The air-launched autonomous micro observer

Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of the Atmospheric and Oceanic Technology 39(4), (2022): 491–502, https://doi.org/10.1175/jtech-d-21-0046.1.The Air-Launched Autonomous Micro Observer (ALAMO) is a versatile profiling float that can be launched from an aircraft to make temperature and salinity observations of the upper ocean for over a year with high temporal sampling. Similar in dimensions and weight to an airborne expendable bathythermograph (AXBT), but with the same capability as Argo profiling floats, ALAMOs can be deployed from an A-sized (sonobuoy) launch tube, the stern ramp of a cargo plane, or the door of a small aircraft. Unlike an AXBT, however, the ALAMO float directly measures pressure, can incorporate additional sensors, and is capable of performing hundreds of ocean profiles compared to the single temperature profile provided by an AXBT. Upon deployment, the float parachutes to the ocean, releases the air-deployment package, and immediately begins profiling. Ocean profile data along with position and engineering information are transmitted via the Iridium satellite network, automatically processed, and then distributed by the Global Telecommunications System for use by the operational forecasting community. The ALAMO profiling mission can be modified using the two-way Iridium communications to change the profiling frequency and depth. Example observations are included to demonstrate the ALAMO’s utility.This work was supported by the National Oceanographic and Atmospheric Administration under Grants NA13OAR4830233 (as part of CINAR Sandy Supplemental funding from the Disaster Relief Appropriations Act of 2013) and NA14OAR4320158 and by Office of Naval Research under Grants N0001416WX01384, N0001416WX01262, and N000141512293. ALAMO floats are commercially available from MRV Systems, LLC (https://www.mrvsys.com)

Internal wave activity in the deep Gulf of Mexico

Internal wave activity in the Gulf of Mexico (GoM) is investigated using a fleet of profiling floats. The floats continuously measured temperature and salinity as they drifted at a parking depth of 1500 dbar, allowing for the reconstruction of 2615 time series of isopycnal displacements. Thanks to the dense sampling of the eastern part of the GoM (east of 90°W), the geographical distribution of the internal waves displacement variance and available potential energy (APE) is revealed. The Loop Current (LC) influence region, between the Yucatan shelf to the west and the southern West Florida shelf to the east exhibits increased displacement variance and APE both in the continuum and near-inertial bands, while the north-eastern and central GoM show reduced internal wave activity. As the LC position fluctuates between a retracted and extended mode, we assessed the impact of the presence or absence of the LC in the increased internal wave activity region. It is shown that in the LC influence region, APE is increased (decreased) when the LC is present (absent), suggesting a strong control of the LC on deep internal waves activity. The 1500 dbar flow velocity, bottom roughness, and float altitude also seem to contribute to increased internal waves APE, but their influence is more subtle. Oppositely, no correlation with wind speed or wind intermittency is found

Results of the first Arctic Heat Open Science Experiment

Author Posting. © American Meteorological Society, 2018. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 99 (2018): 513-520, doi:10.1175/BAMS-D-16-0323.1.Seasonally ice-covered marginal seas are among the most difficult regions in the Arctic to study. Physical constraints imposed by the variable presence of sea ice in all stages of growth and melt make the upper water column and air–sea ice interface especially challenging to observe. At the same time, the flow of solar energy through Alaska’s marginal seas is one of the most important regulators of their weather and climate, sea ice cover, and ecosystems. The deficiency of observing systems in these areas hampers forecast services in the region and is a major contributor to large uncertainties in modeling and related climate projections. The Arctic Heat Open Science Experiment strives to fill this observation gap with an array of innovative autonomous floats and other near-real-time weather and ocean sensing systems. These capabilities allow continuous monitoring of the seasonally evolving state of the Chukchi Sea, including its heat content. Data collected by this project are distributed in near–real time on project websites and on the Global Telecommunications System (GTS), with the objectives of (i) providing timely delivery of observations for use in weather and sea ice forecasts, for model, and for reanalysis applications and (ii) supporting ongoing research activities across disciplines. This research supports improved forecast services that protect and enhance the safety and economic viability of maritime and coastal community activities in Alaska. Data are free and open to all (see www.pmel.noaa.gov/arctic-heat/).This work was supported by NOAA Ocean and Atmospheric Research and the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) under NOAA Cooperative Agreement NA15OAR4320063 and by the Innovative Technology for Arctic Exploration (ITAE) program at JISAO/PMEL. Jayne, Robbins, and Ekholm were supported by ONR (N00014-12-10110)

Autonomous and Lagrangian ocean observations for Atlantic tropical cyclone studies and forecasts

Author Posting. © The Oceanography Society, 2017. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 30, no. 2 (2017): 92–103, doi:10.5670/oceanog.2017.227.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.The NOAA/AOML component of this work was originally funded by the Disaster Relief Appropriations Act of 2013, also known as the Sandy Supplemental, and is currently funded through NOAA research grant NA14OAR4830103 by AOML and CARICOOS, as well as NOAA’s Integrated Ocean Observing System (IOOS). The TEMPESTS component of this work is supported by NOAA through the Cooperative Institute for the North Atlantic Region (NA13OAR4830233) with additional analysis support from the WHOI Summer Student Fellowship Program, Nortek Student Equipment Grant, and the Rutgers University Teledyne Webb Graduate Student Fellowship Program. The drifter component of this work is funded through NOAA grant NA15OAR4320071(11.432) in support of the Global Drifter Program

Argo data 1999-2019: two million temperature-salinity profiles and subsurface velocity observations from a global array of profiling floats.

