Preparation of psychrometric charts for water vapour in Martian atmosphere

Shallcross, David

doi:10.1016/j.ijheatmasstransfer.2004.11.015

Cited by 15 publications

(5 citation statements)

References 9 publications

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“…Water vapor is a significant absorber and emitter of radiation. Since the processes of evaporation and condensation are accompanied by the absorption and release of heat, changes in the amount and distribution of atmospheric water vapor are of fundamental importance in thermal processes of the atmosphere [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Mixing Ratio for Saturated Air over Water

2012

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The role that water vapor plays in atmospheric phenomena is extremely important. Saturation of moist air at a given temperature and pressure occurs if its mixing ratio is such that the moist air can coexist in a stable condition with an associated condensed phase at the same temperature and pressure. The saturation mixing ratio is useful to calculate the relative humidity which is a ratio, expressed in percent, of the amount of water vapor in the air (actual mixing ratio) compared to the amount of water vapor the air can hold (saturation mixing ratio). In this work, an attempt has been made to develop a simple-to-use Arrhenius-type function to estimate the mixing ratio for saturated air over water as a function of pressure and temperature. Estimations have been found to be in excellent agreement with the data reported in the literature, with the average absolute deviation being around 0.4 %.

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Section: Introductionmentioning

confidence: 99%

Estimation of Mixing Ratio for Saturated Air over Water

2012

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“…Water vapour is a significant absorber and emitter of radiation. Since the processes of evaporation and condensation are accompanied by the absorption and release of heat, changes in the amount and distribution of atmospheric water vapour are of fundamental importance in thermal processes of the atmosphere (Singh et al, 2002;Shallcross, 2005;Hasan, 2012). The role water vapour plays in atmospheric phenomena is extremely important.…”

Section: Introductionmentioning

confidence: 99%

Estimation and variation of saturation mixing ratio and mixing ratio over Potiskum, Nigeria

Akpootu¹,

Iliyasu²,

Nouhou,³

et al. 2022

Nig J Bas App Sci

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In this study, the monthly average mean temperature, relative humidity and surface pressure data obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) for a period of thirty-eight (1979 – 2016) years was employed to estimate the saturation mixing ratio and mixing ratio for Potiskum located in the Sahelian climatic zone of Nigeria. The monthly variation of saturation mixing ratio and mixing ratio with the meteorological parameters during the period under focused was investigated. The results revealed that the maximum and minimum values of saturation mixing ratio were found in the months of May and December with 31.7067 g kg-1 and 17.5980 g kg-1, respectively. The maximum and minimum values of saturation mixing ratio were found in the months of September and February with 20.6797 g kg-1 and 4.1153 g kg-1, respectively. The results showed that high values of mixing ratio were observed during the rainy season and low values during the dry season. The monthly variation of saturation mixing ratio with mean temperature and the monthly variation of mixing ratio with relative humidity depicts direct relationship. The monthly variation of saturation mixing ratio with atmospheric pressure and relative humidity and the monthly variation of mixing ratio with mean temperature and atmospheric pressure varies differently.

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“…The widely applied parameter in practice for determining a characteristic of air is the dry-bulb temperature, which is known as the air temperature. Since the obtained air temperature by a thermometer is not function of the air humidity, it is named as dry bulb [3,4]. Conversely, the wet-bulb temperature obtained by a wet thermometer which is affected by the airflow.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Accurate Prediction of Relative Humidity based on Temperature and Wet-Bulb Depression

Ghadiri¹,

Marjani²,

Mohammadinia³

et al. 2020

Preprint

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The main parameters for calculation of relative humidity are the wet-bulb depression and dry bulb temperature. In this work, easy-to-used predictive tools based on statistical learning concepts, i.e., the Adaptive Network-Based Fuzzy Inference System (ANFIS) and Least Square Support Vector Machine (LSSVM) are developed for calculating relative humidity in terms of wet bulb depression and dry bulb temperature. To evaluate the aforementioned models, some statistical analyses have been done between the actual and estimated data points. Results obtained from the present models showed their capabilities to calculate relative humidity for divers values of dry bulb temperatures and also wet-bulb depression. The obtained values of MSE and MRE were 0.132 and 0.931, 0.193 and 1.291 for the LSSVM and ANFIS approaches respectively. These developed tools are user-friend and can be of massive value for scientists especially, those dealing with air conditioning and wet cooling towers systems to have a noble check of the relative humidity in terms of wet bulb depression and dry bulb temperatures.

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Preparation of psychrometric charts for water vapour in Martian atmosphere

Cited by 15 publications

References 9 publications

Estimation of Mixing Ratio for Saturated Air over Water

Estimation of Mixing Ratio for Saturated Air over Water

Estimation and variation of saturation mixing ratio and mixing ratio over Potiskum, Nigeria

Machine Learning Approaches for Accurate Prediction of Relative Humidity based on Temperature and Wet-Bulb Depression

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