<p>Within the development of passive microwave precipitation retrieval techniques, and, in<br>particular, of snowfall detection and retrieval techniques, the possibility to characterize the<br>frozen background surface (snowcover and sea ice conditions) at the time of the overpass<br>appears to be a relevant task. As demonstrated by many recent studies (e.g., Tabkiri et al.,<br>2019, Ebtehaj and Kummerow 2017, Panegrossi et al., 2017), the microwave signal<br>related to snowfall is strongly influenced by the surface conditions, and the response of the<br>observed brightness temperatures to the presence and intensity of snowfall depends on<br>complex interconnections between environmental conditions (surface temperature, water<br>vapor content, snow water path, cloud depth, presence of supercooled droplets) and the<br>different surface conditions (wet or dry snow cover, sea ice concentration and type, etc.).<br>The use of surface classification climatological datasets results inadequate for the purpose<br>because of the extreme variability of the frozen surface conditions. It is therefore<br>necessary to be able to identify the background surface condition as close as possible (in<br>space and time) to that of the observation. The conically scanning GPM Microwave Imager<br>(GMI) and cross-track the Advanced Technology Microwave Sounder (ATMS) are the most<br>advanced currently available microwave radiometers. They are both equipped with<br>channels at several different frequencies that can be exploited both for the identification of<br>the frozen surface conditions and for snowfall detection and retrieval at the time of the<br>overpass over a precipitation event (i.e., Rysman et al., 2018). Moreover, they can be<br>used to analyze the potentials of future radiometers with similar characteristics such as the<br>EPS-SG Microwave Sounder (MWS) and Microwave Imager (MWI), which represent the<br>future in terms of European operational radiometers that can be exploited for precipitation<br>retrieval at all latitudes (including the Polar Regions). In the last years we have developed<br>two frozen surface classification schemes based on the use of GMI and ATMS low<br>frequency channels (from 10 GHz up to 36 GHz) and on ancillary near-surface<br>temperature and columnar water vapor data (obtained from ECMWF global ERA5<br>reanalysis). The algorithm is able to identify 9 classes of soil including different type of<br>snow and sea ice. The results of such classification have been compared with other<br>products, such as the NASA-GPROF soil type classification, and with snowcover and sea<br>ice global datasets (such as GMASI- Autosnow, and SNODAS from NOAA, and ECMWF<br>ERA5). In particular, the comparison with SNODAS over Northern America region shows<br>that the probability of detection of snow-covered surfaces varies between 86% - 98%<br>(79%-95%) for GMI (ATMS) with a relatively small false alarm ratio (10%-30%). The<br>analysis evidenced the main factors limiting the detection capability, such as the moisture<br>content, the presence of orography, the snow cover beam filling and the snow depth.</p>
Effects of weather events on X-SAR returns from ice fields: case-study of Hielo Patagónico Sur, South America
