The Passive microwave Empirical cold Surface Classification Algorithm (PESCA): application to GMI and ATMS

Abstract

This paper describes a new Passive microwave Empirical cold Surface Classification Algorithm (PESCA) developed for snow cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, as several studies have highlighted the influence of snow cover radiative properties on the falling snow passive microwave signature. The developed methodology is based on the exploitation of the lower frequency channels (< 90 GHz), common to most microwave radiometers. The methodology applied to the conically scanning GMI and the cross-track scanning ATMS is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust for both sensors in dry conditions (TPW < 10 mm), and for mean surface elevation < 2500 m, independently of the cloud cover. The algorithm shows very good performance for cold temperatures (2 m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 K and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA [overall detection statistics: POD=0.98(0.92), FAR=0.01(0.08), HSS=0.72(0.69) for ATMS(GMI)]. Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometry, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes appear to be different also at high frequency (>90GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically and cross track scanning radiometers including the future operational EPS-SG mission microwave radiometers.

Fast Radiative Transfer Approximating Ice Hydrometeor Orientation and Its Implication on IWP Retrievals

“…These values are quite comparable to the snow emissivity estimates from our model. Similar values were also reported by Camplani et al [74] for snow covered surfaces.…”

Applications of a CloudSat-TRMM and CloudSat-GPM Satellite Coincidence Dataset

“…In general, the maps of SLALOM snowfall rate for GMI and ATMS in Figure 8 show a good agreement of the retrieval from the two radiometers, with some differences mainly linked to a slight overestimation of the intensities and a larger snowfall area by SLALOM-ATMS with respect to SLALOM-GMI. The maps of the surface characterization from PESCA are very consistent, especially for the detection of snow-covered areas (the red areas depict the region where PESCA is not applicable due to the high temperature and/or moisture conditions [44]). The main differences are in the thickness of the coastal areas (where PESCA is not used), due to the different IFOV size of ATMS and GMI.…”

“…Region A is characterized by the presence of relatively high temperature and supercooled droplets (pink dots in Figure 7, panel 1) on top of the cloud outside the area of coincidence between CPR and snowfall area by SLALOM-ATMS with respect to SLALOM-GMI. The maps of the surface characterization from PESCA are very consistent, especially for the detection of snowcovered areas (the red areas depict the region where PESCA is not applicable due to the high temperature and/or moisture conditions [44]). The main differences are in the thickness of the coastal areas (where PESCA is not used), due to the different IFOV size of ATMS and GMI.…”

“…It is worth noting that during the development of the SLALOM approach for GMI and for ATMS, the impact of the extremely variable frozen surface conditions (snow-covered or sea ice) on the (weak) passive microwave snowfall signal [41][42][43], including references in [43], has been tackled through the exploitation of the low frequency channels (<37 GHz) available in both radiometers. The Passive microwave Empirical cold Surface Classification Algorithm (PESCA) for GMI and ATMS [44] has been recently developed to be able to detect and characterize snow cover (and sea ice) at the time of the satellite overpass.…”

The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

“…The daily time resolution of the sea-ice and snow cover data was chosen to better represent these two highly variable fields. In fact, their extremely variable emissivity has a significant effects on the upwelling radiation (especially in dry conditions) and tends to contaminate the precipitation microwave signal [64][65][66][67]. The choice of low time resolutions (monthly) for the other ERA5 variables, instead, ensures the dominance of the instantaneous information (TBs) over the ancillary one, determining a weak dependence on other data sources.…”