include median filtering and moving average. These algorithms lack an effective threshold to measure the scale of removal or filling. When there are many noises around the missing value, filling will cause the noise range to expand. When the data judged as valid may be noise itself, the noise cannot be effectively removed. In this embodiment, a statistical algorithm is used to process the velocity field data, and the specific steps are as follows:

[0196] (1) Select a sliding window of M*N, equally divide the data size in the window into P intervals, △d is the interval interval, and the wind speed value of the window center point is V ij , the P+1 interval is represented as V ij ±△d / 2.

[0197] (2) The distribution frequency (X1, X2, X3...X P ) and X′ of the wind speed value in the M*N window in different intervals is counted, and X MAX is the maximum value in X1, X2, X3...X P and X′, and VP is the middle value of the interval corresponding to X MAX .

[0198] (3) Let the new value of the window center point be V ij , then the noise removal and missing data filling can be realized as follows, K1 is the threshold for judging whether to remove noise, K2 is the threshold for judging whether to fill missing data, and "0" is used instead of no echo. That is, when the original value V ij of the window center point is not 0, it represents an effective echo point. When X′<= threshold K1, it means that there are few echo points with similar values around the window center point, so the point is a singular point and needs to be removed. When X′> threshold K1, it means that there are many echo points with similar values around the window center point, so the point is a normal point in the window and needs to be retained. When the original value V ij of the window is equal to 0, it is an invalid point, and it needs to be judged whether it is a missing point and whether it needs to be filled. That is, when X MAX >= threshold K2, it means that the point appears frequently in the window, and there are many valid points around it, so it can be filled, and the center point VP of the interval corresponding to X MAX is assigned to V.ij ;X MAX <Threshold K2, indicating that there is no echo or few echo points in the window, and there are few effective points around it, so it is considered that the point is an invalid point and does not need to be filled.

[0199]

[0200] b. Median filtering.

[0201] Median filtering is a classic nonlinear signal processing technique, and its basic idea is mainly based on sorting theory to process data, which can effectively suppress noise interference while protecting the edges of the signal. The signal processing process is as follows: define a sliding window with an odd length, and replace the value of the center point of the window with the median value of the remaining points in the neighborhood of the point. Assuming that there is a set of atmospheric laser radar data y(n), the specific process of median filtering is as follows: define an initial sliding window with a length of (2w+1), then the output value y^(n) of the nth sampling point of the filtered laser radar data y^ can be calculated by the following formula:

[0202] y(n)=med[y(n-w),...,y(n),...,y(n+w)]

[0203] In the formula, med() is the median function. It can be seen that this filtering method is relatively simple and easy to implement. The filtering effect is closely related to the window size. If the window size is too small, there may be more noise residues in the signal, and the filtering effect is not obvious. But if the window size is too large, it may cause the signal to be too smooth, lose important detail information, and cause past noise. Therefore, based on the characteristic that the noise of the laser radar echo signal gradually increases with the increase of the distance, the selection of the window size is generally gradually increased.

[0204] As shown in Figure 9 , the laser fog radar data quality control includes the following steps:

[0205] Step S901, dead time correction.

[0206] The laser radar system is composed of a laser emitting system, an optical receiving system, a photoelectric conversion and data acquisition system, a signal processing system, etc. In the avalanche diode of the photoelectric conversion link, the upper limit of the photon counting mode counting rate depends on the pulse pair resolution, that is, the minimum time interval that each pulse can be separated. The inverse of the pulse pair resolution is the maximum counting rate. However, since most events in the light counting area usually occur randomly, the counting pulses may overlap. Considering the possibility of pulse overlap, the actual maximum counting rate is about one tenth of the calculated value. If we denote the real counting rate as N, the measured counting rate as M, and the time resolution as t, then the real counting rate N can be expressed as:

[0207]

[0208] For example, the time resolution of a laser radar is 100 ns, and the cumulative sampling number in one minute is 58400. Therefore, the measured count rate M1 = Data1 / (58400*100), the true count rate N1 = M1 / (1-M1*t), and t is the dead time, which is generally provided by the manufacturer. After obtaining the true count rate, the true photon number Data1 = N1*100*58400 can be obtained. The original data of the radar is corrected according to the formula.

