CN110927828A - Low-orbit detection thermal layer atmosphere multi-factor separation and feature extraction method - Google Patents
Low-orbit detection thermal layer atmosphere multi-factor separation and feature extraction method Download PDFInfo
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Abstract
The invention provides a low-orbit detection thermal layer atmosphere multi-factor separation and feature extraction method, which comprises low-orbit atmospheric multi-source influence factor separation and disturbance atmospheric feature extraction in a magnetic storm process. Wherein, low rail atmosphere multisource influence factor separation includes: reconstructing a detection data sequence according to the nearest condition after the detection track data are subjected to rail lifting separation based on the low-orbit detection atmosphere data and the external geophysical condition data at the same time; correcting the altitude of the atmosphere in the processing range based on an empirical mode; classifying and denoising the recombined data based on a method combining time sequence superposition median and error analysis; and (3) based on gridding atmospheric parameters of multi-condition classification processing, separating multi-source influence factors of high-rise atmosphere, and extracting influence characteristics of local time, latitude, season, geomagnetism and solar radiation of the atmosphere of the hot layer. The invention has small error of feature extraction.
Description
Technical Field
The invention belongs to the technical field of high-rise atmospheric analysis and prediction, and particularly relates to a low-orbit detection thermal layer atmospheric multi-factor separation and feature extraction method.
Background
High-rise atmospheric changes are influenced by various geophysical conditions and are a common result driven by both internal and external sources. The external drive is mainly solar radiation and geomagnetic disturbance. During magnetic storm, energy generated by interaction of solar wind and the magnetic layer enters the high-latitude ionized layer and the thermal layer in the forms of Joule heating, particle sedimentation and the like, and during magnetic disturbance, the solar wind energy coupled into the atmosphere is remarkably enhanced to cause the thermal layer to generate a series of complex physical processes to respond to geomagnetic disturbance, so that atmospheric composition and temperature in a polar region are changed, and the atmospheric circulation is changed by heating expansion. The atmospheric density change influence factors during storm are numerous, and the characteristics of a single magnetic storm, such as the position of a interplanetary magnetic field, the change of the solar wind speed and the solar wind density influence the change intensity and the space-time distribution characteristics of the high-rise atmosphere, so that the analysis result of the individual magnetic storm has no universality. Meanwhile, the high-rise atmospheric changes have strong dependence on local time, seasons, altitude and the like. In order to extract the response changes of the thermal layer atmospheric density to the various sources of influence, each source of influence must be separated out.
The satellite detection feature determines the difficulty of covering all conditions and spatio-temporal ranges. According to the characteristics of the satellite orbit, the satellite detection data is detected when the data is concentrated in the morning/evening or the data needs a certain time period to gradually cover the whole place, and meanwhile, the satellite has larger orbit height attenuation in the life cycle of the satellite. For external conditions, such as large individual differences of geomagnetic activity events, event classification criteria for statistical analysis are difficult to define, and thus process features of atmospheric parameter changes in magnetic storm events are difficult to extract.
Currently, the extraction of multi-scale features of high-rise atmosphere based on detection data mainly utilizes a multi-factor fitting method of observation data, and can analyze the change features of an affected source under the condition of removing (minimizing) other affected factors based on classification of different conditions. Whether a multi-factor empirical fitting method or a classification and segmentation research method based on different conditions is adopted, the current processing is mostly based on scattered point fitting or statistical median calculation, the accuracy of an analysis result depends on the quality of an analysis sample, for detection results obtained by fusing multiple influence factors together, especially for different change weights caused by different influence sources, different error sources are accumulated in full sample analysis without considering time sequence change, small-scale signals are easily covered, and the error increase and even failure of the analysis result can be caused. In addition, the results of the scatter-point fitting for a small number of samples can be severely distorted.
Most of the existing thermal layer atmosphere change processing under the geomagnetic disturbance condition does not consider the development process of the magnetic storm, or considers the development process of the magnetic storm but only analyzes individual cases of the magnetic storm. Compared with background parameters of atmospheric disturbance in a magnetic storm process, most of the processing in the prior art is static day atmospheric density/wind field calculated based on an empirical mode, and because systematic deviation of detection data is not considered, accumulated errors can be generated in the disturbed atmospheric density/wind field calculated according to static day background atmosphere.
