CN117851393A - Data assimilation method and system for inferring seawater quality changes using tide gauge station records - Google Patents

Abstract

The invention provides a data assimilation method and a system for recording and presuming sea water quality change by utilizing a tide station, and relates to the technical field of ocean observation. Comprises selecting a tide station; calculating global land quality migration change to obtain a sea level fingerprint effect; extracting dynamic sea level change of a climate mode, and interpolating the dynamic sea level to a record data position of a tide station; establishing an observation equation according to the selected tide station, and associating state quantity and observed quantity by using a sparse matrix; introducing random variables at each tide station to make up for the defect of simulating local dynamic sea level change by a climate mode; determining the state of the tide station at the initial moment, and optimizing and adjusting constraint parameters of state quantity and correlation coefficients between adjacent tide stations; based on Kalman filtering and smoothing, an optimal state quantity is estimated, and the global sea water mass increment is estimated. The invention improves the accuracy of sea water quality estimation, and realizes the coincidence and compatibility of mode and model prediction and observation data of the tide station.

Description

Data assimilation method and system for inferring seawater quality changes using tide gauge station records

Technical Field

The invention belongs to the technical field of ocean observation, and in particular relates to a data assimilation method and system for inferring seawater quality changes by using tide gauge station records.

Background technique

The main causes of long-term sea level changes include two parts: one is the change in seawater density caused by temperature and salinity changes, and the other is the increase in seawater mass caused by land mass migration. Tide gauges record long-term changes in sea level in coastal areas and are valuable data; climate models can simulate dynamic sea level changes, mainly sea level changes caused by temperature and salinity changes and ocean circulation; glacier models, hydrological models and observational data can give the contribution of different components of land mass changes to sea level rise, and the sea level fingerprint effect can describe the spatial variation characteristics of these contributions in the ocean area.

To study the contribution of seawater quality changes to sea level rise in the 20th century, Hay et al. (2015) proposed a data assimilation method, the core point of which is to assume that the main sources of contribution to seawater quality increase come from Greenland, West Antarctica and global mountain glaciers, where the melting of Greenland and West Antarctica is spatially homogenized, and then use the sea level fingerprint theory to predict the spatial characteristics of their contribution to sea level rise. In addition, it is assumed that the spatial characteristics of the sea level fingerprint caused by the melting of global mountain glaciers are homogenized. The dynamic sea level output by the combined climate model is used as the observation constraint of the selected global tide gauge stations, and the contribution of the three contributing sources to the increase in seawater quality is estimated by running Kalman filtering and smoothing. In addition to estimating the contribution of seawater quality, this method can also reconstruct the trend of sea level rise at the tide gauge station to supplement the missing records of the tide gauge station.

The inventors found that directly using land mass migration to study the increase in seawater mass faces some technical problems, such as significant differences in the error characteristics of different data sources, and incompatibility between model output and observational data. Therefore, it is necessary to construct a technical method to make the various components of land mass migration compatible with tide gauge records. The aforementioned existing sea level data assimilation technology can achieve long-term reconstruction of sea level changes at tide gauges, but the simultaneous estimation of multiple mass contribution sources faces greater uncertainty, or requires stronger prior constraints to achieve accurate quantitative estimates.

Summary of the invention

In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a data assimilation method and system for inferring changes in seawater quality using tide gauge station records, avoiding the strong prior constraints in the prior art, reducing the number of parameters to be estimated, and introducing random variables to make up for the lack of simulation capabilities of climate models in local areas. Through these technical improvements, the accuracy of seawater quality estimation can be improved, and the consistency and compatibility of pattern and model predictions with tide gauge station observation data can be achieved.

To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

A first aspect of the present invention provides a data assimilation method for inferring changes in seawater quality using tide gauge station records.

