ALBERT

Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hand, K., Phillips, C., Murray, A., Garvin, J., Maize, E., Gibbs, R., Reeves, G., San Martin, A., Tan-Wang, G., Krajewski, J., Hurst, K., Crum, R., Kennedy, B., McElrath, T., Gallon, J., Sabahi, D., Thurman, S., Goldstein, B., Estabrook, P., Lee, S. W., Dooley, J. A., Brinckerhoff, W. B., Edgett, K. S., German, C. R., Hoehler, T. M., Hörst, S. M., Lunine, J. I., Paranicas, C., Nealson, K., Smith, D. E., Templeton, A. S., Russell, M. J., Schmidt, B., Christner, B., Ehlmann, B., Hayes, A., Rhoden, A., Willis, P., Yingst, R. A., Craft, K., Cameron, M. E., Nordheim, T., Pitesky, J., Scully, J., Hofgartner, J., Sell, S. W., Barltrop, K. J., Izraelevitz, J., Brandon, E. J., Seong, J., Jones, J.-P., Pasalic, J., Billings, K. J., Ruiz, J. P., Bugga, R. V., Graham, D., Arenas, L. A., Takeyama, D., Drummond, M., Aghazarian, H., Andersen, A. J., Andersen, K. B., Anderson, E. W., Babuscia, A., Backes, P. G., Bailey, E. S., Balentine, D., Ballard, C. G., Berisford, D. F., Bhandari, P., Blackwood, K., Bolotin, G. S., Bovre, E. A., Bowkett, J., Boykins, K. T., Bramble, M. S., Brice, T. M., Briggs, P., Brinkman, A. P., Brooks, S. M., Buffington, B. B., Burns, B., Cable, M. L., Campagnola, S., Cangahuala, L. A., Carr, G. A., Casani, J. R., Chahat, N. E., Chamberlain-Simon, B. K., Cheng, Y., Chien, S. A., Cook, B. T., Cooper, M., DiNicola, M., Clement, B., Dean, Z., Cullimore, E. A., Curtis, A. G., Croix, J-P. de la, Pasquale, P. Di, Dodd, E. M., Dubord, L. A., Edlund, J. A., Ellyin, R., Emanuel, B., Foster, J. T., Ganino, A. J., Garner, G. J., Gibson, M. T., Gildner, M., Glazebrook, K. J., Greco, M. E., Green, W. M., Hatch, S. J., Hetzel, M. M., Hoey, W. A., Hofmann, A. E., Ionasescu, R., Jain, A., Jasper, J. D., Johannesen, J. R., Johnson, G. K., Jun, I., Katake, A. B., Kim-Castet, S. Y., Kim, D. I., Kim, W., Klonicki, E. F., Kobeissi, B., Kobie, B. D., Kochocki, J., Kokorowski, M., Kosberg, J. A., Kriechbaum, K., Kulkarni, T. P., Lam, R. L., Landau, D. F., Lattimore, M. A., Laubach, S. L., Lawler, C. R., Lim, G., Lin, J. Y., Litwin, T. E., Lo, M. W., Logan, C. A., Maghasoudi, E., Mandrake, L., Marchetti, Y., Marteau, E., Maxwell, K. A., Namee, J. B. Mc, Mcintyre, O., Meacham, M., Melko, J. P., Mueller, J., Muliere, D. A., Mysore, A., Nash, J., Ono, H., Parker, J. M., Perkins, R. C., Petropoulos, A. E., Gaut, A., Gomez, M. Y. Piette, Casillas, R. P., Preudhomme, M., Pyrzak, G., Rapinchuk, J., Ratliff, J. M., Ray, T. L., Roberts, E. T., Roffo, K., Roth, D. C., Russino, J. A., Schmidt, T. M., Schoppers, M. J., Senent, J. S., Serricchio, F., Sheldon, D. J., Shiraishi, L. R., Shirvanian, J., Siegel, K. J., Singh, G., Sirota, A. R., Skulsky, E. D., Stehly, J. S., Strange, N. J., Stevens, S. U., Sunada, E. T., Tepsuporn, S. P., Tosi, L. P. C., Trawny, N., Uchenik, I., Verma, V., Volpe, R. A., Wagner, C. T., Wang, D., Willson, R. G., Wolff, J. L., Wong, A. T., Zimmer, A. K., Sukhatme, K. G., Bago, K. A., Chen, Y., Deardorff, A. M., Kuch, R. S., Lim, C., Syvertson, M. L., Arakaki, G. A., Avila, A., DeBruin, K. J., Frick, A., Harris, J. R., Heverly, M. C., Kawata, J. M., Kim, S.-K., Kipp, D. M., Murphy, J., Smith, M. W., Spaulding, M. D., Thakker, R., Warner, N. Z., Yahnker, C. R., Young, M. E., Magner, T., Adams, D., Bedini, P., Mehr, L., Sheldon, C., Vernon, S., Bailey, V., Briere, M., Butler, M., Davis, A., Ensor, S., Gannon, M., Haapala-Chalk, A., Hartka, T., Holdridge, M., Hong, A., Hunt, J., Iskow, J., Kahler, F., Murray, K., Napolillo, D., Norkus, M., Pfisterer, R., Porter, J., Roth, D., Schwartz, P., Wolfarth, L., Cardiff, E. H., Davis, A., Grob, E. W., Adam, J. R., Betts, E., Norwood, J., Heller, M. M., Voskuilen, T., Sakievich, P., Gray, L., Hansen, D. J., Irick, K. W., Hewson, J. C., Lamb, J., Stacy, S. C., Brotherton, C. M., Tappan, A. S., Benally, D., Thigpen, H., Ortiz, E., Sandoval, D., Ison, A. M., Warren, M., Stromberg, P. G., Thelen, P. M., Blasy, B., Nandy, P., Haddad, A. W., Trujillo, L. B., Wiseley, T. H., Bell, S. A., Teske, N. P., Post, C., Torres-Castro, L., Grosso, C. Wasiolek, M. Science goals and mission architecture of the Europa Lander mission concept. The Planetary Science Journal, 3(1), (2022): 22, https://doi.org/10.3847/psj/ac4493.