Disclosure of Invention
The invention aims to provide a low-orbit detection thermal layer atmosphere multi-factor separation and feature extraction method to solve the technical problem.
The invention provides a low-orbit detection thermal layer atmosphere multi-factor separation and feature extraction method, which comprises the steps of low-orbit atmosphere multi-source influence factor separation and disturbance atmosphere feature extraction in a magnetic storm process;
the low-orbit atmospheric multi-source impact factor separation comprises:
1) reconstructing a detection data sequence according to the nearest condition after the detection track data are subjected to rail lifting separation based on the low-orbit detection atmosphere data and the external geophysical condition data at the same time; the nearest condition is a condition with the minimum difference of other factors when a certain influence source is processed and analyzed;
2) correcting the altitude of the atmosphere in the processing range based on an empirical mode;
3) classifying and denoising the recombined data based on a method combining time sequence superposition median and error analysis;
4) based on gridding atmospheric parameters subjected to multi-condition classification processing, multi-source influence factors of high-rise atmosphere are separated, and influence characteristics of local time, latitude, seasons, geomagnetism and solar radiation of the atmosphere of the hot layer are extracted;
the extraction of the disturbance atmospheric features in the magnetic storm process comprises the following steps:
(1) judging the development process of the magnetic storm event based on the combined analysis of the geomagnetism and interplanetary parameters;
(2) refining atmospheric statistic classification during storm based on track detection and magnetic storm event characteristics;
(3) and determining the atmosphere of the dead day nearest to the detection condition, and extracting the atmosphere of the storm time disturbance under different conditions.
Further, the low-orbit atmospheric sounding atmospheric data in the low-orbit atmospheric multi-source influence factor separation step 1) includes satellite sounding atmospheric density, sounding time and sounding position information, and the external geophysical condition data at the same time includes solar activity and geomagnetic activity index data at the same time.
Further, the low-orbit atmospheric multi-source influence factor separation step 4) comprises:
and judging the orbit rising and the orbit falling according to the latitude positions of the satellite orbit when the continuous local time variation characteristics are extracted, and distinguishing the local time of the satellite measurement.
Further, the step (2) of disturbing the atmosphere in the magnetic storm process comprises the following steps:
and performing reclassification statistical processing according to the duration of the main phase of the magnetic storm on the basis of the classification of the intensity of the magnetic storm.
Further, the step (3) of disturbing the atmosphere in the magnetic storm process comprises the following steps:
and (3) backtracking forwards according to the magnetic storm time magnetic activity index sequence, determining the dead-day time by judging the geomagnetic condition, and extracting the atmospheric parameters of the dead day under the condition closest to the storm time detection.
Compared with the prior art, the invention has the beneficial effects that:
(1) according to the invention, according to the characteristics of the satellite orbit, after the detection orbit is subjected to lifting orbit separation, the detection data sequence is recombined according to the nearest principle, and when a certain influence source is processed and analyzed, the condition with the minimum difference of other factors is selected, so that the introduced reprocessing (analysis) error is minimum.
(2) For the atmospheric change of the hot layer under the geomagnetic disturbance condition, the method utilizes the duration of the main phase of the magnetic storm to refine the event on the basis of the classification of the intensity of the magnetic storm, determines the atmospheric parameters of the quiet day on the basis of the nearest detection condition to extract the atmospheric statistical characteristics of the hot layer during the storm, and obtains the atmospheric density/wind field change characteristics of the hot layer in the magnetic storm process with different intensities and different durations on the basis of the atmospheric data of the track detection.
Drawings
FIG. 1 is a flow chart of the low-orbit atmospheric multi-source impact factor separation process based on the nearest neighbor principle according to the present invention;
FIG. 2 is a flow chart of a multi-source condition analysis process of the present invention;
FIG. 3 is a graph of atmospheric density as a function of solar radiation index for equatorial regions of different seasons in accordance with the present invention;
FIG. 4 is a flow chart of the discrimination of magnetic storm event and the analysis and processing of the disturbance atmosphere during storm according to the present invention.
