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Atmosphere
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Weather Radar in Rainfall Estimation
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Weather Radar in Rainfall Estimation

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (1 September 2021) | Viewed by 13410

Special Issue Editors

Dr. Samantha Melani

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Guest Editor
IBE-CNR, LaMMA Consortium, 50019 Sesto Fiorentino-FI, Italy
Interests: satellite meteorology; radarmeteorology; cloud physics
Dr. Andrea Antonini

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Guest Editor
LaMMA Consortium, 50019 Sesto Fiorentino-FI, Italy
Interests: satellite; radar; GNSS meteorology; low-cost sensors

Special Issue Information

Dear Colleagues,

An accurate knowledge of precipitation intensity and its dynamics is a great challenge for a variety of meteorological and climatological problems today, ranging from flood warning to water budget and climatological research. Weather radars are a key instrument in improving the knowledge of atmospheric processes and their prediction over a variety of temporal and spatial scales. Many weather radar networks have been implemented worldwide using different instruments and technologies. Advances in radar hardware and signal processing, as well as in related atmospheric products, have allowed for a better observation of precipitation systems. Additionally, nowcasting and short-term forecasting have improved due to observing integrated systems blending radar measurements with other heterogeneous instruments. This Special Issue focuses on the use of weather radar measurements in understanding and quantifying precipitation processes and how these observations could affect our ability to characterize and predict atmospheric phenomenology. Topics of this Special Issue include but are not limited to:

  • Quantitative precipitation estimation;
  • Precipitation forecasting;
  • Data assimilation in an NWP model;
  • Radar data calibration/validation and merging with other instruments;
  • Meteorological and hydrological nowcasting algorithms;
  • Cloud and precipitation physics;
  • Weather radar networking operational aspects.

Dr. Samantha Melani
Dr. Andrea Antonini
Guest Editors

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. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

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Published Papers (4 papers)

