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Remote Sensing
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Nowcasting of Convective Storms Based on Remote Sensing Data Fusion
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Nowcasting of Convective Storms Based on Remote Sensing Data Fusion

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

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 4318

Special Issue Editors

Dr. Filomena Romano

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Guest Editor
Institute of Methodologies for Environmental Analysis, National Research Council (IMAA/CNR), 85050 Tito Scalo, Potenza, Italy
Interests: cloud remote sensing; cloud radiative forcing; cloud detection and classification; cloud microphysical properties; surface solar irradiance
Special Issues, Collections and Topics in MDPI journals
Dr. Donatello Gallucci

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Guest Editor
National Research Council (Italy) – Institute of Methodologies for Environmental Analysis, CNR-IMAA, C/da S. Loya, Zona Industriale C. P. 27, 85050 Tito Scalo (PZ), Italy
Interests: quantum mechanics; foundations of quantum mechanics; numerical weather prediction; atmospheric physics; WRF; quantum physics; bose-einstein condensates; ultracold quantum gases

Special Issue Information

Dear Colleagues,

Extreme weather events - often leading to heavy precipitation, strong winds and hail - may have severe impacts to ecosystems, and pose an always increasing threat to society by causing disruption at many levels. To the present, there is still need for further research aiming to improve the prediction accuracy and the capacity to face such weather hazards in a changing climate, which is also an ambitious milestone in the World Meteorological Organization strategy.

Nowcasting - (i.e., 0-6 hour lead time forecasting) - provides the ideal framework to achieve such a goal, whereby near real-time atmospheric observations are an essential basis. Today’s state of the art remote sensing technologies offer in fact a unique opportunity for weather science to address this challenging task: satellite platform, ground-based and airborne instruments provide a variety of improved remote sensing data in terms of temporal, spatial, spectral and radiometric resolution. Since these convective-type events tend to rapidly evolve on small spatial scales, exploting these data sources (either separately or via a data fusion approach), has proved to be crucial to improve on the predictability of such events.

In this Special Issue then, scientific community members are invited to submit manuscripts dealing with recent advances in Nowcasting, in terms of new methods, techniques and/or identification of new sets of nowcasting predictors, mainly based on observational remote sensing data integration; papers discussing combination of Numerical Weather Prediction methods and Nowcasting are also welcome.

Dr. Filomena Romano
Dr. Donatello Gallucci
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

  • Nowcasting
  • Convection
  • Remote sensing data fusion
  • Satellite instrument
  • Ground-based instrument
  • Airborne instruments
  • NWP assimilation

Published Papers (3 papers)