The data assimilation method using tide gauge station records to infer changes in seawater quality includes the following steps:

Preprocess the data, including: selecting tide stations and obtaining tide station record data; calculating global land mass migration changes to obtain sea level fingerprint effects; extracting dynamic sea level changes in climate models and interpolating dynamic sea levels to tide station record data;

Observation equations are established based on the selected tide gauges, and sparse matrices are used to associate state quantities and observation quantities. Random variables are introduced at each tide gauge to make up for the shortcomings of climate models in simulating local dynamic sea level changes. The state of the tide gauge station at the initial moment is determined, and the constraint parameters of the state quantity and the correlation coefficient between adjacent tide gauges are optimized and adjusted. Based on Kalman filtering and smoothing, the optimal state quantity is estimated, and the increase in global seawater mass is speculated.

A second aspect of the present invention provides a data assimilation system for inferring changes in seawater quality using tide gauge station records.

The data assimilation system that uses tide gauge records to infer changes in seawater quality includes:

The data preprocessing module is configured to: select tide stations and obtain tide station record data; calculate the global land mass migration changes to obtain the sea level fingerprint effect; extract the dynamic sea level changes of the climate model and interpolate the dynamic sea level to the tide station record data;

The assimilation modeling module is configured as follows: establish observation equations based on the selected tide gauge stations, and use sparse matrices to associate state quantities and observation quantities; introduce random variables at each tide gauge station to make up for the shortcomings of climate models in simulating local dynamic sea level changes; determine the state of the tide gauge station at the initial moment, optimize and adjust the constraint parameters of the state quantity and the correlation coefficient between adjacent tide gauge stations; estimate the optimal state quantity based on Kalman filtering and smoothing, and speculate on the increase in global seawater mass.

A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the data assimilation method for inferring seawater quality changes using tide gauge station records as described in the first aspect of the present invention.

The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the data assimilation method for inferring changes in seawater quality using tide gauge station records as described in the first aspect of the present invention are implemented.

One or more of the above technical solutions have the following beneficial effects:

The present invention provides a data assimilation method and system for inferring seawater quality changes using tide gauge station records, constructs a new framework for sea level data assimilation theory, and introduces random variables based on tide gauge station records, climate models and sea level fingerprint effects to enhance the simulation capability of climate models in local areas, make up for the inadequacy of climate models in local area simulation capability, improve the estimation of global seawater quality increase, avoid strong prior constraints, and reduce the number of parameters to be estimated. Through these technical improvements, the accuracy of seawater quality estimation is improved, and the consistency and compatibility of pattern and model predictions with tide gauge station observation data are achieved.

The present invention assumes that land mass migration based on different data sources has the same error characteristics, and integrates the sea level fingerprint effects caused by different land mass migrations into a whole for estimation, thereby improving the land mass migration estimation, increasing the compatibility between different mass components, and ultimately achieving accurate estimation of the seawater mass increase.

Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

FIG. 1 is a flow chart of a method according to a first embodiment.

FIG. 2( a ) is a comparison diagram of the sea level rise curves obtained by the method of the present invention and the prior art method.

FIG. 2( b ) is a graph showing seawater quality and dynamic sea level obtained by the method of the present invention.

FIG3 is a comparison diagram of the seawater quality obtained by the method of the present invention and the total amount of the input sea level fingerprint.

FIG. 4 is a long-term trend diagram of sea level obtained by using the method of the present invention.

Detailed ways

It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

Terminology explanation:

GIA: glacial isostatic adjustment

CMIP6: Coupled Model Intercomparison Project Phase 6

SLF: sea level fingerprint

Sterody: stereodynamic sea level

GMSL: global mean sea level

Embodiment 1

This embodiment discloses a data assimilation method for inferring changes in seawater quality using tide gauge station records.

As shown in Figure 1, the data assimilation method for inferring seawater quality changes using tide gauge station records includes the following steps:

Preprocess the data, including: selecting tide stations and obtaining tide station record data; calculating global land mass migration changes to obtain sea level fingerprint effects; extracting dynamic sea level changes in climate models and interpolating dynamic sea levels to tide station record data;

Observation equations are established based on the selected tide gauges, and sparse matrices are used to associate state quantities and observation quantities. Random variables are introduced at each tide gauge to make up for the shortcomings of climate models in simulating local dynamic sea level changes. The state of the tide gauge station at the initial moment is determined, and the constraint parameters of the state quantity and the correlation coefficient between adjacent tide gauges are optimized and adjusted. Based on Kalman filtering and smoothing, the optimal state quantity is estimated, and the increase in global seawater mass is speculated.

