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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (31 October 2016) | Viewed by 81298

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Dr. Guifu Zhang

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Guest Editor

School of Meteorology, and Advanced Radar Research Center, University of Oklahoma, Norman, OK, USA
Interests: Radar theory and technology, weather observation and measurement, cloud and precipitation physics, remote sensing, wave propagation and scattering in geophysical media, signal processing and data analysis

Special Issue Information

Dear Colleagues,

Weather radars measure wave scattering by hydrometeors in cloud and precipitation, which provides us with fine-resolution, four-dimensional information. Weather radars play critical roles in weather observation, detection of hazards, classification and quantification of precipitation, and forecasting. Recently, weather radars have been (or are being) upgraded with dual-polarization capability, which allows them to provide multi-parameter measurements with unprecedented quality and information. The new technology of radar polarimetry and its new measurements offer great opportunities in the understanding and advancement of meteorology. They allow us to better study cloud and precipitation microphysics, their dynamics, as well as the connection between them.

In addition to the advances in weather radar technology, there are many challenges in optimally utilizing weather radar measurements. This is because radar observations are remote measurements, radar parameters are not linearly related to the weather state parameters, and radar measurements contain errors, noise, clutter, and artifacts. There is on-going research to address data quality issues, to verify radar measurements with in-situ or ground measurements, to connect the weather phenomena with radar signatures, to link weather states with radar parameters, to retrieve cloud microphysics, to improve model microphysics parameterization, and to improve quantitative precipitation estimation and forecast. Manuscripts on these topics would be very welcome in this Special Issue.

Dr. Guifu Zhang
Guest Editor

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Keywords

Weather radar polarimetry
Dual-polarization capability
Radar signatures
Signal processing
Calibration and data quality control
In-situ or ground verification
Cloud and precipitation physics
Microphysics retrieval
Quantitative precipitation estimation
Quantitative precipitation forecast
Data assimilation

Published Papers (13 papers)

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6092 KiB

Open AccessArticle

Using Satellite and Lightning Data to Track Rapidly Developing Thunderstorms in Data Sparse Regions

by Morné Gijben and Estelle De Coning

Atmosphere 2017, 8(4), 67; https://doi.org/10.3390/atmos8040067 - 20 Apr 2017

Cited by 18 | Viewed by 5555

Abstract

Radar systems provide the most useful information about the intensity, movement, and characteristics of severe thunderstorms, but are expensive to maintain and require extensive maintenance. In South Africa, some areas are not covered by radar systems, while very few operational radar systems exist in other southern African countries. Despite these shortcomings, all meteorological centers still have to warn the public of pending severe weather events. The Nowcasting Satellite Application Facility (NWC SAF) in Europe developed software that utilizes satellite data to identify and track rapidly developing thunderstorms (RDT). The NWC software was installed at the South African Weather Service in 2014. Initially, the RDT product was validated against lightning data and the results showed that the RDT product could provide very useful information on possible severe or intense convective storms. This study focusses on the effects of including lightning as an ancillary dataset into the algorithms and then validating the RDT product against radar data. Twenty-five summer cases were considered to determine whether the inclusion of lightning data had a positive effect on the accuracy of the RDT product, when compared to radar data. The results of this study show that in the majority of the cases, the inclusion of lightning data was beneficial to the RDT product. On average the Probability of Detection (POD) improved by 6.6%, the Heidke Skill Score (HSS) by 4.6%, and the False alarm ratio (FAR) by 0.1%. To our knowledge, South Africa is the only African country which is running the NWC SAF software operationally and which has performed an evaluation of the product over Africa against observations from radar systems and lightning sensors. The outcomes of this study are very encouraging for other countries in Africa where convection and severe convection often occur and sophisticated data sources are absent. Initial studies over East Africa indicate that the RDT product can benefit operational practices for the nowcasting of severe convection events. Full article

(This article belongs to the Special Issue Radar Meteorology)

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11769 KiB

Open AccessArticle

Operational Application of Optical Flow Techniques to Radar-Based Rainfall Nowcasting

by Wang-chun Woo and Wai-kin Wong

Atmosphere 2017, 8(3), 48; https://doi.org/10.3390/atmos8030048 - 25 Feb 2017

Cited by 129 | Viewed by 11230

Abstract

Hong Kong Observatory has been operating an in-house developed rainfall nowcasting system called “Short-range Warning of Intense Rainstorms in Localized Systems (SWIRLS)” to support rainstorm warning and rainfall nowcasting services. A crucial step in rainfall nowcasting is the tracking of radar echoes to generate motion fields for extrapolation of rainfall areas in the following few hours. SWIRLS adopted a correlation-based method in its first operational version in 1999, which was subsequently replaced by optical flow algorithm in 2010 and further enhanced in 2013. The latest optical flow algorithm employs a transformation function to enhance a selected range of reflectivity for feature tracking. It also adopts variational optical flow computation that takes advantage of the Horn–Schunck approach and the Lucas–Kanade method. This paper details the three radar echo tracking algorithms, examines their performances in several significant rainstorm cases and summaries verification results of multi-year performances. The limitations of the current approach are discussed. Developments underway along with future research areas are also presented. Full article

