Estimation of surface heat fluxes via variational assimilation of land surface temperature, air temperature and specific humidity into a coupled land surface-atmospheric boundary layer model

Tajfar, E.; Bateni, Sayed M.; Lakshmi, Venkataraman; Ek, Michael

doi:10.1016/j.jhydrol.2020.124577

Cited by 27 publications

(30 citation statements)

References 109 publications

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“…Ri is a measure of atmospheric stability. The stability correction function (f ) proposed by Caparrini et al [45] performed well in previous studies [49][50][51]53,57,[81][82][83], and thus was used in this study. It is given by f (Ri) = 1 + 2(1 -e 10Ri ).…”

Section: Sensible and Latent Heat Fluxesmentioning

confidence: 92%

“…It is worth mentioning that the assumption of daily constant EF and monthly constant C HN are widely used in VDA studies [42,44,46,47,49,50,53,55,56,[81][82][83] to estimate H and LE.…”

Section: Sensible and Latent Heat Fluxesmentioning

confidence: 99%

“…A mixed-layer model, which performs well compared to the complicated large eddy simulation models, is used to simulate ABL processes [81][82][83]90,91]. The profiles of potential temperature (θ) and specific humidity (q) are assumed to be constant with height throughout the mixed layer.…”

Section: Atmospheric Boundary Layer (Abl) Modelmentioning

confidence: 99%

“…As shown in Figure 1, the thin layer between the ground and the mixed layer is called the surface layer (SL). The surface layer is assumed to be 10% of the mixed-layer top, h (i.e., z SL = 0.1h), and is convectively unstable during the growth phase of the boundary layer [73,79,81,83].…”

Section: Atmospheric Boundary Layer (Abl) Modelmentioning

confidence: 99%

“…where σ is the Stefan-Boltzman constant, ε ad is the effective emissivity above the mixed layer [73,81,83,[94][95][96], T h+ is the air temperature exactly above the mixed layer [95,96], and ε s is the surface emissivity.…”

Section: Radiative Fluxesmentioning

confidence: 99%

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Feasibility of Estimating Turbulent Heat Fluxes via Variational Assimilation of Reference-Level Air Temperature and Specific Humidity Observations

et al. 2020

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This study investigated the feasibility of partitioning the available energy between sensible (H) and latent (LE) heat fluxes via variational assimilation of reference-level air temperature and specific humidity. For this purpose, sequences of reference-level air temperature and specific humidity were assimilated into an atmospheric boundary layer model (ABL) within a variational data assimilation (VDA) framework to estimate H and LE. The VDA approach was tested at six sites (namely, Arou, Audubon, Bondville, Brookings, Desert, and Willow Creek) with contrasting climatic and vegetative conditions. The unknowns of the VDA system were the neutral bulk heat transfer coefficient (CHN) and evaporative fraction (EF). EF estimates were found to agree well with observations in terms of magnitude and day-to-day fluctuations in wet/densely vegetated sites but degraded in dry/sparsely vegetated sites. Similarly, in wet/densely vegetated sites, the variations in the CHN estimates were found to be consistent with those of the leaf area index (LAI) while this consistency deteriorated in dry/sparely vegetated sites. The root mean square errors (RMSEs) of daily H and LE estimates at the Arou site (wet) were 25.43 (Wm−2) and 55.81 (Wm−2), which are respectively 57.6% and 45.4% smaller than those of 60.00 (Wm−2) and 102.21 (Wm−2) at the Desert site (dry). Overall, the results show that the VDA system performs well at wet/densely vegetated sites (e.g., Arou and Willow Creek), but its performance degrades at dry/slightly vegetated sites (e.g., Desert and Audubon). These outcomes show that the sequences of reference-level air temperature and specific humidity have more information on the partitioning of available energy between the sensible and latent heat fluxes in wet/densely vegetated sites than dry/slightly vegetated sites.

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Section: Sensible and Latent Heat Fluxesmentioning

confidence: 92%

“…It is worth mentioning that the assumption of daily constant EF and monthly constant C HN are widely used in VDA studies [42,44,46,47,49,50,53,55,56,[81][82][83] to estimate H and LE.…”

Section: Sensible and Latent Heat Fluxesmentioning

confidence: 99%

Section: Atmospheric Boundary Layer (Abl) Modelmentioning

confidence: 99%

Section: Atmospheric Boundary Layer (Abl) Modelmentioning

confidence: 99%

Section: Radiative Fluxesmentioning

confidence: 99%

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Feasibility of Estimating Turbulent Heat Fluxes via Variational Assimilation of Reference-Level Air Temperature and Specific Humidity Observations

et al. 2020

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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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A Machine Learning-Based Model for Predicting Temperature Under the Effects of Climate Change

Shams¹,

Tarek²,

Elshewey³

et al. 2023

Studies in Big Data

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Estimation of surface heat fluxes via variational assimilation of land surface temperature, air temperature and specific humidity into a coupled land surface-atmospheric boundary layer model

Cited by 27 publications

References 109 publications

Feasibility of Estimating Turbulent Heat Fluxes via Variational Assimilation of Reference-Level Air Temperature and Specific Humidity Observations

Feasibility of Estimating Turbulent Heat Fluxes via Variational Assimilation of Reference-Level Air Temperature and Specific Humidity Observations

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

A Machine Learning-Based Model for Predicting Temperature Under the Effects of Climate Change

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