Review of paper: "Land surface parameter optimisation through data assimilation: the adJULES system"

2016 ◽  
Author(s):  
Anonymous
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Surface Parameter ◽  
Parameter Optimisation ◽  
Land Surface Parameter

scholarly journals Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

2016 ◽  
Vol 9 (8) ◽  
pp. 2833-2852 ◽  
Author(s):  
Nina M. Raoult ◽  
Tim E. Jupp ◽  
Peter M. Cox ◽  
Catherine M. Luke
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Gross Primary Production ◽  
Land Surface Model ◽  
Weather Forecast ◽  
Surface Model ◽  
Land Surface Parameter ◽  
Estimation System ◽  
Using Data ◽  
The Uk

Abstract. Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate–carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model–data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85 % of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter.

scholarly journals Optimally Merging Precipitation to Minimize Land Surface Modeling Errors

2010 ◽  
Vol 49 (3) ◽  
pp. 415-423 ◽  
Author(s):  
M. Tugrul Yilmaz ◽  
Paul Houser ◽  
Roshan Shrestha ◽  
Valentine G. Anantharaj
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Modeling ◽  
Surface Model ◽  
Surface Parameter ◽  
Land Surface Modeling ◽  
Precipitation Product ◽  
Land Surface Parameter ◽  
Modeling Errors

Abstract This paper introduces a new method to improve land surface model skill by merging different available precipitation datasets, given that an accurate land surface parameter ground truth is available. Precipitation datasets are merged with the objective of improving terrestrial water and energy cycle simulation skill, unlike most common methods in which the merging skills are evaluated by comparing the results with gauge data or selected reference data. The optimal merging method developed in this study minimizes the simulated land surface parameter (soil moisture, temperature, etc.) errors using the Noah land surface model with the Nelder–Mead (downhill simplex) method. In addition to improving the simulation skills, this method also impedes the adverse impacts of single-source precipitation data errors. Analysis has indicated that the results from the optimally merged precipitation product have fewer errors in other land surface states and fluxes such as evapotranspiration (ET), discharge R, and skin temperature T than do simulation results obtained by forcing the model using the precipitation products individually. It is also found that, using this method, the true knowledge of soil moisture information minimized land surface modeling errors better than the knowledge of other land surface parameters (ET, R, and T). Results have also shown that, although it does not have the true precipitation information, the method has associated heavier weights with the precipitation product that has intensity, amount, and frequency that are similar to those of the true precipitation.

Regularized kernel-based BRDF model inversion method for ill-posed land surface parameter retrieval

2007 ◽  
Vol 111 (1) ◽  
pp. 36-50 ◽  
Author(s):  
Yanfei Wang ◽  
Xiaowen Li ◽  
Zuhair Nashed ◽  
Feng Zhao ◽  
Hua Yang ◽  
...  
Keyword(s):  
Land Surface ◽  
Inversion Method ◽  
Surface Parameter ◽  
Model Inversion ◽  
Land Surface Parameter ◽  
Ill Posed ◽  
Parameter Retrieval
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