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Robert T. Ryan
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Robert T. Ryan
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Robert T. Ryan

Abstract

A vertical wind tunnel was constructed to study the behavior of large, low-surface-tension drops in free fall. The tunnel is simple, but provides a low turbulence (0.7%) flow which stably supports large water drops falling at terminal velocity. The influence of reduced surface tension on maximum drop size, drop terminal velocity, and drop shape was investigated. It was found that drops of low surface tension break up at a smaller size than drops with normal surface tension, are more deformed than drops of equal mass having normal surface tension, and have a lower terminal velocity than drops of equal mass and normal surface tension. Drops only partially coated with surfactant cannot be stably supported and undergo violent oscillations. Before any field testing of possible cloud modification by reducing rainwater surface tension is warranted, further investigation of the behavior of low-surface-tension drops should be undertaken and, in particular, the behavior of drops only partially coated with surfactant should be studied.

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Daniel T. McCoy
,
Ryan Eastman
,
Dennis L. Hartmann
, and
Robert Wood

Abstract

Decreases in subtropical low cloud cover (LCC) occur in climate model simulations of global warming. In this study 8-day-averaged observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) spanning 2002–14 are combined with European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis to compute the dependence of the observed variability of LCC on various predictor variables. Large-scale thermodynamic and dynamic predictors of LCC are selected based on insight from large-eddy simulations (LESs) and observational analysis. It is found that increased estimated inversion strength (EIS) is associated with increased LCC. Drying of the free troposphere is associated with decreased LCC. Decreased LCC accompanies subsidence in regions of relatively low EIS; the opposite is found in regions of high EIS. Finally, it is found that increasing sea surface temperature (SST) leads to a decrease in LCC. These results are in keeping with previous studies of monthly and annual data. Based upon the observed response of LCC to natural variability of the control parameters, the change in LCC is estimated for an idealized warming scenario where SST increases by 1 K and EIS increases by 0.2 K. For this change in EIS and SST the LCC is inferred to decrease by 0.5%–2.7% when the regression models are trained on data observed between 40°S and 40°N and by 1.1%–1.4% when trained on data from trade cumulus–dominated regions. When the data used to train the regression model are restricted to stratocumulus-dominated regions the change in LCC is highly uncertain and varies between −1.6% and +1.4%, depending on the stratocumulus-dominated region used to train the regression model.

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R. T. Ryan
,
H. H. Blau Jr.
,
P. C. von Thüna
,
M. L. Cohen
, and
G. D. Roberts

Abstract

An improved single-particle light scattering instrument for measurement of cloud microstructure has been built and used in field studies. Cloud particle size and number information is measured over 12 sizing intervals, in the range 4 to 85 μ diameter. The microstructure can be observed in real time and with a spatial resolution not previously reported. The general features of water cloud droplet size and number distributions are consistent with previous direct capture and replication studies. The transition from water to ice phase regions in cumuliform clouds can be inferred from dramatic changes observed in the distribution features. Results are also presented for stratus and cirrus cloud penetrations.

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Erica Rosenblum
,
Julienne Stroeve
,
Sarah T. Gille
,
Camille Lique
,
Robert Fajber
,
L. Bruno Tremblay
,
Ryan Galley
,
Thiago Loureiro
,
David G. Barber
, and
Jennifer V. Lukovich

