Study of wind speed and relative humidity using stochastic technique in a semi-arid climate region

Mahmud, Suhail; Bhuiyan, Al Masum; Sarmin, Nusrat; Elahee, Sanjida

doi:10.3934/environsci.2020010

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…The mean residence times of water in the troposphere fall in the same interval, Section 2. The stochastic behaviour of RH is statistically similar to that of turbulent air motion [81]. In contrast, the RH values at the sea surface are remarkably stable on climatological timescales [82].…”

Section: Relative Humidity: Cause and Effect Of Evaporationmentioning

confidence: 59%

Relative Humidity: A Control Valve of the Steam Engine Climate

Feistel

Hellmuth

2021

J. Hum. Earth Future

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In the words of Heinrich Hertz in 1885, the Earth is a “gigantic steam engine”. On average, of the planet’s cross section exposed to sunlight, 72 % belong to the global ocean. With a delay of only 2-3 months, most of the heat absorbed there is released by evaporation rather than by thermal radiation. Water vapour is the dominating “greenhouse gas” of the marine troposphere with a typical relative humidity (RH) of 80 % at the surface. Observing the heat transport across the ocean surface permits insight in the powerhouse of the “steam engine”, controlled by the RH at the surface, a quantity that is often considered the “Cinderella” among the climate data. RH of the troposphere also controls cloud formation that is equally fundamental as challenging for climate research. As a precise and perfectly consistent thermodynamic basis for the description of such processes, the new oceanographic standard TEOS-10 was introduced by UNESCO/IOC in 2010 and IUGG in 2011. Its equations cover all thermodynamic properties of liquid water, seawater, ice and humid air, as well as their mutual equilibria and phase transitions. For harmonisation of the inconsistent RH definitions of humid air between meteorology and climatology, the relative fugacity has been defined as a physically more reasonable RH substitute that does not rely on the approximation of ideal gases. Doi: 10.28991/HEF-2021-02-02-06 Full Text: PDF

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Section: Relative Humidity: Cause and Effect Of Evaporationmentioning

confidence: 59%

Relative Humidity: A Control Valve of the Steam Engine Climate

Feistel

Hellmuth

2021

J. Hum. Earth Future

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“…In estimations conducted with this algorithm, the measurement values of the Van GDM measurement station could be estimated at close proximity of 14.81%. The qq‐plot 44 graph for data sets, which includes a comparison of estimates and realized values, is included in Figure 7. As can be seen from the chart, there are very close relationships between estimate results and actual values.…”

Section: Findings and Discussionmentioning

confidence: 99%

Estimation of target station data using satellite data and deep learning algorithms

Yayla

Harmanci

2020

Int J Energy Res

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In this study, an innovative model has been developed for wind speed estimation through the Deep Learning method using hourly wind speed data from the measurement stations of the General Directorate of Meteorology in Van and Hakkari provinces in Turkey in conjunction with simultaneous satellite images from Eumetsat. Obtained satellite images were used during the introduction of the model, while wind speed data were used at the output stage. As a result of the findings, it was found that 85% accuracy performance could be achieved to provide sufficient insight for systems that are widely established worldwide. The model, developed as a result of the study, eliminates the need to install wind measuring stations for any region on earth within the satellite field in terms of determining wind potential. Since the field of view of the Meteosat 7 satellite covers the whole of Eastern Europe, it was determined that it could predict a high rate of up to 6 hours later by the method used in image analysis. The systems to be controlled with this method will be able to examine the weather events instantly at each point in the satellite field of view and make more accurate decisions. Also, companies will be able to perform a more detailed and rapid field scan compared to existing limited methods, and reduce initial investment costs and operating costs in terms of renewable energy resources investments.

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“…El Paso is often found to be in non-attainment of the US National Ambient Air Quality Standards (NAAQS) for O 3 , CO, PM 10 , and PM 2.5 . This non-attainment is due to the combination of a high population concentration, industrial influences, and weather conditions [23]. To capture the full topographic and meteorological characteristics of this study area, data from six different stations were used.…”

Section: Data Backgroundmentioning

confidence: 99%

Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region

et al. 2021

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In the last decade, ground-level ozone exposure has led to a significant increase in environmental and health risks. Thus, it is essential to measure and monitor atmospheric ozone concentration levels. Specifically, recent improvements in machine learning (ML) processes, based on statistical modeling, have provided a better approach to solving these risks. In this study, we compare Naive Bayes, K-Nearest Neighbors, Decision Tree, Stochastic Gradient Descent, and Extreme Gradient Boosting (XGBoost) algorithms and their ensemble technique to classify ground-level ozone concentration in the El Paso-Juarez area. As El Paso-Juarez is a non-attainment city, the concentrations of several air pollutants and meteorological parameters were analyzed. We found that the ensemble (soft voting classifier) of algorithms used in this paper provide high classification accuracy (94.55%) for the ozone dataset. Furthermore, variables that are highly responsible for the high ozone concentration such as Nitrogen Oxide (NOx), Wind Speed and Gust, and Solar radiation have been discovered.

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Study of wind speed and relative humidity using stochastic technique in a semi-arid climate region

Cited by 9 publications

References 12 publications

Relative Humidity: A Control Valve of the Steam Engine Climate

Relative Humidity: A Control Valve of the Steam Engine Climate

Estimation of target station data using satellite data and deep learning algorithms

Machine Learning Techniques Applied to Predict Tropospheric Ozone in a Semi-Arid Climate Region

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