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M. J. Schwartz
,
J. W. Barrett
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P. W. Fieguth
,
P. W. Rosenkranz
,
M. S. Spina
, and
D. H. Staelin

Abstract

An imaging microwave radiometer with eight double-sideband channels centered on the 118-GHz oxygen resonance was flown on a high-altitude aircraft over a tropical cyclone in the Coral Sea. The measurements clearly resolved an eyewall of strong convection and a warm core within the eye. Brightness temperatures observed within the eye were approximately 10 K warmer than those observed in clear air 100 km or more away. This warming extended somewhat beyond the eyewall in the highest (most opaque) channel. The temperature profile in the eye, central pressure, and convective cell-top altitudes are inferred from the data.

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A. J. Gasiewski
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J. W. Barrett
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P. G. Bonanni
, and
D. H. Staelin

Abstract

Passive microwave imagine of O2emissions using the 118.75-GHz(1) resonance has been investigated for tropospheric and stratosphere remote sensing of atmospheric temperature and precipitation. An imaging millimeter-wave spectrometer (MTS) using eight double-sideband channels centered around the 118.75-GHz O2 resonance, and including a fixed-beam 53.65-GHz radiometer and video camera was constructed. The MTS collected data during 33 flights of the NASA ER-2 high-altitude aircraft in 1986 during the Genesis of Atlantic Lows Experiment (GALE) and the Cooperative Huntsvilie Meteorological Experiment (COHMEX), yielding the first high spatial resolution microwave images of atmospheric O2 brightness.

The isolated 118-GHz fine offers higher spatial resolution and precipitation sensitivity than O2 lines in the 5-mm band near 60 GHz. The brightness temperature perturbations of clouds in nonprecipitating regions are typically twice as large in the 118-GHz channels relative to comparable 60-GHz channels. However, observations over cirrus anvils show that the 118-GHz brightnesses are not adversely sensitive to some optically opaque cloud cover. Thus, these channels are expected to be useful for temperature sounding in the presence of clouds, although retrieval ambiguities can result from variations in the water vapor profile and surface emissivity. The demonstration of 118-GHz temperature profile retrievals is left for a subsequent paper.

Over deep convective precipitation, 118-GHz brightness temperature images are characterized by decreases of up to 200 K due to strong scattering in the storm core. The amplitudes of the 118-GHz brightness perturbations contain information on the altitude of the cell top. The shape of the 118-GHz spectrum is also suggested to contain altitude information by virtue of the various peaking altitudes of the 118-GHz weighting functions. Precipitation cells observed by the MTS sometimes appear in bands or rows, and have been accompanied by periodic radiance structures.

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E. A. Smith
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J. E. Lamm
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R. Adler
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J. Alishouse
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K. Aonashi
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E. Barrett
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P. Bauer
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W. Berg
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A. Chang
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R. Ferraro
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J. Ferriday
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S. Goodman
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N. Grody
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C. Kidd
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D. Kniveton
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C. Kummerow
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G. Liu
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F. Marzano
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A. Mugnai
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W. Olson
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G. Petty
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A. Shibata
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R. Spencer
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F. Wentz
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T. Wilheit
, and
E. Zipser

Abstract

The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 satellite precipitation retrieval algorithms, implemented for application with Special Sensor Microwave/Imager (SSM/I) passive microwave (PMW) measurements and run for a set of rainfall case studies at full resolution–instantaneous space–timescales. The cases are drawn from over the globe during all seasons, for a period of 7 yr, over a 60°N–17°S latitude range. Ground-based data were used for the intercomparisons, principally based on radar measurements but also including rain gauge measurements. The goals of PIP-2 are 1) to improve performance and accuracy of different SSM/I algorithms at full resolution–instantaneous scales by seeking a better understanding of the relationship between microphysical signatures in the PMW measurements and physical laws employed in the algorithms; 2) to evaluate the pros and cons of individual algorithms and their subsystems in order to seek optimal “front-end” combined algorithms; and 3) to demonstrate that PMW algorithms generate acceptable instantaneous rain estimates.

It is found that the bias uncertainty of many current PMW algorithms is on the order of ±30%. This level is below that of the radar and rain gauge data specially collected for the study, so that it is not possible to objectively select a best algorithm based on the ground data validation approach. By decomposing the intercomparisons into effects due to rain detection (screening) and effects due to brightness temperature–rain rate conversion, differences among the algorithms are partitioned by rain area and rain intensity. For ocean, the screening differences mainly affect the light rain rates, which do not contribute significantly to area-averaged rain rates. The major sources of differences in mean rain rates between individual algorithms stem from differences in how intense rain rates are calculated and the maximum rain rate allowed by a given algorithm. The general method of solution is not necessarily the determining factor in creating systematic rain-rate differences among groups of algorithms, as we find that the severity of the screen is the dominant factor in producing systematic group differences among land algorithms, while the input channel selection is the dominant factor in producing systematic group differences among ocean algorithms. The significance of these issues are examined through what is called “fan map” analysis.

The paper concludes with a discussion on the role of intercomparison projects in seeking improvements to algorithms, and a suggestion on why moving beyond the “ground truth” validation approach by use of a calibration-quality forward model would be a step forward in seeking objective evaluation of individual algorithm performance and optimal algorithm design.

