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Warner L. Ecklund
,
Christopher R. Williams
,
Paul E. Johnston
, and
Kenneth S. Gage

Abstract

A 3-GHz profiler has been developed by the National Oceanic and Atmospheric Administration’s Aeronomy Laboratory to observe the evolution and vertical structure of precipitating cloud systems. The profiler is very portable, robust, and relatively inexpensive, so that continuous, unattended observations of overhead precipitation can be obtained, even at remote locations. The new profiler is a vertically looking Doppler radar that operates at S band, a commonly used band for scanning weather radars (e.g., WSR-88D). The profiler has many features in common with the 915-MHz profiler developed at the Aeronomy Laboratory during the past decade primarily for measurement of lower-tropospheric winds in the Tropics. This paper presents a description of the new profiler and evaluates it in the field in Illinois and Australia in comparison with UHF lower-tropospheric profilers. In Illinois, the new profiler was evaluated alongside a collocated 915-MHz profiler at the Flatland Atmospheric Observatory. In Australia it was evaluated alongside a 920-MHz profiler during the Maritime Continent Thunderstorm Experiment. The results from these campaigns confirm the approximate 20-dB improvement in sensitivity, as expected for Rayleigh scatter. The results show that the new profiler provides a substantial improvement in the ability to observe deep cloud systems in comparison with the 915-MHz profilers.

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Ali Tokay
,
Peter Hartmann
,
Alessandro Battaglia
,
Kenneth S. Gage
,
Wallace L. Clark
, and
Christopher R. Williams

Abstract

Observations from a 16-month field study using two vertically pointing radars and a disdrometer at Wallops Island are analyzed to examine the consistency of the multi-instrument observations with respect to reflectivity and Z–R relations. The vertically pointing radars were operated at S and K bands and had a very good agreement in reflectivity at a gate centered on 175 and 177 m above ground level over a variety of storms. This agreement occurred even though the sampling volumes were of different size and even though the S band measured the reflectivity factor directly, whereas the K-band radar deduced it from attenuated K-band measurements. Indeed, the radar agreement in reflectivity at the collocated range gates was superior to that between the disdrometer and either radar. This is attributed in large part to the spatial separation of the disdrometer and radar sample volumes, although the lesser agreement observed in a prior collocated disdrometer–disdrometer comparison suggests the larger size of the radar sample volumes as well as the better overlap also play a role. Vertical variations in the observations were examined with the aid of the two radar profilers. As expected, the agreement between the disdrometer reflectivity and the reflectivity seen in the vertically pointing radars decreased with height. The effect of these vertical variations on determinations of Z–R relation coefficients was then examined, using a number of different methods for finding the best-fitting coefficients. The coefficient of the Z–R relation derived from paired disdrometer rain rate and radar reflectivity decreased with height, while the exponent of the Z–R relation increased with height. The coefficient and exponent of the Z–R relations also showed sensitivity to the choice of derivation method [linear and nonlinear least squares, fixed exponent, minimizing the root-mean-square difference (RMSD), and probability matching]. The influence of the time lag between the radar and disdrometer measurements was explored by examining the RMSD in reflectivity for paired measurements between 0- and 4-min lag. The no-lag conditions had the lowest RMSD up to 400 m, while 1-min lag gave the lowest RMSD at higher heights. The coefficient and exponent of the Z–R relations, on the other hand, did not have a significant change between no-lag- and 1-min-lag-based pairs.

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Andrew J. Newman
,
Paul A. Kucera
,
Christopher R. Williams
, and
Larry F. Bliven

Abstract

This paper develops a technique for retrieving snowflake size distributions (SSDs) from a vertically pointing 915-MHz vertical profiler. Drop size distributions (DSDs) have been retrieved from 915-MHz profilers for several years using least squares minimization to determine the best-fit DSD to the observed Doppler spectra. This same premise is used to attempt the retrieval of SSDs. A nonlinear search, the Levenberg–Marquardt (LM) method, is used to search the physically realistic solution space and arrive at a best-fit SSD from the Doppler spectra of the profiler. The best fit is assumed to be the minimum of the squared difference of the log of the observed and modeled spectrum power over the precipitation portion of the spectrum. A snowflake video imager (SVI) disdrometer was collocated with the profiler and provided surface estimates of the SSDs. The SVI also provided estimates of crystal type, which is critical in attempting to estimate the density–size relationship. A method to vary the density–size relationship during the event was developed as well. This was necessary to correctly scale the SVI SSDs for comparison to the profiler-estimated distributions. Five events were examined for this study, and good overall agreement was found between the profiler and SVI for the lowest profiler gate (225 m AGL). Vertical profiles of SSDs were also produced and appear to be physically reasonable. Uncertainty estimates using simulated Doppler spectra show that the retrieval uncertainties are larger than that for rainfall and can approach and exceed 100% for situations with large spectral broadening as a result of atmospheric turbulence. The larger uncertainties are attributed to the lack of unique Doppler spectra for quite different SSDs, resulting in a less well-behaved solution space than that of rainfall retrievals.

