Spectrum analysis method, device and equipment for vertical coupling process of mars atmosphere

Abstract

The application relates to a spectrum analysis method, device and equipment for a vertical coupling process of a Mars atmosphere, time series data is constructed based on a characteristic variable of a Mars atmospheric layer by acquiring the characteristic variable of the Mars atmospheric layer; a multi-order vector autoregressive model is constructed according to the time series data; a time length is set, the characteristic variable of the Mars atmospheric layer after the set time length is taken as input, zero hypothesis testing is performed on lag influence coefficients in the multi-order vector autoregressive model to test the causal relationship between the characteristic variables; based on the causal relationship between the characteristic variables, a matrix of the lag influence coefficients is subjected to frequency domain relationship conversion to obtain the correlation of the characteristic variables in the frequency domain and calculate PSD values; based on a specific frequency, PDC values of the characteristic variables are acquired; and based on the PSD values and the PDC values, the vertical interaction relationship between the characteristic variables is analyzed by performing asymptotic normal distribution statistics on the PDC values.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of spectrum analysis, in particular to a spectrum analysis method, device and equipment for vertical coupling process of Mars atmosphere. BACKGROUND

[0002] The density of Mars atmosphere is less than one percent of that of the Earth's atmosphere, mainly composed of carbon dioxide, nitrogen and argon. Although the Mars atmosphere is very thin, it still shows complex climate and extreme weather events, including global dust storm, polar warming and annular polar vortex, etc. The research on the atmospheric composition of Mars, such as water vapor and carbon dioxide, and atmospheric phenomena such as dust storm, is very important for exploring the habitability of Mars. The evolution of Mars atmosphere can be used as a reference for related research on the Earth's atmosphere. In addition, the probe such as Mars rover is also running in the interior of Mars atmosphere. Therefore, the Mars atmosphere is the basis for human research on Mars.

[0003] There is a significant interaction between the atmospheric layer of Mars. The interaction between the atmospheric layer refers to the vertical interaction between the atmospheric layer through radiation, dynamics and chemical processes. The vertical interaction can significantly affect the atmospheric circulation, and the changed atmospheric circulation has a counteraction on the development of dust. In addition, the vertical interaction on Mars greatly affects the redistribution of atmospheric elements such as chemical composition (such as water, ozone, etc.), thermal structure (such as polar warming), mesoscale cloud group (such as water and carbon dioxide), etc. The existence of vertical interaction has an important influence on the atmospheric circulation of Mars, which is directly related to the prediction accuracy of the model and the normal flight work of the orbiting probe, so it has become the top priority of the current research on Mars atmosphere.

[0004] However, it is difficult to show the directionality and strength of the vertical interaction in the traditional time domain analysis, for example, the traditional time domain analysis can only show the accompanying relationship between the thermal forcing of dust on the zonal circulation and the zonal circulation filtering of the tide, but it is difficult to show the cause and effect relationship. SUMMARY

[0005] Therefore, it is necessary to provide a spectrum analysis method, device and equipment for vertical coupling process of Mars atmosphere, which can analyze the interaction process and principle between the atmospheric layer of Mars.

[0006] A spectrum analysis method for vertical coupling process of Mars atmosphere, the method comprising:

[0007] Obtaining a characteristic variable of the atmospheric layer of Mars, and constructing time series data based on the characteristic variable of the atmospheric layer of Mars;

[0008] Constructing a multi-order vector autoregressive model according to the time series data;

[0009] The time length is set, and the Martian atmospheric boundary layer characteristic variable after the set time length is taken as input to perform zero hypothesis test on the lag influence coefficient in the multi-order vector autoregressive model to test the causal relationship between the characteristic variables;

[0010] Based on the causal relationship between the characteristic variables, the matrix of the lag influence coefficient is converted in the frequency domain to obtain the correlation of the characteristic variables in the frequency domain, and the PSD value is calculated;

[0011] Based on the specific frequency, the PDC value of each characteristic variable is obtained;

[0012] Based on the PSD value and the PDC value, the vertical interaction relationship between the characteristic variables is analyzed by performing asymptotic normal distribution statistics on the PDC value.

[0013] In one embodiment, the Martian atmospheric boundary layer characteristic variable is obtained, and time series data is constructed based on the Martian atmospheric boundary layer characteristic variable, including:

[0014] The Martian atmospheric boundary layer characteristic variable includes dust, zonal wind and tide;

[0015] After weighting the dust, the zonal wind and the tide vertically according to the mass, the time series data is constructed.

[0016] In one embodiment, a multi-order vector autoregressive model is constructed according to the time series data, and the multi-order vector autoregressive model is represented as:

[0017]

[0018] In the formula, p represents the order of the model, X(t)=[X1(t),X2(t),X3(t)] represents the time series data; X1(t) represents the dust; X2(t) represents the zonal wind; X3(t) represents the tide; ε i (t) represents the innovation vector; X j (t-k) represents the time series data of the time series variable j with a lead of k; a ij (k) is the lag influence coefficient, which represents the k-order lag influence of X j (t-k) on X i (t); t represents the time variable.

