Mean dynamic topography and oceanographic parameters estimated from an inverse model and satellite geodesy, with link to model result in one single NetCDF file (392 MB), including the inverse data error covariance

Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography-the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99-110, 2012, doi:10.1016/j.jog.2011.07.0069; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie, 2012, http://nbn-resolving.de/nbn:de:hbz:5n-29199). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates.

Spectral reflectance library of selected biotic and abiotic coral reef features in Glovers Reef, Belize

Underwater spectral reflectance was measured for selected biotic and abiotic coral reef features of Glovers Reef, Belize from March 6 - 10, 2005. Spectral reflectance's of 63 different benthic types were obtained in-situ. An Ocean Optics USB2000 spectrometer was deployed in an custom made underwater housing with a 0.5 m fiber-optic probe mounted next to an artificial light source. Spectral readings were collected with the probe (bear fibre) about 5 cm from the target to ensure that the target would fill the field of view of the fiber optic (FOV diameter ~4.4 cm), as well as to reduce the attenuating effect of the intermediate water (Roelfsema et al., 2006). Spectral readings included for one target included: 1 reading of the covered spectral fibre to correct for instrument noise, 1 reading of spectralon panel mounted on divers wrist to measure incident ambient light, and 8 readings of the target. Spectral reflectance was calculated for each target by first subtracting the instrument noise reading from each other reading. The corrected target readings were then divided by the corrected spectralon reading resulting in spectral reflectance of each target reading. An average target spectral reflectance was calculated by averaging the eight individual spectral reflectance's of the target. If an individual target spectral reflectance was visual considered an outlier, it was not included in the average spectral reflectance calculation. See Roelfsema at al. (2006) for additional info on the methodology of underwater spectra collection.

Colored dissolved organic matter (cDOM) absorption measurements in Antarctic lakes

The datasets present measurements of cDOM absorption of lakes located in Antarctic oasis during the summer periods from 2013 to 2016. In summer season of 2013 water samples were collected on Fildes Peninsula (King George Island, West Antarctica) - Bellingshausen Station, Russia. Investigated lakes on Fides Peninsula were completely or partly free from ice cover during water sampling. In summer seasons of 2014-2016 water samples were collected on Vestfold Hills, Reuer Island and Larsemann Hills Oasis (East Antarctica) - Progress station, Russia. During 2014-2016 summer season part of lakes on Larsemann Hills Oasis were free from ice cover, some of the lakes were completely covered by ice and were drilled before sampling. Part of the water samples from Progress Station (2015) has not been filtered. cDOM is operationally defined by the chosen filter pore size. Samples have been consistently filtrated through 0.7 µm pore size glas fibre filters. cDOM filtrates have been stored in darkness and have been measured after the expedition using the dual-beam Specord200 laboratory spectrometer (Jena Analytik) at the Otto Schmidt Laboratory OSL, Arctic and Antarctic Research Institute, St. Petersburg, Russia. The OSL cDOM protocol (Heim and Roessler, 2016) prescribes 3 Absorbance (A) measurements per sample from UV to 750 nm against ultra-pure water. The absorption coefficient, a, is calculated by a = 2.303A/L, where L is the pathlength of the cuvette [m], and the factor 2.303 converts log10 to loge. The output of the calculation is a continuous spectrum of a. The cDOM a spectra are used to determine the exponential slope value for specific wavelength ranges, S by fitting the data between min and max wavelength to an exponential function. We provide cDOM absorption coefficients for the wavelengths 254, 260, 350, 375, 400, 412, 440, 443 nm [1/m] and Slope values for three different UV, VIS, wavelength ranges: 275 to 295 nm, 350 to 400 nm, 300 to 500 nm [1/nm]. All data were carried out by scientists from Arctic and Antarctic Research Institute and Saint Petersburg State University of Russia during Russian Antarctic Expedition in 2013-2016.

Potential of RapidEye image data for sediment monitoring of the German tidal flats

The Wadden Sea is located in the southeastern part of the North Sea forming an extended intertidal area along the Dutch, German and Danish coast. It is a highly dynamic and largely natural ecosystem influenced by climatic changes and anthropogenic use of the North Sea. Changes in the environment of the Wadden Sea, natural or anthropogenic origin, cannot be monitored by the standard measurement methods alone, because large-area surveys of the intertidal flats are often difficult due to tides, tidal channels and unstable underground. For this reason, remote sensing offers effective monitoring tools. In this study a multi-sensor concept for classification of intertidal areas in the Wadden Sea has been developed. Basis for this method is a combined analysis of RapidEye (RE) and TerraSAR-X (TSX) satellite data coupled with ancillary vector data about the distribution of vegetation, mussel beds and sediments. The classification of the vegetation and mussel beds is based on a decision tree and a set of hierarchically structured algorithms which use object and texture features. The sediments are classified by an algorithm which uses thresholds and a majority filter. Further improvements focus on radiometric enhancement and atmospheric correction. First results show that we are able to identify vegetation and mussel beds with the use of multi-sensor remote sensing. The classification of the sediments in the tidal flats is a challenge compared to vegetation and mussel beds. The results demonstrate that the sediments cannot be classified with high accuracy by their spectral properties alone due to their similarity which is predominately caused by their water content.