# Passive Microwave Imagers, Their Applications, and Benefits: A Review
## 2 PMWI Applications and Impact Areas
### Applications and Benefits of PMWIs
#### 2.1.4 Nwp  
Currently, NWPs are the state of the art in forecasts ranging more than a few hours into the future, and they are a core component of early warning systems [3]. To initiate NWP models, data assimilation methods are used to combine satellite observations, conventional observations, and short-range forecasts to assess the initial state of the atmosphere [19]. A great example of the use of microwave data in NWP models is the European Center for Medium-Range Weather Forecast (ECMWF) and NOAA National Centers for Environmental Prediction Global Forecast System (NCEP GFS) model [2; 19; 69]. As of now, the volume of satellite observations by far exceeds other data types used in NWPs [2], and among them, microwave observations have the highest overall impact on the quality of the final product [3; 19]. This is mostly related to the ability of microwave instruments to gather information in cloudy conditions. The benefits of microwave data vary with location,with the highest benefits in locations with sparse on-site observations [3]. For instance, microwave observations provide a higher benefit over the Northern Hemisphere compared to the Southern Hemisphere [3]. Additionally, ECMWF reported that denial of microwave data in NWP models would be equivalent to losing 2-4 years of technological advances in NWP models. Most importantly, ECMWF reported that almost 40% of improvement in short-range forecast skill is associated with PMW observations from sounders and imagers, and removing these data causes the largest loss of skill compared to other satellite data. [PERSON] et al. reported a relative Forecast Sensitivity to observation Impact (FSOI) of 3% for microwave Arctic observations for ECMWF [19]. [PERSON] et al. reported a FSOI value of around 27% for microwave sounders and imagers in the Met Office global NWP system for August-October 2019 [70]. Additionally, the authors reported that through data denial experiments, denial of microwave sounder and imager data in the Met Office global NWP system for the period of August-November 2019 resulted in the biggest performance loss compared to both observations and ECMWF analysis [70]. In addition, [PERSON] et al. evaluated the effect of incorporating GPM GMI clear-sky radiance into the track and intensity forecasts of two Atlantic hurricanes during the 2015 and 2016 hurricane seasons. They used the Hurricane Weather Research and Forecasting (HWRF) Model, which is based on the NOAA NCEP GFS. The forecast results indicated that assimilating GMI clear-sky radiance positively influences both track and intensity forecasts, with the impact varying depending on the phase of hurricane development [71]. Moreover, [PERSON] et al. assessed the forecast impact of WindSat data in the NCEP GFS and reported that integrating National Environmental Satellite, Data, and Information Service (NESDIS) Coriolis WindSat improves the forecast at mid-latitudes and in the tropics [72]. Furthermore, [PERSON] et al. applied an Ensemble Kalman Filter (EnKF) to estimate 24 h forecast impacts of assimilated observations, including wind data from WindSat. This approach that was implemented with NCEP GFS EnKF quantified the overall positive effects of assimilated data, highlighting the significance of satellite radiance observations, particularly for moisture variables [73].