# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
## 1 Introduction  
It is critical to monitor and predict land surface variables related to vegetation dynamics, water and carbon fluxes, which are essential for better understanding complex land-atmosphere interactions and climate change [1; 2; 3]. Process-based land surface models (LSMs) make it possible to simulate land surface variables and represent complex land surface processes [4]. A series of models have been developed to study land and lower atmosphere conditions. Among them, the Noah with multi-parameterization model (Noah-MP model) significantly improves the simulations of land surface vegetation and heat fluxes with its advantages of using multiple optional parameterization schemes to interpret the differences in modeling simulations [5]. While LSMs can provide temporally and spatially continuous simulations of land surface variables, there are still substantial uncertainties caused by initial conditions, forcing data, interactions between land surface and atmosphere, and model physics within LSMs, leading to significant differences between simulation and reality [6]. Data assimilation (DA) methods that can merge observational information into process-based LSMs can constrain parameters and optimize the simulations of land surface variables. Most data assimilation schemes are developed with variational-based or ensemble-based methods to improve the performance of LSMs [4; 5; 6; 7; 8]. Based on the assimilation theories, various observational variables such as land surface temperature (LST), leaf area index (LAI), and soil moisture(SM) observations have been assimilated into LSMs, improving the simulations of land surface variables from LSMs. For instance, assimilation of SM improves the estimation of evapotranspiration [9; 10], and assimilation of LAI optimizes estimates of carbon fluxes of gross primary production (GPP) and net ecosystem exchange (NEE) [11; 12; 13]. It is notable that these traditional data assimilation frameworks are built with a single or dual observational variable assimilated into theprocess-based models [11; 14; 15]. [PERSON] et al. [16] found that root zone soil moisture (RZSM) can benefit from the assimilation of SM and LAI, which indicates that assimilation of multi-variables at once may improve the performance of land surface variable simulations. Recently, a multi-pass land data assimilation scheme (MLDAS) has been proposed, which can assimilate different observational variables into LSMs to optimize simulations of land surface variables all at once [17]. As different observed variables are jointly assimilated, MLDAS potentially improves the capability of LSM simulations. Although some data assimilation schemes have been constructed within LSMs to better simulate land surface variables, few studies are focused on the potential of improvement of predictivity skills brought by data assimilation. It is believed that uncertainties of land surface forecast mainly arise from initial conditions (ICs), meteorological forcing, and errors in model structure, and initial conditions play an important role among them. [PERSON] and [PERSON] [7] evaluated the relative importance of initial conditions and meteorological forcing in hydrologic forecast and discovered that initial conditions dominated the forecast in some cases. [PERSON] et al. [18] assimilated microwave brightness temperature into a land surface model for better monitoring and forecast of agricultural drought. The results showed that data assimilation especially sequential data assimilation may provide more accurate initial conditions that lead to better LAI and SM forecasts, which were likely to benefit agricultural drought monitoring. Quantities of data assimilation experiments have proved that accurate initial conditions are crucial for prediction, but the observational data are assimilated into the LSM separately in those experiments. It is a topic of interest to test whether assimilating several land surface variables into LSM jointly would improve prediction of LSMs.