Dynamical method for reconstructing tropical ocean near-surface flow field using multi-source satellite data

Abstract

The application discloses a kind of dynamics methods for reconstructing tropical ocean near-surface flow using multi-source satellite data, obtains and pre-processes satellite observation data and buoy data;Using satellite observation sea level and simplified dynamic method reconstructs beta plane approximation quasi-geostrophic flow and geostrophic flow;Beta plane approximation quasi-geostrophic flow and geostrophic flow are fused into tropical ocean near-surface quasi-geostrophic flow using weight function;Using sea surface 10m wind speed and near-surface wind-driven Ekman flow parameterization scheme to calculate tropical ocean near-surface wind-driven Ekman flow;Fusion tropical ocean near-surface quasi-geostrophic flow and tropical ocean near-surface wind-driven Ekman flow, preliminary obtain the reconstruction field of tropical ocean near-surface flow;Using real-time buoy data to evaluate and test preliminary reconstruction field, obtain more reliable tropical ocean near-surface flow field;According to application requirement, output multiple types of tropical ocean near-surface flow.The application calculation principle is simple, reconstructs fast, can objectively and accurately reflect the dynamic characteristics of actual tropical ocean near-surface flow field.

Description

Technical Field

[0001] The present invention belongs to the field of tropical ocean dynamic environment analysis and forecasting, and specifically relates to a dynamic method for reconstructing tropical ocean near-surface flow fields using multi-source satellite data. Background Art

[0002] The circulation state of the ocean near-surface current field profoundly affects the redistribution of heat and salinity in the ocean near-surface layer, and further affects human life and development. Therefore, how to more accurately characterize the ocean near-surface current field has always been a hot research topic. However, due to the limitations of observation methods, observation instruments and observation costs, people are currently unable to directly conduct large-scale ocean current observations, making the actual available ocean current observation data very scarce. Therefore, existing studies generally obtain large-scale ocean current information through indirect inversion and calculation, including: (1) Directly using drifting buoys and free-drifting profile buoys to estimate surface and middle-layer currents respectively; (2) Using three-dimensional temperature and salinity elements and dynamic diagnostic methods to invert the ocean current field. However, this method is limited by the quantity and quality of temperature and salinity elements, and its application in most sea areas is very limited; (3) Multi-source satellite observations based on remote sensing combined with dynamic methods to invert the ocean surface current field. Method (3) has benefited from the steady improvement of satellite remote sensing observation technology in recent years. Satellite remote sensing can directly acquire ocean environmental information on sea surface temperature, sea surface height, and sea surface winds over a wide range. Its high temporal and spatial resolution, wide range, high accuracy, and quasi-synchronous nature make indirect inference algorithms increasingly popular and widely used. For ocean regions far from the equator, existing studies generally use the absolute geostrophic current derived from the geostrophic balance relationship and altimeter data, and the sum of the Ekman current velocity derived from Ekman dynamics and remotely sensed wind speeds as the ocean surface current. However, near the equator (5°S to 5°N), the traditional geostrophic relationship is no longer applicable, requiring relatively complex equatorial dynamics to explain the relevant dynamical processes in this region. Therefore, it is imperative to develop a simple dynamical method to reconstruct relatively accurate near-surface current fields in tropical oceans. Summary of the Invention

[0003] Purpose of the invention: The present invention proposes a dynamic method for reconstructing the near-surface flow field of tropical oceans using multi-source satellite data. This method takes into account the dynamic differences in different tropical sea areas. The calculation principle is relatively simple, does not require heavy hardware support, and can objectively and accurately reflect the dynamic characteristics of the actual near-surface flow field of tropical oceans.

[0004] Technical Solution: The present invention provides a dynamic method for reconstructing the near-surface flow field of tropical oceans using multi-source satellite data, comprising the following steps:

[0005] (1) Acquisition and processing of measured satellite data and buoy data:

[0006] (2) Using sea surface height and simplified dynamical methods to reconstruct the β-plane approximate quasi-geostrophic and geostrophic currents;

[0007] (3) Using the weight function, the β-plane approximate quasi-geostrophic current and the geostrophic current are merged into the near-surface quasi-geostrophic current in the tropical ocean;

[0008] (4) Calculate the wind-driven Ekman current near the surface of the tropical ocean using the wind speed at 10 m above the sea surface and the parameterization scheme of the wind-driven Ekman current near the surface;

[0009] (5) By integrating the quasi-geostrophic current near the surface of the tropical ocean and the wind-driven Ekman current near the surface of the tropical ocean, a preliminary reconstruction of the near-surface flow field of the tropical ocean is obtained;

[0010] (6) Use real-time buoy data to evaluate and verify the preliminary reconstructed field to obtain a more reliable near-surface current field in the tropical ocean;

[0011] (7) Output different types of tropical ocean near-surface currents according to application requirements.