Detailed Description
The present invention is described in detail with reference to the embodiments shown in the drawings, but it should be understood that these embodiments are not intended to limit the present invention, and those skilled in the art should understand that functional, methodological, or structural equivalents or substitutions made by these embodiments are within the scope of the present invention.
The embodiment provides a low-orbit detection thermal layer atmosphere multi-factor separation and feature extraction method, which comprises the steps of low-orbit atmosphere multi-source influence factor separation and disturbance atmosphere feature extraction in a magnetic storm process;
referring to fig. 1, the low-orbit atmospheric multi-source impact factor separation includes:
1) reconstructing a detection data sequence according to the nearest condition after the detection track data are subjected to rail lifting separation based on the low-orbit detection atmosphere data and the external geophysical condition data at the same time; the nearest condition is a condition with the minimum difference of other factors when a certain influence source is processed and analyzed;
2) correcting the altitude of the atmosphere in the processing range based on an empirical mode;
3) classifying and denoising the recombined data based on a method combining time sequence superposition median and error analysis;
4) based on gridding atmospheric parameters subjected to multi-condition classification processing, multi-source influence factors of high-rise atmosphere are separated, and influence characteristics of local time, latitude, seasons, geomagnetism and solar radiation of the atmosphere of the hot layer are extracted;
referring to fig. 4, the extracting of the disturbing atmosphere feature in the magnetic storm process includes:
(1) judging the development process of the magnetic storm event based on the combined analysis of the geomagnetism and interplanetary parameters;
(2) refining atmospheric statistic classification during storm based on track detection and magnetic storm event characteristics;
(3) and determining the atmosphere of the dead day nearest to the detection condition, and extracting the atmosphere of the storm time disturbance under different conditions.
The present invention is described in further detail below.
The invention aims at the defects that the satellite detection data is the most advantageous detection means at present, but the satellite detection characteristics determine that all conditions and space-time ranges are difficult to cover, and the error of more sources is easy to accumulate without considering the full-scatter processing of time sequence change. In order to extract the statistical information of the atmospheric change of the hot layer in the magnetic storm event process, a more refined statistical classification method of the magnetic storm event and a method for determining the atmosphere of the quiet day based on the nearest detection condition are provided, so that the atmospheric density/wind field change characteristics of the hot layer in the magnetic storm process with different intensities and different durations can be obtained based on the track detection atmospheric data. The specific technical scheme is as follows:
1. low-orbit atmospheric multi-source influence factor separation based on 'nearest neighbor' principle
The nearest 'proximity' principle is that when processing and analyzing a certain influence source, the condition with the minimum difference of other factors is selected, so that the introduced reprocessing or analysis error is minimized.
As shown in fig. 1, multi-condition high-rise influence source separation is realized by a series of processing and analysis of low-orbit detection atmosphere data and simultaneous external geophysical condition (including solar activity and geomagnetic activity) data (exogenous condition data), and a corresponding global analysis result is obtained. The input parameters comprise satellite detection atmosphere density, detection time and detection position information, and solar activity and geomagnetic activity index data at the same time. In the operation process, firstly, the data preprocessing is carried out on the input file, including the quality control of the data and the data standardization processing. The quality control is to eliminate the unqualified data according to the judgment standard. The data normalization includes the unification of time resolution of different elements and the high normalization of the atmospheric density data at different times of the detection of the same detector. And correspondingly judging the time sequences of different elements, and processing the time sequences into the same resolution by utilizing an interpolation or statistical median method. The high normalization for atmospheric density may utilize empirical atmospheric patterns such as Msis00, JB2008, DTM2013, and the like. And then, judging and classifying according to different conditions by comprehensively utilizing external geophysical condition data and the standardized atmospheric density, and processing the data into multi-dimensional gridding atmospheric density data. After classification denoising processing is carried out, analysis methods such as time sequence superposition analysis, statistical median, weight interpolation and the like are comprehensively utilized to calculate and extract the influence characteristics of local time, latitude, season, geomagnetism and solar radiation of the atmosphere of the hot layer.
