Observed Atmospheric and Surface Variability on Heterogeneous Terrain at the Hectometer Scale and Related Advective Transports

2019 ◽  
Vol 124 (16) ◽  
pp. 9407-9422 ◽  
Author(s):  
Gemma Simó ◽  
Joan Cuxart ◽  
Maria A. Jiménez ◽  
Daniel Martínez‐Villagrasa ◽  
Rodrigo Picos ◽  
...  
Keyword(s):  
Heterogeneous Terrain ◽  
Surface Variability

Measuring the Thermal Conductivity of Porous, Transparent SiO2 Films With Time Domain Thermoreflectance

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
Patrick E. Hopkins ◽  
Bryan Kaehr ◽  
Leslie M. Phinney ◽  
Timothy P. Koehler ◽  
Anne M. Grillet ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Time Domain ◽  
Optical Measurements ◽  
Conductivity Measurements ◽  
Glass Slides ◽  
Thermally Conductive ◽  
Surface Variability ◽  
Porous Nanomaterials ◽  
Structural Aspects ◽  
Nanoporous Structures

Nanocomposites offer unique capabilities of controlling thermal transport through the manipulation of various structural aspects of the material. However, measurements of the thermal properties of these composites are often difficult, especially porous nanomaterials. Optical measurements of these properties, although ideal due to the noncontact nature, are challenging due to the large surface variability of nanoporous structures. In this work, we use a vector-based thermal algorithm to solve for the temperature change and heat transfer in which a thin film subjected to a modulated heat source is sandwiched between two thermally conductive pathways. We validate our solution with time domain thermoreflectance measurements on glass slides and extend the thermal conductivity measurements to SiO2-based nanostructured films.

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>

Surface Variability of Short-wavelength Radiation and Temperature on Exoplanets around M Dwarfs

2017 ◽  
Vol 837 (2) ◽  
pp. L27 ◽  
Author(s):  
Xin Zhang ◽  
Feng Tian ◽  
Yuwei Wang ◽  
Jimy Dudhia ◽  
Ming Chen
Keyword(s):  
Short Wavelength ◽  
M Dwarfs ◽  
Wavelength Radiation ◽  
Surface Variability ◽  
Short Wavelength Radiation

Time changes in spatial structure of surface variability in the Southern Great Plains

2003 ◽  
Vol 26 (4) ◽  
pp. 407-415 ◽  
Author(s):  
Michael H. Cosh ◽  
Jery Stedinger ◽  
Wilfried Brutsaert
Keyword(s):  
Spatial Structure ◽  
Great Plains ◽  
Southern Great Plains ◽  
Time Changes ◽  
Surface Variability
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