[0209] Step S902, geometric overlap factor correction.

[0210] Generally, in the blind area, the light beam emitted by the laser is not in the receiving field of view of the telescope; in the transition area, the atmospheric return signal cannot be completely received by the telescope, causing the received return signal to be inconsistent with the actual return signal, and the ratio of the number of photons received by the telescope to the actual number of photons is called the geometric overlap factor. In the data processing process, this situation needs to be corrected, that is, the geometric overlap factor correction.

[0211] The geometric overlap factor is a parameter that approaches 1 with distance, which is generally provided by the manufacturer. If the geometric overlap factor is O(R), the corrected signal is:

[0212]

[0213] Where P(R, λ) is the original return signal; P'(R, λ) is the signal after geometric overlap factor correction; R is the height from the radar, in meters; and λ is the wavelength of the laser emission, in nanometers.

[0214] Step S903, background noise subtraction.

[0215] Background noise is caused by stray light in the atmosphere. The data measured by the laser radar not only contains aerosol, cloud and other signals, but also contains sky background noise signals.

[0216] According to the vertical distribution characteristics of the atmosphere, the upper atmosphere is relatively clean and almost does not contain aerosols and clouds. Therefore, the upper atmospheric return signal is generally used for background noise subtraction. The average value of the signal in the upper atmosphere 1-2 km is selected as the background noise, and the original return signal is subtracted to obtain the true atmospheric return signal. For example, the range of the laser radar is 60 km, and the average value of the signal in any 2 km between 50-60 km can be selected as the background signal for subtraction. The calculation method is shown in the following formula:

[0217] P''(R, λ) = P(R, λ) - P bg (R, λ)

[0218] Wherein, P(R, λ) is the original echo signal; P"(R, λ) is the signal after background noise deduction, P bg (R, λ) is the signal of background noise.

[0219] Step S904, data smoothing processing.

[0220] Since the atmospheric state is changing at any time, and is affected by the random error of the detection device, the original signal output by the laser radar acquisition card has jitter phenomenon, for this case, the echo signal can be smoothed to reduce signal jitter.

[0221] The data smoothing processing is to average the echo signals on adjacent distance points, and the distance corresponding to the average echo value is also the average of the distances. The number of points for calculating the average value is determined according to the actual signal quality, such as 3-point smoothing, 5-point smoothing, etc. The data smoothing processing also changes the distance resolution of the echo signal, for example, the distance resolution of a group of aerosol lidar echo signals is △r, after N-point smoothing, the distance resolution is N*△r.

[0222] Step S905, cloud and aerosol classification.

[0223] The optical properties of clouds and aerosols are different, and the selection of different aerosol lidar ratios during data processing has a greater impact on the inversion results, so it is necessary to distinguish the cloud and aerosol data.

[0224] Since the backscattering coefficient of the cloud is much larger than that of the aerosol, when the laser is transmitted in the atmosphere and encounters a cloud, the distance correction signal (RCS) will rapidly increase. The slope of the RCS curve is calculated, and according to the positive and negative changes of the slope value, the cloud layer or the aerosol layer can be determined.

[0225] If the slope of the RCS curve is continuously negative from a certain height point, and then changes from negative to positive and then continuously positive, it is determined that the point is the boundary point between layers. After the whole curve is divided into layers according to the method, the interval range of each layer can be obtained, and then several parameters such as the maximum slope, the minimum slope and the aspect ratio of each layer are evaluated, and if the score exceeds a certain threshold, it is determined as a cloud layer, otherwise it is an aerosol layer or uncertain.

[0226] The data with cloud is marked, and is excluded in the subsequent inversion of aerosol extinction coefficient and backscattering coefficient.

[0227] Step S906, data quality verification.

