Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

Details

• Journal Title:
  Remote Sensing
• Personal Author:
  Balsamo, Gianpaolo ; Agusti-Panareda, Anna ; Albergel, Clement ; Arduini, Gabriele ; Beljaars, Anton ; Bidlot, Jean ; Blyth, Eleanor ; Bousserez, Nicolas ; Boussetta, Souhail ; Brown, Andy ; Buizza, Roberto ; Buontempo, Carlo ; Chevallier, Frédéric ; Choulga, Margarita ; Cloke, Hannah ; Cronin, Meghan F. ; Dahoui, Mohamed ; De Rosnay, Patricia ; Dirmeyer, Paul A. ; Drusch, Matthias ; Dutra, Emanuel ; Ek, Michael B. ; Gentine, Pierre ; Hewitt, Helene ; Keeley, Sarah P.E. ; Kerr, Yann ; Kumar, Sujay ; Lupu, Cristina ; Mahfouf, Jean-François ; McNorton, Joe ; Mecklenburg, Susanne ; Mogensen, Kristian ; Muñoz-Sabater, Joaquín ; Orth, Rene ; Rabier, Florence ; Reichle, Rolf ; Ruston, Ben ; Pappenberger, Florian ; Sandu, Irina ; Seneviratne, Sonia I. ; Tietsche, Steffen ; Trigo, Isabel F. ; Uijlenhoet, Remko ; Wedi, Nils ; Woolway, R. Iestyn ; Zeng, Xubin
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; PMEL (Pacific Marine Environmental Laboratory)
• Description:
  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.
• Keywords:
  Atmospheric Science Remote Sensing Atmospheric Science Remote Sensing earth-observations | earth system modelling | direct and inverse methods | arctic sea-ice | numerical weather prediction | terrestrial chlorophyll | fluorescence | vegetation optical depth | radiative-transfer model | snow | water equivalent | soil-moisture product | pacific cold-tongue | methane | emissions | data assimilation | Remote Sensing
• Source:
  Remote Sensing, 10(12), 2038
• DOI:
  https://doi.org/10.3390/rs10122038
• Document Type:
  Journal Article
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Library
• Main Document Checksum:
  urn:sha256:3195dbfa503343fb43b19972e1c48447d8c0ef37046bfb8615c315d417215a4b
• Download URL:
  https://repository.library.noaa.gov/view/noaa/51291/noaa_51291_DS1.pdf
• File Type:
  [PDF - 22.51 MB ]