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Wong, A. P. S., Wijffels, S. E., Riser, S. C., Pouliquen, S., Hosoda, S., Roemmich, D., Gilson, J., Johnson, G. C., Martini, K., Murphy, D. J., Scanderbeg, M., Bhaskar, T. V. S. U., Buck, J. J. H., Merceur, F., Carval, T., Maze, G., Cabanes, C., Andre, X., Poffa, N., Yashayaev, I., Barker, P. M., Guinehut, S., Belbeoch, M., Ignaszewski, M., Baringer, M. O., Schmid, C., Lyman, J. M., McTaggart, K. E., Purkey, S. G., Zilberman, N., Alkire, M. B., Swift, D., Owens, W. B., Jayne, S. R., Hersh, C., Robbins, P., West-Mack, D., Bahr, F., Yoshida, S., Sutton, P. J. H., Cancouet, R., Coatanoan, C., Dobbler, D., Juan, A. G., Gourrion, J., Kolodziejczyk, N., Bernard, V., Bourles, B., Claustre, H., D'Ortenzio, F., Le Reste, S., Le Traon, P., Rannou, J., Saout-Grit, C., Speich, S., Thierry, V., Verbrugge, N., Angel-Benavides, I. M., Klein, B., Notarstefano, G., Poulain, P., Velez-Belchi, P., Suga, T., Ando, K., Iwasaska, N., Kobayashi, T., Masuda, S., Oka, E., Sato, K., Nakamura, T., Sato, K., Takatsuki, Y., Yoshida, T., Cowley, R., Lovell, J. L., Oke, P. R., van Wijk, E. M., Carse, F., Donnelly, M., Gould, W. J., Gowers, K., King, B. A., Loch, S. G., Mowat, M., Turton, J., Rama Rao, E. P., Ravichandran, M., Freeland, H. J., Gaboury, I., Gilbert, D., Greenan, B. J. W., Ouellet, M., Ross, T., Tran, A., Dong, M., Liu, Z., Xu, J., Kang, K., Jo, H., Kim, S., & Park, H. Argo data 1999-2019: two million temperature-salinity profiles and subsurface velocity observations from a global array of profiling floats. Frontiers in Marine Science, 7, (2020): 700, doi:10.3389/fmars.2020.00700.In the past two decades, the Argo Program has collected, processed, and distributed over two million vertical profiles of temperature and salinity from the upper two kilometers of the global ocean. A similar number of subsurface velocity observations near 1,000 dbar have also been collected. This paper recounts the history of the global Argo Program, from its aspiration arising out of the World Ocean Circulation Experiment, to the development and implementation of its instrumentation and telecommunication systems, and the various technical problems encountered. We describe the Argo data system and its quality control procedures, and the gradual changes in the vertical resolution and spatial coverage of Argo data from 1999 to 2019. The accuracies of the float data have been assessed by comparison with high-quality shipboard measurements, and are concluded to be 0.002°C for temperature, 2.4 dbar for pressure, and 0.01 PSS-78 for salinity, after delayed-mode adjustments. Finally, the challenges faced by the vision of an expanding Argo Program beyond 2020 are discussed.AW, SR, and other scientists at the University of Washington (UW) were supported by the US Argo Program through the NOAA Grant NA15OAR4320063 to the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) at the UW. SW and other scientists at the Woods Hole Oceanographic Institution (WHOI) were supported by the US Argo Program through the NOAA Grant NA19OAR4320074 (CINAR/WHOI Argo). The Scripps Institution of Oceanography's role in Argo was supported by the US Argo Program through the NOAA Grant NA15OAR4320071 (CIMEC). Euro-Argo scientists were supported by the Monitoring the Oceans and Climate Change with Argo (MOCCA) project, under the Grant Agreement EASME/EMFF/2015/1.2.1.1/SI2.709624 for the European Commission

Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix

The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 m of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many societally important ocean features that they observe. Determining these distributions is challenging because float advection is difficult to predict. Using no external models, transition matrices based on existing Argo trajectories provide statistical inferences about Argo float motion. We use the 24 years of Argo locations to construct an optimal transition matrix that minimizes estimation bias and uncertainty. The optimal array is determined to have a 28° 3 28° spatial resolution with a 90-day time step. We then use the transition matrix to predict the probability of future float locations of the core Argo array, the Global Biogeochemical Array, and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) array. A comparison of transition matrices derived from floats using Argos system and Iridium communication methods shows the impact of surface displacements, which is most apparent near the equator. Additionally, we demonstrate the utility of transition matrices for validating models by comparing the matrix derived from Argo floats with that derived from a particle release experiment in the Southern Ocean State Estimate (SOSE)

Using existing Argo trajectories to statistically predict future float positions with a transition matrix

Abstract: The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 meters of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many societally important ocean features that they observe. Determining these distributions is challenging because float advection is difficult to predict. Using no external models, transition matrices based on existing Argo trajectories provide statistical inferences about Argo float motion. We use the 24 years of Argo locations to construct an optimal transition matrix that minimizes estimation bias and uncertainty. The optimal array is determined to have a 2°×2° spatial resolution with a 90 day timestep. We then use the transition matrix to predict the probability of future float locations of the Core Argo array, the Global Biogeochemical Array, and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) array. A comparison of transition matrices derived from floats using Argos System and Iridium communication methods shows the impact of surface displacements, which is most apparent near the equator. Additionally, we demonstrate the utility of transition matrices for validating models by comparing the matrix derived from Argo floats with that derived from a particle release experiment in the Southern Ocean State Estimate (SOSE)