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Remote Sensing for Precipitation Measurements and Lightning Meteorology

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

Deadline for manuscript submissions: 26 May 2024 | Viewed by 6058

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Special Issue Editors

Dr. Stefano Dietrich

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Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Area di Ricerca di Roma Tor Vergata, Via Fosso del Cavaliere 100, 00133 Rome, Italy
Interests: satellite meteorology; atmospheric electricity; cloud microphysics
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Prof. Dr. Yoav Yair

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School of Sustainability, Reichman University (IDC Herzliya), 8 University Street, Herzliya 4610101, Israel
Interests: atmospheric electricity; lightning (on Earth and other planets); space weather; solar–terrestrial relations and transient luminous events (sprites); dust storm electrification; cloud microphysics
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Dr. Leo Pio D'Adderio

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Dr. Alessandra Mascitelli

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Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Area di Ricerca di Roma Tor Vergata, Via Fosso del Cavaliere 100, 00133 Roma, Italy
Interests: satellite-based and ground-based meteorology

Special Issue Information

Dear Colleagues,

In recent years, there has been a growing interest in the study of extreme natural events, mainly linked to the strong impacts on human society, especially infrastructure and people. Due to the availability of increasingly dense observational ground networks and satellite sensors with continuously improving capabilities, the possibility of accurately measuring different parameters relevant to remote characterization precipitation and electrical processes (e.g., lightning) continue to expand. Reprocessing of datasets is beginning to provide sufficient spatial and temporal coverage for the study of climate trends. An increasing ability to obtain detailed precipitation features with a high temporal frequency and short delay makes real-time precipitation monitoring an indispensable tool for warning and a basis for the nowcasting of severe events or for the assimilation into forecast models. Thunderstorms exhibit the unique coupling between electrical and precipitation processes, and thus remotely sensing lightning enables monitoring and alerting against flash floods, hail, and other extreme phenomena.

Dr. Stefano Dietrich
Prof. Dr. Yoav Yair
Dr. Leo Pio D'Adderio
Dr. Alessandra Mascitelli
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. 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

satellite-based meteorology
precipitation
ground-based sensors
lightning
cloud microphysics
nowcasting

Published Papers (4 papers)

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Research

17 pages, 5833 KiB

Open AccessArticle

Lightning Stroke Strength and Its Correlation with Cloud Macro- and Microphysics over the Tibetan Plateau

by Lei Wei, Chen Xu and Zhuling Sun

Remote Sens. 2024, 16(5), 876; https://doi.org/10.3390/rs16050876 - 1 Mar 2024

Viewed by 590

Abstract

Lightning stroke strength, characterized by energy and peak currents, over the Tibetan Plateau (TP), is investigated by utilizing datasets from the World Wide Lightning Location Network and the Chinese Cloud-to-Ground Lightning Location System during 2016–2019. Focused on the south-central (SC) and southeast (SE) of the TP, it reveals that SE-TP experiences strokes with larger average energy and peak currents. Strong strokes (energy ≥ 100 kJ or peak currents ≥ |100| kA), exhibiting bimodal distribution in winter and summer, are more frequent and have larger average values over the SE-TP than the SC-TP, with diurnal distribution indicating peaks in energy and positive strokes in the middle of the night and negative strokes peaking in the morning. Utilizing the ECMWF/ERA-5 and MERRA-2 reanalysis, we find that stronger strokes correlate with thinner charge zone depths and larger CIWCFs but stable warm cloud depths and zero-degree levels over the SC-TP. Over the SE-TP, stronger strokes are associated with smaller CIWCFs and show turning points for warm cloud depths and zero-degree levels. Thicker charge zone depths correlate with stronger negative strokes but weaker positive strokes. Generating strokes of similar strength over the SC-TP requires larger CIWCFs, thinner warm cloud depths, and lower zero-degree levels than over the SE-TP. Full article

(This article belongs to the Special Issue Remote Sensing for Precipitation Measurements and Lightning Meteorology)

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19 pages, 1524 KiB

Open AccessArticle

Predicting Eastern Mediterranean Flash Floods Using Support Vector Machines with Precipitable Water Vapor, Pressure, and Lightning Data

by Saed Asaly, Lee-Ad Gottlieb, Yoav Yair, Colin Price and Yuval Reuveni

Remote Sens. 2023, 15(11), 2916; https://doi.org/10.3390/rs15112916 - 2 Jun 2023

Cited by 2 | Viewed by 1414

Abstract

Flash floods in the Eastern Mediterranean (EM) region are considered among the most destructive natural hazards, which pose a significant challenge to model due to their high complexity. Machine learning (ML) methods have made a significant contribution to the advancement of flash flood prediction systems by providing cost-effective solutions with improved performance, enabling the modeling of the complex mathematical expressions underlying physical processes of flash floods. Thus, the development of ML methods for flash flood prediction holds the potential to mitigate risks, inform policy recommendations, minimize loss of human life, and reduce property damage caused by flash floods. Here, we present a novel approach for improving flash flood predictions in the EM region using Support Vector Machines (SVMs) with a combination of precipitable water vapor (PWV) data, derived from ground-based global navigation satellite system (GNSS) receivers, along with surface pressure measurements, and nearby lightning occurrence data to predict flash floods in an arid region of the EM. The SVM model was trained on historical data from 2004 to 2019 and was used to forecast the likelihood of flash floods in the region. The study found that integrating nearby lightning data with the other variables significantly improved the accuracy of flash flood prediction compared to using only PWV and surface pressure measurements. The results of the SVM model were validated using observed flash flood events, and the model was found to have a high predictive accuracy with an area under the receiver operating characteristic curve of 0.93 for the test set. The study provides valuable insights into the potential of utilizing a combination of meteorological and lightning data for improving flash flood forecasting in the Eastern Mediterranean region. Full article