[0228] The original echo data are checked in quality, corresponding quality control identification code and quality control type code are given by comprehensively considering the score of RCS curve, signal-to-noise ratio of original echo signal and consistency with atmospheric molecule signal fitting and the like.

[0229] The signal-to-noise ratio is the ratio of signal to noise, and the calculation formula is:

[0230]

[0231] As described above, P bg (R, lambda) is a background noise signal, and P(R, lambda) is an original echo signal.

[0232] The consistency with atmospheric molecule signal fitting refers to that, in a region above the boundary layer and with almost 0 aerosol content in the atmosphere, the echo signal can be assumed to be related to only the density of molecules, therefore, after taking log, the curve of the molecular backscattering signal calculated according to the laser radar equation and the real echo after background subtraction and distance square correction should be basically consistent, that is, consistent.

[0233] The embodiment of the present application also provides a sea-air-space multi-source meteorological detection data fusion device, as shown in the figure, the device 1000 comprises: Figure 10

[0234] The first quality control module 1001 is configured to feed the satellite observation data after quality control and format conversion to a data receiving end;

[0235] The second quality control module 1002 is configured to convert the airborne meteorological radar reflectivity factor, speed and spectral width of the airborne platform coordinate system into a geodetic coordinate system, and perform quality control and format conversion on the data collected by the airborne platform converted into the geodetic coordinate system and the sounding data, and then feed the data to the data receiving end;

[0236] The third quality control module 1003 is configured to perform quality control and format conversion on the data detected by the detection equipment and then feed the data to the data receiving end, wherein the detection equipment at least includes one of a ground automatic weather observation station, an X-band weather radar, an S-band weather radar, a C-band weather radar, a P-band wind profile radar, an L-band wind profile radar, a laser wind measuring radar, a laser fog measuring radar, a millimeter wave cloud radar and a millimeter wave wind measuring radar;

[0237] The fourth quality control module 1004 is configured to perform quality control and format conversion on the meteorological sensor data and then feed the data to the data receiving end, wherein the meteorological sensor data includes data collected by meteorological sensors arranged on an automatic weather station, a buoy and / or a ship.

[0238] ​It should be noted that the various device structures described in the embodiments belong to the same technical concept as the methods described above, and achieve the same technical effects through the same principles, and thus will not be described here.

[0239] The embodiment of the present application further provides a readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method according to any one of the above embodiments.

[0240] Furthermore, although example embodiments have been described herein, the scope of their embodiments includes any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of the various embodiments interchangeably), adaptations and / or alterations based on the present disclosure. The elements of the claims are to be construed in the broadest reasonable manner consistent with the language and the context. Accordingly, they shall not be construed to exclude any changing of features by equivalents or substitutions therefore even if the present specification explicitly states that it includes only what is illustrated in the examples and / or that alternatives are unattainable. Therefore, the specification and examples should be regarded as illustrative rather than restrictive, and they are intended to be solely for purposes of exemplification so as to enable a full and enabling disclosure of the true scope and spirit of the application. Accordingly, the mere fact that an element is not recited in the claims does not exclude it from the scope of the application.

[0241] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) can be used in combination with each other. Other examples can be used in addition to those described, such as one of ordinary skill in the art would appreciate from the foregoing description. Still further, in the above Detailed Description, various features can be grouped together or described in a single embodiment for the purpose of streamlining the disclosure. This should not be interpreted as intending that an unclaimed application is necessarily comprised of more features than are recited in any claim. Rather, inventive subject matter can be less than all of the features of a specific embodiment. Thus, the following claims are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment. The scope of the application should be determined, not by the ability to describe all possibilities of the application with accuracy, but by the appended claims and their equivalents, as interpreted in accordance with laws of patent claims.