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Scott F. Blair
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Jennifer M. Laflin
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Dennis E. Cavanaugh
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Kristopher J. Sanders
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Scott R. Currens
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Justin I. Pullin
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Dylan T. Cooper
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Derek R. Deroche
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Jared W. Leighton
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Robert V. Fritchie
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Mike J. Mezeul II
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Barrett T. Goudeau
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Stephen J. Kreller
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John J. Bosco
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Charley M. Kelly
, and
Holly M. Mallinson

Abstract

A field research campaign, the Hail Spatial and Temporal Observing Network Effort (HailSTONE), was designed to obtain physical high-resolution hail measurements at the ground associated with convective storms to help address several operational challenges that remain unsatisfied through public storm reports. Field phases occurred over a 5-yr period, yielding hail measurements from 73 severe thunderstorms [hail diameter ≥ 1.00 in. (2.54 cm)]. These data provide unprecedented insight into the hailfall character of each storm and afford a baseline to explore the representativeness of the climatological hail database and hail forecasts in NWS warning products. Based upon the full analysis of HailSTONE observations, hail sizes recorded in Storm Data as well as hail size forecasts in NWS warnings frequently underestimated the maximum diameter hailfall occurring at the surface. NWS hail forecasts were generally conservative in size and at least partially calibrated to incoming hail reports. Storm mode played a notable role in determining the potential range of maximum hail size during the life span of each storm. Supercells overwhelmingly produced the largest hail diameters, with smaller maximum hail sizes observed as convection became progressively less organized. Warning forecasters may employ a storm-mode hail size forecast philosophy, in conjunction with other radar-based hail detection techniques, to better anticipate and forecast hail sizes during convective warning episodes.

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James D. Doyle
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Jonathan R. Moskaitis
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Joel W. Feldmeier
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Ronald J. Ferek
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Mark Beaubien
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Michael M. Bell
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Daniel L. Cecil
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Robert L. Creasey
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Patrick Duran
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Russell L. Elsberry
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William A. Komaromi
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John Molinari
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David R. Ryglicki
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Daniel P. Stern
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Christopher S. Velden
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Xuguang Wang
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Todd Allen
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Bradford S. Barrett
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Peter G. Black
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Jason P. Dunion
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Kerry A. Emanuel
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Patrick A. Harr
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Lee Harrison
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Eric A. Hendricks
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Derrick Herndon
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William Q. Jeffries
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Sharanya J. Majumdar
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James A. Moore
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Zhaoxia Pu
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Robert F. Rogers
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Elizabeth R. Sanabia
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Gregory J. Tripoli
, and
Da-Lin Zhang

Abstract

Tropical cyclone (TC) outflow and its relationship to TC intensity change and structure were investigated in the Office of Naval Research Tropical Cyclone Intensity (TCI) field program during 2015 using dropsondes deployed from the innovative new High-Definition Sounding System (HDSS) and remotely sensed observations from the Hurricane Imaging Radiometer (HIRAD), both on board the NASA WB-57 that flew in the lower stratosphere. Three noteworthy hurricanes were intensively observed with unprecedented horizontal resolution: Joaquin in the Atlantic and Marty and Patricia in the eastern North Pacific. Nearly 800 dropsondes were deployed from the WB-57 flight level of ∼60,000 ft (∼18 km), recording atmospheric conditions from the lower stratosphere to the surface, while HIRAD measured the surface winds in a 50-km-wide swath with a horizontal resolution of 2 km. Dropsonde transects with 4–10-km spacing through the inner cores of Hurricanes Patricia, Joaquin, and Marty depict the large horizontal and vertical gradients in winds and thermodynamic properties. An innovative technique utilizing GPS positions of the HDSS reveals the vortex tilt in detail not possible before. In four TCI flights over Joaquin, systematic measurements of a major hurricane’s outflow layer were made at high spatial resolution for the first time. Dropsondes deployed at 4-km intervals as the WB-57 flew over the center of Hurricane Patricia reveal in unprecedented detail the inner-core structure and upper-tropospheric outflow associated with this historic hurricane. Analyses and numerical modeling studies are in progress to understand and predict the complex factors that influenced Joaquin’s and Patricia’s unusual intensity changes.

Open access
David C. Leon
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Jeffrey R. French
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Sonia Lasher-Trapp
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Alan M. Blyth
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Steven J. Abel
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Susan Ballard
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Andrew Barrett
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Lindsay J. Bennett
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Keith Bower
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Barbara Brooks
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Phil Brown
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Cristina Charlton-Perez
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Thomas Choularton
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Peter Clark
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Chris Collier
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Jonathan Crosier
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Zhiqiang Cui
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Seonaid Dey
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David Dufton
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Chloe Eagle
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Michael J. Flynn
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Martin Gallagher
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Carol Halliwell
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Kirsty Hanley
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Lee Hawkness-Smith
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Yahui Huang
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Graeme Kelly
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Malcolm Kitchen
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Alexei Korolev
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Humphrey Lean
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Zixia Liu
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John Marsham
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Daniel Moser
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John Nicol
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Emily G. Norton
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David Plummer
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Jeremy Price
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Hugo Ricketts
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Nigel Roberts
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Phil D. Rosenberg
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David Simonin
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Jonathan W. Taylor
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Robert Warren
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Paul I. Williams
, and
Gillian Young

Abstract

The Convective Precipitation Experiment (COPE) was a joint U.K.–U.S. field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly as a result of the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the United States. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve numerical weather prediction (NWP) model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the U.K. BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360° volume scans over 10 elevation angles approximately every 5 min and was augmented by two Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper i) provides an overview of the COPE field campaign and the resulting dataset, ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone, and iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.

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