scholarly journals High-Temporal Global Rainfall Maps from Satellite Passive Microwave Radiometers

10.5772/9027 ◽  
2010 ◽  
Author(s):  
Shoichi Shige ◽  
Satoshi Kida ◽  
Tomoya Yamamoto ◽  
Takuji Kubota ◽  
Kazumasa Aonashi
Keyword(s):  
Passive Microwave ◽  
Microwave Radiometers

A 1DVAR-Based Snowfall Rate Algorithm for Passive Microwave Radiometers

2020 ◽  
pp. 297-313
Author(s):  
Huan Meng ◽  
Cezar Kongoli ◽  
Ralph R. Ferraro
Keyword(s):  
Passive Microwave ◽  
Microwave Radiometers

Spectral linear mixing model applied to data from passive microwave radiometers for sea ice mapping in the Antarctic Peninsula

2021 ◽  
pp. 1-27
Author(s):  
Fernando Luis Hillebrand ◽  
Ulisses Franz Bremer ◽  
Marcos Wellausen Dias de Freitas ◽  
Juliana Costi ◽  
Cláudio Wilson Mendes Júnior ◽  
...  
Keyword(s):  
Sea Ice ◽  
Antarctic Peninsula ◽  
Mixing Model ◽  
Passive Microwave ◽  
Linear Mixing Model ◽  
Microwave Radiometers ◽  
The Antarctic

scholarly journals Snow Depth Retrieval on Arctic Sea Ice From Passive Microwave Radiometers—Improvements and Extensions to Multiyear Ice Using Lower Frequencies

2018 ◽  
Vol 123 (10) ◽  
pp. 7120-7138 ◽  
Author(s):  
Philip Rostosky ◽  
Gunnar Spreen ◽  
Sinead L. Farrell ◽  
Torben Frost ◽  
Georg Heygster ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Passive Microwave ◽  
Arctic Sea ◽  
Microwave Radiometers ◽  
Multiyear Ice ◽  
Lower Frequencies

Parametric Rainfall Retrieval Algorithms for Passive Microwave Radiometers

2003 ◽  
Vol 42 (10) ◽  
pp. 1480-1496 ◽  
Author(s):  
Dong-Bin Shin ◽  
Christian Kummerow
Keyword(s):  
Passive Microwave ◽  
Retrieval Algorithms ◽  
Microwave Radiometers ◽  
Rainfall Retrieval

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

2021 ◽  
Author(s):  
Andrea Camplani ◽  
Daniele Casella ◽  
Paolo Sanò ◽  
Giulia Panegrossi
Keyword(s):  
Snow Cover ◽  
Classification Algorithm ◽  
Passive Microwave ◽  
Radiative Properties ◽  
Maximum Temperature ◽  
Satellite Measurements ◽  
Cold Surface ◽  
Surface Classification ◽  
Microwave Radiometers ◽  
Cross Track

AbstractThis 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.

Daily Rainfall Estimate by Emissivity Temporal Variation from 10 Satellites

2021 ◽  
Author(s):  
Yalei You ◽  
S. Joseph Munchak ◽  
Christa Peters-Lidard ◽  
Sarah Ringerud
Keyword(s):  
Temporal Variation ◽  
Low Frequency ◽  
Daily Rainfall ◽  
Passive Microwave ◽  
Advanced Technology ◽  
Precipitation Measurement ◽  
Comparable Performance ◽  
Microwave Imager ◽  
Microwave Radiometers ◽  
Global Precipitation

AbstractRainfall retrieval algorithms for passive microwave radiometers often exploits the brightness temperature depression due to ice scattering at high frequency channels (≥ 85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low frequency channels (19, 24 and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Special Sensor Microwave Imager/Sounder (SSMIS), the Advanced Technology Microwave Sounder (ATMS), and four Advanced Microwave Sounding Unit-A (AMSU-A). Four different satellite combination schemes are used to derive the Δe for daily rainfall estimates. They are all-10-satellites, 5-imagers, 6-satellites with very different equator crossing times, and GMI-only. Results show that Δe from all-10-satellites has the best performance with a correlation of 0.60 and RMSE of 6.52 mm, comparing with the integrated multi-satellite retrievals (IMERG) final run product. The 6-satellites scheme has comparable performance with all-10-satellites scheme. The 5-imagers scheme performs noticeably worse with a correlation of 0.49 and RMSE of 7.28 mm, while the GMI-only scheme performs the worst with a correlation of 0.25 and RMSE of 11.36 mm. The inferior performance from the 5-imagers and GMI-only schemes can be explained by the much longer revisit time, which cannot accurately capture the emissivity temporal variation.

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