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The quality control (QC) of ocean observational data, essential to establish a high-quality global ocean database, is one of the basic data pre-processing steps in oceanography research, marine monitoring, and forecasting. With the introduction of various advanced instruments in recent decades, oceanographic surveys have expanded from coastal regions to open oceans. However, as ocean in-situ observations are obtained using different instruments that offer heterogeneous data qualities, it is paramount that bad data could be accurately and efficiently identified via QC to provide a reliable global ocean database. In this review, we briefly summarize the latest progress of QC for oceanic in-situ observations, and mainly focus on temperature and salinity data. The similarities and differences between QC schemes developed by various ocean organizations are introduced. We also discuss the performances of the various QC schemes and identify the key challenges. Based on the discussions, several recommendations are proposed for future improvements in the QC for ocean observations.

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities. In 2023, the sea surface temperature (SST) and upper 2000 m ocean heat content (OHC) reached record highs. The 0–2000 m OHC in 2023 exceeded that of 2022 by 15 ± 10 ZJ (1 Zetta Joules = 10 21 Joules) (updated IAP/CAS data); 9 ± 5 ZJ (NCEI/NOAA data). The Tropical Atlantic Ocean, the Mediterranean Sea, and southern oceans recorded their highest OHC observed since the 1950s. Associated with the onset of a strong El Niño, the global SST reached its record high in 2023 with an annual mean of ∼0.23°C higher than 2022 and an astounding > 0.3°C above 2022 values for the second half of 2023. The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

High-quality ocean in situ profile observations are fundamental for ocean and climate research and operational oceanographic applications. Here we describe a new global ocean subsurface temperature profile database named the Chinese Academy of Science (CAS) Oceanography Data Center version 1 (CODC-v1). This database contains over 17 million temperature profiles between 1940–2023 from all available instruments. The major data source is the World Ocean Database (WOD), but CODC-v1 also includes some data from some Chinese institutes which are not available in WOD. The data are quality-controlled (QC-ed) by a new QC system that considers the skewness of local temperature distributions, topographic barriers, and the shift of temperature distributions due to climate change. Biases in Mechanical Bathythermographs (MBTs), eXpendable Bathythermographs (XBTs), and Bottle data (OSD) are all corrected using recently proposed correction schemes, which makes CODC-v1 a bias-corrected dataset. These aspects ensure the data quality of the CODC-v1 database, making it suitable for a wide spectrum of ocean and climate research and applications.

Changes in the global ocean salinity reflect the evolution of the global hydrological cycle. These secular changes are assessed using seawater salinity profiles obtained during the past ~80 years. Here, we introduce a new global ocean salinity profiles database named CODC-S (the Chinese Academy of Science (CAS) Oceanography Data Center – Salinity component), which encompasses over 11 million in-situ salinity profiles from 1940 to 2023 obtained by means of several instrument types. These salinity profiles are quality-controlled (QC-ed) using a new automated salinity quality control system named CODC-QC-S (the CODC Quality Control system – Salinity component), consisting of 11 distinct quality checks. By applying time-varying, flow-dependent, and topographical-dependent 0.5% and 99.5% quantile thresholds, the CODC-QC-S defines local climatology salinity ranges without the assumption of Gaussian distribution. The CODC-S database, together with the newly proposed QC algorithm, has undergone extensive evaluations, including comparisons with the benchmark data and climatology, as well as analyses of global and basin-scale long-term salinity changes before and after QC. These validations demonstrate that the quality of salinity data in the CODC-S is time-, depth-, region- and instrument type-dependent. The eight-decade quality-homogeneous salinity profiles from the CODC-S database can support diverse oceanographic and climatic research such as monitoring water cycle changes and freshwater/overturning transports.

