ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • ddc:550  (4)
  • 2020-2023  (4)
  • 2021  (4)
  • 1
    Publication Date: 2022-03-29
    Description: A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic‐abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.
    Description: Plain Language Summary: A reanalysis is a unique set of continuous variables produced by optimally merging a numerical model and observed data. The data are merged with the model using available uncertainty estimates to generate the best possible estimate of the target variables. The framework for generating a reanalysis consists of the model, the data, and the model‐data‐fusion algorithm. The very specific requirements of reanalysis frameworks have led to the development of Earth‐compartment specific reanalysis for the atmosphere, the ocean and land. Here, we review atmospheric and oceanic reanalyzes, and in more detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic, and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Based on a review of existing achievements, we identify five major steps required to develop reanalysis for terrestrial ecosystem to shed more light on biotic and abiotic interactions. In the future, terrestrial ecosystem reanalysis will deliver continuous data streams on the state and the development of terrestrial ecosystems.
    Description: Key Points: Reanalyzes provide decades‐long model‐data‐driven harmonized and continuous data sets for new scientific discoveries. Novel global scale reanalyzes quantify the biogeochemical ocean cycle, terrestrial carbon cycle, land surface, and hydrologic processes. New observation technology and modeling capabilities allow in the near future production of advanced terrestrial ecosystem reanalysis.
    Description: European Union's Horizon 2020 research and innovation programme
    Description: Deutsche Forschungsgemeinschaft
    Description: U.S. Department of Energy
    Description: Emory University's Halle Institute for Global Research and the Halle Foundation Collaborative Research
    Description: NSF
    Description: NASA
    Description: Natural Environment Research Council
    Description: European Union'’s Horizon 2020 research and innovation programme
    Description: NSERC Discovery program, the Ocean Frontier Institute, and MEOPAR
    Description: Research Foundation Flanders (FWO)
    Description: Helmholtz Association
    Description: NASA Terrestrial Ecosystems
    Keywords: ddc:550
    Language: English
    Type: doc-type:article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 2
    Publication Date: 2022-04-04
    Description: Observations in polar regions show that sea ice deformations are often narrow linear features. These long bands of deformations are referred to as Linear Kinematic Features (LKFs). Viscous‐plastic sea ice models have the capability to simulate LKFs and more generally sea ice deformations. Moreover, viscous‐plastic models simulate a larger number and more refined LKFs as the spatial resolution is increased. Besides grid spacing, other aspects of a numerical implementation, such as the placement of velocities and the associated degrees of freedom, may impact the formation of simulated LKFs. To explore these effects this study compares numerical solutions of sea ice models with different velocity staggering in a benchmark problem. Discretizations based on A‐,B‐, and C‐grid systems on quadrilateral meshes have similar resolution properties as an approximation with an A‐grid staggering on triangular grids (with the same total number of vertices). CD‐grid approximations with a given grid spacing have properties, specifically the number and length of simulated LKFs, that are qualitatively similar to approximations on conventional Arakawa A‐grid, B‐grid, and C‐grid approaches with half the grid spacing or less, making the CD‐discretization more efficient with respect to grid resolution. One reason for this behavior is the fact that the CD‐grid approach has a higher number of degrees of freedom to discretize the velocity field. The higher effective resolution of the CD‐discretization makes it an attractive alternative to conventional discretizations.
    Description: Plain Language Summary: Sea ice in the Arctic and Antarctic Oceans plays an important role in the exchange of heat and freshwater between the atmosphere and the ocean and hence in the climate in general. Satellite observations of polar regions show that the ice drift sometimes produces long features that are either cracks (leads) and zones of thicker sea ice (pressure ridges). This phenomenon is called deformation. It is mathematically described by the non‐uniform way in which the ice moves. For numerical models of sea ice motion it is difficult to represent this deformation accurately. Details of the numerics may affect the way these models simulate leads and ridges, their number and length. Specifically, we find by comparing different numerical models, that the way the model variables are ordered on a computational grid to solve the mathematical equations of sea ice motion has an effect of how many deformation features can be represented on a grid with a given spacing between grid points. A new discretization (ordering of model variables) turns out to resolve more details of the approximated field than traditional methods.
