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Radar Meteorology

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 34066

Special Issue Editor

Dr. Rütger Rollenbeck

E-Mail Website
Guest Editor
Laboratory for Climatology and Remot Sensing, University of Marburg, Deutschhausstraße 10, 35037 Marburg, Germany
Interests: radar meteorology; atmospheric remote sensing; precipitation: dynamics and hydrological impact; climate variability, extremes, and trends; land surface–atmosphere interactions; solar and eolic energy potential

Special Issue Information

Dear Colleagues,

Radar has developed into an indispensable tool in modern meteorology, and many countries have implemented networks of radar instruments for the continuous monitoring of atmospheric conditions. Radar is used in a wide array of scientific, commercial, and industrial applications ranging from ecological research to aviation, military uses, and public services, amongst many others.

Additionally, the detection capabilities for atmospheric processes have advanced, with new technologies and improved processing methods. Technology is increasingly moving from vacuum-tube-based systems to solid-state hardware and complex signal processing capabilities. Along with these technological achievements, cost-efficiency has improved and radar instruments can now be realized in developing countries, thus improving the global coverage.

Nevertheless, radar remote sensing is still affected by many fundamental and unsolved problems.

This Special Issue aims to collect new developments and methodologies, best practices, and applications of radar for atmospheric remote sensing in the context of weather applications. We welcome submissions that provide the community with the most recent advancements on operational aspects of meteorological radars, including but not limited to:

  • Implementation and operational issues. As ground-based radars require an unobstructed view to achieve their full range, they are frequently operated in harsh mountain environments;
  • Suppression and correction of non-meteorological signals like clutter, anaprop, beam refraction, hardware variations, and many more. Handling of Data gaps and beam shadow;
  • Methodological improvements of signal conversion (reflectivity to rain rate, atmospheric motion);
  • Data processing and validation;
  • Integration of radar with ground-based observations, satellite data, and modelling approaches;
  • Applications and improvements in coverage for remote regions;
  • Applications in weather forecasting, early warning, and public alert;
  • Any use case of radar related to meteorology.

Dr. Rütger Rollenbeck
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • radar operation
  • radar correction
  • technological advances
  • rain-rate conversion
  • radar-derived atmospheric motion
  • processing techniques and data integration
  • radar application and forecasting

Please submit articles addressing the retrieval of precipitation microphysics and different atmospheric properties from polarimetric radars to the special issue Radar Polarimetry.

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Published Papers (7 papers)

