Spatial validation of large‐scale land surface models against monthly land surface temperature patterns using innovative performance metrics
| Autor: | Julian Koch, Amanda L. Siemann, Justin Sheffield, Simon Stisen |
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| Rok vydání: | 2016 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences 0208 environmental biotechnology Empirical orthogonal functions 02 engineering and technology 01 natural sciences 020801 environmental engineering Data set Geophysics Space and Planetary Science Evapotranspiration Earth and Planetary Sciences (miscellaneous) Spatial ecology Common spatial pattern Environmental science Spatial variability Scale (map) 0105 earth and related environmental sciences Remote sensing Hydrosphere |
| Zdroj: | Journal of Geophysical Research: Atmospheres. 121:5430-5452 |
| ISSN: | 2169-8996 2169-897X |
| Popis: | Land surface models (LSMs) are a key tool to enhance process understanding and to provide predictions of the terrestrial hydrosphere and its atmospheric coupling. Distributed LSMs predict hydrological states and fluxes, such as land surface temperature (LST) or actual evapotranspiration (aET), at each grid cell. LST observations are widely available through satellite remote sensing platforms that enable comprehensive spatial validations of LSMs. In spite of the great availability of LST data, most validation studies rely on simple cell to cell comparisons and thus do not regard true spatial pattern information. The core novelty of this study is the development and application of two innovative spatial performance metrics, namely, empirical orthogonal function (EOF) and connectivity analyses, to validate predicted LST patterns by three LSMs (Mosaic, Noah, Variable Infiltration Capacity (VIC)) over the contiguous United States. The LST validation data set is derived from global High-Resolution Infrared Radiometric Sounder retrievals for a30 year period. The metrics are bias insensitive, which is an important feature in order to truly validate spatial patterns. The EOF analysis evaluates the spatial variability and pattern seasonality and attests better performance to VIC in the warm months and to Mosaic and Noah in the cold months. Further, more than 75% of the LST variability can be captured by a single pattern that is strongly correlated to air temperature.The connectivity analysis assesses the homogeneity and smoothness of patterns. The LSMs are most reliable at predicting cold LST patterns in the warm months and vice versa. Lastly, the coupling between aET and LST is investigated at flux tower sites and compared against LSMs to explain the identified LST shortcomings |
| Databáze: | OpenAIRE |
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