Rain Reflectivity Research Articles

Cross-Evaluation of Reflectivity from NEXRAD and Global Precipitation Mission during Extreme Weather Events.

During extreme events such as tropical cyclones, the precision of sensors used to sample the meteorological data is vital to feed weather and climate models for storm path forecasting, quantitative precipitation estimation, and other atmospheric parameters. For this reason, periodic data comparison between several sensors used to monitor these phenomena such as ground-based and satellite instruments, must maintain a high degree of correlation in order to issue alerts with an accuracy that allows for timely decision making. This study presents a cross-evaluation of the radar reflectivity from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement Mission (GPM) and the U.S. National Weather Service (NWS) Next-Generation Radar (NEXRAD) ground-based instrument located in the Caribbean island of Puerto Rico, USA, to determine the correlation degree between these two sensors’ measurements during extreme weather events and normal precipitation events during 2015–2019. GPM at Ku-band and Ka-band and NEXRAD at S-band overlapping scanning regions data of normal precipitation events during 2015–2019, and the spiral rain bands of four extreme weather events, Irma (Category 5 Hurricane), Beryl (Tropical Storm), Dorian (Category 1 hurricane), and Karen (Tropical Storm), were processed using the GPM Ground Validation System (GVS). In both cases, data were classified and analyzed statistically, paying particular attention to variables such as elevation angle mode and precipitation type (stratiform and convective). Given that ground-based radar (GR) has better spatial and temporal resolution, the NEXRAD was used as ground-truth. The results revealed that the correlation coefficient between the data of both instruments during the analyzed extreme weather events was moderate to low; for normal precipitation events, the correlation is lower than that of studies that compared GPM and NEXRAD reflectivity located in other regions of the USA. Only Tropical Storm Karen obtained similar results to other comparative studies in terms of the correlation coefficient. Furthermore, the GR elevation angle and precipitation type have a substantial impact on how well the rain reflectivity correlates between the two sensors. It was found that the Ku-band channel possesses the least bias and variability when compared to the NEXRAD instrument’s reflectivity and should therefore be considered more reliable for future tropical storm tracking and tropical region precipitation estimates in regions with no NEXRAD coverage.

High Performance Pavement Markings Enhancing Camera And LiDAR Detection

It is well known that camera and video sensors have limitations in detecting pavement markings under certain conditions e.g. glare from sunlight or other vehicles, rain, fog etc. First generations of lane keeping systems depend on visual light. Erroneous detection is also resulting from irregular road surfaces such as glossy bitumen sealing strips, rain puddles or simply worn asphalt. The role of higher performing markings and better visual camera detection has been studied with Vedecom France. LiDAR (light detection and ranging) technology could help to fill remaining gaps, as it actively sends out IR (infrared) light, that returns reliable images of the road scenario and pavement markings both day and nighttime. In order to evaluate the opportunities of LiDAR technology for the detection of road markings, 3M Company and the University of Applied Sciences in Dresden decided to work together in a joint research project. All-Weather Elements AWE, are the latest development of high-performance optics, using high index beads to provide reflectivity both in dry and wet condition. It could be determined that high performance markings help to increase the level of detection by both camera and LiDAR sensors. The AWE marking was detected from significantly longer distances, especially in wet and rainy conditions. In combination with common camera based LKA and LDW systems, the LiDAR sensors can increase the overall detection rate of pavement markings. This is especially important for vehicles with higher SAE levels of automated driving and can support the overall safety of vehicles. The research also evaluated existing test methods for wet and rain reflectivity in EN 1436 and ASTM E 2832 and how measured performance correlates with LiDAR detection.

