## 5 Towards Enhanced Use of High Resolution EO Data in Earth System Modelling
### Towards More Comprehensive Model Improvement through Joint Use of Multivariate EO Data  
As illustrated throughout various examples in this paper, satellite-based EO data products are essential to improving and informing model development. However, the potential of the growing suite of EO datasets could be better exploited through the joint use of multiple EO datasets. So far, this is only done in classical data assimilation even though the approach holds promising potential also for improving model structures, and for deriving more accurate, more unique, parameter value sets.  
For example, Ref. [13] showed that robust improvements in the parameter calibration of the H-TESSEL model are only possible with multiple observation-based reference datasets. For this purpose, they used several EO datasets in conjunction with in situ measurements. This allows overcoming their individual shortcomings; e.g., the satellite-based soil moisture offers unprecedented spatial coverage but is available only during relatively short time periods and is constrained to the surface layer, whereas in situ soil moisture measurements cover longer time periods and multiple soil depths. Such a comprehensive approach helps to advance model development as it can capture and adjust various couplings within a model. In contrast, calibration against individual datasets such as land surface temperature results in degraded model performance with respect to other variables such as soil moisture. This is due to an imperfect representation of the energy-water coupling in the model. In this way, the use of different combinations of reference datasets for training and validation enables a targeted identification of flawed couplings within a model.  
While the suite of EO datasets is growing, the complexity of state-of-the-art (land surface and hydrological) models is also increasing. The related increasing number of parameters renders it difficult to sufficiently constrain such models. The potential of a comprehensive model calibration through an adequate ratio between the numbers of model parameters and employed reference datasets has been illustrated in [8]. They used a conceptual water balance model with few parameters and calibrated it thoroughly against a range of EO and in situ datasets. As a result, this simplistic model reached similar (and partly better) performance in simulating key hydrological variables such as soil moisture and runoff.  
Despite the success of a simplistic model in this particular case, the increased complexity of state-of-the-art models is required in the context of coupled weather forecasting systems, or Earth system models [324]. This indicates that the use of emerging new EO datasets is absolutely required  
Figure 27: Model complexity, model resolution and ensemble, and High Performance Computing and data volume trade-offs. Courtesy of Peter Bauer, ECMWF.  
to better constrain those complex operational models. As a result, the improved calibration of individual modules, such as H-TESSEL, within a weather forecasting system, can contribute to advance temperature and precipitation forecasting skills, as exemplified in [325].  
Following this avenue, it is critical to better link model development efforts--for example to enable to explicit representation of (to be) observed EO variables--with the planning and design of upcoming EO missions. This way, the growing suite of EO data products can contribute to an improved physical understanding of land-atmosphere interactions, and furthermore to more accurate weather predictions as well as climate projections.