[0019] In one embodiment, the time length is set, and the Martian atmospheric boundary layer characteristic variable after the set time length is taken as input to perform zero hypothesis test on the lag influence coefficient in the multi-order vector autoregressive model to test the causal relationship between the characteristic variables, including:

[0020] A zero hypothesis test is performed on lag influence coefficients in the multi-order vector autoregressive model with the sandstorm, the zonal wind and the tide after a set time length as inputs, denoted as:

[0021] H0:a ij (k)=0,k=1,...,p

[0022] H1:k∈{1,...,p},a ij (k)≠0;

[0023] When the zero hypothesis H0 is not established, it is considered that the past of the variable X j (t-k) helps to predict the future of X i (t), that is, X j (t-k) is the Granger reason of X i (t).

[0024] In one embodiment, based on the causal relationship between each feature variable, the matrix of lag influence coefficients is converted in the frequency domain to obtain the correlation of each feature variable in the frequency domain, denoted as:

[0025]

[0026] In the formula, δ ij is a Kronecker symbol, λ is a specific frequency.

[0027] In one embodiment, based on the specific frequency, the PDC value of each feature variable is obtained, including:

[0028] The gPDC information flow of X j (t-k) to X i (t) at the specific frequency λ is the PDC value π ij (λ), denoted as:

[0029]

[0030] In the formula, s is the column of is the Hermite transpose, and S is (I K ⊙C) -1 for PDC, I K is a K×K unit matrix.

[0031] In one embodiment, based on the PSD value and the PDC value, the vertical interaction relationship between each feature variable is analyzed by performing asymptotic normal distribution statistics on the PDC value, including:

[0032] The PDC value is subject to an asymptotic normal distribution, which is represented by an asymptotic normal distribution function, and is:

[0033]

[0034] wherein n s is the number of observations available, γ 2 is a frequency-dependent variable.

[0035] A spectrum analysis device for a vertical coupling process of a Mars atmosphere, the device comprising:

[0036] a data construction module configured to obtain characteristic variables of a Mars atmospheric layer, and construct time series data based on the characteristic variables of the Mars atmospheric layer;

[0037] a model construction module configured to construct a multi-order vector autoregressive model according to the time series data;

[0038] a test module configured to set a time length, and take the characteristic variables of the Mars atmospheric layer after the set time length as input, and perform a zero hypothesis test on lag influence coefficients in the multi-order vector autoregressive model to test a causal relationship between the characteristic variables;

[0039] a frequency domain conversion module configured to perform a frequency domain relationship conversion on a matrix of the lag influence coefficients based on the causal relationship between the characteristic variables, obtain a correlation of the characteristic variables in a frequency domain, and calculate PSD values;

[0040] a PDC value calculation module configured to obtain PDC values of the characteristic variables based on a specific frequency;

[0041] an analysis module configured to analyze a vertical interaction relationship between the characteristic variables by performing an asymptotic normal distribution statistics on the PDC values based on the PSD values and the PDC values.

[0042] A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of the preceding claims when executing the computer program.

[0043] The spectrum analysis method, device and equipment for the vertical coupling process of the Mars atmosphere, by acquiring characteristic variables of the Mars atmosphere stratification, constructing time series data based on the characteristic variables of the Mars atmosphere stratification; constructing a multi-order vector autoregressive model according to the time series data; setting a time length, and taking the characteristic variables of the Mars atmosphere stratification after the set time length as input, performing zero hypothesis test on lag influence coefficients in the multi-order vector autoregressive model to test the causal relationship between the characteristic variables; based on the causal relationship between the characteristic variables, performing frequency domain relationship conversion on a matrix of the lag influence coefficients to obtain the correlation of the characteristic variables in the frequency domain, and calculating PSD values; based on a specific frequency, acquiring PDC values of the characteristic variables; based on the PSD values and the PDC values, performing asymptotic normal distribution statistics on the PDC values to analyze the vertical interaction relationship between the characteristic variables.

[0044] The present application quantitatively analyzes the interaction process and principle between the atmospheric stratifications by judging the causal relationship between the characteristic variables, and simultaneously considers the PSD values and the PDC values when performing the spectrum analysis, so as to judge whether the characteristic variables at the same frequency have a significant influence, and then analyze the interaction between the atmospheric stratifications of Mars. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 It is a flowchart of the spectrum analysis method for the vertical coupling process of the Mars atmosphere in an embodiment;

[0046] Figure 2 It is a result diagram of EXP1 in the early spring of the southern hemisphere (Ls = 180°-210°) during the first Martian year in an embodiment; wherein, Figure 2 (a) is an average dust mass mixing ratio diagram, Figure 2 (b) is a zonal wind diagram, Figure 2 (c) is an amplitude diagram of the diurnal migration tide DW1;

[0047] Figure 3 It is a result diagram of EXP2 in the early spring of the southern hemisphere (Ls = 180°-210°) during the first Martian year in an embodiment; wherein, Figure 3 (a) is an average dust mass mixing ratio diagram, Figure 3 (b) is a zonal wind diagram, Figure 3 (c) is an amplitude diagram of the diurnal migration tide DW1;

[0048] Figure 4 It is a result diagram of EXP3 in the early spring of the southern hemisphere (Ls = 180°-210°) during the first Martian year in an embodiment; wherein, Figure 4 (a) is an average dust mass mixing ratio diagram, Figure 4(b) is a plot of the zonal winds, Figure 4 (c) is a plot of the amplitude of the diurnal migrating tide DW1 ;

[0049] Figure 5 Results for EXP4 during the early southern spring (Ls = 180°-210°) of the first Martian year simulated in one embodiment; where, Figure 5 (a) is a plot of the average dust mass mixing ratio, Figure 5 (b) is a plot of the zonal winds, Figure 5 (c) is a plot of the amplitude of the diurnal migrating tide DW1 ;