[0012] Furthermore, the implementation process of step (1) is as follows:

[0013] Invalid and abnormal data in the measured data are checked and eliminated, and then different types of measured data are converted into a unified format required for field reconstruction. The satellite data include sea surface height and wind speed data at 10 m above the sea surface; the buoy data include drifting buoy and anchored buoy current velocity data.

[0014] Furthermore, the implementation process of step (2) is as follows:

[0015] (21) According to the continuous linear stable fluid equilibrium state equation, the following dynamic framework is simplified:

[0016]

[0017] Where f is the Coriolis parameter, Ω is the angular velocity of the Earth's rotation; h m is the depth of the mixed layer; u, v represent the latitudinal and longitudinal velocities of the equatorial region, respectively; g is the acceleration of gravity; H is the sea level; x, y represent the latitudinal and longitudinal directions, respectively; τ x ,τ y are the latitudinal and longitudinal wind stress components respectively; ρ0 is the mean sea surface density; v is the vertical linear drag coefficient; u e ,v e are the zonal and meridional wind-induced Ekman flow components, respectively;

[0018] Among them, the wind stress component τ x ,τ y Calculated by the block formula:

[0019]

[0020] Among them, ρ a is the air density, is the wind speed vector at 10 m above sea level;

[0021] Multiplying equation (2) by the imaginary number i and combining it with equation (1) yields:

[0022]

[0023] in, as well as

[0024] The surface current in tropical oceans is considered to be the sum of the quasi-geostrophic current and the wind-driven Ekman current. Then formula (4) is expanded into:

[0025]

[0026] in for the quasi-geostrophic flow;

[0027] (22) For tropical seas far from the equator, the geostrophic current is directly calculated using the following formula:

[0028]

[0029] (23) For the near-equatorial region, the quasi-geostrophic flow is regarded as the geostrophic flow. and β-plane approximate quasi-geostrophic flow The sum of the two, first calculate the geostrophic current according to formula (7), and then According to formula (5) and β plane approximation, it is deduced:

[0030]

[0031] For formula (8) and Perform orthogonal polynomial fitting separately:

[0032]

[0033] Among them, n is the number of terms of orthogonal polynomial fitting, and it should also satisfy:

[0034]

[0035] After the satellite observation of sea level height is obtained and preprocessed, the geostrophic current of the entire region is calculated according to formula (7): Then, the approximate quasi-geostrophic flow in the β plane near the equator is calculated according to equations (8) to (12):

[0036] Furthermore, the step (3) is implemented as follows:

[0037] The weight function is used to combine the geostrophic flow and the β-plane approximate quasi-geostrophic flow into the quasi-geostrophic flow, that is:

[0038]

[0039] The weight coefficient is determined by the following formula:

[0040]

[0041] Furthermore, the implementation process of step (4) is as follows:

[0042] Driven by a stable wind field, the linear stable uniform flow in the open sea area satisfies the following dynamic constraints:

[0043]

[0044] Where p is the pressure; according to equations (15) and (16):

[0045]

[0046] Get the zonal and longitudinal wind-induced Ekman flow components u e ,v e :

[0047]

[0048] in, v is the fluid viscosity coefficient; z represents the depth.

[0049] Furthermore, the implementation process of step (6) is as follows:

[0050] The real-time anchored buoy and drifting buoy velocity components (u o , v o ) respectively perform quadratic surface least squares fitting:

[0051] U(x,y)=C0+C1x+C2y+C3x 2 +C4y 2 +C5xy(20)

[0052] The velocity values ​​of the initial reconstructed field that are greater than twice the standard deviation of the entire area are eliminated and replaced with the velocity values ​​calculated by the fitting coefficients C0 to C5.

