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- Author or Editor: Ali D. S. Ali x
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Abstract
The thermal emission spectrum of the atmosphere near the 118 GHz oxygen resonance has been measured from the NASA Convair-990 aircraft as it flew over clear air and storms. The instrument viewed the ground 45° from nadir with a 7.5° beamwidth. Brightness temperatures were measured in six bands 200 MHz wide centered at frequencies 821–1891 MHz from the line at 118.7505 GHz. The double-sideband super-heterodyne receiver had ∼1 K sensitivity for 1 s integration. Comparison of observed clear air brightness temperatures (from 238 mb) with those computed for a coincident dropsonde yielded agreement within 1.4 K; the retrieved temperature profile agreed with the dropsonde with an average magnitude error of 1.4 K. Observations over precipitation yielded brightness perturbations as large as 30 K.
Abstract
The thermal emission spectrum of the atmosphere near the 118 GHz oxygen resonance has been measured from the NASA Convair-990 aircraft as it flew over clear air and storms. The instrument viewed the ground 45° from nadir with a 7.5° beamwidth. Brightness temperatures were measured in six bands 200 MHz wide centered at frequencies 821–1891 MHz from the line at 118.7505 GHz. The double-sideband super-heterodyne receiver had ∼1 K sensitivity for 1 s integration. Comparison of observed clear air brightness temperatures (from 238 mb) with those computed for a coincident dropsonde yielded agreement within 1.4 K; the retrieved temperature profile agreed with the dropsonde with an average magnitude error of 1.4 K. Observations over precipitation yielded brightness perturbations as large as 30 K.
Abstract
Rainfall retrieval algorithms often assume a gamma-shaped raindrop size distribution (DSD) with three mathematical parameters N
w
, D
m
, and μ. If only two independent measurements are available, as with the dual-frequency precipitation radar on the Global Precipitation Measurement (GPM) mission core satellite, then retrieval algorithms are underconstrained and require assumptions about DSD parameters. To reduce the number of free parameters, algorithms can assume that μ is either a constant or a function of D
m
. Previous studies have suggested μ–Λ constraints [where Λ = (4 + μ)/D
m
], but controversies exist over whether μ–Λ constraints result from physical processes or mathematical artifacts due to high correlations between gamma DSD parameters. This study avoids mathematical artifacts by developing joint probability distribution functions (joint PDFs) of statistically independent DSD attributes derived from the raindrop mass spectrum. These joint PDFs are then mapped into gamma-shaped DSD parameter joint PDFs that can be used in probabilistic rainfall retrieval algorithms as proposed for the GPM satellite program. Surface disdrometer data show a high correlation coefficient between the mass spectrum mean diameter D
m
and mass spectrum standard deviation σ
m
. To remove correlations between DSD attributes, a normalized mass spectrum standard deviation 
Abstract
Rainfall retrieval algorithms often assume a gamma-shaped raindrop size distribution (DSD) with three mathematical parameters N
w
, D
m
, and μ. If only two independent measurements are available, as with the dual-frequency precipitation radar on the Global Precipitation Measurement (GPM) mission core satellite, then retrieval algorithms are underconstrained and require assumptions about DSD parameters. To reduce the number of free parameters, algorithms can assume that μ is either a constant or a function of D
m
. Previous studies have suggested μ–Λ constraints [where Λ = (4 + μ)/D
m
], but controversies exist over whether μ–Λ constraints result from physical processes or mathematical artifacts due to high correlations between gamma DSD parameters. This study avoids mathematical artifacts by developing joint probability distribution functions (joint PDFs) of statistically independent DSD attributes derived from the raindrop mass spectrum. These joint PDFs are then mapped into gamma-shaped DSD parameter joint PDFs that can be used in probabilistic rainfall retrieval algorithms as proposed for the GPM satellite program. Surface disdrometer data show a high correlation coefficient between the mass spectrum mean diameter D
m
and mass spectrum standard deviation σ
m
. To remove correlations between DSD attributes, a normalized mass spectrum standard deviation
