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Katja Friedrich, David E. Kingsmill, Cyrille Flamant, Hanne V. Murphey, and Roger M. Wakimoto

better monitor the temporal and spatial variation of Froude numbers. 6. Summary and conclusions The kinematic and moisture structure of a slow-moving, nonprecipitating cold front observed in west-central Kansas on 10 June 2002 during IHOP has been examined with a wide array of ground-based and airborne instrumentation that included in situ sensors, sounding systems, Doppler radars, a microwave radiometer, and a differential absorption lidar. Intensive observations were collected across a ∼40-km

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Erin M. Dougherty, John Molinari, Robert F. Rogers, Jun A. Zhang, and James P. Kossin

downdrafts from 85 to 100 km in radius. The vertical vorticity cross section ( Fig. 11c ) is consistent with a possible wavenumber-1 asymmetry suggested by the asymmetries in the microwave and radar data (cf. Figs. 7 and 8 ). To the south, where there was predominantly rising motion, vorticity appeared enhanced compared to the north, particularly at 20 km in radius. Vorticity was generally maximized within 50 km both north and south near the primary eyewall, consistent with observations of mature

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Nicholas T. Luchetti, Katja Friedrich, Christopher E. Rodell, and Julie K. Lundquist

sensor and relative humidity are available at 3, 26, and 88 m AGL. All M-4 tower instruments sample at 20 Hz averaged to 1-min output ( Clifton et al. 2013 ; Clifton 2014 ). To avoid tower-wake impacts ( Clifton 2014 ), we only consider winds between 25° and 100° and 175° and 300°; we removed one event at the NWTC. c. Description of remote sensing research instruments In addition to tower observations, both sites use a Radiometrics MWR-3000A microwave radiometer and Leosphere/NRG WindCube lidars. A

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E. R. Toracinta, Daniel J. Cecil, Edward J. Zipser, and Stephen W. Nesbitt

). Observations of midlatitude thunderstorms ( Dye et al. 1986, 1989 ), tropical island convection ( Carey and Rutledge 2000 ), and tropical oceanic convection ( Petersen et al. 1996 ) show that rapid cloud electrification occurs with the presence of millimeter-sized graupel or frozen drops and high reflectivity (35–40 dB Z ) above 6–7 km. The relationship between flash rate and the 85- and 37-GHz PCTs is consistent with the response of the microwave frequencies to an increase in the number and size of liquid

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Stephen W. Nesbitt, Robert Cifelli, and Steven A. Rutledge

version 5 rainfall estimates from the TRMM microwave imager, precipitation radar, and rain gauges on global, regional, and storm scales. J. Appl. Meteor. , 43 , 1016 – 1036 . Petersen , W. A. , and S. A. Rutledge , 2001 : Regional variability in tropical convection: Observations from TRMM. J. Climate , 14 , 3566 – 3586 . Rickenbach , T. M. , 1999 : Cloud-top evolution of tropical oceanic squall lines from radar reflectivity and infrared satellite data. Mon. Wea. Rev. , 127 , 2951

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Q. Xiao, X. Zou, and Y-H. Kuo

1960s and have substantially improved in accuracy in recent years. It is now widely recognized that satellite products have a positive impact on operational analysis and forecasts, especially in the data-sparse oceanic areas where conventional radiosonde measurements are not available. Among all the observational data from satellites, microwave remote sensing sounding has been proven to be the most informative source of data to provide significant improvements in quantitative measurements of

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Cheng-Zhi Zou and Michael L. Van Woert

resolutions for accurate flux estimates. In particular, radiosonde stations are sparse over the oceans, aircraft observations are collected on an intermittent basis along fixed flight tracks and heights, and satellite cloud-drift winds only contain high- and low-level winds (e.g., Schmetz et al. 1993 ). For this reason, many recent energy and moisture flux studies are based on the wind fields from various analyses and reanalyses (e.g., Bromwich et al. 1995 ; Cullather et al. 1996 , 2000 ; Wang and

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Alfred T. C. Chang and Long S. Chiu

1. Introduction In the estimation of space–time rainfall from satellite measurements, one can distinguish three types of errors:systematic, random, and sampling ( Wilheit 1988 ). Errors in the algorithm assumptions (algorithm error) and the sensor calibration are the main contributors to systematic errors. For example, the nonuniform field of view of microwave sensors introduces a beam-filling bias in rain rate retrievals ( Chiu et al. 1990 ). Nonsystematic errors consist of

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Tzvi Gal-Chen, Brian D. Schmidt, and Louis W. Uccellini

maintain the positive impact on the numerical simulations. This implies that in order to make geostationarysatellite data useful for initializing numerical models, microwave sounding channels should be considered forgeostationary orbit so that frequent observations of the surface-500 mb thickness can be estimated for cloudcovered (but nonprecipitating) areas as well.1. Introduction At present, the operational meteorological satelliteobserving system is designed to retrieve temperatureand humidity

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Simon W. Chang and Teddy R. Holt

microwave emission; therefore, the measured brightnesstemperature and inferred rain rates are more relatedto the internal bulk microphysical properties of theclouds, instead of the height of the clouds in the caseof infrared sensors. The SSM/I rain-rate measurementsare thus less likely to be "fooled" by high stratus orcirrus clouds. Currently, there are three SSM/I's ino~bit, providing up to six observations daily~ The algorithm for rainfall rates has been extensively calibratedand validated against

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