scholarly journals Impact of the 3D morphology on the hygro-thermal transfer of hygroscopic materials: application to spruce wood

2021 ◽  
Vol 2069 (1) ◽  
pp. 012008
Author(s):  
Hiam Dahanni ◽  
Aya Rima ◽  
Kamilia Abahri ◽  
Chady El Hachem ◽  
Hassan Assoum
Keyword(s):  
Relative Humidity ◽  
Morphological Structure ◽  
Anatomical Structure ◽  
Acoustic Properties ◽  
Thermal Transfer ◽  
Spruce Wood ◽  
Hygroscopic Nature ◽  
Micro Tomography ◽  
Mass Transfers ◽  
Humidity Profiles

Abstract Spruce wood is a bio-based material that is well known in the building construction field because of its good thermal and acoustic properties. It has a heterogeneous anatomical structure and also hygroscopic nature which offers the possibility to swell or shrink–in accordance to–relative humidity solicitations. In this context, the aim of this paper is to investigate the influence of the microstructure of spruce wood on the mechanisms of heat and mass transfers. The novelty of this article is that a real 3D spruce wood structure is taken into account to model hygrothermal transfer within the material. A 3D X-ray micro-tomography was investigated for the reconstruction of the material at a resolution of 3.35 μm/pixel. Hygrothermal model was developed in order to predict the influence of the anatomical structure of wood on the material behaviour. The resulting 3D temperature and relative humidity profiles show a significant dependence on the morphological structure of the material and the mechanisms that are at the microscopic scale have an influence on the macroscopic scale.

The Effects of Water Soluble Extractives on the Acoustic Properties of Reed (Arundo donax L.)

Holzforschung ◽  
1999 ◽  
Vol 53 (1) ◽  
pp. 63-67 ◽  
Author(s):  
E. Obataya ◽  
T. Umezawa ◽  
F. Nakatsubo ◽  
M. Norimoto
Keyword(s):  
Weight Loss ◽  
Relaxation Time ◽  
Relative Humidity ◽  
Loss Tangent ◽  
Arundo Donax ◽  
Anatomical Structure ◽  
Water Soluble ◽  
Acoustic Properties ◽  
Parenchyma Cells ◽  
Arundo Donax L

Summary The storage modulus (E′) and the loss tangent (tanδ) of reed (Arundo donax L.) used for woodwinds were measured at 20°C and 60% relative humidity and the effects of water soluble extractives on these properties were discussed. The extractives increased both the E′ and tanδof reed. There was a linear relationship between the tanδ change and the weight loss due to extraction. By using an uniaxial rheological model considering the anatomical structure of reed, the E′ and tanδ of reed were described using the storage moduli, loss tangents, and volume fractions of bundle sheaths and parenchyma cells. It was suggested that the extractives in parenchyma cells increased the modulus of elasticity for parenchyma cells by 25% and reduced the relaxation time of parenchyma cells by a factor of three. The main constituents of extractives were glucose, fructose and sucrose.

scholarly journals Retrieval of Layer Averaged Relative Humidity Profiles from MHS Observations over Tropical Region

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
R. K. Gangwar ◽  
B. S. Gohil ◽  
A. K. Mathur
Keyword(s):  
Relative Humidity ◽  
Radiative Transfer ◽  
Reanalysis Data ◽  
Tropical Region ◽  
Atmospheric Layer ◽  
Ncep Reanalysis ◽  
Microwave Humidity Sounder ◽  
Using Data ◽  
Profile Errors ◽  
Humidity Profiles

The present paper deals with the retrieval of the atmospheric layer averaged relative humidity profiles using data from the Microwave Humidity Sounder (MHS) onboard the MetOp satellite. The retrieval has been innovatively performed by firstly retrieving humidity for pairs of thick overlapping layers (TOLs) used subsequently to derive humidity for associated thin isolated layer (TIL). A water vapour dependent (WVD) algorithm has been developed and applied to infer the humidity of TOLs. Thus, the retrieved profiles have been finally compared with standard algorithm (NORM). These algorithms have been developed based on radiative transfer simulations and study of sensitivities of MHS channels on humidity of various types of layers (TOL, TIL). The algorithm has been tested with MHS data and validated using concurrent radiosonde as well as NCEP reanalysis data indicating profile errors of ~15% and ~19%, respectively.

scholarly journals Inter-comparison of AIRS temperature and relative humidity profiles with AMMA and DACCIWA radiosonde observations over West Africa

2020 ◽  
Author(s):  
Marian Amoakowaah Osei ◽  
Leonard K Amekudzi ◽  
Craig R. Ferguson ◽  
Sylvester Kojo Danuor
Keyword(s):  
Relative Humidity ◽  
West Africa ◽  
Humidity Profiles

scholarly journals Tropospheric water vapour and relative humidity profiles from lidar and microwave radiometry