(This article belongs to the Special Issue Radar Meteorology)

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10216 KiB

Open AccessArticle

Investigation of Weather Radar Quantitative Precipitation Estimation Methodologies in Complex Orography

by Mario Montopoli, Nicoletta Roberto, Elisa Adirosi, Eugenio Gorgucci and Luca Baldini

Atmosphere 2017, 8(2), 34; https://doi.org/10.3390/atmos8020034 - 10 Feb 2017

Cited by 30 | Viewed by 5531

Abstract

Near surface quantitative precipitation estimation (QPE) from weather radar measurements is an important task for feeding hydrological models, limiting the impact of severe rain events at the ground as well as aiding validation studies of satellite-based rain products. To date, several works have analyzed the performance of various QPE algorithms using actual and synthetic experiments, possibly trained by measurement of particle size distributions and electromagnetic models. Most of these studies support the use of dual polarization radar variables not only to ensure a good level of data quality but also as a direct input to rain estimation equations. One of the most important limiting factors in radar QPE accuracy is the vertical variability of particle size distribution, which affects all the acquired radar variables as well as estimated rain rates at different levels. This is particularly impactful in mountainous areas, where the sampled altitudes are likely several hundred meters above the surface. In this work, we analyze the impact of the vertical profile variations of rain precipitation on several dual polarization radar QPE algorithms when they are tested in a complex orography scenario. So far, in weather radar studies, more emphasis has been given to the extrapolation strategies that use the signature of the vertical profiles in terms of radar co-polar reflectivity. This may limit the use of the radar vertical profiles when dual polarization QPE algorithms are considered. In that case, all the radar variables used in the rain estimation process should be consistently extrapolated at the surface to try and maintain the correlations among them. To avoid facing such a complexity, especially with a view to operational implementation, we propose looking at the features of the vertical profile of rain (VPR), i.e., after performing the rain estimation. This procedure allows characterization of a single variable (i.e., rain) when dealing with vertical extrapolations. In this work, a definition of complex orography is also given, introducing a radar orography index to objectively quantify the degree of terrain complexity when dealing with radar QPE in heterogeneous environmental scenarios. Three case studies observed by the research C-band polarization agility Doppler radar named Polar 55C, managed by the Institute of Atmospheric Sciences and Climate (ISAC) at the National Research Council of Italy (CNR), were used to prove the concept of VPR. Our results indicate that the combined algorithm, which merges together differential phase shift (Kdp), single polarization reflectivity factor (Zhh), and differential reflectivity (Zdr), once accurately processed, in most cases performs better among those tested and those that make use of Zhh alone, Kdp alone, and Zhh, and Zdr. Improvements greater than 25% are found for the total rain accumulations in terms of normalized bias when the VPR extrapolation is applied. Full article

(This article belongs to the Special Issue Radar Meteorology)

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4668 KiB

Open AccessFeature PaperArticle

Observations of a Cold Front at High Spatiotemporal Resolution Using an X-Band Phased Array Imaging Radar

by Andrew Mahre, Tian-You Yu, Robert D. Palmer and James M. Kurdzo

Atmosphere 2017, 8(2), 30; https://doi.org/10.3390/atmos8020030 - 06 Feb 2017

Cited by 5 | Viewed by 4817

Abstract

While the vertical structure of cold fronts has been studied using various methods, previous research has shown that traditional methods of observing meteorological phenomena (such as pencil-beam radars in PPI/volumetric mode) are not well-suited for resolving small-scale cold front phenomena, due to relatively low spatiotemporal resolution. Additionally, non-simultaneous elevation sampling within a vertical cross-section can lead to errors in analysis, as differential vertical advection cannot be distinguished from temporal evolution. In this study, a cold front from 19 September 2015 is analyzed using the Atmospheric Imaging Radar (AIR). The AIR transmits a 20-degree fan beam in elevation, and digital beamforming is used on receive to generate simultaneous receive beams. This mobile, X-band, phased-array radar offers temporal sampling on the order of 1 s (while in RHI mode), range sampling of 30 m (37.5 m native resolution), and continuous, arbitrarily oversampled data in the vertical dimension. Here, 0.5-degree sampling is used in elevation (1-degree native resolution). This study is the first in which a cold front has been studied via imaging radar. The ability of the AIR to obtain simultaneous RHIs at high temporal sampling rates without mechanical steering allows for analysis of features such as Kelvin-Helmholtz instabilities and feeder flow. Full article

