Radar Data Quality Research Articles

Lqregularization-based unobserved baselines’ data estimation method for tomographic synthetic aperture radar inversion

The elevation image quality of tomographic synthetic aperture radar (TomoSAR) data depends mainly on the elevation aperture size, number of baselines, and baseline distribution. In TomoSAR, due to the restricted number of baselines with irregular distributions, the elevation imaging quality is always unacceptable using the conventional spectral analysis approach. Therefore, for a given limited number of irregular baselines, the completion of data for the unobserved virtual uniform baseline distribution should be addressed to improve the spectral analysis-based TomoSAR reconstruction quality. We propose an Lq(0<q≤1) regularization-based unobserved baselines’ data estimation method for TomoSAR, which uses the geometric imaging relationship between the observed and unobserved baseline distributions. In the proposed method, we first estimate the transformation matrix between the acquisitions and the data of virtual uniform baseline distribution by solving an optimization problem, before calculating the data for virtual baseline distribution based on the acquisitions and the transformation matrix. Finally, the elevation reflectivity function is recovered using the spectral analysis method based on the estimated data. Compared with the reconstructed results only based on the limited irregular acquisitions, the image recovered using the dataset with a virtual uniform baseline distribution can improve the elevation image quality in an efficient manner.

The value of weather radar data for the estimation of design storms – an analysis for the Hannover region

Abstract. Pure radar rainfall, station rainfall and radar-station merging products are analysed regarding extreme rainfall frequencies with durations from 5 min to 6 h and return periods from 1 year to 30 years. Partial duration series of the extremes are derived from the data and probability distributions are fitted. The performance of the design rainfall estimates is assessed based on cross validations for observed station points, which are used as reference. For design rainfall estimation using the pure radar data, the pixel value at the station location is taken; for the merging products, spatial interpolation methods are applied. The results show, that pure radar data are not suitable for the estimation of extremes. They usually lead to an overestimation compared to the observations, which is opposite to the usual behaviour of the radar rainfall. The merging products between radar and station data on the other hand lead usually to an underestimation. They can only outperform the station observations for longer durations. The main problem for a good estimation of extremes seems to be the poor radar data quality.

Intercomparison of selected fixed-area areal reduction factor methods

Summary The areal reduction factor (ARF) is a concept used in many hydrologic designs to transform a point precipitation frequency estimate of a given duration and frequency to a corresponding areal estimate. Various methods have been proposed in the literature to calculate ARFs. Proposed ARFs could vary significantly, and it is unclear if discrepancies are primarily due to differences in methodologies, the dissimilar datasets used to calculate ARFs, or if they originate from regional uniqueness. Our goal in this study is to analyze differences among ARFs derived from different types of fixed-area ARF methods, which are suitable for use with precipitation frequency estimates. For this intercomparison, all the ARFs were computed using the same, high-quality rainfall-radar merged dataset for a common geographic region. The selected ARFs methods represent four commonly used approaches: empirical methods, methods that are based on the spatial correlation structure of rainfall, methods that rely on the scaling properties of rainfall in space and time, and methods that utilize extreme value theory. The state of Oklahoma was selected as the study area, as it has a good quality radar data and a dense network of rain gauges. Results indicate significant uncertainties in the ARF estimates, regardless of the method used. Even when calculated from the same dataset and for the same geographic area, the ARF estimates from the selected methods differ. The differences are more pronounced for the shorter durations and larger areas. Results also indicate some ARF dependence on the average recurrence intervals.

Operational Quality Control and Enhancement of Radar Data to Support Regional Flash Flood Warning Systems

AbstractThe paper draws from recent experience with operational regional flash flood warning systems deployed worldwide that utilize radar precipitation data as input to provide regional warnings at high spatial resolution. This paper focuses on radar precipitation data quality control and adjustments in context of making the radar data useful in hydrologic application specific to flash flood warnings. Aspects discussed are radar precipitation preprocessing for hydrologic application, identifying regions of inappropriate radar data for hydrologic use, developing bias adjustment strategies using limited rain gauge networks under the radar umbrella, and impacts of radar precipitation uncertainty on soil water estimates that impact in turn predictions and warnings. The bias adjustment strategies presented are twofold: (1) adjusting for the long-term mean bias in the radar precipitation data (climatological bias adjustment), and (2) accounting for event-specific, temporal deviations from the climatological bi...

