Daytime rock surface temperature variability and its implications for mechanical rock weathering: Tenerife, Canary Islands

CATENA ◽  
1990 ◽  
Vol 17 (4-5) ◽  
pp. 449-459 ◽  
Author(s):  
K.A. Jenkins ◽  
B.J. Smith
2021 ◽  
Vol 11 (9) ◽  
pp. 3773
Author(s):  
Simone Mineo ◽  
Giovanna Pappalardo

Infrared thermography is a growing technology in the engineering geological field both for the remote survey of rock masses and as a laboratory tool for the non-destructive characterization of intact rock. In this latter case, its utility can be found either from a qualitative point of view, highlighting thermal contrasts on the rock surface, or from a quantitative point of view, involving the study of the surface temperature variations. Since the surface temperature of an object is proportional to its emissivity, the knowledge of this last value is crucial for the correct calibration of the instrument and for the achievement of reliable thermal outcomes. Although rock emissivity can be measured according to specific procedures, there is not always the time or possibility to carry out such measurements. Therefore, referring to reliable literature values is useful. In this frame, this paper aims at providing reference emissivity values belonging to 15 rock types among sedimentary, igneous and metamorphic categories, which underwent laboratory emissivity estimation by employing a high-sensitivity thermal camera. The results show that rocks can be defined as “emitters”, with emissivity generally ranging from 0.89 to 0.99. Such variability arises from both their intrinsic properties, such as the presence of pores and the different thermal behavior of minerals, and the surface conditions, such as polishing treatments for ornamental stones. The resulting emissivity values are reported and commented on herein for each different studied lithology, thus providing not only a reference dataset for practical use, but also laying the foundation for further scientific studies, also aimed at widening the rock aspects to investigate through IRT.


2021 ◽  
Vol 43 ◽  
pp. 101686
Author(s):  
Juan Carlos Hernández-Padilla ◽  
Manuel J. Zetina-Rejón ◽  
F. Arreguín-Sánchez ◽  
Pablo del Monte-Luna ◽  
José T. Nieto-Navarro ◽  
...  

2004 ◽  
Vol 31 (2) ◽  
pp. 549-560 ◽  
Author(s):  
Tariq Masood Ali Khan ◽  
Dewan Abdul Quadir ◽  
Tad S. Murty ◽  
Majajul Alam Sarker

2021 ◽  
Vol 126 (9) ◽  
Author(s):  
Ming Feng ◽  
Ying Zhang ◽  
Harry H. Hendon ◽  
Michael J. McPhaden ◽  
Andrew G. Marshall

Author(s):  
Raquel Barata ◽  
Raquel Prado ◽  
Bruno Sansó

Abstract. We present a data-driven approach to assess and compare the behavior of large-scale spatial averages of surface temperature in climate model simulations and in observational products. We rely on univariate and multivariate dynamic linear model (DLM) techniques to estimate both long-term and seasonal changes in temperature. The residuals from the DLM analyses capture the internal variability of the climate system and exhibit complex temporal autocorrelation structure. To characterize this internal variability, we explore the structure of these residuals using univariate and multivariate autoregressive (AR) models. As a proof of concept that can easily be extended to other climate models, we apply our approach to one particular climate model (MIROC5). Our results illustrate model versus data differences in both long-term and seasonal changes in temperature. Despite differences in the underlying factors contributing to variability, the different types of simulation yield very similar spectral estimates of internal temperature variability. In general, we find that there is no evidence that the MIROC5 model systematically underestimates the amplitude of observed surface temperature variability on multi-decadal timescales – a finding that has considerable relevance regarding efforts to identify anthropogenic “fingerprints” in observational surface temperature data. Our methodology and results present a novel approach to obtaining data-driven estimates of climate variability for purposes of model evaluation.


Sign in / Sign up

Export Citation Format

Share Document