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Early Career Scientists' (ECS) Contributions to Meteorology (2024)
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Early Career Scientists' (ECS) Contributions to Meteorology (2024)

A special issue of Meteorology (ISSN 2674-0494).

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 5408

Special Issue Editor

Dr. Edoardo Bucchignani

E-Mail Website
Guest Editor
Meteorology Laboratory, CIRA Italian Aerospace Research Center, 81043 Capua, CE, Italy
Interests: regional climate modeling; climate changes; numerical weather prediction models; extreme events
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In 2022, we launched a Special Issue (SI) to provide an opportunity for early career scientists in meteorology to share their valuable results with the scientific community. Due to the success of the initiative, in 2023, we decided to launch a second edition of this SI. Both Issues attracted many young scientists, and a number of relevant papers have already been published. For this reason, we have decided to launch a 2024 edition of this SI.

As with the previous editions, manuscripts on all meteorological topics can be submitted. Examples of exciting subjects that could be addressed in this SI include the following:

  • Current challenging areas in weather models, including (but not limited to) data assimilation techniques, optimization of parameterization schemes, model calibrations, and ensemble forecasting.
  • The rise of machine learning in weather forecasting.
  • Numerical weather prediction in the lower stratosphere.
  • Weather drones, or meteo-drones: unmanned aerial vehicles (UAVs) that record weather conditions.
  • Remote sensing in meteorology (e.g., innovative studies focusing on cloud microphysics, wind profile satellite observations).
  • Urban weather: urban heat islands, the interaction between meteorological and social worlds, and local nowcasting tools for the operational spaces of drones (low atmosphere).

This Special Issue will publish original research articles and reviews where the first author is an early career scientist (a student, a PhD candidate, or a practicing scientist who received their highest certificate within the past 5 years). We will provide additional discounts on the APC (article processing charges) upon request, as well as additional guidance on how to address reviewers’ comments, while the publication process will be as transparent and efficient as possible. The submissions will be assessed by at least two referees, as rigorously as any other paper submitted to Meteorology.

Dr. Edoardo Bucchignani
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. Meteorology is an international peer-reviewed open access quarterly 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 1000 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

  • numerical weather prediction models
  • remote sensing
  • model assessment
  • extreme events
  • urban weather
  • small-scale processes

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issues

Published Papers (5 papers)