Comprehensive Framework for Assessment of Radar-Based Precipitation Data Estimates

AbstractAssessment of radar-based precipitation estimates using rain gauge observations is a critical exercise in evaluating pre-and postcorrected (gauge-adjusted) radar-based precipitation data. A comprehensive assessment framework combining several visual, quantitative, and statistical measures, indexes, and skill scores is proposed and developed for evaluation of radar-based precipitation estimates in space and time. Contingency measures, skill scores, and a few new metrics are proposed and are evaluated along with several indexes. Visual measures provide a quick check of agreement between radar and rain gauge data sets. Quantitative measures provide information about errors, and skill scores assess the quality of radar data for dichotomous (rain and no-rain) events. Summary statistics and hypothesis tests in statistical categories provide insights into distributional aspects of the rain gauge and radar data sets. The framework is used for evaluation of 15-min radar-based precipitation data obtained fr...

Which Polarimetric Variables Are Important for Weather/No-Weather Discrimination?

AbstractRecently, a radar data quality control algorithm has been devised to discriminate between weather echoes and echoes due to nonmeteorological phenomena, such as bioscatter, instrument artifacts, and ground clutter (Lakshmanan et al.), using the values of polarimetric moments at and around a range gate. Because the algorithm was created by optimizing its weights over a large reference dataset, statistical methods can be employed to examine the importance of the different variables in the context of discriminating between weather and no-weather echoes. Among the variables studied for their impact on the ability to identify and censor nonmeteorological artifacts from weather radar data, the method of successive permutations ranks the variance of Zdr, the reflectivity structure of the virtual volume scan, and the range derivative of the differential phase on propagation [PhiDP (Kdp)] as the most important. The same statistical framework can be used to study the impact of calibration errors in variables such as Zdr. The effects of Zdr calibration errors were found to be negligible.

Rainfall Event Analysis for Urban Flooding Study Using Radar Rainfall Data.

Urban flooding is one of the most serious challenges facing urban areas and causesdestruction of properties and major problems for people living in urban areas. Itoccurs in an urban area when intense convective rainfall happens that is higher thanthe capacity of the urban drainage system. Rainfall data can be recorded by radar andby rain gauge. It is essential for urban flooding studies to have access to high qualityrainfall data in attempting to reduce and tackle this challenge. The main aim of thispaper is to test the quality of radar data by means of rainfall event analysis. Radardata obtained from Yorkshire Water were analysed and compared to rain gauge dataduring selected rainfall events in a study area located in North Yorkshire in the UK.Rainfall intensity curves within 5 minute and one-hour intervals, cumulative rainfalldepth curves and normalised bias were used as standard methods for comparisonbetween radar and rain gauge data during the selected rainfall events. Available datawere collected for June, July, and October 2008 and were recorded from Blackhill,Harrogate North, and Helmsley rain gauges, and radar data for these same locationswere also collected. The results of this thesis show that certain errors might have animpact on radar data in terms of quality. Some such errors caused overestimation ofradar rainfall data, such as anomalous propagation error and error related tothunderstorm. However, other errors, such as attenuation, caused underestimation inradar rainfall data.

Applying bias correction for merging rain gauge and radar data

Weather radar provides areal rainfall information with very high temporal and spatial resolution. Radar data has been implemented in several hydrological applications despite the fact that the data suffers from varying sources of error. Several studies have attempted to propose methods for solving these problems. Additionally, weather radar usually underestimates or overestimates the rainfall amount. In this study, a new method is proposed for correcting radar data by implementing the quantile mapping bias correction method. Then, the radar data is merged with observed rainfall by conditional merging and kriging with external drift interpolation techniques. The merging product is analysed regarding the sensitivity of the two investigated methods to the radar data quality. After implementing bias correction, not only did the quality of the radar data improve, but also the performance of the interpolation techniques using radar data as additional information. In general, conditional merging showed greater sensitivity to radar data quality, but performed better than all the other interpolation techniques when using bias corrected radar data. Furthermore, a seasonal variation of interpolation performances has in general been observed. A practical example of using radar data for disaggregating stations from daily to hourly temporal resolution is also proposed in this study.

