Global Analysis of Atmospheric Transmissivity Using Cloud Cover, Aridity and Flux Network Datasets

Abstract

Atmospheric transmissivity (τ) is a critical factor in climatology, which affects surface energy balance, measured at a limited number of meteorological stations worldwide. With the limited availability of meteorological datasets in remote areas across different climatic regions, estimation of τ is becoming a challenging task for adequate hydrological, climatic, and crop modeling studies. The availability of solar radiation data is comparatively less accessible on a global scale than the temperature and precipitation datasets, which makes it necessary to develop methods to estimate τ. Most of the previous studies provided region specific datasets of τ, which usually provide local assessments. Hence, there is a necessity to give the empirical models for τ estimation on a global scale that can be easily assessed. This study presents the analysis of the τ relationship with varying geographic features and climatic factors like latitude, aridity index, cloud cover, precipitation, temperature, diurnal temperature range, and elevation. In addition to these factors, the applicability of these relationships was evaluated for different climate types. Thus, empirical models have been proposed for each climate type to estimate τ by using the most effective factors such as cloud cover and aridity index. The cloud cover is an important yet often overlooked factor that can be used to determine the global atmospheric transmissivity. The empirical relationship and statistical indicator provided the best performance in equatorial climates as the coefficient of determination (r2) was 0.88 relatively higher than the warm temperate (r2 = 0.74) and arid regions (r2 = 0.46). According to the results, it is believed that the analysis presented in this work is applicable for estimating the τ in different ecosystems across the globe.

Temporal and Spatial Variability of Dryness Conditions in Kazakhstan during 1979–2021 Based on Reanalysis Data

“…It can affect large areas as the weather conditions are governed by large-scale circulation processes [19]. Drought is one of the most complicated and least understood natural hazards [20,21] with widespread impacts on water resources [22], net energy budget [23], agricultural production [24], ecosystem functions [25,26], biodiversity [27,28], biogeochemical processes [29], wildfire occurrence [30,31] and local and global economies [32]. Despite numerous studies on changes in dryness and water availability, major uncertainties regarding both past and future aridity changes still remain [33][34][35][36].…”

Assessing the National Water Model’s Streamflow Estimates Using a Multi-Decade Retrospective Dataset across the Contiguous United States

“…It is crucial to acknowledge that data utilized for the classification of climate zones could be influenced by the signa climate change, as well as the impacts of teleconnections and climate variability, includ the implications of diverse low-frequency climate oscillation indices at varying pha This topic warrants substantial attention. However, the primary objective of the pres research is to offer a preliminary delineation of the spatial distribution of aridity zone Regardless, the methodologies derived for climate zone classification align well w both regional and global studies that have explored the spatial variation of the Arid Index [57,58] The monthly variation of PB for regulated flow was assessed in this analysis. The PB for NWM estimates exhibited positive median values for all months, indicating a general propensity for the model to overestimate regulated flow (Figure 5).…”

Long Term Monitoring and Connection between Topography and Cloud Cover Distribution in Serbia

“…This factor (index) varies according to the latitude, being 0.88 or (r2) in an equatorial climate, thus relatively higher than in the warm temperate belt (r2 = 0.74), whereas in arid regions it is lower (r2 = 0.46). Thanks to the estimation of this factor it is possible to find other climate elements at a global scale [53].…”