Estimation of Surface Turbulent Fluxes From Land Surface Moisture and Temperature Via a Variational Data Assimilation Framework

Abdolghafoorian, Abedeh; Farhadi, Leila

doi:10.1029/2018wr024580

Cited by 12 publications

(33 citation statements)

References 76 publications

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“…The LIDA for estimation of evaporative and turbulent heat fluxes is formulated based on the integrated VDA framework proposed by Abdolghafoorian and Farhadi (2019) but extended to work with remotely sensed surface soil moisture and LST data and remotely sensed and/or reanalysis forcing data of precipitation and incoming radiation. The VDA technique is based on the minimization of the cost function, J , which aggregates error between state estimates, X , and observations, X obs , as well as the error of the unknown parameters, Y , with respect to prior estimates, Y ′, over the assimilation interval, [t 0 t 1 ] as

\begin{matrix} lefttrue \\ J = \int_{t_{0}}^{t_{1}} {(][, M ()(, X) - X_{obs})}^{T} {boldC}_{X}^{- 1} [M (boldX) - {boldX}_{italicobs}] dt + {(][, boldY - boldY')}^{T} {boldC}_{Y}^{- 1} [Y - Y'] \\ + \int_{t_{0}}^{t_{1}} {bold-italicλ}^{T} [\frac{d X}{italicdt} - F (X, Y) - w] dt + \int_{t_{0}}^{t_{1}} \int_{t_{0}}^{t_{1}} w {()(, t)}^{T} {boldC}_{w}^{- 1} (t, t') w (t') dt dt' \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

“…The gradient descent method is used to perform the optimization, that is, the optimum value of parameters and estimate of model errors are obtained by setting the first variation of the cost function with respect to X , Y , and w equal to 0 ( δJ = 0). This leads to a set of the so‐called Euler‐Lagrange equations including adjoint models and a set of equations for updating the prior value of parameters, as well as a set of model error estimation equations (see Abdolghafoorian and Farhadi (2019), Equations 22–24).…”

Section: Methodsmentioning

confidence: 99%

“…In this method, N Y new cost functions are introduced, where N Y is the number of unknown parameters. The new cost functions, L Yi (where i = 1: N Y ), consist of the gradient of J with respect to Y i (i.e., ∂ J /∂ Y i ) and are constrained by the physical and adjoint models (see Abdolghafoorian and Farhadi (2019), Equations 25).…”

Section: Methodsmentioning

confidence: 99%

“…Analysis of the error covariance matrix and the correlation matrix between the parameters directs toward the formulation of a well‐posed estimation problem (Abdolghafoorian & Farhadi, 2016; Farhadi et al, 2014). Abdolghafoorian and Farhadi (2019) rigorously tested the feasibility and accuracy of their proposed framework at point scales through a set of synthetic experiments.…”

Section: Introductionmentioning

confidence: 99%

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LIDA: A Land Integrated Data Assimilation Framework for Mapping Land Surface Heat and Evaporative Fluxes by Assimilating Space‐Borne Soil Moisture and Land Surface Temperature

Abdolghafoorian

Farhadi

2020

Water Resources Research

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Land surface heat and evaporative fluxes exchanged between the land and atmosphere play a crucial role in the terrestrial water and energy balance. Regional mapping of these fluxes is hampered by the lack of in situ measurements (with the required coverage and duration) and the high spatial heterogeneity. In this paper, we propose a Land Integrated Data Assimilation framework (LIDA) based on the variational data assimilation technique to estimate the key parameters of surface heat and evaporative fluxes by jointly assimilating Soil Moisture Active Passive (SMAP) data and Geostationary Operational Environmental Satellite (GOES) surface temperature data into a coupled parsimonious land water and energy balance model. The method is implemented over an area of 31,500 km2 in the U.S. Southern Great Plains, and its performance is evaluated through consistency tests, comparison tests, and uncertainty analyses. The maps of retrieved heat and evaporative fluxes are used to analyze a range of feedback mechanisms in land‐atmosphere interaction, such as the dependence of evapotranspiration on vegetation and water availability.