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Research

18 pages, 3598 KiB  
Article
Precipitation Characteristics at Two Locations in the Tropical Andes by Means of Vertically Pointing Micro-Rain Radar Observations
by Jochen Seidel, Katja Trachte, Johanna Orellana-Alvear, Rafael Figueroa, Rolando Célleri, Jörg Bendix, Ciro Fernandez and Christian Huggel
Remote Sens. 2019, 11(24), 2985; https://doi.org/10.3390/rs11242985 - 12 Dec 2019
Cited by 13 | Viewed by 4053
Abstract
In remote areas with steep topography, such as the Tropical Andes, reliable precipitation data with a high temporal resolution are scarce. Therefore, studies focusing on the diurnal properties of precipitation are hampered. In this paper, we investigated two years of data from Micro-Rain [...] Read more.
In remote areas with steep topography, such as the Tropical Andes, reliable precipitation data with a high temporal resolution are scarce. Therefore, studies focusing on the diurnal properties of precipitation are hampered. In this paper, we investigated two years of data from Micro-Rain Radars (MRR) in Cuenca, Ecuador, and Huaraz, Peru, from February 2017 to January 2019. This data allowed for a detailed study on the temporal precipitation characteristics, such as event occurrences and durations at these two locations. Our results showed that the majority of precipitation events had durations of less than 3 h. In Huaraz, precipitation has a distinct annual and diurnal cycle where precipitation in the rainy season occurred predominantly in the afternoon. These annual and diurnal cycles were less pronounced at the site in Cuenca, especially due to increased nocturnal precipitation events compared to Huaraz. Furthermore, we used a fuzzy logic classification of fall velocities and rainfall intensities to distinguish different precipitation types. This classification showed that nightly precipitation at both locations was predominantly stratiform, whereas (thermally induced) convection occurred almost exclusively during the daytime hours. Full article
(This article belongs to the Special Issue Radar Meteorology)
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25 pages, 36416 KiB  
Article
MASS-UMAP: Fast and Accurate Analog Ensemble Search in Weather Radar Archives
by Gabriele Franch, Giuseppe Jurman, Luca Coviello, Marta Pendesini and Cesare Furlanello
Remote Sens. 2019, 11(24), 2922; https://doi.org/10.3390/rs11242922 - 6 Dec 2019
Cited by 11 | Viewed by 5094
Abstract
The use of analog-similar weather patterns for weather forecasting and analysis is an established method in meteorology. The most challenging aspect of using this approach in the context of operational radar applications is to be able to perform a fast and accurate search [...] Read more.
The use of analog-similar weather patterns for weather forecasting and analysis is an established method in meteorology. The most challenging aspect of using this approach in the context of operational radar applications is to be able to perform a fast and accurate search for similar spatiotemporal precipitation patterns in a large archive of historical records. In this context, sequential pairwise search is too slow and computationally expensive. Here, we propose an architecture to significantly speed up spatiotemporal analog retrieval by combining nonlinear geometric dimensionality reduction (UMAP) with the fastest known Euclidean search algorithm for time series (MASS) to find radar analogs in constant time, independently of the desired temporal length to match and the number of extracted analogs. We show that UMAP, combined with a grid search protocol over relevant hyperparameters, can find analog sequences with lower mean square error (MSE) than principal component analysis (PCA). Moreover, we show that MASS is 20 times faster than brute force search on the UMAP embedding space. We test the architecture on real dataset and show that it enables precise and fast operational analog ensemble search through more than 2 years of radar archive in less than 3 seconds on a single workstation. Full article
(This article belongs to the Special Issue Radar Meteorology)
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22 pages, 4524 KiB  
Article
River Discharge Simulation in the High Andes of Southern Ecuador Using High-Resolution Radar Observations and Meteorological Station Data
by Diego Mejía-Veintimilla, Pablo Ochoa-Cueva, Natalia Samaniego-Rojas, Ricardo Félix, Juan Arteaga, Patricio Crespo, Fernando Oñate-Valdivieso and Andreas Fries
Remote Sens. 2019, 11(23), 2804; https://doi.org/10.3390/rs11232804 - 27 Nov 2019
Cited by 15 | Viewed by 4555
Abstract
The prediction of river discharge using hydrological models (HMs) is of utmost importance, especially in basins that provide drinking water or serve as recreation areas, to mitigate damage to civil structures and to prevent the loss of human lives. Therefore, different HMs must [...] Read more.
