## 4 Potential Applications, Discussions, and Perspectives
### Could LDAS-Monde Provide Accurate Initial Conditions for Vegetation Forecasts?  
In the context of NWP, assimilation of satellite observations in atmospheric models has the capacity to mitigate model deficiencies, leading to better estimates of system states. This has been the main driver of the improvement of both weather forecast skill and lead time [60]. Data assimilation is able to produce similar benefits for LSVs forecasting. Seeking to foster a link with applications, LDAS-Monde could not only be used to monitor the LSVs, but could also be integrated into a forecasting system (at different time scales) assuming that it can provide better initial conditions than a model run and that its impact lasts in time. Many applications could benefit from a better representation of the LSVs, from NWP [61], to early warning systems of, for example, agricultural drought and yield forecasts. As a first step towards such early warning systems, Figure 11 shows a comparison between LAI from the two last simulations presented in Table 1: Ini_Model and Ini_Analysis. Figure 11a,b shows monthly RMSD (R) values for the year 2016 for LAI. A strong impact is visible not only from the beginning of the two simulations, but also a few months later (up to April). The four maps of Figure 11c show RMSD differences between Ini_analysis and Ini_model from January to April. All maps are dominated by negative values (in blue), suggesting that Ini_analysis presents a better match with the observed LAI and that analysis effects last in time. Southeastern part of the domain is mainly affected, these areas are dominated by broadleaves forest as well as C3 crops. Large differences between the model and the analysis during winter time (as shown on Figure 2) suggest that the minimum LAI threshold used in the model for these vegetation types has to be revisited. Such results are strongly linked to the time of the year when the simulation is initialised by the analysis, that is, the greater the prior  
Figure 10: (**a**) Monthly anomaly of leaf area index for August 2012 with respect to the 2010–2016 period, (**b**) same as (**a**) for observed leaf area index, and (**c**) same as (**a**) for analysed leaf area index.  
difference between the model and the analysis experiments will be, the stronger the impact. As for LAI, and according to Figure 2a, marked impact would be expected from July to March. It is also very promising that the impact of LAI initialisation lasts in time for several weeks or even months. Those results are in line with findings from [5] who have assimilated biomass and LAI observations at \"site-level\" in CLM4.5. They found that monthly forecasts (and even longer forecasts range) were improved by data assimilation compared with forecasts without data assimilation.  
Although the ISBA LSM does not directly represent grain yield (GY), it is assumed that the regional-scale simulations of above-ground biomass from a generic LSM can provide the inter-annual variability as a proxy for GY [62; 63]. Refs. [13; 64] have also found that the LDAS-Monde analysed above-ground biomass inter-annual variability was in better agreement with that of GY than its open-loop counterpart. These studies have been performed over France using straw cereal GY values from the Agreste French agricultural statistics portal ([[http://agreste.agriculture.gouv.fr](http://agreste.agriculture.gouv.fr)]([http://agreste.agriculture.gouv.fr](http://agreste.agriculture.gouv.fr))). If more evaluations are required to assess LDAS-Monde capacity to represent GY, it paves the way towards potential productivity and yield forecasting system.