(This article belongs to the Special Issue Radar Meteorology)

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4523 KiB

Open AccessArticle

On the Implementation of a Regional X-Band Weather Radar Network

by Andrea Antonini, Samantha Melani, Manuela Corongiu, Stefano Romanelli, Alessandro Mazza, Alberto Ortolani and Bernardo Gozzini

Atmosphere 2017, 8(2), 25; https://doi.org/10.3390/atmos8020025 - 26 Jan 2017

Cited by 17 | Viewed by 6524

Abstract

In the last few years, the number of worldwide operational X-band weather radars has rapidly been growing, thanks to an established technology that offers reliability, high performance, and reduced efforts and costs for installation and maintenance, with respect to the more widespread C- and S-band systems. X-band radars are particularly suitable for nowcasting activities, as those operated by the LaMMA (Laboratory of Monitoring and Environmental Modelling for the sustainable development) Consortium in the framework of its institutional duties of operational meteorological surveillance. In fact, they have the capability to monitor precipitation, resolving very local scales, with good spatial and temporal details, although with a reduced scanning range. The Consortium has recently installed a small network of X-band weather radars that partially overlaps and completes the existing national radar network over the north Tyrrhenian area. This paper describes the implementation of this regional network, detailing the aspects related with the radar signal processing chain that provides the final reflectivity composite, starting from the acquisition of the signal power data. The network performances are then qualitatively assessed for three case studies characterised by different precipitation regimes and different seasons. Results are satisfactory especially during intense precipitations, particularly regarding what concerns their spatial and temporal characterisation. Full article

(This article belongs to the Special Issue Radar Meteorology)

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2459 KiB

Open AccessArticle

Evaluation of Surface Clutter for Future Geostationary Spaceborne Weather Radar

by Xuehua Li, Jianxin He, Chuanzhi Wang, Shunxian Tang and Xiaoyu Hou

Atmosphere 2017, 8(1), 14; https://doi.org/10.3390/atmos8010014 - 17 Jan 2017

Cited by 7 | Viewed by 5681

Abstract

Surface clutter interference will be one of the important problems for the future of geostationary spaceborne weather radar (GSWR). The aim of this work is to provide some numerical analyses on surface clutter interference and part of the performance evaluation for the future implementation of GSWR. The received powers of rain echoes, land and sea surfaces from a radar scattering volume are calculated numerically based on the derived radar equations, assuming a uniform rain layer and appropriate land and sea surface scattering models. An antenna pattern function based on a Bessel curve and Taylor weighting is considered to approximate the realistic spherical antenna of a GSWR. The power ratio of the rain echo signal to clutter (SCR) is then used to evaluate the extension of surface clutter interference. The study demonstrates that the entire region of surface clutter interference in GSWR will be wider than those in tropical rainfall measuring mission precipitation radar (TRMM PR). Most strong surface clutter comes from the antenna mainlobe, and the decrease of clutter contamination through reducing the level of the antenna sidelobe and range sidelobe are not obvious. In addition, the clutter interference is easily affected by rain attenuation in the Ka-band. When rain intensity is greater than 10 mm/h, most of rain echoes at off-nadir scanning angles will not be interfered by surface clutter. Full article

(This article belongs to the Special Issue Radar Meteorology)

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2820 KiB

Open AccessArticle

Ensemble Classification for Anomalous Propagation Echo Detection with Clustering-Based Subset-Selection Method

by Hansoo Lee and Sungshin Kim

Atmosphere 2017, 8(1), 11; https://doi.org/10.3390/atmos8010011 - 13 Jan 2017

Cited by 5 | Viewed by 4315

Abstract

Several types of non-precipitation echoes appear in radar images and disrupt the weather forecasting process. An anomalous propagation echo is an unwanted observation result similar to a precipitation echo. It occurs through radar-beam ducting because of the temperature, humidity distribution, and other complicated atmospheric conditions. Anomalous propagation echoes should be removed because they make weather forecasting difficult. In this paper, we suggest an ensemble classification method based on an artificial neural network and a clustering-based subset-selection method. This method allows us to implement an efficient classification method when a feature space has complicated distributions. By separating the input data into atomic and non-atomic clusters, each derived cluster will receive its own base classifier. In the experiments, we compared our method with a standalone artificial neural network classifier. The suggested ensemble classifier showed 84.14% performance, which was about 2% higher than that of the k-means clustering-based ensemble classifier and about 4% higher than the standalone artificial neural network classifier. Full article

