# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
## 3 Results
### Duration of Impact of Data Assimilation on Prediction  
This work focuses not only on whether data assimilation improves the performance of prediction, but also on how long the effect exists. Previous results have demonstrated that MLDAS does have the potential to improve the prediction skill of Noah-MP compared to OL, and specific comparisons are summarized in Tables 1-3. Therefore, it is necessary to analyze the impact of assimilation on the duration of the prediction. Herein, SM and LE are chosen to demonstrate this issue.  
The overall dynamic changes of \(RD\) over four scenarios from 2003 to 2005 are given in Figures 6 and 7. The \(RD\) of SM prediction is given in Figure 6. As indicated, \(RD\) in each of the four scenarios decreases with the forecast lead time, suggesting that improved prediction skill brought by data assimilation degrades with time. \(RD\) in scenario IV is generally larger than other scenarios and the decreasing tendency is not as clear as other scenarios. Figure 7 shows how \(RD\) of LE prediction changes over forecast lead time. Generally, the trend that \(RD\) diminishes with lead time is clear in scenarios I and II over the 3 years. Minimum fluctuations of \(RD\) can be seen in scenario III (assimilation for 75 days), where \(RD\) is less than 10% and close to 0% over most of the lead time, and corresponding changes can also be seen in scenario III of Figure 6. No significant trend of decreasing \(RD\) was found in scenario IV, indicating that the impact of data assimilation in scenario IV may persist for a longer period.  
Figure 5: Latent heat flux predictions from OL and MLDAS in 2003–2005. Predictions of four assimilation scenarios are given. Columns represent different assimilation scenarios in each year. The red solid line in each subplot represents MLDAS prediction in one future month; the blue dashed line in each subplot represents OL simulation; and the green hollow scatters represent LE benchmarks.  
Figure 6: \(RD\) of soil moisture prediction in four scenarios over 2003–2005. The red solid lines, green dashed lines, blue dotted lines, and cyan dashed lines represent \(RD\) of scenario I, II, III, and IV, respectively. Blue bars are precipitation.  
Figure 7: Same as Figure 6, but for latent heat flux prediction in four scenarios over 2003–2005.  
Scenario I (assimilation for 15 days) of each year (2003, 2004, and 2005) is chosen to show how \(RD\) changes with forecast lead time so that the forecast lead time can be long enough for evaluation. Figure 8 shows the \(RD\) of SM over the 3 years. Overall, the influence of assimilation on prediction also diminishes with forecast lead days. The impact of assimilation lasted for around 80 days, 70 days, and 100 days in 2003, 2004, and 2005, respectively. Then the \(RD\) converges to around 0. Additionally, precipitation is also an essential factor that affects SM simulation. There is a good correspondence between precipitation events and fluctuation of \(RD\) between MLDAS and OL before convergence (as shown in the grey parts). As is shown, \(RD\) between MLDAS and OL becomes smaller when precipitation happens, and \(RD\) increases fast without precipitation (i.e., 20-40 days in 2005), confirming that accurate precipitation inputs are important for SM prediction.  
Figure 9 shows detailed \(RD\) of LE prediction between MLDAS and OL. Similar to SM prediction, the overall tendency of \(RD\) diminishes with lead time and apparent fluctuation can be seen in the first 20 to 30 forecast lead days. The \(RD\) also converged to around 0 at around 60, 55, and 55 lead days in 2003, 2004, and 2005, respectively.  
Figure 8: \(RD\) between OL and MLDAS of soil moisture prediction in scenario I in 2003, 2004, and 2005. The shaded area represents the duration of influence brought by assimilation on future predictions.