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Expendable bathythermograph (XBT) data were the major component of the ocean temperature profile observations from the late 1960s through the early 2000s, and XBTs still continue to provide critical data to monitor surface and subsurface currents, meridional heat transport, and ocean heat content. Systematic errors have been identified in the XBT data, some of which originate from computing the depth in the profile using a theoretically and experimentally derived fall-rate equation (FRE). After in-depth studies of these biases and discussions held in several workshops dedicated to discussing XBT biases, the XBT science community met at the Fourth XBT Science Workshop and concluded that XBT biases consist of 1) errors in depth values due to the inadequacy of the probe motion description done by standard FRE and 2) independent pure temperature biases. The depth error and temperature bias are temperature dependent and may depend on the data acquisition and recording system. In addition, the depth bias also includes an offset term. Some biases affecting the XBT-derived temperature profiles vary with manufacturer/probe type and have been shown to be time dependent. Best practices for historical XBT data corrections, recommendations for future collection of metadata to accompany XBT data, the impact of XBT biases on scientific applications, and challenges encountered are presented in this manuscript. Analysis of XBT data shows that, despite the existence of these biases, historical XBT data without bias corrections are still suitable for many scientific applications, and that bias-corrected data can be used for climate research.

Major Atlantic hurricanes Irma, Jose, and Maria of 2017 reached their peak intensity in September while traveling over the tropical North Atlantic Ocean and Caribbean Sea, where both atmospheric and ocean conditions were favorable for intensification. In situ and satellite ocean observations revealed that conditions in these areas exhibited (i) sea surface temperatures above 28°C, (ii) upper-ocean heat content above 60 kJ cm −2 , and (iii) the presence of low-salinity barrier layers associated with a larger-than-usual extension of the Amazon and Orinoco riverine plumes. Proof-of-concept coupled ocean–hurricane numerical model experiments demonstrated that the accurate representation of such ocean conditions led to an improvement in the simulated intensity of Hurricane Maria for the 3 days preceding landfall in Puerto Rico, when compared to an experiment without the assimilation of ocean observations. Without the assimilation of ocean observations, upper-ocean thermal conditions were generally colder than observations, resulting in reduced air–sea enthalpy fluxes—enthalpy fluxes are more realistically simulated when the upper-ocean temperature and salinity structure is better represented in the model. Our results further showed that different components of the ocean observing system provide valuable information in support of improved TC simulations, and that assimilation of underwater glider observations alone enabled the largest improvement over the 24 h time frame before landfall. Our results, therefore, indicated that ocean conditions were relevant for more realistically simulating Hurricane Maria’s intensity. However, further research based on a comprehensive set of hurricane cases is required to confirm robust improvements to forecast systems.

Miles et al focus on uncrewed ocean gliders and saildrones support hurricane forecasting and research. Components of the sustained ocean observing system are useful for understanding the role of the ocean in hurricane intensity changes. Here, the authors provide a broad overview of the ongoing US hurricane glider project and details of a new effort with the Saildrone USV during the 2021 hurricane season. While this article focuses on the US East Coast, Gulf of Mexico, and Caribbean Sea, similar efforts are underway in Korea, the Philippines, Japan, and China, among other countries.

The initialization of ocean conditions is essential to coupled tropical cyclone (TC) forecasts. This study investigates the impact of ocean observation assimilation, particularly underwater glider data, on high-resolution coupled TC forecasts. Using the coupled Hurricane Weather Research and Forecasting (HWRF) Model–Hybrid Coordinate Ocean Model (HYCOM) system, numerical experiments are performed by assimilating underwater glider observations alone and with other standard ocean observations for the forecast of Hurricane Gonzalo (2014). The glider observations are able to provide valuable information on subsurface ocean thermal and saline structure, even with their limited spatial coverage along the storm track and the relatively small amount of data assimilated. Through the assimilation of underwater glider observations, the prestorm thermal and saline structures of initial upper-ocean conditions are significantly improved near the location of glider observations, though the impact is localized because of the limited coverage of glider data. The ocean initial conditions are best represented when both the standard ocean observations and the underwater glider data are assimilated together. The barrier layer and the associated sharp density gradient in the upper ocean are successfully represented in the ocean initial conditions only with the use of underwater glider observations. The upper-ocean temperature and salinity forecasts in the first 48 h are improved by assimilating both underwater glider and standard ocean observations. The assimilation of glider observations alone does not make a large impact on the intensity forecast due to their limited coverage along the storm track. The 126-h intensity forecast of Hurricane Gonzalo is improved moderately through assimilating both underwater glider data and standard ocean observations.