[0053] Beneficial effects: Compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. This paper constructs a simplified dynamic reconstruction method for inverting the near-surface flow field in tropical oceans. Its dynamic framework incorporates geostrophic equilibrium, equatorial dynamics, and Ekman theory. It also employs a parameterized calculation scheme for wind-induced Ekman currents that exhibits spatiotemporal variations. Furthermore, the reliability of the reconstructed flow field is further improved by evaluating and validating the preliminary reconstructed field using measured buoy data.

[0055] 2. The present invention realizes the construction of tropical ocean near-surface flow field by integrating satellite observations of sea surface height and sea surface wind field, and measured drifting buoy and anchored buoy flow velocity to reconstruct daily, monthly average climate state and climate state;

[0056] 3. This invention solves the problems of difficulty in direct detection of tropical ocean currents, difficulty in obtaining large-scale data, and high observation costs. It has the characteristics of fast reconstruction speed and reliable and effective results.

[0057] 4. The present invention takes into account the dynamic differences in different tropical sea areas. The calculation principle is relatively simple, does not require heavy hardware support, and can objectively and accurately reflect the dynamic characteristics of the actual tropical ocean near-surface flow field. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of the present invention;

[0059] Figure 2 They are the satellite sea surface height and the satellite-observed wind field 10 m above the sea surface on June 15, 2018;

[0060] Figure 3 is based on Figure 2 The wind speed at 10m above the sea surface observed by satellite on June 15, 2018 and the wind stress value τ calculated by the present invention are shown as follows: x and τ y ;

[0061] Figure 4 is based on Figure 2 Satellite-observed sea surface data and the real-time tropical ocean current field, tropical ocean quasi-geostrophic current, and tropical ocean near-surface wind-driven Ekman current on June 15, 2018, reconstructed and evaluated using the method of the present invention are shown;

[0062] Figure 5 is based on Figure 2 A comparison of the satellite-observed sea surface data and the flow field of the local tropical Indian Ocean area and the international mainstream tropical ocean flow field reconstructed by the method of the present invention on June 15, 2018 is shown;

[0063] Figure 6 is based on Figure 2 A comparison diagram of the satellite-observed sea surface data, the tropical ocean current field reconstructed using the method of the present invention, and the international mainstream tropical ocean current field on June 15, 2018;

[0064] Figure 7 The monthly average climatological mean current of the tropical Indian Ocean in January and June is output according to the multi-year (1993-2018) satellite observation sea surface data input in the embodiment and the method of the present invention;

[0065] Figure 8 The monthly average climatological mean current of the tropical ocean in January and June is obtained by inputting multi-year (1993-2018) satellite observation sea surface data and outputting the method of the present invention according to the embodiment;

[0066] Figure 9 It is based on the multi-year (1993-2018) satellite observation sea surface data input in the embodiment and the climatological tropical ocean average current output by the method of the present invention. DETAILED DESCRIPTION

[0067] The present invention will be described in further detail below with reference to the accompanying drawings.

[0068] The present invention provides a dynamic method for reconstructing the near-surface flow field of the tropical ocean using multi-source satellite data, and constructs a simplified dynamic reconstruction method for inverting the tropical ocean flow field. Its dynamic framework includes the geostrophic balance relationship, equatorial dynamics, and Ekman theory. At the same time, a parameterized calculation scheme for wind-induced Ekman flow with spatiotemporal variation characteristics is adopted, making the reconstruction of wind-induced Ekman flow more scientific and reasonable. Figure 1 As shown, it specifically includes the following steps:

[0069] Step 1: Acquire and process measured satellite data and buoy data.

[0070] To avoid potential errors or irregularities in the input measured data, satellite observation data and buoy data must be pre-processed and quality-controlled. This patent eliminates invalid and abnormal data from the measured data through multiple steps, including date verification, position verification, landing verification, format verification, observation element range verification, speed verification, continuity verification, extreme value verification, spike verification, and gradient verification. The data is then converted into a unified format required for subsequent calculations.

[0071] Step 2: Reconstruct the β-plane approximate quasi-geostrophic and geostrophic currents using sea surface height and simplified dynamical methods.