This embodiment mainly includes 3 steps:

Step 1: Data preprocessing, mainly including tide station selection, sea level fingerprint calculation and dynamic sea level extraction. The specific implementation process is as follows:

(1) Select a suitable tide gauge station.

Although tide gauges are widely distributed in coastal areas around the world, there are still large differences in daily observations and maintenance in different regions, resulting in obvious data missing records. In addition, tide gauges in some areas are also affected by local geological, geophysical, climatic and meteorological conditions, resulting in some jump records. These jump records are not sea level change signals, or have little correlation with large-scale sea level change signals.

Therefore, it is necessary to carefully select tide gauges according to the purpose of the study. First, select tide gauges with more than 20 years of valid records after 1950, then conduct a detailed inspection of the initially selected tide gauges and delete abnormal jump records. In addition, delete tide gauges with linear rates exceeding 10 mm/yr, because these tide gauges are very likely to reflect local geophysical signals.

(2) Calculate the sea level fingerprint effect.

It mainly collects components of land mass transfer changes, including Greenland glacier melting, Antarctic ice sheet melting, mountain glacier melting and changes in land water storage.

Based on image data, we restored the spatiotemporal characteristics of Greenland's 20th century melting and quantitatively estimated the melting of Greenland's glaciers and ice sheets;

Estimate ice sheet loss in the Antarctic region based on meteorological observations combined with ice sheet models;

Based on the glacier model, simulate the melting changes of global mountain glaciers in the 20th century;

Combining observational data and model-driven analysis, we reconstruct changes in terrestrial water storage during the 20th century.

Using the above four change components, the global land mass migration changes are obtained. According to the sea level fingerprint theory, under the elastic assumption framework of mass load, the influence of the earth's gravity field, surface deformation and the earth's rotation are considered to calculate the seawater mass redistribution characteristics formed by land mass changes in the ocean area, namely the sea level fingerprint effect.

(3) Extract dynamic sea level changes from climate models.

The CMIP6 model gives the dynamic sea level changes on a global scale, but these outputs are based on regular grids or grids defined by climate models, not dynamic sea level changes at tide gauges. Therefore, it is necessary to use two-dimensional interpolation technology to interpolate the dynamic sea level output by the model to the tide gauges. In addition, it is also necessary to consider the contribution of post-glacial rebound to the current relative sea level and use the output of the GIA model to correct it.

Step 2: Construct a data assimilation framework, which includes five processes: establishing observation equations, introducing random variables, estimating initial states, optimizing parameters, and filtering and smoothing.

Next, the mathematical structure and calculation process of the data assimilation method are described in detail:

The observation equation is established based on the tide gauge station records at time node t:

Z ^t = H ^t X ^t + ε(S1)

Where ^Zt is the observation vector, which contains m valid tide station records at time t (m changes with time), ε is the noise of the tide station records, and the matrix ^Ht is the conversion matrix, which converts the quantity to be estimated (i.e., the state quantity) to the observation vector. It consists of two parts:

In the formula, Sparse matrix: For each row, when the i-th tide station has a record, then/> The i-th element of is 1, otherwise it is 0; /> The dimension is m×n, where n is the total number of tide gauges.

In data assimilation, the state vector ^Xt is defined as:

In the formula, represents the sea level at the i-th tide gauge station at time t; similarly,/> represents the random rate of the sea level at the tide gauge station; ^αt represents the amplitude t of the sea level fingerprint.

During the filtering process, the state transfer matrix Φ converts the state quantity to the next moment:

In the formula, w represents process noise, subscript f represents prediction, and subscript a represents analytical solution, i.e., the result after filtering; Indicates the instantaneous rate of dynamic sea level; /> represents the relative sea level change caused by glacial isostatic adjustment; it should be emphasized that /> and/> In data assimilation, it is the driving factor, that is, each climate model output can obtain the corresponding state quantity estimate (or complete a data assimilation calculation).