Microphysics of Stratiform and Convective Precipitation During Meiyu Season in Eastern China

AbstractThe microphysical structure of Meiyu precipitation in Eastern China is investigated using two‐dimensional video disdrometer (2DVD) and S‐band polarimetric radar observations. The constrained‐gamma raindrop size distribution (DSD) model derived from 2DVD observations performs well in representing Meiyu DSDs. The vertical variability of polarimetric variables and retrieved DSD parameters are then investigated. The results show different patterns of vertical behavior for convective and stratiform rain due to different ice‐phase and precipitation microphysical processes. The radar reflectivity of stratiform rain presents a distinct bright band (with an average echo top between 6 and 7 km). In contrast, the convective rain shows a larger reflectivity with a relatively higher echo top at 8 km. Polarimetric signatures of convective rain above the 0°C isotherm imply the coexistence of rimed particles and aggregates, which is indispensable for the intense precipitation on the ground. However, with a bulk precipitation formed below the melting layer, warm rain processes are still critical pathways for the growth of raindrops and the subsequent generation of heavy rainfall. Furthermore, both convective and stratiform rain is dominated by raindrops <4 mm, and the increase in their rain intensity can mostly be attributed to the increase in raindrop concentration. The identified maritime nature of convective rain has a much higher (roughly more than two times) number concentration of raindrops than that of convection in a similar climate region. This study provides a more comprehensive picture of Meiyu precipitation microphysics in Eastern China.

Attenuation Correction over Ocean for the HIWRAP Dual-Frequency Airborne Scatterometer

Abstract An important objective in scatterometry is the estimation of near-surface wind speed and direction in the presence of rain. We investigate an attenuation correction method using data from the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) dual-frequency scatterometer, which operates at Ku and Ka band with dual conical scans at incidence angles of 30° and 40°. The method relies on the fact that the differential normalized surface cross section, δσ 0 = σ 0(Ka) − σ 0(Ku), is relatively insensitive to wind speed and direction and that this quantity is closely related to the magnitude of the differential path attenuation, δA = A(Ka) − A(Ku), arising from precipitation, cloud, and atmospheric gases. As the method relies only on the difference between quantities measured in the presence and absence of rain, the estimates are independent of radar calibration error. As a test of the method’s accuracy, we make use of the fact that the radar rain reflectivities just above the surface, as seen along different incidence angles, are approximately the same. This yields constraint equations in the form of differences between pairs of path attenuations along different lines of sight to the surface. A second validation method uses the dual-frequency radar returns from the rain just above the surface where it can be shown that the difference between the Ku- and Ka-band-measured radar reflectivity factors provide an estimate of differential path attenuation. Comparisons between the path attenuations derived from the normalized surface cross section and those from these surface-independent methods generally show good agreement.

Evaluation of Unified Model Microphysics in High-resolution NWP Simulations Using Polarimetric Radar Observations

The UK Met Office Unified Model (UM) is employed by many weather forecasting agencies around the globe. This model is designed to run across spatial and time scales and known to produce skillful predictions for large-scale weather systems. However, the model has only recently begun running operationally at horizontal grid spacings of ∼1.5 km [e.g., at the UK Met Office and the Korea Meteorological Administration (KMA)]. As its microphysics scheme was originally designed and tuned for large-scale precipitation systems, we investigate the performance of UM microphysics to determine potential inherent biases or weaknesses. Two rainfall cases from the KMA forecasting system are considered in this study: a Changma (quasi-stationary) front, and Typhoon Sanba (2012). The UM output is compared to polarimetric radar observations in terms of simulated polarimetric radar variables. Results show that the UM generally underpredicts median reflectivity in stratiform rain, producing high reflectivity cores and precipitation gaps between them. This is partially due to the diagnostic rain intercept parameter formulation used in the one-moment microphysics scheme. Model drop size is generally both underand overpredicted compared to observations. UM frozen hydrometeors favor generic ice (crystals and snow) rather than graupel, which is reasonable for Changma and typhoon cases. The model performed best with the typhoon case in terms of simulated precipitation coverage.

