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Ocean practices, intended as a wide spectrum of methodologies supporting ocean-related research, operations, and applications, are constantly developed and improved to enable informed decision-making. Practices start from the idea of an individual or a group and often evolve towards what can be called good or best practices. This bottom-up approach may in principle result in different paths for the evolution of each practice, and ultimately generate situations where it is not clear to a new user how to compare two practices aiming at the same objective, and determine which one is best. Also, although a best practice is supposed to be the result of a multi-institutional collaborative effort based on the principles of evidence, repeatability and comparability, a set of individual requirements is not yet defined in literature for a practice to be considered a good, better, and ultimately a best practice. This paper proposes a method for addressing those questions and presents a new maturity model for ocean practices, built upon existing maturity models for systems and software, developed and adopted in the last decades. The model provides attributes for assessing both the maturity of the practice description and its implementation. It also provides a framework for analyzing gaps and suggesting actions for practice evolution. The model has been tested against a series of widely adopted practices and the results are reported and discussed. This work facilitates a common approach for developing and assessing practices, from which greater interoperability and trust can be achieved.

High-frequency radars (HFRs) provide remote information on ocean surface velocity in extended coastal areas at high resolutions in space (O(km)) and time (O(h)). They directly produce radial velocities (in the radar antenna’s direction) combined to provide total vector velocities in areas covered by at least two radars. HFRs are a key element in ocean observing systems, with several important environmental applications. Here, we provide an assessment of the HFR-TirLig network in the NW Mediterranean Sea, including results from the gap-filling open-boundary modal analysis (OMA) using in situ velocity data from drifters. While the network consists of three radars, only two were active during the assessment experiment, so the test also includes an area where the radial velocities from only one radar system were available. The results, including several metrics, both Eulerian and Lagrangian, and configurations, show that the network performance is very satisfactory and compares well with the previous results in the literature in terms of both the radial and total combined vector velocities where the coverage is adequate, i.e., in the area sampled by two radars. Regarding the OMA results, not only do they perform equally well in the area sampled by the two radars but they also provide results in the area covered by one radar only. Even though obviously deteriorated with respect to the case of adequate coverage, the OMA results can still provide information regarding the velocity structure and speed as well as virtual trajectories, which can be of some use in practical applications. A general discussion on the implications of the results for the potential of remote sensing velocity estimation in terms of HFR network configurations and complementing gap-filling analysis is provided.

High Frequency Radar (HFR) technology refers to land based remote sensing instruments capable of measuring surface currents and ocean waves at ranges up to 200 km or more. HFR technology is widely acknowledged as a cost-efficient tool to monitor coastal regions and has potential use to monitor coastal regions all over the world. Globally, the number of HFR stations is steadily increasing. Regional networks provide real-time data in support of operational activities such as search and rescue operations, fast response in case of maritime accidents and spill of pollutants, and resource management. Each operator needs a general understanding of the working principles in order to ensure that instruments are managed properly. A set of harmonized quality assurance and quality control procedures is recommended, along with an effective approach to HFR data discovery and dissemination, to provide high quality measurements to the end users. Different documents providing best practices for operation and maintenance have emerged in the past years. They are oriented either to Direction Finding (DF) or Beam Forming (BF) systems, or to specific manufacturer’s radar systems. The main objective of this paper is to offer a comprehensive “Best Practices” document in an effort of ensuring consistency between different deployments and harmonized operations of HFR systems. This, regardless of system manufacturer, antenna design and setup. A homogeneous approach is given when possible, general concepts and definitions are introduced in order to provide a framework for both data processing and management steps. Examples are also given from the European HFR users with focus on Near Real Time data flow suitable for operational services.

Academic, government, and private organizations from around the globe have established High Frequency radar (hereinafter, HFR) networks at regional or national levels. Partnerships have been established to coordinate and collaborate on a single global HFR network (http://global-hfradar.org/). These partnerships were established in 2012 as part of the Group on Earth Observations (GEO) to promote HFR technology and increase data sharing among operators and users. The main product of HFR networks are continuous maps of ocean surface currents within 200 km of the coast at high spatial (1–6 km) and temporal resolution (hourly or higher). Cutting-edge remote sensing technologies are becoming a standard component for ocean observing systems, contributing to the paradigm shift toward ocean monitoring. In 2017 the Global HFR Network was recognized by the Joint Technical WMO-IOC Commission for Oceanography and Marine Meteorology (JCOMM) as an observing network of the Global Ocean Observing System (GOOS). In this paper we will discuss the development of the network as well as establishing goals for the future. The U.S. High Frequency Radar Network (HFRNet) has been in operation for over 13 years, with radar data being ingested from 31 organizations including measurements from Canada and Mexico. HFRNet currently holds a collection from over 150 radar installations totaling millions of records of surface ocean velocity measurements. During the past 10 years in Europe, HFR networks have been showing steady growth with over 60 stations currently deployed and many in the planning stage. In Asia and Oceania countries, more than 110 radar stations are in operation. HFR technology can be found in a wide range of applications: for marine safety, oil spill response, tsunami warning, pollution assessment, coastal zone management, tracking environmental change, numerical model simulation of 3-dimensional circulation, and research to generate new understanding of coastal ocean dynamics, depending mainly on each country’s coastal sea characteristics. These radar networks are examples of national inter-agency and inter-institutional partnerships for improving oceanographic research and operations. As global partnerships grow, these collaborations and improved data sharing enhance our ability to respond to regional, national, and global environmental and management issues.

Marine plankton abundance and dynamics in the open and interior ocean is still an unknown field. The knowledge of gelatinous zooplankton distribution is especially challenging, because this type of plankton has a very fragile structure and cannot be directly sampled using traditional net based techniques. To overcome this shortcoming, Computer Vision techniques can be successfully used for the automatic monitoring of this group.This paper presents the GUARD1 imaging system, a low-cost stand-alone instrument for underwater image acquisition and recognition of gelatinous zooplankton, and discusses the performance of three different methodologies, Tikhonov Regularization, Support Vector Machines and Genetic Programming, that have been compared in order to select the one to be run onboard the system for the automatic recognition of gelatinous zooplankton. The performance comparison results highlight the high accuracy of the three methods in gelatinous zooplankton identification, showing their good capability in robustly selecting relevant features. In particular, Genetic Programming technique achieves the same performances of the other two methods by using a smaller set of features, thus being the most efficient in avoiding computationally consuming preprocessing stages, that is a crucial requirement for running on an autonomous imaging system designed for long lasting deployments, like the GUARD1. The Genetic Programming algorithm has been installed onboard the system, that has been operationally tested in a two-months survey in the Ligurian Sea, providing satisfactory results in terms of monitoring and recognition performances.