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.

Large-Area Photo-Mosaicking tool (LAPM tool)

This paper presents a new tool for large-area photo-mosaicking (LAPM tool). This tool was developed specifically for the purpose of underwater mosaicking, and it is aimed at providing end-user scientists with an easy and robust way to construct large photo-mosaics from any set of images. It is notably capable of constructing mosaics with an unlimited number of images on any modern computer (minimum 1.30 GHz, 2 GB RAM). The mosaicking process can rely on both feature matching and navigation data. This is complemented by an intuitive graphical user interface, which gives the user the ability to select feature matches between any pair of overlapping images. Finally, mosaic files are given geographic attributes that permit direct import into ArcGIS. So far, the LAPM tool has been successfully used to construct geo-referenced photo-mosaics with photo and video material from several scientific cruises. The largest photo-mosaic contained more than 5000 images for a total area of about 105,000 m**2. This is the first article to present and to provide a finished and functional program to construct large geo-referenced photo-mosaics of the seafloor using feature detection and matching techniques. It also presents concrete examples of photo-mosaics produced with the LAPM tool.

Study of the fronts of Johnsons and Hurd Glaciers (Livingston Island, Antarctica) from 1957 to 2013, with links to shapefiles

The study of glacier fronts combines different geomatics measurement techniques as the classic survey using total station or theodolite, technical GNSS (Global Navigation Satellite System), using laser-scanner or using photogrammetry (air or ground). The measure by direct methods (classical surveying and GNSS) is useful and fast when accessibility to the glaciers fronts is easy, while it is practically impossible to realize, in the case of glacier fronts that end up in the sea (tide water glaciers). In this paper, a methodology that combines photogrammetric methods and other techniques for lifting the front of the glacier Johnsons, inaccessible is studied. The images obtained from the front, come from a non-metric digital camera; its georeferencing to a global coordinate system is performed by measuring points GNSS support in accessible areas of the glacier front side and applying methods of direct intersection in inaccessible points of the front, taking measurements with theodolite. The result of observations obtained were applied to study the temporal evolution (1957-2014) of the position of the Johnsons glacier front and the position of the Argentina, Las Palmas and Sally Rocks lobes front (Hurd glacier).

Documentation of glaciers and mountain chains in the Nordvestfjord of Scorsby Sound (East Greenland, July 2016) by landscape photography during Maria S. Merian cruise MSM56

From the 12th until the 17th of July 2016, research vessel Maria S. Merian entered the Nordvestfjord of Scorsby Sound (East Greenland) as part of research cruise MSM56, "Ecological chemistry in Arctic fjords". A large variety of chemical and biological parameters of fjord and meltwater were measured during this cruise to characterize biogeochemical fluxes in arctic fjords. The photo documentation described here was a side project. It was started when we were close to the Daugaard-Jensen glacier at the end of the Nordvestfjord and realized that not many people have seen this area before and photos available for scientists are probably rare. These pictures shall help to document climate and landscape changes in a remote area of East Greenland. Pictures were taken with a Panasonic Lumix G6 equipped with either a 14-42 or 45-150 objective (zoom factor available in jpg metadata). Polarizer filters were used on both objectives. The time between taking the pictures and writing down the coordinates was maximally one minute but usually shorter. The uncertainty in position is therefore small as we were steaming slowly most of the time the pictures were taken (i.e. below 5 knots). I assume the uncertainty is in most cases below 200 m radius of the noted position. I did not check the direction I directed the camera to with a compass at the beginning. Hence, the direction that was noted is an approximation based on the navigation map and the positioning of the ship. The uncertainty was probably around +/- 40° but initially (pictures 1-17) perhaps even higher as this documentation was a spontaneous idea and it took some time to get the orientation right. It should be easy, however, to find the location of the mountains and glaciers when being on the respective positions because the mountains have a quite characteristic shape. In a later stage of this documentation, I took pictures from the bridge and used the gyros to approximate the direction the camera was pointed at. Here the uncertainty was much lower (i.e. +/- 20° or better). Directions approximated with the help of gyros have degree values in the overview table. The ship data provided in the MSM56 cruise report will contain all kinds of sensor data from Maria S. Merian sensor setup. This data can also be used to further constrain the position the pictures were taken because the exact time a photo was shot is noted in the metadata of the .jpg photo file. The shipboard clock was set on UTC. It was 57 minutes and 45 seconds behind the time in the camera. For example 12:57:45 on the camera was 12:00:00 UTC on the ship. All pictures provided here can be used for scientific purposes. In case of usage in presentations etc. please acknowledge RV Maria S. Merian (MSM56) and Lennart T. Bach as author. Please inform me and ask for reprint permission in case you want to use the pictures for scientific publications. I would like to thank all participants and the crew of Maria S. Merian Cruise 56 (MSM56, Ecological chemistry in Arctic fjords).

Directional wave measurements using the Offshore Sensing Sailbuoy, link to MATLAB file

An autonomous vessel, the Offshore Sensing Sailbuoy, was used for wave measurements near the Ekofisk oil platform complex in the North Sea (56.5 N, 3.2 E, operated by ConocoPhilllips) from 6 to 20 November 2015. Being 100% wind propelled, the Sailbuoy has two-way communication via the Iridium network and has the capability for missions of six months or more. It has previously been deployed in the Arctic, Norwegian Sea and the Gulf of Mexico, but this was the first real test for wave measurements. During the campaign it held position about 20km northeast of Ekofisk (on the lee side) during rough conditions. Mean wind speed measured at Ekofisk during the campaign was near 9.8m/s, with a maximum of 20.4m/s, with wind mostly from south and south west. A Datawell MOSE G1000 GPS based 2Hz wave sensor was mounted on the Sailbuoy. Mean significant wave height (Hs 1hr) measured was 3m, whereas maximum Hs was 6m. Mean wave period was 7.7s, while maximum wave height, Hmax, was 12.6m. These measurements have been compared with non-directional Waverider observations at the Ekofisk complex. Mean Hs at Ekofisk was 3.1m, while maximum Hs was 6.5m. Nevertheless, the correlation between the two measurements was high (97%). Spectra comparison was also good, except for low Hs (~1m), where the motion of the vessel seemed to influence the measurements. Nevertheless, the Sailbuoy performed well during this campaign, and results suggests that it is a suitable platform for wave measurements in rather rough sea conditions.