Vertical Profiles of Rain Drop-Size Distribution over Tropical Semi-Arid-Region, Kadapa (14.47° N; 78.82° E), India

South-West and North East monsoon are the two important periodic winds, which show high impact on the rain. In general, the South-West (SW) monsoon spreads over the state from June to September and North-East (NE) monsoon from October to December. In this paper we present the details of vertical profiles of rainfall measured with Micro Rain Radar (MRR) deployed at Kadapa (14.47° N; 78.82° E), a semiarid tropical site in Andhra Pradesh, India for the period from 2009 to 2010. This paper discusses the relation between various rain drop size distribution properties such as Rain Reflectivity (Z), Rain Rate (RR), Liquid Water Content(LWC) of the rain in these two monsoons. In the present study, the Z-R relationship is obtained using linear regression. The Strati form and Convective precipitations are found to occur at different rates of rainfall.

Modified Hitschfeld–Bordan Equations for Attenuation-Corrected Radar Rain Reflectivity: Application to Nonuniform Beamfilling at Off-Nadir Incidence

Abstract As shown by Takahashi et al., multiple path attenuation estimates over the field of view of an airborne or spaceborne weather radar are feasible for off-nadir incidence angles. This follows from the fact that the surface reference technique, which provides path attenuation estimates, can be applied to each radar range gate that intersects the surface. This study builds on this result by showing that three of the modified Hitschfeld–Bordan estimates for the attenuation-corrected radar reflectivity factor can be generalized to the case where multiple path attenuation estimates are available, thereby providing a correction to the effects of nonuniform beamfilling. A simple simulation is presented showing some strengths and weaknesses of the approach.

Rain field and reflectivity vertical profile reconstruction from C-band Radar volumetric data

Operating a meteorological radar is generally a challenging task when in presence of a significant beam blockage as in complex orography. Apart from enhanced ground clutter, mountainous obstructions of the radar beam can significantly reduce the radar visibility and, thus, its monitoring capabilities. Self-consistent adaptive techniques to reconstruct vertical profiles of reflectivity (VPR) and near-surface rain-rate fields from high-elevation reflectivity bins are here proposed, compared, and tested for ranges up to 60 km. The methodology is based on statistical estimators trained by a large reflectivity volumetric datasets, classified into stratiform and convective rain regimes and resampled onto a uniform Cartesian grid by means of a modified Cressman technique. For what concerns reflectivity vertical profiles, two methods, respectively named statistical nonlinear reconstruction (NSR) and neural network reconstruction (NNR), are considered. The NSR method is based on the principal component analysis, applied to the radar dataset, in order to extract significant reflectivity-profile variance. A retrieval technique, based on a nonlinear multiple regression scheme, is then used to infer near-surface reflectivity from available high-altitude echoes at a given range. The NNR is based on a three-layer artificial neural network trained by means a feedforward backpropagation algorithm. For what concerns the near-surface rain retrieval, besides a power-law reflectivity-rain-rate (ZR) approach, a three-layer neural network technique is also set up in order to estimate surface rain rate from reconstructed VPR. The proposed reconstruction techniques are here illustrated by using volumetric data acquired by the C-band Doppler single-polarization radar, operated in L'Aquila, Italy. A case study, related to a rainfall event that occurred during fall 2000, is discussed. Using a test area within 60 km from the radar site and simulating the presence of beam obstructions, a comparison of NSR and NNR with conventional area average reconstruction techniques shows that the percentage improvement of both NSR and NNR approaches is significant, both for the error bias (by 30% to more than 50%, depending on altitude) and variance (by 10% to more than 20%). A sensitivity test indicates that the VPR reconstruction procedure is fairly robust to missing data, especially in terms of error bias. The comparison of estimated radar rainfall with rain gauge data measurement is also illustrated. The mean field bias closer to its optimal value and an error variance much smaller is obtained when neural network techniques are applied than with conventional ZR methods for both techniques of reconstruction. With respect to the latter, the obtained improvement is more than 40% in terms of root mean square error and is comparable when estimating near-surface rain rate using either NSR or NNR methods to reconstruct the reflectivity vertical profiles. Limitations, potential, and future developments of the proposed adaptive reconstruction techniques are finally discussed.