[0072] According to the continuous linear stable fluid equilibrium state equation, the following dynamic framework can be simplified:

[0073]

[0074] Where f is the Coriolis parameter, which is Ω is the angular velocity of the Earth's rotation, which is 7.292×10 -5 rad / s, is latitude; h m is the depth of the mixed layer; u and v represent the latitudinal and longitudinal velocities in the equatorial region, respectively; g is the acceleration of gravity, which is 9.780 m / s 2 ; H is the sea level height; x, y represent the latitude and longitude respectively; τ x ,τ y are the latitudinal and longitudinal wind stress components respectively; ρ0 is the average sea surface density, which is 1025 kg / m 3 ;υ is the vertical linear drag coefficient; u e ,v e are the zonal and meridional components of the wind-induced Ekman flow, respectively.

[0075] Among them, the wind stress component τ x ,τ y Calculated by the block formula:

[0076]

[0077] Where ρ a is the air density, which is 1.23 kg / m 3 , is the wind speed vector at 10 m above sea level.

[0078] Multiplying equation (2) by the imaginary number i and combining it with equation (1) yields:

[0079]

[0080] Where, as well as

[0081] If the tropical ocean surface current is regarded as the sum of the quasi-geostrophic current and the wind-driven Ekman current Then formula (4) can be expanded as:

[0082]

[0083] Where, Based on geostrophic flow.

[0084] For tropical sea areas far from the equator, that is, areas outside 5S-5N°, the flow basically conforms to the geostrophic equilibrium relationship. The following scheme is used to directly calculate the geostrophic flow:

[0085]

[0086] For the near-equatorial region (5S-5N°), the traditional geostrophic balance relationship is no longer applicable in this region, so the quasi-geostrophic flow here is regarded as the geostrophic flow. and β-plane approximate quasi-geostrophic flow First, calculate the geostrophic current according to formula (7), and then According to formula (5) and the β-plane approximation (f = βy), it is deduced that:

[0087]

[0088] In order to avoid the singular value caused by f = 0 on the equator, this method is used to calculate the value of and Perform orthogonal polynomial fitting separately:

[0089] n is the number of terms in the orthogonal polynomial fitting, n = 0.75*[5-(-5)] / N, N is the spatial resolution of the satellite observation input, and it should also meet the following requirements:

[0090]

[0091] In summary, after the satellite observation of sea surface height is obtained and preprocessed, the geostrophic current of the entire region is calculated according to formula (7): Then, the approximate quasi-geostrophic flow in the β plane near the equator is calculated according to equations (8) to (12):

[0092] Step 3: Use the weight function to merge the β-plane approximate quasi-geostrophic current and the geostrophic current into the tropical ocean near-surface quasi-geostrophic current.

[0093] In order to avoid the velocity discontinuity problem caused by the difference in the calculation methods of the current velocity in different sea areas, the following weight function is used to combine the geostrophic current and the β-plane approximate quasi-geostrophic current into the quasi-geostrophic current, namely:

[0094]

[0095] The weight coefficient is determined by the following formula:

[0096]

[0097] Step 4: Calculate the near-surface wind-driven Ekman current in the tropical ocean using the sea surface 10 m wind speed and the near-surface wind-driven Ekman current parameterization scheme.

[0098] Driven by a stable wind field, the linear stable uniform flow in the open sea area satisfies the following dynamic constraints:

[0099]

[0100] Where p is pressure. Further, according to equations (15) and (16), we can derive:

[0101]

[0102] The classical analytical solution of formula (17) is:

[0103]

[0104] Where, υ is the fluid viscosity coefficient; z represents the depth.

[0105] In this invention, the above classical analytical solution is simply parameterized:

[0106]

[0107] Where b and θ are empirical parameters derived from measured data, used to calculate the amplitude and deflection of the wind-induced Ekman current, respectively. Their monthly and regional values ​​are shown in Tables 1 and 2, respectively:

[0108] Table 1 shows the values ​​of the empirical parameter b calculated from the measured data.

[0109]

[0110]

[0111] Table 2 shows the values ​​of the empirical parameter θ calculated from the measured data.

