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Remote Sensing of Extreme Weather Events: Monitoring and Modeling

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 7284

Special Issue Editors


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Guest Editor
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Via del Fosso del Cavaliere 100, Rome, Italy
Interests: numerical weather prediction; data assimilation; precipitation; satellite products
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
CIMA Research Foundation, Via A. Magliotto 2, 17100 Savona, Italy
Interests: numerical weather prediction; data assimilation; lightning forecast; precipitation

E-Mail Website
Guest Editor
CIMA Research Foundation, Via A. Magliotto 2, 17100 Savona, Italy
Interests: numerical weather prediction; data assimilation; lightning forecast; precipitation

Special Issue Information

Dear Colleagues,

Extreme weather events involve a large variety of atmospheric phenomena: extreme rainfalls, floods, extreme wind gusts, cold outbreaks, heat waves, droughts, lightning, large hail, tornadoes etc. An increasing number of extreme weather events are occurring over the globe in the last few decades, and their number is expected to increase in the future climate.

For these reasons, it is important to observe, study, and improve predictions of extreme weather events, and this Special Issue aims to collect contributions in these directions.

The aim of this Special Issue is to collect contributions on different aspects of extreme weather events. We encourage several types of studies on the topic: observational studies, statistical and climatological analyses, and predictions of extreme weather events at different spatial and temporal scales. Analyses of risk, vulnerability and impact are also of interest for the Special Issue.

This Special Issue collects studies on past, present and future extreme weather events. Contributions can consider, but are not limited to, the following topics:

  • Observational studies of extreme weather events;
  • Studies on physical processes determining extreme weather events;
  • Modelling studies of extreme weather events;
  • Statistical and climatological analysis of extreme weather events;
  • Extreme weather events in a changing climate;
  • Risk, vulnerability and impacts: assessment, mitigation and impact studies.

Dr. Stefano Federico
Dr. Rosa Claudia Torcasio
Dr. Martina Lagasio
Dr. Vincenzo Mazzarella
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

  • statistics and climatology of extreme weather events
  • observations of extreme weather events
  • predictions of severe extreme weather events at different spatial and temporal scales
  • extreme weather events in a changing climate
  • risk, vulnerability and impacts: assessment, mitigation and impact studies

Published Papers (6 papers)