A high-quality hydrographic observational database is essential for ocean and climate studies and operational applications. Because there are numerous global and regional ocean databases, duplicate data continues to be an issue in data management, data processing and database merging, posing a challenge on effectively and accurately using oceanographic data to derive robust statistics and reliable data products. This study aims to provide algorithms to identify the duplicates and assign labels to them. We propose first a set of criteria to define the duplicate data; and second, an open-source and semi-automatic system to detect duplicate data and erroneous metadata. This system includes several algorithms for automatic checks using statistical methods (such as Principal Component Analysis and entropy weighting) and an additional expert (manual) check. The robustness of the system is then evaluated with a subset of the World Ocean Database (WOD18) with over 600,000 in-situ temperature and salinity profiles. This system is an open-source Python package (named DC_OCEAN) allowing users to effectively use the software. Users can customize their settings. The application result from the WOD18 subset also forms a benchmark dataset, which is available to support future studies on duplicate checks, metadata error identification, and machine learning applications. This duplicate checking system will be incorporated into the International Quality-controlled Ocean Database (IQuOD) data quality control system to guarantee the uniqueness of ocean observation data in this product.

Expendable bathythermographs (XBTs) have been widely deployed for ocean monitoring since the late 1960s. Improving the quality of XBT data is a vital task in climatology. Many factors (e.g., temperature, probe type, and manufacturing time) have been identified as major influences of XBT systematic bias. In addition, the recording system (RS) has long been suspected as another factor. However, this factor has not been taken into account in any global XBT correction schemes, partly because its impact is poorly understood. Here, based on analysis of an XBT–CTD side-by-side dataset and a global collocated reference dataset, the influence of RSs on the pure temperature error (PTE) is examined. Results show a clear time dependency of PTE on the RS, with maximum values occurring in the 1970s. In addition, the method used to convert thermistor resistance into temperature in the RS (using a resistance–temperature equation) has changed over time. These changes, together with the decadal changes in RSs, might contribute a small error (10% on average) to the RS dependency. Here, an improvement of global XBT bias correction that accounts for the RS dependency is proposed. However, more than 70% of historical global XBT data are missing RS-type information. We investigate several assumptions about the temporal distribution of RS types, and all scenarios lead to at least a ~50% reduction in the time variation of PTE compared with the uncorrected data. Therefore, the RS dependency should be taken into account in updated XBT correction schemes, which would have further implications for climatology studies.

Ocean observational gridded products are vital for climate monitoring, ocean and climate research, model evaluation, and supporting climate mitigation and adaptation measures. This paper describes the 4th version of the Institute of Atmospheric Physics (IAPv4) ocean temperature and ocean heat content (OHC) objective analysis product. It accounts for recent developments in quality control (QC) procedures, climatology, bias correction, vertical and horizontal interpolation, and mapping and is available for the upper 6000 m (119 levels) since 1940 (more reliable after ∼ 1957) for monthly and 1°×1° temporal and spatial resolutions. IAPv4 is compared with the previous version, IAPv3, and with the other data products, sea surface temperatures (SSTs), and satellite observations. It has a slightly stronger long-term upper 2000 m OHC increase than IAPv3 for 1955–2023, mainly because of newly developed bias corrections. The IAPv4 0–2000 m OHC trend is also higher during 2005–2023 than IAPv3, mainly because of the QC process update. The uppermost level of IAPv4 is consistent with independent SST datasets. The month-to-month OHC variability for IAPv4 is desirably less than IAPv3 and the other OHC products investigated in this study, the trend of ocean warming rate (i.e., warming acceleration) is more consistent with the net energy imbalance at the top of the atmosphere than IAPv3, and the sea level budget can be closed within uncertainty. The gridded product is freely accessible at https://doi.org/10.12157/IOCAS.20240117.002 for temperature data (Cheng et al., 2024a) and at https://doi.org/10.12157/IOCAS.20240117.001 for ocean heat content data (Cheng et al., 2024b).

