A Reduced-Adjoint Variational Data Assimilation for Estimating Soil Moisture Profile from Surface Soil Moisture Observations

Parisa Heidary; Leila Farhadi; Muhammad Altaf

doi:10.1109/IGARSS47720.2021.9554864

A Reduced-Adjoint Variational Data Assimilation for Estimating Soil Moisture Profile from Surface Soil Moisture Observations

Parisa Heidary, Leila Farhadi, Muhammad Altaf

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English (US)
Title of host publication	2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Publisher	IEEE
ISBN (Print)	978-1-6654-4762-1
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9554864
State	Published - Oct 12 2021

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10.1109/IGARSS47720.2021.9554864

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title = "A Reduced-Adjoint Variational Data Assimilation for Estimating Soil Moisture Profile from Surface Soil Moisture Observations",

abstract = "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.",

author = "Parisa Heidary and Leila Farhadi and Muhammad Altaf",

note = "KAUST Repository Item: Exported on 2022-05-18",

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doi = "10.1109/IGARSS47720.2021.9554864",

language = "English (US)",

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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.

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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 -

A Reduced-Adjoint Variational Data Assimilation for Estimating Soil Moisture Profile from Surface Soil Moisture Observations

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