Calibration of aerodynamic roughness over the Tibetan Plateau with Ensemble Kalman Filter analysed heat flux

Abstract

Abstract. Aerodynamic roughness height (Z om ) is a key parameter required in several land surface hydrological models, since errors in heat flux estimation are largely dependent on optimization of this input. Despite its significance, it remains an uncertain parameter which is not readily determined. This is mostly because of non-linear relationship in Monin-Obukhov similarity (MOS) equations and uncertainty of vertical characteristic of vegetation in a large scale. Previous studies often determined aerodynamic roughness using a minimization of cost function over MOS relationship or linear regression over it, traditional wind profile method, or remotely sensed vegetation index. However, these are complicated procedures that require a high accuracy for several other related parameters embedded in serveral equations including MOS. In order to simplify this procedure and reduce the number of parameters in need, this study suggests a new approach to extract aerodynamic roughness parameter from single or two heat flux measurements analyzed via Ensemble Kalman Filter (EnKF) that affords non-linearity. So far, to our knowledge, no previous study has applied EnKF to aerodynamic roughness estimation, while the majority of data assimilation study have paid attention to updates of other land surface state variables such as soil moisture or land surface temperature. The approach of this study was applied to grassland in semi-arid Tibetan Plateau and maize on moderately wet condition in Italy. It was demonstrated that aerodynamic roughness parameter can be inversely tracked from heat flux EnKF final analysis. The aerodynamic roughness height estimated in this approach was consistent with eddy covariance method and literature value. Through a calibration of this parameter, this adjusted the sensible heat previously overestimated and latent heat flux previously underestimated by the original Surface Energy Balance System (SEBS) model. It was considered that this improved heat flux estimation especially during the summer Monsoon period, based upon a comparison with precipitation and soil moisture field measurement. For an advantage of this approach over other previous methodologies, this approach is useful even when eddy covariance data are absent at a large scale and is time-variant over vegetation growth, as well as is not directly affected by saturation problem of remotely sensed vegetation index.

Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

“…The SMAP retrieval system uses the VWC, but it is estimated with the Normalized Difference Vegetation Index (NDVI) from visible near infrared reflectance from the EOS MODIS and NPP/JPSS VIIRS instruments. The NDVI barely reads the vertical characteristics of canopy [81], and is easily saturated by low-level vegetation. For these reasons, Holmes et al [72] found that the vegetation models introduce uncertainties up to 25 K. They also stated that the auxiliary vegetation database regime results in large variations in simulating brightness temperature by 40 K. Crow et al [59] also discussed that a spatial pattern in soil moisture retrievals is influenced primarily by vegetation distribution, and found that the presence of vegetation changes the brightness temperature simulations at H-polarization up to 30 K, and at V-polarization up to 20 K.…”

Surface-sensible and latent heat fluxes over the Tibetan Plateau from ground measurements, reanalysis, and satellite data

“…Ma et al, 2003;Tanaka et al, 2003;Liu et al, 2009;Bian et al, 2012), meadow (Gu et al, 2005;Yao et al, 2008Yao et al, , 2011, and glacial and alpine areas (Zou et al, 2009;Yang et al, 2011c;Chen et al, 2012;Zhang et al, 2013). Advanced methods have been developed to retrieve SEB from improved parameterization of routine meteorological observations (Yang et al, 2002(Yang et al, , 2003Chen et al, 2010Chen et al, , 2013bGuo et al, 2011b;Lee et al, 2012) satellite observations (Y. Ma et al, 2006Ma et al, , 2012W.…”

Comparison of satellite-based evapotranspiration estimates over the Tibetan Plateau

“…The knowledge of ET is essential for the study of land-atmosphere interactions, and assessment of the impacts of and feedbacks to the global change (Shi and Liang, 2014). In order to characterise the distribution of ET over the TP, different methods using micrometeorological measurements (Yang et al, 2003;Lee et al, 2012;Chen et al, 2013b;Zhang et al, 2007), remote sensing products (Ma et al, , 2006Chen et al, 2013a) and the combined use of both data sources (Ma et al, 2003(Ma et al, , 2011You et al, 2014) have been investigated over the last decades. In addition, land surface models have also been applied to simulate ET over the TP (Gerken et al, 2012;Yang et al, 2009).…”