DEnKF–Variational Hybrid Snow Cover Fraction Data Assimilation for Improving Snow Simulations with the Common Land Model

Jianhui Xu,Lin Dong,Hong Shu

doi:10.3390/rs61110612

Abstract

This study assesses the analysis performance of a hybrid DEnKF-variational data assimilation (DA) method (DEnVar) for assimilating the MODIS snow cover fraction (SCF) into the Common Land Model (CoLM). Coupling a deterministic ensemble Kalman filter (DEnKF) with a one-dimensional variational DA method (1DVar), DEnVar without observation perturbations is a two-step DA method. That is, the analysis ensemble mean and analysis error covariance of DEnKF are introduced into the 1DVar hybrid cost function, and the analysis mean of DEnKF is replaced by the 1DVar analysis. The analysis performance of DEnVar was experimentally compared with DEnKF, 1DVar, and EnVar (hybrid ensemble-variational DA) at five sites in the Altay region of China from November 2008 to March 2009. From our results, it is shown that the four DA experiments can improve snow simulations at most sites when the available MODIS SCF is assimilated. The DEnVar experiment using the hybrid error covariance shows the best analysis performance among the four DA experiments at most sites. Furthermore, sensitivity tests show that DEnVar is slightly sensitive to the weighting coefficient, which controls the respective weights of ensemble- and (National Meteorological Center) NMC-based error covariances, but is highly sensitive to the observation error. DEnVar obtains better analysis performance when using the ensemble-based analysis error covariance rather than the hybrid error covariance coupling ensemble-based analysis and static NMC-based error covariances. The inaccurate distribution of observation error may invalidate the DEnVar method.

Highlights

Seasonal snow cover plays a crucial role in the Earth’s hydrological processes, land surface energy balance, and climate [1,2,3] due to its high albedo and low thermal conductivity [4,5]
The error of the analyzed snow depth (SD) in the DEnKF-1DVar hybrid method (DEnVar)-Beta 0.5 experiment was reduced by 57.82% (NERP of 57.82%, see Figure 4) as compared with the Common Land Model (CoLM) simulations at the Qinghe site, and the errors in other data assimilation (DA) experiments were reduced by 18.12%, 51.13%, and
EnVar and DEnVar are coupled DA methods, which are comparable to the method of Zhang et al [36]

Summary

Introduction

Seasonal snow cover plays a crucial role in the Earth’s hydrological processes, land surface energy balance, and climate [1,2,3] due to its high albedo and low thermal conductivity [4,5]. The snow cover fraction (SCF) is a key parameter in the studies of both hydrology and climatology [6]. SCF is derived from snowpack state such as snow depth (SD) or snow water equivalent (SWE). SD and SWE are important internal variables of hydrological and climatic models. SWE is the product of SD and snow density. They have significant impacts on climatic and hydrological simulations and snowmelt runoff predictions [7]. Substantial snow accumulation frequently causes severe snow disasters, such as frostbite, death for both animals and people, and traffic disruption [8]. The accurate estimates of SD and SWE are important for reduction, mitigation, and prevention of snow-related disasters [9]

Methods

Results

Conclusion