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, Olson et al. (1999 , 2006) and Grecu and Olson (2006 , hereafter GO06) developed methods that directly interpreted satellite passive microwave signatures in terms of heating vertical structure. The first two of these studies utilized cloud-resolving model simulations to synthesize microwave radiances; the model relationships between radiances and heating profiles were then employed in a Bayesian methodology for inferring heating profiles from satellite microwave sensor observations. These
, Olson et al. (1999 , 2006) and Grecu and Olson (2006 , hereafter GO06) developed methods that directly interpreted satellite passive microwave signatures in terms of heating vertical structure. The first two of these studies utilized cloud-resolving model simulations to synthesize microwave radiances; the model relationships between radiances and heating profiles were then employed in a Bayesian methodology for inferring heating profiles from satellite microwave sensor observations. These
the GEC on subseasonal time scales. The goal of this study is to develop a method for directly estimating electric fields above individual electrified clouds from common 37- and 85-GHz passive microwave observations. By coalescing the long record of satellite passive microwave observations at or near these frequencies taken by the Special Sensor Microwave Imager (SSM/I; Hollinger et al. 1990 ), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI; Kummerow et al. 1998 ), and Global
the GEC on subseasonal time scales. The goal of this study is to develop a method for directly estimating electric fields above individual electrified clouds from common 37- and 85-GHz passive microwave observations. By coalescing the long record of satellite passive microwave observations at or near these frequencies taken by the Special Sensor Microwave Imager (SSM/I; Hollinger et al. 1990 ), Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI; Kummerow et al. 1998 ), and Global
. (2007 , and references therein). Most of the emphasis of the work was on tropospheric water vapor and the comparison of various measurement systems, both in situ and remote. However, there were some significant differences between the in situ systems in the upper troposphere and lower stratosphere, and these differences would be difficult to resolve by the two standard remote sounding systems of precipitable water vapor (PWV), namely the dual-frequency microwave radiometer (MWR) and the global
. (2007 , and references therein). Most of the emphasis of the work was on tropospheric water vapor and the comparison of various measurement systems, both in situ and remote. However, there were some significant differences between the in situ systems in the upper troposphere and lower stratosphere, and these differences would be difficult to resolve by the two standard remote sounding systems of precipitable water vapor (PWV), namely the dual-frequency microwave radiometer (MWR) and the global
often contaminated by those due to snow and sand particles on the surface. Seto et al. (2005 , hereinafter S05 ) developed RNC methods for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) ( Kummerow et al. 1998 ) over land using a statistical database. The database statistically summarizes the land surface brightness temperature of the TMI pixels judged to be no-rain pixels by simultaneous TRMM precipitation radar (PR) observations. Therefore, these methods, called PR
often contaminated by those due to snow and sand particles on the surface. Seto et al. (2005 , hereinafter S05 ) developed RNC methods for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) ( Kummerow et al. 1998 ) over land using a statistical database. The database statistically summarizes the land surface brightness temperature of the TMI pixels judged to be no-rain pixels by simultaneous TRMM precipitation radar (PR) observations. Therefore, these methods, called PR
1. Introduction A strong need is emerging to have accurate radiative transfer simulations from realistic cloudy and rainy scenes at high microwave frequencies. First, efforts are made to assimilate satellite microwave radiation from cloudy and rainy atmospheres within numerical weather prediction (NWP) models. As a first step, precipitation affected satellite observations at microwave frequencies up to 22 GHz are assimilated in the European Centre for Medium-Range Weather Forecasts (ECMWF
1. Introduction A strong need is emerging to have accurate radiative transfer simulations from realistic cloudy and rainy scenes at high microwave frequencies. First, efforts are made to assimilate satellite microwave radiation from cloudy and rainy atmospheres within numerical weather prediction (NWP) models. As a first step, precipitation affected satellite observations at microwave frequencies up to 22 GHz are assimilated in the European Centre for Medium-Range Weather Forecasts (ECMWF
