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Atmosphere
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Advances in Severe Weather Forecast
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Advances in Severe Weather Forecast

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (22 January 2024) | Viewed by 9818

Special Issue Editor

Dr. Massimo Milelli

E-Mail Website
Guest Editor
Meteorology and Climatology Department, CIMA Research Foundation, 17100 Savona, Italy
Interests: NWP; COSMO/ICON model; synoptic meteorology; severe weather; urban meteorology; data assimilation; post-processing; verification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One of the most important challenges in atmospheric science is extreme weather event forecast. Severe events cause a lot of damage to infrastructures, nature, and people, and they are unusual and rare. The frequency of these events is increasing due to climate change; therefore, it is crucial to study their predictability. Nevertheless, the topic of this issue is not the relation with climate change but the study of the phenomena from the very-short-range (nowcasting) to the medium-range (one week ahead) point of view, considering both deterministic and probabilistic approaches. High-resolution numerical weather prediction models such as COSMO, ICON, WRF, etc. or global models such as IFS, GFS, etc. may be considered. Sensitivity studies on data assimilation and physical parametrizations are welcome, together with analysis of the performance of operational simulations in selected case studies. Moreover, a crucial point is the objective validation of the forecast which can be performed with various types of observations (satellite, radar, ground networks, soundings, personal weather stations, etc.).

Summarizing, this Special Issue aims to provide an overview of the most recent applications of NWP in the following (not exhaustive) list of topics:

  • Extreme precipitation (both rain and snow);
  • Heatwaves;
  • Windstorms;
  • Dust Storms;
  • Thunderstorms;
  • Mediterranean/tropical cyclones;
  • Typhoons;
  • Hurricanes;
  • Tornadoes;
  • Hailstorms.

Dr. Massimo Milelli
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

  • meteorology
  • NWP
  • extreme events
  • validation
  • nowcasting
  • short- and medium-range weather forecast
  • data assimilation
  • physical parametrizations
  • ensemble

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Published Papers (4 papers)