[0050] Figure 6 Results for EXP5 during the early southern spring (Ls = 180°-210°) of the first Martian year simulated in one embodiment; where, Figure 6 (a) is a plot of the average dust mass mixing ratio, Figure 6 (b) is a plot of the zonal winds, Figure 6 (c) is a plot of the amplitude of the diurnal migrating tide DW1 ;

[0051] Figure 7 Results for EXP6 during the early southern spring (Ls = 180°-210°) of the first Martian year simulated in one embodiment; where, Figure 7 (a) is a plot of the average dust mass mixing ratio, Figure 7 (b) is a plot of the zonal winds, Figure 7 (c) is a plot of the amplitude of the diurnal migrating tide DW1 ;

[0052] Figure 8 Results for EXP1 during the early southern spring (Ls = 270°-300°) of the first Martian year simulated in one embodiment; where, Figure 8 (a) is a plot of the average dust mass mixing ratio, Figure 8 (b) is a plot of the zonal winds, Figure 8 (c) is a plot of the amplitude of the diurnal migrating tide DW1 ;

[0053] Figure 9 Results for EXP2 during the early southern spring (Ls = 270°-300°) of the first Martian year simulated in one embodiment; where, Figure 9 (a) is a plot of the average dust mass mixing ratio, Figure 9 (b) is a plot of the zonal winds, Figure 9 (c) is a plot of the amplitude of the diurnal migrating tide DW1 ;

[0054] Figure 10Fig. 6 is a plot of results for EXP 3 during the early southern spring (Ls = 270°-300°) of the first Martian year simulated in one embodiment; wherein, Figure 10 (a) is a plot of the average dust-to-mass mixing ratio, Figure 10 (b) is a plot of the zonal wind, Figure 10 (c) is a plot of the amplitude of the diurnal migrating tide DW1 ;

[0055] Figure 11 Fig. 7 is a plot of results for EXP 4 during the early southern spring (Ls = 270°-300°) of the first Martian year simulated in one embodiment; wherein, Figure 11 (a) is a plot of the average dust-to-mass mixing ratio, Figure 11 (b) is a plot of the zonal wind, Figure 11 (c) is a plot of the amplitude of the diurnal migrating tide DW1 ;

[0056] Figure 12 Fig. 8 is a plot of results for EXP 5 during the early southern spring (Ls = 270°-300°) of the first Martian year simulated in one embodiment; wherein, Figure 12 (a) is a plot of the average dust-to-mass mixing ratio, Figure 12 (b) is a plot of the zonal wind, Figure 12 (c) is a plot of the amplitude of the diurnal migrating tide DW1 ;

[0057] Figure 13 Fig. 9 is a plot of results for EXP 6 during the early southern spring (Ls = 270°-300°) of the first Martian year simulated in one embodiment; wherein, Figure 13 (a) is a plot of the average dust-to-mass mixing ratio, Figure 13 (b) is a plot of the zonal wind, Figure 13 (c) is a plot of the amplitude of the diurnal migrating tide DW1 ;

[0058] Figure 14 Fig. 10 is a plot of the frequency domain distribution of PDC values and power spectral density (PSD) for EXP 1 under the no gravity wave condition in one embodiment, wherein, Figure 14 (a) is the distribution of PDC values for the dust effect on the zonal wind, Figure 14 (b) is the distribution of PDC values for the zonal wind effect on DW1, Figure 14 (c) is the distribution of PDC values for the dust effect on DW1, Figure 14 (d) is the power spectral density of DW1 itself, Figure 14 (e) is the power spectral density of the dust itself, Figure 14 (f) is the power spectral density of the zonal wind itself;

[0059] Figure 15Fig. 1 is a schematic diagram of the frequency domain distribution of PDC value and power spectral density (PSD) in EXP3 under the condition of no gravity wave in one embodiment, wherein, Figure 15 (a) is the PDC value distribution of the influence of sand dust on zonal wind, Figure 15 (b) is the PDC value distribution of the influence of zonal wind on DW1, Figure 15 (c) is the PDC value distribution of the influence of sand dust on DW1, Figure 15 (d) is the power spectral density of DW1 itself, Figure 15 (e) is the power spectral density of sand dust itself, Figure 15 (f) is the power spectral density of zonal wind itself;

[0060] Figure 16 Fig. 2 is a schematic diagram of the frequency domain distribution of PDC value and power spectral density (PSD) in EXP5 under the condition of no gravity wave in one embodiment, wherein, Figure 16 (a) is the PDC value distribution of the influence of sand dust on zonal wind, Figure 16 (b) is the PDC value distribution of the influence of zonal wind on DW1, Figure 16 (c) is the PDC value distribution of the influence of sand dust on DW1, Figure 16 (d) is the power spectral density of DW1 itself, Figure 16 (e) is the power spectral density of sand dust itself, Figure 16 (f) is the power spectral density of zonal wind itself;

[0061] Figure 17 Fig. 3 is a structural block diagram of a frequency spectrum analysis device for vertical coupling process of Mars atmosphere in one embodiment;

[0062] Figure 18 Fig. 4 is an internal structural diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0064] The inventors found that there are not many quantitative studies on the qualitative research between atmospheric layers in the process of implementing the present scheme, and the interaction between sand dust, tides and circulation in the frequency domain has not been fully studied due to the lack of quantitative frequency domain analysis in vertical interaction research.