Description: Europa is a premier target for advancing both planetary science and astrobiology, as well as for opening a new window into the burgeoning field of comparative oceanography. The potentially habitable subsurface ocean of Europa may harbor life, and the globally young and comparatively thin ice shell of Europa may contain biosignatures that are readily accessible to a surface lander. Europa's icy shell also offers the opportunity to study tectonics and geologic cycles across a range of mechanisms and compositions. Here we detail the goals and mission architecture of the Europa Lander mission concept, as developed from 2015 through 2020. The science was developed by the 2016 Europa Lander Science Definition Team (SDT), and the mission architecture was developed by the preproject engineering team, in close collaboration with the SDT. In 2017 and 2018, the mission concept passed its mission concept review and delta-mission concept review, respectively. Since that time, the preproject has been advancing the technologies, and developing the hardware and software, needed to retire risks associated with technology, science, cost, and schedule.

Description: K.P.H., C.B.P., E.M., and all authors affiliated with the Jet Propulsion Laboratory carried out this research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (grant No. 80NM0018D0004). J.I.L. was the David Baltimore Distinguished Visiting Scientist during the preparation of the SDT report. JPL/Caltech2021.

Description: 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): 160–168, doi:10.5670/oceanog.2017.238.

Description: Future ocean observing systems will rely heavily on autonomous vehicles to achieve the persistent and heterogeneous measurements needed to understand the ocean’s impact on the climate system. The day-to-day maintenance of these arrays will become increasingly challenging if significant human resources, such as manual piloting, are required. For this reason, techniques need to be developed that permit autonomous determination of sampling directives based on science goals and responses to in situ, remote-sensing, and model-derived information. Techniques that can accommodate large arrays of assets and permit sustained observations of rapidly evolving ocean properties are especially needed for capturing interactions between physical circulation and biogeochemical cycling. Here we document the first field program of the Satellites to Seafloor project, designed to enable a closed loop of numerical model prediction, vehicle path-planning, in situ path implementation, data collection, and data assimilation for future model predictions. We present results from the first of two field programs carried out in Monterey Bay, California, over a period of three months in 2016. While relatively modest in scope, this approach provides a step toward an observing array that makes use of multiple information streams to update and improve sampling strategies without human intervention.

Description: This work is funded by the Keck Institute for Space Studies (generously supported by the W.M. Keck Foundation) through the project “Science-driven Autonomous and Heterogeneous Robotic Networks: A Vision for Future Ocean Observation”

Description: Submesoscale fronts arising from mesoscale stirring are ubiquitous in the ocean and have a strong impact on upper-ocean dynamics. This work presents a method for optimizing the sampling of ocean fronts with autonomous vehicles at meso- and submesoscales, based on a combination of numerical forecast and autonomous planning. This method uses a 48-h forecast from a real-time high-resolution data-assimilative primitive equation ocean model, feature detection techniques, and a planner that controls the observing platform. The method is tested in Monterey Bay, off the coast of California, during a 9-day experiment focused on sampling subsurface thermohaline-compensated structures using a Seaglider as the ocean observing platform. Based on model estimations, the sampling “gain,” defined as the magnitude of isopycnal tracer variability sampled, is 50% larger in the feature-chasing case with respect to a non-feature-tracking scenario. The ability of the model to reproduce, in space and time, thermohaline submesoscale features is evaluated by quantitatively comparing the model and glider results. The model reproduces the vertical (~50–200 m thick) and lateral (~5–20 km) scales of subsurface subducting fronts and near-bottom features observed in the glider data. The differences between model and glider data are, in part, attributed to the selected glider optimal interpolation parameters and to uncertainties in the forecasting of the location of the structures. This method can be exported to any place in the ocean where high-resolution data-assimilative model output is available, and it allows for the incorporation of multiple observing platforms.