1) Continuous local time and latitude conditions
The satellite detection feature determines the difficulty of covering all conditions and spatio-temporal ranges. According to the characteristics of the satellite orbit, the satellite detection data is detected when the data is concentrated in the morning/evening or the data needs a certain time period to gradually cover the whole place, and meanwhile, the satellite has larger orbit height attenuation in the life cycle of the satellite. For example, the CHAMP satellite orbit is a near circular polar orbit with an inclination of 87.3 degrees, with a period around the earth of about 94 minutes, approximately every 130 days when it covers the entire solar field. To improve the satellite life, it can be seen that the satellite has undergone 4 orbital transfers. The GOCE satellite was maintained at a height of 230-275 kilometers (km) from 2009, 12 months 1 to 2013, 10 months 20. The satellite detects the local sun at 6-8 hours (morning/afternoon) due to the quasi-morning and evening orbit, and the later Local Time (LT) is changed from 6LT to about 8LT during the orbit precession.
For detecting atmospheric wind field changes caused by satellite altitude attenuation, most of the numerical and empirical models show that the thermal level wind field above 250km, especially between 300-450km, is essentially invariant to altitude. Thus, the height difference effect in the CHAMP data described above is negligible. But the altitude-induced density variation is not negligible, and to avoid atmospheric density variation due to satellite orbital altitude variation, the observed total mass density data ρ (h) is normalized to a uniform calibration altitude using an empirical model, for example if corrected to 400 km: ρ (400km) ═ ρ (h). ρM(400km)/ρM(h) The subscript M here indicates the mode density of the calibration altitude and the altitude h at which the satellite orbits. The error introduced by the above process increases with increasing height difference.
The satellite orbit is divided into a descending section and an ascending section according to the flight direction, and when each time interval is concentrated in the same solar place, the difference between the two time intervals is 12 hours. When continuous local time variation characteristics are extracted, firstly, the orbit rising and the orbit falling are judged according to the satellite orbit latitude positions, and the satellite measurement local time is distinguished. When the lifting rail is judged, a continuous rail needs to be determined, and the set conditions are as follows: (1) determining a starting point (e.g., CHAMP satellite 87N) according to the satellite characteristics; (2) judging that the time interval between the starting points is less than 1 orbit period of the satellite; (3) it is determined that the data within the track is not a default value. The satellite can be divided into 4 sectors according to the time of the satellite: 04-08LT is morning sector, 10-14LT is noon sector, 16-20LT is evening sector, and 22-02LT is night sector. The satellite data time sampling is 10s, 1-2 data are guaranteed to exist in each geographical latitude in the process of track advancing, therefore, the latitude direction is processed into 1-degree resolution data, and the geographical latitude and the geomagnetic latitude are different, and the track ascending and the track descending are determined according to the geographical latitude.
The most "adjacent" processing conditions taken when extracting the continuous local variation features are as follows: (1) firstly, classifying and reconstructing a data sequence after separation of lifting rails, wherein according to the rail characteristics of the lifting rails with 12-hour difference, the morning side and the faint side extracted during processing are the same rail (half rail lifting and half rail lowering), and the noon side/night side result is the same rail measurement result, so that the morning side/the faint side (noon side/night side) are ensured to be in the same external condition. (2) Based on the characteristics of the stepwise advancing changes in orbital location, the analysis of the entire local time (0-24LT) segment ensures continuous satellite orbit measurements for adjacent locations, such that adjacent locations correspond to the most similar external conditions. (3) Because the sequences are taken as relatively adjacent sequences and the minimum detection height deviation is ensured, after the statistical average height of the analyzed data sequence is determined, the data of other detection points are corrected to the average height by using an empirical mode, thereby ensuring the minimum introduced height error.
