TY - JOUR
T1 - Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication
AU - De Lannoy, Gabriëlle J.M.
AU - Bechtold, Michel
AU - Albergel, Clément
AU - Brocca, Luca
AU - Calvet, Jean Christophe
AU - Carrassi, Alberto
AU - Crow, Wade T.
AU - de Rosnay, Patricia
AU - Steele-Dunne, Susan
AU - More Authors, null
PY - 2022
Y1 - 2022
N2 - The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.
AB - The beginning of the 21st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.
KW - data assimilation
KW - land surface modeling
KW - microwave remote sensing
KW - snow
KW - soil moisture
KW - targeted observations
KW - vegetation
UR - http://www.scopus.com/inward/record.url?scp=85139259981&partnerID=8YFLogxK
U2 - 10.3389/frwa.2022.981745
DO - 10.3389/frwa.2022.981745
M3 - Review article
AN - SCOPUS:85139259981
SN - 2624-9375
VL - 4
JO - Frontiers in Water
JF - Frontiers in Water
M1 - 981745
ER -