Claims

1. A method for fusing multi-source meteorological sounding data in air, land, sea, space, characterized in that, The method comprises: After quality control of satellite observation data, format conversion is performed and then fed to a data receiving end; The airborne platform coordinate system of reflectivity factor, velocity and spectral width of airborne weather radar is converted into a geodetic coordinate system, and the data collected by the airborne platform converted into the geodetic coordinate system and the sounding data are subjected to quality control and format conversion and then fed to the data receiving end; After quality control and format conversion of the data detected by the detection equipment, the data are fed to the data receiving end, wherein the detection equipment at least includes one of a ground automatic weather observation station, an X-band weather radar, an S-band weather radar, a C-band weather radar, a P-band wind profile radar, an L-band wind profile radar, a laser wind lidar, a laser fog lidar, a millimeter wave cloud radar and a millimeter wave wind lidar; After quality control and format conversion of the meteorological sensor data, the data are fed to the data receiving end, wherein the meteorological sensor data includes data collected by meteorological sensors arranged on an automatic weather station, a buoy and / or a ship. The quality control of the data collected by the airborne platform converted into the geodetic coordinate system and the sounding data comprises the following steps: The data collected by the airborne platform converted into the geodetic coordinate system and the sounding data are subjected to ground clutter suppression, velocity ambiguity resolution, data consistency evaluation, noise and interference removal, clutter and false echo filtering, atmospheric attenuation correction and error correction and calibration. The ground clutter suppression step comprises: Fixed target clutter and slow target clutter are filtered or suppressed by using space-time two-dimensional adaptive processing and / or adopting digital signal processing technology. The velocity ambiguity resolution step comprises: Irregular double waveforms A and B are designed, the maximum unambiguous velocities of the waveforms A and B are different and are coprime, and in the case that the ambiguous velocities of the same target velocity on the A wave and the B wave are the same, the target velocity is determined as the real target velocity. The data consistency evaluation step comprises: The radar echo intensities at different altitudes and different time points are compared to ensure the consistency of the data in time and space. The noise and interference removal step comprises: The effects of electromagnetic interference and random noise are removed by using a filter and / or a smoothing algorithm. The clutter and false echo filtering step comprises: The clutter and false echo caused by ground objects, flying birds and insects in the radar data are removed. The atmospheric attenuation correction step comprises: The radar data are subjected to atmospheric attenuation correction by using an atmospheric attenuation model or measured data. The error correction and calibration step comprises: Systematic errors of the radar equipment are corrected, and the radar data are calibrated to ensure that the radar data at different times and different places have unified scales and standards; the systematic errors of the radar equipment include antenna pointing errors and distance errors.

2. The method of claim 1, wherein, The satellite observation data are subjected to quality control by the following method: Terrain test: satellite observation data with land, sea ice, snow and mixed underlying surface are removed; Extreme value test: satellite observation data with a brightness temperature less than 70K or greater than 320K are removed; Threshold test: satellite observation data with a brightness temperature standard deviation greater than 6K and satellite observation data with an observed brightness temperature minus a simulated brightness temperature greater than 3.5K are removed.

3. The method of claim 1, wherein, The airborne weather radar reflectivity factor, velocity, and spectral width in the airborne platform coordinate system are converted into the geodetic coordinate system by the following method: Real-time acquisition of the attitude information of the airborne platform collected by the attitude sensor, the attitude information including the pitch angle, the yaw angle, and the roll angle; In the case of jitter or attitude change during flight, the attitude change is determined according to the real-time acquired attitude information, and the coordinate system of the airborne platform is adjusted to keep the antenna stationary relative to the earth; Real-time acquisition of the operating parameters of the aircraft, the operating parameters including the position, the velocity, and the acceleration, the operating parameters being measured relative to the geodetic coordinate system; According to the operating parameters of the aircraft and the attitude information of the airborne platform, the coordinates of the airborne platform are converted into the geodetic coordinate system.

