The Passive Microwave Empirical Cold Surface Classification Algorithm (PESCA) Application to GMI and ATMS

• Published in: Journal of hydrometeorology Vol. 22; no. 7; pp. 1727 - 1744
• Main Authors: Camplani, Andrea, Casella, Daniele, Sanò, Paolo, Panegrossi, Giulia
• Format: Journal Article
• Language: English
• Published: Boston American Meteorological Society 01.07.2021
• Subjects: Ablation; Algorithms; Classification; Cloud cover; Cold surfaces; Decision trees; Detection; Exploitation; False alarms; Global precipitation; High frequency; Hydrologic cycle; Land area; Maximum temperatures; Microwave imagery; Microwave radiometers; Microwave signatures; Precipitable water; Precipitation; Probability theory; Radiation; Radiometers; Retrieval; Satellites; Scanning; Snow; Snow cover; Snowfall; Spatial discrimination; Spatial resolution; SPECIAL COLLECTION: Global Precipitation Measurement (GPM); Spectral signatures; Statistical methods; Temperature; Northern Hemisphere
• ISSN: 1525-755X; 1525-7541; 1525-7541
• DOI: 10.1175/JHM-D-20-0260.1

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, since several studies have highlighted the influence of snow-cover radiative properties on the falling-snow passive microwave signature. The developed method is based on the exploitation of the lower-frequency channels (<90 GHz), common to most microwave radiometers. The method applied to the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the cross-track-scanning Advanced Technology Microwave Sounder (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 both for sensors in dry conditions (total precipitable water < 10 mm) and for mean surface elevation < 2500 m, independent 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 and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA (overall detection statistics: probability of detection is 0.98 for ATMS and 0.92 for GMI, false alarm ratio is 0.01 for ATMS and 0.08 for GMI, and Heidke skill score is 0.72 for ATMS and 0.69 for GMI). Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometries, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes also appear to be different at high frequency (>90 GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically scanning and cross-track-scanning radiometers, including the future operational EUMETSAT Polar System Second Generation (EPS-SG) mission microwave radiometers.