The prediction of river discharge using hydrological models (HMs) is of utmost importance, especially in basins that provide drinking water or serve as recreation areas, to mitigate damage to civil structures and to prevent the loss of human lives. Therefore, different HMs must be tested to determine their accuracy and usefulness as early warning tools, especially for extreme precipitation events. This study simulated the river discharge in an Andean watershed, for which the distributed HM Runoff Prediction Model (RPM) and the semi-distributed HM Hydrologic Modelling System (HEC-HMS) were applied. As precipitation input data for the RPM model, high-resolution radar observations were used, whereas the HEC-HMS model used the available meteorological station data. The obtained simulations were compared to measured discharges at the outlet of the watershed. The results highlighted the advantages of distributed HM (RPM) in combination with high-resolution radar images, which estimated accurately the discharges in magnitude and time. The statistical analysis showed good to very good accordance between observed and simulated discharge for the RPM model (R2: 0.85–0.92; NSE: 0.77–0.82), whereas for the HEC-HMS model accuracies were lower (R2: 0.68–0.86; NSE: 0.26–0.78). This was not only due to the application of means values for the watershed (HEC-HMS), but also to limited rain gauge information. Generally, station network density in tropical mountain regions is poor, for which reason the high spatiotemporal precipitation variability cannot be detected. For hydrological simulation and forecasting flash floods, as well as for environmental investigations and water resource management, meteorological radars are the better choice. The greater availability of cost-effective systems at the present time also reduces implementation and maintenance costs of dense meteorological station networks. Full article
(This article belongs to the Special Issue Radar Meteorology)
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26 pages, 13271 KiB  
Article
A Study of Vertical Structures and Microphysical Characteristics of Different Convective Cloud–Precipitation Types Using Ka-Band Millimeter Wave Radar Measurements
by Jiafeng Zheng, Peiwen Zhang, Liping Liu, Yanxia Liu and Yuzhang Che
Remote Sens. 2019, 11(15), 1810; https://doi.org/10.3390/rs11151810 - 1 Aug 2019
Cited by 6 | Viewed by 5166
Abstract
Millimeter wave cloud radar (MMCR) is one of the primary instruments employed to observe cloud–precipitation. With appropriate data processing, measurements of the Doppler spectra, spectral moments, and retrievals can be used to study the physical processes of cloud–precipitation. This study mainly analyzed the [...] Read more.
Millimeter wave cloud radar (MMCR) is one of the primary instruments employed to observe cloud–precipitation. With appropriate data processing, measurements of the Doppler spectra, spectral moments, and retrievals can be used to study the physical processes of cloud–precipitation. This study mainly analyzed the vertical structures and microphysical characteristics of different kinds of convective cloud–precipitation in South China during the pre-flood season using a vertical pointing Ka-band MMCR. Four kinds of convection, namely, multi-cell, isolated-cell, convective–stratiform mixed, and warm-cell convection, are discussed herein. The results show that the multi-cell and convective–stratiform mixed convections had similar vertical structures, and experienced nearly the same microphysical processes in terms of particle phase change, particle size distribution, hydrometeor growth, and breaking. A forward pattern was proposed to specifically characterize the vertical structure and provide radar spectra models reflecting the different microphysical and dynamic features and variations in different parts of the cloud body. Vertical air motion played key roles in the microphysical processes of the isolated- and warm-cell convections, and deeply affected the ground rainfall properties. Stronger, thicker, and slanted updrafts caused heavier showers with stronger rain rates and groups of larger raindrops. The microphysical parameters for the warm-cell cloud–precipitation were retrieved from the radar data and further compared with the ground-measured results from a disdrometer. The comparisons indicated that the radar retrievals were basically reliable; however, the radar signal weakening caused biases to some extent, especially for the particle number concentration. Note that the differences in sensitivity and detectable height of the two instruments also contributed to the compared deviation. Full article
(This article belongs to the Special Issue Radar Meteorology)
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20 pages, 5978 KiB  
Article
Optimization of X-Band Radar Rainfall Retrieval in the Southern Andes of Ecuador Using a Random Forest Model
by Johanna Orellana-Alvear, Rolando Célleri, Rütger Rollenbeck and Jörg Bendix
Remote Sens. 2019, 11(14), 1632; https://doi.org/10.3390/rs11141632 - 10 Jul 2019
Cited by 17 | Viewed by 5594
Abstract
Despite many efforts of the radar community, quantitative precipitation estimation (QPE) from weather radar data remains a challenging topic. The high resolution of X-band radar imagery in space and time comes with an intricate correction process of reflectivity. The steep and high mountain [...] Read more.