Effects of Rain Rate and Wind Magnitude on SeaWinds Scatterometer Wind Speed Errors

Rain within the footprint of the SeaWinds scatterometer on the QuikSCAT satellite causes more significant errors than existed with its predecessor, the NASA scatterometer (NSCAT) on Advanced Earth Observing Satellite-I (ADEOS-I). Empirical relations are developed that show how the rain-induced errors in the scatterometer wind magnitude depend on both the rain rate and on the wind magnitude. These relations are developed with collocated National Data Buoy Center (NDBC) buoy measurements (to provide accurate sea surface winds) and simultaneous Next Generation Weather Radar (NEXRAD) observations of rain reflectivity. An analysis, based on electromagnetic scattering theory, interprets the dependence of the scatterometer wind errors on volumetric rain rate over a range of wind and rain conditions. These results demonstrate that the satellite scatterometer responds to rain in a manner similar to that of meteorological radars, with a Z–R relationship. These observations and results indicate that the combined (wind and rain) normalized radar cross section will lead to erroneously large wind estimates when the rain-related radar cross section exceeds a particular level that depends on the rain rate and surface wind speed.

Feasibility of Using Mountain Return for the Correction of Ground-Based X-Band Weather Radar Data

Abstract The Marseilles Hydrometeorological Experiment has been designed to improve rainfall measurement techniques over the space scales and timescales useful for urban hydrology applications. Among other sensors, an X-band light configuration weather radar system was set up near the city. The main objective of the present paper is to test the feasibility of using mountain return to correct rain attenuation effects, an application of the well-known “surface reference technique” developed for spaceborne radar configurations. The radar siting and scan procedures were defined so as to obtain both (i) rain reflectivity measurements free of ground detection over a large domain and (ii) strong mountain return for the path-integrated attenuation (PIA) estimation in a reduced azimuthal sector. The so-called PIA constraint equation is proposed to relate the measured rain reflectivity profile in one direction to the corresponding mountain PIA. A study of this equation for the 23 September 1993 rain event provides ...

Cloud observations with a polarimetric 33 GHz and 95 GHz radar

The University of Massachusetts' Microwave Remote Sensing Laboratory (MIRSL) has developed a unique high spatial resolution multiparameter radar under sponsorship from the Department of Energy's Atmospheric Radiation Measurement (ARM) program. The Cloud Profiling Radar System (CPRS) uses a single one-meter diameter dielectric lens antenna to make collocated polarimetric and Doppler measurements at both 33 GHz and 95 GHz. The polarization of each transmitted pulse at either frequency can be selected on a pulse-to-pulse basis. The radar and supporting hardware are mounted on a truck that serves as a mobile laboratory. The truck-based platform permits CPRS to operate in remote locations and also serves as an economical means of transporting the system. This paper describes the CPRS hardware and presents preliminary vertically pointing observations of mixed-phase stratus clouds obtained in the summer of 1993 during the first field test of the system. Measurements show Mie scattering in the ice region, melting layer and rain region of the clouds observed. To illustrate CPRS potential for particle sizing, models of differential reflectivity and differential mean Doppler velocity are used to estimate median volume diameter,D 0, from dual-wavelength reflectivity and Doppler measurments of rain.

Reconstruction of the spatial distribution of radar reflectivity of precipitation through linear-inversion techniques

The authors present a method aiming to remove the smoothing effect due to the antenna beamwidth in the reconstruction of the rain reflectivity based on radar measurements. The function to be reconstructed is the spatial distribution Z(x, y, h) of the radar reflectivity Z of the precipitation, which is shown to be related by a Fredholm integral equation to the power received by the radar. This integral equation is transformed into a set of linear equations and subsequently solved through a constrained-linear-inversion technique. The method is then applied to the cases of both stratiform and convective rain, leading to an improved estimation of the reflectivity-peak location and intensity, and of the overall reflectivity profile.