The structure of the transfer matrix Φ is:

Where y _SLF is a vector containing the rate of change of the sea level fingerprint at the tide gauge station, that is, It should be noted that /> represents the linear trend of the sea level fingerprint during the period 1950-2020, while/> represents the rate of the sea level fingerprint at time t, and the relationship between the two is:

Where α represents the amplitude of the linear velocity of the sea level fingerprint at time t.

Using formulas S3-S6, the process of sea level change between two adjacent moments can be expressed as:

Formula S1 and Formula S4 constitute the basic formula for data assimilation:

In the formula, R represents the observation noise, and the matrix Q represents the covariance matrix of the state quantity, which has the following form:

In the formula, I _(n+1)×(n+1) is the unit matrix, and σ ² is a constraint parameter that affects and the range of α(t) over time, in this paper σ is set to 1 mm yr ^-1 ; V _TG defines the correlation between tide gauges, which depends mainly on the distance between them:

In the formula, Variance of tide gauge stations after removing linear trends; τ is taken as 500km; D represents the distance between tide gauge stations. When D≤300km, the correlation is calculated using formula S10. If the distance D>300km, the correlation between tide gauge stations is no longer considered.

The specific calculation formula for data assimilation is:

In the formula, is the Kalman gain matrix, _vt is the update to the observation, and its variance is _Ft .

The smoothing calculation process of data assimilation is:

In the formula, L _t = Φ-K _t H ^t , and Represents the smoothed state quantity.

After making preliminary estimates of the states of seawater quality changes, random variables, and tide gauge station reconstruction, the state of the tide gauge station at the initial moment is determined to reduce the uncertainty in the estimation of other state quantities; the constraint parameters of the state quantities and the correlation coefficients between adjacent tide gauge stations are further optimized and adjusted; finally, Kalman filtering and smoothing are implemented to estimate the optimal state quantity.

Step 3: Analyze the output results and evaluate the causes of sea level rise, mainly including comparing and quantifying the contribution of seawater mass, analyzing the contribution of seawater specific volume, analyzing and reconstructing the sea level time series, explaining the causes of sea level rise, and uncertainty analysis.

The sea level data assimilation framework can not only infer the increase in global seawater mass, but also reconstruct the long-term trend of sea level changes at tide gauges and estimate the contribution of dynamic sea level changes. The spatial characteristics of global seawater mass changes obey the spatial characteristics of the input sea level fingerprint effect. There is only a difference in amplitude between the two. The inferred seawater mass changes are compatible with tide gauge observations. Based on the dynamic sea level changes output by the climate model, combined with the estimation of random variables, new dynamic sea level changes at tide gauges will be obtained. If the inferred seawater mass changes and the newly estimated dynamic sea level changes can explain the reconstructed sea level changes, it is considered that the cause of sea level rise has been successfully revealed. Since data assimilation considers the output of multiple climate models, time series driven by different models can be obtained, the degree of discreteness between them can be estimated, and the uncertainty of the inferred results can be reflected.

The experimental effects obtained by the present invention are described as follows:

As shown in FIG2(a), a comparison diagram of the global mean sea level rise curve (global mean, solid line) reconstructed by the present invention and the sea level rise curve (dashed line) reconstructed by the prior art is shown; as shown in FIG2(b), the long-term trends of ocean mass (ocean mass, short dashed line), dynamic sea level (sterodynamic, long dashed line) and the sum of the two (Sum, long and short dashed lines), the solid line is the same as FIG2(a);

As shown in FIG3 , it is a comparison diagram of the seawater mass inferred by the present invention and the input seawater mass, the solid line represents the inferred ocean mass increase in the present invention; the dotted line represents the input sea level fingerprint total (SLF total);

Figure 4 shows the long-term trend of sea level (mm/yr):

(a) in Figure 4 is a diagram of the sea level rise trend at the tide gauge station reconstructed by the present invention; (b) in Figure 4 is the trend of the sum of the seawater mass and the seawater specific volume estimated by the present invention; (c) in Figure 4 is the difference in trends (a-b).