). Observations from the Special Sensor Microwave Imager (SSM/I) sensor aboard multiple polar-orbiting platforms indicate increases in global water vapor from the record’s inception in 1987 ( Wentz and Schabel 2000 ; Trenberth et al. 2005 ). Both sets of observations are consistent with predictions of anthropogenic climate change. It appears that issues associated with calibration, orbital drift, and other effects have been addressed to the degree necessary to reveal long-term global trends. New results
). Observations from the Special Sensor Microwave Imager (SSM/I) sensor aboard multiple polar-orbiting platforms indicate increases in global water vapor from the record’s inception in 1987 ( Wentz and Schabel 2000 ; Trenberth et al. 2005 ). Both sets of observations are consistent with predictions of anthropogenic climate change. It appears that issues associated with calibration, orbital drift, and other effects have been addressed to the degree necessary to reveal long-term global trends. New results
Retrieving Global Wilson Currents from Electrified Clouds Using Satellite Passive Microwave Observationsregridding is to use the large volume of AMPR measurements rather than a small number of coincident TRMM overpasses to determine coefficients in the Peterson et al. (2015) algorithm that are compatible with the TMI scan geometry. b. The Peterson et al. (2015) ER-2 algorithm The passive microwave electric field retrieval algorithm from Peterson et al. (2015) uses the AMPR 37- or 85-GHz observations of clouds below the aircraft to estimate the electric field that is measured by LIP at the ER-2
regridding is to use the large volume of AMPR measurements rather than a small number of coincident TRMM overpasses to determine coefficients in the Peterson et al. (2015) algorithm that are compatible with the TMI scan geometry. b. The Peterson et al. (2015) ER-2 algorithm The passive microwave electric field retrieval algorithm from Peterson et al. (2015) uses the AMPR 37- or 85-GHz observations of clouds below the aircraft to estimate the electric field that is measured by LIP at the ER-2
the temperature trend of the earth’s atmosphere and its spatial structure remains a challenge. Temperature trends derived from conventional radiosonde observations are questionable because they are subject to large regional and temporal errors due to varying observational practices in different countries. In addition, radiosonde stations are too sparse for determining spatial trend patterns. The Microwave Sounding Unit (MSU) on board the NOAA polar-orbiting satellites is uniquely positioned to
the temperature trend of the earth’s atmosphere and its spatial structure remains a challenge. Temperature trends derived from conventional radiosonde observations are questionable because they are subject to large regional and temporal errors due to varying observational practices in different countries. In addition, radiosonde stations are too sparse for determining spatial trend patterns. The Microwave Sounding Unit (MSU) on board the NOAA polar-orbiting satellites is uniquely positioned to
over the ocean, microwave emissivity over land is highly variable because of a plethora of surface characteristics that include soil moisture, soil texture, surface roughness, land-cover type, and vegetation optical depth. During the last three decades, substantial progress has been made in retrieving LSE from passive microwave (PMW) sensors. Retrieval algorithms are broadly based on land surface models and direct satellite observations ( Ferraro et al. 2013 ; Turk et al. 2014 ; Ringerud et al
over the ocean, microwave emissivity over land is highly variable because of a plethora of surface characteristics that include soil moisture, soil texture, surface roughness, land-cover type, and vegetation optical depth. During the last three decades, substantial progress has been made in retrieving LSE from passive microwave (PMW) sensors. Retrieval algorithms are broadly based on land surface models and direct satellite observations ( Ferraro et al. 2013 ; Turk et al. 2014 ; Ringerud et al
aircraft in the scene to make photographic or video observations, and tower-based operations are suspended during inclement weather. Microwave radiometer data represent another source of whitecap information. As in ocean surface optical images, microwave brightness temperature T bp increases sharply in the presence of whitecaps (surface foams); subscript p is polarization and is either vertical (V) or horizontal (H) in this paper. Several investigations of whitecap retrieval from T bp data have
aircraft in the scene to make photographic or video observations, and tower-based operations are suspended during inclement weather. Microwave radiometer data represent another source of whitecap information. As in ocean surface optical images, microwave brightness temperature T bp increases sharply in the presence of whitecaps (surface foams); subscript p is polarization and is either vertical (V) or horizontal (H) in this paper. Several investigations of whitecap retrieval from T bp data have
