Quality Control of Weather Radar Data Using Polarimetric Variables

2014 ◽  
Vol 31 (6) ◽  
pp. 1234-1249 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Christopher Karstens ◽  
John Krause ◽  
Lin Tang
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
Weather Radar ◽  
Radar Data ◽  
The United States ◽  
Skill Score ◽  
Ground Clutter ◽  
Local Variance ◽  
Average Probability ◽  
Differential Reflectivity

Abstract Because weather radar data are commonly employed in automated weather applications, it is necessary to censor nonmeteorological contaminants, such as bioscatter, instrument artifacts, and ground clutter, from the data. With the operational deployment of a widespread polarimetric S-band radar network in the United States, it has become possible to fully utilize polarimetric data in the quality control (QC) process. At each range gate, a pattern vector consisting of the values of the polarimetric and Doppler moments, and local variance of some of these features, as well as 3D virtual volume features, is computed. Patterns that cannot be preclassified based on correlation coefficient ρHV, differential reflectivity Zdr, and reflectivity are presented to a neural network that was trained on historical data. The neural network and preclassifier produce a pixelwise probability of precipitation at that range gate. The range gates are then clustered into contiguous regions of reflectivity, with bimodal clustering carried out close to the radar and clustering based purely on spatial connectivity farther away from the radar. The pixelwise probabilities are averaged within each cluster, and the cluster is either retained or censored depending on whether this average probability is greater than or less than 0.5. The QC algorithm was evaluated on a set of independent cases and found to perform well, with a Heidke skill score (HSS) of about 0.8. A simple gate-by-gate classifier, consisting of three simple rules, is also introduced in this paper and can be used if the full QC method is not able to be applied. The simple classifier has an HSS of about 0.6 on the independent dataset.

A Neural-Network Quality Control scheme for improved Quantitative Precipitation Estimation accuracy on the UK weather radar network

2021 ◽  
Author(s):  
Nawal Husnoo ◽  
Timothy Darlington ◽  
Sebastián Torres ◽  
David Warde
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
Weather Radar ◽  
Radar Data ◽  
Superior Performance ◽  
Ground Clutter ◽  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
Precipitation Estimation ◽  
The Uk

AbstractIn this work, we present a new Quantitative-Precipitation-Estimation (QPE) quality-control (QC) algorithm for the UK weather radar network. The real-time adaptive algorithm uses a neural network (NN) to select data from the lowest useable elevation scan to optimize the combined performance of two other radar data correction algorithms: ground clutter mitigation (using CLEAN-AP) and vertical profile of reflectivity (VPR) correction. The NN is trained using 3D tiles of observed uncontaminated weather signals that are systematically combined with ground-clutter signals collected under dry weather conditions. This approach provides a way to simulate radar signals with a wide range of clutter contamination conditions and with realistic spatial structures while providing the uncontaminated “truth” with respect to which the performance of the QC algorithm can be measured. An evaluation of QPE products obtained with the proposed QC algorithm demonstrates superior performance as compared to those obtained with the QC algorithm currently used in operations. Similar improvements are also illustrated using radar observations from two periods of prolonged precipitation, showing a better balance between overestimation errors from using clutter-contaminated low-elevation radar data and VPR-induced errors from using high-elevation radar data.

scholarly journals Monitoring the impacts of weather radar data quality control for quantitative application at the continental scale

2020 ◽  
Vol 27 (4) ◽  
Author(s):  
Daniel Michelson ◽  
Bjarne Hansen ◽  
Dominik Jacques ◽  
François Lemay ◽  
Peter Rodriguez
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Weather Radar ◽  
Radar Data ◽  
Data Quality Control ◽  
Continental Scale

Nowcasting lightning during RELAMPAGO

2020 ◽  
Author(s):  
Evan Ruzanski ◽  
Venkatachalam Chandrasekar ◽  
Ivan Arias
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
The United States ◽  
Extreme Weather Events ◽  
Optimization Approach ◽  
Lightning Flash ◽  
Convective Systems ◽  
Physical Mechanisms ◽  
Weather Events ◽  
Future Loss