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Anthony C. Riddle
,
Leslie M. Hartten
,
David A. Carter
,
Paul E. Johnston
, and
Christopher R. Williams

Abstract

One limiting factor in atmospheric radar observations is the inability to distinguish the often weak atmospheric signals from fluctuations of the noise. This study presents a minimum threshold of usability, SNRmin, for signal-to-noise ratios obtained from wind profiling radars. The basic form arises from theoretical considerations of radar noise; the final form includes empirical modifications based on radar observations. While SNRmin was originally developed using data from the 50-MHz profiler at Poker Flat, Alaska, it works well with data collected from a wide range of locations, frequencies, and parameter settings. It provides an objective criterion to accept or reject individual spectra, can be quickly applied to a large quantity of data, and has a false-alarm rate of approximately 0.1%. While this threshold’s form depends on the methods used to calculate SNR and spectral moments, variations of the threshold could be developed for use with data processed by other methods.

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Ali Tokay
,
David A. Short
,
Christopher R. Williams
,
Warner L. Ecklund
, and
Kenneth S. Gage

Abstract

The motivation for this research is to move in the direction of improved algorithms for the remote sensing of rainfall, which are crucial for meso- and large-scale circulation studies and climate applications through better determinations of precipitation type and latent heating profiles. Toward this end a comparison between two independent techniques, designed to classify precipitation type from 1) a disdrometer and 2) a 915-MHz wind profiler, is presented, based on simultaneous measurements collected at the same site during the Intensive Observing Period of the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. Disdrometer-derived quantities such as differences in drop size distribution parameters, particularly the intercept parameter N 0 and rainfall rate, were used to classify rainfall as stratiform or convective. At the same time, profiler-derived quantities, namely, Doppler velocity, equivalent reflectivity, and spectral width, from Doppler spectra were used to classify precipitation type in four categories: shallow convective, deep convective, mixed convective–stratiform, and stratiform.

Overall agreement between the two algorithms is found to be reasonable. Given the disdrometer stratiform classification, the mean profile of reflectivity shows a distinct bright band and associated large vertical gradient in Doppler velocity, both indicators of stratiform rain. For the disdrometer convective classification the mean profile of reflectivity lacks a bright band, while the vertical gradient in Doppler velocity below the melting level is opposite to the stratiform case. Given the profiler classifications, in the order shallow–deep–mixed–stratiform, the composite raindrop spectra for a rainfall rate of 5 mm h−1 show an increase in D 0, the median volume diameter, consistent with the dominant microphysical processes responsible for drop formation. Nevertheless, the intercomparison does reveal some limitations in the classification methodology utilizing the disdrometer or profiler algorithms in isolation. In particular, 1) the disdrometer stratiform classification includes individual cases in which the vertical profiles appear convective, but these usually occur at times when the disdrometer classification is highly variable; 2) the profiler classification scheme also appears to classify precipitation too frequently as stratiform by including cases that have small vertical Doppler velocity gradients at the melting level but no bright band; and 3) the profiler classification scheme includes a category of mixed (stratiform–convective) precipitation that has some features in common with deep convection (e.g., enhanced spectral width above the melting level) but other features in common with stratiform precipitation (e.g., well-developed melting layer signature). Comparison of the profiler-derived vertical structure with disdrometer-determined rain rates reveals that almost all cases of rain rates greater than 10 mm h−1 are convective. For rain rates less than 5 mm h−1 all four profiler-determined precipitation classes are well represented.

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Kenneth Sassen
,
James R. Campbell
,
Jiang Zhu
,
Pavlos Kollias
,
Matthew Shupe
, and
Christopher Williams

Abstract

During the recent Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL) Florida Area Cirrus Experiment (FACE) field campaign in southern Florida, rain showers were probed by a 0.523-μm lidar and three (0.32-, 0.86-, and 10.6-cm wavelength) Doppler radars. The full repertoire of backscattering phenomena was observed in the melting region, that is, the various lidar and radar dark and bright bands. In contrast to the ubiquitous 10.6-cm (S band) radar bright band, only intermittent evidence is found at 0.86 cm (K band), and no clear examples of the radar bright band are seen at 0.32 cm (W band), because of the dominance of non-Rayleigh scattering effects. Analysis also reveals that the relatively inconspicuous W-band radar dark band is due to non-Rayleigh effects in large water-coated snowflakes that are high in the melting layer. The lidar dark band exclusively involves mixed-phase particles and is centered where the shrinking snowflakes collapse into raindrops—the point at which spherical particle backscattering mechanisms first come into prominence during snowflake melting. The traditional (S band) radar brightband peak occurs low in the melting region, just above the lidar dark-band minimum. This position is close to where the W-band reflectivities and Doppler velocities reach their plateaus but is well above the height at which the S-band Doppler velocities stop increasing. Thus, the classic radar bright band is dominated by Rayleigh dielectric scattering effects in the few largest melting snowflakes.