2) Seasonal, geomagnetic and solar radiation condition processing
And after the lifting rail separation and denoising re-fusion processing are carried out, carrying out classification characteristic extraction on the multi-dimensional data. Full latitude results were divided into different seasons (mar., jun., sep., Dec) at different local times (Dawn in the morning, Dusk in the evening, Noon in the afternoon, and Night). The four seasons are respectively northern hemisphere winter and southern hemisphere summer: 12 months, 1-2 months (corresponding to the dates of 1-60 days and 335-365/366 days), 3-5 months of spring (corresponding to the dates of 61-152 days), 6-8 months of northern hemisphere summer and southern hemisphere winter (corresponding to the dates of 153-224 days), and 9-11 months of autumn (corresponding to the dates of 245-335 days). As shown in fig. 2.
The geomagnetic activity is characterized by a geomagnetic index (including a Kp index, a Dst index, and the like, described below). Since the atmospheric density of the thermal layer is affected by factors such as the local time and the season in addition to the geomagnetic activity, the classification is performed under the same conditions for the factors other than the geomagnetic activity in order to minimize the influence of the other factors. The hot layer in the magnetic storm process is an extremely complex and variable system, and the processing analysis and the system construction of the event process are independently carried out on the hot layer. According to the geomagnetic activity index, two conditions of geomagnetic calmness and small disturbance are considered, namely when Kp is less than 3 and geomagnetic calmness is achieved, and when Kp is more than or equal to 3 and less than 5 and small disturbance is achieved. Since the hot layer atmosphere has a delayed reaction with respect to the geomagnetic activity, Kp < 3 for the first 3-6 hours needs to be satisfied in determining the geomagnetic quiet period, in addition to taking into account Kp at that time.
Considering that the composite index P10.7 of historical solar radiation levels describes the energy input of the thermal and ionospheric layers better than the F10.7 index, the present solution indicates the effect of solar radiation levels using the P10.7 index. P10.7 and F10.7 are both solar radiation indices, where P10.7 ═ 0.5(F10.7+ F10.7)81days)，F10.781daysIs an 81 balance average F10.7 index. After the F10.7 time series is processed to generate the corresponding P10.7 index, the atmospheric data under the same other conditions are processed according to the standard of every 10sfu (radiation index unit: 1 sfu-10)-22Wm-2Hz-1) And carrying out segmented statistics to calculate the statistical median and the corresponding standard deviation of the statistical median, wherein the interval of each segment P10.7 is 10sfu resolution. The latitude data is reprocessed and divided into 7 segments of an equatorial segment (-5 degrees S-5 degrees N), low latitude (5-25 degrees), middle latitude (25-45 degrees) and a sub-polar region (45-75 degrees) of the north and south hemispheres, and then the statistical median of the detection values along the 7 segments of orbits is taken as the density value of the segment. And analyzing to obtain the distribution of the latitude (7: equator, low latitude, middle latitude and high latitude in the south-north hemisphere), different local time periods (4) and different seasons (4) of atmospheric parameters along with the solar radiation index P10.7. Figure 3 gives an example of a partial result. 2. Disturbance atmospheric feature extraction in magnetic storm process
In order to extract the change process and characteristics of the atmosphere of the hot layer in the magnetic storm process, all magnetic storm events with observed data are processed and analyzed. The development process of the magnetic storm event is judged through combined analysis of geomagnetism and interplanetary parameters, statistical classification of the storm atmosphere is refined by combining track detection and magnetic storm event characteristics, and the storm disturbance atmosphere under different conditions is extracted after the quiet day atmosphere closest to the detection condition is determined.
Fig. 4 shows a disturbing atmosphere feature extraction flow in the magnetic storm process. Firstly, determining the process of a magnetic storm event based on the geomagnetic activity index, then refining the magnetic storm according to the development process of the magnetic storm on the basis of intensity classification of the magnetic storm to obtain the event category of the most similar condition, and on the basis, extracting the parameters of an upstream interplanetary and a magnetic layer in a corresponding time period and determining the variation disturbance of the atmosphere of a downstream thermal layer. In order to analyze atmospheric density disturbance caused by a magnetic storm, the original background atmospheric density needs to be removed from the observed total atmospheric density, the scheme designs forward backtracking according to a geomagnetic activity index sequence of the magnetic storm, determines the static day time by judging a geomagnetic Kp condition, and extracts the static day atmosphere under the condition most similar to the storm detection. The design program realizes the classification from the judgment of the magnetic storm event to different standards, realizes the judgment of conditions such as local time, latitude and the like and the extraction of the atmospheric density of the quiet day by combining the orbit atmospheric density data, and obtains the statistical analysis results under different conditions on the basis of analyzing all the cases.