<p>The Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) international field campaign occurred June 1, 2018, to April 30, 2019. This campaign was comprised of more than 150 scientists from 10 organizations. Data was collected to investigate different phases of the life cycle of thunderstorms that occur in Argentina to better understand the physical mechanisms that cause the initiation and growth of organized convective systems in some of the most intense storms on the planet. The main focus of the project was to develop new conceptual models for forecasting extreme weather events that will hopefully lead to reductions in future loss of life and property.</p><p>This presentation shows the performance of a recently developed model for estimating ice mass aloft, a key component in the atmospheric electrification process, and a method for nowcasting lightning activity using C-band weather radar and Global Lightning Dataset (GLD360) data from RELAMPAGO. This nowcasting method uses a grid-based approach to make specific forecasts of lightning in space and time. The method estimates ice mass aloft in the region where electrification occurs using a numerical optimization approach to essentially reframe a simplified bulk microphysical model into a completely data-driven model. Previous results using WSR-88D S-band radar data in the United States showed that using this model significantly improved nowcasts of first-flash lightning occurrence versus the traditional weather radar-based ice mass estimator as well as using lightning flash-rate density directly.</p>

scholarly journals Ground Clutter Filtering Dual-Polarized, Staggered PRT Sequences

2006 ◽  
Vol 23 (8) ◽  
pp. 1114-1130 ◽  
Author(s):  
M. Sachidananda ◽  
Dusan S. Zrnic
Keyword(s):  
Radar Data ◽  
Differential Phase ◽  
Ground Clutter ◽  
Spectral Coefficients ◽  
Repetition Time ◽  
Dual Polarized ◽  
Differential Reflectivity ◽  
Simulated Time ◽  
Simulated Time Series ◽  
Position Indicator

Abstract A procedure to filter the ground clutter from a dual-polarized, staggered pulse repetition time (PRT) sequence and recover the complex spectral coefficients of the weather signal is presented. While magnitude spectra are sufficient for estimation of the spectral moments from staggered PRT sequences, computation of differential phase in dual-polarized radars requires recovery of the complex spectra. Herein a method is given to recover the complex spectral coefficients after the ground clutter is filtered. Under the condition of “narrow” spectra, it is possible to recover the differential phase, ΦDP, and the copolar correlation coefficient, ρhv, accurately, in addition to the differential reflectivity, ZDR. The technique is tested on simulated time series and on actual radar data. The efficacy of the method is demonstrated on plan position indicator (PPI) plots of polarimetric variables.

scholarly journals Processing of 3D Weather Radar Data with Application for Assimilation in the NWP Model

2014 ◽  
Vol 18 (3) ◽  
pp. 31-39 ◽  
Author(s):  
Katarzyna Ośródka ◽  
Jan Szturc ◽  
Bogumił Jakubiak ◽  
Anna Jurczyk
Keyword(s):  
Quality Control ◽  
Data Assimilation ◽  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Weather Radar ◽  
Radar Data ◽  
Field Pattern ◽  
The Impact ◽  
Radar Data Assimilation

Abstract The paper is focused on the processing of 3D weather radar data to minimize the impact of a number of errors from different sources, both meteorological and non-meteorological. The data is also quantitatively characterized in terms of its quality. A set of dedicated algorithms based on analysis of the reflectivity field pattern is described. All the developed algorithms were tested on data from the Polish radar network POLRAD. Quality control plays a key role in avoiding the introduction of incorrect information into applications using radar data. One of the quality control methods is radar data assimilation in numerical weather prediction models to estimate initial conditions of the atmosphere. The study shows an experiment with quality controlled radar data assimilation in the COAMPS model using the ensemble Kalman filter technique. The analysis proved the potential of radar data for such applications; however, further investigations will be indispensable.

scholarly journals Clear-Air Turbulence (CAT) Identification with X-Band Dual Polarimetric Radar Based on Bayesian Approach

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1691
Author(s):  
Jianli Ma ◽  
Li Luo ◽  
Mingxuan Chen ◽  
Siteng Li
Keyword(s):  
Frequency Distribution ◽  
Removal Rate ◽  
Low Frequency ◽  
Weather Radar ◽  
Radar Data ◽  
Probability Density Distribution ◽  
Air Turbulence ◽  
X Band ◽  
Differential Reflectivity

The echo of weather radar is seriously disturbed by clear-air turbulence echo (CAT) which needs identifying and eliminating to improve the data quality of weather radar. Using the data observed with the five X-band dual polarimetric radars in Changping, Fangshan, Miyun, Shunyi, and Tongzhou, Beijing in 2018, the probability density distribution (PDD) of the horizontal texture of four radar moments reflectively factor (ZH), differential reflectivity (ZDR), correlation coefficient (ρHV), differential propagation phase shift (ΦDP), and then the CAT is identified and removed using Bayesian method. The results show that the radar data can be effectively improved after the CAT has been eliminated, which include: (1) the removal rate of CAT is more than 98.2% in the analyzed cases. (2) In the area with high-frequency distribution of CAT, the CAT can be effectively suppressed; in the area with low-frequency distribution, some weather echo in the edge with SNR < 15 dB may be mistakenly identified as CAT, but the proportion of meteorological echoes to the total echoes is more than 85%, which indicate that the error rate is very low and does not affect the radar operation.