[0065] On the other hand, the zonal wind is also found to be crucial in the vertical interaction of the Martian atmosphere and can also play an intermediate role in the process of dynamic vertical interaction. This is because the zonal wind is the main momentum component of the atmospheric circulation, which modulates the tides through the Doppler shift effect, affects the outbreak, propagation and development of dust storms, and absorbs the momentum and energy dragged by the gravity wave (gravity wave) while filtering the gravity wave to a specific frequency. The gravity wave, dust and tides in turn have an impact on the zonal wind. The convergence of tidal heat and momentum flux has a strong forcing effect on the zonal wind.

[0066] In short, in the vertical interaction of dust and thermal tides, the zonal wind is an important link in the signal transmission between the two tides, and the gravity wave offsets the modulation of the zonal wind by the dust by slowing down the wind. In addition, due to the lack of signal analysis in the vertical interaction study, the interaction between dust, tides and circulation and the relationship between the zonal wind SAO and the zonal wind in the frequency domain have not been fully studied. Therefore, when analyzing this vertical interaction process, it is necessary to consider the zonal wind and gravity wave as a whole.

[0067] The traditional view qualitatively proves that there is a vertical interaction between dust and tides, and the zonal wind field can significantly affect the vertical propagation of thermal tides. However, the present application will quantitatively answer what frequency domain the dust signal interacts with the thermal tides, under what conditions and what strength, and what specific role the zonal wind plays in it.

[0068] The embodiments of the present application will be described in detail below with reference to the accompanying drawings of the embodiments of the present application.

[0069] In one embodiment, as shown in Figure 1 a frequency spectrum analysis method for vertical coupling process of Martian atmosphere is provided, comprising the following steps:

[0070] Step 102, obtaining the characteristic variables of the Martian atmosphere layer, and constructing time series data based on the characteristic variables of the Martian atmosphere layer.

[0071] Specifically, the characteristic variables of the Martian atmosphere layer include the convective layer dust X1(t), the zonal wind at the top of the convective layer X2(t) and the intermediate layer tide X3(t); by weighting the convective layer dust X1(t), the zonal wind at the top of the convective layer X2(t) and the intermediate layer tide X3(t) vertically, the meridional weighting factor is cosφ, wherein φ is the latitude before performing the partial directional coherence (PDC) analysis. The time series data for partial directional coherence (PDC) analysis is constructed by the three characteristic variables, that is, the vector signal X(t) = [X1(t), X2(t), X3(t)].

[0072] Step 104, constructing a multi-order vector autoregressive model according to the time series data.

[0073] It can be understood that, in this step, the Granger causality is used as a measurement index to measure the causality between variables in the vertical coupling process of the Martian atmosphere, a multi-order vector autoregressive model is constructed, and by comparing the probability distribution of variable A at this moment with all information at the last moment and the probability distribution of variable A at this moment with all information at the last moment except variable B, it is determined whether variable B has a causal relationship with variable A.

[0074] Specifically, the p-order vector autoregressive model, abbreviated as VAR[p], is used for the vector signal X(t), which is defined by the following equation:

[0075]

[0076] wherein ε i (t) is a new information vector with a new information covariance C = [σ ij ], representing the residual error between the true value X i (t) and the regression result of the VAR[p] autoregressive model, and specifically:

[0077]

[0078] In the formula, p represents the order of the model, X(t) = [X1(t), X2(t), X3(t)] represents time series data; X1(t) represents dust; X2(t) represents zonal wind; X3(t) represents tide; ε i (t) represents a new information vector; X j (t-k) represents time series data of the previous variable j with a time lag of k; a ij (k) is a lag influence coefficient, representing the k-order lag influence of X j (t-k) on X i (t); t represents a time variable.

[0079] If a ij (k) ≠ 0 and statistically significant, then X j (t-k) has a Granger causal relationship with X i (t). This causal relationship means that X j (t) indeed helps to predict X i (t), and vice versa.

[0080] Step 106, set the time length, and use the Martian atmospheric layer characteristic variables after the set time length as input to perform zero hypothesis test on the lag influence coefficients in the multi-order vector autoregressive model to test the causal relationship between the characteristic variables.

[0081] Specifically, a time length is set, and the sand, zonal wind and tide after the time length are taken as inputs to set the lag effect coefficient a ij (k) of the multi-order vector autoregressive model

[0082] H0: a ij (k) = 0, k = 1,..., p

[0083] H1: k ∈ {1,..., p}, a ij (k) ≠ 0

[0084] When the null hypothesis H0 is not established, it is considered that the past of the variable X j (t-k) helps to predict the future of X i (t), that is, X j (t-k) is the Granger cause of X i (t).

[0085] Step 108, based on the causal relationship between each characteristic variable, the matrix of the lag effect coefficient is converted in the frequency domain, the correlation of each characteristic variable in the frequency domain is obtained, and the PSD value is calculated.

[0086] Specifically, considering the ternary time series X(t) = [X1(t), X2(t), X3(t)] with a p-order vector autoregressive process VAR(p) defined in step 102, the coefficient a ij (k) describes the lag effect of X j (t-k) on X i (t), and the frequency domain representation of the matrix A(k) composed of the coefficient a ij (k) can be denoted as By converting the matrix of the lag effect coefficient a ij (k) in the frequency domain, it is denoted as:

[0087]

[0088] In the formula, δ ij is the Kronecker symbol, λ is a specific frequency.

