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Michael Peterson, Chuntao Liu, Douglas Mach, Wiebke Deierling, and Christina Kalb

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

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Michael Peterson, Wiebke Deierling, Chuntao Liu, Douglas Mach, and Christina Kalb

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

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Eui-Seok Chung, Brian J. Soden, and Viju O. John

homogenized upper-tropospheric water vapor dataset with a long-term stability. The rectification and intersatellite calibration of SSM/T-2 measurements are deferred because of the lack of calibration information in the 2000s. In addition, the AMSU-B and MHS 183.31 ± 3 GHz channel observations will be analyzed to produce a continuous dataset of midtropospheric water vapor suitable for climate studies. 2. Satellite-based microwave radiometer observations Satellite-based radiance measurements of the 183-GHz

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Y. Hu and X. Zou

appears ragged or is partially obscured or if the TC center is totally covered by a central dense overcast cloud field ( Wimmers and Velden 2010 ). Microwave observations from polar-orbiting satellites can penetrate clouds except for heavy precipitation and can well reveal convective organizations and eyewall structures, beneficial for locating TC centers. The ARCHER was applied to 37- and 85–92-GHz microwave observations from imagers, e.g., the Special Sensor Microwave Imager, the Special Sensor

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F. Joseph Turk, Ramon Padullés, Estel Cardellach, Chi O. Ao, Kuo-Nung Wang, David D. Morabito, Manuel de la Torre Juarez, Mayra Oyola, Svetla Hristova-Veleva, and J. David Neelin

-coincident observations between ROHP and the passive microwave (MW) radiometric satellite observations that make up the current GPM satellite constellation ( Kidd et al. 2018 ) are used to simulate the Δ ϕ profile by the associated three-dimensional precipitation conditions. Depending upon the sensor, these radiometers scan a wide swath and whose equivalent blackbody brightness temperature (TB) imagery can provide three-dimensional context to the precipitation structure that is not discernable from the ROHP Δ ϕ

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Haobo Tan, Jietai Mao, Huanhuan Chen, P. W. Chan, Dui Wu, Fei Li, and Tao Deng

verification and comparison of results obtained by four radiative transfer models using ground-based, remote sensing observations of brightness temperatures, and it is found that MonoRTM simulates the brightness temperatures of microwave radiations very well. b. Principal component analysis and stepwise regression When the regression method is used in forecasting, there could be a number of predictors that may not be mutually independent but rather are related to each other to a certain extent. In such

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H. Dong and X. Zou

1. Introduction Satellite microwave temperature sounders, humidity sounders, and imagers have provided complementary global observations of the global atmospheric and Earth surface variables for several decades. It is important to ensure the highest possible accuracy and precision of these observations before they are assimilated into numerical weather prediction (NWP) models. Although the Advanced Technology Microwave Sounder (ATMS) on board the Suomi–National Polar-Orbiting Partnership

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Andrew Manaster, Lucrezia Ricciardulli, and Thomas Meissner

1. Introduction Spaceborne active and passive microwave sensors have been monitoring global ocean surface winds for more than three decades ( Bourassa et al. 2010 ; Wentz et al. 2017 ). These measurements have been extensively validated versus in situ observations from anemometers on moored buoys and are highly accurate in the low to moderate wind regime (0–15 m s −1 ). In this wind regime, wind observations from satellites and buoys, converted to a reference height of 10 m, typically agree

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Patrick C. Meyers, Ralph R. Ferraro, and Nai-Yu Wang

-global rain-rate estimates every 3–4 h. The microwave spectrum is particularly sensitive to water in all states, allowing for retrievals of water vapor, liquid precipitation, and surface snow cover. Rainfall interacts with the microwave emissions from the earth’s surface, such that convective regions can be identified by the scattering of surface emissions by suspended snow, ice, and water ( Ferraro et al. 1998 ). Spaceborne passive microwave (PMW) imagers have been used to monitor global precipitation

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Stephen Po-Chedley and Qiang Fu

Wang W. , 2010 : Stability of the MSU-derived atmospheric temperature trend . J. Atmos. Oceanic Technol. , 27 , 1960 – 1971 . Zou, C.-Z. , and Wang W. , 2012 : Inter-satellite calibration of AMSU-A observations for weather and climate applications . J. Geophys. Res. , 116 , D23113 , doi:10.1029/2011JD016205 . Zou, C.-Z. , Goldberg M. D. , Cheng Z. , Grody N. C. , Sullivan J. T. , Cao C. , and Tarpley D. , 2006 : Recalibration of microwave sounding unit for climate

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