PA
P. Alinaghi
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Clouds, specifically shallow clouds, are known as a major source of uncertainty in global climate models. Shallow clouds over the global oceans show different spatial patterns and organizations that may be influenced by climate change. Besides, the frequency of these patterns can change the climate feedback of marine clouds in the subtropics. Experts in atmospheric sciences have proposed different kinds of organization metrics. In a recent paper (Janssens et al., 2021), 21 cloud organization metrics were collected and computed over 5000 satellite scenes of shallow clouds located near the east of Barbados. Almost all of the existing metrics of cloud organization are bulk parameters of the cloud field. Accordingly, over the same data as Janssens et al. (2021), this project aims to define a metric that originated from the mutual arrangements of the individual clouds in the field. To this end, network theory as a mathematical tool is employed to define new cloud organization metrics to explore how cloud objects interact with and coordinate concerning each other. It should be noted that this research is the first in which cumulus cloud fields are considered as complex spatial networks. In this regard, an important question is whether a new network metric can distinctly explain a variability which has not been captured by previously defined metrics in cloud organization. To address the research question, we utilize a multivariate regression model to understand whether a linear combination of principal components of the existing metrics can encapsulate a variation in newly proposed network metrics. We found that degree standard deviation and mean of clustering coefficient are two network metrics that can distinctly capture a variability that has not been encapsulated by the existing metrics in cumulus cloud organizations. Degree standard deviation simultaneously measures the homogeneity of the cloud size distribution and the distance between nearest neighboring clouds distribution. A large value of the average clustering coefficient indicates that the network field consists of either large clouds located on corners of triangles or relatively small clouds closely coagulated. Additionally, two sensitivity analyses were performed to understand how the main results are influenced by either center-spacing or edge-spacing distance between clouds and the nodes (clouds) located close to the boundaries of the field. Finally, one should note that all of the results of network theory-based approaches are comprehensively affected by how the network is defined. Defining a network that combines both geometric and physical interaction between cloud objects will be the future work of this research.
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Clouds, specifically shallow clouds, are known as a major source of uncertainty in global climate models. Shallow clouds over the global oceans show different spatial patterns and organizations that may be influenced by climate change. Besides, the frequency of these patterns can change the climate feedback of marine clouds in the subtropics. Experts in atmospheric sciences have proposed different kinds of organization metrics. In a recent paper (Janssens et al., 2021), 21 cloud organization metrics were collected and computed over 5000 satellite scenes of shallow clouds located near the east of Barbados. Almost all of the existing metrics of cloud organization are bulk parameters of the cloud field. Accordingly, over the same data as Janssens et al. (2021), this project aims to define a metric that originated from the mutual arrangements of the individual clouds in the field. To this end, network theory as a mathematical tool is employed to define new cloud organization metrics to explore how cloud objects interact with and coordinate concerning each other. It should be noted that this research is the first in which cumulus cloud fields are considered as complex spatial networks. In this regard, an important question is whether a new network metric can distinctly explain a variability which has not been captured by previously defined metrics in cloud organization. To address the research question, we utilize a multivariate regression model to understand whether a linear combination of principal components of the existing metrics can encapsulate a variation in newly proposed network metrics. We found that degree standard deviation and mean of clustering coefficient are two network metrics that can distinctly capture a variability that has not been encapsulated by the existing metrics in cumulus cloud organizations. Degree standard deviation simultaneously measures the homogeneity of the cloud size distribution and the distance between nearest neighboring clouds distribution. A large value of the average clustering coefficient indicates that the network field consists of either large clouds located on corners of triangles or relatively small clouds closely coagulated. Additionally, two sensitivity analyses were performed to understand how the main results are influenced by either center-spacing or edge-spacing distance between clouds and the nodes (clouds) located close to the boundaries of the field. Finally, one should note that all of the results of network theory-based approaches are comprehensively affected by how the network is defined. Defining a network that combines both geometric and physical interaction between cloud objects will be the future work of this research.
In the well-known process of the turbine design, turbulence intensity (TI) plays a vital role in prediction of the power output and loads on the turbine's structure. TI is believed to be an important statistical parameter of the wind speed that can be extracted from the signals recorded by the dedicated sensors in the wind energy area. Despite the limitations of the mast-mounted sensors, they are probably more popular than LIDARs in wind energy applications. Although the sonic anemometers are reference tools in measuring turbulent features of wind, they are expensive instruments to be employed in a large-scale. In this regard, the cup anemometers appear to be the most commonly used instruments in the wind energy community. Accordingly, it would be tremendously advantageous if the 1-Hz cup anemometer data can be employed with the synthetic down-scaling idea to build the turbulence-like velocity signal fields. In this research, small-scale fluctuations are constructed via the Fractal Interpolation (FI) technique. In addition, this study aims to assess the compatibility of the FI technique in enhancing the cup anemometer data. The analysis has been carried out for the data collected in September 2018. Through this analysis, it is deduced that the cup anemometer data can be improved using the FI method. Subsequently, by applying the FI method, in most of the cases, the standard deviation values of the cup anemometer data are increased.
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In the well-known process of the turbine design, turbulence intensity (TI) plays a vital role in prediction of the power output and loads on the turbine's structure. TI is believed to be an important statistical parameter of the wind speed that can be extracted from the signals recorded by the dedicated sensors in the wind energy area. Despite the limitations of the mast-mounted sensors, they are probably more popular than LIDARs in wind energy applications. Although the sonic anemometers are reference tools in measuring turbulent features of wind, they are expensive instruments to be employed in a large-scale. In this regard, the cup anemometers appear to be the most commonly used instruments in the wind energy community. Accordingly, it would be tremendously advantageous if the 1-Hz cup anemometer data can be employed with the synthetic down-scaling idea to build the turbulence-like velocity signal fields. In this research, small-scale fluctuations are constructed via the Fractal Interpolation (FI) technique. In addition, this study aims to assess the compatibility of the FI technique in enhancing the cup anemometer data. The analysis has been carried out for the data collected in September 2018. Through this analysis, it is deduced that the cup anemometer data can be improved using the FI method. Subsequently, by applying the FI method, in most of the cases, the standard deviation values of the cup anemometer data are increased.