Frozen Surface Classification Scheme for ATMS and GMI
Abstract
Within the development of passive microwave precipitation retrieval techniques, and, inparticular, of snowfall detection and retrieval techniques, the possibility to characterize thefrozen background surface (snowcover and sea ice conditions) at the time of the overpassappears to be a relevant task. As demonstrated by many recent studies (e.g., Tabkiri et al.,2019, Ebtehaj and Kummerow 2017, Panegrossi et al., 2017), the microwave signalrelated to snowfall is strongly influenced by the surface conditions, and the response of theobserved brightness temperatures to the presence and intensity of snowfall depends oncomplex interconnections between environmental conditions (surface temperature, watervapor content, snow water path, cloud depth, presence of supercooled droplets) and thedifferent surface conditions (wet or dry snow cover, sea ice concentration and type, etc.).The use of surface classification climatological datasets results inadequate for the purposebecause of the extreme variability of the frozen surface conditions. It is thereforenecessary to be able to identify the background surface condition as close as possible (inspace and time) to that of the observation. The conically scanning GPM Microwave Imager(GMI) and cross-track the Advanced Technology Microwave Sounder (ATMS) are the mostadvanced currently available microwave radiometers. They are both equipped withchannels at several different frequencies that can be exploited both for the identification ofthe frozen surface conditions and for snowfall detection and retrieval at the time of theoverpass over a precipitation event (i.e., Rysman et al., 2018). Moreover, they can beused to analyze the potentials of future radiometers with similar characteristics such as theEPS-SG Microwave Sounder (MWS) and Microwave Imager (MWI), which represent thefuture in terms of European operational radiometers that can be exploited for precipitationretrieval at all latitudes (including the Polar Regions). In the last years we have developedtwo frozen surface classification schemes based on the use of GMI and ATMS lowfrequency channels (from 10 GHz up to 36 GHz) and on ancillary near-surfacetemperature and columnar water vapor data (obtained from ECMWF global ERA5reanalysis). The algorithm is able to identify 9 classes of soil including different type ofsnow and sea ice. The results of such classification have been compared with otherproducts, such as the NASA-GPROF soil type classification, and with snowcover and seaice global datasets (such as GMASI- Autosnow, and SNODAS from NOAA, and ECMWFERA5). In particular, the comparison with SNODAS over Northern America region showsthat the probability of detection of snow-covered surfaces varies between 86% - 98%(79%-95%) for GMI (ATMS) with a relatively small false alarm ratio (10%-30%). Theanalysis evidenced the main factors limiting the detection capability, such as the moisturecontent, the presence of orography, the snow cover beam filling and the snow depth.
- Publication:
-
EGU General Assembly Conference Abstracts
- Pub Date:
- May 2020
- DOI:
- 10.5194/egusphere-egu2020-18844
- Bibcode:
- 2020EGUGA..2218844C