Gust front statistical characteristics and automatic identification algorithm for CINRAD

Gust front is a kind of meso- and micro-scale weather phenomenon that often causes serious ground wind and wind shear. This paper presents an automatic gust front identification algorithm. Totally 879 radar volume-scan samples selected from 21 gust front weather processes that occurred in China between 2009 and 2012 are examined and analyzed. Gust front echo statistical features in reflectivity, velocity, and spectrum width fields are obtained. Based on these features, an algorithm is designed to recognize gust fronts and generate output products and quantitative indices. Then, 315 samples are used to verify the algorithm and 3 typical cases are analyzed. Major conclusions include: 1) for narrow band echoes intensity is between 5 and 30 dBZ, widths are between 2 and 10 km, maximum heights are less than 4 km (89.33% are lower than 3 km), and the lengths are between 50 and 200 km. The narrow-band echo is higher than its surrounding echo. 2) Gust fronts present a convergence line or a wind shear in the velocity field; the frontal wind speed gradually decreases when the distance increases radially outward. Spectral widths of gust fronts are large, with 87.09% exceeding 4 m s−1. 3) Using 315 gust front volume-scan samples to test the algorithm reveals that the algorithm is highly stable and has successfully recognized 277 samples. The algorithm also works for small-scale or weak gust fronts. 4) Radar data quality has certain impact on the algorithm.

Long-term diagnostics of precipitation estimates and the development of radar hardware monitoring within a radar product data quality management system

Quality is key to ensuring that the potential offered by weather radar networks is realized. To achieve optimum quality, a comprehensive radar data quality management system, designed to monitor the end-to-end radar data processing chain and evaluate product quality, is being developed at the UK Met Office. Three contrasting elements of this system are described: monitoring of key radar hardware performance indicators; generation of long-term integrations of radar products; and monitoring of radar reflectivity factor using synthesized observations from numerical weather prediction model fields. Examples of each component are presented and ways in which the different types of monitoring information have been used to both identify issues with the radar product data quality and help formulate solutions are given.Editor Z.W. Kundzewicz; Guest editor R.J. MooreCitation Harrison, D., Georgiou, S., Gaussiat, N., and Curtis, A., 2013. Long-term diagnostics of precipitation estimates and the development of radar hardware monitoring within a radar product data quality management system. Hydrological Sciences Journal, 59 (7), 1327–1342. http://dx.doi.org/10.1080/02626667.2013.841316

Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables

The United States Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network has been upgraded to dual-polarization capabilities, providing operational and research meteorologists with a wealth of new information regarding the types and distributions of hydrometeors within precipitating storms, as well as a means for improved radar data quality. In addition to the conventional moments of reflectivity factor at horizontal polarization (ZH), Doppler velocity (Vr), and Doppler spectrum width (W), the new variables available from upgraded radars are the differential reflectivity (ZDR), differential propagation phase shift (ΦDP), specific differential phase (KDP), and the co-polar correlation coefficient (ρhv or CC). In the first part of this review series, a description of the polarimetric radar variables available from the newly polarimetric WSR-88D radars is provided. An emphasis is made on their physical meaning and interpretation in the context of operational meteorology.

Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios

Summary This study investigates the performance of merging radar and rain gauge data for different high temporal resolutions and rain gauge network densities. Three different geostatistical interpolation techniques: Kriging with external drift, indicator kriging with external drift and conditional merging were compared and evaluated by cross validation. Ordinary kriging was considered as the reference method without using radar data. The study area is located in Lower Saxony, Germany, and covers the measuring range of the radar station Hanover. The data used in this study comprise continuous time series from 90 rain gauges and the weather radar that is located near Hanover over the period from 2008 until 2010. Seven different temporal resolutions from 10 min to 6 h and five different rain gauge network density scenarios were investigated regarding the interpolation performance of each method. Additionally, the influence of several temporal and spatial smoothing-techniques on radar data was evaluated and the effect of radar data quality on the interpolation performance was analyzed for each method. It was observed that smoothing of the gridded radar data improves the performance in merging rain gauge and radar data significantly. Conditional merging outperformed kriging with an external drift and indicator kriging with an external drift for all combinations of station density and temporal resolution, whereas kriging with an external drift performed similarly well for low station densities and rather coarse temporal resolutions. The results of indicator kriging with an external drift almost reached those of conditional merging for very high temporal resolutions. Kriging with an external drift appeared to be more sensitive in regard to radar data quality than the other two methods. Even for 10 min temporal resolutions, conditional merging performed better than ordinary kriging without radar information. This illustrates the benefit of merging rain gauge and radar data even for very high temporal resolutions.

