# Hailstorm Detection by Satellite Microwave Radiometers
## 1 Introduction  
The potential of the Advanced Microwave Sounding Unit-B (AMSU-B) and of its successors the Microwave Humidity Sounder (MHS) and the Advanced Technology Microwave Sounder (ATMS) in characterizing precipitating systems is amply demonstrated in the literature. [PERSON] et al. [1] employed the AMSU channels in retrieving a suite of hydrological products relevant for weather forecasting and climate monitoring. [PERSON] and [PERSON] [2] first developed a neural network, recently revisited by [PERSON] et al. [3], to identify rain clouds and retrieve rain rates by exploiting the sensitivity of the water vapor absorption bands at 183.31 GHz. Further methods for precipitation retrievals were focused on discriminating between solid and liquid precipitation. The frequency range 90-190 GHz was used [4; 5] to estimate rain rates and classify stratiform, convection and snowfall precipitation types. Several authors [6; 7; 8; 9] demonstrated that the bands at 183.31 GHz can isolate convective clouds by mapping their characteristics over the Tropics and the Mediterranean basin. In the last 10 years, the scientific community increasingly focused its attention on a better understanding of ice hydrometeors in general and on improving the detection of hailstorms in particular. Studies to devise methods of hailstorm detection from satellites started from the exploitation of visible (VIS) and infrared (IR) sensors on geostationary satellites (e.g., [10]) dwelling on their-frequent scanning of thunderstorm development areas (e.g., [11]). These sensors have also been used to investigate the structure of the storm top and retrieve vertical profiles of cloud particles (e.g., [12; 13]). Such approaches try to link the presence of deep convection with strong updrafts and haifall as spotted at the ground by hail reporting and radar networks (e.g., [14]). Ground-based approaches are more numerous and dwell on hailpad networks (e.g., [15]), statistics using large-scale environmental conditions [16], or radar data (e.g., [17; 18; 19]). Recently a physically based stochastic event catalog of hail occurrence over Europe [20] and a radar-based climatology of damaging hailstroms in Brisbane and Sydney [21] were published. The first studies using passive microwave (PMW) sensors focused on millimeter and submillimeter wave bands (e.g., [22]). However, it was only with the studies on the response of frequencies around 37 GHz that the signal from hail or large ice hydmeteors within severe storms over land [23; 24; 25] was first exploited. These original works were successively reconsidered and improved by [26; 27; 28] by combining the frequencies at 37 and 85 GHz to establish a relationship between the occurrence of hail and strong brightness temperature (TB) depression. [PERSON] et al. [29] proposed a new prototype AMSU-based hail detection algorithm and derived a hail climatology. [PERSON] et al. [30] have used 16 years of PMW and radar observations from the Tropical Rainfall Measuring Mission (TRMM) and hail reports from China and the US to infer differences in hail properties in the two countries. More recently, the same datasets combined with observations from the Global Precipitation Measurement (GPM) mission Core Observatory and ground-based dual-polarization radar signatures were employed to define the correlation between hail occurrence and vertical characteristics of Ku-band reflectivity [31]. The hailstorm structure detection capabilities of the GPM Dual-frequency Precipitation Radar (DPR) were further exploited in combination with the GPM Microwave Imager (GMI) [32; 33; 34; 35]. In this study, the hail-induced scattering signal is exploited to develop a method for detecting hail-bearing clouds. The general model has been suggested by the distribution law of signal attenuation as a function of hail size. A probability-based method is developed where a logistic model of growth is used to fit the TB depression due to the scattering from hail.