4. The method of claim 1, wherein, The weather sensor data are quality controlled by the following method: Data acquisition verification, including the use of a verification mechanism during data acquisition, the verification mechanism including sensor calibration, hardware device state monitoring, and data acquisition software stability testing; After data acquisition, data preprocessing is performed, including denoising, smoothing, standardization, and normalization operations; Data integrity check, including determining whether the data record is complete, whether the timestamp is accurate, whether the data is missing, and whether there are abnormal situations; Data outlier identification, including identifying outliers that do not conform to conventional weather data using statistical methods, comparative analysis methods, or expert experience methods; Data consistency analysis, including comparing data between different sensors or different observation sites to check whether the data are consistent; In the case of identifying outliers or inconsistent data, data correction and modification are performed, wherein the correction method includes using historical data, adjacent site data, or algorithm models for interpolation, smoothing, or replacement After that, the modification method includes hardware replacement, software upgrade, or re-observation for sensor failure or operation error; Data storage management, including storing data in a preset data storage format, storage medium, backup strategy, and security measures; Data quality evaluation, including determining the overall quality level of the data according to the completeness, accuracy, consistency, and availability of the evaluation data.

5. The method of claim 1, wherein, The data detected by the X-band weather radar, S-band weather radar, and C-band weather radar are quality controlled by the following method: Ground clutter suppression processing, including one or more of the following steps: digital signal processing, spatial filtering, MTI filter, adaptive frequency domain filter, and clutter reduction decision processing. In the case of using multiple processing steps, the results obtained by multiple processing steps are compared, and the optimal processing result is selected, The digital signal processing step includes: processing the radar echo signal based on median filtering, mean filtering, and adaptive filtering to filter out or suppress fixed target clutter and slow target clutter, and to retain the echo of moving targets; The spatial filtering processing step includes: using the echo signals received by multiple antennas for spatial filtering to suppress ground clutter from specific directions, including sky and ground echoes; The MTI filter processing step includes: using the MTI filter to eliminate the echo of fixed targets and slow-moving clutter by subtracting the pulses in adjacent repetition periods, thereby retaining the echo of moving targets; The adaptive frequency domain filter processing step includes: using the adaptive frequency domain filter to adaptively determine the filtering position by analyzing the shape of the spectrum, and to restore the filtered weather signals by the Gaussian fitting method; The clutter reduction decision processing step includes: combining the adaptive filter and the CMD algorithm to improve the data quality of the weather radar; In the case that the target speed observed by the radar exceeds the maximum unambiguous speed of the radar, a speed ambiguity elimination process is performed, which includes one or more combinations of Doppler shift compensation, phase coding and repetition frequency diversity; The Doppler shift compensation includes: measuring the Doppler shift of the target, and calculating the speed of the target according to the radar system parameters, thereby eliminating the error caused by speed ambiguity; The phase coding includes: adding a set of phase coding sequences to the transmitted signal, so that the radar can distinguish different speed echoes and solve the speed ambiguity problem; The repetition frequency diversity includes: using multiple different repetition frequencies, so that the radar can receive multiple echo signals of different speeds at the same time, thereby eliminating the speed ambiguity; In the case that the radar uses a pulse repetition frequency higher than a set value to cause range folding, the range folding is eliminated by random phase coding, adjusting the pulse repetition frequency, using an elevation higher than the current elevation for scanning, or changing the detection location; The adjusting the pulse repetition frequency includes changing the pulse repetition frequency to change the maximum detection distance of the radar, thereby eliminating the range folding in the area of interest.

6. The method of claim 1, wherein, The data detected by a P-band wind profile radar, an L-band wind profile radar and a millimeter wave radar are quality controlled by ground clutter suppression, median filtering or smoothing filtering, The ground clutter suppression includes: designing an IIR notch filter according to a clutter map, and then performing linear interpolation between the two edge points of the notch; The median filtering includes: determining a neighborhood of a certain center point of data to be processed; sorting the values of each data in the neighborhood, and taking the middle value as the new value of the center point data; and using the median filtering to smooth the data after moving the window by time and height; The smoothing filtering includes: taking an observation object Y, a corresponding independent variable X of Y, Δx as the interval of the independent variable X, equally spaced independent variables xi (i=1, 2, 3, …, n), and an observation data sequence yi (i=1, 2, 3, …, n) obtained according to the value of the dependent variable Y; and filtering the noise components in the observation data sequence by using sliding smoothing, which includes: taking the data of the ith point and its nearby points, determining a fitting straight line equation according to the least square principle, and calculating the dependent variable of the ith point based on the straight line equation as the data value after sliding smoothing.