2014 ◽  
Vol 7 (5) ◽  
pp. 1201-1211 ◽  
Author(s):  
F. Navas-Guzmán ◽  
J. Fernández-Gálvez ◽  
M. J. Granados-Muñoz ◽  
J. L. Guerrero-Rascado ◽  
J. A. Bravo-Aranda ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Water Vapour ◽  
Microwave Radiometer ◽  
Radiosonde Data ◽  
Mixing Ratio ◽  
Raman Lidar ◽  
Night Time ◽  
New Approach ◽  
Lidar Measurements ◽  
Humidity Profiles

Abstract. In this paper, we outline an iterative method to calibrate the water vapour mixing ratio profiles retrieved from Raman lidar measurements. Simultaneous and co-located radiosonde data are used for this purpose and the calibration results obtained during a radiosonde campaign in summer and autumn 2011 are presented. The water vapour profiles measured during night-time by the Raman lidar and radiosondes are compared and the differences between the methodologies are discussed. Then, a new approach to obtain relative humidity profiles by combination of simultaneous profiles of temperature (retrieved from a microwave radiometer) and water vapour mixing ratio (from a Raman lidar) is addressed. In the last part of this work, a statistical analysis of water vapour mixing ratio and relative humidity profiles obtained during 1 year of simultaneous measurements is presented.

scholarly journals Inter-Comparison of AIRS Temperature and Relative Humidity Profiles with AMMA and DACCIWA Radiosonde Observations over West Africa

2020 ◽  
Vol 12 (16) ◽  
pp. 2631
Author(s):  
Marian Amoakowaah Osei ◽  
Leonard Kofitse Amekudzi ◽  
Craig R. Ferguson ◽  
Sylvester Kojo Danuor
Keyword(s):  
Relative Humidity ◽  
West Africa ◽  
Water Vapour ◽  
Radiosonde Data ◽  
Weather Information ◽  
Significant Difference ◽  
High Impact Weather ◽  
Mean Square Difference ◽  
June July ◽  
Humidity Profiles

The vertical profiles of temperature and water vapour from the Atmospheric InfraRed Sounder (AIRS) have been validated across various regions of the globe as an effort to provide a substitute for radiosonde observations. However, there is a paucity of inter-comparisons over West Africa where local convective processes dominate and radiosonde observations (RAOBs) are limited. This study validates AIRS temperature and relative humidity profiles for selected radiosonde stations in West Africa. Radiosonde data were obtained from the AMMA and DACCIWA campaigns which spanned 2006–2008 and June–July 2016 respectively and offered a period of prolonged radiosonde observations in West Africa. AIRS performance was evaluated with the bias and root mean square difference (RMSD) at seven RAOB stations which were grouped into coastal and inland. Evaluation was performed on diurnal and seasonal timescales, cloud screening conditions and derived thunderstorm instability indices. At all timescales, the temperature RMSD was higher than the AIRS accuracy mission goal of ±1 K. Relative humidity RMSD was satisfactory with deviations <20% and <50% for both lower and upper troposphere respectively. AIRS retrieval of water vapour under cloudy and cloud-free conditions had no significant difference whereas cloud-free temperature was found to be more accurate. The seasonal evolution of some thunderstorm convective indices were also found to be comparable for AIRS and RAOB. The ability of AIRS to capture the evolution of these indices imply it will be a useful dataset for the African Science for Weather Information and Forecasting Techniques (SWIFT) high impact weather studies.

Estimation of fine-scale relative humidity profiles: an issue for understanding the atmospheric water cycle

2020 ◽  
Author(s):  
Veronique Michot ◽  
Helene Brogniez ◽  
Mathieu Vrac ◽  
Soulivanh Thao ◽  
Helene Chepfer ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Quantile Regression ◽  
Water Vapour ◽  
Climate Models ◽  
Water Cycle ◽  
Skill Score ◽  
Horizontal Resolution ◽  
Atmospheric Water ◽  
Global Knowledge ◽  
Humidity Profiles

&lt;p&gt;The multi-scale interactions at the origin of the links between clouds and water vapour are essential for the Earth's energy balance and thus the climate, from local to global. Knowledge of the distribution and variability of water vapour in the troposphere is indeed a major issue for the understanding of the atmospheric water cycle. At present, these interactions are poorly known at regional and local scales, i.e. within 100km, and are therefore poorly represented in numerical climate models. This is why we have sought to predict cloud scale relative humidity profiles in the intertropical zone, using a non-parametric statistical downscaling method called quantile regression forest. The procedure includes co-located data from 3 satellites: CALIPSO lidar and CloudSat radar, used as predictors and providing cloud properties at 90m and 1.4km horizontal resolution respectively; SAPHIR data used as a predictor and providing relative humidity at an initial horizontal resolution of 10km. Quantile regression forests were used to predict relative humidity profiles at the CALIPSO and CloudSat scales. These predictions are able to reproduce a relative humidity variability consistent with the cloud profiles and are confirmed by values of coefficients of determination greater than 0.7, relative to observed relative humidity, and Continuous Rank Probability Skill Score between 0 and 1, relative to climatology. Lidar measurements from the NARVAL 1&amp;2 campaigns and radiosondes from the EUREC4A campaigns were also used to compare Relative Humidity profiles at the SAPHIR scale and at the scale of forest regression prediction by quantile regression.&lt;/p&gt;

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