A method for multi-scale inversion of ocean thermohaline structure and dynamic height

Abstract

The present application belongs to the technical field of marine environmental parameter inversion and marine dynamics observation, and specifically relates to a marine temperature-salinity structure and dynamic height multi-scale inversion method, which comprises the following steps: collecting round-trip sound wave propagation time and seafloor pressure data measured by PIES arranged on the seafloor of a target sea area; pre-processing the data to generate baroclinic contribution sound propagation time data; obtaining temperature-salinity profile data corresponding to the PIES position based on high spatiotemporal resolution marine model data, and calculating model sound propagation time; performing multi-scale dynamics signal separation on the model data and PIES data; constructing a multi-scale gravity empirical mode mapping function; using the mapping function to invert each scale temperature-salinity profile and dynamic height; and superimposing the results of each scale to generate a multi-scale synthesized total temperature-salinity structure and total dynamic height. The present application is based on multi-scale joint inversion of PIES data and high-resolution numerical data, and improves the ability to capture and invert submesoscale and high-frequency dynamic processes.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of marine environmental parameter inversion and marine dynamics observation, and particularly relates to a marine temperature-salinity structure and dynamic height multi-scale inversion method. BACKGROUND

[0002] The temperature, salinity (referred to as temperature-salinity structure) and dynamic height of the ocean are important physical characteristics of marine dynamic processes. The temperature-salinity structure directly reflects the density characteristics of seawater, and the dynamic height is formed by the vertical integration of the seawater density anomaly caused by the temperature-salinity structure. The two are closely related and jointly determine the three-dimensional flow field structure of the ocean. Accurate acquisition of multi-scale temperature-salinity structure and dynamic height is of key significance for analyzing and predicting ocean circulation, climate change, energy and material transport, and local marine ecological environment.

[0003] At present, the observation of the marine temperature-salinity structure and dynamic height mainly relies on satellite remote sensing, shipborne observation equipment (such as CTD), Argo floats, underwater gliders and seabed anchor observation systems. Satellite altimeters have the advantage of global long-term continuous observation and have become an important means of studying large-scale and mesoscale dynamic processes of the ocean. However, due to the limitations of spatial resolution and observation period of satellite altimeters (spatial scale generally greater than 100 kilometers, time scale generally greater than 10 days), it is difficult to effectively distinguish sub-mesoscale (1 to 50 kilometers) and tidal processes. Although shipborne CTD and Argo floats can provide accurate temperature-salinity profile data, the observation cost is high, and the spatial coverage is limited, making it difficult to observe small-scale processes continuously.

[0004] In recent years, pressure-sensing inverse echo sounders (PIES) have been widely used in the observation and research of marine temperature-salinity structure and dynamic height. However, the traditional gravity empirical mode (GEM) method widely used at present usually constructs a static empirical mode mapping relationship with historical discrete observation of temperature-salinity data, lacks time continuity and fine scale information, and therefore cannot effectively capture and invert the rapid changes of sub-mesoscale and high-frequency processes, resulting in limited inversion accuracy and scale resolution.

[0005] On the other hand, high-resolution numerical models can simulate the temperature-salinity structure changes of mesoscale, sub-mesoscale and tidal scale in the ocean, providing high-quality and continuous temperature-salinity structure simulation data for the study of high-frequency and sub-mesoscale dynamic processes. The fusion of such model data and PIES observation data can significantly enhance the scale resolution of marine dynamic processes. However, at present, there is still a lack of an effective technical method to combine the temperature-salinity information of high-resolution models with PIES high-frequency observation data to establish a multi-scale mapping relationship that can accurately invert the temperature-salinity structure and dynamic height.

[0006] Therefore, there is an urgent need in the art to propose a technical method for accurately and multi-scale joint inversion of ocean thermohaline structure and dynamic height. SUMMARY

[0007] The application aims to provide a multi-scale inversion method of ocean thermohaline structure and dynamic height based on pressure sensing inverse echo sounder (PIES), which realizes a multi-scale joint inversion method based on PIES data and high-resolution model data by innovatively constructing a multi-scale gravity empirical mode mapping function (MS-GEM-EMMF), thereby effectively improving the inversion accuracy and scale resolution capability of thermohaline structure and dynamic height, so as to overcome the defects of the above-mentioned existing inversion methods.

