scholarly journals Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

2013 ◽  
Vol 35 (3) ◽  
pp. 577-606 ◽  
Author(s):  
Rolf H. Reichle ◽  
Gabriëlle J. M. De Lannoy ◽  
Barton A. Forman ◽  
Clara S. Draper ◽  
Qing Liu
Keyword(s):  
Data Assimilation ◽  
Water Cycle ◽  
Satellite Observations ◽  
Land Data Assimilation

The Optimality of Potential Rescaling Approaches in Land Data Assimilation

2013 ◽  
Vol 14 (2) ◽  
pp. 650-660 ◽  
Author(s):  
M. Tugrul Yilmaz ◽  
Wade T. Crow
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Least Squares ◽  
Optimal Solution ◽  
Least Squares Regression ◽  
Linear Least Squares ◽  
Optimal Analysis ◽  
Land Data Assimilation ◽  
Temporal Statistics ◽  
Hydrological Variables

Abstract It is well known that systematic differences exist between modeled and observed realizations of hydrological variables like soil moisture. Prior to data assimilation, these differences must be removed in order to obtain an optimal analysis. A number of rescaling approaches have been proposed for this purpose. These methods include rescaling techniques based on matching sampled temporal statistics, minimizing the least squares distance between observations and models, and the application of triple collocation. Here, the authors evaluate the optimality and relative performances of these rescaling methods both analytically and numerically and find that a triple collocation–based rescaling method results in an optimal solution, whereas variance matching and linear least squares regression approaches result in only approximations to this optimal solution.

scholarly journals Reviews and syntheses: Flying the satellite into your model: on the role of observation operators in constraining models of the Earth system and the carbon cycle

2017 ◽  
Vol 14 (9) ◽  
pp. 2343-2357 ◽  
Author(s):  
Thomas Kaminski ◽  
Pierre-Philippe Mathieu
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Automatic Differentiation ◽  
Satellite Observations ◽  
Spectral Radiance ◽  
Special Focus ◽  
Earth System ◽  
The Earth ◽  
Retrieval Scheme

Abstract. The vehicles that fly the satellite into a model of the Earth system are observation operators. They provide the link between the quantities simulated by the model and the quantities observed from space, either directly (spectral radiance) or indirectly estimated through a retrieval scheme (biogeophysical variables). By doing so, observation operators enable modellers to properly compare, evaluate, and constrain their models with the model analogue of the satellite observations. This paper provides the formalism and a few examples of how observation operators can be used in combination with data assimilation techniques to better ingest satellite products in a manner consistent with the dynamics of the Earth system expressed by models. It describes commonalities and potential synergies between assimilation and classical retrievals. This paper explains how the combination of observation operators and their derivatives (linearizations) form powerful research tools. It introduces a technique called automatic differentiation that greatly simplifies both the development and the maintenance of code for the evaluation of derivatives. Throughout this paper, a special focus lies on applications to the carbon cycle.

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