Retrieving soil temperature profile by assimilating MODIS LST products with ensemble Kalman filter

Abstract Proper estimation of initial state variables and model parameters are vital importance for determining the accuracy of numerical model prediction. In this work, we develop a one-dimensional land data assimilation scheme based on ensemble Kalman filter and Common Land Model version 3.0 (CoLM). This scheme is used to improve the estimation of soil temperature profile. The leaf area index (LAI) is also updated dynamically by MODIS LAI production and the MODIS land surface temperature (LST) products are assimilated into CoLM. The scheme was tested and validated by observations from four automatic weather stations (BTS, DRS, MGS, and DGS) in Mongolian Reference Site of CEOP during the period of October 1, 2002 to September 30, 2003. Results indicate that data assimilation improves the estimation of soil temperature profile about 1 K. In comparison with simulation, the assimilation results of soil heat fluxes also have much improvement about 13 W m− 2 at BTS and DGS and 2 W m− 2 at DRS and MGS, respectively. In addition, assimilation of MODIS land products into land surface model is a practical and effective way to improve the estimation of land surface variables and fluxes.

[1]  H. Madsen,et al.  Bias aware Kalman filters: Comparison and improvements , 2006 .

[2]  Jeffrey P. Walker,et al.  Extended versus Ensemble Kalman Filtering for Land Data Assimilation , 2002 .

[3]  V. Lakshmi,et al.  A simple surface temperature assimilation scheme for use in land surface models , 2000 .

[4]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[5]  Z. Wan,et al.  Quality assessment and validation of the MODIS global land surface temperature , 2004 .

[6]  Dennis McLaughlin,et al.  Recent developments in hydrologic data assimilation , 1995 .

[7]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[8]  R. Dickinson,et al.  The Common Land Model , 2003 .

[9]  Dan Watt,et al.  Quality Assessment , 2009, Encyclopedia of Database Systems.

[10]  Jeff Dozier,et al.  A generalized split-window algorithm for retrieving land-surface temperature from space , 1996, IEEE Trans. Geosci. Remote. Sens..

[11]  Hong Li,et al.  Advanced data assimilation methods- EKF and EnKF , 2006 .

[12]  Robert N. Miller,et al.  Data assimilation into nonlinear stochastic models , 1999 .

[13]  R. Dickinson,et al.  Biosphere-Atmosphere Transfer Scheme (BATS) version le as coupled to the NCAR community climate model. Technical note. [NCAR (National Center for Atmospheric Research)] , 1993 .

[14]  R. Daley.  Atmospheric Data Analysis , 1991 .

[15]  Chunlin Huang,et al.  Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter , 2008 .

[16]  Michael Ghil,et al.  Advanced data assimilation in strongly nonlinear dynamical systems , 1994 .

[17]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[18]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[19]  Zhao-Liang Li,et al.  Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data , 2002 .

[20]  Praveen Kumar,et al.  Assimilation of near-surface temperature using extended Kalman filter , 2003 .

[21]  D. Lettenmaier,et al.  Assimilating remotely sensed snow observations into a macroscale hydrology model , 2006 .

[22]  D. McLaughlin,et al.  Hydrologic Data Assimilation with the Ensemble Kalman Filter , 2002 .

[23]  G. McCabe,et al.  Assimilation of snow covered area information into hydrologic and land-surface models , 2006 .

[24]  B. Bonan,et al.  The NCAR Land Surface Model (LSM Version 1.0) Coupled to the NCAR Community Climate Model , 1996 .