Despite many efforts of the radar community, quantitative precipitation estimation (QPE) from weather radar data remains a challenging topic. The high resolution of X-band radar imagery in space and time comes with an intricate correction process of reflectivity. The steep and high mountain topography of the Andes enhances its complexity. This study aims to optimize the rainfall derivation of the highest X-band radar in the world (4450 m a.s.l.) by using a random forest (RF) model and single Plan Position Indicator (PPI) scans. The performance of the RF model was evaluated in comparison with the traditional step-wise approach by using both, the Marshall-Palmer and a site-specific Z–R relationship. Since rain gauge networks are frequently unevenly distributed and hardly available at real time in mountain regions, bias adjustment was neglected. Results showed an improvement in the step-wise approach by using the site-specific (instead of the Marshall-Palmer) Z–R relationship. However, both models highly underestimate the rainfall rate (correlation coefficient < 0.69; slope up to 12). Contrary, the RF model greatly outperformed the step-wise approach in all testing locations and on different rainfall events (correlation coefficient up to 0.83; slope = 1.04). The results are promising and unveil a different approach to overcome the high attenuation issues inherent to X-band radars. Full article
(This article belongs to the Special Issue Radar Meteorology)
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18 pages, 7847 KiB  
Article
Decoupling between Precipitation Processes and Mountain Wave Induced Circulations Observed with a Vertically Pointing K-Band Doppler Radar
by Sergi Gonzalez, Joan Bech, Mireia Udina, Bernat Codina, Alexandre Paci and Laura Trapero
Remote Sens. 2019, 11(9), 1034; https://doi.org/10.3390/rs11091034 - 1 May 2019
Cited by 14 | Viewed by 4506
Abstract
Recent studies reported that precipitation and mountain waves induced low tropospheric level circulations may be decoupled or masked by greater spatial scale variability despite generally there is a connection between microphysical processes of precipitation and mountain driven air flows. In this paper we [...] Read more.
Recent studies reported that precipitation and mountain waves induced low tropospheric level circulations may be decoupled or masked by greater spatial scale variability despite generally there is a connection between microphysical processes of precipitation and mountain driven air flows. In this paper we analyse two periods of a winter storm in the Eastern Pyrenees mountain range (NE Spain) with different mountain wave induced circulations and low-level turbulence as revealed by Micro Rain Radar (MRR), microwave radiometer and Parsivel disdrometer data during the Cerdanya-2017 field campaign. We find that during the event studied mountain wave wind circulations and low-level turbulence do not affect neither the snow crystal riming or aggregation along the vertical column nor the surface particle size distribution of the snow. This study illustrates that precipitation profiles and mountain induced circulations may be decoupled which can be very relevant for either ground-based or spaceborne remote sensing of precipitation. Full article
(This article belongs to the Special Issue Radar Meteorology)
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17 pages, 2181 KiB  
Article
A Random Forest Method to Forecast Downbursts Based on Dual-Polarization Radar Signatures
by Bruno L. Medina, Lawrence D. Carey, Corey G. Amiot, Retha M. Mecikalski, William P. Roeder, Todd M. McNamara and Richard J. Blakeslee
Remote Sens. 2019, 11(7), 826; https://doi.org/10.3390/rs11070826 - 6 Apr 2019
Cited by 15 | Viewed by 4384
Abstract
The United States Air Force’s 45th Weather Squadron provides wind warnings, including those for downbursts, at the Cape Canaveral Air Force Station and Kennedy Space Center (CCAFS/KSC). This study aims to provide a Random Forest model that classifies thunderstorms’ downburst and null events [...] Read more.
The United States Air Force’s 45th Weather Squadron provides wind warnings, including those for downbursts, at the Cape Canaveral Air Force Station and Kennedy Space Center (CCAFS/KSC). This study aims to provide a Random Forest model that classifies thunderstorms’ downburst and null events using a 35-knot wind threshold to separate these two categories. The downburst occurrence was assessed using a dense network of wind observations around CCAFS/KSC. Eight dual-polarization radar signatures that are hypothesized to have physical implications for downbursts at the surface were automatically calculated for 209 storms and ingested into the Random Forest model. The Random Forest model predicted null events more correctly than downburst events, with a True Skill Statistic of 0.40. Strong downburst events were better classified than those with weaker wind magnitudes. The most important radar signatures were found to be the maximum vertically integrated ice and the peak reflectivity. The Random Forest model presented a more reliable performance than an automated prediction method based on thresholds of single radar signatures. Based on these results, the Random Forest method is suggested for continued operational development and testing. Full article
(This article belongs to the Special Issue Radar Meteorology)
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