[0008] The technical scheme adopted by the application to achieve the above-mentioned purpose is: a multi-scale inversion method of ocean thermohaline structure and dynamic height, comprising the following steps:

[0009] S1: collecting the round-trip sound wave propagation time and seabed pressure data measured by the PIES arranged on the seabed of the target sea area at a set frequency;

[0010] S2: preprocessing the data collected in step S1 to generate baroclinic contribution sound propagation time data with a resolution of not less than 1 hour ;

[0011] S3: based on high spatiotemporal resolution ocean model data, obtaining the thermohaline profile data corresponding to the PIES position near the target sea area, and calculating the model sound propagation time ;

[0012] S4: multi-scale dynamic signal separation of the model data and PIES data;

[0013] S5: based on the multi-scale dynamic signal separation data, constructing a multi-scale gravity empirical mode mapping function, and establishing a statistical relationship between the scale-dependent sound propagation time and the thermohaline structure;

[0014] S6: using the multi-scale gravity empirical mode mapping function to invert the thermohaline profile and dynamic height of each scale;

[0015] S7: superimposing the results of each scale to generate a multi-scale synthesized total thermohaline structure and total dynamic height.

[0016] The observation frequency of the PIES is not less than 1 time per hour, which is suitable for sub-mesoscale and high-frequency process observation.

[0017] In step S2, the data preprocessing includes the following steps:

[0018] S21: using the 3σ criterion to remove outliers in the sound wave propagation time data;

[0019] S22: Correcting long-period drift error of the instrument using linear regression;

[0020] S23: Calculate the barotropic component using seabed pressure data, and dynamically compensate for sound propagation time using the following formula:

[0021]

[0022] in, For the original propagation time, This represents the change in seabed pressure. For reference density, Let c be the acceleration due to gravity, and c be the speed of sound.

[0023] In step S3, the high spatiotemporal resolution ocean model data satisfies the following parameters:

[0024] Temporal resolution: 1 hour; Horizontal spatial resolution: ≤1 / 48°; Vertical resolution: 1 meter within a depth of 0-200 meters, and 10-50 meters within a depth of 200-1000 meters; Includes physical processes of sub-mesoscale eddies, fronts, and tidal forcing.

[0025] Step S3 specifically includes:

[0026] S31: Use sinc interpolation to unify the temporal resolution of model data and PIES observation data;

[0027] S32: Locate the nearest PIES deployment latitude and longitude data in the loop traversal mode data, and use cubic spline layered interpolation to the PIES deployment station to obtain the corresponding T / S data;

[0028] S33: Obtain seawater density based on T / S data. And the speed of sound c, thus obtaining the model sound propagation time as:

[0029]

[0030] Where c is the speed of sound. Let g be the density of seawater and g be the acceleration due to gravity. The bottom reference pressure for the PIES station.

[0031] In steps S4 and S6, the multi-scale dynamic signal separation is performed using time-domain bandpass filtering. Specific scale-frequency band divisions include, but are not limited to:

[0032] Mesoscale: period > 10 days; Submesoscale: period 1.5 to 10 days; Diurnal tide: period 18 to 36 hours; Semidiurnal tide: period 10 to 18 hours.

[0033] The step S5 specifically comprises the following steps.

[0034] S51: data layering processing:

[0035] The temperature-salinity data T / S separated by multi-scale is layered according to water depth, and each layer has a thickness of N meters; each scale band is independently layered processed to ensure that the temperature-salinity profile of different scales is independently modeled;

[0036] S52: for each layer of data, the weighted least squares method is used to fit the sound propagation time and the corresponding temperature-salinity data T / S is a cubic polynomial relationship, that is:

[0037]

[0038] wherein, ~ is a fitting coefficient, and ε is a residual error.

[0039] According to the confidence of the temperature-salinity data, the weight is distributed, and the high-confidence data occupies a higher weight in the fitting to improve the robustness of the mapping relationship.

[0040] S53: cross-validation optimization:

[0041] The data is divided into a training set and a validation set, and the fitting effect of different polynomial orders is verified; the residual sum of squares (RSS) of each order model is calculated, and the polynomial order with the smallest RSS is selected as the final model; if the cubic polynomial is the optimal one, it is directly used; if there is no significant improvement in higher order, overfitting is avoided, and the cubic polynomial form is maintained.

