An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI).

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters.

Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll-a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll-a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll-a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of overall algorithm uncertainty were roughly equal for two chlorophyll-a algorithms-the standard NASA OC4 algorithm based on blue/green bands and a MERIS 3-band algorithm based on red/NIR bands-with RMS error of 0.416 and 0.437 for each in log chlorophyll-a units, respectively. However, it is clear that each algorithm performs better at different chlorophyll-a ranges. When a blending approach is used based on an optical water type classification, the overall RMS error was reduced to 0.320. Bias and relative error were also reduced when evaluating the blended chlorophyll-a product compared to either of the single algorithm products. As a demonstration for ocean color applications, the algorithm blending approach was applied to MERIS imagery over Lake Erie. We also examined the use of this approach in several coastal marine environments, and examined the long-term frequency of the OWTs to MODIS-Aqua imagery over Lake Erie.

Generalized ocean color inversion model for retrieving marine inherent optical properties.

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

Ultrastructural features of NUT midline carcinoma.

Midline carcinomas associated with the nuclear protein in testis (NUT) gene rearrangement are rare, aggressive tumors that have been diagnosed most commonly in the head, neck, mediastinum, and upper aerodigestive tract. The ultrastructural features associated with this tumor have thus far received only brief comment and have never been illustrated. The authors provide a more extensive description and illustrate the electron microscopic findings in a typical case of NUT midline carcinoma, confirmed by cytogenetic and fluorescence in situ hybridization studies. This tumor was composed of cells displaying large, irregularly shaped nuclei with prominent compact nucleoli and abundant cytoplasm containing prominent bundles of tonofilaments, occasional clusters of pleomorphic granules, small numbers of lipid inclusions, and rare glycogen deposits. The cells exhibited stubby microvillous projections, were intermittently enveloped by basal lamina, and were interjoined by numerous well-formed desmosomal-type junctions and occasional junctional complexes. The authors propose that this constellation of ultrastructural features can prove helpful in discriminating NUT midline carcinoma from similar appearing entities.

Improvement in diagnostic and therapeutic arthrocentesis via constant compression.

We hypothesized that constant compression of the knee would mobilize residual synovial fluid and promote successful arthrocentesis. Two hundred and ten knees with grade II-III osteoarthritis were included in this paired design study: (1) conventional arthrocentesis was performed with manual compression and success and volume (milliliters) determined; and (2) the intra-articular needle was left in place, and a circumferential elastomeric brace was tightened on the knee to provide constant compression. Arthrocentesis was attempted again and additional fluid volume was determined. Diagnostic procedural cost-effectiveness was determined using 2017 US Medicare costs. No serious adverse events were noted in 210 subjects. In the 158 noneffusive (dry) knees, sufficient synovial fluid for diagnostic purposes (≥ 2 ml) was obtained in 5.0% (8/158) without compression and 22.8% (36/158) with compression (p = 0.0001, z for 95% CI = 1.96), and the absolute volume of arthrocentesis fluid obtained without compression was 0.28 ± 0.79 versus 1.10 ± 1.81 ml with compression (293% increase, p = 0.0001). In the 52 effusive knees, diagnostic synovial fluid (≥ 2 ml) was obtained in 75% (39/52) without compression and 100% (52/52) with compression (p = 0.0001, z for 95% CI = 1.96), and the absolute volume of arthrocentesis without compression was 14.7 ± 13.8 versus 25.3 ± 15.5 ml with compression (72.1% increase, p = 0.0002). Diagnostic procedural cost-effectiveness was $655/sample without compression and $387/sample with compression. The new technique of constant compression via circumferential mechanical compression mobilizes residual synovial fluid beyond manual compression improving the success, cost-effectiveness, and yield of diagnostic and therapeutic arthrocentesis in both the effusive and noneffusive knee.