[0089] Through the formula, the correlation of each characteristic variable in the frequency domain is obtained, and the PSD value (power spectral density) is calculated through the above formula.

[0090] Step 110, based on the specific frequency, the PDC value of each characteristic variable is obtained.

[0091] Specifically, based on step 108, the gPDC information flow of X j (t-k) on X i (t) at a specific frequency λ is the PDC value πij (λ), is denoted as:

[0092]

[0093] where s is is denoted as is the Hermitian transpose, S is (I K ⊙C) -1 for PDC, I K is a KxK identity matrix.

[0094] It can be understood that the PDC value π ij (λ) is normalized, indicating that the signal flowing from X j (t-k) to X i (t) accounts for the proportion of all signals flowing out of X j (t-k).

[0095] In step 112, based on the PSD value and the PDC value, the vertical interaction relationship between each characteristic variable is analyzed by performing asymptotic normal distribution statistics on the PDC value.

[0096] Specifically, the PDC value is subject to an asymptotic normal distribution, which is represented by an asymptotic normal distribution function, as follows:

[0097]

[0098] where n s is the number of available observations, and γ 2 is a frequency-dependent variable.

[0099] Through the above steps, the gPDC of the multi-order vector autoregressive model is used to understand the mutual relationship between the Mars atmospheric processes (zonal wind, tides, and dust changes).

[0100] The present scheme directly and quantitatively shows the frequency and strength of the interaction between dust, zonal wind, tides, and gravity waves through the PDC method. The PDC value from unit a to unit b represents the influence of a on b, while the power spectral density (PSD) represents the energy density, both in the frequency domain. The PDC value and the PSD need to be considered together, rather than separately, because looking at one of them alone does not make much sense for analyzing the interaction. Generally speaking, the PDC value at a certain frequency corresponds to the strength of the interaction, while the PSD value corresponds to the energy density. We believe that when the PDC value of a on b and the PSD of both are high at the same frequency, a has a significant influence on b at that frequency and the influence acts on the frequency band where the energy density is concentrated, so it is worth paying attention to. However, if they are both high at different frequencies, the influence is relatively insignificant.

[0101] The spectrum analysis method, device and equipment for the vertical coupling process of the Mars atmosphere, by acquiring the Mars atmosphere stratification characteristic variables, constructing time series data based on the Mars atmosphere stratification characteristic variables; constructing a multi-order vector autoregressive model according to the time series data; setting a time length, and taking the Mars atmosphere stratification characteristic variables after the set time length as input, performing zero hypothesis test on the lag influence coefficients in the multi-order vector autoregressive model to test the causal relationship between the characteristic variables; based on the causal relationship between the characteristic variables, performing frequency domain relationship conversion on the matrix of the lag influence coefficients to obtain the correlation of the characteristic variables in the frequency domain, and calculating the PSD value; based on a specific frequency, acquiring the PDC value of each characteristic variable; based on the PSD value and the PDC value, by performing asymptotic normal distribution statistics on the PDC value, the vertical interaction relationship between the characteristic variables is analyzed.

[0102] The application quantitatively analyzes the interaction process and principle between the atmospheric stratifications by judging the causal relationship between the characteristic variables, and simultaneously considers the PSD value and the PDC value when performing the spectrum analysis, so as to judge whether the characteristic variables at the same frequency have a significant influence, and then analyze the interaction between the Mars atmospheric stratifications.

[0103] In one of the embodiments, the scheme proposed in the application is verified by experiments.

[0104] Firstly, the Mars Planetary Climate Model (Mars PCM-LMDZ) with Laboratoire de Météorologie Dynamique Zoom (LMDZ) as the kernel is used to carry out six Mars atmosphere simulation experiments with a length of 10 Mars years. The planetary climate model is used to simulate the coupling of the atmospheric stratification. Based on three initial fields (cold, hot and climate state conditions) under the widely used Mars Climate Database (MCD) v6.1, the existence of gravity (with / without gravity wave) is simulated by controlling the gravity wave module, so as to realize the simulation of the coupling of the stratification wave signal. The Mars PCM-LMDZ simulation results are analyzed based on the partial directed coherence (PDC), the performance form of the stratification coupling in the frequency domain is discussed, and it is compared with the intrinsic atmospheric phenomenon.

[0105] Secondly, the experimental parameters are set. By carrying out the Mars PCM-LMDZ simulation in the global range, 64*48*49 grid points are contained along two horizontal and vertical dimensions (the model top is located at a height of about 249.5 km from the ground surface).

[0106] Six experiments (EXP1-EXP6) with / without GWs in "cold", "warm" and "climatic" scenarios were performed by running the PCM. The temperature, dust mass mixing ratio (mr) and zonal wind in the model results were analyzed to investigate the zonal wind and the diurnal westward migrating tide (DW1) in the dust convective layer (~40 km), the upper troposphere and lower mesosphere (UTLM, 20-50 km) and the middle mesosphere (50-100 km) over Mars during 10 Martian years (MYs).

