CN-122019514-A - Drift correction method for Argo salinity profile observation data

CN122019514ACN 122019514 ACN122019514 ACN 122019514ACN-122019514-A

Abstract

The invention discloses a drift correction method for Argo salinity profile observation data, which relates to the technical field of ocean observation data processing and correction, and specifically comprises the steps of obtaining historical time sequence salinity profile observation data corresponding to an Argo buoy, constructing a climatic state salinity estimation model, respectively introducing large-scale space parameters and small-scale space parameters into the climatic state salinity estimation model to obtain large-scale salinity variation and small-scale salinity variation, calculating and adding weights of the large-scale salinity variation and the small-scale salinity variation to obtain a climatic state salinity objective estimation value, collecting the salinity profile observation value in real time, comparing the salinity profile observation value with the climatic state salinity objective estimation value, judging whether the salinity profile observation value has drift errors, fitting the climatic state potential conductivity value to obtain the corrected climatic state potential conductivity value if the drift errors exist, and converting the corrected climatic state potential conductivity value into the corresponding correction salinity value.

Inventors

LIU QINGSONG
LI LI
LV RUI
LIU XIN
WANG XIAOLEI
SONG MINGXIU
CAO JIXIN
WANG SHAOJUN
Zeng Chongji
Yin Tienan

Assignees

华能烟台新能源有限公司
中国华能集团清洁能源技术研究院有限公司
华能海上风电科学技术研究有限公司

Dates

Publication Date: 20260512
Application Date: 20251127

Claims (8)

A method for correcting drift of argo salinity profile observations, the method comprising: numbering a plurality of Argo buoys in a target sea area, obtaining historical time series salinity profile observation data of the Argo buoys corresponding to the numbering, and constructing a climatic state salinity estimation model; Respectively introducing a large-scale space parameter and a small-scale space parameter into the climatic state salinity estimation model to obtain a large-scale salinity variation and a small-scale salinity variation; The salinity profile observation data acquisition device is preset, salinity profile observation values are acquired in real time and are compared with climate state salinity objective estimation values for analysis, and whether drift errors exist in the salinity profile observation values is judged; If drift errors exist, fitting the climate state potential conductivity value based on a weighted least square method to obtain a corrected climate state potential conductivity value; And converting the corrected climate state potential conductivity value into a corresponding corrected salinity value, correcting the salinity profile observed value acquired in real time based on the corrected salinity value, completing drift correction of the salinity profile observed data, and outputting the corrected salinity profile observed value.
2. The method of correcting for drift in Argo salinity profile according to claim 1, wherein the time-series salinity profile comprises salinity profile, temperature profile, pressure profile, time of observation, buoy position coordinates and buoy number.
3. The method of correcting drift of Argo salinity profile observations as defined in claim 1 wherein the large scale spatial parameters comprise ocean current characteristics parameters, large scale topography parameters and global climate system parameters.
4. The method for correcting drift of Argo salinity profile observation according to claim 1 wherein the small scale space parameters comprise a local water mixing parameter, a small scale vortex parameter and a local topography parameter.
5. The method of correcting for drift in an Argo salinity profile observation according to claim 1, wherein obtaining an objective estimate of climatic state salinity comprises: The climatic state salinity estimation model respectively introduces a large-scale space parameter and a small-scale space parameter to obtain a large-scale salinity variation and a small-scale salinity variation; the dynamic allocation mechanism based on Bayes optimization is adopted to calculate the weights of the large-scale salinity variation and the small-scale salinity variation, which comprises, Constructing an objective function of the large-scale parameter contribution degree m and the small-scale parameter contribution degree 1-m, taking the root mean square error of the minimized climatic state salinity estimation model as an optimization target, and determining an optimal weight combination when the root mean square error reaches the minimum value by iteratively adjusting the m value; To obtain the weights of the large-scale salinity variation and the small-scale salinity variation.
6. The method of correcting for drift of Argo salinity profile observations as defined in claim 1, wherein collecting salinity profile observations in real time and comparing the salinity profile observations with objective estimates of climatic salinity for analysis, and determining whether there is a drift error in the salinity profile observations comprises: A preset comparison time window, wherein a salinity profile observed value and a climatic state salinity objective estimated value which are acquired in real time are divided into a continuous preset comparison time window according to a time sequence; Calculating deviation of the salinity profile observed value and the climate state salinity objective estimated value acquired in real time in each preset comparison time window, and presetting a threshold value of an allowable variation range of the climate state salinity objective estimated value; If the salinity profile observed value acquired in real time is in the threshold value of the allowable variation range of the objective estimated value of the salinity in the preset climatic state, judging that the salinity profile observed value has no drift error; If the salinity profile observed value acquired in real time is not in the threshold value of the allowable variation range of the salinity objective estimated value of the preset climate state, judging that the salinity profile observed value has drift errors.
7. The method of correcting for drift in an Argo salinity profile observation according to claim 1, wherein obtaining corrected values of the conductivity of the climate attitude potential comprises: If drift errors exist, a salinity value deviation sequence corresponding to the buoy salinity profile observed value is obtained; calculating a climatic state potential conductivity value of a salinity value deviation sequence corresponding to the buoy salinity profile observation value and a climatic state potential conductivity value of a climatic state salinity objective estimation value; Respectively constructing corresponding fitting functions for the climatic state potential conductivity values of the salinity value deviation sequences corresponding to the buoy salinity profile observed values and the climatic state potential conductivity values of the climatic state salinity objective estimated values; Taking a climatic state potential conductivity value fitting function of the climatic state salinity objective estimated value as a reference function; Correcting the climate state potential conductivity value fitting function of the salinity value deviation sequence corresponding to the buoy salinity profile observation value based on a weighted least square method, and taking a reference function as a target; and adjusting the climate state potential conductivity value of the salinity value deviation sequence corresponding to the buoy salinity profile observation value through iterative calculation until the weighted residual square sum converges to a preset convergence threshold value, so as to obtain the corrected climate state potential conductivity value.
The drift correction system for the Argo salinity profile observation data, which is applied to the drift correction method for the Argo salinity profile observation data according to any one of claims 1-7, is characterized by comprising a number and data acquisition module, a first generation module, a first judgment analysis module, a second generation module and a salinity value correction module; Numbering a plurality of Argo buoys in a target sea area, obtaining historical time series salinity profile observation data of the Argo buoys corresponding to the numbering, and constructing a climatic state salinity estimation model; The system comprises a generation module I, a weather state salinity estimation module, a calculation module and a calculation module, wherein the weather state salinity estimation module is respectively introduced with a large-scale space parameter and a small-scale space parameter to obtain a large-scale salinity variation and a small-scale salinity variation; A judgment and analysis module; the salinity profile observation data acquisition device is preset, salinity profile observation values are acquired in real time and are compared with climate state salinity objective estimation values for analysis, and whether drift errors exist in the salinity profile observation values is judged; If drift errors exist, fitting the climate state potential conductivity value based on a weighted least square method to obtain a corrected climate state potential conductivity value; And the corrected climatic potential conductivity value is converted into a corresponding corrected salinity value, the salinity profile observed value acquired in real time is corrected based on the corrected salinity value, drift correction of the salinity profile observed data is completed, and the corrected salinity profile observed value is output.