7. The method of claim 1, wherein, The data detected by a laser wind measurement radar are quality controlled by the following method: Noise and missing value processing: Select a sliding window of M*N, divide the data size in the window into P intervals equally, △d is the interval, the wind speed value of the center point of the window is V ij , the P+1th interval is expressed as V ij ±△d / 2; The distribution frequency of the wind speed value in the M*N window in different intervals X 1, X 2, X 3... X P ) and X ´, let X MAX be X 1, X 2, X 3... X P and X ´ the maximum value, take VP as X MAX the middle value of the corresponding interval; Let the new value of the center point of the window be V’ ij The noise elimination and missing data filling are realized by the following formula: ； In the formula K 1 is the discrimination threshold of whether to reject noise, K 2 is the discrimination threshold of whether to fill in missing data, when the original value of the window center point V ij is not 0, it represents that this time is an effective echo point, when X ´<=threshold K 1, the point is rejected; when X ´>threshold K 1, the corresponding point is retained; When the original value in the window V ij When it is equal to 0, it is an invalid point. X MAX >=threshold K 2, then fill in X MAX The center point VP of the corresponding interval is assigned to V ij ;like X MAX <Threshold K 2, no filling is performed; The data detected by the laser fog radar is quality controlled by the following method: Dead time correction, the calculation formula is: ； wherein, N represents the true count rate, M represents the measured count rate, t represents the time resolution; Geometric overlap factor correction, the calculation formula is: ； wherein, is the original echo signal; is the signal corrected for the geometric overlap factor; R is the height from the radar; is the laser emission wavelength, O(R) is the geometric overlap factor; Background noise deduction, the calculation formula is: ； wherein is the original echo signal; is the signal after background noise subtraction, is the signal of the background noise; Smooth processing of echo signals; Cloud and aerosol classification: the slope of the corrected signal curve is calculated, and according to the positive and negative change of the slope value, the cloud layer or aerosol layer is judged. Data quality verification: according to the score of the RCS curve, the signal-to-noise ratio of the original echo signal and the consistency with the atmospheric molecular signal fitting, the original echo data is quality verified, and the corresponding quality control identification code and quality control type code are given, wherein the consistency with the atmospheric molecular signal fitting refers to, in the region above the boundary layer, the aerosol content in the atmosphere is lower than the set threshold, in the case that the set echo signal is only related to the density of molecules, the molecular backscattering signal calculated according to the laser radar equation is consistent with the curve after the background is deducted and the distance square is corrected, after taking log, within the set error, and the atmospheric molecular signal, it is determined to have consistency.

8. An apparatus for fusing multi-source meteorological sounding data in space, land, air and sea using the method of any one of claims 1 to 7, characterized in that, The device comprises: A first quality control module configured to perform quality control on satellite observation data, perform format conversion, and feed to a data receiving end; A second quality control module configured to convert airborne weather radar reflectivity factor, speed, and spectral width of an airborne platform coordinate system into a geodetic coordinate system, perform quality control on data collected by the airborne platform converted into the geodetic coordinate system and sounding data, and perform format conversion and feed to a data receiving end; A third quality control module configured to perform quality control on data detected by a detection device and format conversion, and feed to a data receiving end, the detection device including at least one of a ground automatic weather observation station, an X-band weather radar, an S-band weather radar, a C-band weather radar, a P-band wind profile radar, an L-band wind profile radar, a laser wind detection radar, a laser fog detection radar, a millimeter wave cloud radar, and a millimeter wave wind detection radar; A fourth quality control module configured to perform quality control on meteorological sensor data and format conversion, and feed to a data receiving end, the meteorological sensor data including data collected by meteorological sensors installed on an automatic weather station, a buoy, and / or a ship. 9.A non-transitory computer-readable storage medium storing instructions, when executed by a processor, perform the method according to any one of claims 1 to 7.

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