Numerical Techniques and Cloud-Scale Processes for High-Resolution Models

2006 ◽  
Author(s):  
James D. Doyle
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregor Luetzenburg ◽  
Aart Kroon ◽  
Anders A. Bjørk

AbstractTraditionally, topographic surveying in earth sciences requires high financial investments, elaborate logistics, complicated training of staff and extensive data processing. Recently, off-the-shelf drones with optical sensors already reduced the costs for obtaining a high-resolution dataset of an Earth surface considerably. Nevertheless, costs and complexity associated with topographic surveying are still high. In 2020, Apple Inc. released the iPad Pro 2020 and the iPhone 12 Pro with novel build-in LiDAR sensors. Here we investigate the basic technical capabilities of the LiDAR sensors and we test the application at a coastal cliff in Denmark. The results are compared to state-of-the-art Structure from Motion Multi-View Stereo (SfM MVS) point clouds. The LiDAR sensors create accurate high-resolution models of small objects with a side length > 10 cm with an absolute accuracy of ± 1 cm. 3D models with the dimensions of up to 130 × 15 × 10 m of a coastal cliff with an absolute accuracy of ± 10 cm are compiled. Overall, the versatility in handling outweighs the range limitations, making the Apple LiDAR devices cost-effective alternatives to established techniques in remote sensing with possible fields of application for a wide range of geo-scientific areas and teaching.


2011 ◽  
Vol 15 (3) ◽  
pp. 545-563 ◽  
Author(s):  
Céline Scheidt ◽  
Jef Caers ◽  
Yuguang Chen ◽  
Louis J. Durlofsky

1997 ◽  
Vol 341 ◽  
pp. 1-18 ◽  
Author(s):  
WILLIAM J. MERRYFIELD ◽  
GREG HOLLOWAY

The physical basis for parameterizing topographic stress due to unresolved eddies is examined in a quasi-geostrophic barotropic model. Topographic stress parameterization is shown to represent two effects of eddies: attraction of the flow to a statistical equilibrium featuring topographically correlated mean currents, and dissipation of potential enstrophy. Performance is evaluated by comparing parameterized low-resolution models with explicit high-resolution models.


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