## 2 EO Satellite and In Situ Observations for Earth Surface
### Ground-Based Networks  
A great wealth of Earth system observations are available to constrain land surface state. These observations continue to grow in volume and diversity but gaps still exist for certain key parameters (e.g., soil moisture, snow depth and river discharge). Despite their limited coverage and limited expansion, in situ observations continue to be the backbone of the observing system used by NWP land-surface applications. Some of the key in situ parameters used in NWP are: surface pressure, 2 m temperature, 2 m humidity, snow depth, soil temperature and wind (see Figure 4). They are mainly provided by the Synoptic Operations (SYNOP) and METeorological Aerodrome Report (METAR) networks and by radiosondes. These observations are used to constrain the atmospheric model, land-surface and snow analyses together. Large spatial gaps and representativity issues [50] continue to affect soil moisture and snow observations. In addition, most variables are retrieved in the atmosphere so that the constraint on the land surface is only indirect. Indeed, for instance, the soil moisture updates reflect changes in the surface energy partitioning, which affects the atmosphere (e.g., air temperature and humidity). Any incorrect physical representation between the surface (soil moisture) and the atmosphere (fluxes) would lead to incorrect soil moisture inversion. In the past five years, the number of observations available from the SYNOP (Synoptic observations) and METAR (Meteorological Report at the Airports) has increased (Figure 5) thanks to the inclusion of automatic data reports not previously considered. Long-term, large scales networks of ground measurements are also of paramount importance. For instance, detailed ground based soil moisture information is provided by in situ soil moisture databases such as the International Soil Moisture Network (ISMN) [51]. Ground-based measurements of soil moisture and temperature from ISMN [52; 53; 54; 55] have been used to evaluate the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analyses, forecasts and reanalyses ability to represent soil moisture and temperature. Verification studies using in situ networks contribute to a better understanding of the deficiencies in models and point to processes that need a better representation. Figure 3: Median Land Surface Temperature (LST, \({}^{\circ}\)C) for all 12 UTC LST estimates in July 2016, derived from GOES-13, MSG/SEVIRI and Himawari-8 observations (upper panel; the circles mark the respective 70\({}^{\circ}\) disks). The median LST diurnal cycle for July 2016 is shown in the lower panels, for two pixels (desert landscape in Northern Africa, lower-left, and Savannah in South America, lower-right). Particular attention must also be devoted to rainfall measurements, for which numerous datasets exist combining in situ rain gauge observations from the SYNOP and SYNOP-like national networks with satellite and ground-based remote sensing to obtained gridded products, as recently compared in a study [56]. The combination of multi-sources observations with models to ensure meteorological consistency is advocated as a way to increase accuracy. However, precipitation estimation for hydrological applications remains a challenge and motivates the continuous effort of the International Precipitation Working Group (IPWG), a permanent Working Group of the Coordination Group for  
Figure 4: Number of available observations per unit area of \(1^{\circ}\times 1^{\circ}\) from the SYNOP and METAR networks in January, February and March 2018 for 2 m temperature (**upper**) and snow depth (**lower**). Figure 5: Daily global data counts from SYNOP and METAR observations. The sudden increase of data counts in 2013 is related to the inclusion of existing METAR observations not previously sourced. Meteorological Satellites, active since 2001 [57].