A study on WRF radar data assimilation for hydrological rainfall prediction

Abstract. Mesoscale numerical weather prediction (NWP) models are gaining more attention in providing high-resolution rainfall forecasts at the catchment scale for real-time flood forecasting. The model accuracy is however negatively affected by the "spin-up" effect and errors in the initial and lateral boundary conditions. Synoptic studies in the meteorological area have shown that the assimilation of operational observations, especially the weather radar data, can improve the reliability of the rainfall forecasts from the NWP models. This study aims at investigating the potential of radar data assimilation in improving the NWP rainfall forecasts that have direct benefits for hydrological applications. The Weather Research and Forecasting (WRF) model is adopted to generate 10 km rainfall forecasts for a 24 h storm event in the Brue catchment (135.2 km2) located in southwest England. Radar reflectivity from the lowest scan elevation of a C-band weather radar is assimilated by using the three-dimensional variational (3D-Var) data-assimilation technique. Considering the unsatisfactory quality of radar data compared to the rain gauge observations, the radar data are assimilated in both the original form and an improved form based on a real-time correction ratio developed according to the rain gauge observations. Traditional meteorological observations including the surface and upper-air measurements of pressure, temperature, humidity and wind speed are also assimilated as a bench mark to better evaluate and test the potential of radar data assimilation. Four modes of data assimilation are thus carried out on different types/combinations of observations: (1) traditional meteorological data; (2) radar reflectivity; (3) corrected radar reflectivity; (4) a combination of the original reflectivity and meteorological data; and (5) a combination of the corrected reflectivity and meteorological data. The WRF rainfall forecasts before and after different modes of data assimilation are evaluated by examining the rainfall temporal variations and total amounts which have direct impacts on rainfall–runoff transformation in hydrological applications. It is found that by solely assimilating radar data, the improvement of rainfall forecasts are not as obvious as assimilating meteorological data; whereas the positive effect of radar data can be seen when combined with the traditional meteorological data, which leads to the best rainfall forecasts among the five modes. To further improve the effect of radar data assimilation, limitations of the radar correction ratio developed in this study are discussed and suggestions are made on more efficient utilisation of radar data in NWP data assimilation.

Improved Doppler Velocity Dealiasing for Radar Data Assimilation and Storm-Scale Vortex Detection

The Doppler velocity dealiasing technique based on alias-robust VAD and variational (AR-Var) analyses developed at the National Severe Storms Laboratory for radar data quality control and assimilation is further improved in its two-step procedures: the reference check in the first step and the continuity check in the second step. In the first step, the alias-robust variational analysis is modified adaptively and used in place of the alias-robust velocity-azimuth display (VAD) analysis for all scan modes (rather than solely the WSR-88D volume coverage pattern 31 with the Nyquist velocityvNreduced below 12 m s−1and the TDWR Mod80 withvNreduced below 15 m s−1), so more raw data can pass the stringent threshold conditions used by the reference check in the first step. This improves the dealiased data coverage without false dealiasing to better satisfy the high data quality standard required by radar data assimilation. In the second step, new procedures are designed and added to the continuity check to increase the dealiased data coverage over storm-scale areas threatened by intense mesocyclones and their generated tornados. The performances of the improved dealiasing technique versus the existing techniques are exemplified by the results obtained for tornadic storms scanned by the operational KTLX radar in Oklahoma.

Data Quality Control of Wind Profiler Radar Based on Extended Kalman Filter

As a new type of Doppler wind radar, Wind profiler radar often suffers from a variety of meteorological factors. These interferences have great effect on the detection data precision, which leads to radar data quality problem. A data processing method based on Extended Kalman Filter (EKF) is presented in this paper, which focuses on the nonlinear problems of wind data of wind profiler radar. This method is the development of traditional Kalman Filter (KF) in practical engineering applications. To verify the validity of this first-order EKF from different situation, one day’s and one moment’s wind data are selected respectively, as examples for filtering. EKF is better for data processing of wind profiler radar and has some engineering application prospects.

A Real-Time and Offline Quality Control Methodology for SeaSonde High-Frequency Radar Currents

Abstract A near-real-time and offline quality control methodology for SeaSonde systems is proposed. It is applied on radial current maps and is based on the determination of the signal-to-noise ratio (SNR) values of the Doppler lines that contribute to the hourly radial current at each range-bearing (R, θ) pair, under the assumption that SNR is a proxy for radar data quality. The retrieval of the sequence of Doppler lines is performed through a minimization procedure that takes advantage of the statistical descriptors output in the short-term radial maps. The separation of the contributing Doppler velocities into valid observations and anomalous velocities is based on their spectral quality factor and on a range-dependent noise threshold derived from statistics (average and standard deviation) of the signal amplitudes in the tails of the Doppler spectra. The final product of the quality control procedure is a radial current map, in which Doppler velocities are weighted by their SNR values and their spectral quality factors, and averaged to produce an output that is fully compatible with the proprietary software. This procedure is fast, despite the fact that a large number of combinations might be required during the retrieval of the Doppler lines, and effective, because it removes both evident spikes as well as Doppler velocities that are not clearly identified as anomalous velocities. In principle, this approach can be used to fill gaps in the radar coverage without the need for interpolation in time or space, proved that the Doppler velocities satisfy predetermined SNR constraints.

