|Home||<< 1 2 3 4 5 6 7 8 9 >>|
|Guimond, S. R., Bourassa, M. A., & Reasor, P. D. (2011). A Latent Heat Retrieval and Its Effects on the Intensity and Structure Change of Hurricane Guillermo (1997). Part I: The Algorithm and Observations. J. Atmos. Sci., 68(8), 1549–1567.|
|Guimond, S. R., Heymsfield, G. M., & Turk, F. J. (2010). Multiscale Observations of Hurricane Dennis (2005): The Effects of Hot Towers on Rapid Intensification. J. Atmos. Sci., 67(3), 633–654.|
|Guimond, S. R., & Reisner, J. M. (2012). A Latent Heat Retrieval and Its Effects on the Intensity and Structure Change of Hurricane Guillermo (1997). Part II: Numerical Simulations. J. Atmos. Sci., 69(11), 3128–3146.|
|Henson, J. I., Muller-Karger, F., Wilson, D., Morey, S. L., Maul, G. A., Luther, M., et al. (2006). Strategic geographic positioning of sea level gauges to aid in early detection of tsunamis in the Intra-Americas Sea. Science of Tsunami Hazards, 25(3), 173–207.|
|Holbach, H. M., & Bourassa, M. A. (2012). The effects of gap wind induced vorticity, monsoon trough and ITCZ on tropical cyclogenesis. In IEEE International Symposium on Geoscience and Remote Sensing IGARSS (pp. 2074–2077).|
|Hong, S. - Y., Park, H., Cheong, H. - B., Kim, J. - E. E., Koo, M. - S., Jang, J., et al. (2013). The Global/Regional Integrated Model system (GRIMs). Asia-Pacific J Atmos Sci, 49(2), 219–243.|
Huang, T., Armstrong, E. M., Bourassa, M. A., Cram, T. A., Elya, J., Greguska, F., et al. (2019). An Integrated Data Analytics Platform. Mar. Sci., 6.
Abstract: An Integrated Science Data Analytics Platform is an environment that enables the confluence of resources for scientific investigation. It harmonizes data, tools and computational resources to enable the research community to focus on the investigation rather than spending time on security, data preparation, management, etc. OceanWorks is a NASA technology integration project to establish a cloud-based Integrated Ocean Science Data Analytics Platform for big ocean science at NASA�s Physical Oceanography Distributed Active Archive Center (PO.DAAC) for big ocean science. It focuses on advancement and maturity by bringing together several NASA open-source, big data projects for parallel analytics, anomaly detection, in situ to satellite data matchup, quality-screened data subsetting, search relevancy, and data discovery.
Our communities are relying on data available through distributed data centers to conduct their research. In typical investigations, scientists would (1) search for data, (2) evaluate the relevance of that data, (3) download it, and (4) then apply algorithms to identify trends, anomalies, or other attributes of the data. Such a workflow cannot scale if the research involves a massive amount of data or multi-variate measurements. With the upcoming NASA Surface Water and Ocean Topography (SWOT) mission expected to produce over 20PB of observational data during its 3-year nominal mission, the volume of data will challenge all existing Earth Science data archival, distribution and analysis paradigms. This paper discusses how OceanWorks enhances the analysis of physical ocean data where the computation is done on an elastic cloud platform next to the archive to deliver fast, web-accessible services for working with oceanographic measurements.
Keywords: big data, Cloud computing, Ocean science, data analysis, Matchup, anomaly detection, open source
|Kanamitsu, M., Yulaeva, E., Li, H., & Hong, S. - Y. (2013). Catalina Eddy as revealed by the historical downscaling of reanalysis. Asia-Pacific J Atmos Sci, 49(4), 467–481.|
Kelly, T. B., Davison, P. C., Goericke, R., Landry, M. R., Ohman, M. D., & Stukel, M., R. (2019). The Importance of Mesozooplankton Diel Vertical Migration for Sustaining a Mesopelagic Food Web. FRONTIERS IN MARINE SCIENCE, 6.
Abstract: We used extensive ecological and biogeochemical measurements obtained from quasi-Lagrangian experiments during two California Current Ecosystem Long-Term Ecosystem Research cruises to analyze carbon fluxes between the epipelagic and mesopelagic zones using a linear inverse ecosystem model (LIEM). Measurement constraints on the model include C-14 primary productivity, dilution-based microzooplankton grazing rates, gut pigment-based mesozooplankton grazing rates (on multiple zooplankton size classes), Th-234:U-238 disequilibrium and sediment trap measured carbon export, and metabolic requirements of micronekton, zooplankton, and bacteria. A likelihood approach (Markov Chain Monte Carlo) was used to estimate the resulting flow uncertainties from a sample of potential flux networks. Results highlight the importance of mesozooplankton active transport (i.e., diel vertical migration) in supplying the carbon demand of mesopelagic organisms and sequestering carbon dioxide from the atmosphere. In nine water parcels ranging from a coastal bloom to offshore oligotrophic conditions, mesozooplankton active transport accounted for 18-84% (median: 42%) of the total carbon transfer to the mesopelagic, with gravitational settling of POC (12-55%; median: 37%), and subduction (2-32%; median: 14%) providing the majority of the remainder. Vertically migrating zooplankton contributed to downward carbon flux through respiration and excretion at depth and via mortality losses to predatory zooplankton and mesopelagic fish (e.g., myctophids and gonostomatids). Sensitivity analyses showed that the results of the LIEM were robust to changes in nekton metabolic demand, rates of bacterial production, and mesozooplankton gross growth efficiency. This analysis suggests that prior estimates of zooplankton active transport based on conservative estimates of standard (rather than active) metabolism are likely too low.
Kent, E. C., Rayner, N. A., Berry, D. I., Eastman, R., Grigorieva, V. G., Huang, B., et al. (2019). Observing Requirements for Long-Term Climate Records at the Ocean Surface. Front. Mar. Sci., 6, 441.
Abstract: Observations of conditions at the ocean surface have been made for centuries, contributing to some of the longest instrumental records of climate change. Most prominent is the climate data record (CDR) of sea surface temperature (SST), which is itself essential to the majority of activities in climate science and climate service provision. A much wider range of surface marine observations is available however, providing a rich source of data on past climate. We present a general error model describing the characteristics of observations used for the construction of climate records, illustrating the importance of multi-variate records with rich metadata for reducing uncertainty in CDRs. We describe the data and metadata requirements for the construction of stable, multi-century marine CDRs for variables important for describing the changing climate: SST, mean sea level pressure, air temperature, humidity, winds, clouds, and waves. Available sources of surface marine data are reviewed in the context of the error model. We outline the need for a range of complementary observations, including very high quality observations at a limited number of locations and also observations that sample more broadly but with greater uncertainty. We describe how high-resolution modern records, particularly those of high-quality, can help to improve the quality of observations throughout the historical record. We recommend the extension of internationally-coordinated data management and curation to observation types that do not have a primary focus of the construction of climate records. Also recommended is reprocessing the existing surface marine climate archive to improve and quantify data and metadata quality and homogeneity. We also recommend the expansion of observations from research vessels and high quality moorings, routine observations from ships and from data and metadata rescue. Other priorities include: field evaluation of sensors; resources for the process of establishing user requirements and determining whether requirements are being met; and research to estimate uncertainty, quantify biases and to improve methods of construction of CDRs. The requirements developed in this paper encompass specific actions involving a variety of stakeholders, including funding agencies, scientists, data managers, observing network operators, satellite agencies, and international co-ordination bodies.