Advancements in Thunderstorm Nowcasting and Atmospheric Electricity Monitoring by Remote Sensing

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

Deadline for manuscript submissions: 15 September 2024 | Viewed by 721

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 Remote Sensing.

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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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 (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

17 pages, 5425 KiB  
Article
Data-Driven Prediction of Severe Convection at Deutscher Wetterdienst (DWD): A Brief Overview of Recent Developments
by Richard Müller and Axel Barleben
Atmosphere 2024, 15(4), 499; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15040499 - 19 Apr 2024
Viewed by 395
Abstract
Thunderstorms endanger life and infrastructure. The accurate and precise prediction of thunderstorms is therefore helpful to enable protection measures and to reduce the risks. This manuscript presents the latest developments to improve thunderstorm forecasting in the first few hours. This includes the description [...] Read more.
Thunderstorms endanger life and infrastructure. The accurate and precise prediction of thunderstorms is therefore helpful to enable protection measures and to reduce the risks. This manuscript presents the latest developments to improve thunderstorm forecasting in the first few hours. This includes the description and discussion of a new Julia-based method (JuliaTSnow) for the temporal extrapolation of thunderstorms and the blending of this method with the numerical weather prediction model (NWP) ICON. The combination of ICON and JuliaTSnow attempts to overcome the limitations associated with the pure extrapolation of observations with atmospheric motion vectors (AMVs) and thus increase the prediction horizon. For the blending, the operational ICON-D2 is used, but also the experimental ICON-RUC, which is implemented with a faster data assimilation update cycle. The blended products are evaluated against lightning data. The critical success index (CSI) for the blended RUC product is higher for all forecast time steps. This is mainly due to the higher resolution of the AMVs (prediction hours 0–2) and the rapid update cycle of ICON-RUC (prediction hours 2–6). The results demonstrate the potential of the rapid update cycle to improve the short-term forecasts of thunderstorms. Moreover, the transition between AMV-driven nowcasting to NWP is much smoother in the blended RUC product, which points to the advantages of fast data assimilation for seamless predictions. The CSI is well above the critical value of 0.5 for the 0–2 h forecasts. Values below 0.5 mean that the number of hits (correct informations) is lower than the number of failures, which results from the missed cells plus false alarms. The product is then no longer useful in forecasting thunderstorms with a spatial accuracy of 0.3 degrees. Unfortunately, with RUC, the CSI also drops below 0.5 when the last forecast is more than 3 h away from the last data assimilation, indicating the lack of model physics to accurately predict thunderstorms. This lack is simply a result of chaos theory. Within this context, the role of NWP in comparison with artificial intelligence (AI) is discussed, and it is concluded that AI could replace physical short-term forecasts in the near future. Full article
Show Figures

Figure 1

Back to TopTop