Abstract

An algorithm has been developed that classifies precipitating clouds into either stratiform, mixed stratiform/convective, deep convective, or shallow convective clouds by analyzing the vertical structure of reflectivity, velocity, and spectral width derived from measurements made with the vertical beam of a 915-MHz Doppler wind profiler. The precipitating clouds classified as stratiform and convective clouds match the physical and radar properties deduced by Doppler weather radars in the GATE and EMEX programs. The mixed stratiform/convective cloud category is a hybrid regime containing a melting-layer signature associated with stratiform clouds yet is turbulent above the melting level similar to convective clouds. Shallow convective clouds have hydrometeors confined entirely below the melting level implying that warm rain processes are occurring exclusively. The algorithm is illustrated by classifying precipitating clouds from 10 months of observations at Manus Island (2°S, 147°E) in the western Pacific. The sensitivity of the algorithm to threshold criteria is investigated using the Manus Island data.

Abstract

This paper describes a method of absolutely calibrating and routinely monitoring the reflectivity calibration from a scanning weather radar using a vertically profiling radar that has been absolutely calibrated using a collocated surface disdrometer. The three instruments have different temporal and spatial resolutions, and the concept of upscaling is used to relate the small resolution volume disdrometer observations with the large resolution volume scanning radar observations. This study uses observations collected from a surface disdrometer, two profiling radars, and the National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) scanning weather radar during the Texas–Florida Underflight-phase B (TEFLUN-B) ground validation field campaign held in central Florida during August and September 1998.

The statistics from the 2062 matched profiling and scanning radar observations during this 2-month period indicate that the WSR-88D radar had a reflectivity 0.7 dBZ higher than the disdrometer-calibrated profiler, the standard deviation was 2.4 dBZ, and the 95% confidence interval was 0.1 dBZ. This study implies that although there is large variability between individual matched observations, the precision of a series of observations is good, allowing meaningful comparisons useful for calibration and monitoring.

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.

Abstract

UHF (boundary layer) and VHF (troposphere–stratosphere) wind profilers have operated at Christmas Island (2°N, 157°W) in the central equatorial Pacific from 1986 to 2002. Observed profiles of winds are sparse over the tropical oceans, but these are critical for understanding convective organization and the interaction of convection and waves. While the zonal winds below about 10 km have previously shown good agreement with the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (RI), significant differences were found above a height of 10 km that were attributed to the low detectability of the wind signal in the profiler observations. Meridional winds at all levels show less agreement, with differences attributed to errors of representativeness and the sparseness of observations in the region. This paper builds on previous work using the Christmas Island wind profilers and presents the results of reprocessing the 17-yr profiler record with techniques that enhance the detectability of the signal at upper heights. The results are compared with nearby rawinsonde soundings obtained during a special campaign at Christmas Island and the RI, NCEP–Department of Energy (DOE) reanalysis (RII), and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40). The newly processed profiler zonal and meridional wind observations show good agreement with rawinsonde observations from 0.5 to 19 km above sea level, with difference statistics similar to other studies. There is also significant improvement in the agreement of RI and RII reanalysis and profiler upper-level zonal and meridional winds from previous studies. A comparison of RII and ERA-40 reanalysis shows that difference statistics between the reanalyses are similar in magnitude to differences between the profiler and the individual reanalyses.

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

Comparisons of data taken by collocated Doppler wind profilers using 100-, 500-, and 1000-m pulse lengths show that the velocity profiles obtained with the longer pulses are displaced in height from contemporaneous profiles measured with the shorter pulses. These differences are larger than can be expected from random measurement errors. In addition, there is evidence that the 500-m pulse may underestimate the wind speed when compared with the 100-m pulse.

The standard radar equation does not adequately account for the conditions under which observations are made. In particular, it assumes that atmospheric reflectivity is constant throughout the pulse volume and that observations can be assigned to the peak of the range-weighting function. However, observations from several tropical profilers show that reflectivity gradients with magnitudes greater than 10 dB km⁻¹ are common. Here, a more general radar equation is used to simulate the radar response to the atmosphere. The simulation shows that atmospheric reflectivity gradients cause errors in the range placement. Observed reflectivity gradients can be used to calculate a correction to the range location of the observations that helps to reduce these errors.

Examples of these errors and the application of the correction to selected cases are shown. The evidence presented shows that reflectivity gradients are the main cause of the pervasive differences observed between the different radar observations.

Abstract

A method is presented that increases the detectability of weak clear-air signals by averaging Doppler spectra from coplanar wind profiler beams. The method, called coplanar spectral averaging (CSA), is applied to both simulated wind profiler spectra and to 1 yr of archived spectra from a UHF profiler at Christmas Island (1 October 1999–30 September 2000). A collocated 50-MHz wind profiler provides a truth for evaluating the CSA technique.

In the absence of precipitation, it was found that CSA, when combined with a fuzzy logic quality control, increases the height coverage of the 1-hourly averaged UHF profiler winds by over 600 m (two range gates). CSA also increased the number of good wind estimates at each observation range by about 10%–25% over the standard consensus method.

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

In this paper a five-beam wind profiler and a collocated meteorological tower are used to estimate the accuracy of four-beam and three-beam wind profiler techniques in measuring horizontal components of the wind. In the traditional three-beam technique, the horizontal components of wind are derived from two orthogonal oblique beams and the vertical beam. In the less used four-beam method, the horizontal winds are found from the radial velocities measured with two orthogonal sets of opposing coplanar beams. In this paper the observations derived from the two wind profiler techniques are compared with the tower measurements using data averaged over 30 min. Results show that, while the winds measured using both methods are in overall agreement with the tower measurements, some of the horizontal components of the three-beam-derived winds are clearly spurious when compared with the tower-measured winds or the winds derived from the four oblique beams. These outliers are partially responsible for a larger 30-min, three-beam standard deviation of the profiler/tower wind speed differences (2.2 m s⁻¹), as opposed to that from the four-beam method (1.2 m s⁻¹). It was also found that many of these outliers were associated with periods of transition between clear air and rain, suggesting that the three-beam technique is more sensitive to small-scale variability in the vertical Doppler velocity because of its reliance on the point measurement from the vertical beam, while the four-beam method is surprisingly robust. Even after the removal of the rain data, the standard deviation of the wind speed error from the three-beam method (1.5 m s⁻¹) is still much larger than that from the four-beam method. Taken together, these results suggest that the spatial variability of the vertical airflow in nonrainy periods or hydrometeor fall velocities in rainy periods makes the vertical beam velocities significantly less representative over the area across the three beams, and decreases the precision of the three-beam method. It is concluded that profilers utilizing the four-beam wind profiler technique have better reliability than wind profilers that rely on the three-beam wind profiler technique.