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An important objective of the Global Precipitation Measurement (GPM) mission is the detection of falling snow, since it accounts for a significant fraction of precipitation in the mid-high latitudes. The GPM Core Observatory carries the first spaceborne Dual-frequency Precipitation Radar (DPR), designed with enhanced sensitivity to detect lighter liquid and solid precipitation. The primary goal of this study is to assess the DPR's ability to identify snowfall using near-coincident CloudSat Cloud Profiling Radar (CPR) observations and products as an independent reference dataset. CloudSat near global coverage and high sensitivity of the W-band CPR make it very suitable for snowfall-related research. While DPR/CPR radar sensitivity disparities contribute substantially to snowfall detection differences, this study also analyzes other factors such as precipitation phase discriminators that produce snowfall identification discrepancies. Results show that even if the occurrence of snowfall events correctly detected by DPR products is quite small compared to CPR (around 5–7%), the fraction of snowfall mass is not negligible (29–34%). A direct comparison of CPR and DPR reflectivities illustrates that DPR misdetection is worsened by a noise-reducing DPR algorithm component that corrects the measured reflectivity. This procedure eliminates the receiver noise and side lobe clutter effects, but also removes radar signals related to snowfall events that are associated with relatively low reflectivity values. In an effort to increase DPR signal fidelity associated with snowfall, this paper proposes a simple algorithm using matched DPR Ku/Ka radar reflectivities producing an increase of the fraction of snowfall mass detected by DPR up to 59%.

Weather radar is a key tool to study microphysical properties of clouds and precipitation. In literature, quantitative comparisons have been made between space borne and ground radars to provide deep insight into the internal structure of clouds and cloud systems (Gaussiat et al. 2004). In particular in the last years, several studies have been published comparing data from W-band Cloud Profiling Radar (CPR) on board CloudSat (CS), operating at 94 GHz, and ground radar observations. Impressive progresses in cloud physics and dynamics investigations and benefits for applications, from nowcasting to climate studies (Stephens et al. 2002), have been reached by means of CS. Hudak et al. (2008) describe the validation data from the Environment Canada radar network and the precipitation detection algorithm in the CS product. Protat et al. (2009) compare CS reflectivities and basic ice cloud properties with ground-based radar observation, showing generally good agreements between the two datasets. Liu et al. (2010) compare the observed vertical structure of cloud occurrence and reflectivity distributions from CS and ground-based millimeter-wavelength cloud radar in the tropical western Pacific region. The present study aims at presenting method and preliminary results of a quantitative comparison between data from CS, and data from a ground radar, operating at the frequency of 5.6 GHz (hereinafter GR), to investigate microphysical properties of a cloud system. In particular we focus our attention on the ice component of the cloud, firstly because it is the main portion of the system and secondly it is well investigated by both of the available radars.

On 5 September 2015 a violent hailstorm hit the Gulf and the city of Naples in Italy. The storm originated over the Tyrrhenian Sea dropping 7-10 cm diameter hailstones along its path. During its mature phase, at 08:47 UTC, the hailstorm was captured by one overpass of the Global Precipitation Measurement mission Core Observatory (GPM-CO) embarking the GPM Microwave Imager (GMI) and the Ka/Ku-band Dual-frequency Precipitation Radar (DPR). In this paper, observations by both GMI and DPR are thoroughly analyzed in conjunction with other spaceborne and ground-based measurements, to show how the GPM-CO integrates established observational tools in monitoring, understanding, and characterizing severe weather. Rapid-scan MSG SEVIRI images show an extremely rapid development, with 10.8 mu m cloud-top temperatures dropping by 65 K in 40 min down to 198 K. The Lightning NETwork registered over 37,000 strokes in 5 h, with intracloud positive stroke fraction increasing during the regeneration phases, when ground-based polarimetric radar and DPR support the presence of large graupel/hail particles. DPR Ku 40 dBZ and 20 dBZ echo top heights at 14 km and 16 km, respectively, indicate strong updraft and deep overshooting. GMI extremely low brightness temperatures (TBs) in correspondence of the convective core (158, 97, 67, and 87 K at 18.7, 36.5, 89 and 166 GHz) are compatible with the presence of massive ice particles. In two years of GPM global observations the storm ranks as fourth and first in terms of minimum 36.5 and 18.7 GHz (V-pol) TBs, respectively. This study illustrates GPM-CO sensing capabilities for characterizing the structure of such severe hailstorm, while providing observational evidence of its intensity and rarity, both globally and over the Mediterranean area. (C) 2017 Elsevier B.V. All rights reserved.