[0107] Table 1. Model configurations

[0108]

[0109] Finally, the results were analyzed, Figures 2 to 7 The simulated mean dust mass mixing ratio, zonal wind and the amplitude of the diurnal migrating tide DW1 during the early southern spring (Ls = 180°-210°) in the first Martian year are shown. Figures 8 to 13 The simulated mean dust mass mixing ratio, zonal wind and the amplitude of the diurnal migrating tide DW1 during the early southern spring (Ls = 270°-300°) in the first Martian year are shown. Figures 2 to 13 In the middle, the horizontal axis represents latitude (°) and the vertical axis represents height (km)

[0110] Figures 2 to 7 It is shown that, in the southern spring, the equatorial DW1 amplitude maximum is located at about 70 km, and the non-equatorial amplitude minima at high latitudes at the same height. The Mars Climate Sounder (MCS) observations ( Figure 4 The seasonal variation of the DW1 amplitude maximum height in the middle is reproduced by the southern spring ( Figure 10 (c)) being lower than the southern summer ( Figure 4 (c)).

[0111] At the same time, the simulated dust distribution is in good agreement with the observations. In Ls = 180-210 and 270-300, the southern dust develops and reaches higher altitudes than the northern ( Figure 4 (a) and Figure 10 (a)). The latter is more vigorous due to the stronger solar radiation. Our results also show that, in the "warm" dust background ( Figure 6 (a), Figure 7 (a)), the mean dust mass mixing ratio at 40 km can reach nearly 5*10-6 kg / kg, regardless of the presence of GWs. The dust reaching the upper troposphere (~40 km) can exert a significant thermal forcing on the circulation and tides.

[0112] The coupling between dust, zonal winds, tides and gravity waves is demonstrated by PDC method. The PDC results show that the zonal winds have a significant semiannual impact on DW1 regardless of the gravity wave and dust conditions.

[0113] In addition, the SAO of zonal winds and the impact of dust on zonal winds are distinguished. The SAO of zonal winds leads to the semiannual scale power spectral density of zonal winds and the PDC value of zonal winds to DW1. Dust modulates zonal winds through thermal forcing. As shown in Figures 14 to 16 The PDC values and the frequency domain distribution of power spectral density (PSD) in EXP1, 3, 5 without gravity wave conditions are given, and the "cold" (low dust), "climatic state" and "warm" (high dust) dust background here refers to EXP2, EXP4 and EXP6 in Table 1, respectively.

[0114] Based on the analysis results of the method provided by the present application, it is found that with the change of the background dust content, the impact of dust on DW1 and zonal winds is continuously enhanced on the seasonal to subseasonal scale, so that in the warm background, this impact is even almost as significant as the SAO of zonal winds. With the advantage of quantitative frequency domain analysis in comparing the strength of the two influences, we distinguish the SAO of zonal winds and the impact of dust on zonal winds: the SAO of zonal winds dominates the power spectral density of zonal winds and the PDC value of zonal winds to DW1 on the semiannual scale, which is largely due to the strong "handedness" of the Martian climate caused by the topography dichotomy [3, 104]; and dust modulates zonal winds on the seasonal to subseasonal scale through thermal forcing.

[0115] The zonal winds transmit the seasonal to subseasonal dust storm signal from the lower atmosphere (<~40 km) to the tides in the middle layer (50~100 km) through the Doppler shift tides they pass through. With the increase of dust content, this interaction through zonal winds gradually dominates the vertical interaction, and the PDC values and PSD distributions of the three tend to be consistent. In the "warm" background, active dust activity leads to the seasonal to subseasonal impact of dust on zonal winds Figure 16 (a)).

[0116] In addition, the thermal effect transfers dust energy to zonal winds and DW1, leading to the seasonal-subseasonal cycle energy peak of DW1 Figure 16 (c) and zonal winds Figure 16 (e). The three elements Figure 14The semiannual peak common to the PSD of the zonal wind (c), the temperature (d), and the dust content (e) represents the dominant of the SAO in the "cold" (low dust) background. The SAO of the mean wind modulates the propagation of the tides on the semiannual scale. The background zonal wind has a semiannual-scale forcing on the propagation of the tides, which dominates the seasonal variations of the diurnal tide in the middle and upper atmosphere of the Earth and the mesosphere and the upper thermosphere of Mars. We observe that the semiannual peak of the zonal wind and the tides interaction is always present, although its strength varies with the dust and the gravity waves. Moreover, these are the result of the SAO of the zonal wind.

[0117] The PDC also explains the variations of the gravity waves. We find that the influence of the gravity waves is limited to the seasonal to subseasonal and annual regions, with no significant influence on the semiannual interaction. This is reflected in the more significant seasonal to subseasonal dust influence on DW1, which is due to the weakening of the gravity wave drag. The gravity waves drive large-scale heating / cooling through adiabatic downward / upward large-scale vertical motions linked to the significantly varying zonal wind. Above 1 Pa (~ 50 km), the gravity waves mainly slow down the intensity of the wind. The increase of the background dust content is thought to favor the development of the meridional circulation but suppress the gravity waves, while the weakening of the gravity wave sources and the more stable mean zonal flow reduce the gravity wave drag events in the dust storms. Therefore, the eastward wind is accelerated to a higher speed than it would have been without the gravity waves.

[0118] In the experiments, the turning off of the gravity waves directly eliminates the source of the zonal wind slowdown, leading to an enhancement of the dust influence on the zonal wind. Therefore, the eastward wind in the southern spring, when the dust activity is high, is stronger due to the reduction of the gravity wave drag.

