|Home||<< 1 2 3 4 5 6 7 8 9 10 >>|
|Hughes, P. J., Bourassa, M. A., Rolph, J. J., & Smith, S. R. (2012). Averaging-Related Biases in Monthly Latent Heat Fluxes. J. Atmos. Oceanic Technol., 29(7), 974–986.|
Jacob, J. C., Armstrong, E. M., Bourassa, M. A., Cram, T., Elya, J. L., Greguska, F. R., III, et al. (2018). OceanWorks: Enabling Interactive Oceanographic Analysis in the Cloud with Multivariate Data. In American Geophysical Union (Vol. Fall Meeting).
Abstract: NASA's Advanced Information System Technology (AIST) Program sponsors the OceanWorks project to establish an integrated data analytics center at the Physical Oceanography Distributed Active Archive Center (PO.DAAC). OceanWorks provides a series of interoperable capabilities that are essential for cloud-scale oceanographic research. These include big data analytics, data search with subsecond response, intelligent ranking of search results, subsetting based on data quality metrics, and rapid spatiotemporal matchup of satellite measurements with distributed in situ data. The software behind OceanWorks is being developed as an open source project in the Apache Incubator Science Data Analytics Platform (SDAP – http://sdap.apache.org). In this presentation we describe how OceanWorks enables efficient, scalable, interactive and interdisciplinary oceanographic analysis with multivariate data.
Interactivity is enabled by a number of SDAP features. First, SDAP provides Representational State Transfer (REST) interfaces to a number of built-in cloud analytics to compute time series, time-averaged maps, correlation maps, climatological maps, Hovmöller maps, and more. To access these, users simply navigate to a properly constructed parameterized URL in their web browser or issue web services calls in a variety of programming languages or in a Jupyter notebook. Alternatively, Python clients can make function calls via the NEXUS Command Line Interface (CLI). Authenticated users can even inject their own custom code via REST calls or the CLI.
To enable interdisciplinary science, OceanWorks provides access to a rich collection of multivariate satellite and in situ measurements of the oceans (e.g., sea surface temperature, height and salinity, chlorophyll and circulation) and other Earth science data (e.g., aerosol optical depth and wind speed), coupled with on-demand processing capabilities close to the data. We partition the data across space or time into tiles and store them into cloud-aware databases that are collocated with the computations. We will provide examples of scientific studies directly enabled by OceanWorks' multivariate data and cloud analytics.
Keywords: 910 Data assimilation, integration and fusion, INFORMATICSDE: 1916 Data and information discovery, INFORMATICSDE: 1926 Geospatial, INFORMATICSDE: 1942 Machine learning, INFORMATICS
|Josey, S. A., & Smith, S. R. (2006). Guidelines for evaluation of air-sea heat, freshwater, and momentum flux datasets. Southampton, UK: National Oceanography Center.|
|Kennedy, A. J., Griffin, M. L., Morey, S. L., Smith, S. R., & O'Brien, J. J. (2007). Effects of El Niño-Southern Oscillation on sea level anomalies along the Gulf of Mexico coast. J. Geophys. Res., 112(C5).|
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.
|Legler, D. M., Smith, S. R., & O'Brien, J. J. (1999). An archive of underway surface meteorology data from WOCE. In CLIMAR99, WMO, Vancouver, Canada (pp. 42–45).|
|Legler, D. M., Smith, S. R., & Stricherz, J. N. (1996). TOGA COARE IOP Surface Meteorology Archive [cdrom]. Tallahassee, FL: Florida State University, COAPS.|
|Legler, D. M., Smith, S. R., & Stricherz, J. N. (1997). WOCE Southern Hemisphere Surface Meteorology. Tallahassee, FL: Center for Ocean-Atmospheric Prediction Studies, Florida State University.|
|O'Brien, J. J., Bourassa, M. A., & Smith, S. R. (2005). Climate variability in ocean surface turbulent fluxes. Annual Report: The State of the Ocean and the Ocean Observing System for Climate. Silver Spring, MD, 20910. USA: NOAA Office of Climate Observation.|
|O'Brien, J. J., Bourassa, M. A., & Smith, S. R. (2005). U.S. research vessel surface meteorology data assembly center. Annual Report: The State of the Ocean and the Ocean Observing System for Climate. Silver Spring, MD, 20910. USA: NOAA Office of Climate Observation.|