# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 4 Applications Related to the Water Cycle
### Assimilation and Validation in NWP Models  
Meteorological applications of satellite precipitation data encompass assimilation into, and validation of, NWP models, verification of hurricane track predictions, and nowcasting of severe weather. Ten years ago, precipitation observations were thought to add little benefit to global weather forecasts. Nowadays, at the European Centre for Medium-range Weather Forecasts (ECMWF), satellite MW radiances sensitive to humidity, cloud, and precipitation provide around 20% of short-range forecast impact. This makes them one of the most important data sources, rivaling in impact with MW temperature sounding observations [252]. Satellite data assimilation is rapidly moving beyond the \"clear-sky\" approach that discards any observation affected by cloud, and operational forecasting centers assimilate cloud- and precipitation-affected radiances operationally [253]. Both 3D- and 4D-Var methods, together with hybrid methods, are applied to improve the initial model state with emphasis on the convective scale driven by the increasingly finer model spatial resolution [254]. As previously discussed, radiometrically-appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature, need to be derived from the inversion of the radiative transfer equation to improve on existing retrieval schemes. This is, for example, the scope of the Microwave Integrated Retrieval System (MiRS, [255]), developed by NOAA, which bridges the gap between retrieval and assimilation.  
The efforts to validate satellite precipitation products and use them as validation tools of NWP model outputs have increased notably during the past decade. A cross comparison between the performance of satellite-derived estimates of precipitation occurrence, amount, and intensity, and model outputs shows that the former are more accurate during the warm season and at lower latitudes, where rainfall is essentially convective in nature [256]. NWP models perform better during the cold season, when non-convective precipitation dominates. Note that the verification and validation of models is strictly linked to space and time scales, and is thus very different for NWP and climate models [96; 257]. While for climate-scale rainfall the requirement is that the algorithm provides a good estimate on average so that errors on shorter time and space scales are not relevant, the use of short-term precipitation estimates cannot tolerate such errors. Hydrologists, for example, need accurate estimates of the rain volume at the catchment scale, while the NWP community places more value in the correct rain location and type than to retrieving the correct amount. In this direction, an interesting and novel approach is the quantification of the uncertainties of GMI precipitation retrievals based on precipitation system properties, such as the size and intensity of the system, which helps in removing zonal and seasonal biases, thus confirming the importance of using the information on the whole precipitation systems instead of individual pixels in the precipitation retrieval [258].