CloudSat-based assessment of GPM microwave imager snowfall observation capabilities

Giulia Panegrossi, Jean François Rysman, Daniele Casella, Anna Cinzia Marra, Paolo Sanò, Mark S. Kulie

Research output: Contribution to journalArticlepeer-review

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 ΔTB) 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 Δ 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 ΔTB-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.

Original languageEnglish
Article number1263
JournalRemote Sensing
Volume9
Issue number12
DOIs
Publication statusPublished - 1 Dec 2017
Externally publishedYes

Keywords

  • CALIPSO
  • CPR
  • CloudSat
  • GPM
  • High latitudes
  • Passive microwave
  • Remote sensing of precipitation
  • Snowfall detection

Fingerprint

Dive into the research topics of 'CloudSat-based assessment of GPM microwave imager snowfall observation capabilities'. Together they form a unique fingerprint.

Cite this