Frozen Surface Classification Scheme for ATMS and GMI
Authors: Casella Daniele , Camplani Andrea , Sanò Paolo , Panegrossi Giulia , Kulie Mark 
Published: March 2020

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