Arguez, A., Smith, S. R., & O'Brien, J. J. (2002). The relationship between low-frequency North Atlantic sea surface temperatures and Eastern North American climate . COAPS Technical Report 02-6. Tallahassee, FL: Center for Ocean-Atmospheric Prediction Studies, Florida State University.
Arguez, A., Yu, P., & O'Brien, J. J. (2008). A New Method for Time Series Filtering near Endpoints. J. Atmos. Oceanic Technol. , 25 (4), 534–546.
Armstrong, E. M., Bourassa, M. A., Cram, T., Elya, J. L., Greguska, F. R., III, Huang, T., et al. (2018). An information technology foundation for fostering interdisciplinary oceanographic research and analysis. In American Geophysical Union (Vol. Fall Meeting).
Abstract: Before complex analysis of oceanographic or any earth science data can occur, it must be placed in the proper domain of computing and software resources. In the past this was nearly always the scientist's personal computer or institutional computer servers. The problem with this approach is that it is necessary to bring the data products directly to these compute resources leading to large data transfers and storage requirements especially for high volume satellite or model datasets. In this presentation we will present a new technological solution under development and implementation at the NASA Jet Propulsion Laboratory for conducting oceanographic and related research based on satellite data and other sources. Fundamentally, our approach for satellite resources is to tile (partition) the data inputs into cloud-optimized and computation friendly databases that allow distributed computing resources to perform on demand and server-side computation and data analytics. This technology, known as NEXUS, has already been implemented in several existing NASA data portals to support oceanographic, sea-level, and gravity data time series analysis with capabilities to output time-average maps, correlation maps, Hovmöller plots, climatological averages and more. A further extension of this technology will integrate ocean in situ observations, event-based data discovery (e.g., natural disasters), data quality screening and additional capabilities. This particular activity is an open source project known as the Apache Science Data Analytics Platform (SDAP) (https://sdap.apache.org), and colloquially as OceanWorks, and is funded by the NASA AIST program. It harmonizes data, tools and computational resources for the researcher allowing them to focus on research results and hypothesis testing, and not be concerned with security, data preparation and management. We will present a few oceanographic and interdisciplinary use cases demonstrating the capabilities for characterizing regional sea-level rise, sea surface temperature anomalies, and ocean hurricane responses.
Armstrong, E. M., Bourassa, M. A., Cram, T. A., DeBellis, M., Elya, J., Greguska III, F. R., et al. (2019). An Integrated Data Analytics Platform. Front. Mar. Sci. , 6 , 354.
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.
Arrocha, G. (2006). Variability of Intraseasonal Precipitation Extremes Associated with ENSO in Panama . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Extensive analysis has been conducted over past decades showing the impacts of El Niño-Southern Oscillation (ENSO) on various regions throughout the world. However, these studies have not analyzed data from many stations in Panama, or they have not analyzed long periods of observations. For these reasons, they often miss climatological differences within the region induced by topography, or they do not possess enough observations to adequately study its climatology. Accordingly, the current study focuses on ENSO impacts on precipitation specific to the Isthmus of Panama. Results will be useful for agricultural and water resources planning and Panama Canal operations. Monthly total precipitation data were provided by Empresa de Transmisión Eléctrica S.A., which includes 32 stations with records from 1960 to 2004. The year is split into three seasons: two wet seasons (Early and Late Wet), one dry season (Dry). The country is also divided into regions according to similarities in the stations' climatology and geographic locations. Upper and lower precipitation extremes are associated with one of the three ENSO phases (warm, cold or neutral) to estimate their percentages of occurrences. The differences between each ENSO phases' seasonal precipitation distributions are statistically examined. Statistical analyses show effects of ENSO phases that vary by season and geographical region. Cold and warm ENSO years affect the southwestern half of the country considerably during the Late Wet season. Cold ENSO phases tend to increase rainfall, and the warm phase tends to decrease it. The opposite is true for the Caribbean coast. The Dry season experiences drier conditions in warm ENSO years, and the Early Wet season does not show any statistically significant difference between ENSO years' rainfall distributions.
Arruda, W. Z. (2002). Eddies along western boundaries . Ph.D. thesis, Florida State University, Tallahassee, FL.
Arruda, W. Z., Nof, D., & O'Brien, J. J. (2004). Does the Ulleung eddy owe its existence to beta and nonlinearities? Deep Sea Research Part I: Oceanographic Research Papers , 51 (12), 2073–2090.
Bai, X., Cocke, S., LaRow, T. E., O'Brien, J. J., & Shin, D. W. (2006). Paradox of SST and lower tropospheric temperature trends over the tropical Pacific ocean . Research Activities in Atmospheric and Ocean Modeling, CAS/JSC Working Group on Numerical Experimentation.
Baigorria, G., Jones, J., Shin, D., Mishra, A., & Ingram, K. T., Jones, J. W., O'Brien, J. J., Roncoli, M. C., Fraisse, C., Breuer, N. E., Bartels, W.-L., Zierden, D. F., Letson, D. (2007). Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs. Clim. Res. , 34 , 211–222.
Baigorria, G. A., Chelliah, M., Mo, K. C., Romero, C. C., Jones, J. W., O'Brien, J. J., et al. (2010). Forecasting Cotton Yield in the Southeastern United States using Coupled Global Circulation Models. Agronomy Journal , 102 (1), 187.
