Effect of land–atmosphere process parameterizations on the PM simulation of a river valley city with complex topography

Wang, Ying; Líu, Hao; Liu, Yang; Wang, Sitong; Wang, Lixia; Li, Xuechao

doi:10.1016/j.atmosres.2022.106505

Cited by 2 publications

(1 citation statement)

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“…Processes at the land-atmosphere interface are mainly driven by water and energy fluxes at the land surface, which are mediated by vegetation (Koch et al, 2017;Simon et al, 2021), land use, and topography. These surface fluxes have proved to control the overlying atmospheric distributions of water vapor, temperature, precipitation, and cloud properties, modulating the hydrological cycle and the surface energy budget (Dickinson, 1995;Dirmeyer et al, 2013;Y. Wang et al, 2023;Wu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The observed spatio-temporal patterns of Land surface temperature over the Contiguous United States

Torres-Rojas,

Waterman,

Cai

et al. 2023

Preprint

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Surface fluxes and their related processes and states tend to recur and remain consistent across various spatial and temporal scales forming patterns. For multiple applications, identifying spatio-temporal patterns is desirable, as they provide information about the dynamics of the processes involved. This is especially true for land surface temperature, a key variable that plays a primary role in the energy and water exchange between land and atmosphere. This study introduces the Empirical Spatio-Temporal Covariance Function (ESTCF) as a tool to identify and characterize spatio-temporal patterns in remotely sensed land surface temperature fields. The method is demonstrated over the Contiguous United States by splitting the entire area into 1.0°x1.0° domains. The summer day-time surface temperature ESTCFs are derived for each domain, and a parametric covariance model is fitted. Clustering analysis is then applied to detect areas with similar spatio-temporal land surface temperature dynamics. The results are assessed to determine if particular spatio-temporal features are present in domains where landscape characteristics make interactions with the atmosphere likely. The proposed tool accurately characterizes the spatio-temporal interdependence of the fields, summarizing features such as spatio-temporal variance, spatial coherence structure, temporal persistence, and space-time interactions. The increased temporal persistence and space-time interaction drive the grouping in mountainous and coastal domains. The tools introduced here provide a pathway to formally identify and summarize the spatio-temporal patterns observed in remotely sensed fields and relate those to more complex processes within the Soil-Vegetation-Atmosphere System.

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