SeaWiFS (version R2010.0) Monthly Global 9 km Microplankton Fraction, links to NetCDF files

Phytoplankton cell size is important to biogeochemical and food web processes. The goal of this study is to estimate phytoplankton cell size distribution from satellite imagery of spectral remote sensing reflectance (Rrs(lambda)). Previous studies have indicated phytoplankton size classes have distinctive absorption spectra despite the physiological and taxonomic variability within an assemblage. For this study, the chlorophyll specific absorption spectra for phytoplankton size class extremes, pico- and microphytoplankton, are weighted by the percent microplankton (Sfm) and are the basis of phytoplankton size retrieval from SeaWiFS imagery. Satellite retrievals of Sfm are done through implementation of a forward optical model look-up table (LUT) that incorporates the range of absorption and scattering variability due to phytoplankton size, chlorophyll concentration ([Chl]) and dissolved and detrital matter (acdm(443)) in the global ocean from which Rrs(lambda) is calculated by the radiative transfer software, Hydrolight. The Hydrolight modeled Rrs(lambda) options for a given combination of [Chl] and acdm(443) within the LUT vary only due to Sfm. For a given pixel, the LUT search space was limited by satellite imagery of [Chl] and acdm(443). Within the narrowed search space, SeaWiFS Rrs(lambda) was matched with the closest LUT Rrs(lambda) option and the associated Sfm was assigned. Thresholds at which changes in Rrs(lambda) due to Sfm could be discerned were established in terms of [Chl] and acdm(443). In situ high-precision liquid chromatography-derived estimates of cell size are used in conjunction with matched daily satellite estimates of Sfm for validation and agree well. A single month is displayed as an example of the Sfm retrieval.

Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.

Reference list of 269 sources used for exploratory search

Today's digital libraries (DLs) archive vast amounts of information in the form of text, videos, images, data measurements, etc. User access to DL content can rely on similarity between metadata elements, or similarity between the data itself (content-based similarity). We consider the problem of exploratory search in large DLs of time-oriented data. We propose a novel approach for overview-first exploration of data collections based on user-selected metadata properties. In a 2D layout representing entities of the selected property are laid out based on their similarity with respect to the underlying data content. The display is enhanced by compact summarizations of underlying data elements, and forms the basis for exploratory navigation of users in the data space. The approach is proposed as an interface for visual exploration, leading the user to discover interesting relationships between data items relying on content-based similarity between data items and their respective metadata labels. We apply the method on real data sets from the earth observation community, showing its applicability and usefulness.

Helicopter-borne sea ice thickness measurements during cruises ARK-XX/2 and ARK-XXII/2 (SPACE) in the Arctic Ocean

Helicopter-borne electromagnetic sea ice thickness measurements were performed over the Transpolar Drift in late summers of 2001, 2004, and 2007, continuing ground-based measurements since 1991. These show an ongoing reduction of modal and mean ice thicknesses in the region of the North Pole of up to 53 and 44%, respectively, since 2001. A buoy derived ice age model showed that the thinning was mainly due to a regime shift from predominantly multi- and second-year ice in earlier years to first-year ice in 2007, which had modal and mean summer thicknesses of 0.9 and 1.27 m. Measurements of second-year ice which still persisted at the North Pole in April 2007 indicate a reduction of late-summer second-year modal and mean ice thicknesses since 2001 of 20 and 25% to 1.65 and 1.81 m, respectively. The regime shift to younger and thinner ice could soon result in an ice free North Pole during summer.