Area and shape distortions in open-source discrete global grid systems

A Discrete Global Grid System(DGGS)is a type of spatial reference system that tessellates the globe into many individual,evenly spaced,and well-aligned cells to encode location and,thus,can serve as a basis for data cube construction.This facilitates integration and aggregation of multi-resolution data from various sources to rapidly calculate spatial statistics.We calculated normalized area and compactness for cell geometries from 5 open-source DGGS implementations-Uber H3,Google S2,RiskAware OpenEAGGR,rHEALPix by Landcare Research New Zealand,and DGGRID by Southern Oregon University-to evaluate their suitability for a global-level statistical data cube.We conclude that the rHEALPix and OpenEAGGR and DGGRID ISEA-based DGGS definitions are most suitable for global statistics because they have the strongest guarantee of equal area preservation-where each cell covers almost exactly the same area on the globe.Uber H3 has the smallest shape distortions,but Uber H3 and Google S2 have the largest variations in cell area.However,they provide more mature software library functionalities.DGGRID provides excellent functionality to construct grids with desired geometric properties but as the only implementation does not provide functions for traversal and navigation within a grid after its construction.

EASE-DGGS:a hybrid discrete global grid system for Earth sciences

Although we live in an era of unprecedented quantities and access to data,deriving actionable information from raw data is a hard problem.Earth observation systems(EOS)have experienced rapid growth and uptake in recent decades,and the rate at which we obtain remotely sensed images is increasing.While significant effort and attention has been devoted to designing systems that deliver analytics ready imagery faster,less attention has been devoted to developing analytical frameworks that enable EOS to be seamlessly integrated with other data for quantitative analysis.Discrete global grid systems(DGGS)have been proposed as one potential solution that addresses the challenge of geospatial data integration and interoperability.Here,we propose the systematic extension of EASE-Grid in order to provide DGGS-like characteristics for EOS data sets.We describe the extensions as well as present implementation as an application programming interface(API),which forms part of the University of Minnesota’s GEMS(Genetic x Environment x Management x Socioeconomic)Informatics Center’s API portfolio.

The Baseline Evaluation of LASG/IAP Climate System Ocean Model (LICOM) Version 2

The baseline performance of the latest version （version 2） of an intermediate resolution, stand-alone climate oceanic general circulation model, called LASG/IAP （State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics/Institute of Atmospheric Physics） Climate system Ocean Model （LICOM）, has been evaluated against the observation by using the main metrics from Griffies et al. in 2009. In general, the errors of LICOM2 in the water properties and in the circulation are comparable with the models of Coordinated Ocean-ice Reference Experiments （COREs）. Some common biases are still evident in the present version, such as the cold bias in the eastern Facific cold tongue, the warm biases off the east coast of the basins~ the weak poleward heat transport in the Atlantic, and the relatively large biases in the Arctic Ocean. A unique systematic bias occurs in LICOM2 over the Southern Ocean, compared with CORE models. It seems that this bias may be related to the sea ice process around the Antarctic continent.