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Research

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20 pages, 7370 KiB  
Article
Application of Severe Weather Nowcasting to Case Studies in Air Traffic Management
by Laura Esbrí, Tomeu Rigo, María Carmen Llasat, Riccardo Biondi, Stefano Federico, Olga Gluchshenko, Markus Kerschbaum, Martina Lagasio, Vincenzo Mazzarella, Massimo Milelli, Antonio Parodi, Eugenio Realini and Marco-Michael Temme
Atmosphere 2023, 14(8), 1238; https://doi.org/10.3390/atmos14081238 - 1 Aug 2023
Viewed by 1817
Abstract
Effective and time-efficient aircraft assistance and guidance in severe weather environments remains a challenge for air traffic control. Air navigation service providers around the globe could greatly benefit from specific and adapted meteorological information for the controller position, helping to reduce the increased [...] Read more.
Effective and time-efficient aircraft assistance and guidance in severe weather environments remains a challenge for air traffic control. Air navigation service providers around the globe could greatly benefit from specific and adapted meteorological information for the controller position, helping to reduce the increased workload induced by adverse weather. The present work proposes a radar-based nowcasting algorithm providing compact meteorological information on convective weather near airports for introduction into the algorithms intended to assist in air-traffic management. The use of vertically integrated liquid density enables extremely rapid identification and short-term prediction of convective regions that should not be traversed by aircraft, which is an essential requirement for use in tactical controller support systems. The proposed tracking and nowcasting method facilitates the anticipation of the meteorological situation around an airport. Nowcasts of centroid locations of various approaching thunderstorms were compared with corresponding radar data, and centroid distances between nowcasted and observed storms were computed. The results were analyzed with Method for the Object-Based Evaluation from the Model Evaluation tools software (MET-10.0.1, Developmental Testbed Center, Boulder, CO, US) and later integrated into an assistance arrival manager software, showing the potential of this approach for automatic air traffic assistance in adverse weather scenarios. Full article
(This article belongs to the Special Issue Advances in Severe Weather Forecast)
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19 pages, 8104 KiB  
Article
Assimilation of FY-3D and FY-3E Hyperspectral Infrared Atmospheric Sounding Observation and Its Impact on Numerical Weather Prediction during Spring Season over the Continental United States
by Qi Zhang and Min Shao
Atmosphere 2023, 14(6), 967; https://doi.org/10.3390/atmos14060967 - 1 Jun 2023
Cited by 1 | Viewed by 1868
Abstract
As a part of the World Meteorological Organization (WMO) Global Observing System, HIRAS-1 and HIRAS-2’s observations’ impact on improving the accuracy of numerical weather prediction (NWP) can be summarized into two questions: (1) Will HIRAS observation help the NWP system to improve its [...] Read more.
As a part of the World Meteorological Organization (WMO) Global Observing System, HIRAS-1 and HIRAS-2’s observations’ impact on improving the accuracy of numerical weather prediction (NWP) can be summarized into two questions: (1) Will HIRAS observation help the NWP system to improve its accuracy? (2) Which instrument has the greater impact on NWP? To answer the questions, four experiments are designed here: (I) the HIRAS-1 experiment, which assimilates the principal component (PC) scores derived from HIRAS-1 radiance observation from the FY-3D satellite; (II) the HIRAS-2 experiment, which assimilates HIRAS-2 (onboard the FY-3E satellite) radiance-observation-derived PC scores; (III) the J-01 experiment, which assimilates JPSS1 CrIS radiance-observation-derived PC scores; (IV) the control experiment. Each experiment generated a series of forecasts with 24 h lead-time from 16 March 2022 to 12 April 2022 using the Unified Forecast System Short-Range Weather application. Forecast evaluation using radiosonde and aircraft observation reveals: (a) for upper-level variables (i.e., temperature and specific humidity), assimilating HIRAS observation can improve the NWP’s performance by decreasing the standard deviation (Stdev) and increasing the anomaly correlation coefficient (ACC); (b) according to the multi-category Heidke skill score, HIRAS assimilation experiments, especially the HIRAS-2 experiment, have a higher agreement with hourly precipitation observations; (c) based on two tornado-outbreak case studies, which occurred on 30 March 2022 and 5 April 2022, HIRAS observation can increase the predicted intensity of 0–1 km storm relative helicity and decrease the height of the lifted condensation level at tornado outbreak locations; and (d) compared to CrIS, HIRAS-2 still has room for improvement. Full article
(This article belongs to the Special Issue Advances in Severe Weather Forecast)
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26 pages, 16832 KiB  
Article
A Framework to Predict Community Risk from Severe Weather Threats Using Probabilistic Hazard Information (PHI)
by Jooho Kim, Patrick A. Campbell and Kristin Calhoun
Atmosphere 2023, 14(5), 767; https://doi.org/10.3390/atmos14050767 - 23 Apr 2023
Cited by 4 | Viewed by 2367
Abstract
Community assets, including physical structures and critical infrastructure, provide the essential services that underpin our communities. Their destruction or incapacitation from severe weather threats such as hail and tornadoes can have a debilitating impact on a community’s quality of life, economy, and public [...] Read more.
Community assets, including physical structures and critical infrastructure, provide the essential services that underpin our communities. Their destruction or incapacitation from severe weather threats such as hail and tornadoes can have a debilitating impact on a community’s quality of life, economy, and public health. Recently, prototype Probabilistic Hazard Information (PHI) from the NOAA Forecasting a Continuum of Environmental Threats (FACETs) program has been developed to reflect the rapidly changing nature of severe weather threats to support forecasters, emergency management agencies, and the public. This study develops a holistic framework to merge PHI with a geodatabase of local infrastructure and community assets to predict possible impacts during events and to assist with post-event recovery. To measure the degree of damage of each building, this study uses the predicted intensity from forecasters along with damage indicators from the Enhanced Fujita scale for a range of wind speeds associated with the predicted intensity. The proposed framework provides the possibility of (1) live prediction of risks to community assets due to local vulnerability, and (2) provision of detailed damage assessments, such as degree of damage of systems or assets, and affected areas, to emergency agencies, infrastructure managers, and the public immediately following an event. With further refinement and verification, this community risk assessment prediction may be able to better communicate possible impacts and improve community resiliency from severe weather threats by supporting multiple phases of emergency management, including preparedness, response, and recovery. Full article
(This article belongs to the Special Issue Advances in Severe Weather Forecast)
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Review

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17 pages, 701 KiB  
Review
A Survey of Deep Learning-Based Lightning Prediction
by Xupeng Wang, Keyong Hu, Yongling Wu and Wei Zhou
Atmosphere 2023, 14(11), 1698; https://doi.org/10.3390/atmos14111698 - 17 Nov 2023
Cited by 3 | Viewed by 2844
Abstract
The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, [...] Read more.
The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, the rapid advancement and successful application of data-driven deep learning across diverse sectors, particularly in computer vision and spatio-temporal data analysis, have opened up innovative avenues for enhancing both the accuracy and efficiency of lightning prediction. This article presents a comprehensive review of the broad spectrum of existing lightning prediction methodologies. Starting from traditional numerical forecasting techniques, the path to the most recent breakthroughs in deep learning research are traversed. For these diverse methods, we shed light on their progression and summarize their capabilities, while also predicting their future development trajectories. This exploration is designed to enhance understanding of these methodologies to better utilize their strengths, navigate their limitations, and potentially integrate these techniques to create novel and powerful lightning prediction tools. Through such endeavors, the aim is to bolster preparedness against the growing unpredictability of climate and ensure a proactive stance towards lightning prediction. Full article
(This article belongs to the Special Issue Advances in Severe Weather Forecast)
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