Figures 2(a) and 2(b) show the main output results of data assimilation. For the reconstructed global mean sea level, the results of the present invention are consistent with those of the references, and the inferred changes in seawater mass are given. The results show that the increase in seawater mass is the dominant factor in sea level rise, especially before 1980. The inferred changes in seawater mass and the newly estimated dynamic sea level well explain the reconstructed global mean sea level, providing an interpretation perspective for the study of the causes of sea level rise.

Comparing the input seawater quality with the inferred seawater quality (Figure 3), it is found that there is a significant difference between the two around 1980. Although the inferred seawater quality is constrained by tide station records under the framework of data assimilation theory, it is also driven by climate model output. Therefore, the accuracy of the model output will also significantly affect the inferred seawater quality changes.

In addition to the global average, the data assimilation results can also give the changes in seawater quality at the tide gauge stations and their contribution to sea level rise, see Figure 4. In all the selected tide gauges, the sum of the inferred seawater quality and the newly estimated dynamic sea level is consistent with the reconstructed sea level rise trend, which is reflected in the trend difference (Figure 4c). This result provides a new perspective for understanding the causes of local sea level rise.

Embodiment 2

This embodiment discloses a data assimilation system for inferring changes in seawater quality using tide gauge station records.

The data assimilation system that uses tide gauge records to infer changes in seawater quality includes:

The data preprocessing module is configured to: select tide stations and obtain tide station record data; calculate the global land mass migration changes to obtain the sea level fingerprint effect; extract the dynamic sea level changes of the climate model and interpolate the dynamic sea level to the tide station record data;

The assimilation modeling module is configured as follows: establish observation equations based on the selected tide gauge stations, and use sparse matrices to associate state quantities and observation quantities; introduce random variables at each tide gauge station to make up for the shortcomings of climate models in simulating local dynamic sea level changes; determine the state of the tide gauge station at the initial moment, optimize and adjust the constraint parameters of the state quantity and the correlation coefficient between adjacent tide gauge stations; estimate the optimal state quantity based on Kalman filtering and smoothing, and speculate on the increase in global seawater mass.

In this embodiment, we use the MATLAB language to independently compile a data processing software system that can preprocess sea level observation data and model data, automatically access the data assimilation module, assimilate these data, run Kalman filtering and smoothing, and save output variables.

Embodiment 3

The purpose of this embodiment is to provide a computer-readable storage medium.

A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the data assimilation method for inferring changes in seawater quality using tide gauge station records as described in Example 1 of the present disclosure.

Embodiment 4

The purpose of this embodiment is to provide an electronic device.

An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in the data assimilation method for inferring changes in seawater quality using tide gauge station records as described in Example 1 of the present disclosure are implemented.

The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method of the present invention.

Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device. The present invention is not limited to any specific combination of hardware and software.

Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Technical personnel in the relevant field should understand that various modifications or variations that can be made by technical personnel in the field without creative work on the basis of the technical solution of the present invention are still within the scope of protection of the present invention.

Claims (10)

1. The data assimilation method for recording the presumed sea water quality change by utilizing the tide station is characterized by comprising the following steps:

preprocessing data, including: selecting a tide station, and acquiring record data of the tide station; calculating global land quality migration change to obtain a sea level fingerprint effect; extracting dynamic sea level change of a climate mode, and interpolating the dynamic sea level to a record data position of a tide station;

establishing an observation equation according to the selected tide station, and associating state quantity and observed quantity by using a sparse matrix; introducing random variables at each tide station to make up for the defect of simulating local dynamic sea level change by a climate mode; determining the state of the tide station at the initial moment, and optimizing and adjusting constraint parameters of state quantity and correlation coefficients between adjacent tide stations; based on Kalman filtering and smoothing, an optimal state quantity is estimated, and the global sea water mass increment is estimated.