Description

Drift correction method for Argo salinity profile observation data Technical Field The invention relates to the technical field of ocean observation data processing and correction, in particular to a drift correction method for Argo salinity profile observation data. Background The Argo is an international ocean observation plan, mainly collects data such as temperature, salinity, pressure and the like in the ocean in the world through a floating type observation buoy (called an Argo buoy), can automatically float between the ocean surface and the deep sea, continuously collects and transmits ocean profile data, particularly salinity and temperature data, and is of great importance for researching ocean circulation, climate change, ocean ecosystem, weather forecast and the like. In the Chinese application with the application publication number of CN120314529A, an error correction system and method for marine environment observation data are disclosed, wherein the error correction system comprises a data acquisition, compensation modeling, error analysis, data compensation, data fusion and feedback optimization module. The data acquisition module acquires ADCP flow measurement, GPS, IMU, temperature, pressure and water quality monitoring data. The error analysis module detects drift errors by using Kalman filtering and a hidden Markov model and analyzes environmental errors based on a long-term memory network. The data compensation module optimizes the compensation model and improves the data precision. The data fusion module adopts particle filtering and Bayesian optimization algorithm to fuse the multi-sensor data. And the feedback optimization module dynamically adjusts the weight of the compensation model according to the error correction data, so that the system adaptability is improved. The invention improves the accuracy of the ocean current monitoring data and provides more reliable data support for the underwater environment research. The problems of the prior art are that the ocean environment has extremely complexity, various space-time scale phenomena exist in the ocean, the large-scale ocean circulates to the small-scale vortex motion, the salinity observation data of the Argo buoy inevitably drift in the long-term observation process, meanwhile, the simple anomalies are identified manually by virtue of the knowledge of the ocean characteristics of the ocean and the past data experience, then abnormal values are removed, certain limitations exist, such as lower efficiency and larger influence of acceptation factors, and therefore, the method and the system for correcting the drift of the salinity profile observation data of the Argo buoy are needed to solve the problems in the prior art. Disclosure of Invention In order to solve the above technical problems, an aspect of the present invention provides a drift correction method for Argo salinity profile observation data, the method comprising: numbering a plurality of Argo buoys in a target sea area, obtaining historical time series salinity profile observation data of the Argo buoys corresponding to the numbering, and constructing a climatic state salinity estimation model; Respectively introducing a large-scale space parameter and a small-scale space parameter into the climatic state salinity estimation model to obtain a large-scale salinity variation and a small-scale salinity variation; The salinity profile observation data acquisition device is preset, salinity profile observation values are acquired in real time and are compared with climate state salinity objective estimation values for analysis, and whether drift errors exist in the salinity profile observation values is judged; If drift errors exist, fitting the climate state potential conductivity value based on a weighted least square method to obtain a corrected climate state potential conductivity value; And converting the corrected climate state potential conductivity value into a corresponding corrected salinity value, correcting the salinity profile observed value acquired in real time based on the corrected salinity value, completing drift correction of the salinity profile observed data, and outputting the corrected salinity profile observed value. In a preferred embodiment, the time series salinity profile observations include salinity profile observations, temperature profile data, pressure profile data, observation times, buoy position coordinates, and buoy numbers. In a preferred embodiment, the large scale space parameters include ocean circulation characterization parameters, large scale topography parameters, and global climate system parameters. In a preferred embodiment, the small scale spatial parameters include local water mixing parameters, small scale vorticity parameters, and local topography parameters. In a preferred embodiment, obtaining an objective estimate of climatic state salinity comprises: The climatic state salinity estimation model respectively introdu