CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities

Abstract

Abstract:The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60 • N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 ∆TB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 ∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg·m −2 , IWP > 0.24 kg·m −2 over land, and SIC > 57%, TPW > 5.1 kg·m −2 over sea). The complex combined 166 ∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms.

SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

“…The majority of the coincidences are found at around 60°N/S (Figure 2 (see also Figure 1 from Panegrossi et al [43]). Numerous ancillary products are also available, including surface information (ground elevation, land-sea flag), CPR precipitation products [e.g., 2C-SNOW-PROFILE (2CSP)] and environmental variables [total precipitable water (TPW), 2-meter temperature (T2m)].…”

“…Several studies (e.g., [40][41][42][43]48]) indicated that the presence of supercooled droplets on the top of the snow clouds could strongly affect TBs from high frequency channels. Specifically, supercooled droplets tend to partially mask scattering by snow crystals, which complicates the detection of snow using passive microwave radiometers.…”

“…Finally, Panegrossi et al [43] showed that the sensitivity of TBs to snow depends on the characteristics of the background surface (e.g., open sea, sea ice and snow-cover). In order to evaluate the skill of the SLALOM algorithm for these different surface types, we added daily sea ice concentration observations derived from Advanced Microwave Scanning Radiometer (AMSR-E/AMSR2) measurements [51] to the GPM-CPR database.…”

“…Recently, Panegrossi et al [43] highlighted the impact of falling snow on the high frequency GMI channels using CloudSat CPR as a reference. This study focuses on higher latitude snowfall systems (around 60 • N/S) and includes very weak snowfall events that are not considered in studies where DPR is used as a reference [26,33].…”

“…This study focuses on higher latitude snowfall systems (around 60 • N/S) and includes very weak snowfall events that are not considered in studies where DPR is used as a reference [26,33]. In particular, Panegrossi et al [43] identified the environmental conditions under which snow detection could be optimally achieved (e.g., high atmospheric moisture and sea ice coverage) and characterized the sensitivity of GMI high-frequency channels and the polarization signal at 166 GHz to falling snow. They also highlighted the impact of supercooled droplets on the GMI signal, showing that a supercooled droplet layer on top of a snow cloud can filter out the scattering signal of the snow particles below.…”