# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
## 4 Discussion  
### Can MLDAS Improve the near Future Prediction Performance?  
Most \(NIC_{RMSD}\) of H, LE, and SM are positive within four data assimilation scenarios, indicating that MLDAS outperforms OL during the prediction period. It is widely accepted that forecasting errors arise from the uncertainty of initial conditions, model structure and parameters, interactions between land surface and atmosphere, and atmospheric forcing. Previous studies have found that accurate initial conditions are essential for better predictions of land surface variables in LSM [7; 27; 28]. [PERSON] and [PERSON] [7] evaluated the relative importance of initial conditions and meteorological forcing in seasonal hydrological forecast and found that initial conditions were more important than meteorological forcing. Here, the initial conditions provided by process-based models (MLDAS or OL) are crucial for future prediction as they determine where the prediction begins. In particular, the forcing data used in our study are from AmeriFlux observations instead of forecasted atmospheric reanalysis, making it possible to highlight the influence of initial conditions on prediction. However, given that the forcing data used here are AmeriFlux observations with high accuracy, the impact of initial conditions may become overestimated and uncertainties in forcing data may not be well represented. Here, for a point-scale simulation driven by AmeriFlux observations, the prediction was likely to be more accurate if the initial condition was close to the \"benchmark\". For instance, in Figure 3, the initial conditions of SM prediction are different between MLDAS and OL in assimilation-prediction scenario I (assimilation for 15 days) in 2004 (DOY 136, the edge of the grey region), with SM provided by MLDAS (red solid line) closer to the \"benchmark\". Accordingly, \(NIC_{RMSD}\) is 50.51%, indicating that assimilation (assimilation for 15 days) can improve the performance of prediction, despite being rare in scenario I. Hence, initial conditions play an essential role in the performance of prediction [18].  
Due to the climatic characteristics of the site, though overall prediction skill of SM, H, and LE is increased in MLDAS compared with that of OL, prediction performance in  
Figure 9: \(RD\) between OL and MLDAS of latent heat flux prediction in scenario I in 2003, 2004, and 2005. The shaded area represents the duration of influence brought by assimilation on future predictions.  
different assimilation scenario varies. For instance, as mentioned above, the prediction performance of scenario I is not as good as expected and even degrades in some experiments such as SM, H, and LE prediction in 2005. For the other three scenarios (especially scenario IV) MLDAS received better responses in most experiments. The seasonal transition of land surface variables such as LAI may influence the prediction skill in different assimilation scenarios. As in scenario I, initial conditions are in the spring/summer transition with LAI around 0, which can be seen in Figure 2. Such a low LAI is likely to bring high uncertainties to data assimilation, which lowers the accuracy and reliability of initial conditions and worsens the prediction skill of MLDAS. However, as in scenarios II to IV, the prediction experiments are conducted during the summer or summer/autumn transition with relatively higher LAI. Furthermore, assimilation of SIF in pass IV updates Vcmax25, which is by default a fixed parameter depending on vegetation type in Noah-MP, leading to better LAI simulation. Hence, LAI is assimilated effectively, and lower uncertainty is introduced so that initial conditions are more reliable, contributing to better prediction performance of MLDAS. Regional climate and corresponding phenology are crucial in the forecast framework dominated by initial conditions [7; 18], so we suggest that initial conditions for prediction should be carefully checked, considering the uncertainties introduced from climate variations of assimilated land surface variables.