(This article belongs to the Special Issue Radar Meteorology)

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8656 KiB

Open AccessArticle

Rain Attenuation Correction of Reflectivity for X-Band Dual-Polarization Radar

by Liang Feng, Hui Xiao, Guang Wen, Zongfei Li, Yue Sun, Qi Tang and Yanan Liu

Atmosphere 2016, 7(12), 164; https://doi.org/10.3390/atmos7120164 - 17 Dec 2016

Cited by 8 | Viewed by 5208

Abstract

In order to improve the performance of X-band dual-polarization radars, it is necessary to conduct attenuation correction before using the X-band radar data. This study analyzes a variety of attenuation correction methods for the X-band radar reflectivity, and proposes a high-resolution slide self-consistency correction (SSCC) method, which is an improvement of Kim et al.’s method based on Bringi et al.’s original method. The new method is comprehensively evaluated with the observational data of convective cloud, stratiform cloud, and the stratiform cloud with embedded convection. Comparing with the intrinsic reflectivity at X-band calculated from the reflectivity at S-band, it is found that the new method can effectively reduce the correction errors when calculating differential propagation shift increments using the conventional self-consistency attenuation correction method. This method can efficiently correct the X-band dual-polarization radar reflectivity, in particular, for the echoes with reflectivity greater than 35 dBZ. Full article

(This article belongs to the Special Issue Radar Meteorology)

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4746 KiB

Open AccessArticle

Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method

by Mohammad-Hossein Golbon-Haghighi, Guifu Zhang, Yinguang Li and Richard J. Doviak

Atmosphere 2016, 7(6), 83; https://doi.org/10.3390/atmos7060083 - 15 Jun 2016

Cited by 19 | Viewed by 9351

Abstract

A novel dual-polarization and dual-scan (DPDS) classification algorithm is developed for clutter detection in weather radar observations. Two consecutive scans of dual-polarization radar echoes are jointly processed to estimate auto- and cross-correlation functions. Discriminants are then defined and estimated in order to separate clutter from weather based on their physical and statistical properties. An optimal Bayesian classifier is used to make a decision on clutter presence from the estimated discriminant functions. The DPDS algorithm is applied to the data collected with the KOUN polarimetric radar and compared with the existing detection methods. It is shown that the DPDS algorithm yields a higher probability of detection and lower false alarm rate in clutter detection. Full article

(This article belongs to the Special Issue Radar Meteorology)

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1501 KiB

Open AccessArticle

Sea Surface Wind Measurement by Airborne Weather Radar Scanning in a Wide-Size Sector

by Alexey Nekrasov, Alena Khachaturian, Vladimir Veremyev and Mikhail Bogachev

Atmosphere 2016, 7(5), 72; https://doi.org/10.3390/atmos7050072 - 23 May 2016

Cited by 11 | Viewed by 4913

Abstract

We suggest a conceptual approach for measuring the near-surface wind vector over water using the airborne weather radar, in addition to its standard meteorological and navigation applications. The airborne weather radar operates in the ground-mapping mode in the range of high to medium incidence angles as a scatterometer. Using the aircraft rectilinear flight over the water surface, measuring the geometry and the geophysical model function, we show that the wind vector can be successfully recovered from the azimuth normalized radar cross-section data obtained from a scanning sector of up to ±100°. The efficiency and accuracy of the proposed wind vector measurement algorithms are supported by computer simulations indicating their potential as a powerful tool for the wind field reconstruction. Some limitations and recommendations of the suggested approach are further discussed. Full article

(This article belongs to the Special Issue Radar Meteorology)

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2795 KiB

Open AccessArticle

Radar Estimation of Intense Rainfall Rates through Adaptive Calibration of the Z-R Relation

by Andrea Libertino, Paola Allamano, Pierluigi Claps, Roberto Cremonini and Francesco Laio

Atmosphere 2015, 6(10), 1559-1577; https://doi.org/10.3390/atmos6101559 - 22 Oct 2015