Global ocean oxygen concentrations have declined in the past decades, posing threats to marine life and human society. High-quality and bias-free observations are crucial to understanding ocean oxygen changes and assessing their impact. Here, we propose a new automated quality control (QC) procedure for ocean profile oxygen data. This procedure consists of a suite of 10 quality checks, with outlier rejection thresholds being defined based on underlying statistics of the data. The procedure is applied to three main instrumentation types: bottle casts, CTD (conductivity–temperature–depth) casts, and Argo profiling floats. Application of the quality control procedure to several manually quality-controlled datasets of good quality suggests the ability of the scheme to successfully identify outliers in the data. Collocated quality-controlled oxygen profiles obtained by means of the Winkler titration method are used as unbiased references to estimate possible residual biases in the oxygen sensor data. The residual bias is found to be negligible for electrochemical sensors typically used on CTD casts. We explain this as the consequence of adjusting to the concurrent sample Winkler data. Our analysis finds a prevailing negative residual bias with the magnitude of several µmol kg−1 for the delayed-mode quality-controlled and adjusted profiles from Argo floats varying among the data subsets adjusted by different Argo Data Assembly Centers (DACs). The respective overall DAC- and sensor-specific corrections are suggested. We also find the bias dependence on pressure, a feature common to both AANDERAA optodes and SBE43-series sensors. Applying the new QC procedure and bias adjustments resulted in a new global ocean oxygen dataset from 1920 to 2023 with consistent data quality across bottle samples, CTD casts, and Argo floats. The adjusted Argo profile data are available at the Marine Science Data Center of the Chinese Academy of Sciences (https://doi.org/10.12157/IOCAS.20231208.001, Gouretski et al., 2024).

Lidar-based 3D object detection and classification is a critical task for autonomous driving. However, inferencing from exceedingly sparse 3D data in real-time is a formidable challenge. Complex-YOLO solves the problem of point cloud disorder and sparsity by projecting it onto the bird’s-eye view and realizes real-time 3D object detection based on LiDAR. However, Complex-YOLO has no object height detection, a shallow network depth, and poor small-size object detection accuracy. To address these issues, this paper has made the following improvements: (1) adds a multi-scale feature fusion network to improve the algorithm’s capability to detect small-size objects; (2) uses a more advanced RepVGG as the backbone network to improve network depth and overall detection performance; and (3) adds an effective height detector to the network to improve the height detection. Through experiments, we found that our algorithm’s accuracy achieved good performance on the KITTI dataset, while the detection speed and memory usage were very superior, 48FPS on RTX3070Ti and 20FPS on GTX1060, with a memory usage of 841Mib.

IntroductionHeadaches, dizziness and memory loss of unspecific causes are the most common non-acute ischemia symptoms in the ageing population, which are often associated with cerebral small vessel disease (CSVD) imaging markers; however, there is insufficient evidence concerning their association with the development of cognitive decline. This study aims to investigate risk factors, clinical course, cerebral and retinal imaging changes, proteomics features of non-symptomatic ischaemia symptomatic patients with cognitive decline.Methods and analysisThe Non-Acute Symptomatic Cerebral Ischemia Registration study is a multicentre, registry-based, prospective observational study, is designed to investigate the cognitive decline in non-acute ischaemia symptomatic patients. We will recruit 500 non-acute ischaemia symptomatic patients from four tertiary hospitals in China. For this study, non-acute ischaemia symptoms will be defined as headaches, dizziness and memory loss. Patients with headaches, dizziness or memory loss over 50 years of age will be included. Clinical features, cognitive assessment, cerebral and retinal imaging data, and a blood sample will be collected after recruitment. Patients will be followed up by structured telephone interviews at 1, 2, 3, 4, 5 years after recruitment. This study will improve our knowledge of the development of cognitive decline in non-acute ischaemia symptomatic patients and factors affecting the cognitive outcomes, which will eventually elucidate underlying pathways and mechanisms of cognitive decline in these patients and facilitate the optimisation of individualised interventions for its prevention and treatment.Ethics and disseminationEthics approval is obtained from The Biomedical Research Ethics Committee of West China Hospital, Sichuan University (Reference No. 2016 (335)). We will present our findings at national and international conferences and peer-reviewed journals in stroke and neurology.Trial registration numberChiCTR-COC-17013056.