[0119] In summary, to determine how the dust, the tides, and the circulation interact in the frequency domain and how the variations of the background dust and the gravity waves affect the interaction, the method provided by the present invention performs a signal analysis of the interaction. It is found that the natural frequency of the dust activity is seasonal to subseasonal. The dust imposes this high-frequency energy on the mean wind. The zonal wind transfers the seasonal to subseasonal dust storm signal to the tides in the upper atmosphere through Doppler shifting. The seasonal variations of the background dust lead to a seasonal to subseasonal signal. When the background dust content is high enough, the seasonal to subseasonal signal is enhanced. Moreover, the dust thermal forcing cools the air near the surface and leads to a weakening of the topographic effects, including the Martian bimodal terrain dichotomy that causes the SAO, so the semiannual influence of the SAO is weakened. Therefore, the influence of the seasonal to subseasonal zonal wind on DW1 is enhanced, while the influence of the semiannual zonal wind on DW1 is weakened to the same importance as the high background dust.

[0120] The impact of gravity waves is limited to the seasonal to sub-seasonal and annual frequency domain, and is not significant for semi-annual interaction. The gravity wave drag acts to reduce the zonal wind speed, offsetting the modulation of the zonal wind speed by the dust. However, when the dust activity is strong enough, the baroclinicity and convective stability generated by the dust activity weaken the gravity wave generation source, and the increase in convective stability leads to the stabilization of the mean zonal flow, and the gravity wave drag is greatly weakened.

[0121] Previous studies have shown that the zonal wind speed is mainly affected by thermal forcing, and is modulated by the variability of dust. However, the frequency and intensity of the modulation have not been fully quantified. By changing the background dust content, the present invention found that the impact of dust on DW1 and zonal wind is most significant at the seasonal to sub-seasonal scale. The zonal wind transmits the seasonal to sub-seasonal dust storm signal from the lower atmosphere to the middle atmosphere through the Doppler shift tide of itself. With the increase of dust content, this interaction gradually dominates in the vertical interaction, and the PDC values and PSD distributions of the three tend to be consistent. In the warm background, the SAO of dust on the zonal wind is almost equally important. In addition, the present invention distinguishes the SAO of the zonal wind and the impact of dust on the zonal wind. The SAO of the zonal wind leads to the semi-annual scale power spectral density of the zonal wind and the PDC value of the zonal wind to DW1. Dust modulates the zonal wind through thermal forcing.

[0122] The present invention analyzes the vertical coupling processes in the Martian atmosphere by introducing the PDC technique, and uses the frequency domain analysis method to study the interrelation between atmospheric processes. This makes up for the lack of causality in traditional time domain analysis. By comprehensively evaluating the interaction between atmospheric processes in the vertical direction, the impact of these processes on the reception or transmission of information in the atmosphere is revealed. This analysis method can even help us discover feedback effects, and thus gain a deeper understanding of the complex phenomena in the Martian atmosphere.

[0123] It should be understood that, although Figure 1 The steps in the flowchart of the present invention are displayed in sequence according to the direction of the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order limitation for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps in the present invention can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be alternately executed with at least part of other steps or sub-steps or stages of other steps.

[0124] In one embodiment, as Figure 17As shown, a spectrum analysis device for a Mars atmospheric vertical coupling process is provided, comprising a data construction module 200, a model construction module 202, a verification module 204, a frequency domain conversion module 206, a PDC value calculation module 208 and an analysis module 210, wherein:

[0125] The data construction module 200 is configured to obtain characteristic variables of a Mars atmospheric layer, and construct time series data based on the characteristic variables of the Mars atmospheric layer.

[0126] The model construction module 202 is configured to construct a multi-order vector autoregressive model according to the time series data.

[0127] The verification module 204 is configured to set a time length, and take the characteristic variables of the Mars atmospheric layer after the set time length as input, to perform a zero hypothesis test on lag influence coefficients in the multi-order vector autoregressive model, so as to verify the causal relationship between the characteristic variables.

[0128] The frequency domain conversion module 206 is configured to perform frequency domain relationship conversion on a matrix of the lag influence coefficients based on the causal relationship between the characteristic variables, to obtain the correlation of the characteristic variables in the frequency domain, and to calculate PSD values.

[0129] The PDC value calculation module 208 is configured to obtain PDC values of the characteristic variables based on a specific frequency.

[0130] The analysis module 210 is configured to analyze the vertical interaction relationship between the characteristic variables based on the PSD values and the PDC values, by performing asymptotic normal distribution statistics on the PDC values.

[0131] The specific limitations of the spectrum analysis device for the Mars atmospheric vertical coupling process can refer to the limitations of the spectrum analysis method for the Mars atmospheric vertical coupling process in the foregoing, which will not be repeated here. Each module in the spectrum analysis device for the Mars atmospheric vertical coupling process can be realized by software, hardware and a combination thereof, in whole or in part. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform the operations corresponding to each module.

[0132] In one embodiment, a computer device is provided, which can be a server, and an internal structure diagram of the computer device can be as shown in Figure 18The computer device includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store the spectral analysis data of the vertical coupling process of the Martian atmosphere. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a spectral analysis method of a vertical coupling process of a Martian atmosphere.

[0133] Those skilled in the art can understand that, Figure 18 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0134] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the following steps:

[0135] Step 102, obtaining a characteristic variable of a Martian atmospheric layer, and constructing time series data based on the characteristic variable of the Martian atmospheric layer.

[0136] Step 104, constructing a multi-order vector autoregressive model according to the time series data.

[0137] Step 106, setting a time length, and taking the characteristic variable of the Martian atmospheric layer after the set time length as input, performing a zero hypothesis test on the lag influence coefficient in the multi-order vector autoregressive model to test the causal relationship between the characteristic variables.