Cited by 20 | Viewed by 5926

Abstract

Rainfall intensity estimation from weather radar is still significantly uncertain, due to local anomalies, radar beam attenuation, inappropriate calibration of the radar reflectivity factor (Z) to rainfall rate (R) relationship, and sampling errors. The aim of this work is to revise the use of the power-law equation commonly adopted to relate radar reflectivity and rainfall rate to increase the estimation quality in the presence of intense rainfall rates. We introduce a quasi real-time procedure for an adaptive in space and time estimation of the Z-R relation. The procedure is applied in a comprehensive case study, which includes 16 severe rainfall events in the north-west of Italy. The study demonstrates that the technique outperforms the classical estimation methods for most of the analysed events. The determination coefficient improves by up to 30% and the bias values for stratiform events decreases by up to 80% of the values obtained with the classical, non-adaptive, Z-R relations. The proposed procedure therefore shows significant potential for operational uses. Full article

(This article belongs to the Special Issue Radar Meteorology)

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4245 KiB

Open AccessArticle

Storm Identification, Tracking and Forecasting Using High-Resolution Images of Short-Range X-Band Radar

by Sajid Shah, Riccardo Notarpietro and Marco Branca

Atmosphere 2015, 6(5), 579-606; https://doi.org/10.3390/atmos6050579 - 06 May 2015

Cited by 17 | Viewed by 6764

Abstract

Rain nowcasting is an essential part of weather monitoring. It plays a vital role in human life, ranging from advanced warning systems to scheduling open air events and tourism. A nowcasting system can be divided into three fundamental steps, i.e., storm identification, tracking and nowcasting. The main contribution of this work is to propose procedures for each step of the rain nowcasting tool and to objectively evaluate the performances of every step, focusing on two-dimension data collected from short-range X-band radars installed in different parts of Italy. This work presents the solution of previously unsolved problems in storm identification: first, the selection of suitable thresholds for storm identification; second, the isolation of false merger (loosely-connected storms); and third, the identification of a high reflectivity sub-storm within a large storm. The storm tracking step of the existing tools, such as TITANand SCIT, use only up to two storm attributes, i.e., center of mass and area. It is possible to use more attributes for tracking. Furthermore, the contribution of each attribute in storm tracking is yet to be investigated. This paper presents a novel procedure called SALdEdA (structure, amplitude, location, eccentricity difference and areal difference) for storm tracking. This work also presents the contribution of each component of SALdEdA in storm tracking. The second order exponential smoothing strategy is used for storm nowcasting, where the growth and decay of each variable of interest is considered to be linear. We evaluated the major steps of our method. The adopted techniques for automatic threshold calculation are assessed with a 97% goodness. False merger and sub-storms within a cluster of storms are successfully handled. Furthermore, the storm tracking procedure produced good results with an accuracy of 99.34% for convective events and 100% for stratiform events. Full article

(This article belongs to the Special Issue Radar Meteorology)

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218 KiB

Open AccessTechnical Note

Calibration Accuracy of the Dual-Polarization Receivers of the C-Band Swiss Weather Radar Network

by Marco Gabella, Marco Boscacci, Maurizio Sartori and Urs Germann

Atmosphere 2016, 7(6), 76; https://doi.org/10.3390/atmos7060076 - 31 May 2016

Cited by 11 | Viewed by 4726

Abstract

The electromagnetic power that comes from the Sun has been proved to be an effective reference for checking the quality of dual-polarization weather radar receiver. Operational monitoring methods have been developed and implemented for determining the electromagnetic antenna pointing, assessing the receiver stability, and the differential reflectivity offset. So far, the focus has been on relative calibration: horizontal and vertical polarization have been mutually compared and evaluated versus the reference mainly in terms of standard deviation of the error. Radar receivers have been able to capture and describe the monthly variability (slowly varying component) of the microwave signal emitted by the Sun. In this paper, we present results from a novel Sun-based method aiming at the absolute calibration of dual-polarization weather radar receivers. To obtain best results, the radar receiver has to be off-line for a few minutes during the tracking of the Sun in order to have the antenna beam axis pointing at the center of the Sun. Among the five polarimetric weather radar receivers of the Swiss network, radar “WEI” located at an altitude of 2850 m next to Davos shows the best absolute agreement with the Dominion Radio Astrophysical Observatory (DRAO) reference for both horizontal (H) and vertical (V) polarization. Albis radar, which is located at an altitude of 938 m near Zurich, shows the largest difference: the radar receiver is too low compared to the Sun reference by −1.62 (−1.25) dB for the H (V) channel. Interestingly, the standard deviation of the error is smaller than ±0.17 dB for all Swiss radar receivers. With a standard deviation of ±0.04 dB Albis radar shows the best relative agreement between H and V. These results are encouraging and MeteoSwiss is planning to repeat off-line Sun-tracking measurements in the future on a regular basis. Full article

(This article belongs to the Special Issue Radar Meteorology)

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