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Advancements in Thunderstorm Nowcasting and Atmospheric Electricity Monitoring by Remote Sensing

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

Deadline for manuscript submissions: 15 September 2024 | Viewed by 2555

Special Issue Editor

Special Issue Information

Dear Colleagues,

Thunderstorms are among the most dangerous meteorological hazards worldwide. Therefore, the monitoring and short-term forecasting of thunderstorms are important means of protecting lives and infrastructure. Despite the great advances in numerical weather prediction, it is still difficult to accurately model thunderstorms as the underlying physics are not linear. This is one reason why thunderstorm forecasting is so important and usually outperforms NWP in the first few hours. However, physical methods that rely on atmospheric motion vectors do not account for the life cycle of thunderstorm cells, so as the forecast horizon increases, the number of false alarms and missed cells increases. Therefore, better consideration of the life cycle information obtained through analysis of lightning intensity and radar and satellite reflectivity is needed to improve nowcasting. This can be conducted using physical approaches, but in recent years, Deep Learning and other artificial intelligence methods have gained considerable importance.

This Special Issue aims to reflect advances in thunderstorm nowcasting based on Remote Sensing. Thus the topic covers several sections of Remote Sensing: Atmospheric Remote Sensing, Environmental Remote Sensing, Earth Observation Data and Earth Observation for Emergency Management.

  • Recent advances in lightning networks and radar and satellite systems and their benefit for thunderstorm nowcasting;
  • Recent advances concerning atmospheric motion vectors;
  • Recent advances in analysis and monitoring of thunderstorm life cycles;
  • Recent advances in convective initiation;
  • Recent advances concerning data fusion for thunderstorm nowcasting;
  • Recent advances in the use of artificial intelligence for thunderstorm nowcasting;
  • Recent advances in emergency management based on thunderstorm nowcasting.

You may choose our Joint Special Issue in Atmosphere.

Dr. Richard Müller
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

  • thunderstorms
  • convective initiation
  • lightning
  • life cycle
  • hazards

Published Papers (2 papers)

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Research

25 pages, 17826 KiB  
Article
Estimation of Lightning Activity of Squall Lines by Different Lightning Parameterization Schemes in the Weather Research and Forecasting Model
by Dongxia Liu, Han Yu and Chunfa Sun
Remote Sens. 2023, 15(20), 5070; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15205070 - 23 Oct 2023
Viewed by 947
Abstract
Based on three-dimensional lightning data and an S-band Doppler radar, a strong relationship was identified between lightning activity and the radar volume of squall lines. A detailed analysis of the squall line investigates the relationship following an exponential relationship. According to the correlation [...] Read more.
Based on three-dimensional lightning data and an S-band Doppler radar, a strong relationship was identified between lightning activity and the radar volume of squall lines. A detailed analysis of the squall line investigates the relationship following an exponential relationship. According to the correlation between lightning and the radar volume, three radar-volume-based lightning parameterization schemes, named the V30dBZ, V35dBZ, and V40dBZ lightning schemes, have been established and introduced into the weather research and forecasting (WRF) model. The performance of lightning precondition by different lightning parameterization schemes was evaluated, including the radar-volume-based schemes (V30dBZ, V35dBZ, and V40dBZ), as well as existing lightning schemes (PR92_1, PR92_2, and the Lightning Potential Index (LPI)). The evaluation shows that the simulated spatial lightning density and temporal lightning frequency by the radar-volume-based lightning schemes are more consistent with the observations. While the two PR_92 lightning schemes significantly underestimated the magnitude of lightning density. The radar-volume-based lightning parameterization schemes are proven to be more reliable in estimating lightning activity than other lightning schemes. Full article
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25 pages, 8317 KiB  
Article
Applying Time-Expended Sampling to Ensemble Assimilation of Remote-Sensing Data for Short-Term Predictions of Thunderstorms
by Huanhuan Zhang, Jidong Gao, Qin Xu and Lingkun Ran
Remote Sens. 2023, 15(9), 2358; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15092358 - 29 Apr 2023
Cited by 2 | Viewed by 1100
Abstract
By sampling perturbed state vectors from each ensemble forecast at additional time levels shifted by ±τ (where τ is a selected time interval) from the analysis time, time-expanded sampling (TES) can not only sample timing errors (or phase errors) but also triple the [...] Read more.
By sampling perturbed state vectors from each ensemble forecast at additional time levels shifted by ±τ (where τ is a selected time interval) from the analysis time, time-expanded sampling (TES) can not only sample timing errors (or phase errors) but also triple the analysis ensemble size for covariance construction without increasing the forecast ensemble size. In this study, TES was applied to the convection-allowing ensemble-based warn-on-forecast system (WoFS), for four severe storm events, to reduce the computational costs that constrain real-time applications in the assimilation of remote-sensing data from radars and the geostationary satellite GOES-16. For each event, TES was implemented against a 36-member control experiment (E36) by reducing the forecast ensemble size to 12 but tripling the analysis ensemble size to 12 × 3 = 36 with τ = 2.5 min, 5 min and 7.5 min in three TES experiments, named E12×3τ2.5, E12×3τ5 and E12×3τ7.5, respectively. A 0–6-h forecast was created hourly after the second hour during the assimilation in each experiment. The assimilation statistics were evaluated for each experiment applied to each event and were found to be little affected by the TES, while reducing the computational cost. The forecasts produced in each experiment were verified against multi-sensor observed/estimated rainfall, reported tornadoes and damaging winds for each event. The verifications indicated that the forecasts produced in the three TES experiments had about the same capability and quality as that in the E36 for predicting hourly rainfall and the probabilities of tornadoes and damaging winds; in addition, the predictive capability and quality were not sensitive to τ, although they were slightly enhanced by selecting τ = 7.5 min. These results suggest that TES is attractive and useful for cost-saving real-time applications of WoFS in the assimilation of remote-sensing data and the generation of short-term severe-weather forecasts. Full article
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