Multilag Correlation Estimators for Polarimetric Radar Measurements in the Presence of Noise

Abstract The quality of polarimetric radar data degrades as the signal-to-noise ratio (SNR) decreases. This substantially limits the usage of collected polarimetric radar data to high SNR regions. To improve data quality at low SNRs, multilag correlation estimators are introduced. The performance of the multilag estimators for spectral moments and polarimetric parameters is examined through a theoretical analysis and by the use of simulated data. The biases and standard deviations of the estimates are calculated and compared with those estimates obtained using the conventional method.

Long‐term monitoring of French polarimetric radar data quality and evaluation of several polarimetric quantitative precipitation estimators in ideal conditions for operational implementation at C‐band

AbstractThis article presents the evaluation of several polarimetric quantitative precipitation estimation (Pol‐QPE) algorithm candidates for operational implementation in the Météo‐France polarimetric weather radars (nine C‐band and one S‐band out of a total of 24 radars in 2011). Since the performance of the algorithms depends on both the quality of the polarimetric variables and the applied polarimetric processing, the article first describes the current polarimetric processing chain and then provides a comprehensive analysis of the quality of the polarimetric variables.Three types of QPE algorithms have been studied at C‐band in ideal conditions (short ranges, no partial beam blocking, no vertical profile of reflectivity effects):(i) algorithms based on simple Z‐R relationships with and without attenuation correction using the differential phase Φdp,(ii) an algorithm based on reflectivity (Zh) and differential reflectivity (Zdr), and(iii) algorithms based on specific differential phase (Kdp).Evaluation against rain‐gauges, performed hourly, confirms the superiority of polarimetric algorithms as repeatedly reported in the literature. It also shows that the benefits brought by algorithms based on power variables, Zh and Zdr, are critically dependent upon the capability to correctly calibrate them. Long‐term monitoring shows that, with the current radar systems and operational calibration procedures, a stability of ±0.4 dB is achieved on Zdr. In that context, QPE algorithms based on Kdp, which are considered to be immune to partial beam blocking, calibration, attenuation, etc., are preferred for operational use until improvements are made to the calibration of Zdr. To this end, a hybrid algorithm based on attenuation‐corrected Zh for low rain rates and Kdp for higher rain rates has been designed and tested Copyright © 2012 Royal Meteorological Society

The Influence of Airborne Doppler Radar Data Quality on Numerical Simulations of a Tropical Cyclone

Abstract The impact of airborne Doppler radar data assimilation on improving numerical simulations of tropical cyclones (TCs) has been well recognized. However, the influence of radar data quality on the numerical simulation of tropical cyclones has not been given much attention. It is commonly assumed that higher quality radar data would be more beneficial to numerical simulations of TCs. This study examines the impact of the radar data quality control on assimilation of the airborne Doppler radar reflectivity and radial velocity observations in a numerical simulation of Typhoon Jangmi (2008). It is found that the quality of radar data has a strong influence on the numerical simulation of Typhoon Jangmi in terms of its track, intensity, and precipitation structures. Specifically, results suggest that a trade-off between the data quality and data coverage is necessary for different purposes in practical applications, as the higher quality data contribute to intensity forecast improvements, whereas data of lower quality but having better coverage are more beneficial to accurate track forecasting.

An operational tool to quality control 2D radar reflectivity data for assimilation in COSMO-DE

An operational tool has been designed to enhance the quality of 2D radar reflectivity data for assimilation in COSMO-DE within the German Weather Service (DWD). This article describes the operational algorithms including their testing, the creation of local and composite quality-index fields and their application to improve data assimilation. In the first step, algorithms have been developed and tested to define and identify some of the most severe errors in radar data: corrupt images, low-reflectivity phenomena, which occur under special meteorological conditions, spokes, rings and clutter remnants/speckles. The algorithms use simple but effective tests based on statistical and textural characteristics of spurious signals. The results are stored in an 8-bit quality-index field created concurrently with the radar data at each radar station. It contains quality flags for each individual range bin plus some header information on the overall quality of the underlying data set. The independent coding of error bits enables a differentiated a posteriori decision on whether a range bin is to be used for a given application. These local quality index fields are then used to create a radar precipitation composite, accompanied by a quality-index composite, covering Germany. This tool has now been applied operationally throughout the German radar network. As a result, the radar data quality in data assimilation could be increased.