    Description: Key Points: The placement of the sea ice velocity has a mayor influence on the number of simulated linear kinematic features (LKFs). The CD‐grid resolves twice as many LKFs compared to A, B, C‐grids. A, B, C‐grids on quadrilateral meshes resolve a similar number of LKFs as A‐grids on triangular meshes (with the same total number of nodes).
    Keywords: ddc:550 ; ddc:551.343
    Language: English
    Type: doc-type:article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 3
    Publication Date: 2022-04-01
    Description: Porosity is one of the key properties of fluvial sediments. It is defined as the ratio of pore volume to total volume. In river science, porosity is often assumed to be spatially constant, which might be a gross simplification of reality. Ignoring the spatial variations in porosity can cause errors in morphological, ecological, hydrological, hydrogeological and sedimentological applications. Although detailed information about spatial porosity variations can be obtained from porosity measurements at field sites, such information has never been collected where these variations might be important. In this study, field porosity measurements were carried out to quantify the magnitude of the spatial porosity variation for four different sedimentological environments of a braided river: a confluence, a tributary delta, a braid bar and a secondary channel. A nuclear density gauge was used for the measurement of porosity. The nuclear density gauge proved to be a time‐saving and labour‐saving technique that produces accurate porosity values with a root mean square error of 0.03. The four sedimentological environments showed significant differences in porosity, with mean porosity being lower for confluence and bar than for delta and secondary channel. Semi‐variogram analysis showed the absence of any spatial correlation in porosity for distances beyond 4 m. This shows that distance cannot be used as a parameter for porosity extrapolation in a fluvial system unless the extrapolation distance is less than 4 m. At least eight measurements of porosity are required to obtain a reliable estimate of mean porosity in a sedimentary environment, i.e. with uncertainty 〈0.03. Although grain size characteristics were found to have a significant impact on porosity, the relationships between these parameters and porosity were not very strong in this study. The unique porosity dataset, presented in this article, provides a valuable source of information for researchers and river managers.
    Description: Deutsche Forschungsgemeinschaft
    Keywords: ddc:550
    Language: English
    Type: doc-type:article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 4
    Publication Date: 2022-12-01
    Description: The German continental seismic reflection program DEKORP (DEutsches KOntinentales Reflexionsseismisches Programm) was carried out in the years between 1984 and 1999. The aim of DEKORP was to investigate the deep crustal structure of Germany with high-resolution near-vertical incidence (mostly vibro)seismic acquisition, supplemented by wide-angle seismic and other target-oriented piggy-back experiments, all complemented by optimized methods of data processing and interpretation. The DEKORP project was an equivalent to many other deep-seismic programs world-wide such as COCORP, BIRPS, LITHOPROBE, ECORS, CROP, BELCORP, IBERSEIS and others. The resulting DEKORP database consists of approximately 40 crustal-scale 2D-seismic reflection lines covering a total of ca. 4 700 km and one 3D-seismic survey covering ca. 400 km², recorded in close connection with the German Continental Deep Drilling Program (KTB). Nowadays, re-recording of these seismic traverses in the same extent and quality would often not be possible anymore due to increased acquisition costs and tightened permission requirements. Therefore these datasets provide unique and deep insights into the subsurface below Germany covering the earth’s crust from the surface to the upper mantle. Currently, many of the original raw data are still stored on old storage media and in formats, which can only be read by special devices, programs and experts. To prevent the final loss of this valuable geoscientific treasure an initiative at GFZ transcripts all relevant DEKORP data to modern formats and media. Over the last few years the demand for DEKORP data continuously increased. Several academic institutions and commercial companies reprocess and/or reinterpret these data, which lead to significant improvements in the quality of the results. Fields of applications are geothermal development, hazard analysis, hydrocarbon/shale gas exploration, underground gas storage, tunnel construction, disposal of nuclear waste and more. To simplify the data access for the scientific as well as for the commercial geo-community, a well-structured provision and utilisation concept is being developed. The concept includes so-called data publications with DOIs, a defined license model and automised retrieval for each of the surveys providing raw data, processed data, meta data, related links and more. The plan aims to have all relevant DEKORP datasets compiled and prepared for access via web interface till 2022.
    Description: poster
    Keywords: ddc:550
    Language: English
    Type: doc-type:conferenceObject
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...