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\begin{matrix} lefttrue \\ J = \int_{t_{0}}^{t_{1}} {(][, M ()(, X) - X_{obs})}^{T} {boldC}_{X}^{- 1} [M (boldX) - {boldX}_{italicobs}] dt + {(][, boldY - boldY')}^{T} {boldC}_{Y}^{- 1} [Y - Y'] \\ + \int_{t_{0}}^{t_{1}} {bold-italicλ}^{T} [\frac{d X}{italicdt} - F (X, Y) - w] dt + \int_{t_{0}}^{t_{1}} \int_{t_{0}}^{t_{1}} w {()(, t)}^{T} {boldC}_{w}^{- 1} (t, t') w (t') dt dt' \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

LIDA: A Land Integrated Data Assimilation Framework for Mapping Land Surface Heat and Evaporative Fluxes by Assimilating Space‐Borne Soil Moisture and Land Surface Temperature

Abdolghafoorian

Farhadi

2020

Water Resources Research

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“…Therefore, soil moisture assimilation has a powerful influence on the improvement in heat flux predictions. 107 When using predicted LST in surface energy balance models, the uncertainties in H estimation by VDA schemes are mainly due to errors in the C HN and LST estimates. Similarly, the uncertainties in λET are influenced by EF, C HN , and LST measurements.…”

Section: Data Assimilation Approachesmentioning

confidence: 99%

Review of approaches for the estimation of sensible heat flux in remote sensing-based evapotranspiration models

Mohan

Kanchirapuzha

Varma

2020

J. Appl. Rem. Sens.

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We recapitulate the approaches of sensible heat flux (H) estimation, which is a critical parameter in the remote sensing (RS)-based evapotranspiration (ET) models. We propose a classification scheme for the ET models considering their distinctions in approaches for the estimation of H. Adhering to the proposed classification scheme, the theoretical backgrounds of H estimation in the single-source and two-source RS-based ET models are discussed in brief, along with their unique characteristics. We addressed the role of critical parameters that influenced the H computation under each model and presented the related progress in the research. The importance of data assimilation techniques, as well as the application of unmanned aerial vehicles for the uninterrupted estimation of turbulent heat flux, are discussed in the context of single-source and two-source models. The influence of scale on the validation of the models and the impact of the aggregation methods are discussed. We compared the performance of the popular ET models for the estimation of H, utilizing the information obtained from peer-reviewed articles. The limitations related to the RS datasets in terms of spatial and temporal resolution and the scope of alleviating the shortcomings using the future satellite missions are discussed. We conclude by pointing toward the current challenges and the prospective domain of research, which needs to be addressed critically in the future.

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Assimilating synthetic land surface temperature in a coupled land–atmosphere model

Sgoff

Schomburg

Schmidli

et al. 2020

Quart J Royal Meteoro Soc

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A realistic simulation of the atmospheric boundary layer (ABL) depends on an accurate representation of the land-atmosphere coupling. Land surface temperature (LST) plays an important role in this context and the assimilation of LST can lead to improved estimates of the boundary layer and its processes. We assimilated synthetic satellite LST retrievals derived from a nature run as truth into a fully coupled, state-of-the-art land-atmosphere numeric weather prediction model. As assimilation system a local ensemble transform Kalman filter was used and the control vector was augmented by the soil temperature and humidity. To evaluate the concept of the augmented control vector, two-day case-studies with different control vector settings were conducted for clear-sky periods in March and August 2017. These experiments with hourly LST assimilation were validated against the nature run and overall, the RMSE of atmospheric and soil temperature of the first-guess (and analysis) were reduced. The temperature estimate of the ABL was particularly improved during daytime as was the estimate of the soil temperature during the whole diurnal cycle. The best impact of LST assimilation on the soil and the ABL was achieved with the augmented control vector. Through the coupling between the soil and the atmosphere, the assimilation of LST can have a positive impact on the temperature forecast of the ABL even after 15 hr because of the memory of the soil. These encouraging results motivate further work towards the assimilation of real satellite LST retrievals. K E Y W O R D S data assimilation, land-atmosphere coupling, land surface temperature, LETKF 1 INTRODUCTION The coupling between soil and atmosphere plays an important role for atmospheric boundary layer (ABL) development (

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Estimation of Surface Turbulent Fluxes From Land Surface Moisture and Temperature Via a Variational Data Assimilation Framework

Cited by 12 publications

References 76 publications

LIDA: A Land Integrated Data Assimilation Framework for Mapping Land Surface Heat and Evaporative Fluxes by Assimilating Space‐Borne Soil Moisture and Land Surface Temperature

LIDA: A Land Integrated Data Assimilation Framework for Mapping Land Surface Heat and Evaporative Fluxes by Assimilating Space‐Borne Soil Moisture and Land Surface Temperature

Review of approaches for the estimation of sensible heat flux in remote sensing-based evapotranspiration models

Assimilating synthetic land surface temperature in a coupled land–atmosphere model

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