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.

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.

Abstract

Profilers operating in the UHF range are sensitive to both Bragg scattering from radio refractive index structure and to Rayleigh scattering from small point targets. Identification of the scattering process is critical for proper interpretation of these observations, especially the data collected from the vertical incident beam. This study evaluates the performance of Doppler velocity thresholds as a means to separate air motions from hydrometeor motions in vertical incident profiler observations. This evaluation consists of three different steps. First, using two collocated profilers operating at different frequencies, the observations are unambiguously identified as Bragg or Rayleigh scattering processes. Second, the observations are separated into either air or hydrometeor motion using only the data from one profiler. The third step quantitatively evaluates the performance of the single profiler separation techniques by counting the number of correct classifications and adjusting the count by the number of incorrect classifications.

Constant Doppler velocity threshold methods are acceptable methods to separate air motions from hydrometeor motions only after the correct threshold is determined. This study presents a cluster analysis method that robustly and objectively separates air from hydrometeor motions. The introduced cluster analysis produces two thresholds. The first threshold is a Doppler velocity threshold that is a function of reflectivity. The second threshold is the maximum reflectivity in which the Doppler velocity threshold divides the observations into two statistical distributions using the Kolmogorov–Smirnov statistical test. The cluster analysis method quantitatively performs better than constant Doppler velocity threshold methods, and is a repeatable, self-adapting, statistically based procedure.

ABSTRACT

The NOAA W-band radar was deployed on a P-3 aircraft during a study of storm fronts off the U.S. West Coast in 2015 in the second CalWater (CalWater-2) field program. This paper presents an analysis of measured equivalent radar reflectivity factor Z _em profiles to estimate the path-averaged precipitation rate and profiles of precipitation microphysics. Several approaches are explored using information derived from attenuation of Z _em as a result of absorption and scattering by raindrops. The first approach uses the observed decrease of Z _em with range below the aircraft to estimate column mean precipitation rates. A hybrid approach that combines Z _em in light rain and attenuation in stronger rain performed best. The second approach estimates path-integrated attenuation (PIA) via the difference in measured and calculated normalized radar cross sections (NRCS _m and NRCS _c , respectively) retrieved from the ocean surface. The retrieved rain rates are compared to estimates from two other systems on the P-3: a Stepped Frequency Microwave Radiometer (SFMR) and a Wide-Swath Radar Altimeter (WSRA). The W-band radar gives reasonable values for rain rates in the range 0–10 mm h⁻¹ with an uncertainty on the order of 1 mm h⁻¹. Mean profiles of Z _em, raindrop Doppler velocity, attenuation, and precipitation rate in bins of rain rate are also computed. A method for correcting measured profiles of Z _em for attenuation to estimate profiles of nonattenuated profiles of Z _e is examined. Good results are obtained by referencing the surface boundary condition to the NRCS values of PIA. Limitations of the methods are discussed.

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 Z–R 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).

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.

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, SNR_min, 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 SNR_min 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.

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.

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.

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.