[0042] S54: constructing a multi-scale mapping function:

[0043] Each scale component is independently repeated steps S51-S53 to finally form a layered and multi-scale mapping function library; and the multi-scale gravity empirical mode mapping function is constructed.

[0044] The step S6 specifically comprises the following steps.

[0045] S61: the PIES baroclinic contribution sound propagation time data is subjected to multi-scale dynamic signal separation to obtain different scale propagation times ;

[0046] S62: the different scale propagation times obtained in step S61 are taken as input data, and are substituted into the constructed mapping function to obtain the corresponding scale temperature-salinity profile by inversion;

[0047] S63: the corresponding multi-scale ocean dynamic height SH is calculated for each scale obtained in step S62.

[0048] The corresponding partial scale sea dynamic height SH is calculated, that is:

[0049]

[0050] wherein, is the dynamic height of each scale, is the reference depth of the PIES station, represents the seawater density calculated by inputting the T / S profile obtained by inversion into the Gibbs-SeaWater formula library, wherein, is the long-term average density, is the obtained density anomaly.

[0051] The step S7 is specifically:

[0052] The partial scale summation obtains the comprehensive multi-scale temperature (Tscale), salinity (Sscale) and dynamic height (Hscale), that is:

[0053]

[0054]

[0055]

[0056] wherein, scale represents a scale component.

[0057] The present application has the following beneficial effects and advantages:

[0058] 1. The present application innovatively proposes a multi-scale gravity empirical mode mapping function, realizes multi-scale joint inversion based on PIES data and high-resolution numerical mode data, effectively overcomes the static empirical mode mapping relationship of the traditional method which only relies on historical discrete observation data, significantly improves the capture and inversion ability of submesoscale and high-frequency dynamic process, and can effectively distinguish the ocean process with a spatial scale of 2 kilometers and a time scale of several hours.

[0059] 2. The method proposed in the present application utilizes the high-frequency information contained in the PIES observation data and the submesoscale dynamic information in the high-resolution numerical mode data, realizes the organic fusion of the two kinds of data in the statistical characteristics. Since the high-resolution numerical mode can accurately simulate the statistical characteristics of the multi-scale temperature and salinity structure of the ocean, and the echo propagation time record of the PIES high-frequency continuous observation has good consistency in the statistical sense, the accuracy and consistency of the inversion results in different scales are ensured.

[0060] ​​​3. The method can finely extract and analyze the contribution of different scale ocean processes to dynamic height changes, such as mesoscale (more than 10 days), sub-mesoscale (1.5-10 days), and high-frequency tidal scale (3-36 hours), significantly improving the resolution of different scale ocean dynamic processes. The method can extract and restore important sub-mesoscale and high-frequency signals that are filtered out or ignored by traditional methods from PIES high-frequency observation data, effectively avoiding the loss of high-frequency detail information, and improving the sensitivity of the inversion results to ocean dynamic changes.

[0061] 4. Compared with satellite remote sensing and shipborne equipment, the observation cost is lower and the spatial and temporal resolution is higher, and it is particularly suitable for long-term, continuous, multi-scale observation of ocean temperature and salinity structure and dynamic height. Using the high-frequency observation capability of PIES equipment, combined with high-resolution multi-scale numerical simulation data, long-term continuous fine observation of ocean internal dynamic processes can be efficiently and economically realized. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 The flowchart of the inversion method of the present application is shown.

[0063] Figure 2 The shallow sea 0.5-3 day sub-mesoscale temperature anomaly inversion example of the present embodiment is shown.

[0064] Figure 3 The shallow sea 3-10 day sub-mesoscale temperature anomaly inversion example of the present embodiment is shown.

[0065] Figure 4 The 10-day or more mesoscale temperature inversion example of the present embodiment is shown.

[0066] Figure 5 The comprehensive multi-scale temperature inversion result example of the present embodiment is shown.

[0067] Figure 6 The real value example of the present embodiment is shown. DETAILED DESCRIPTION

[0068] The technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0069] In practical application, the multi-scale inversion method of ocean temperature and salinity structure and dynamic height based on pressure sensor inverse echo sounder (PIES) is shown in the following embodiments.

