Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
StandardStandard
Yn: Remote Sensing, Cyfrol 10, Rhif 12, 14.12.2018.
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
HarvardHarvard
APA
CBE
MLA
VancouverVancouver
Author
RIS
TY - JOUR
T1 - Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review
AU - Balsamo, Gianpaolo
AU - Agusti-Parareda, Anna
AU - Albergel, Clement
AU - Arduini, Gabriele
AU - Beljaars, Anton
AU - Bidlot, Jean
AU - Bousserez, Nicolas
AU - Boussetta, Souhail
AU - Brown, Andy
AU - Buizza, Roberto
AU - Buontempo, Carlo
AU - Chevallier, Frederic
AU - Choulga, Margarita
AU - Cloke, Hannah
AU - Cronin, Meghan F.
AU - Dahoui, Mohamed
AU - De Rosnay, Patricia
AU - Dirmeyer, Paul A.
AU - Drusch, Matthias
AU - Dutra, Emanuel
AU - Ek, Michael B.
AU - Gentine, Pierre
AU - Hewitt, Helene
AU - Keeley, Sarah P. E.
AU - Kerr, Yann
AU - Kumar, Sujay
AU - Lupu, Cristina
AU - Mahfouf, Jean-Francois
AU - McNorton, Joe
AU - Mecklenburg, Susanne
AU - Mogensen, Kristian
AU - Munoz-Sabater, Joaquin
AU - Orth, Rene
AU - Rabier, Florence
AU - Reichle, Rolf
AU - Ruston, Ben
AU - Pappenberger, Florian
AU - Sandu, Irina
AU - Seneviratne, Sonia I.
AU - Tietsche, Steffen
AU - Trigo, Isabel F.
AU - Uijlenhoet, Remko
AU - Wedi, Nils
AU - Woolway, R. Iestyn
AU - Zeng, Xubin
PY - 2018/12/14
Y1 - 2018/12/14
N2 - In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.
AB - In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.
KW - earth-observations
KW - earth system modelling
KW - direct and inverse methods
U2 - 10.3390/rs10122038
DO - 10.3390/rs10122038
M3 - Article
VL - 10
JO - Remote Sensing
JF - Remote Sensing
SN - 2072-4292
IS - 12
ER -