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Spatial validation of large-scale land surface models against monthly land surface temperature patterns using innovative performance metrics

Spatial validation of large-scale land surface models against monthly land surface temperature patterns using innovative performance metrics
Spatial validation of large-scale land surface models against monthly land surface temperature patterns using innovative performance metrics
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 a
30 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
2169-8996
1-65
Koch, Julian
cd3ad6ad-7f37-43e5-8c55-78968e6ed46e
Siemann, Amanda
84c9ac23-73b3-4bf9-9dc1-a1c6bff1dd42
Stisen, Simon
764b4acb-d20f-41fd-8696-08203aa0556d
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Koch, Julian
cd3ad6ad-7f37-43e5-8c55-78968e6ed46e
Siemann, Amanda
84c9ac23-73b3-4bf9-9dc1-a1c6bff1dd42
Stisen, Simon
764b4acb-d20f-41fd-8696-08203aa0556d
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b

Koch, Julian, Siemann, Amanda, Stisen, Simon and Sheffield, Justin (2016) Spatial validation of large-scale land surface models against monthly land surface temperature patterns using innovative performance metrics. Journal of Geophysical Research: Atmospheres, 1-65. (doi:10.1002/2015JD024482).

Record type: Article

Abstract

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 a
30 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

Text
NLDAS_LST_juko_review.pdf - Accepted Manuscript
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More information

Accepted/In Press date: 5 May 2016
e-pub ahead of print date: 21 May 2016
Organisations: Global Env Change & Earth Observation, Geography & Environment

Identifiers

Local EPrints ID: 397504
URI: https://eprints.soton.ac.uk/id/eprint/397504
ISSN: 2169-8996
PURE UUID: b2b81cc6-f3cb-4d71-8eeb-1fd10ded13db
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

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Date deposited: 04 Jul 2016 07:55
Last modified: 10 Dec 2019 01:32

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