## 3 Results and Discussion
### Validation  
#### 3.2.1 Seasonal Cycle of LAI, WG2, GPP and LE  
Domain averaged LAI, WG2, GPP, and LE time series are shown in Figure 5 The LAI maximums provided by the satellite did not show such annual variability as simulated by the models. In 2010 the model produced the highest LAI values (Figure 5, first row), about 4 m\({}^{2}\)/m\({}^{2}\), while the satellite had a maximum 3-3.5 m\({}^{2}\)/m\({}^{2}\). In 2010 and in 2014 the models significantly overestimated the LAI values, and the cycle was also longer than for the satellite observations. During these years, large amounts of precipitation fell on the domain, resulting in a longer growing season and higher amounts of above-ground biomass. The vegetation in the model was increased due to heavy rainfall predicted by ALADIN at the beginning of the growing season. The amount of the forecasted precipitation was about twice as much as it was in reality (not shown). All extra precipitation was going to run-off above saturation and did not influence vegetation growth negatively.  
The assimilation was able to compensate for differences between the open-loop and the satellite data in 2008, 2011, and 2014. In 2008, LAI values given by the open-loop were underestimated compared to the satellite data, due to hot and dry springs. The assimilation compensated for the lack of biomass, creating larger amounts of GPP and LE flux (Figure 5, second and fourth row). In 2012, there was a drought during the very warm spring and summer seasons, as well as a small amount of precipitation, which resulted in a short cycle by both simulations. GPP, LE was also low, suggesting a lack of vegetation. The precipitation deficits reached 83% of the long-term average and the soil moisture was also kept low, between 0.15-0.22 m\({}^{3}\)/m\({}^{3}\) (Figure 5, third row).  
Figure 4: Jacobian values of dLAI/dWG2 for 2008 and 2009.  
LAI is an important parameter for carbon and water fluxes, but soil moisture also has an important effect on these fluxes. The largest impact on WG2 assimilation was in 2012 and the first half of 2013, resulting in lower GPP and LE values.  
Former studies [1; 7; 8] noted that the beginning of the growing season and the senescence were delayed by the models. In our experiment, the maximum of the growing phase was delayed by the simulations in 2008, 2009, and 2014, and the senescence was shifted in some years (2008, 2010, and 2014).  
Figure 5: Time series of open-loop (blue), observed (green) and assimilation (orange) LAI (**first row**), GPP (**second row**), WG2 (**third row**), and LE (**fourth row**) averaged over Hungary from 2008 to 2015.  
The impact of the assimilation over Hungary was examined with respect to the residuals (differences between the simulations and the satellite observations) for LAI for the period 2008-2015. Figure 6 shows LAI SAT-ASS and SAT-OP points, calculated as the area mean of the entire domain. As expected, LDAS was able to reduce the differences compared to the open-loop simulation. The distribution of the residuals shows seasonal patterns, concentrating at around 0 in winter and increasing for the rest of the year. A large overestimation produced by both simulations (the simulated LAI values exceeded the satellite data) occurred in the wet periods of 2010 and 2014, respectively.