Long-Term Radar Observations of the Melting Layer of Precipitation and Their Interpretation

Abstract In this study, 600 h of vertically pointing X-band radar data and 50 h of UHF boundary layer wind profiler data were processed and analyzed to characterize quantitatively the structure and the causes of the radar signature from melting precipitation. Five classes of vertical profiles of reflectivity in rain were identified, with three of them having precipitation undergoing a transition between the solid and liquid phase. Only one of them, albeit the most common, showed a radar brightband signature. In-depth study of the bright band and its dependence on precipitation intensity reveals that the ratio of the brightband peak reflectivity to the rainfall reflectivity is constant at 8 dB below 0.5 mm h−1 and then increases to reach 13 dB at 2.5 mm h−1 and 16 dB at 5 mm h−1. The equivalent reflectivity factor of snow just above the melting layer is on average 1–2 dB below the reflectivity of rain just below the melting layer, independent of precipitation intensity. The classical brightband explanation...

Technique for calibration of meteorological radars using differential phase

Meteorological radars that are capable of making both polarisation diversity and phase measurements require an accurate method for absolute power calibration to achieve their full potential. A technique is described which uses a combination of differential reflectivity and differential phase measurements in rain to achieve this. The method is demonstrated using data obtained with the 3 GHz Chilbolton radar, which was recently upgraded to measure differential phase and Doppler velocity. The sensitivity of this technique to the assumed drop size distribution is estimated, and shown to have a minor effect on the accuracy of the calibration.

Axis Ratios and Oscillations of Raindrops

Abstract Axis ratios were determined for about 3500 raindrop images recorded in summertime rainshowers by an instrumented aircraft. These ratios were used to determine the mean axis ratios and oscillation amplitudes of raindrops. A filtering algorithm using Fourier descriptors was applied to the raindrop images to reduce the quantization noise and the systematic errors, and simulations were used to estimate the standard errors of the measurement procedure. Drops with diameters <4 mm were observed to be slightly more spherical than would be expected for drops in equilibrium. Oscillation amplitudes were found to be typically ±10% in axis ratio for light to moderate rainfall rates, and such oscillations can account for the departures from equilibrium values. The effects of these axis ratios and oscillations on the differential radar reflectivity of rain are calculated and discussed.

Physical modeling and analysis of rain and clouds by anisotropic scaling multiplicative processes

We argue that the basic properties of rain and cloud fields (particularly their scaling and intermittency) are best understood in terms of coupled (anisotropic and scaling) cascade processes. We show how such cascades provide a framework not only for theoretically and empirically investigating these fields, but also for constructing physically based stochastic models. This physical basis is provided by cascade scaling and intermittency, which is of broadly the same sort as that specified by the dynamical (nonlinear, partial differential) equations. Theoretically, we clarify the links between the divergence of high‐order statistical moments, the multiple scaling and dimensions of the fields, and the multiplicative and anisotropic nature of the cascade processes themselves. We show how such fields can be modeled by fractional integration of the product of appropriate powers of conserved but highly intermittent fluxes. We also empirically test these ideas by exploiting high‐resolution radar rain reflectivities. The divergence of moments is established by direct use of probability distributions, whereas the multiple scaling and dimensions required the development of new empirical techniques. The first of these estimates the “trace moments” of rain reflectivities, which are used to determine a moment‐dependent exponent governing the variation of the various statistical moments with scale. This exponent function in turn is used to estimate the dimension function of the moments. A second technique called “functional box counting,” is a generalization of a method first developed for investigating strange sets and permits the direct evaluation of another dimension function, this time associated with the increasingly intense regions. We further show how the different intensities are related to singularities of different orders in the field. This technique provides the basis for another new technique, called “elliptical dimensional sampling,” which permits the elliptical dimension rain (describing its stratification) to be directly estimated: it yields del = 2.22 ± 0.07, which is less than that of an isotropic rain field (del = 3), but significantly greater than that of a completely flat (stratified) two‐dimensional field (del = 2).

