A Reduced-Adjoint Variational Data Assimilation for Estimating Soil Moisture Profile from Surface Soil Moisture Observations
Type
Conference PaperKAUST Department
Biological and Environmental Science and Engineering (BESE) DivisionKing Abdullah University of Science and Technology
Water Desalination and Reuse Research Center (WDRC)
Date
2021-10-12Permanent link to this record
http://hdl.handle.net/10754/677980
Metadata
Show full item recordAbstract
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.Citation
Heidary, P., Farhadi, L., & Altaf, M. U. (2021). A Reduced-Adjoint Variational Data Assimilation for Estimating Soil Moisture Profile from Surface Soil Moisture Observations. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. https://doi.org/10.1109/igarss47720.2021.9554864Publisher
IEEEConference/Event name
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSSISBN
978-1-6654-4762-1Additional Links
https://ieeexplore.ieee.org/document/9554864/https://ieeexplore.ieee.org/document/9554864/
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9554864
ae974a485f413a2113503eed53cd6c53
10.1109/IGARSS47720.2021.9554864