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David G. Lerach
,
Steven A. Rutledge
,
Christopher R. Williams
, and
Robert Cifelli

Abstract

This study describes the vertical structure of mesoscale convective systems (MCSs) that characterized the 2004 North American monsoon utilizing observations from a 2875-MHz (S band) profiler and a dual-polarimetric scanning Doppler radar. Both instrument platforms operated nearly continuously during the North American Monsoon Experiment (NAME). A technique was developed to identify dominant hydrometeor type using S-band (profiler) reflectivity along with temperature. The simplified hydrometeor identification (HID) algorithm matched polarimetric scanning radar fuzzy logic–based HID results quite well. However, the simplified algorithm lacked the ability to identify ice hydrometeors below the melting layer and on occasion, underestimated the vertical extent of graupel because of a profiler reflectivity bias.

Three of the strongest NAME convective rainfall events recorded by the profiler are assessed in this study. Stratiform rain exhibited a reflectivity bright band and strong Doppler velocity gradient within the melting layer. Convective rainfall exhibited high reflectivity and Doppler velocities exceeding 3 (−10) m s−1 in updrafts (downdrafts). Low-density graupel persisted above the melting layer, often extending to 10 km, with high-density graupel observed near 0°C. Doppler velocity signatures suggested that updrafts and downdrafts were often tilted, though estimating the degree of tilt would have required a more three-dimensional view of the passing storms. Cumulative frequency distributions (CFDs) of reflectivity were created for stratiform and convective rainfall and were found to be similar to results from other tropical locations.

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John R. Mecikalski
,
John K. Williams
,
Christopher P. Jewett
,
David Ahijevych
,
Anita LeRoy
, and
John R. Walker

Abstract

The Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.

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Albert J. Williams 3rd
,
John S. Tochko
,
Richard L. Koehler
,
William D. Grant
,
Thomas F. Gross
, and
Christopher V. R. Dunn

Abstract

A vertical array of acoustic current meters measures the vector flow field in the lowest 5 m of the oceanic boundary layer. By resolving the velocity to 0.03 cm s−1 over 15 cm paths, it samples the dominant turbulent eddies responsible for Reynolds stress to within 50 cm of the bottom. Profiles through the inner boundary layer, from six sensor pods, of velocity, turbulent kinetic energy, and Reynolds stress can be recorded for up 10 four months with a 2 Hz sample rate and 20 min averaging interval. We can study flow structure and spectra from as many as four event-triggered recordings of unaveraged samples, each lasting one hour, during periods of intense sediment transport. Acoustic transducer multiplexing permits 24 axes to be interfaced to a single receiving circuit. Electrical reversal of transducers in each axis eliminates zero drift. A deep-sea tripod supports the sensor array rigidly with minimum flow disturbance, yet releases on command for free vehicle recovery.

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Vickal V. Kumar
,
Alain Protat
,
Christian Jakob
,
Christopher R. Williams
,
Surendra Rauniyar
,
Graeme L. Stephens
, and
Peter T. May

Abstract

Cumulus parameterizations in general circulation models (GCMs) frequently apply mass-flux schemes in their description of tropical convection. Mass flux constitutes the product of the fractional area covered by cumulus clouds in a model grid box and the vertical velocity within the cumulus clouds. The cumulus area fraction profiles can be derived from precipitating radar reflectivity volumes. However, the vertical velocities are difficult to observe, making the evaluation of mass-flux schemes difficult. In this paper, the authors develop and evaluate a parameterization of vertical velocity in convective (cumulus) clouds using only radar reflectivities collected by a C-band polarimetric research radar (CPOL), operating at Darwin, Australia. The parameterization is trained using vertical velocity retrievals from a dual-frequency wind profiler pair located within the field of view of CPOL. The parametric model uses two inputs derived from CPOL reflectivities: the 0-dBZ echo-top height (0-dBZ ETH) and a height-weighted column reflectivity index (Z HWT). The 0-dBZ ETH determines the shape of the vertical velocity profile, while Z HWT determines its strength. The evaluation of these parameterized vertical velocities using (i) the training dataset, (ii) an independent wind-profiler-based dataset, and (iii) 1 month of dual-Doppler vertical velocity retrievals indicates that the statistical representation of vertical velocity is reasonably accurate up to the 75th percentile. However, the parametric model underestimates the extreme velocities. The method allows for the derivation of cumulus mass flux and its variability on current GCM scales based only on reflectivities from precipitating radar, which could be valuable to modelers.

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