A simplified land data assimilation scheme and its application to soil moisture experiments in 2002 (SMEX02)

2003 ◽  
Vol 39 (12) ◽  
Author(s):  
Mahadevan Pathmathevan ◽  
Toshio Koike ◽  
Xin Li ◽  
Hideyuki Fujii
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Data Assimilation Scheme ◽  
Land Data Assimilation ◽  
Assimilation Scheme

A 3DVAR land data assimilation scheme: Part 2. Test with ECMWF ERA-40

2006 ◽  
Author(s):  
Lanjun Zou ◽  
Wei Gao ◽  
Tongwen Wu ◽  
Xiaofeng Xu ◽  
Bingyu Du ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Data Assimilation Scheme ◽  
Land Data Assimilation ◽  
Assimilation Scheme

A 3DVAR land data assimilation scheme Part 1: Mathematical design

2006 ◽  
Author(s):  
Lanjun Zou ◽  
Wei Gao ◽  
Tongwen Wu ◽  
Xiaofeng Xu ◽  
Bingyu Du ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Data Assimilation Scheme ◽  
Land Data Assimilation ◽  
Assimilation Scheme ◽  
Mathematical Design

An Indirect Data Assimilation Scheme for Deep Soil Temperature in the Pleim–Xiu Land Surface Model

2009 ◽  
Vol 48 (7) ◽  
pp. 1362-1376 ◽  
Author(s):  
Jonathan E. Pleim ◽  
Robert Gilliam
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Deep Soil ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

Abstract The Pleim–Xiu land surface model (PX LSM) has been improved by the addition of a second indirect data assimilation scheme. The first, which was described previously, is a technique in which soil moisture is nudged according to the biases in 2-m air temperature and relative humidity between the model- and observation-based analyses. The new technique involves nudging the deep soil temperature in the soil temperature force–restore (FR) model according to model bias in 2-m air temperature only during nighttime. While the FR technique is computationally efficient and very accurate for the special conditions for which it was derived, it is very dependent on the deep soil temperature that drives the restoration term of the surface soil temperature equation. Thus, adjustment of the deep soil temperature to optimize the 2-m air temperature during the night, when surface forcing is minimal, provides significant advantages over other methods of deep soil moisture initialization. Simulations of the Weather Research and Forecasting Model (WRF) using the PX LSM with and without the new deep soil temperature nudging scheme demonstrate substantial benefits of the new scheme for reducing error and bias of the 2-m air temperature. The effects of the new nudging scheme are most pronounced in the winter (January 2006) during which the model’s cold bias is greatly reduced. Air temperature error and bias are also reduced in a summer simulation (August 2006) with the greatest benefits in less vegetated and more arid regions. Thus, the deep temperature nudging scheme complements the soil moisture nudging scheme because it is most effective for conditions in which the soil moisture scheme is least effective, that is, when evapotranspiration is not important (winter and arid climates).

A nonparametric sequential data assimilation scheme for soil moisture flow

2021 ◽  
Vol 593 ◽  
pp. 125865
Author(s):  
Yakun Wang ◽  
Liangsheng Shi ◽  
Tianfang Xu ◽  
Qiuru Zhang ◽  
Ming Ye ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Sequential Data ◽  
Moisture Flow ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme ◽  
Sequential 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 Modeling of the Melting Layer. Part IV: Brightband Bulk Parameterization

2008 ◽  
Vol 65 (6) ◽  
pp. 1991-2001 ◽  
Author(s):  
Catherine Heyraud ◽  
Wanda Szyrmer ◽  
Stéphane Laroche ◽  
Isztar Zawadzki
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
Melting Layer ◽  
Observation Operator ◽  
Data Assimilation Scheme ◽  
Bulk Model ◽  
Assimilation Scheme ◽  
Bulk Parameterization ◽  
Good Agreement ◽  
Extract Information

Abstract In this paper a simplified UHF-band backscattering parameterization for individual melting snowflakes is proposed. This parameterization is a function of the density, shape, and melted fraction, and is used here in a brightband bulk modeling study. A 1D bulk model is developed where aggregation and breakup are neglected. Model results are in good agreement with detailed bin-model results and simulate the radar brightband observations well. It is shown the model can be seen as an observation operator that could be introduced into a data assimilation scheme to extract information contained in the radar data measurements.

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