Satellite and in situ observations for advancing global earth surface modelling
A review
Gianpaolo Balsamo (ECMWF)
Anna Agusti-Panareda (ECMWF)
Clement Albergel (Meteo France)
Gabriele Arduini (ECMWF)
Anton Beljaars (ECMWF)
Jean Bidlot (ECMWF)
Nicolas Bousserez (ECMWF)
Souhail Boussetta (ECMWF)
Andy Brown (ECMWF)
Roberto Buizza (ECMWF)
Carlo Buontempo (ECMWF)
Frederic Chevallier (Laboratoire des Sciences du Climat et de l'Environment)
Margarita Choulga (ECMWF)
Hannah Cloke (University of Reading)
Meghan F. Cronin (National Oceanic and Atmospheric Administration)
Mohamed Dahoui (ECMWF)
Patricia De Rosnay (ECMWF)
Paul A. Dirmeyer (Center for Ocean-Land-Atmosphere Studies)
Matthias Drusch (European Space Agency (ESA))
Emanuel Dutra (Universidade de Lisboa)
Michael B. Ek (University Corporation for Atmospheric Research)
Pierre Gentine (Columbia University)
Helene Hewitt (UK MetOffice)
Sarah P.E. Keeley (ECMWF)
Yann Kerr (Centre d’Études Spatiales de la Biosphère - Centre national de la recherche scientifique)
Sujay Kumar (NASA Goddard Space Flight Center)
Cristina Lupu (ECMWF)
Jean Francois Mahfouf (Meteo France)
Joe McNorton (ECMWF)
Susanne Mecklenburg (European Space Agency (ESA))
Kristian Mogensen (ECMWF)
Joaquín Muñoz-Sabater (ECMWF)
Rene Orth (Max Planck Institute for Biogeochemistry)
Florence Rabier (ECMWF)
Rolf Reichle (NASA Goddard Space Flight Center)
Ben Ruston (Naval Research Laboratory)
Florian Pappenberger (ECMWF)
Irina Sandu (ECMWF)
Sonia I. Seneviratne (ETH Zürich)
Steffen Tietsche (ECMWF)
Isabel F. Trigo (Instituto Português do Mar e da Amosfera (IPMA))
Remko Uijlenhoet (Wageningen University & Research)
Nils Wedi (ECMWF)
R. Iestyn Woolway (University of Reading)
Xubin Zeng (University of Arizona)
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Abstract
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.