2. The data assimilation method using tide station record presuming sea water quality variation as claimed in claim 1, wherein tide station with effective record over 20 years is selected, abnormal jump record is deleted, and tide station with linear rate exceeding set value is deleted.

3. The data assimilation method for estimating sea water quality variation by using tide station record according to claim 1, wherein sea level fingerprint effects caused by different land quality migration are integrated into one whole to estimate assuming that land quality migration based on different data sources has the same error characteristics.

4. A data assimilation method for estimating sea water quality change using tide station recording according to claim 3, wherein the sea level fingerprint effect obtaining process is:

taking the change of land water reserves, namely the Greenland island glacier ablation, the Antarctic glacier cover ablation and the mountain glacier ablation into consideration to obtain the global land quality migration change;

under the elastic assumption frame of mass load, the influence of the earth gravity field, earth surface deformation and earth rotation is considered, the sea water mass redistribution characteristic formed by land mass change in the ocean area is calculated, and the sea level fingerprint effect is obtained.

5. The data assimilation method for presuming sea water quality change using a tide station record as claimed in claim 1, wherein: establishing an observation equation based on the tide station record, and at a time node t:

Z ^t ＝H ^t X ^t +ε

wherein Z is ^t Is the observation vector, which contains m valid tide station records at time t, ε is the noise of the tide station records, matrix H ^t Is a transformation matrix;

state vector X ^t The definition is as follows:

in the method, in the process of the invention,representing the sea level of the ith tide station at time t; />Representing the random rate of sea level at the tide station; alpha ^t An amplitude t representing a sea level fingerprint;

in the filtering process, the state transition matrix Φ converts the state quantity to the next time:

wherein w represents process noise, subscript f represents prediction, subscript a represents analysis solution, i.e. filtered result;representing the instantaneous rate of dynamic sea level; />Indicating the relative sea level change due to glacier equalization adjustment.

6. The data assimilation method for estimating sea water quality variation using tide station recording according to claim 5, wherein the process of sea level variation between adjacent two moments is:

in SL (SL) ^t The sea level at the tide station at the current moment; SL (SL) device ^t+1 The sea level is at the tide station at the next moment;a random rate for sea level at the tide station;

representing the velocity of the sea level fingerprint at time t:

in the method, in the process of the invention,rate change for sea level fingerprint at the tide station; alpha represents the amplitude of the linear velocity of the sea level fingerprint at time t.

7. The data assimilation method for presuming sea water quality change using a tide station record as claimed in claim 5, wherein: process noise w-N (0, Q), matrix Q represents the covariance matrix of the state quantity:

wherein I is _(n+1)×(n+1) Is a unit array, sigma ² As constraint parameters, influenceAnd a (t) ranges over time; v (V) _TG Correlation between tide stations is defined:

in the method, in the process of the invention,the tide station removes the variance after the linear trend; the value of tau is 500km; d represents the distance between tide stations, when D is largeAt the set point, the correlation between the tide stations is not considered.

8. Utilize tide station record to speculate data assimilation system of sea water quality change, its characterized in that: comprising the following steps:

a data preprocessing module configured to: selecting a tide station, and acquiring record data of the tide station; calculating global land quality migration change to obtain a sea level fingerprint effect; extracting dynamic sea level change of a climate mode, and interpolating the dynamic sea level to a record data position of a tide station;

an assimilation modeling module configured to: establishing an observation equation according to the selected tide station, and associating state quantity and observed quantity by using a sparse matrix; introducing random variables at each tide station to make up for the defect of simulating local dynamic sea level change by a climate mode; determining the state of the tide station at the initial moment, and optimizing and adjusting constraint parameters of state quantity and correlation coefficients between adjacent tide stations; based on Kalman filtering and smoothing, an optimal state quantity is estimated, and the global sea water mass increment is estimated.

9. A computer readable storage medium having a program stored thereon, which when executed by a processor, implements the steps of the data assimilation method for recording a speculative sea water quality change using a tide station according to any one of claims 1-7.

10. Electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, implements the steps of the data assimilation method of any of claims 1-7 for recording speculative sea water quality changes using a tide station.