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Kenneth S. Gage
,
Christopher R. Williams
,
Wallace L. Clark
,
Paul E. Johnston
, and
David A. Carter

Abstract

Doppler radar profilers are widely used for routine measurement of wind, especially in the lower troposphere. The same profilers with minor modifications are useful tools for precipitation research. Specifically, the profilers are now increasingly being used to explore the structure of precipitating cloud systems and to provide calibration and validation of other instruments used in precipitation research, including scanning radars and active and passive satellite-borne sensors. A vertically directed profiler is capable of resolving the vertical structure of precipitating cloud systems that pass overhead. Standard profiler measurements include reflectivity, reflectivity-weighted Doppler velocity, and spectral width. This paper presents profiler observations of precipitating cloud systems observed during Tropical Rainfall Measuring Mission (TRMM) Ground Validation field campaigns. The observations show similarities and differences between convective systems observed in Florida; Brazil; and Kwajalein, Republic of the Marshall Islands. In addition, it is shown how a profiler can be calibrated using a collocated Joss–Waldvogel disdrometer, how the profiler can then be used to calibrate a scanning radar, and how the profiler may be used to retrieve drop size distributions.

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Robert A. Houze Jr
,
Stacy Brodzik
,
Courtney Schumacher
,
Sandra E. Yuter
, and
Christopher R. Williams

Abstract

The Kwajalein, Marshall Islands, Tropical Rainfall Measuring Mission (TRMM) ground validation radar has provided a multiyear three-dimensional radar dataset at an oceanic site. Extensive rain gauge networks are not feasible over the ocean and, hence, are not available to aid in calibrating the radar or determining a conversion from reflectivity to rain rate. This paper describes methods used to ensure the calibration and allow the computation of quantitative rain maps from the radar data without the aid of rain gauges. Calibration adjustments are made by comparison with the TRMM satelliteborne precipitation radar. The additional steps required to convert the calibrated reflectivity to rain maps are the following: correction for the vertical profile of reflectivity below the lowest elevation angle using climatological convective and stratiform reflectivity profiles; conversion of reflectivity (Z) to rain rate (R) with a relationship based on disdrometer data collected at Kwajalein, and a gap-filling estimate. The time series of rain maps computed by these procedures include low, best, and high estimates to frame the estimated overall uncertainty in the radar rain estimation. The greatest uncertainty of the rain maps lies in the calibration of the radar (±30%). The estimation of the low-altitude vertical profile of reflectivity is also a major uncertainty (±15%). The ZR and data-gap uncertainties are relatively minor (±5% or less). These uncertainties help to prioritize the issues that need to be addressed to improve quantitative rainfall mapping over the ocean and provide useful bounds when comparing radar-derived rain estimates with other remotely sensed measures of oceanic rain (such as from satellite passive microwave sensors).

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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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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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Kenneth S. Gage
,
Christopher R. Williams
,
Paul E. Johnston
,
Warner L. Ecklund
,
Robert Cifelli
,
Ali Tokay
, and
David A. Carter

Abstract

The National Oceanic and Atmospheric Administration’s Aeronomy Laboratory has modified a standard 915-MHz profiler for use as a precipitation profiler in support of Tropical Rainfall Measuring Mission ground validation field campaigns. This profiler was modified to look vertically with a fixed dish antenna. It was operated during the Texas and Florida Underflights Experiment (TEFLUN) A in south Texas in April–May 1998 and during TEFLUN B in central Florida in August–September 1998. Collocated with the profiler was a Distromet, Inc., RD-69 Joss–Waldvogel disdrometer in Texas and Florida and a two-dimensional video disdrometer in Florida. The disdrometers are used to calibrate the profiler at the lowest range gates. At higher altitudes, the calibrated profiler reflectivities are compared with observations made by scanning radars such as the Weather Surveillance Radar-1988 Doppler in Dickinson, Texas, and Melbourne, Florida, and the S-band Doppler dual-polarization radar in Florida. The authors conclude that it is possible to use profilers as transfer standards to calibrate and to validate the reflectivities measured by the scanning radars.

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Robert Cifelli
,
Christopher R. Williams
,
Deepak K. Rajopadhyaya
,
Susan K. Avery
,
Kenneth S. Gage
, and
P. T. May

Abstract

Drop-size distribution characteristics were retrieved in eight tropical mesoscale convective systems (MCS) using a dual-frequency (UHF and VHF) wind profiler technique. The MCSs occurred near Darwin, Australia, during the 1993/94 wet season and were representative of the monsoon (oceanic) regime. The retrieved drop-size parameters were compared with corresponding rain gauge and disdrometer data, and it was found that there was good agreement between the measurements, lending credence to the profiler retrievals of drop-size distribution parameters. The profiler data for each MCS were partitioned into a three-tier classification scheme (i.e., convective, mixed convective–stratiform, and stratiform) based on a modified version of to isolate the salient microphysical characteristics in different precipitation types. The resulting analysis allowed for an examination of the drop-size distribution parameters in each category for a height range of about 2.1 km in each MCS.

In general, the distributions of all of the retrieved parameters showed the most variability in convection and the least in stratiform, with the mixed convective–stratiform category usually displaying intermediate characteristics. Although there was significant overlap in the range of many of the parameter distributions, the mean profiles were distinct. In the stratiform region, there was minimal vertical structure for all of the drop-size distribution parameters. This result suggests an equilibrium between depletion (e.g., evaporation) and growth (e.g., coalescence) over the height range examined. In contrast, the convective parameter distributions showed a more complicated structure, probably as a consequence of the complex microphysical processes occurring in the convective precipitation category.

Reflectivity–rainfall (Z–R) relations of the form Z = AR B were developed for each precipitation category as a function of height using linear regressions to the profiler retrievals of R and Z in log space. Similar to findings from previous studies, the rainfall decreased for a given reflectivity as the precipitation type changed from convective to stratiform. This result primarily was due to the fact that the coefficient A in the best-fit stratiform Z–R was approximately a factor of 2 greater than the convective A at all heights. The coefficient A generally increased downward with height in each category; the exponent B showed a small decrease (stratiform), almost no change (convective), or a slight increase (mixed convective–stratiform). Consequently, the amount by which convective rain rate exceeded stratiform (for a given reflectivity) varied significantly as a function of height, ranging from about 15% to over 80%.

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