scholarly journals Estimating Daily Dew Point Temperature Based on Local and Cross-Station Meteorological Data Using CatBoost Algorithm

2022 ◽  
Vol 130 (2) ◽  
pp. 671-700
Author(s):  
Fuqi Yao ◽  
Jinwei Sun ◽  
Jianhua Dong
Keyword(s):  
Meteorological Data ◽  
Dew Point ◽  
Point Temperature ◽  
Dew Point Temperature

scholarly journals Relationship between weather parameters and risk of exertional heat injuries during military training

2021 ◽  
Author(s):  
BH Poon ◽  
AW Gorny ◽  
KY Zheng ◽  
WK Cheong
Keyword(s):  
Risk Mitigation ◽  
Meteorological Data ◽  
Military Training ◽  
Armed Forces ◽  
Dew Point ◽  
Heat Injury ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Weather Parameters ◽  
The Relationship

Introduction: The Singapore Armed Forces (SAF) collaborated with the Meteorological Service Singapore (MSS) to study the relationship between weather parameters and the incidents of exertional heat injury (EHI) to mitigate the risk of EHI in a practical manner. Methods: Data from the SAF’s heat injury registry and MSS’ meteorological data from 2012 to 2018 were used to establish a consolidated dataset of EHI incidents and same-day weather parameters rank-ordered in deciles. Poisson regression modelling was used to determine the incidence rate ratios (IRRs) of the EHI, referencing the first decile of weather parameters. Two frames of analysis were performed - the first described the relationship between the weather parameters and the adjusted IRR for the same day (D), and the second described the relationship between the weather parameters and the adjusted IRR on the following day (D+1). Results: For wet-bulb temperature, the IRR on D+1 approximated unity for the first nine deciles but rose to 3.09 at the tenth decile. For dew-point temperature, the IRR on D+1 approximated unity for the first nine deciles but rose to 3.48 at the tenth decile. By designating a single dew-point temperature cut-off at  25.1°C (transition between the ninth and tenth decile), the adjusted IRR on D +1 was 2.26 on days with dew-point temperature  25.1°C,. Conclusion: Integrating the data from the SAF and MSS demonstrated that a dew-point temperature ≥ 25.1°C on D correlates statistically with the risk of EHI on D +1and could be used to supplement the risk mitigation system.

Observed spatiotemporal changes in air temperature, dew point temperature and relative humidity over Myanmar during 2001–2019

2021 ◽  
Vol 134 (1) ◽  
Author(s):  
Zin Mie Mie Sein ◽  
Irfan Ullah ◽  
Vedaste Iyakaremye ◽  
Kamran Azam ◽  
Xieyao Ma ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Dew Point ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Spatiotemporal Changes

scholarly journals The Influence of Air Temperature on the Dew Point Temperature in Benin City, Nigeria

2017 ◽  
Vol 21 (4) ◽  
pp. 657 ◽  
Author(s):  
KE Ukhurebor ◽  
TB Batubo ◽  
IC Abiodun ◽  
E Enoyoze
Keyword(s):  
Air Temperature ◽  
Dew Point ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Benin City

scholarly journals Improving efficiency and lowering operating costs of evaporative cooling

2021 ◽  
Vol 338 ◽  
pp. 01027
Author(s):  
Jan Taler ◽  
Bartosz Jagieła ◽  
Magdalena Jaremkiewicz
Keyword(s):  
Electricity Consumption ◽  
Open Circuit ◽  
Dew Point ◽  
Operating Costs ◽  
Water Evaporation ◽  
Total Water ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Adiabatic Cooling ◽  
Positive Effect

Cooling towers, or so-called evaporation towers, use the natural effect of water evaporation to dissipate heat in industrial and comfort installations. Water, until it changes its state of aggregation, from liquid to gas, consumes energy (2.257 kJ/kg). By consuming this energy, it lowers the air temperature to the wet-bulb temperature, thanks to which the medium can be cooled below the ambient temperature. Evaporative solutions are characterized by continuous water evaporation (approx. 1.5% of the total water flow) and low electricity consumption (high EER). Evaporative (adiabatic) cooling also has a positive effect on the reduction of electricity consumption of cooled machines. Lowering the relative humidity (RH) by approx. 2% lowers the wet-bulb temperature by approx. 0.5°C, which increases the efficiency of the tower, operating in an open circuit, expressed in kW, by approx. 5%, while reducing water consumption and treatment costs. The use of the M-Cycle (Maisotsenko cycle) to lower the temperature of the wet thermometer to the dew point temperature will reduce operating costs and increase the efficiency of cooled machines.

scholarly journals Theoretical Analysis of Acid Dew Point Temperature and Vapour Condensation Characteristics of the Flue Gas

2020 ◽  
Vol 721 ◽  
pp. 012048
Author(s):  
Ning Wang ◽  
Xuemin Liu ◽  
Jian Guan ◽  
Guoli Qi ◽  
Jianfei Zhang ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Flue Gas ◽  
Dew Point ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Vapour Condensation

scholarly journals Estimating Daily Dew Point Temperature Using Machine Learning Algorithms

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 582 ◽  
Author(s):  
Sultan Noman Qasem ◽  
Saeed Samadianfard ◽  
Hamed Sadri Nahand ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Gene Expression Programming ◽  
Machine Learning Algorithms ◽  
Dew Point ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
M5 Model Tree ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Input Parameters

In the current study, the ability of three data-driven methods of Gene Expression Programming (GEP), M5 model tree (M5), and Support Vector Regression (SVR) were investigated in order to model and estimate the dew point temperature (DPT) at Tabriz station, Iran. For this purpose, meteorological parameters of daily average temperature (T), relative humidity (RH), actual vapor pressure (Vp), wind speed (W), and sunshine hours (S) were obtained from the meteorological organization of East Azerbaijan province, Iran for the period 1998 to 2016. Following this, the methods mentioned above were examined by defining 15 different input combinations of meteorological parameters. Additionally, root mean square error (RMSE) and the coefficient of determination (R2) were implemented to analyze the accuracy of the proposed methods. The results showed that the GEP-10 method, using three input parameters of T, RH, and S, with RMSE of 0.96°, the SVR-5, using two input parameters of T and RH, with RMSE of 0.44, and M5-15, using five input parameters of T, RH, Vp, W, and S with RMSE of 0.37 present better performance in the estimation of the DPT. As a conclusion, the M5-15 is recommended as the most precise model in the estimation of DPT in comparison with other considered models. As a conclusion, the obtained results proved the high capability of proposed M5 models in DPT estimation.

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