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

2015 ◽  
Vol 32 (6) ◽  
pp. 1209-1223 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Christopher Karstens ◽  
John Krause ◽  
Kim Elmore ◽  
Alexander Ryzhkov ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Control Algorithm ◽  
Radar Data ◽  
Errors In Variables ◽  
Data Quality Control ◽  
Reference Dataset ◽  
Ground Clutter ◽  
Statistical Framework ◽  
The Impact

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.

scholarly journals Quality Control of Antenna Alignment and Receiver Calibration Using the Sun: Adaptation to Midrange Weather Radar Observations at Low Elevation Angles

2015 ◽  
Vol 32 (5) ◽  
pp. 927-942 ◽  
Author(s):  
Patricia Altube ◽  
Joan Bech ◽  
Oriol Argemí ◽  
Tomeu Rigo
Keyword(s):  
Quality Control ◽  
Goodness Of Fit ◽  
Local Area Network ◽  
Control Method ◽  
Selection Process ◽  
Weather Radar ◽  
Area Network ◽  
The Sun ◽  
Ground Clutter ◽  
Radar Antenna

AbstractA quality control method for combined online monitoring of weather radar antenna pointing biases and receiver calibration using solar signals detected by an operational radar is adapted for application to midrange radar data (80–150 km). As the original method was developed using long-range data, additional criteria based on robust statistical estimators are imposed in the sun signature detection and selection process, allowing to discard observations biased by ground clutter or precipitation and to remove very influential outliers. The validity ranges of the physical model describing the solar interferences detected by the scanning radar antenna are explicitly defined and an equation for estimation of the effective scanning width in reception is provided in a thorough theoretical derivation. The method proposed reveals its sensitivity to changes in the antenna pointing accuracy and receiver calibration when applied to operational data obtained with three C-band radars during one year. A comparative study on the goodness of fit between a three- and a five-parameter model highlights the effect on the stability and accuracy of the antenna and receiver parameters retrieved for each radar system, considering the dissimilar information content of the observations collected by each radar. The performance of the proposed methodology under the effects of the presence of ground clutter and radio local area network interferences is discussed in the results presented.

scholarly journals Usefulness of the United States National Weather Service Radar Display as Rated by Website Users

2018 ◽  
Vol 10 (4) ◽  
pp. 673-691 ◽  
Author(s):  
Michelle E. Saunders ◽  
Kevin D. Ash ◽  
Jennifer M. Collins
Keyword(s):  
United States ◽  
Spatial Clustering ◽  
Weather Radar ◽  
Weather Conditions ◽  
Radar Data ◽  
The United States ◽  
Perceived Usefulness ◽  
National Weather Service ◽  
Weather Information ◽  
Weather Service

Abstract Weather radar is now widely viewed by the general public in the United States via television, computers/tablets, and smartphones. Anyone can consult near-real-time maps and animations of weather radar data when weather conditions are a factor. However, the usefulness of weather radar data for each user depends on a complex interaction of factors. There have been few studies providing conceptual arguments and empirical data to better understand what the most important factors are and to comprehend patterns of public weather radar use across the United States. The first part of this research provides a basic conceptual framework for research investigating the usefulness of weather radar displays as a source of weather information and as a decision aid. The second part aims to uncover several factors that influence the perceived usefulness rating of the National Weather Service (NWS) website’s weather radar display at both national and regional levels using variables gathered from the 2014 NWS customer satisfaction survey alongside relevant geographic and climatological variables. Data analyses include spatial clustering and ordinal regression utilized within a generalized linear model methodology. Overall, respondents who are more familiar with the NWS and their products, as well as those who indicate they are more likely to take action based on information provided by the NWS, are more likely to find the NWS radar display useful. Geographically, the NWS radar display is most useful to persons residing in the southern United States. Lightning is the most important hazard associated with higher radar usefulness ratings.

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