On the Optimality of Dubins Paths across Heterogeneous Terrain

2008 ◽  
pp. 457-470 ◽  
Author(s):  
Ricardo G. Sanfelice ◽  
Emilio Frazzoli
Keyword(s):  
Heterogeneous Terrain

Estimating Surface Melt on the Larsen Ice Shelf Using a Deep Neural Network: Opportunities and Challenges

2021 ◽  
Author(s):  
Zhongyang Hu ◽  
Peter Kuipers Munneke ◽  
Stef Lhermitte ◽  
Maaike Izeboud ◽  
Michiel van den Broeke
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Neural Network ◽  
Climate Model ◽  
Climate Modeling ◽  
Coefficient Of Determination ◽  
Ice Shelf ◽  
Heterogeneous Terrain ◽  
Surface Melt ◽  
Larsen Ice Shelf

<p>Presently, surface melt over Antarctica is estimated using climate modeling or remote sensing. However, accurately estimating surface melt remains challenging. Both climate modeling and remote sensing have limitations, particularly in the most crucial areas with intense surface melt.  The motivation of our study is to investigate the opportunities and challenges in improving the accuracy of surface melt estimation using a deep neural network. The trained deep neural network uses meteorological observations from automatic weather stations (AWS) and surface albedo observations from satellite imagery to improve surface melt simulations from the regional atmospheric climate model version 2.3p2 (RACMO2). Based on observations from three AWS at the Larsen B and C Ice Shelves, cross-validation shows a high accuracy (root mean square error = 0.898 mm.w.e.d<sup>−1</sup>, mean absolute error = 0.429 mm.w.e.d<sup>−1</sup>, and coefficient of determination = 0.958). The deep neural network also outperforms conventional machine learning models (e.g., random forest regression, XGBoost) and a shallow neural network. To compute surface melt for the entire Larsen Ice Shelf, the deep neural network is applied to RACMO2 simulations. The resulting, corrected surface melt shows a better correlation with the AWS observations in AWS 14 and 17, but not in AWS 18. Also, the spatial pattern of the surface melt is improved compared to the original RACMO2 simulation. A possible explanation for the mismatch at AWS 18 is its complex geophysical setting. Even though our study shows an opportunity to improve surface melt simulations using a deep neural network, further study is needed to refine the method, especially for complicated, heterogeneous terrain.</p>

Turbulence intensity and turbulent kinetic energy parameters over a heterogeneous terrain of Loess Plateau

2015 ◽  
Vol 32 (9) ◽  
pp. 1291-1302 ◽  
Author(s):  
Ping Yue ◽  
Qiang Zhang ◽  
Runyuan Wang ◽  
Yaohui Li ◽  
Sheng Wang
Keyword(s):  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Loess Plateau ◽  
Turbulence Intensity ◽  
Heterogeneous Terrain ◽  
Energy Parameters

On the Determination and Characteristics of Effective Roughness Length for Heterogeneous Terrain

2003 ◽  
Vol 20 (1) ◽  
pp. 71-76 ◽  
Author(s):  
Zhong Zhong ◽  
Ming Zhao ◽  
Bingkai Su ◽  
Jianping Tang
Keyword(s):  
Roughness Length ◽  
Heterogeneous Terrain ◽  
Effective Roughness ◽  
Effective Roughness Length

Aerosol mapping over rugged heterogeneous terrain with imaging spectrometer data

2002 ◽  
Author(s):  
Stephan Bojinski ◽  
Daniel Schlaepfer ◽  
Michael E. Schaepman ◽  
Johannes Keller
Keyword(s):  
Heterogeneous Terrain ◽  
Imaging Spectrometer ◽  
Spectrometer Data

Interpretation of surface flux measurements in heterogeneous terrain during the Monsoon '90 experiment

1994 ◽  
Vol 30 (5) ◽  
pp. 1227-1239 ◽  
Author(s):  
D. I. Stannard ◽  
J. H. Blanford ◽  
W. P. Kustas ◽  
W. D. Nichols ◽  
S. A. Amer ◽  
...  
Keyword(s):  
Surface Flux ◽  
Heterogeneous Terrain ◽  
Flux Measurements

scholarly journals Spatial random downscaling of rainfall signals in Andean heterogeneous terrain

2015 ◽  
Vol 22 (4) ◽  
pp. 383-402 ◽  
Author(s):  
A. Posadas ◽  
L. A. Duffaut Espinosa ◽  
C. Yarlequé ◽  
M. Carbajal ◽  
H. Heidinger ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Complex Terrain ◽  
Tropical Rainfall Measuring Mission ◽  
Decision Makers ◽  
Remotely Sensed Data ◽  
Heterogeneous Terrain ◽  
Peruvian Andes ◽  
High Plateau ◽  
The Poor

Abstract. Remotely sensed data are often used as proxies for indirect precipitation measures over data-scarce and complex-terrain areas such as the Peruvian Andes. Although this information might be appropriate for some research requirements, the extent at which local sites could be related to such information is very limited because of the resolution of the available satellite data. Downscaling techniques are used to bridge the gap between what climate modelers (global and regional) are able to provide and what decision-makers require (local). Precipitation downscaling improves the poor local representation of satellite data and helps end-users acquire more accurate estimates of water availability. Thus, a multifractal downscaling technique complemented by a heterogeneity filter was applied to TRMM (Tropical Rainfall Measuring Mission) 3B42 gridded data (spatial resolution ~ 28 km) from the Peruvian Andean high plateau or \\textit{Altiplano} to generate downscaled rainfall fields that are relevant at an agricultural scale (spatial resolution ~ 1 km).

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