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Research

20 pages, 1814 KiB  
Article
Performance Evaluation of a Nowcasting Modelling Chain Operatively Employed in Very Small Catchments in the Mediterranean Environment for Civil Protection Purposes
by Martina Raffellini, Federica Martina, Francesco Silvestro, Francesca Giannoni and Nicola Rebora
Atmosphere 2021, 12(6), 783; https://doi.org/10.3390/atmos12060783 - 18 Jun 2021
Cited by 2 | Viewed by 2559
Abstract
The Hydro-Meteorological Centre (CMI) of the Environmental Protection Agency of Liguria Region, Italy, is in charge of the hydrometeorological forecast and the in-event monitoring for the region. This region counts numerous small and very small basins, known for their high sensitivity to intense [...] Read more.
The Hydro-Meteorological Centre (CMI) of the Environmental Protection Agency of Liguria Region, Italy, is in charge of the hydrometeorological forecast and the in-event monitoring for the region. This region counts numerous small and very small basins, known for their high sensitivity to intense storm events, characterised by low predictability. Therefore, at the CMI, a radar-based nowcasting modelling chain called the Small Basins Model Chain, tailored to such basins, is employed as a monitoring tool for civil protection purposes. The aim of this study is to evaluate the performance of this model chain, in terms of: (1) correct forecast, false alarm and missed alarm rates, based on both observed and simulated discharge threshold exceedances and observed impacts of rainfall events encountered in the region; (2) warning times respect to discharge threshold exceedances. The Small Basins Model Chain is proven to be an effective tool for flood nowcasting and helpful for civil protection operators during the monitoring phase of hydrometeorological events, detecting with good accuracy the location of intense storms, thanks to the radar technology, and the occurrence of flash floods. Full article
(This article belongs to the Special Issue Weather Radar in Rainfall Estimation)
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22 pages, 8170 KiB  
Article
Performing Hydrological Monitoring at a National Scale by Exploiting Rain-Gauge and Radar Networks: The Italian Case
by Giulia Bruno, Flavio Pignone, Francesco Silvestro, Simone Gabellani, Federico Schiavi, Nicola Rebora, Pietro Giordano and Marco Falzacappa
Atmosphere 2021, 12(6), 771; https://doi.org/10.3390/atmos12060771 - 15 Jun 2021
Cited by 21 | Viewed by 4263
Abstract
Hydrological monitoring systems relying on radar data and distributed hydrological models are now feasible at large-scale and represent effective early warning systems for flash floods. Here we describe a system that allows hydrological occurrences in terms of streamflow at a national scale to [...] Read more.
Hydrological monitoring systems relying on radar data and distributed hydrological models are now feasible at large-scale and represent effective early warning systems for flash floods. Here we describe a system that allows hydrological occurrences in terms of streamflow at a national scale to be monitored. We then evaluate its operational application in Italy, a country characterized by various climatic conditions and topographic features. The proposed system exploits a modified conditional merging (MCM) algorithm to generate rainfall estimates by blending data from national radar and rain-gauge networks. Then, we use the merged rainfall fields as input for the distributed and continuous hydrological model, Continuum, to obtain real-time streamflow predictions. We assess its performance in terms of rainfall estimates from MCM, using cross-validation and comparison with a conditional merging technique at an event-scale. We also assess its performance against rainfall fields from ground-based data at catchment-scale. We further evaluate the performance of the hydrological system in terms of streamflow against observed data (relative error on high flows less than 25% and Nash–Sutcliffe Efficiency greater than 0.5 for 72% and 46% of the calibrated study sections, respectively). These results, therefore, confirm the suitability of such an approach, even at national scale, over a wide range of catchment types, climates, and hydrometeorological regimes, and for operational purposes. Full article
(This article belongs to the Special Issue Weather Radar in Rainfall Estimation)
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26 pages, 38633 KiB  
Article
The 3D Neural Network for Improving Radar-Rainfall Estimation in Monsoon Climate
by Nurulhani Roslan, Mohd Nadzri Md Reba, Syarawi M. H. Sharoni and Mohammad Shawkat Hossain
Atmosphere 2021, 12(5), 634; https://doi.org/10.3390/atmos12050634 - 17 May 2021
Cited by 3 | Viewed by 2856
Abstract
The reflectivity (Z)—rain rate (R) model has not been tested on single polarization radar for estimating monsoon rainfall in Southeast Asia, despite its widespread use for estimating heterogeneous rainfall. The artificial neural network (ANN) regression has been applied to the radar [...] Read more.
The reflectivity (Z)—rain rate (R) model has not been tested on single polarization radar for estimating monsoon rainfall in Southeast Asia, despite its widespread use for estimating heterogeneous rainfall. The artificial neural network (ANN) regression has been applied to the radar reflectivity data to estimate monsoon rainfall using parametric Z-R models. The 10-min reflectivity data recorded in Kota Bahru radar station (in Malaysia) and hourly rain record in nearby 58 gauge stations during 2013–2015 were used. The three-dimensional nearest neighbor interpolation with altitude correction was applied for pixel matching. The non-linear Levenberg Marquardt (LM) regression, integrated with ANN regression minimized the spatiotemporal variability of the proposed Z-R model. Results showed an improvement in the statistical indicator, when LM and ANN overestimated (6.6%) and underestimated (4.4%), respectively, the mean total rainfall. For all rainfall categories, the ANN model has a positive efficiency ratio of >0.2. Full article
(This article belongs to the Special Issue Weather Radar in Rainfall Estimation)
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16 pages, 9784 KiB  
Article
A Novel Convective Storm Location Prediction Model Based on Machine Learning Methods
by Hansoo Lee, Jonggeun Kim, Eun Kyeong Kim and Sungshin Kim
Atmosphere 2021, 12(3), 343; https://doi.org/10.3390/atmos12030343 - 6 Mar 2021
Cited by 5 | Viewed by 2139
Abstract
A weather radar is a frequently used device in remote sensing to identify meteorological phenomena using electromagnetic waves. It can observe atmospheric conditions in a wide area with a remarkably high spatiotemporal resolution, and its observation results are useful to meteorological research and [...] Read more.
A weather radar is a frequently used device in remote sensing to identify meteorological phenomena using electromagnetic waves. It can observe atmospheric conditions in a wide area with a remarkably high spatiotemporal resolution, and its observation results are useful to meteorological research and services. Recent research on data analysis using radar data has concentrated on applying machine learning techniques to solve complicated problems, including quality control, quantitative precipitation estimation, and convective storm prediction. Convective storms, which consist of heavy rains and winds, are closely related to real-life and cause significant loss of life and property. This paper proposes a novel approach utilizing the given convective storms’ temporal properties based on machine learning models to predict future locations. The experimental results showed that the machine learning-based prediction models are capable of nowcasting future locations of convective storms with a slight difference. Full article
(This article belongs to the Special Issue Weather Radar in Rainfall Estimation)
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