Automatic ocean temperature and salinity observation data quality control method and system based on climate state range threshold

Abstract

The invention provides an automatic ocean thermohaline observation data quality control method and system based on a climatic state range threshold, and belongs to the field of data processing systems. The method comprises the following steps: acquiring ocean thermohaline observation data; performing data preprocessing on the observation data; performing core module quality control on the preprocessed observation data; basic information inspection, depth incremental inspection, instrument maximum depth inspection, local maximum depth inspection, global range inspection, seawater freezing point inspection, equivalence inspection, local climate state inspection, peak inspection, extreme value inspection, global gradient inspection, local gradient climate state inspection and instrument characteristic inspection are performed in sequence; marking and outputting quality control results of all the examinations; carrying out statistics on output results of the quality control marks; and outputting the observation data file with the quality control mark. The method is used for carrying out quality control on the temperature and salinity data of ocean observation, and aims to provide a solution thought for rapidly providing a high-quality observation data set at low cost.

Description

technical field [0001] The invention belongs to the field of data processing systems, in particular to an automatic ocean temperature-salinity observation data quality control method based on the climate range threshold. Background technique [0002] The quality control of ocean temperature and salinity observation data is the basis for establishing a high-quality ocean science database, which is of great significance for promoting ocean science and interdisciplinary research, marine environment prediction and forecasting, and marine disaster early warning. In recent decades, with the development of various ocean observation technologies and equipment, and the continuous expansion of the depth and breadth of ocean surveys, ocean science has entered the era of big data. Data quality control refers to the comprehensive quality assessment of marine observation data through manual review or computer algorithm-assisted methods, identifying and eliminating false observation data (...

AI Technical Summary

Problems solved by technology

But in fact, affected by factors such as the number of observations and changes in the marine environment, marine multi-source observation data are often skewed or even irregularly distributed.

If it is assumed that the data is only normally distributed, some outliers will not be accurately identified

[0007] (2) The traditional quality control system did not consider the flow dependence of the ocean circulation when constructing the threshold (range) of the climate state, and did not adapt to the complex ocean topographic conditions, which resulted in the inability to observe in some areas with little or no observation data. Accurately construct the range of historical climate change in the region, resulting in too low accuracy of quality control

[0008] (3) Traditional automated quality control systems often need to be combined with manual review for data quality control, because a single automated quality control system often has a low probability of false detection (unable to correctly mark outliers) and a low probability of misjudgment (correct data Mislabeled as an outlier) High case

[0009] (4) Some traditional quality control systems are mainly realized through manual inspection (expert quality control). This type of system consumes a lot of time, manpower, and financial resources, and the cost is too high. At the same time, this type of system cannot adapt to the increasing Ocean real-time data flow and the ability to build a real-time linked ocean database are not time-sensitive in terms of efficiency

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