[0112] θ 15～25°N 5～15°N 0～5°N 0～5°S 5～15°S 15～25°S January -61.67 -62.46 -51.69 57.57 60.85 66.60 February -60.75 -62.57 -53.23 58.15 61.57 66.77 March -61.43 -61.51 -51.48 56.05 60.82 64.62 April -62.97 -61.93 -51.38 56.50 60.27 63.01 May -64.76 -62.31 -50.45 56.88 61.19 61.72 June -63.53 -60.42 -49.96 57.13 61.02 59.72 July -65.02 -62.87 -50.82 56.35 59.70 58.11 August -64.94 -63.85 -52.43 56.96 59.20 57.61 September -63.90 -61.68 -51.28 57.10 59.40 59.22 October -63.21 -60.46 -50.38 56.39 60.04 61.07 November -62.76 -60.26 -48.02 53.73 58.95 63.89 December -62.99 -63.64 -51.32 56.62 60.27 65.45

[0113] Step 5: Combine the tropical ocean near-surface quasi-geostrophic current and the tropical ocean near-surface wind-driven Ekman current to preliminarily obtain the tropical ocean near-surface current reconstruction field.

[0114] Step 6: Use real-time buoy data to evaluate and verify the preliminary reconstructed field to obtain a more reliable tropical ocean near-surface current field.

[0115] The real-time anchored buoy and drifting buoy velocity components (u o , v o ) respectively use formula (20) to perform quadratic surface least squares fitting:

[0116] U(x,y)=C0+C1x+C2y+C3x 2 +C4y 2 +C5xy(20)

[0117] The velocity values ​​of the preliminary reconstructed field that are greater than twice the standard deviation of the entire area are eliminated and replaced by the velocity values ​​calculated using the fitting coefficients (C0~C5) of formula (20).

[0118] Step 7: Output different types of tropical ocean near-surface currents (including daily, monthly average climate state, and climate state) according to application requirements.

[0119] The following is a further demonstration of the advantages of the method and method of the present invention in combination with specific sample data:

[0120] The sea surface height selected in this embodiment has a daily temporal resolution and a 1 / 4° spatial resolution. It is provided by AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic data) and is affiliated with the CMSMS (Copernicus Marine Environment and Monitoring Service) program. The program aims to provide high-quality fused altimeter products for scientific research and related applications such as marine applications, climate forecasting, geophysics and biochemistry. In step 1 of this embodiment, a 10m sea surface wind field (CCMP) with a daily temporal resolution and a 1 / 4° spatial resolution is selected. The CCMP dataset combines cross-calibrated satellite microwave wind and instrument observations and uses the variational analysis method (VAM) to produce a high-resolution (0.25°) gridded product. The wind field is inverted by observations of some satellite-borne passive and active microwave instruments, and the wind speed is highly integrated with numerous microwave radiation equipment platforms. All its data are based on a 10m reference surface. The drifting buoy data used in step 1 of this embodiment has a temporal resolution of 6 hours and a duration from 1979 to the present, and is affiliated with the Global Buoy Program (GDP). The moored buoy data used in step 1 of this example is provided by a global array of moored buoys in tropical oceans, a joint effort of multiple countries. This array aims to provide real-time data for climate research and forecasting. This measured data is open source and is the preferred input for this invention; the source of the data is not limited in this invention.

[0121] Figure 2 The spatial distribution of sea surface height and sea surface wind speed on June 15, 2018 is shown. After inputting the sea surface height, Figure 2 As shown in (a), the geostrophic current is obtained by the method described in step 2 of the present invention. and the β-plane approximate quasi-geostrophic flow in the near-equatorial region Then, the quasi-geostrophic current in the tropical ocean is calculated using the scheme described in step 3. Figure 4 After inputting the sea surface wind field, as shown in (b) Figure 2 (b) vector arrow, first use the method shown in step 2 (4) of the present invention to calculate the regional wind stress component, as shown in Figure 3 Then, the wind-driven Ekman current near the surface of the tropical ocean is calculated using the method described in step 4 of the present invention, as shown in FIG. Figure 4As shown in (c). The sum of the quasi-geostrophic current and the wind-driven Ekman current in the tropical ocean near the surface is the tropical ocean near the surface current preliminarily reconstructed by this method. Using the measured buoy velocity data described in step 1 and the scheme described in step 6, the velocity value of the preliminary reconstructed field is evaluated and corrected to generate a quasi-real-time reconstructed field, as shown in Figure 4 Finally, multiple types of tropical ocean current fields are output according to different requirements, such as Figures 5 to 9 shown.

