Suitability Assessment of Weather Networks for Wind Data Measurements in the Athabasca Oil Sands Area

Dhananjay Deshmukh,Anil Gupta,Quazi K Hassan,M Razu Ahmed,John Albino Dominic,Gopal Achari

doi:10.3390/cli10020010

Dhananjay Deshmukh, Anil Gupta + Show 4 more

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https://doi.org/10.3390/cli10020010

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Abstract

The Athabasca Oil Sands Area (AOSA) in Alberta, Canada, is considered to have a high density of weather stations. Therefore, our objective was to determine an optimal network for the wind data measurement that could sufficiently represent the wind variability in the area. We used available historical data records of the weather stations in the three networks in AOSA, i.e., oil sands monitoring (OSM) water quantity program (WQP) and Wood Buffalo Environmental Association (WBEA) edge sites (ES) and meteorological towers (MT) of the air program. Both graphical and quantitative methods were implemented to find the correlations and similarities in the measurements between weather stations in each network. The graphical method (wind rose diagram) was found as a functional tool to understand the patterns of wind directions, but it was not appropriate to quantify and compare between wind speed data of weather stations. Therefore, we applied the quantitative method of the Pearson correlation coefficient (r) and absolute average error (AAE) in finding a relationship between the wind data of station pairs and the percentage of similarity (PS) method in quantifying the closeness/similarity. In the correlation analyses, we found weak to strong correlations in the wind data of OSM WQP (r = 0.04–0.69) and WBEA ES (r = 0.32–0.77), and a strong correlation (r = 0.33–0.86) in most of the station pairs of the WBEA MT network. In the case of AAE, we did not find any acceptable value within the standard operating procedure (SOP) threshold when logically combining the values of the u and v components together. In the similarity analysis, minor similarities were identified between the stations in the three networks. Hence, we presumed that all weather stations would be required to measure wind data in the AOSA.

Highlights

Introduction published maps and institutional affilWind is an important atmospheric element when we think about the current weather condition and predicting the future
To understand the redundancy of the stations, we demonstrated that a wind rose diagram would be appropriate for visual comparisons of the wind datasets among weather stations to understand various patterns of the wind magnitude and direction in the networks
Comparing the dominant wind directions from wind rose diagrams for a station pair was straightforward, but it was difficult for the wind speeds

Summary

Introduction

Wind is an important atmospheric element when we think about the current weather condition and predicting the future. It carries temperature and moisture from one place to another, and weather conditions vary with the shift of wind speed and direction. The wind blows due to uneven heating of the Earth’s surface by the Sun (solar radiation). In this process, the Sun heats the Earth’s surface and warms up the surface air. Denser cold air from the surrounding high-pressure zone blows toward the low-pressure zone due to the pressure gradient, and that causes surface wind [1]. The surface wind recorded at the weather station is directly related to the characteristics of the landscape of the site, i.e., latitude, the roughness of the terrain, surrounding vegetation, and any elevated surface structures [2,3]

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