TY - GEN
T1 - A Reduced-Adjoint Variational Data Assimilation for Estimating Soil Moisture Profile from Surface Soil Moisture Observations
AU - Heidary, Parisa
AU - Farhadi, Leila
AU - Altaf, Muhammad
N1 - KAUST Repository Item: Exported on 2022-05-18
PY - 2021/10/12
Y1 - 2021/10/12
N2 - Soil moisture plays an important role in the global water cycle and has an important impact on weather and climate, energy fluxes at the land surface, hence, the accurate estimation of this state variable is important. In this work, the potential of using surface soil moisture measurements (e.g. satellite soil moisture) to retrieve initial soil moisture profile will be explored in a synthetic study, using a reduced-adjoint variational data assimilation (hereafter RA- VDA) and a nonlinear 1D-soil water model (HYDRUS-1D). The proposed RA-VDA applies the Proper Orthogonal Decomposition (POD) technique to approximate the adjoint model in the reduced space. The accuracy and performance of the proposed RA-VDA method is illustrated with different synthetic experiments in a nonlinear physical model.
AB - Soil moisture plays an important role in the global water cycle and has an important impact on weather and climate, energy fluxes at the land surface, hence, the accurate estimation of this state variable is important. In this work, the potential of using surface soil moisture measurements (e.g. satellite soil moisture) to retrieve initial soil moisture profile will be explored in a synthetic study, using a reduced-adjoint variational data assimilation (hereafter RA- VDA) and a nonlinear 1D-soil water model (HYDRUS-1D). The proposed RA-VDA applies the Proper Orthogonal Decomposition (POD) technique to approximate the adjoint model in the reduced space. The accuracy and performance of the proposed RA-VDA method is illustrated with different synthetic experiments in a nonlinear physical model.
UR - http://hdl.handle.net/10754/677980
UR - https://ieeexplore.ieee.org/document/9554864/
U2 - 10.1109/IGARSS47720.2021.9554864
DO - 10.1109/IGARSS47720.2021.9554864
M3 - Conference contribution
SN - 978-1-6654-4762-1
BT - 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
PB - IEEE
ER -