Identification

Title

A microwave land data assimilation system: Scheme and preliminary evaluation over China

Abstract

To make use of satellite microwave observations for estimating soil moisture, a dual-pass land data assimilation system (DLDAS) is developed in this paper by incorporating a dual-pass assimilation framework into the Community Land Model version 3 (CLM3). In the DLDAS, the model state (volumetric soil moisture content) and model parameters are jointly optimized using the gridded Advanced Microwave Scanning Radiometer-EOS (AMSR-E) satellite brightness temperature (Tb) data through a radiative transfer model (RTM), which acts as an observation operator to provide a link between the model states and the observational variable (i.e., Tb). The DLDAS embeds a state assimilation pass and a parameter calibration pass. In the assimilation pass, the whole soil moisture profiles are assimilated from the Tb data using an ensemble-based four-dimensional variational assimilation method (En4DVar). Simultaneously, several key parameters in the RTM are also optimized using the ensemble Proper Orthogonal Decomposition-based parameter calibration approach (EnPOD_P) in the parameter optimization pass to account for their high variability or uncertainty. To quantify the impacts of the Tb assimilation on CLM3-calculated soil moisture, the original CLM3 (Sim) and the DLDAS (Ass) were run separately over China on a 0.5° grid forced with identical, observation-based atmospheric forcing from 2004 to 2008. Soil moisture data from 226 stations over China are averaged over seven different climate divisions and compared with the soil moisture from the Sim and Ass runs. It is found that the assimilation of the AMSR-E Tb data through the DLDAS greatly improves the soil moisture content within the top 10 cm with reduced mean biases and enhanced correlations with the station data in all divisions except for southwest China, where the current satellite sensors may have difficulties in measuring soil moisture due to the dense vegetation and complex terrain over this region. The results suggest that the AMSR-E Tb data can be used to improve soil moisture simulations over many regions and the DLDAS is a promising new tool for estimating soil moisture content from satellite Tb data.

Resource type

document

Resource locator

Unique resource identifier

code

https://n2t.org/ark:/85065/d77d2vmm

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2010-11-06T00:00:00Z

Frequency of update

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Conformity

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version of format

Constraints related to access and use

Constraint set

Use constraints

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact