Multiparameter Radar Measurements Research Articles

The Queensland Cloud Seeding Research Program

As a response to extreme water shortages in southeast Queensland, Australia, brought about by reduced rainfall and increasing population, the Queensland government decided to explore the potential for cloud seeding to enhance rainfall. The Queensland Cloud Seeding Research Program (QCSRP) was conducted in the southeast Queensland region near Brisbane during the 2008/09 wet seasons. In addition to conducting an initial exploratory, randomized (statistical) cloud seeding study, multiparameter radar measurements and in situ aircraft microphysical data were collected. This comprehensive set of observational platforms was designed to improve the physical understanding of the effects of both ambient aerosols and seeding material on precipitation formation in southeast Queensland clouds. This focus on gaining physical understanding, along with the unique combination of modern observational platforms utilized in the program, set it apart from previous cloud seeding research programs. The overarching goals of the ...

Errors in Estimating Raindrop Size Distribution Parameters Employing Disdrometer and Simulated Raindrop Spectra

Abstract There have been debates and differences of opinion over the validity of using drop size distribution (DSD) models to characterize precipitation microphysics and to retrieve DSD parameters from multiparameter radar measurements. In this paper, simulated and observed rain DSDs are used to evaluate moment estimators. Seven estimators for gamma DSD parameters are evaluated in terms of the biases and fractional errors of five integral parameters: radar reflectivity (ZH), differential reflectivity (ZDR), rainfall rate (R), mean volume diameter (Dm), and total number concentration (NT). It is shown that middle-moment estimators such as M234 (using the second-third-fourth moments) produce smaller errors than lower- and higher-moment estimators if the DSD follows the gamma distribution. However, if there are model errors, the performance of M234 degrades. Even though the DSD parameters can be biased in moment estimators, integral parameters are usually not. Maximum likelihood (ML) and L-moment (LM) estimators perform similarly to low-moment estimators such as M012. They are sensitive to both model error and the measurement errors of the low ends of DSDs. The overall differences among M234, M246, and M346 are not substantial for the five evaluated parameters. This study also shows that the discrepancy between the radar and disdrometer observations cannot be reduced by using these estimators. In addition, the previously found constrained-gamma model is shown not to be exclusively determined by error effects. Rather, it is equivalent to the mean function of normalized DSDs derived through Testud’s approach, and linked to precipitation microphysics.

Sensitivity of multiparameter radar rainfall algorithms

The most commonly used polarimetric radar measurements in rainfall estimation are reflectivity at one polarization (ZH), differential reflectivity (ZDR), and specific differential propagation phase (KDP). The reflectivity measurement requires knowledge of absolute power and is prone to calibration errors. Differential reflectivity is a relative power measurement and is not affected by absolute calibration errors. However, all the algorithms that use differential reflectivity also include reflectivity and therefore are prone to absolute calibration errors. Algorithms to estimate rainfall from specific differential propagation phase are immune to absolute calibration error and attenuation effects [Zrnic and Ryzhkov, 1996]. However, specific differential propagation phase is very noisy at low rain rates. In addition, specific differential propagation phase is estimated as the slope of differential propagation phase measurements over a path. Consequently, there is a trade‐off between accuracy and resolution of KDP. Thus there are advantages and disadvantages of each multiparameter radar measurement that translate into the error structure of algorithms involving multiparameter radar measurements. This paper presents a quantitative evaluation of the performance of five different polarimetric radar rainfall algorithms. The performance of the five algorithms is evaluated in the presence of physical variability in rainfall, radar measurement errors, and systematic calibration error.

Detection and estimation of reflectivity gradients in the radar resolution volume using multiparameter radar measurements

Multiparameter radar measurements of rainfall, namely, reflectivity factor Z/sub H/, differential reflectivity Z/sub DR/, and specific differential phase K/sub DP/ lie in a constrained three-dimensional (3D) space and therefore measurement of Z/sub H/, Z/sub DR/, or, K/sub DP/ should be consistent with the other two. This self-consistency relationship between Z/sub H/, Z/sub DR/, and K/sub DP/ is valid when the radar resolution volume is homogeneous. When there are reflectivity gradients within the radar resolution cell, the self-consistency relation is perturbed. This perturbation can be utilized to detect the presence of gradients in the radar resolution volume. This paper presents a technique to detect and estimate reflectivity gradients in the resolution cell. The technique is evaluated using theoretical analysis as well as experimental data collected by the NCAR CP-2 radar. It is demonstrated that the presence of reflectivity gradients larger than a few decibels can be detected using the algorithm developed in this paper.

Raindrop axis ratios and size distributions in Florida rainshafts: an assessment of multiparameter radar algorithms

Eleven penetrations of rainshafts by the University of Wyoming King Air (WKA) aircraft equipped with a two-dimensional (2D) optical array probe are studied in coordination with multiparameter radar measurements from the National Center for Atmospheric Research (NCAR) CP-2 radar collected in a multicellular storm that occurred on August 8, 1991, of the Convective and Precipitation/Electrification (CaPE) experiment. A comparison is made between the mass-weighted mean diameter (D/sub m/) and rainrate (R) computed from the nine-size spectra and their estimates from multiparameter radar algorithms based on Z/sub dr/, and Z/sub h/. It was found that D/sub m/ could be estimated with a mean bias of 0.07 mm and a standard deviation of 0.35 mm. Rainrates (in the range of 10-60 mmh/sup -1/) could be estimated from Z/sub h/, and Z/sub dr/ with a mean bias of 1-4% and fractional standard error (FSE) of 30-40% depending on the estimator used. Raindrop axis ratios are analyzed as a function of volume equivalent spherical diameter (D/sub eq/) in the range 2-6 mm. The mean axis ratio versus the D/sub eq/ relationship was found to be consistent with previous data from the High Plains (from Colorado and Montana). A study of fluctuations of axis ratio (about their mean value) showed that most drops have axis ratios close to their mean values with oscillation amplitudes to be typically /spl plusmn/10% in axis ratio, again consistent with the earlier High Plains results.

Development of a neural network based algorithm for radar snowfall estimation

Using radar to measure snowfall accumulation has been a research topic in radar meteorology for decades. Traditionally, a parametric reflectivity-snowfall (Z-S) relationship is used to estimate ground snowfall amounts based on radar observations. However, the accuracy and reliability of Z-S relationship are limited by the wide variability of the Z-S relationship with snowfall type. In this paper, the authors introduce a neural network based approach to address the problem of snowfall estimation from radar by taking into account the vertical structure of precipitation. The motivation for using a multilayer feedforward neural network (MFNN), such as the radial-basis function (RBF) network, is the good universal function approximation capability of the network. The network is trained using vertical reflectivity profiles averaged over a 9-km/sup 2/ area as the input and ground snowfall amounts as the target output. Separate data, which are not part of the training data, are used to test the generalization performance of the RBF network after the training is done. Radar reflectivity data collected by the CSU-CHILL multiparameter radar and ground snowfall measurements recorded by snowgages located at the Stapleton International Airport (SIA), Stapleton, CO, and the Denver International Airport (DIA), Denver, CO, during the Winter and Icing and Storms Projects (WISP94) were used for this study. The snowfall estimates from the RBF network are shown to be better than those obtained from conventional Z-S algorithms. The neural network based approach provides an alternate method to the snowfall estimation problem.

Radiowave propagation research in the tropics using a transportable multiparameter radar system

Although accurate models for Earth-space microwave propagation, based on extensive measured data, exist for temperate regions of the world, this is not the case for the tropics. With the proliferation of satellite communications in these areas, it has become important to test the validity of currently recommended techniques for calculating circuit availability and co-channel interference levels due to attenuation by precipitation and scattering from precipitation, respectively. Multiparameter radar measurements, employing both polarimetric and Doppler techniques and made over a significant period of time on a statistically-valid basis, provide an ideal tool for these investigations. The paper describes the design considerations for, and construction of such a radar. A selection of its data products are presented, illustrating its potential for radio propagation and meteorological research.

Development of a neural network based algorithm for rainfall estimation from radar observations

Rainfall estimation based on radar measurements has been an important topic in radar meteorology for more than four decades. This research problem has been addressed using two approaches, namely a) parametric estimates using reflectivity-rainfall relation (Z-R relation) or equations using multiparameter radar measurements such as reflectivity, differential reflectivity, and specific propagation phase, and b) relations obtained by matching probability distribution functions of radar based estimates and ground observations of rainfall. In this paper the authors introduce a neural network based approach to address this problem by taking into account the three-dimensional (3D) structure of precipitation. A three-layer perceptron neural network is developed for rainfall estimation from radar measurements. The neural network is trained using the radar measurements as the input and the ground raingage measurements as the target output. The neural network based estimates are evaluated using data collected during the Convection and Precipitation Electrification (CaPE) experiment conducted over central Florida in 1991. The results of the evaluation show that the neural network can be successfully applied to obtain rainfall estimates on the ground based on radar observations. The rainfall estimates obtained from neural network are shown to be better than those obtained from several existing techniques. The neural network based rainfall estimate offers an alternate approach to the rainfall estimation problem, and it can be implemented easily in operational weather radar systems.

Radar and Surface Measurement of Rainfall during CaPE: 26 July 1991 Case Study

Abstract Conventional usage of multiparameter radar measurements for rainfall estimation has been associated with tracking the variability of the raindrop size distribution. The use of multiparameter radar measurements in a statistical framework to estimate rainfall is presented in this paper. The techniques developed in this paper are applied to the radar and rain gauge measurement of rainfall observed on 26 July 1991, during the Convection and Precipitation Electrification program. Conventional pointwise estimates of rainfall are also compared. The probability matching procedure, when applied to the radar and surface measurements shows that multiparameter radar algorithms can match the probability distribution functions better than the reflectivity based algorithms, thereby indicating the potential of multiparameter radar measurements for statistical approach to rainfall estimation.

Weather radar calibration by means of the metallic sphere and multiparameter radar measurements

Absolute calibration is an important task in order to obtain reliable and accurate measurements of radar observables and derived meteorological parameters. In this paper two procedures are described; the first one is based on measuring the power received from a metallic sphere and the second utilizes the rainfall estimates obtained by the multiparameter radar measurements. The accuracy of the latter method is studied in detail from the simulation of the dual polarization measurables. The theoretical results are compared with radar measurements relative to a rain event.

Optimization of Multiparameter Radar Estimates of Rainfall

The estimates of rainfall rate derived from a multiparameter radar based on reflectivity factor (R sub ZH), differential reflectivity (R sub DR), and specific differential propagation phase (R sub DP) have widely varying accuracies over the dynamic range of the natural occurrence of rainfall. This paper presents a framework to optimally combine the three estimates, R sub zH, R sub DR, and R sub DP, to derive the best estimate of rainfall using coherent multiparameter radars. The optimization procedure is demonstrated for application to multiparameter radar measurements at C band.

Rainfall and Clutter Discrimination by Means of Dual-linear Polarization Radar Measurements

The presence of undetected mixed-phase precipitation or superimposed intense clutter can cause serious errors in the estimation of rainfall rate and other parameters of precipitation occurring in the radar scattering volume. To reduce or avoid these errors it is necessary to distinguish between the rain echo, and that due to other types of precipitation, and between precipitation radar echoes and ground clutter. Multiparameter radar measurements may be exploited for this discrimination. In particular, it is demonstrated that dual-linear polarization measurements may play a major role in this process. Sample radar data are employed to illustrate several different tests to classify radar data: the results refer to comparisons of dual-polarized echoes due to precipitation (rain or mixed-phase event) and land with echoes from land alone. This is illustrated by example through the application of a series of tests on a clutter-contaminated dual-polarized dataset obtained during the May Polarization Experiments (MAYPOLE) 1984 field program in Colorado.

Rainfall Parameter Estimation from Dual-Radar Measurements Combining Reflectivity Profile and Path-integrated Attenuation

Abstract Rain rate estimation properties of multiparameter radar measurements combining radar reflectivity and microwave attenuations are studied through simulations using a two-year disdrometer dataset. In the first simulation, properties of “complete” multiparameter measurements, where error-free measurements are assumed for each radar resolution cell, are investigated. The result indicates that dual-parameter (DP) measurements provide an excellent accuracy in rain rate estimation. Most of the DP measurements possible from space are “semi-” dual-parameter (SDP) measurements, defined as DP measurements in which spatial resolution for the attenuation measurement is coarser than the resolution required for rain profiling. Considering this fact, an SDP measurement simulation is also made. The SDP measurement is an extension of the concept of the DP measurement in terms of the spatial resolution, and can provide information on raindrop size distribution (DSD) by employing a “two-scale” DSD model. It is shown...

Error Structure of Multiparameter Radar and Surface Measurements of Rainfall. Part III: Specific Differential Phase

Abstract Parts I and II of this three part paper dealt with the error structure of differential reflectivity and X-band specific attenuation in rainfall as estimated by radar and surface disdrometers. In this Part III paper we focus on the error structure of the specific differential phase (KDP, °km−1) measurement in rainfall. This allows us to analyze three estimators of rainfall rate, the first based on the reflectivity factor ZH, the second based on combining reflectivity and ZDR, [R(ZH, ZDR)], and the third based on KDP alone, R(KDP). Simulations are used to model random errors in ZH, ZDR and KDP. Physical variations in the raindrop size distribution (RSD) are modeled by varying the gamma parameters (N0, D0, m) over a range typically found in natural rainfall. Thus, our simulations incorporate physical fluctuations onto which random measurement errors have been superimposed. Radar-derived estimates of R(ZH, ZDR) and R(KDP) have been intercompared using data obtained in convective rainfall with the NSS...

Microwave Radiative Transfer Studies Using Combined Multiparameter Radar and Radiometer Measurements during COHMEX

Theoretical calculations of the upwelling microwave radiances from clouds containing layers of rain, ice, and a melting region were performed at frequencies of 18, 37, and 92 GHz. These frequencies coincide with high-resolution microwave radiometer measurements taken aboard the NASA ER-2 high-altitude aircraft during the summer 1986 Cohmex (Cooperative Huntsville Meteorological Experiment) in Alabama. For purposes of brightness temperature computations, the storms were modeled with rain, melting phase, and ice layers. The melting phase region was composed of water-coated ice spheres defined by a 'melt index' in terms of the volume fraction of water. Single scatter albedo, scattering, and extinction coefficients were computed at the above frequencies as a function of the rain rate and melt index. In addition, multiparameter radar observations of the storm were mapped into a Cartesian space and averaged over regions comparable to the radiometer footprint.

Multiparameter Radar Measurements in Colorado Convective Storms. Part II: Hail Detection Studies

Abstract In this part II paper we focus on hail detection using the NCAR CP-2 radar measurements from which ZH, ZDR, LDR, and dual-frequency ratio can be derived. A major feature of this paper relates to rigorous computations of multiparameter observables, including circular polarization observables, from model hail distributions, and to consideration of propagation effects in moderate-to-intense rainfall. Multiparameter radar measurements of hail are presented using data gathered during the MAYPOLE'83 and '84 field programs. These hail measurements are found to be consistent with model computations. Hail signature locations using ZDR, dual-frequency ratio, and LDR are shown to be in excellent agreement. The vertical structure of ZDR, (below the melting level), LDR (above the melting level), and dual-frequency ratio yield new and interesting information on hail shaft structure and vertical extent. Examples of radar data from MAYPOLE '84 are used to illustrate these inferences.

Microphysical Interpretation of Multiparameter Radar Measurements in Rain. Part III: Interpretation and Measurement of Propagation Differential Phase Shift between Orthogonal Linear Polarizations

As radar waves having different polarizations propagate through a collection of nonspherical oriented hydrometeors, a phase difference between the waves appears. In a collection of uniformly horizontally oriented quiescent water drops, the rate of change of the propagation differential phase shift with increasing distance from the radar is proportional to the product of the liquid water content and the departure from unity of the mass-weighted mean axis ratio of the drops provided the radar wavelength is much larger than the drops. The appropriateness however, of such a simple relation to natural rain 'in which some drops assume complex shapes and a variety of orientations through the processes of collision, coalescence, break-up and oscillation remains to be determined.

Microphysical Interpretation of Multi-Parameter Radar Measurements in Rain. Part II: Estimation of Raindrop Distribution Parameters by Combined Dual-Wavelength and Polarization Measurements

The radar reflectivity weighted mean axis ratio R¯ and the variance of the axis ratio distribution σR2 of an ensemble of raindrops can be estimated from polarization radar measurements. If the shape function relating axis ratio to drop size is known or measured, then these quantities may be transformed into the radar reflectivity weighted mean diameter D and, in the case of quiescent drops, into the variance of the raindrop size distribution σD2. Although these parameters are of intrinsic meteorological interest, estimates of the rainfall rate in still air R0 and of liquid water content W can also be constructed. In principle, estimates using both D¯ and σD2 appear, potentially, to be more accurate than estimates based only on D¯. Realization of this potential improvement probably depends on the development of techniques to extract that component of σD2 arising solely from the raindrop size distribution.

Microphysical Interpretation of Multi-Parameter Radar Measurements in Rain. Part I: Interpretation of Polarization Measurements and Estimation of Raindrop Shapes

The backscattered electric field received by a radar is related to the transmitted field by a backscatter matrix. In the case of single raindrops the elements in this matrix are determined by raindrop size and shape. At long radar wavelength certain combinations of elements, however, only depend on raindrop shape. These combinations form the basis of current linear and circular polarization measurables. An ensemble of raindrops will usually exhibit a variety of shapes because of the variation in raindrop size and because of oscillations induced by drop oscillations. This shape variation can be described by an axis ratio distribution. Since at long wavelengths in rain the radar polarization measurables are largely determined by shape, they are sensitive to this axis ratio distribution. In this study, the sensitivity of some linear and circular polarization measurables to the mean and variance of the raindrop axis ratio distribution is investigated. These measurables are then used to formulate estimators of radar-power-weighted mean axis ratio and variance. These estimators appear to be applicable to a wide variety of drop size distributions.