[0138] Step 108, based on the causal relationship between the characteristic variables, performing a frequency domain relationship conversion on the matrix of the lag influence coefficient to obtain the correlation of the characteristic variables in the frequency domain and calculate the PSD value.

[0139] Step 110, based on a specific frequency, obtaining the PDC value of each characteristic variable.

[0140] Step 112, based on the PSD value and the PDC value, performing asymptotic normal distribution statistics on the PDC value to analyze the vertical interaction relationship between the characteristic variables.

[0141] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, databases, or other media in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0142] The technical features of the above embodiments can be combined in any way. In order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, but as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0143] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of the patent protection of the present application should be subject to the appended claims.

Claims

1. A method for spectral analysis of the vertical coupling process of the Martian atmosphere, characterized in that, The method comprises: acquiring Mars atmospheric layer characteristic variables, and constructing time series data based on the Mars atmospheric layer characteristic variables; constructing a multi-order vector autoregressive model according to the time series data; setting a time length, and taking the Mars atmospheric layer characteristic variables after the time length as input, performing zero hypothesis testing on lag influence coefficients in the multi-order vector autoregressive model to test the causal relationship between the characteristic variables; based on the causal relationship between the characteristic variables, performing frequency domain relationship conversion on a matrix of the lag influence coefficients to obtain the correlation of the characteristic variables in the frequency domain, and calculating PSD values; based on a specific frequency, acquiring PDC values of the characteristic variables; based on the PSD values and the PDC values, performing asymptotic normal distribution statistics on the PDC values to analyze the vertical interaction relationship between the characteristic variables; wherein the PSD value is a power spectral density, and the PDC value is a partial directional coherence value; acquiring Mars atmospheric layer characteristic variables, and constructing time series data based on the Mars atmospheric layer characteristic variables, comprising: the Mars atmospheric layer characteristic variables include dust, zonal wind and tide; after weighting the dust, the zonal wind and the tide vertically according to mass, time series data is constructed; a multi-order vector autoregressive model is constructed according to the time series data, and the multi-order vector autoregressive model is represented as: ； In the formula, Indicates the order of the model. Represents time series data; Indicates sand and dust; Indicates zonal wind; Indicates tides; Represents the innovation vector; Indicates being ahead of time Variables before time series Time series data; The lag effect coefficient represents... right of The effect of hysteresis; Represents a time variable; , .

2. The method of spectral analysis of the process of vertical coupling of the Martian atmosphere according to claim 1, characterized by, setting a time length, and taking the Mars atmospheric layer characteristic variables after the time length as input, performing zero hypothesis testing on lag influence coefficients in the multi-order vector autoregressive model to test the causal relationship between the characteristic variables, comprising: setting a time length, and taking the dust, the zonal wind and the tide after the time length as input, performing zero hypothesis testing on lag influence coefficients in the multi-order vector autoregressive model, represented as: ； When the null hypothesis is not true, then the variable is considered to be a Granger cause of if past values of help to predict future values of .

3. The method of spectral analysis of the vertical coupling process of the Martian atmosphere according to claim 1 or 2, characterized by, based on the causal relationship between the characteristic variables, performing frequency domain relationship conversion on a matrix of the lag influence coefficients to obtain the correlation of the characteristic variables in the frequency domain, represented as: ； wherein is the Kronecker symbol, , is a specific frequency.

4. The method of spectral analysis of the process of vertical coupling of the Martian atmosphere according to claim 3, characterized by, based on a specific frequency, acquiring PDC values of the characteristic variables, comprising: to a specific frequency upper to the gPDC information stream for PDC values is represented as: ； where s is , the coefficient , the matrix , the frequency domain representation of , , the column of , , the Hermitian transpose of , , the identity matrix , and , the innovation covariance.

5. The method of spectral analysis of the process of vertical coupling of the Martian atmosphere according to claim 4, characterized by, based on the PSD values and the PDC values, performing asymptotic normal distribution statistics on the PDC values to analyze the vertical interaction relationship between the characteristic variables, comprising: the PDC value obeys an asymptotic normal distribution, and is represented by an asymptotic normal distribution function, which is: ； wherein is the number of observations available, is the frequency-dependent variable.

6. A spectral analysis device for a Mars atmospheric vertical coupling process, characterized by, the device comprises: a data construction module configured to acquire Mars atmospheric layer characteristic variables, and construct time series data based on the Mars atmospheric layer characteristic variables; a model construction module configured to construct a multi-order vector autoregressive model according to the time series data; a testing module configured to set a time length, and take the Mars atmospheric layer characteristic variables after the time length as input, perform zero hypothesis testing on lag influence coefficients in the multi-order vector autoregressive model to test the causal relationship between the characteristic variables; The frequency domain conversion module is configured to perform frequency domain relationship conversion on the matrix of the lag influence coefficients based on the causal relationship between the characteristic variables, to obtain the correlation of the characteristic variables in the frequency domain, and to calculate PSD values; The PDC value calculation module is configured to obtain PDC values of the characteristic variables based on specific frequencies. The analysis module is configured to analyze the vertical interaction relationship between the characteristic variables by performing asymptotic normal distribution statistics on the PDC values based on the PSD values and the PDC values. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6 when the computer program is executed by the processor. The processor implements the steps of the method in any one of claims 1 to 5 when executing the computer program.

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