Modeling for the performance of navigation, control and data post-processing of underwater gliders

Underwater gliders allow efficient monitoring in oceanography. In contrast to buoys, which log oceanographic data at individual depths at only one location, gliders can log data over a period of up to one year by following predetermined routes. In addition to the logged data from the available sensors, usually a conductivity-temperature-depth (CTD) sensor, the depth-average velocity can also be estimated using the horizontal glider velocity and the GPS update in a dead-reckoning algorithm. The horizontal velocity is also used for navigation or planning a long-term glider mission. This paper presents an investigation to determine the horizontal glider velocity as accurately as possible. For this, Slocum glider flight models used in practice will be presented and compared. A glider model for a steady-state gliding motion based on this analysis is described in detail. The approach for estimating the individual model parameters using nonlinear regression will be presented. In this context, a robust method to accurately detect the angle of attack is presented and the requirements of the logged vehicle data for statistically verified model parameters are discussed. The approaches are verified using logged data from glider missions in the Indian Ocean from 2016 to 2018. It is shown that a good match between the logged and the modeled data requires a time-varying model, where the model parameters change with respect to time. A reason for the changes is biofouling, where organisms settle and grow on the glider. The proposed method for deciphering an accurate horizontal glider velocity could serve to improve the dead-reckoning algorithm used by the glider for calculating depth-average velocity and for understanding its errors. The depth-average velocity is used to compare ocean current models from CMEMS and HYCOM with the glider logged data

High Frequency Radar Wind Turbine Interference Community Working Group Report

Land-based High Frequency (HF) Radars provide critically important observations of the coastal ocean that will be adversely affected by the spinning blades of utility-scale wind turbines. Pathways to mitigate the interference of turbines on HF radar observations exist for small number of turbines; however, a greatly increased pace of research is required to understand how to minimize the complex interference patterns that will be caused by the large arrays of turbines planned for the U.S. outer continental shelf. To support the U.S.’s operational and scientific needs, HF radars must be able to collect high-quality measurements of the ocean’s surface inand around areas with significant numbers of wind turbines. This is a solvable problem, but given the rapid pace of wind energy development, immediate action is needed to ensure that HF radar wind turbine interference mitigation efforts keep pace with the planned build out of turbines

Annual and Seasonal Surface Circulation Over the Mid-Atlantic Bight Continental Shelf Derived From a Decade of High Frequency Radar Observations

A decade (2007–2016) of hourly 6-km-resolution maps of the surface currents across the Mid-Atlantic Bight (MAB) generated by a regional scale High Frequency Radar network are used to reveal new insights into the spatial patterns of the annual and seasonal mean surface flows. Across the 10-year time series, temporal means and interannual and intra-annual variability are used to quantify the variability of spatial surface current patterns. The 10-year annual mean surface flows are weaker and mostly cross-shelf near the coast, increasing in speed and rotating to more alongshore directions near the shelfbreak, and increasing in speed and rotating to flow off-shelf in the southern MAB. The annual mean surface current pattern is relatively stable year to year compared to the hourly variations within a year. The 10-year seasonal means exhibit similar current patterns, with winter and summer more cross-shore while spring and fall transitions are more alongshore. Fall and winter mean speeds are larger and correspond to when mean winds are stronger and cross-shore. Summer mean currents are weakest and correspond to a time when the mean wind opposes the alongshore flow. Again, intra-annual variability is much greater than interannual, with the fall season exhibiting the most interseasonal variability in the surface current patterns. The extreme fall seasons of 2009 and 2011 are related to extremes in the wind and river discharge events caused by different persistent synoptic meteorological conditions, resulting in more or less rapid fall transitions from stratified summer to well-mixed winter conditions

Wind Speed Inversion in High Frequency Radar Based on Neural Network

Wind speed is an important sea surface dynamic parameter which influences a wide variety of oceanic applications. Wave height and wind direction can be extracted from high frequency radar echo spectra with a relatively high accuracy, while the estimation of wind speed is still a challenge. This paper describes an artificial neural network based method to estimate the wind speed in HF radar which can be trained to store the specific but unknown wind-wave relationship by the historical buoy data sets. The method is validated by one-month-long data of SeaSonde radar, the correlation coefficient between the radar estimates and the buoy records is 0.68, and the root mean square error is 1.7 m/s. This method also performs well in a rather wide range of time and space (2 years around and 360 km away). This result shows that the ANN is an efficient tool to help make the wind speed an operational product of the HF radar

Operation and Application of a Regional High-Frequency Radar Network in the Mid-Atlantic Bight

The Mid-Atlantic Regional Coastal Ocean Observing System (MARCOOS) High- Frequency Radar Network, which comprises 13 long-range sites, 2 medium-range sites, and 12 standard-range sites, is operated as part of the Integrated Ocean Observing System. This regional implementation of the network has been operational for 2 years and has matured to the point where the radars provide consistent coverage from Cape Cod to Cape Hatteras. A concerted effort was made in the MARCOOS project to increase the resiliency of the radar stations from the elements, power issues, and other issues that can disable the hardware of the system. The quality control and assurance activities in the Mid-Atlantic Bight have been guided by the needs of the Coast Guard Search and Rescue Office. As of May,, 2009, these quality-controlled MARCOOS High-Frequency Radar totals are being served through the Coast Guard\u27s Environmental Data Server to the Coast Guard Search and Rescue Optimal Planning System. In addition to the service to U.S. Coast Guard Search and Rescue Operations, these data support water quality, physical oceanographic, and fisheries research throughout the Mid-Atlantic Bight

Annual and Seasonal Surface Circulation Over the Mid Atlantic Bight Continental Shelf Derived From a Decade of High Frequency Radar Observations

A decade (2007–2016) of hourly 6‐km‐resolution maps of the surface currents across the Mid‐Atlantic Bight (MAB) generated by a regional‐scale High Frequency Radar network are used to reveal new insights into the spatial patterns of the annual and seasonal mean surface flows. Across the 10‐year time series, temporal means and interannual and intra‐annual variability are used to quantify the variability of spatial surface current patterns. The 10‐year annual mean surface flows are weaker and mostly cross‐shelf near the coast, increasing in speed and rotating to more alongshore directions near the shelfbreak, and increasing in speed and rotating to flow off‐shelf in the southern MAB. The annual mean surface current pattern is relatively stable year to year compared to the hourly variations within a year. The 10‐year seasonal means exhibit similar current patterns, with winter and summer more cross‐shore while spring and fall transitions are more alongshore. Fall and winter mean speeds are larger and correspond to when mean winds are stronger and cross‐shore. Summer mean currents are weakest and correspond to a time when the mean wind opposes the alongshore flow. Again, intra‐annual variability is much greater than interannual, with the fall season exhibiting the most interseasonal variability in the surface current patterns. The extreme fall seasons of 2009 and 2011 are related to extremes in the wind and river discharge events caused by different persistent synoptic meteorological conditions, resulting in more or less rapid fall transitions from stratified summer to well‐mixed winter conditions

Chatpal Chatbot dialogue data set

The scripts used in the ChatPal chatbot are freely available as an output from the ChatPal project. The datasets contain the chatbot utterances in English, Swedish, Finnish and Scottish Gaelic. Any replies collected from users through the ChatPal chatbot are not included in these data. Datasets are available in csv format and contain Unicode character encodings (UTF-8). Disclaimer: The datasets are open access, should be used appropriately and can be repurposed. However, the ChatPal project team are not responsible for how you chose to use the data or repurpose the content