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Data analysis methods in physical oceanography
A practical reference to established and modern data analysis techniques in earth and ocean sciences. Its five major sections address data acquisition and recording, data processing and presentation, statistical methods and error handling, analysis of spatial data fields, and time series analysis methods.
Book
Data analysis methods in physical oceanography
Data Analysis Methods in Physical Oceanography, Third Edition is a practical reference to established and modern data analysis techniques in earth and ocean sciences.Its five major sections address data acquisition and recording, data processing and presentation, statistical methods and error handling, analysis of spatial data fields, and time.
eBook
Potential Use of Chat GPT in Global Warming
Climate change is a major global challenge that requires the integration of many different scientific disciplines, including atmospheric science, oceanography, and ecology. The complexity and scale of the problem require sophisticated tools and techniques to understand, model, and project future climate conditions. Artificial intelligence and natural language processing technologies, such as ChatGPT, have the potential to play a critical role in advancing our understanding of climate change and improving the accuracy of climate projections. ChatGPT can be used in a variety of ways to aid climate research, including in model parameterization, data analysis and interpretation, scenario generation, and model evaluation. This technology provides researchers and policy-makers with a powerful tool for generating and analyzing different climate scenarios based on a wide range of data inputs, and for improving the accuracy of climate projections. The author acknowledges asking chatGPT questions regarding its uses for Climate Change Research. Some of the uses that it states are possible now and some are potentials for the future. The author has analyzed and edited the replies of chat GPT.
Journal Article
Measuring the Value of Research Data: A Citation Analysis of Oceanographic Data Sets
Evaluation of scientific research is becoming increasingly reliant on publication-based bibliometric indicators, which may result in the devaluation of other scientific activities--such as data curation--that do not necessarily result in the production of scientific publications. This issue may undermine the movement to openly share and cite data sets in scientific publications because researchers are unlikely to devote the effort necessary to curate their research data if they are unlikely to receive credit for doing so. This analysis attempts to demonstrate the bibliometric impact of properly curated and openly accessible data sets by attempting to generate citation counts for three data sets archived at the National Oceanographic Data Center. My findings suggest that all three data sets are highly cited, with estimated citation counts in most cases higher than 99% of all the journal articles published in Oceanography during the same years. I also find that methods of citing and referring to these data sets in scientific publications are highly inconsistent, despite the fact that a formal citation format is suggested for each data set. These findings have important implications for developing a data citation format, encouraging researchers to properly curate their research data, and evaluating the bibliometric impact of individuals and institutions.
Journal Article
Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data
Color vision deficiency (CVD) affects more than 4% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perception of scientific data by as many viewers as possible. We developed a Python module, cmaputil, to create CVD-optimized colormaps, which imports colormaps and modifies them to be perceptually uniform in CVD-safe colorspace while linearizing and maximizing the brightness range. The module is made available to the science community to enable others to easily create their own CVD-optimized colormaps. Here, we present an example CVD-optimized colormap created with this module that is optimized for viewing by those without a CVD as well as those with red-green colorblindness. This colormap, cividis, enables nearly-identical visual-data interpretation to both groups, is perceptually uniform in hue and brightness, and increases in brightness linearly.
Journal Article
DATA PROCESSING AND MANAGEMENT AT PMEL
Over the last 50 years, the landscape of marine data management has been transformed. Previously, each research project held its data privately and managed them as local files on disk; today, it is standard practice to share data collaboratively over the internet, often integrated with web tools that provide a global community of scientists with ready access to data analysis and visualization. NOAA Pacific Marine Environmental Laboratory (PMEL) developers and data managers have made and continue to make pivotal contributions toward this evolution. This article examines contributions that include a community-wide standard for metadata storage (e.g., climate and forecast [CF] metadata conventions), a widely used desktop computer tool (PyFerret), a pioneering web server providing visualization and analysis of distributed data (Live Access Server), tailor-made data management systems for uncrewed ocean platforms, and new developments in applications of machine learning to data quality control. We also describe the evolution of in-house PMEL data management, from PMEL developed tools to an open-science, interoperable data approach.