Millions of in situ ocean temperature profiles have been collected historically using various instrument types with varying sensor accuracy and then assembled into global databases. These are essential to our current understanding of the changing state of the oceans, sea level, Earth’s climate, marine ecosystems and fisheries, and for constraining model projections of future change that underpin mitigation and adaptation solutions. Profiles distributed shortly after collection are also widely used in operational applications such as real-time monitoring and forecasting of the ocean state and weather prediction. Before use in scientific or societal service applications, quality control (QC) procedures need to be applied to flag and ultimately remove erroneous data. Automatic QC (AQC) checks are vital to the timeliness of operational applications and for reducing the volume of dubious data which later require QC processing by a human for delayed mode applications. Despite the large suite of evolving AQC checks developed by institutions worldwide, the most effective set of AQC checks was not known. We have developed a framework to assess the performance of AQC checks, under the auspices of the International Quality Controlled Ocean Database (IQuOD) project. The IQuOD-AQC framework is an open-source collaborative software infrastructure built in Python (available from https://github.com/IQuOD ). Sixty AQC checks have been implemented in this framework. Their performance was benchmarked against three reference datasets which contained a spectrum of instrument types and error modes flagged in their profiles. One of these (a subset of the Quality-controlled Ocean Temperature Archive (QuOTA) dataset that had been manually inspected for quality issues by its creators) was also used to identify optimal sets of AQC checks. Results suggest that the AQC checks are effective for most historical data, but less so in the case of data from Mechanical Bathythermographs (MBTs), and much less effective for Argo data. The optimal AQC sets will be applied to generate quality flags for the next release of the IQuOD dataset. This will further elevate the quality and historical value of millions of temperature profile data which have already been improved by IQuOD intelligent metadata and observational uncertainty information ( https://doi.org/10.7289/v51r6nsf ).

Sudden tropical cyclone (TC) intensification has been linked with high values of upper ocean heat content contained in mesoscale features, particularly warm ocean eddies, provided that atmospheric conditions are also favorable. Although understanding of air-sea interaction for TCs is evolving, this manuscript summarizes some of the current work being carried out to investigate the role that the upper ocean plays in TC intensification and the use of ocean parameters in forecasting TC intensity.

The first eXpendable BathyThermographs (XBTs) were deployed in the 1960s in the North Atlantic Ocean. In 1967 XBTs were deployed in operational mode to provide a continuous record of temperature profile data along repeated transects, now known as the Global XBT Network. The current network is designed to monitor ocean circulation and boundary current variability, basin-wide and trans-basin ocean heat transport, and global and regional heat content. The ability of the XBT Network to systematically map the upper ocean thermal field in multiple basins with repeated trans-basin sections at eddy-resolving scales remains unmatched today and cannot be reproduced at present by any other observing platform. Some repeated XBT transects have now been continuously occupied for more than 30 years, providing an unprecedented long-term climate record of temperature and geostrophic velocity profiles that are used to understand variability in ocean heat content, sea level change, and meridional ocean heat transport. Here, we present key scientific advances in understanding the changing ocean and climate system supported by XBT observations. Improvement in XBT data quality and its impact on computations, particularly of ocean heat content, are presented. Technology development for probes, launchers, and transmission techniques are also discussed. Finally, we offer new perspectives for the future of the Global XBT Network.

The tropical Atlantic basin is one of seven global regions where tropical cyclones (TCs) commonly originate, intensify, and affect highly populated coastal areas. Under appropriate atmospheric conditions, TC intensification can be linked to upper-ocean properties. Errors in Atlantic TC intensification forecasts have not been significantly reduced during the last 25 years. The combined use of in situ and satellite observations, particularly of temperature and salinity ahead of TCs, has the potential to improve the representation of the ocean, more accurately initialize hurricane intensity forecast models, and identify areas where TCs may intensify. However, a sustained in situ ocean observing system in the tropical North Atlantic Ocean and Caribbean Sea dedicated to measuring subsurface temperature, salinity, and density fields in support of TC intensity studies and forecasts has yet to be designed and implemented. Autonomous and Lagrangian platforms and sensors offer cost-effective opportunities to accomplish this objective. Here, we highlight recent efforts to use autonomous platforms and sensors, including surface drifters, profiling floats, underwater gliders, and dropsondes, to better understand air-sea processes during high-wind events, particularly those geared toward improving hurricane intensity forecasts. Real-time data availability is key for assimilation into numerical weather forecast models.

Since the 1970s, eXpendable BathyThermographs (XBTs) have provided the simplest and most cost-efficient solution for rapid sampling of temperature vs. depth profiles of the upper part of the ocean along ship transects. This manual, compiled by the Ship of Opportunity Program Implementation Panel (SOOPIP) a subgroup of the Global Ocean Observing System (GOOS) Observations Coordination Group (OCG) Ship Observations Team (SOT) together with members of the XBT Science Team, aims to improve the quality assurance of XBT data by establishing best practices for field measurements and promoting their adoption by the global operational and scientific community. The measurement system components include commercially available expendable temperature probes, the launcher, the data acquisition (DAQ) hardware, a Global Navigation Satellite System (GNSS) receiver, an optional satellite transmitter, and a computer with software controls. The measurement platform can be any sea-going vessel with available space for the equipment and operator, and capable of oceanic voyages across the regions of interest. Adoption of a standard methodology in the installation and deployment of the measurement system will lead to data quality improvements with subsequent impact on the computation and understanding of changes in the near surface ocean properties (e.g., heat content), ocean circulation dynamics, and their relationship to climate variability.