Abstract

The Arctic seasonal halocline impacts the exchange of heat, energy, and nutrients between the surface and the deeper ocean, and it is changing in response to Arctic sea ice melt over the past several decades. Here, we assess seasonal halocline formation in 1975 and 2006–12 by comparing daily, May–September, salinity profiles collected in the Canada Basin under sea ice. We evaluate differences between the two time periods using a one-dimensional (1D) bulk model to quantify differences in freshwater input and vertical mixing. The 1D metrics indicate that two separate factors contribute similarly to stronger stratification in 2006–12 relative to 1975: 1) larger surface freshwater input and 2) less vertical mixing of that freshwater. The larger freshwater input is mainly important in August–September, consistent with a longer melt season in recent years. The reduced vertical mixing is mainly important from June until mid-August, when similar levels of freshwater input in 1975 and 2006–12 are mixed over a different depth range, resulting in different stratification. These results imply that decadal changes to ice–ocean dynamics, in addition to freshwater input, significantly contribute to the stronger seasonal stratification in 2006–12 relative to 1975. These findings highlight the need for near-surface process studies to elucidate the impact of lateral processes and ice–ocean momentum exchange on vertical mixing. Moreover, the results may provide insight for improving the representation of decadal changes to Arctic upper-ocean stratification in climate models that do not capture decadal changes to vertical mixing.

Open access
EXECUTIVE COMMITTEE
,
David D. Houghton
,
Paul D. Try
,
Warren M. Washington
,
Robert T. Ryan
,
Margaret A. LeMone
,
Richard S. Greenfield
,
Richard E. Hallgren
, and
Kenneth C. Spengler
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EXECUTIVE COMMITTEE
,
Donald R. Johnson
,
Robert T. Ryan
,
William D. Bonner
,
James R. Mahoney
,
Kristina B. Katsaros
,
Ronald D. McPherson
,
Richard E. Hallgren
, and
Kenneth C. Spengler
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Corey K. Potvin
,
Burkely T. Gallo
,
Anthony E. Reinhart
,
Brett Roberts
,
Patrick S. Skinner
,
Ryan A. Sobash
,
Katie A. Wilson
,
Kelsey C. Britt
,
Chris Broyles
,
Montgomery L. Flora
,
William J. S. Miller
, and
Clarice N. Satrio

Abstract

Thunderstorm mode strongly impacts the likelihood and predictability of tornadoes and other hazards, and thus is of great interest to severe weather forecasters and researchers. It is often impossible for a forecaster to manually classify all the storms within convection-allowing model (CAM) output during a severe weather outbreak, or for a scientist to manually classify all storms in a large CAM or radar dataset in a timely manner. Automated storm classification techniques facilitate these tasks and provide objective inputs to operational tools, including machine learning models for predicting thunderstorm hazards. Accurate storm classification, however, requires accurate storm segmentation. Many storm segmentation techniques fail to distinguish between clustered storms, thereby missing intense cells, or to identify cells embedded within quasi-linear convective systems that can produce tornadoes and damaging winds. Therefore, we have developed an iterative technique that identifies these constituent storms in addition to traditionally identified storms. Identified storms are classified according to a seven-mode scheme designed for severe weather operations and research. The classification model is a hand-developed decision tree that operates on storm properties computed from composite reflectivity and midlevel rotation fields. These properties include geometrical attributes, whether the storm contains smaller storms or resides within a larger-scale complex, and whether strong rotation exists near the storm centroid. We evaluate the classification algorithm using expert labels of 400 storms simulated by the NSSL Warn-on-Forecast System or analyzed by the NSSL Multi-Radar/Multi-Sensor product suite. The classification algorithm emulates expert opinion reasonably well (e.g., 76% accuracy for supercells), and therefore could facilitate a wide range of operational and research applications.

Significance Statement

We have developed a new technique for automatically identifying intense thunderstorms in model and radar data and classifying storm mode, which informs forecasters about the risks of tornadoes and other high-impact weather. The technique identifies storms that are often missed by other methods, including cells embedded within storm clusters, and successfully classifies important storm modes that are generally not included in other schemes, such as rotating cells embedded within quasi-linear convective systems. We hope the technique will facilitate a variety of forecasting and research efforts.

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EXECUTIVE COMMITTEE
,
Robert T. Ryan
,
Warren M. Washington
,
Donald R. Johnson
,
William D. Bonner
,
Margaret A. LeMone
,
Ronald D. McPherson
,
Richard E. Hallgren
, and
Kenneth C. Spengler
Full access