Journal Article
Ship Trajectories Pre-processing Based on AIS Data
Data derived from the Automatic Identification System (AIS) plays a key role in water traffic data mining. However, there are various errors regarding time and space. To improve availability, AIS data quality dimensions are presented for detecting errors of AIS tracks including physical integrity, spatial logical integrity and time accuracy. After systematic summary and analysis, algorithms for error pre-processing are proposed. Track comparison maps and traffic density maps for different types of ships are derived to verify applicability based on the AIS data from the Chinese Zhoushan Islands from January to February 2015. The results indicate that the algorithms can effectively improve the quality of AIS trajectories.
Journal Article
MAIA—A machine learning assisted image annotation method for environmental monitoring and exploration
Digital imaging has become one of the most important techniques in environmental monitoring and exploration. In the case of the marine environment, mobile platforms such as autonomous underwater vehicles (AUVs) are now equipped with high-resolution cameras to capture huge collections of images from the seabed. However, the timely evaluation of all these images presents a bottleneck problem as tens of thousands or more images can be collected during a single dive. This makes computational support for marine image analysis essential. Computer-aided analysis of environmental images (and marine images in particular) with machine learning algorithms is promising, but challenging and different to other imaging domains because training data and class labels cannot be collected as efficiently and comprehensively as in other areas. In this paper, we present Machine learning Assisted Image Annotation (MAIA), a new image annotation method for environmental monitoring and exploration that overcomes the obstacle of missing training data. The method uses a combination of autoencoder networks and Mask Region-based Convolutional Neural Network (Mask R-CNN), which allows human observers to annotate large image collections much faster than before. We evaluated the method with three marine image datasets featuring different types of background, imaging equipment and object classes. Using MAIA, we were able to annotate objects of interest with an average recall of 84.1% more than twice as fast as compared to \"traditional\" annotation methods, which are purely based on software-supported direct visual inspection and manual annotation. The speed gain increases proportionally with the size of a dataset. The MAIA approach represents a substantial improvement on the path to greater efficiency in the annotation of large benthic image collections.
Journal Article
High-resolution bathymetry by deep-learning-based image superresolution
Seafloor mapping to create bathymetric charts of the oceans is important for various applications. However, making high-resolution bathymetric charts requires measuring underwater depths at many points in sea areas, and thus, is time-consuming and costly. In this work, treating gridded bathymetric data as digital images, we employ the image-processing technique known as superresolution to enhance the resolution of bathymetric charts by estimating high-resolution images from low-resolution ones. Specifically, we use the recently-developed deep-learning methodology to automatically learn the geometric features of ocean floors and recover their details. Through an experiment using bathymetric data around Japan, we confirmed that the proposed method outperforms naive interpolation both qualitatively and quantitatively, observing an eight-dB average improvement in peak signal-to-noise ratio. Deep-learning-based bathymetric image superresolution can significantly reduce the number of sea areas or points that must be measured, thereby accelerating the detailed mapping of the seafloor and the creation of high-resolution bathymetric charts around the globe.
Journal Article
Detection of pelagic habitat hotspots for skipjack tuna in the Gulf of Bone-Flores Sea, southwestern Coral Triangle tuna, Indonesia
Using remote sensing of sea surface temperature (SST), sea surface height anomaly (SSHA) and chlorophyll-a (Chl-a) together with catch data, we investigated the detection and persistence of important pelagic habitat hotspots for skipjack tuna in the Gulf of Bone-Flores Sea, Indonesia. We analyzed the data for the period between the northwest and southeast monsoon 2007-2011. A pelagic hotspot index was constructed from a model of multi-spectrum satellite-based oceanographic data in relation to skipjack fishing performance. Results showed that skipjack catch per unit efforts (CPUEs) increased significantly in areas of highest pelagic hotspot indices. The distribution and dynamics of habitat hotspots were detected by the synoptic measurements of SST, SSHA and Chl-a ranging from 29.5° to 31.5°C, from 2.5 to 12.5 cm and from 0.15 to 0.35 mg m-3, respectively. Total area of hotspots consistently peaked in May. Validation of skipjack CPUE predicted by our model against observed data from 2012 was highly significant. The key pelagic habitat corresponded with the Chl-a front, which could be related to the areas of relatively high prey abundance (enhanced feeding opportunity) for skipjack. We found that the area and persistence of the potential skipjack habitat hotspots for the 5 years were clearly identified by the 0.2 mg m-3 Chl-a isopleth, suggesting that the Chl-a front provides a key oceanographic indicator for global understanding on skipjack tuna habitat hotspots in the western tropical Pacific Ocean, especially within Coral Triangle tuna.
Journal Article