1) Method for determining and classifying magnetic storm events
The geomagnetic storm is a strong magnetic field disturbance caused by interplanetary disturbance, and due to insufficient data and the complex influence of atmospheric development forms of thermal layers during the storm, the typical forms of atmospheric development changes in the magnetic storm process are still difficult to be given. In combination with the satellite orbit characteristics, the method is divided into four local time periods: morning, evening, noon and night. Since the daily observations were mainly concentrated in the same place, the up-and down-tracks of the track had a 12 hour time difference, the above-mentioned noon-hour analysis and night correspond to the same magnetic storm case, with the corresponding morning-hour results corresponding to the same magnetic storm event as the coma. In order to obtain the two-dimensional continuous distribution of the atmosphere, gridding interpolation of weighted average is carried out on the orbit data, and empty areas which are not sampled and are not detected among the orbits are filled with weighted interpolation.
The geomagnetic activity can be described by using indexes such as Dst and Kp, and for the occurrence and development of a magnetic storm, that is, the magnetic storm generally has a development process of three stages, namely an initial phase, a main phase and a recovery phase, the magnetic storm is generally judged by using the Dst index, so that the process of event development is judged by using the Dst index in the scheme, and classified statistics is performed on the event development process. And classifying the magnetic storm according to the strength of the magnetic storm and the duration of the initial phase of the magnetic storm by combining two types of standards. Firstly, according to Dst exponential extreme value (Dst)min) The intensity classification is carried out, and the method is mostly adopted in historical research, and Dst is usedminInstantiating similar magnetic storms as 0 value pairsDuring the course, the superposition statistical analysis is carried out, the characteristics of the magnetic storm with different intensities before and after the peak value can be extracted by using the method, but the time intervals from the beginning to the development to the extreme value of the magnetic storm are possibly very different, so that the method cannot judge the development change process of the high-rise atmospheric parameters before and after the beginning of the magnetic storm. In order to solve the problems, reclassification statistical processing is carried out according to the duration of the main phase of the magnetic storm after the classification of the intensity of the magnetic storm is carried out.
The initial phase usually means the beginning of a magnetic storm, but is not a necessary process for a magnetic storm. While hot/ionospheric F-layer disturbances generally start with the main phase of magnetic storm (MPO). Therefore, the scheme selects the MPO as the reference zero of the time sequence superposition analysis, and the MPO duration (delta t) is used according tom) The magnetic storm is refined into different categories, and the method for determining the MPO starting time is as follows: first according to DstminBack-tracking forwards, and sequentially searching forwards to determine the moment when the Dst index starts to rise and then falls, namely the DstminIn the previous peak period, it should be noted that in order to remove the false signal caused by the small disturbance of Dst, the peak value of a certain time period needs to be determined, and thus the MPO start time is determined. δ t as shown in FIG. 4mDuration from instantaneous to long, e.g. deltat, respectivelymThe treatment time is less than 5h (h).
2) Method for extracting atmosphere parameters in quiet days
The atmospheric disturbance caused by the magnetic storm needs to remove the static day background value of the corresponding condition from the total observed value during the storm, most of historical research is static day atmospheric density calculated based on an empirical mode, but detection data has systematic deviation, and disturbance atmospheric parameters calculated as the static day background reference atmosphere accumulate larger errors. According to the scheme, the static day detection atmosphere of the nearest storm time detection atmosphere exogenous condition based on satellite detection is adopted as the static day background value. The scheme combines the geomagnetic activity index Dst and the Kp sequence to judge and determine the static day time. Since the atmosphere in the hot layer has a delayed reaction with respect to the geomagnetic disturbance, that is, the historical geomagnetic disturbance has an influence on the hot layer, Kp at that time is not only considered at that time, but also Kp < 3 is required to satisfy the previous 3-6 hours when determining the geomagnetic quiet period. The quiet day parameter is according to DstminAnd (4) backtracking forwards, judging according to the Kp index of the geomagnetic activity, and if Kp of 6 continuous hours in the adjacent time is less than 3, determining the observation result of the satellite at the same position as a static day characteristic value relative to an atmospheric parameter during storm. If DstminIf no magnetostatic days meeting the conditions exist in the first 120 hours, the analysis is abandoned due to the influence of the satellite orbit local time shift. The static day size determined by the method has similar external conditions such as solar radiation, season, latitude and the like with the storm atmosphere analyzed and compared, so that the determined disturbing atmosphere density is mainly caused by geomagnetic disturbance.