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18 pages, 2310 KiB  
Article
Data Assimilation of Satellite-Derived Rain Rates Estimated by Neural Network in Convective Environments: A Study over Italy
by Rosa Claudia Torcasio, Mario Papa, Fabio Del Frate, Alessandra Mascitelli, Stefano Dietrich, Giulia Panegrossi and Stefano Federico
Remote Sens. 2024, 16(10), 1769; https://doi.org/10.3390/rs16101769 - 16 May 2024
Viewed by 244
Abstract
The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data [...] Read more.
The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model’s precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms. Full article
(This article belongs to the Special Issue Remote Sensing of Extreme Weather Events: Monitoring and Modeling)
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31 pages, 14168 KiB  
Article
Towards the Accurate Automatic Detection of Mesoscale Convective Systems in Remote Sensing Data: From Data Mining to Deep Learning Models and Their Applications
by Mikhail Krinitskiy, Alexander Sprygin, Svyatoslav Elizarov, Alexandra Narizhnaya, Andrei Shikhov and Alexander Chernokulsky
Remote Sens. 2023, 15(14), 3493; https://doi.org/10.3390/rs15143493 - 11 Jul 2023
Cited by 2 | Viewed by 1421
Abstract
Mesoscale convective systems (MCSs) and associated hazardous meteorological phenomena cause considerable economic damage and even loss of lives in the mid-latitudes. The mechanisms behind the formation and intensification of MCSs are still not well understood due to limited observational data and inaccurate climate [...] Read more.
Mesoscale convective systems (MCSs) and associated hazardous meteorological phenomena cause considerable economic damage and even loss of lives in the mid-latitudes. The mechanisms behind the formation and intensification of MCSs are still not well understood due to limited observational data and inaccurate climate models. Improving the prediction and understanding of MCSs is a high-priority area in hydrometeorology. One may study MCSs either employing high-resolution atmospheric modeling or through the analysis of remote sensing images which are known to reflect some of the characteristics of MCSs, including high temperature gradients of cloud-top, specific spatial shapes of temperature patterns, etc. However, research on MCSs using remote sensing data is limited by inadequate (in size) databases of satellite-identified MCSs and poorly equipped automated tools for MCS identification and tracking. In this study, we present (a) the GeoAnnotateAssisted tool for fast and convenient visual identification of MCSs in satellite imagery, which is capable of providing AI-generated suggestions of MCS labels; (b) the Dataset of Mesoscale Convective Systems over the European Territory of Russia (DaMesCoS-ETR), which we created using this tool, and (c) the Deep Convolutional Neural Network for the Identification of Mesoscale Convective Systems (MesCoSNet), constructed following the RetinaNet architecture, which is capable of identifying MCSs in Meteosat MSG/SEVIRI data. We demonstrate that our neural network, optimized in terms of its hyperparameters, provides high MCS identification quality (mAP=0.75, true positive rate TPR=0.61) and a well-specified detection uncertainty (false alarm ratio FAR=0.36). Additionally, we demonstrate potential applications of the GeoAnnotateAssisted labelling tool, the DaMesCoS-ETR dataset, and the MesCoSNet neural network in addressing MCS research challenges. Specifically, we present the climatology of axisymmetric MCSs over the European territory of Russia from 2014 to 2020 during summer seasons (May to September), obtained using MesCoSNet with Meteosat MSG/SEVIRI data. The automated identification of MCSs by the MesCoSNet artificial neural network opens up new avenues for previously unattainable MCS research topics. Full article
(This article belongs to the Special Issue Remote Sensing of Extreme Weather Events: Monitoring and Modeling)
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24 pages, 4941 KiB  
Article
Warm Core and Deep Convection in Medicanes: A Passive Microwave-Based Investigation
by Giulia Panegrossi, Leo Pio D’Adderio, Stavros Dafis, Jean-François Rysman, Daniele Casella, Stefano Dietrich and Paolo Sanò
Remote Sens. 2023, 15(11), 2838; https://doi.org/10.3390/rs15112838 - 30 May 2023
Cited by 6 | Viewed by 1573
Abstract
Mediterranean hurricanes (Medicanes) are characterized by the presence of a quasi-cloud-free calm eye, spiral-like cloud bands, and strong winds around the vortex center. Typically, they reach a tropical-like cyclone (TLC) phase characterized by an axisymmetric warm core without frontal structures. Yet, some of [...] Read more.
Mediterranean hurricanes (Medicanes) are characterized by the presence of a quasi-cloud-free calm eye, spiral-like cloud bands, and strong winds around the vortex center. Typically, they reach a tropical-like cyclone (TLC) phase characterized by an axisymmetric warm core without frontal structures. Yet, some of them are not fully symmetrical, have a shallow warm-core structure, and a weak frontal activity. Finding a clear definition and potential classification of Medicanes based on their initiation and intensification processes, understanding the role of convection, and identifying the evolution to a TLC phase are all current research topics. In this study, passive microwave (PMW) measurements and products are used to characterize warm core (WC) and deep convection (DC) for six Medicanes that occurred between 2014 and 2021. A well-established methodology for tropical cyclones, based on PMW temperature sounding channels, is used to identify the WC while PMW diagnostic tools and products (e.g., cloud-top height (CTH) and ice water path (IWP)), combined with lightning data, are used for DC detection and characterization. The application of this methodology to Medicanes highlights the possibility to describe their WC depth, intensity, and symmetry and to identify the cyclone center. We also analyze to what extent the occurrence and characteristics of the WC are related to the Medicane’s intensity and DC development. The results show that Medicanes reaching full TLC phase are associated with deep and symmetric WCs, and that asymmetric DC features in the proximity of the center, and in higher CTH and IWP values, with scarce lighting activity. Medicanes that never develop to a fully TLC structure are associated with a shallower WC, weaker and more sparse DC activity, and lower CTHs and IWP values. Ultimately, this study illustrates the potential of PMW radiometry in providing insights into dynamic and thermodynamic processes associated with Medicanes’ WC characteristics and evolution to TLCs, thus contributing to the ongoing discussion about Medicanes’ definition. Full article
(This article belongs to the Special Issue Remote Sensing of Extreme Weather Events: Monitoring and Modeling)
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17 pages, 10796 KiB  
Article