[0122] Figures 5 to 9 The superiority of the flow field reconstruction method of the present invention and the diversity of output types are demonstrated from different angles. Figure 5 is based on Figure 2 The satellite observation sea surface data shown and the flow field of the local tropical Indian Ocean reconstructed by the method of the present invention (such as Figure 5 (a), spatial resolution 1 / 4×1 / 4°) and the international mainstream tropical ocean current field (OSCAR, Figure 5 (b), spatial resolution 1 / 3×1 / 3°. A comparison of the flow field on June 15, 2018, shows that the reconstructed flow field using this method has a roughly consistent spatial pattern with mainstream international velocity products in the same region, and the inversion results for the flow field characteristics of some ocean eddies are consistent. However, the reconstruction results using this method capture smaller-scale signals, resulting in a more reasonable flow field structure, greater continuity of velocity values, and more pronounced fluctuations near the equator. This result demonstrates the significant advantages of this method in reconstructing flow fields when the temporal and spatial resolution is increased. Figure 6 (a) and Figure 6 (b) is based on Figure 2 The figure shows a comparison of the satellite observation sea surface data and the tropical ocean current field reconstructed by the method of the present invention and the OSCAR flow field on June 15, 2018. The figure shows that the tropical ocean near-surface current field reconstructed by the present method based on the simplified dynamic model clearly identifies (1) the North Equatorial Current flowing westward around 13°N; (2) the southeast coastal current of the Arabian Sea in summer; (3) the South Equatorial Current flowing westward around 10°S; (4) the East African coastal current crossing the equator; (5) the north-south fluctuation of the flow direction in the near-equatorial region, etc. Compared with the OSCAR flow field, the velocity values ​​reconstructed by the present method are more continuous, the velocity distribution is more reasonable, the velocity anomaly caused by the abnormal sea surface height gradient in the coastal area is avoided, and the dynamic characteristics are more consistent with the actual dynamic process. Appendix Figure 7 The monthly average climatological mean currents in the tropical Indian Ocean in January and June are obtained based on the multi-year (1993-2018) satellite observation sea surface data and the output of the method of the present invention, as shown in FIG. Figure 7 (a) and Figure 7As shown in (b). It can be seen that this reconstruction method reasonably reconstructs the changes in the surface circulation of the tropical Indian Ocean caused by the changes in the monsoon circulation characteristics. Among them, due to the influence of the monsoon, the eastern coastal current of the Arabian Sea shows obvious changes in flow direction in winter and summer. The flow direction distribution of the entire basin is also basically inconsistent. The more famous Somali Current also clearly shows the characteristics of seasonal anomalies. The flow speed and direction of the Bay of Bengal also show different seasonal variation characteristics in winter and summer. In addition, although the flow direction in the near-equatorial region is eastward, the flow direction distribution in the southeast Indian Ocean shows certain seasonal changes. Many seasonal changes show that the reconstruction results of this method are more reliable in depicting seasonal circulation due to the introduction of the Ekman flow parameterization scheme that changes with time and space, and have irreplaceable advantages. Figure 8 The monthly average climatological mean currents of the tropical ocean in January and June are obtained based on the multi-year (1993-2018) satellite observation sea surface data and the output of the method of the present invention, as shown in FIG. Figure 8 (a) and Figure 8 This figure shows that the reconstruction method basically reflects the characteristics of the tropical ocean current system in different months. The position of the equatorial convergence zone changes with the seasons, resulting in relatively large changes in the equatorial current system. Figure 9 The results are based on the input of multi-year (1993-2018) satellite-observed sea surface data and the climatological tropical ocean mean current output by the method of the present invention. Several important tropical ocean characteristics are clearly inverted and generally consistent with the large-scale characteristics of ocean circulation, further demonstrating the rationality and reliability of this reconstruction method using satellite observation data to reconstruct the tropical ocean near-equatorial current.

