# Passive Microwave Imagers, Their Applications, and Benefits: A Review
## 2 PMWI Applications and Impact Areas
### Applications and Benefits of PMWIs
#### 2.1.2 Global Precipitation and Extreme Events  
Precipitation (rain and snowfall) retrievals [53; 54; 55; 22; 56] benefit from using PMWI due to their superior spatial resolution compared to sounders, which enables more accurate and detailed precipitation measurements and improves flood forecasting. Additionally, SMMR, SSM/I, SSMIS, and AMSR-E sensors have been pivotal in detecting flood occurrence signals and dynamic surface water fractions. The gridded Passive Microwave (PMW) Earth System Data Record (ESDR)--a part of the NASA Making Earth System Data Records for Use in Research Environments (MEASURE) program--provides data derived from the mentioned PMWIs [57]. Additionally, surface soil moisture retrievals obtained from the TMI X-band (10.7 GHz) radiometer, along with the TRMM TMI surface SOIL Moisture dataset generated from the Land Parameter Retrieval Model (LPRM_TMI_SOILM2), are used for forecasting storm-event-scale runoff ratios, which are used for flood forecasting [58]. AMSR2, GMI, and SSMIS at different frequencies are also utilized for rainfall rate and accumulation estimates during extreme precipitation events for studying floods [59]. Figure 2 shows the GPM Core Observatory (GPM-CO) observations of a storm over the Carolina coastlines. Moreover, IMERG [1] and TRMM Multi-satellite Precipitation Analysis (TMPA) datasets are Level 3 products that blend Level 2 precipitation data from different satellites and sensors to provide precipitation data, including from GMI and TMI PMWIs, which are useful for flood discharge estimation and near-real-time flood forecasting [60]. Climate Prediction Center MORPHing technique 2 (CMORPH2) is another Level 3 dataset that merges Level 2 precipitation data from a constellation of satellites, including PMWIs, and provides global satellite-based precipitation analyses with 0.05\({}^{\circ}\) by 0.05\({}^{\circ}\) and 30 min spatial and temporal resolution [61; 62]. The channels at frequencies above 150 GHz are preferred for detecting precipitation areas, and channels at 89 GHz and 150 GHz are considered the two most crucial features for precipitation retrieval. Therefore, future precipitation imagers that incorporate a broad range of these high-frequency channels will enhance measurement precision. By leveraging the precise precipitation rate information obtained from various PMW sensors, researchers and forecasters can better assess flood risks, estimate flood discharge, and facilitate near-real-time flood forecasting [62]. The timely data obtained from PMWI sensors are instrumental in early warning systems, significantly reducing the impact of floods on affected communities [63]. Figure 2: GPM-CO observation over a storm on the Carolina coast. The blues and purples represent snow, whereas the greens to reds represent rainfall (source: figure created by [38]). The use of PMWI data to estimate snow cover and snow water equivalent started in the late 1970s [21]. With the advent of PMW high-frequency channels (150 GHz and higher), the sensitivity to snowfall microphysics has improved, and these channels are more commonly used for snow retrieval [20; 22; 53; 28]. Other PMWIs such as the AMSR-E aboard NASA's Aqua satellite, operating across multiple frequency bands from 6.9 GHz to 89 GHz, are instrumental in monitoring land surface temperatures during strong snowstorms and disasters. Furthermore, this sensor operates at 36.5 GHz and 89 GHz frequencies and provides crucial data for understanding the dynamics of hail formation and distribution and snowfall retrieval, being important for studying storms, particularly hailstorms. Moreover, the X-band (approximately 10 GHz) gained popularity for studying deep snow conditions, particularly since the launch of AMSR-E in 2002 [21]. Another example usage of PMWI data is the Snow Retrieval Algorithm for GMI (SLALOM) and NESDIS snowfall rate products, both of which use PMWI data as part of their input [20; 22; 53; 64].