## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Land-Surface Reservoirs
#### 3.1.1 Soil
Considering ground measurements sites where fraction of bare ground was greater than 0.2, The Root Mean Squared Difference (RMSD) of IFS's soil moisture with the observations decreased from 0.110 m\({}^{3}\) m\({}^{-3}\) to 0.088 m\({}^{3}\) m\({}^{-3}\). Figure 7 illustrates the impact of the new parameterisation on soil moisture over the USA. The old and new schemes (first and second panel from the left) shows the August soil moisture in the western part of the USA is much lower than with the old scheme. These changes correlate with the bare ground fraction (third panel). This is clearly beneficial, as can be demonstrated by verification based on the Soil Climate Analysis Network (SCAN) over the USA (see Figure 7 for the location of the stations). The positive differences in the right panel of Figure 7 indicate a reduction of the root-mean-square error (RSME) of soil moisture particularly at high bare ground fractions. A better match with Soil Moisture and Ocean salinity (SMOS) satellite observations [53] reinforced the generality of the results obtained from the in situ comparison, and confirmed once again the synergy of satellite and ground-based observations for the model development. At NASA, the Catchment land surface model [91] has been continuously developed for use in the Goddard Earth Observing System NWP and reanalysis products [92] and the SMAP L4 SM product. The latter product in particular depends critically on the skill of the land surface model. Some of the model developments prior to the launch of SMAP were focused on a revised set of soil texture and soil hydraulic parameters [93], as well as on the calibration of the microwave radiative transfer model that converts the modeled soil moisture and temperature values into L-band passive microwave temperatures [94]. Within the L4 SM assimilation system, these modeled radiances were then confronted with the SMAP observations, with deviations between the modeled and observed radiances resulting in adjustments to the modeled soil moisture [95]. Since the first release of the L4 SM product in 2015, new input parameter datasets for land cover, topography, and vegetation height were derived based on recent, high-quality satellite observations [96; 97]. Land cover inputs were updated using the GlobCover2009 product, which is based on satellite observations from the Medium Resolution Imaging Spectrometer. Topographic statistics now rely on observations from the Shuttle Radar Topography Mission. Finally, vegetation height inputs are derived from space-borne Lidar measurements. Additionally, SMAP Level-2 soil moisture retrievals [98] were used to calibrate a particular Catchment model parameter that governs the recharge of soil moisture from the model's root-zone excess reservoir into the surface excess reservoir [99]. Specifically, the replenishment of soil moisture near the surface from below under non-equilibrium conditions was substantially reduced, which brought the model's surface soil moisture better in line with the SMAP Level-2 soil moisture retrievals (not shown). The benefits of this change are illustrated in Figure 8, which shows that the surface soil moisture dynamics from the new model better agree than the old model with independent in situ measurements at SMAP core validation sites [45]. Figure 8a shows an example of surface soil moisture time series for one such site at Little River, Georgia, USA. Figure 8b quantifies the improvements across 18 sites. Between the old and new model versions, the average correlation increased from 0.64 to 0.67 and the bias decreased from 0.032 to 0.017 m\({}^{3}\) m\({}^{-3}\), respectively, while the unbiased RMSE remains unchanged at 0.041 m\({}^{3}\) m\({}^{-3}\). The improved Catchment model version will be used in future versions of the L4 SM product, thereby resulting in an improved soil moisture data product.