Analysis of a Dual-Wavelength Surface Reference Radar Technique

Among the methods that have been proposed for estimating precipitation parameters from a down-looking radar are those that use the scattered power from the surface to infer path attenuation. In this paper, an analysis of a dual-wavelength surface reference technique (DSRT) is presented. The principal of the method is that the ratio of the backscattered power from the surface at one wavelength to that at a second wavelength is proportional to the differential attenuation of the signals through the intervening rain. To account for differences in the backscattering cross section of the surface a 0°, and in the radar calibration constant at the two wavelengths, measurements are made of the surface return power during clear air conditions. The characteristics of the method are best understood by comparing it to two closely related methods: the single-wavelength surface reference technique (SRT) and the standard dual-wavelength technique (DWT). A comparison between the DWT and the DSRT shows that the rain rate estimates are identical in form and that the surface reference can be obtained from the standard estimate by replacing the statistics of the rain reflectivity with those of ?°Advantages of the DSRT relative to its single wavelength counterpart occur when the wavelength correlation in ?° is high or when the mean values of ?° at the two wavelengths are nearly equal. To treat the methods under less restrictive assumptions a simulation is used. Error statistics of the rain rate estimates are presented for a space-borne radar operating at an attenuating wavelength.

Achieving improved frequency response of the rain structure from spaceborne radar altimeters by employing deconvolution methods

A technique is described for arriving at improved measurements of the rain structure near the earth's surface from satellite‐borne radars. The method uses deconvolution and Fourier transform procedures and assumes a knowledge of the antenna beam pattern. As an example, the method is directed toward the application of future spaceborne radar altimeters that may contain additional range gates to permit the measurement of rain in the lower troposphere. Using a radar‐measured rain reflectivity profile acquired at Wallops Island, Va., in a simulation example, it is specifically demonstrated that improved measurements of the spatial frequency spectrum of the rain structure may be deduced by means of deconvolution methods, as compared to the measured spectrum derived by beam averaging. Rain measurements are considered vital from the standpoint of providing a flag for altimeter data that may be corrupted by rain. Such measurement capabilities should also provide sorely needed data on rain over the oceans where few or no such data are available for the meteorologist or the communicator interested in space diversity communications.

Rain cell size statistics as a function of rain rate for attenuation modeling

Rain cell size statistics as a function of rain rate have been deduced by employing a radar data base of rain reflectivity data acquired over a three-year period at Wallops Island, VA. These cell statistics have important applications in slant path rain attenuation modeling and remote sensing of the earth's surface from space at frequencies above 10 GHz.

Space diversity performance prediction for earth‐satellite paths using radar modeling techniques

Both single‐terminal and two‐terminal joint probability rain fade distributions for earth‐satellite paths at 28.56 GHz are derived for Wallops Island, Virginia, by using radar modeling techniques. An array of parallel paths is modeled in the vertical plane which contained the COMSTAR satellite and a terminal receiving the 28.56 GHz COMSTAR beacon. The paths are displaced 1 km apart (8–100 km from the radar) at a fixed elevation angle (θ0 = 45°). They pass through the volume whose rain environment has been monitored as radar rain reflectivity levels and stored on magnetic tape. An attenuation at 28.56 GHz and a ground rain rate level is calculated for each path and for each radar scan. Both single and joint terminal conditional fade statistics are derived for an ensemble of such arrays, where the conditioning constant is the long‐term rain rate distribution. The single and joint terminal distributions are calculated by multiplying the conditional fade statistics by the measured long‐term rain rate distribution. As a partial check, the radar‐derived single‐terminal fade distribution at Wallops Island is compared with the directly measured levels obtained over a 3‐year period showing very good agreement. A family of diversity gain curves is calculated from the joint probability distributions corresponding to different probability levels. These are demonstrated to be represented by a single diversity gain function defined as the ‘relative diversity gain.’ The radar‐derived relative diversity gain is compared with those obtained from other investigations as well as with derived autocorrelation function results showing good agreement.