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Research

22 pages, 5309 KiB  
Article
A Space-Time Variational Method for Retrieving Upper-Level Vortex Winds from GOES-16 Rapid Scans over Hurricanes
by Qin Xu, Li Wei, Kang Nai, Huanhuan Zhang and Robert Rabin
Remote Sens. 2024, 16(1), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16010032 - 20 Dec 2023
Viewed by 731
Abstract
A space-time variational method is developed for retrieving upper-level vortex winds from geostationary satellite rapid infrared scans over hurricanes. In this method, new vortex-flow-dependent correlation functions are formulated for the radial and tangential components of the vortex wind. These correlation functions are used [...] Read more.
A space-time variational method is developed for retrieving upper-level vortex winds from geostationary satellite rapid infrared scans over hurricanes. In this method, new vortex-flow-dependent correlation functions are formulated for the radial and tangential components of the vortex wind. These correlation functions are used to construct the background error covariance matrix and its square root matrix. The resulting square root matrix is then employed to precondition the cost function, constrained by an advection equation formulated for rapidly scanned infrared image movements. This newly formulated and preconditioned cost function is more suitable for deriving upper-level vortex winds from GOES-16 rapid infrared scans over hurricanes than the cost function in the recently adopted optical flow technique. The new method was applied to band-13 (10.3 µm) brightness temperature images scanned every min from GOES-16 over Hurricanes Laura on 27 August 2020 and Hurricanes Ida on 29 August 2021. The retrieved vortex winds were shown to not only be much denser than operationally produced atmospheric motion vectors (AMVs) but also more rotational and better organized around the eyewall than the super-high-resolution AMVs derived from optical-flow technique. By comparing their component velocities (projected along radar beams) with limited radar velocity observations available near the cloud top, the vortex winds retrieved using the new method were also shown to be more accurate than the super-high-resolution AMVs derived from the optical-flow technique. The new method is computationally efficient for real-time applications and potentially useful for hurricane wind nowcasts. Furthermore, the combined use of VF-dependent covariance functions and imagery advection equation is not only novel but was also found to be critically important for the improved performance of the method. This finding implies that similar combined approaches can be developed with improved performance for retrieving vortex flows rapidly scanned using other types of remote sensing on different scales, such as tornadic mesocyclones rapidly scanned by phased-array radars. Full article
(This article belongs to the Special Issue Nowcasting of Convective Storms Based on Remote Sensing Data Fusion)
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21 pages, 2492 KiB  
Article
Impact of Multi-Thresholds and Vector Correction for Tracking Precipitating Systems over the Amazon Basin
by Helvecio B. Leal, Alan J. P. Calheiros, Henrique M. J. Barbosa, Adriano P. Almeida, Arturo Sanchez, Daniel A. Vila, Sâmia R. Garcia and Elbert E. N. Macau
Remote Sens. 2022, 14(21), 5408; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215408 - 28 Oct 2022
Viewed by 1336
Abstract
Different algorithms for forecasting and tracking meteorological systems have been developed over the years. Many of them are used to study cloud propagation, precipitation and lightning for nowcasting. Therefore, it is necessary to define carefully the parameters (e.g., intensity thresholds and minimum size) [...] Read more.
Different algorithms for forecasting and tracking meteorological systems have been developed over the years. Many of them are used to study cloud propagation, precipitation and lightning for nowcasting. Therefore, it is necessary to define carefully the parameters (e.g., intensity thresholds and minimum size) that impact tracking of these variables. In order to represent the physical aspects of rain propagation over the Amazon region, several methods of correction and displacement detection were studied. Different parameters were used to validate the methods based on the extrapolated rain cell. A probability detection of 78.4% and 68.6% was achieved for 20 dBZ thresholds during the wet and dry season, respectively. However, the POD decreases for higher reflectivity thresholds. The results for corrections by Inner Nuclei showed that embedded convection can dictate the propagation of rain cells. Split and merge corrections performed well; however, they applied only to a few cases. Corrections performed better for precipitating systems with larger areas and longer duration. The correction methods showed similar skills for both seasons. Which shows that they are able to monitor rain cells throughout the year. The automated combination of different methods for the 20 dBZ threshold proved to be the best choice for tracking rainfall in the Amazon region. Full article
(This article belongs to the Special Issue Nowcasting of Convective Storms Based on Remote Sensing Data Fusion)
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16 pages, 13512 KiB  
Article
Tracking Atmospheric Moisture Changes in Convective Storm Environments Using GEO ABI and LEO CrIS Data Fusion
by Elisabeth Weisz and W. Paul Menzel
Remote Sens. 2022, 14(21), 5327; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215327 - 25 Oct 2022
Cited by 1 | Viewed by 1288
Abstract
The synergistic use of data from advanced space-borne instruments of different designs onboard different satellite platforms with different orbital tracks provides advantages in various applications over the use of individual data sets alone. For example, high vertical resolution sounding profiles from advanced sounders [...] Read more.
The synergistic use of data from advanced space-borne instruments of different designs onboard different satellite platforms with different orbital tracks provides advantages in various applications over the use of individual data sets alone. For example, high vertical resolution sounding profiles from advanced sounders like CrIS (Cross-track Infrared Sounder) in a low Earth orbit (LEO) and a high horizontal plus temporal resolution radiance measurements from geostationary (GEO) imagers like ABI (Advanced Baseline Imager) can be effectively combined to benefit severe weather monitoring, prediction, and warning systems. The spatial and temporal fusion approach allows LEO products, such as atmospheric moisture, to be created with increased spatial detail at every GEO measurement time, generating a GEO hyperspectral sounder-like perspective. To demonstrate the potential benefit of a GEO and LEO (i.e., ABI and CrIS) data fusion to real-time applications, time sequences of the moisture profile fusion results are presented in two case studies, namely a tornado outbreak in Nebraska on 5 May 2021 and a severe storm occurrence in Texas on 24 May 2022. The implications of the fusion results for nowcasting and warning operations via comparisons to numerical model forecasts and weather radar reflectivity data are discussed. Full article
(This article belongs to the Special Issue Nowcasting of Convective Storms Based on Remote Sensing Data Fusion)
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