Analysis of hydrological spatial and temporal characteristic scales over the Contiguous United States using GOES-16 Land Surface Temperature retrievals

Torres-Rojas, Laura; Waterman, Tyler; Cai, Jiaxuan; Chaney, Nathaniel W.

doi:10.1002/essoar.10511740.1

Cited by 1 publication

(1 citation statement)

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“…The GOES-16 LST and SST products used in this study are freely available from NOAA's Comprehensive Large Array-Data Stewardship System (CLASS). The data that support the findings of this study, including the scripts to reproject the original data to a WSG84 projection, combine LST and SST products for the CONUS region, merge individual hourly files into weekly netCDF4 files, extract the data for 1°x1° domains over CONUS, compute the daytime summer ESTCF for those domains, and analyze the results (i.e., mapping and clustering) are preserved at https://doi.org/10.5281/zenodo.8428629 (Torres-Rojas & Chaney, 2023).…”

Section: Open Researchsupporting

confidence: 62%

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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Section: Open Researchsupporting

confidence: 62%