Improved sensible and latent heat flux estimation of community land model by using ensemble Kalman filter assimilation

Chaoshun Liu; Shijie Shu; Wei Gao

doi:10.1117/12.2021713

24 September 2013 Improved sensible and latent heat flux estimation of community land model by using ensemble Kalman filter assimilation

Chaoshun Liu, Shijie Shu, Wei Gao

Author Affiliations +

Proceedings Volume 8869, Remote Sensing and Modeling of Ecosystems for Sustainability X; 886917 (2013) https://doi.org/10.1117/12.2021713
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

Abstract

Accurate estimation of variability of the surface heat fluxes is very important in the hydrological, meteorological, and agricultural applications. Community land model (CLM) may be used to continuously predict the temporal and spatial sensible and latent heat flux, however, its output is contaminated by uncertainties of the model’s parameters, model structure and forcing data. The aim of this paper is to improve the sensible and latent heat flux prediction of CLM by using data assimilation schemes with Ensemble Kalman Filter (EnKF) algorithm. The data assimilation results are compared against eddy-covariance observations collected at three sites (Arou, Guantan and Yingke) in the northwest of China including grassland, forestland, and cropland cover types. The CLM usually overestimates the sensible heat flux while underestimates the latent heat flux at the three observation sites. The comparison results indicate that data assimilation method improves the estimation of surface sensible and latent heat fluxes from the model, with clear reduction in the resulting uncertainty of estimated fluxes. The average reductions in the RMSE and MAE values of all sites are 40.62 and 25.83 W/m² while the average decline of MAE values were 33.80 and 26.92 W/m² for sensible and latent heat fluxes, respectively, while the most significant reductions in the RMSE values are 67.70 and 30.40 W/m² for sensible and latent heat flux with EnKF algorithm, respectively. Although this study clearly implies that the assimilation of sensible and latent heat fluxes EnKF algorithm has the potential to improve the surface heat fluxes predictions of CLM, further research is required to make definitive conclusions when assimilation of sensible and latent heat fluxes derived from real remote sensing data into CLM. Furthermore, the good approximation of the model and measurement errors and the assimilation multi-source data simultaneously into the CLM may produce better results.

Citation Download Citation

Chaoshun Liu, Shijie Shu, and Wei Gao "Improved sensible and latent heat flux estimation of community land model by using ensemble Kalman filter assimilation", Proc. SPIE 8869, Remote Sensing and Modeling of Ecosystems for Sustainability X, 886917 (24 September 2013); https://doi.org/10.1117/12.2021713

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available