[0070] EMBODIMENT

[0071] As Figure 1 shown, a flow chart of the inversion method of the application, the application is a kind of ocean temperature and salinity structure and dynamic height multi-scale inversion method, it is realized based on the way of pressure sensing inverse echo sounder (PIES) and high resolution mode, specific method includes the following steps:

[0072] S1: obtaining the round-trip sound propagation time and seabed pressure data measured by PIES deployed in the target sea area, the sound propagation time data and bottom pressure data are collected at a frequency of 24 times per hour;

[0073] S2: the sound wave round-trip propagation time data and seabed pressure data are subjected to outlier rejection, long-period drift correction, and the measured propagation time is corrected by using seabed pressure data (i.e. positive pressure component) to process the 1 hour resolution baroclinic contribution sound propagation time (T ) data;

[0074] S21: 3σ criterion is used to remove outliers in sound propagation time data;

[0075] S22: linear regression method is used to correct instrument long-period drift error;

[0076] S23: the positive pressure component is calculated using seabed pressure data, and the sound propagation time is dynamically compensated by the following formula:

[0077]

[0078] Wherein, is the original propagation time, is the amount of change of seabed pressure, is the reference density, is the acceleration of gravity, and c is the sound speed.

[0079] S3: using high resolution ocean numerical model data to loop through to find the data closest to the PIES deployment latitude and longitude in the model data, using cubic spline layered interpolation to the PIES deployment station, obtaining the high spatiotemporal resolution temperature and salinity (T / S) profile data near the target sea area, using sinc interpolation method to unify the time resolution of model data and PIES observation data;And the propagation time is calculated by the following formula :

[0080]

[0081] Wherein, c is the sound speed, which is calculated by T / S at the station obtained by S31 through any sound speed empirical formula that meets the use conditions of the observed sea area, is the seawater density calculated by T / S, g is the acceleration of gravity, The bottom reference pressure of the PIES station.

[0082] The high-temporal and high-spatial resolution ocean model data meet the following parameters:

[0083] The time resolution is 1 hour, the horizontal spatial resolution is ≤1 / 48°, the vertical resolution is 1 meter within a depth of 0-200 meters and 10-50 meters within a depth of 200-1000 meters, and the physical processes include submesoscale eddies, fronts and tidal forcing.

[0084] S4: The mode data described in S3 are subjected to multi-scale dynamic signal separation by using time-domain band-pass filtering processing, and the specific scale frequency band division includes but is not limited to: mesoscale (>10 days), submesoscale (1.5 days-10 days), diurnal tide (18 hours-36 hours), and semidiurnal tide (10 hours-18 hours).

[0085] S5: The T / S after scale separation and Data are used to construct a multi-scale gravity empirical mode mapping function (MS-GEM-EMMF) by using a cubic polynomial fitting method for each scale.

[0086] S51: Data layering processing:

[0087] The temperature and salinity data T / S after multi-scale separation are layered according to the water depth, and each layer has a thickness of N meters; each scale frequency band is independently subjected to layering processing to ensure that the temperature and salinity profiles of different scales are independently modeled.

[0088] S52: For each layer of data, a weighted least squares method is used to fit the sound propagation time and the cubic polynomial relationship of the corresponding temperature and salinity data T / S, that is:

[0089]

[0090] wherein, are fitting coefficients, and ϵ is the residual error.

[0091] According to the confidence of the temperature and salinity data, the high-confidence data occupies a higher weight in the fitting to improve the robustness of the mapping relationship.

[0092] S53: Cross-validation optimization:

[0093] The data are divided into a training set and a validation set, the fitting effect of different polynomial orders is verified, the residual sum of squares (RSS) of each order model is calculated, and the polynomial order with the smallest RSS is selected as the final model; if the cubic polynomial is the optimal one, it is directly used; if there is no significant improvement in higher order, overfitting is avoided, and the cubic polynomial form is maintained. ​

[0094] S54: Constructing the scaling function:

[0095] Steps S51 to S53 are repeated independently for each scale component to form a hierarchical-scale mapping function library; thus completing the construction of multi-scale gravity empirical mode mapping functions.

[0096] S6: The PIES baroclinic contribution sound propagation time described in step S2 ( The data is subjected to multi-scale dynamic signal separation, and the separation scale frequency band is consistent with that described in step S4, to obtain the propagation time at different scales. ).

[0097] S7: The propagation times at different scales obtained in step S6 ( Using this as input data, the mapping function constructed in step S5 is substituted to obtain the temperature and salinity profile at the corresponding scale.