Research on the Monitoring Ability of Fengyun-Based Quantitative Precipitation Estimates for Capturing Heavy Precipitation: A Case Study of the “7·20” Rainstorm in Henan Province, China
by Hao Wu, Bin Yong and Zhehui Shen
Remote Sens. 2023, 15(11), 2726; https://doi.org/10.3390/rs15112726 - 24 May 2023
Cited by 2 | Viewed by 1082
Abstract
One of the important tasks of the Chinese geostationary and meteorological satellite Fengyun-2 (FY2) series is to provide quantitative precipitation estimates (QPE) with high spatiotemporal resolutions for East Asia. To analyze the monitoring capabilities of FY2-based QPEs in extreme rainfall events, this study [...] Read more.
One of the important tasks of the Chinese geostationary and meteorological satellite Fengyun-2 (FY2) series is to provide quantitative precipitation estimates (QPE) with high spatiotemporal resolutions for East Asia. To analyze the monitoring capabilities of FY2-based QPEs in extreme rainfall events, this study comprehensively evaluated and compared the performances of FY-2G and FY-2H QPEs for the “7.20” rainstorm in Henan province, China from 17 July 2021 to 22 July 2021. Three continuous metrics and three categorical metrics were adopted to assess the accuracies of FY-2G and FY-2H QPEs, referenced by gauge observations from 116 meteorological stations. The results show that the FY-2G QPE has lower BIAS (−9.64% for FY-2G, −46.22% for FY-2H) and RMSE (5.83 mm/h for FY-2G, 8.4 mm/h for FY-2H) and higher CC (0.57 for FY-2G, 0.24 for FY-2H) than FY-2H QPE in this rainstorm event. Moreover, the FY-2G QPE is not only more consistent with the ground reference with respect to the rainfall amount, but also has higher detecting capability in the “7.20” rainstorm event when compared with the FY-2H QPE. The FY-2G QPE presented a higher capability to correctly capture the precipitation event for the “7.20” rainstorm because of higher POD (probability of detection) and CSI (critical success index) relative to FY-2H QPE, especially in complex topography. From the spatial distribution of precipitation amount, the FY-2G QPE captured the rainstorm center of extreme precipitation more accurately relative to the latest FY-2H product. On the other hand, the previous generation of FY-2G QPE was closer to the continuous rainfall process and precipitation duration with ground observations than the latest FY-2H QPE. Therefore, the precipitation retrieval algorithm of FY-2H QPE still had room to improve. It is necessary to introduce error correction algorithms, especially in complex topography for rainstorm events. Full article
(This article belongs to the Special Issue Remote Sensing of Extreme Weather Events: Monitoring and Modeling)
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14 pages, 3545 KiB  
Technical Note
Early Detection and Analysis of an Unpredicted Convective Storm over the Negev Desert
by Shilo Shiff, Amir Givati, Steve Brenner and Itamar M. Lensky
Remote Sens. 2023, 15(21), 5241; https://doi.org/10.3390/rs15215241 - 4 Nov 2023
Viewed by 811
Abstract
On 15 September 2015, a convective storm yielded heavy rainfalls that caused the strongest flash flood in the last 50 years in the South Negev Desert (Israel). None of the operational forecast models predicted the event, and thus, no warning was provided. We [...] Read more.
On 15 September 2015, a convective storm yielded heavy rainfalls that caused the strongest flash flood in the last 50 years in the South Negev Desert (Israel). None of the operational forecast models predicted the event, and thus, no warning was provided. We analyzed this event using satellite, radar, and numerical weather prediction model data. We generated cloud-free climatological values on a pixel basis using Temporal Fourier Analysis on a time series of MSG geostationary satellite data. The discrepancy between the measured and climatological values was used to detect “cloud-contaminated” pixels. This simple, robust, fast, and accurate method is valuable for the early detection of convection. The first clouds were detected 30 min before they were detected by the official MSG cloud mask, 4.5 h before the radar, and 10 h before the flood reached the main road. We used the “severe storms” RGB composite and the satellite-retrieved vertical profiles of cloud top temperature–particle’s effective radius relations as indicators for the development of a severe convective storm. We also reran the model with different convective schemes, with much-improved results. Both the satellite and model-based analysis provided early warning for a very high probability of flooding a few hours before the actual flooding occurred. Full article
(This article belongs to the Special Issue Remote Sensing of Extreme Weather Events: Monitoring and Modeling)
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14 pages, 8135 KiB  
Technical Note
Assessment of Extreme Ocean Winds within Intense Wintertime Windstorms over the North Pacific Using SMAP L-Band Radiometer Observations
by Mikhail Pichugin, Irina Gurvich and Anastasiya Baranyuk
Remote Sens. 2023, 15(21), 5181; https://doi.org/10.3390/rs15215181 - 30 Oct 2023
Cited by 1 | Viewed by 1092
Abstract
Here, we examine extreme ocean winds associated with intense wintertime extratropical windstorms over the North Pacific. The study was mainly based on NASA Soil Moisture Active Passive (SMAP) L-band radiometer observations allowing the retrieval of ocean wind speeds up to 70 m/s regardless [...] Read more.
Here, we examine extreme ocean winds associated with intense wintertime extratropical windstorms over the North Pacific. The study was mainly based on NASA Soil Moisture Active Passive (SMAP) L-band radiometer observations allowing the retrieval of ocean wind speeds up to 70 m/s regardless of precipitation intensity. Additionally, we assessed the ability of atmospheric reanalysis ERA5 and the Climate Forecast System Version 2 (CFSv2) to reproduce high-wind features within severe windstorms, particularly those associated with “explosive” cyclogenesis. The analysis identified 145 windstorm events with hurricane-force (HF) wind zones within the SMAP L-band radiometer swath from 2015 to 2023. These windstorms develop most frequently over two areas: southeast of Kamchatka and south of Alaska, spanning 40–47°N latitudes. Both reanalysis datasets significantly underestimated HF wind speeds compared to SMAP measurements, but CFSv2 tends to reproduce more-intense windstorms than ERA5. Among the notable new findings is that the SMAP data revealed two distinct groups in maximum wind speed distribution, indicating the existence of a separate class of severe windstorm events with a distinct mechanism for extreme wind formation related probably to a Shapiro–Keyser cyclogenesis and the presence of sting jet (SJ) feature. The study highlights the potential of SMAP measurements to study wind extremes and underscores the need for improvements in operational predictive models to better reproduce the formation of SJ windstorms. Full article
(This article belongs to the Special Issue Remote Sensing of Extreme Weather Events: Monitoring and Modeling)
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