[0123] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A dynamic method for reconstructing the near-surface flow field of tropical oceans using multi-source satellite data, characterized by: The following steps are involved: (1) Acquisition and processing of measured satellite data and buoy data: (2) Using sea surface height and simplified dynamical methods to reconstruct the β-plane approximate quasi-geostrophic and geostrophic currents; (3) Using the weight function, the β-plane approximate quasi-geostrophic current and the geostrophic current are merged into the near-surface quasi-geostrophic current in the tropical ocean; (4) Calculate the wind-driven Ekman current near the surface of the tropical ocean using the wind speed at 10 m above the sea surface and the parameterization scheme of the wind-driven Ekman current near the surface; (5) By integrating the quasi-geostrophic current near the surface of the tropical ocean and the wind-driven Ekman current near the surface of the tropical ocean, a preliminary reconstruction of the near-surface flow field of the tropical ocean is obtained; (6) Use real-time buoy data to evaluate and verify the preliminary reconstructed field to obtain a more reliable near-surface current field in the tropical ocean; (7) Output different types of tropical ocean near-surface currents according to application requirements; The implementation process of step (2) is as follows: (21) According to the continuous linear stable fluid equilibrium state equation, the following dynamic framework is simplified: Where f is the Coriolis parameter, Ω is the angular velocity of the Earth's rotation; h m is the depth of the mixed layer; u, v represent the latitudinal and longitudinal velocities of the equatorial region, respectively; g is the acceleration of gravity; H is the sea level; x, y represent the latitudinal and longitudinal directions, respectively; τ x ,τ y are the latitudinal and longitudinal wind stress components respectively; ρ0 is the mean sea surface density; υ is the vertical linear drag coefficient; u e ,v e are the zonal and meridional wind-induced Ekman flow components, respectively; Among them, the wind stress component τ x ,τ y Calculated by the block formula: Among them, ρ a is the air density, is the wind speed vector at 10 m above sea level; Multiplying equation (2) by the imaginary number i and combining it with equation (1) yields: in, as well as The surface current in the tropical ocean is considered to be the sum of the quasi-geostrophic current and the wind-driven Ekman current. Then formula (4) is expanded into: in for the quasi-geostrophic flow; (22) For tropical seas far from the equator, the geostrophic current is directly calculated using the following formula: (23) For the near-equatorial region, the quasi-geostrophic flow is regarded as the geostrophic flow. and β-plane approximate quasi-geostrophic flow The sum of the two, first calculate the geostrophic current according to formula (7), and then According to formula (5) and β plane approximation, it is deduced: For formula (8) and Perform orthogonal polynomial fitting separately: Where n is the number of terms in the orthogonal polynomial fitting, is the latitude, and should also meet the following requirements: After the satellite observation of sea level height is obtained and preprocessed, the geostrophic current of the entire region is calculated according to formula (7): Then, the approximate quasi-geostrophic flow in the β plane near the equator is calculated according to equations (8) to (12): The step (3) is implemented as follows: The weight function is used to combine the geostrophic flow and the β-plane approximate quasi-geostrophic flow into the quasi-geostrophic flow, that is: The weight coefficient is determined by the following formula: The implementation process of step (4) is as follows: Driven by a stable wind field, the linear stable uniform flow in the open sea area satisfies the following dynamic constraints: Where p is the pressure; according to equations (15) and (16): Get the zonal and longitudinal wind-induced Ekman flow components u e ,v e : in, υ is the fluid viscosity coefficient; z represents the depth.

2. The method for reconstructing the tropical ocean near-surface flow field using multi-source satellite data according to claim 1 is characterized in that: The implementation process of step (1) is as follows: Invalid and abnormal data in the measured data are checked and eliminated, and then different types of measured data are converted into a unified format required for field reconstruction. The satellite data include sea surface height and wind speed data at 10 m above the sea surface; the buoy data include drifting buoy and anchored buoy current velocity data.

3. The method for reconstructing the tropical ocean near-surface flow field using multi-source satellite data according to claim 1, characterized in that: The implementation process of step (6) is as follows: The real-time anchored buoy and drifting buoy velocity components (u o , v o ) perform quadratic surface least squares fitting respectively: U(x,y)=C0+C1x+C2y+C3x 2 +C4y 2 +C5xy(20) The velocity values ​​of the initial reconstructed field that are greater than twice the standard deviation of the entire area are eliminated and replaced with the velocity values ​​calculated by the fitting coefficients C0 to C5.

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