[0098] S8: Calculate the corresponding subscale ocean dynamic height (SH) for the temperature and salinity field obtained at each scale in step S7:

[0099]

[0100] In the formula, For dynamic height at various scales, The reference depth for PIES positioning. This represents the seawater density calculated from the T / S profile obtained through inversion and input into the Gibbs-SeaWater formula library, where... For long-term average density, for The density anomaly was obtained.

[0101] S9: Superimpose all the subscale temperature-salinity profiles obtained in step S7 and step S8, as well as SH, i.e., sum the subscales to obtain the comprehensive multiscale temperature. ),salinity( ) and power height ( ):

[0102]

[0103]

[0104]

[0105] In the formula, scale represents the scale component.

[0106] Example 2:

[0107] The effects of the present invention will now be described with reference to the accompanying drawings.

[0108] Taking the temperature inversion of shallow sea (water depth of about 111 meters) as an example, using the high-resolution ocean numerical model MITgcmllc4320, the high-temporal and spatial resolution temperature and salinity (T / S) profile data near the target sea area are obtained, and the mapping function of the T / S time series corresponding to the PIES position and the sound propagation time is established. The MITgcmllc4320 model data has a time resolution of once per hour and a spatial resolution of 1 / 48°, and the vertical resolution is 1-50 meters in the depth range of 0-1000 meters, which meets the resolution requirements of inversion.

[0109] The frequency band of shallow sea inversion is divided into 0.5 days-3 days (T1) Figure 2 ), 3 days-10 days (T2) Figure 3 ) and more than 10 days (T3) Figure 4 , in order to distinguish the shallow submesoscale and mesoscale processes. The comprehensive inversion results (such as Figure 5 ) after superposition can accurately capture the high-frequency changes that cannot be captured by traditional inversion methods (such as Figure 6 ).

[0110] Therefore, the method of the present application can effectively extract and restore important submesoscale and high-frequency signals filtered out or ignored by traditional methods from PIES high-frequency observation data, effectively avoid the loss of high-frequency detail information, and improve the inversion accuracy.

[0111] In summary, the method proposed by the present application can not only significantly improve the inversion accuracy and scale resolution of ocean temperature and salinity structure and dynamic height, but also has obvious advantages in cost, efficiency, long-term continuous observation ability and the like, and can provide important technical support for analyzing and predicting ocean circulation changes, climate variation, energy transport and local marine ecological environment and the like.

[0112] In this specification, the present application has been described with reference to its specific embodiments. The above embodiments are the preferred embodiments of the present patent, and are not intended to limit the scope of the present application. It should be noted that the present application is not limited to the above specific embodiments, and any improvement, change, combination, replacement and the like made by those skilled in the art without departing from the principles of the present application are within the scope of the claims of the present application.

[0113] While the preferred embodiments of the application have been described, additional variations and modifications can be made to these embodiments by those skilled in the art once they have the benefit of the foregoing description without departing from the spirit and scope of the application. Accordingly, it is intended that the appended claims be interpreted as including all such variations and modifications as fall within the spirit and scope of the application. It is apparent that those skilled in the art can modify and adapt the application without departing from the spirit and scope of the application. It is therefore intended that the application not be limited to the disclosed embodiments, but that it can also cover modifications and variations within the scope of the present application.

Claims

1. A method for ocean thermohaline structure and dynamic height multi-scale inversion, characterized in that, The method comprises the following steps: S1: collecting the round-trip sound wave propagation time and seabed pressure data measured by the PIES arranged on the seabed of the target sea area at a set frequency; S2: pre-processing the data collected in step S1 to generate baroclinic contribution sound travel time data with no less than 1 hour resolution ; S3: Based on high spatiotemporal resolution ocean model data, obtain temperature and salinity profile data corresponding to the PIES location near the target sea area, and calculate the model sound propagation time ; S4: performing multi-scale dynamic signal separation on the model data and the PIES data; S5: based on the data of the multi-scale dynamic signal separation, constructing a multi-scale gravity empirical mode mapping function, and establishing a statistical relationship between the multi-scale sound propagation time and the temperature-salinity structure; S6: using the multi-scale gravity empirical mode mapping function to invert the temperature-salinity profile and the dynamic height of each scale; S7: superimposing the results of each scale to generate a multi-scale synthesized total temperature-salinity structure and total dynamic height.