The invention provides a method for minimizing reprocessing (analyzing) errors and extracting and separating different influence sources of high-rise atmospheric density/wind field based on the most 'adjacent' principle according to satellite orbit characteristics, and a method for determining atmosphere parameters in quiet days based on the most adjacent detection conditions and extracting statistical characteristics of storm-time hot-zone atmosphere based on the combined classification of magnetic storm intensity and main phase duration, so that the efficiency and the accuracy of multi-time-space multi-factor characteristic analysis of the hot-zone atmospheric parameters can be effectively improved, and the method is suitable for processing physical parameters such as low-orbit detection atmospheric density, temperature and wind field.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (5)
1. A low-orbit detection thermal layer atmosphere multi-factor separation and feature extraction method is characterized by comprising the steps of low-orbit atmosphere multi-source influence factor separation and disturbance atmosphere feature extraction in a magnetic storm process;
the low-orbit atmospheric multi-source impact factor separation comprises:
1) reconstructing a detection data sequence according to the nearest condition after the detection track data are subjected to rail lifting separation based on the low-orbit detection atmosphere data and the external geophysical condition data at the same time; the nearest condition is a condition with the minimum difference of other factors when a certain influence source is processed and analyzed;
2) correcting the altitude of the atmosphere in the processing range based on an empirical mode;
3) classifying and denoising the recombined data based on a method combining time sequence superposition median and error analysis;
4) based on gridding atmospheric parameters subjected to multi-condition classification processing, multi-source influence factors of high-rise atmosphere are separated, and influence characteristics of local time, latitude, seasons, geomagnetism and solar radiation of the atmosphere of the hot layer are extracted;
the extraction of the disturbance atmospheric features in the magnetic storm process comprises the following steps:
(1) judging the development process of the magnetic storm event based on the combined analysis of the geomagnetism and interplanetary parameters;
(2) refining atmospheric statistic classification during storm based on track detection and magnetic storm event characteristics;
(3) and determining the atmosphere parameters of the dead day nearest to the detection condition, and extracting the atmosphere disturbed by the storm under different conditions.
2. The low-orbit detection thermal layer atmosphere multi-factor separation and feature extraction method according to claim 1, wherein the low-orbit detection atmosphere data in the low-orbit detection atmospheric multi-source influence factor separation step 1) comprises satellite detection atmosphere density, detection time and detection position information, and the simultaneous external geophysical condition data comprises simultaneous solar activity and geomagnetic activity index data.
3. The low-orbit detection thermal-layer atmospheric multi-factor separation and feature extraction method according to claim 2, wherein the low-orbit atmospheric multi-source influence factor separation step 4) comprises:
and judging the orbit rising and the orbit falling according to the latitude positions of the satellite orbit when the continuous local time variation characteristics are extracted, and distinguishing the local time of the satellite measurement.
4. The low-orbit detection thermal-layer atmosphere multifactor separation and feature extraction method according to claim 1, wherein the step (2) of disturbing atmosphere in the magnetic storm process comprises:
and performing reclassification statistical processing according to the duration of the main phase of the magnetic storm on the basis of the classification of the intensity of the magnetic storm.
5. The low-orbit detection thermal-layer atmosphere multifactor separation and feature extraction method according to claim 1, wherein the step (3) of disturbing atmosphere in the magnetic storm process comprises:
and (3) backtracking forwards according to the magnetic storm time magnetic activity index sequence, determining the dead-day time by judging the geomagnetic condition, and extracting the atmospheric parameters of the dead day under the condition closest to the storm time detection.
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