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

2019 ◽  
Author(s):  
Abedeh Abdolghafoorian ◽  
Leila Farhadi
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Turbulent Fluxes ◽  
Variational Data Assimilation ◽  
Surface Moisture

scholarly journals A Comparison of the Triangle Retrieval and Variational Data Assimilation Methods for Surface Turbulent Flux Estimation

2005 ◽  
Vol 6 (6) ◽  
pp. 1063-1072 ◽  
Author(s):  
Steven A. Margulis ◽  
Jongyoun Kim ◽  
Terri Hogue
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Land Surface ◽  
Measurement Errors ◽  
Structural Model ◽  
Turbulent Fluxes ◽  
Surface Fluxes ◽  
Variational Data Assimilation ◽  
Model Errors

Abstract Future operational frameworks for estimating surface turbulent fluxes over the necessary spatial and temporal scales will undoubtedly require the use of remote sensing products. Techniques used to estimate surface fluxes from radiometric surface temperature generally fall into two categories: retrieval-based and data assimilation approaches. Up to this point, there has been little comparison between retrieval- and assimilation-based techniques. In this note, the triangle retrieval method is compared to a variational data assimilation approach for estimating surface turbulent fluxes from radiometric surface temperature observations. Results from a set of synthetic experiments and an application using real data from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site indicate that the assimilation approach performs slightly better than the triangle method because of the robustness of the estimation to measurement errors and parsimony of the system model, which leads to fewer sources of structural model errors. Future comparison work using retrieval and data assimilation algorithms will provide more insight into the optimal approach for diagnosis of land surface fluxes using remote sensing observations.

scholarly journals Land-atmosphere interactions in an high resolution atmospheric simulation coupled with a surface data assimilation scheme

2009 ◽  
Vol 9 (5) ◽  
pp. 1613-1624 ◽  
Author(s):  
L. Campo ◽  
F. Castelli ◽  
D. Entekhabi ◽  
F. Caparrini
Keyword(s):  
Energy Balance ◽  
Data Assimilation ◽  
Surface Energy ◽  
Energy Budget ◽  
Land Surface ◽  
Surface States ◽  
Atmospheric Model ◽  
Turbulent Fluxes ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

Abstract. A valid tool for the retrieving of the turbulent fluxes that characterize the surface energy budget is constituted by the remote sensing of land surface states. In this study sequences of satellite-derived observations (from SEVIRI sensors aboard the Meteosat Second Generation) of Land Surface Temperature have been used as input in a data assimilation scheme in order to retrieve parameters that describe energy balance at the ground surface in the Tuscany region, in central Italy, during summer 2005. A parsimonious 1-D multiscale variational assimilation procedure has been followed, that requires also near surface meteorological observations. A simplified model of the surface energy balance that includes such assimilation scheme has been coupled with the limited area atmospheric model RAMS, in order to improve in the latter the accuracy of the energy budget at the surface. The coupling has been realized replacing the assimilation scheme products, in terms of surface turbulent fluxes and temperature and humidity states during the meteorological simulation. Comparisons between meteorological model results with and without coupling with the assimilation scheme are discussed, both in terms of reconstruction of surface variables and of vertical characterization of the lower atmosphere. In particular, the effects of the coupling on the moisture feedback between surface and atmosphere are considered and estimates of the precipitation recycling ratio are provided. The results of the coupling experiment showed improvements in the reconstruction of the surface states by the atmospheric model and considerable influence on the atmospheric dynamics.

scholarly journals Partitioning Evapotranspiration into Soil Evaporation and Canopy Transpiration via a Two-Source Variational Data Assimilation System

2016 ◽  
Vol 17 (9) ◽  
pp. 2353-2370 ◽  
Author(s):  
Tongren Xu ◽  
Sayed M. Bateni ◽  
Steven A. Margulis ◽  
Lisheng Song ◽  
Shaomin Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Soil Evaporation ◽  
Variational Data Assimilation ◽  
Heat Diffusion ◽  
Heihe River ◽  
Source Surface ◽  
Heat Diffusion Equation ◽  
Canopy Transpiration ◽  
Mean Square Errors

Abstract The primary objective of this study is to assess the accuracy of the two-source variational data assimilation (TVDA) system for partitioning evapotranspiration (ET) into soil evaporation (ETS) and canopy transpiration (ETC). Its secondary aim is to compare performance of the TVDA system with the commonly used two-source surface energy balance (TSEB) method. A combination of eddy-covariance-based ET observations and stable-isotope-based measurements of the ratio of evaporation and transpiration to total evapotranspiration (ETS/ET and ETC/ET) over an irrigated cropland site (the so-called Daman site) in the middle reach of the Heihe River basin (northwestern China) was used to investigate these objectives. The results indicate that the TVDA method predicts ETS and ETC more accurately than TSEB. Root-mean-square errors (RMSEs) of midday (1300–1500 LT) averaged soil and canopy latent heat flux (LES and LEC) estimates from TVDA are 23.1 and 133.0 W m−2, respectively. Corresponding RMSE values from TSEB are 41.9 and 156.0 W m−2. Compared to TSEB, the TVDA method takes advantage of all of the information in land surface temperature observations in the estimation period by leveraging a dynamic model (the heat diffusion equation) and thus can generate more accurate LES and LEC estimates.

scholarly journals The Land Variational Ensemble Data Assimilation fRamework: LaVEnDAR

2019 ◽  
Author(s):  
Ewan Pinnington ◽  
Tristan Quaife ◽  
Amos Lawless ◽  
Karina Williams ◽  
Tim Arkebauer ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Time Window ◽  
Variational Data Assimilation ◽  
Surface Model ◽  
State Variables ◽  
Maize Crop ◽  
Area Index ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

Abstract. The Land Variational Ensemble Data Assimilation fRamework (LaVEnDAR) implements the method of Four-Dimensional Ensemble Variational data assimilation for land surface models. Four-Dimensional Ensemble Variational data assimilation negates the often costly calculation of a model adjoint required by traditional variational techniques (such as 4DVar) for optimising parameters/state variables over a time window of observations. In this paper we implement LaVEnDAR with the JULES land surface model. We show the system can recover seven parameters controlling crop behaviour in a set of twin experiments. We run the same experiments at the Mead continuous maize FLUXNET site in Nebraska, USA to show the technique working with real data. We find that the system accurately captures observations of leaf area index, canopy height and gross primary productivity after assimilation and improves posterior estimates of the amount of harvestable material from the maize crop by 74 %. LaVEnDAR requires no modification to the model that it is being used with and is hence able to keep up to date with model releases more easily than other data assimilation methods.

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