2. The method according to claim 1, wherein, The observation frequency of the PIES is not less than 1 time per hour, so as to be applicable to the observation of sub-mesoscale and high-frequency processes.

3. The method according to claim 1, wherein, In step S2, the data is preprocessed, comprising the following steps: S21: using the 3σ criterion to remove outliers in the sound wave propagation time data; S22: correcting the long-period drift error of the instrument by linear regression method; S23: calculating the barometric component by using the seabed pressure data, and dynamically compensating the sound propagation time by the following formula: ； wherein, is the original travel time, is the change in bottom pressure, is the reference density, is the acceleration of gravity, and c is the sound speed.

4. The method according to claim 1, wherein, In step S3, the high-temporal and spatial resolution ocean model data meet the following parameters: Time resolution: 1 hour; horizontal spatial resolution: ≤1 / 48°; vertical resolution: 1 meter within 0-200 meters in depth, 10-50 meters within 200-1000 meters in depth; containing physical processes of sub-mesoscale eddy, front and tidal forcing.

5. The method according to claim 1, wherein, The step S3 specifically comprises: S31: using the sinc interpolation method to unify the time resolution of the model data and the PIES observation data; S32: traversing the model data to find the data of the nearest PIES arrangement longitude and latitude, using cubic spline layered interpolation to the PIES arrangement site to obtain the corresponding T / S data; S33: Obtain seawater density according to T / S data and sound speed c, and then obtain the model sound propagation time as: ； where c is the sound speed, is the seawater density, g is the gravitational acceleration, is the bottom reference pressure of the PIES station.

6. The method according to claim 1, wherein, In steps S4 and S6, the multi-scale dynamic signal separation is processed by time domain band-pass filtering, and the specific scale frequency band division includes but is not limited to: Mesoscale: period > 10 days; sub-mesoscale: period 1.5 days-10 days; diurnal tide: period 18 hours-36 hours; semidiurnal tide: period 10 hours-18 hours.

7. The method according to claim 1, wherein, The step S5 specifically comprises the following steps: S51: data layered processing: The temperature-salinity data T / S after multi-scale separation is layered according to the water depth, and each layer has a thickness of N meters; each scale frequency band is independently processed to ensure that the temperature-salinity profile of different scales is independently modeled; S52: For each layer of data, the weighted least squares method is used to fit the sound propagation time The cubic polynomial relationship with the corresponding temperature and salinity data T / S, that is: ； wherein ~ are fitting coefficients and e is the residual error. According to the confidence of the temperature-salinity data, the weight is allocated, and the high-confidence data occupies a higher weight in the fitting to improve the robustness of the mapping relationship; S53: cross-validation optimization: The data is divided into a training set and a validation set to verify the fitting effect of different polynomial orders; the residual sum of squares RSS of each model is calculated, and the polynomial order with the smallest RSS is selected as the final model; if the cubic polynomial is the optimal one, it is directly used; if there is no significant improvement in higher order, overfitting is avoided, and the cubic polynomial form is maintained; S54: constructing a scale mapping function: Each scale component is independently repeated steps S51-S53 to finally form a layered-scale mapping function library; and the multi-scale gravity empirical mode mapping function is constructed.

8. The method according to claim 7, wherein, The step S6 specifically comprises: S61: PIES barocline contribution sound propagation time Multiscale dynamic signal separation is performed on the data to obtain different scale propagation times ; S62: Different scale propagation time obtained in step S61 is compared with the corresponding scale propagation time in the database to obtain the corresponding scale propagation time difference. As input data, the constructed mapping function is substituted, and the temperature and salinity profile corresponding to the scale is obtained by inversion. S63: calculating the corresponding scale-segmented sea dynamic height SH for each scale-segmented temperature-salinity field obtained in the step S62.

9. The method according to claim 8, wherein, In the step S63, the corresponding scale-segmented sea dynamic height SH is calculated, i.e.: ； where, is the dynamic height for each scale, is the reference depth of the PIES station, is the seawater density calculated by the Gibbs-SeaWater equation library with the T / S profile from inversion, where, is the long-term average density, is the is the density anomaly.

10. The method of claim 1, wherein, The step S7 specifically comprises: The temperature is integrated over the scales to obtain a comprehensive multiscale temperature , salinity and dynamic height , i.e.: ； ； ； Wherein, scale represents the scale component.

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