(This article belongs to the Special Issue Remote Sensing for Precipitation Measurements and Lightning Meteorology)

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14 pages, 5940 KiB

Open AccessArticle

Precipitation Microphysics of Locally-Originated Typhoons in the South China Sea Based on GPM Satellite Observations

by Xingtao Huang, Zuhang Wu, Yanqiong Xie, Yun Zhang, Lifeng Zhang, Hepeng Zheng and Wupeng Xiao

Remote Sens. 2023, 15(10), 2657; https://doi.org/10.3390/rs15102657 - 19 May 2023

Cited by 1 | Viewed by 1421

Abstract

Locally-originated typhoons in the South China Sea (SCS) are characterized by long duration, complex track, and high probability of landfall, which tend to cause severe wind, rainstorm, and flood disasters in coastal regions. Therefore, it is of great significance to conduct research on typhoon precipitation microphysics in the SCS. Using GPM satellite observations, the precipitation microphysics of typhoons in the SCS are analyzed by combining case and statistical studies. The precipitation of Typhoon Ewiniar (2018) in the SCS is found to be highly asymmetric. In the eyewall, the updraft is strong, the coalescence process of particles is distinct, and the precipitation is mainly concentrated in large raindrops. In the outer rainbands, the “bright-band” of melting layer is distinct, the melting of ice particles and the evaporation of raindrops are distinct, and there exist a few large raindrops in the precipitation. Overall, the heavy precipitation of typhoons in the SCS is composed of higher concentration of smaller raindrops than that in the western Pacific (WP), leading to a more “oceanic deep convective” feature of typhoons in the SCS. While the heavy precipitation of typhoons in the SCS is both larger in drop size and number concentration than that in the North Indian Ocean (NIO), leading to more abundant rainwater of typhoons in the SCS. For the relatively weak precipitation (R < 10 mm h⁻¹), the liquid water path (LWP) of typhoons in the SCS is higher than that of the NIO, while the ice water path (IWP) of the locally-originated typhoons in the SCS is lower than that of the WP. For the heavy precipitation (R ≥ 10 mm h⁻¹), the LWP and IWP of typhoons in the SCS are significantly higher than those in the WP and NIO. Full article

(This article belongs to the Special Issue Remote Sensing for Precipitation Measurements and Lightning Meteorology)

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27 pages, 10334 KiB

Open AccessArticle

Performance Analysis of Precipitation Datasets at Multiple Spatio-Temporal Scales over Dense Gauge Network in Mountainous Domain of Tajikistan, Central Asia

by Manuchekhr Gulakhmadov, Xi Chen, Aminjon Gulakhmadov, Muhammad Umer Nadeem, Nekruz Gulahmadov and Tie Liu

Remote Sens. 2023, 15(5), 1420; https://doi.org/10.3390/rs15051420 - 2 Mar 2023

Cited by 3 | Viewed by 1710

Abstract

Cryospheric and ecological studies become very complicated due to the absence of observed data, particularly in the mountainous regions of Central Asia. Performance analysis of Satellite-Based Precipitation Datasets (SBPD) is very critical before their direct hydro-climatic applications. This study assessed the ground validation of four SBPDs (IMERG, TRMM, PERSIANN-CDR, and PERSIANN-CSS). From January 2000 to December 2013, all SBPD data were analyzed on daily, monthly, seasonal (winter, spring, summer, autumn), and annual scales at the entire spatial domain and point-to-pixel scale. The performance of SBPD was analyzed by using evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (r-Bias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI). Results revealed that: (1) IMERG’s spatiotemporal tracking ability is better as compared to other datasets with appropriate ranges (CC > 0.8 and r-BIAS (±10)). The performance of all SBPDs is more capable on a monthly scale as compared to a daily scale. (2) In terms of POD, the IMERG outperformed all other SBPD on daily and seasonal scales. All SBPD showed underestimations in the summer season, and PERSIANN-CCS showed the most significant underestimation (−70). Moreover, the IMERG signposted the most satisfactory performance in all seasons. (3) All SBPD showed better performance in capturing the light precipitation events as indicated by the Probability Density Function (PDF%). Moreover, the performance of PERSIANN-CDR and TRMM is acceptable at low topography; the performance of PERSIANN-CCS is very poor in diverse topographical and climatic conditions over Tajikistan. Therefore, we advocate the use of daily, monthly, and seasonal estimations of IMERG precipitation product for hydro-climatic applications over the mountainous domain of Central Asia. Full article

(This article belongs to the Special Issue Remote Sensing for Precipitation Measurements and Lightning Meteorology)

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Precipitation Microphysics of Locally-Originated Typhoons in the South China Sea Based on GPM Satellite Observations
Author: Wu
Highlights: 1.First report on the precipitation microphysics of locally-originated typhoons in the South China Sea 2.The coalescence (melting) process of liquid (ice) particles is more distinct in the eyewall (outer rainbands) 3.The precipitation is more “oceanic deep convective” of typhoons in the South China Sea than western Pacific

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