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Research

26 pages, 7006 KiB  
Article
Relation Between Major Climatic Indices and Subseasonal Precipitation in Rio Grande do Sul State, Brazil
by Angela Maria de Arruda, Luana Nunes Centeno and André Becker Nunes
Meteorology 2025, 4(1), 5; https://doi.org/10.3390/meteorology4010005 - 19 Feb 2025
Viewed by 149
Abstract
This study analyzed the correlation between climate indices—El Niño–Southern Oscillation (NINO34), Southern Oscillation Index (SOI), Antarctic Oscillation (AOC), Sea Surface Temperature in the southwestern Atlantic (ISSTRG2 + RG3), South Atlantic Subtropical High (SASH), Pacific Decadal Oscillation (PDO), and Madden–Julian Oscillation (MJO)—and precipitation in [...] Read more.
This study analyzed the correlation between climate indices—El Niño–Southern Oscillation (NINO34), Southern Oscillation Index (SOI), Antarctic Oscillation (AOC), Sea Surface Temperature in the southwestern Atlantic (ISSTRG2 + RG3), South Atlantic Subtropical High (SASH), Pacific Decadal Oscillation (PDO), and Madden–Julian Oscillation (MJO)—and precipitation in Rio Grande do Sul (RS) during 45-day subseasonal periods from 2006 to 2022. Precipitation data from 670 rain gauges were categorized into three clusters: cluster 1, located in western RS, displayed the lowest precipitation variation; cluster 2, in eastern RS, exhibited the greatest variability; and cluster 3, situated in northern RS. ENSO demonstrated the strongest positive correlation with precipitation during spring in clusters 1 and 3 (0.65–0.79), while PDO also correlated positively, especially in summer and spring. AOC exhibited negative correlations, most pronounced in spring. Significant inter-index correlations were identified, including a high positive correlation between SASH and AOC (0.7) and a high negative correlation between NINO34 and SOI (−0.73). Within clusters, NINO34 and PDO showed low positive correlations with precipitation (0.24–0.32), while SOI demonstrated low negative correlations (−0.21 to −0.30). Seasonal analysis revealed that NINO34 influenced summer and spring precipitation, correlating with above-average rainfall during El Niño events. SASH and PDO also showed positive correlations with summer and spring rainfall, with PDO’s positive phase associated with a 25% increase in precipitation. These findings provide valuable insights into the complex interactions between global climatic indices and regional precipitation patterns, enhancing the understanding of subseasonal climate variability in RS and supporting the development of more accurate climate prediction models for the region. Full article
(This article belongs to the Special Issue Early Career Scientists' (ECS) Contributions to Meteorology (2024))
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25 pages, 7222 KiB  
Article
Precipitation Forecasting and Drought Monitoring in South America Using a Machine Learning Approach
by Juliana Aparecida Anochi and Marilia Harumi Shimizu
Meteorology 2025, 4(1), 1; https://doi.org/10.3390/meteorology4010001 - 25 Dec 2024
Viewed by 819
Abstract
Climate forecasting is essential for energy production, agricultural activities, transportation, and civil defense sectors, serving as a foundation for decision-making and risk management. This study addresses the challenge of accurately predicting extreme droughts in South America, a region highly vulnerable to climate variability. [...] Read more.
Climate forecasting is essential for energy production, agricultural activities, transportation, and civil defense sectors, serving as a foundation for decision-making and risk management. This study addresses the challenge of accurately predicting extreme droughts in South America, a region highly vulnerable to climate variability. By employing a supervised neural network (NN) within a machine learning framework, we developed a methodology to forecast precipitation and subsequently calculate the Standardized Precipitation Index (SPI) for predicting drought conditions across the continent. The proposed model was trained with precipitation data from the Global Precipitation Climatology Project (GPCP) for the period 1983–2023. It provided monthly drought forecasts, which were validated against observational data and compared with predictions from the North American Multi-Model Ensemble (NMME). Key findings indicate the neural network’s ability to capture complex precipitation patterns and predict drought conditions. The model’s architecture effectively integrates precipitation data, demonstrating superior performance metrics compared to traditional approaches like the NMME. This study reinforces the relevance of using machine learning algorithms as a robust tool for drought prediction, providing critical information that can assist in decision-making for sustainable water resource management. Full article
(This article belongs to the Special Issue Early Career Scientists' (ECS) Contributions to Meteorology (2024))
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21 pages, 21427 KiB  
Article
Evolution of Synoptic Systems Associated with Lake-Effect Snow Events over Northwestern Pennsylvania
by Jake Wiley and Christopher Elcik
Meteorology 2024, 3(4), 391-411; https://doi.org/10.3390/meteorology3040019 - 20 Nov 2024
Viewed by 1283
Abstract
This study investigates the synoptic conditions associated with lake-effect snow (LES) over northwestern Pennsylvania with a focus on classifying cases based on the tracks of cyclones influencing the region, including Nor’easters (NEs), Alberta Clippers (ACs), Colorado Lows (COs), and Great Lakes Lows (GLs). [...] Read more.
This study investigates the synoptic conditions associated with lake-effect snow (LES) over northwestern Pennsylvania with a focus on classifying cases based on the tracks of cyclones influencing the region, including Nor’easters (NEs), Alberta Clippers (ACs), Colorado Lows (COs), and Great Lakes Lows (GLs). Synoptic composites were constructed using the North American Regional Reanalysis (NARR) for all cases, as well as each cyclone group, using an LES repository spanning from 2006–2020. Additionally, 95 percent bootstrapped confidence intervals were created for each cyclone track to compare the initial mesoscale environmental properties (i.e., surface lake/air temperature and wind direction/speed) and LES impact (i.e., duration, maximum snowfall, and property damage). Synoptic composites of all LES cases exhibited an archetypal LES synoptic pattern consisting of an upper-level low geopotential height anomaly over the Hudson Bay and surface dipole structure centered across the Great Lakes basin. Regarding the different tracks, NEs and COs featured dynamic support in the form of enhanced turbulent mixing and synoptic vertical forcing, while ACs and GLs had greater thermodynamic support in the form of higher lapse rates and heightened heat and moisture fluxes. However, the bootstrapping analysis revealed minimal differences in LES impact between the cyclone types. Full article
(This article belongs to the Special Issue Early Career Scientists' (ECS) Contributions to Meteorology (2024))
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23 pages, 21344 KiB  
Article
Vertical Structure of Heavy Rainfall Events in Brazil
by Eliana Cristine Gatti, Izabelly Carvalho da Costa and Daniel Vila
Meteorology 2024, 3(3), 310-332; https://doi.org/10.3390/meteorology3030016 - 23 Sep 2024
Viewed by 1021
Abstract
Intense rainfall events frequently occur in Brazil, often leading to rapid flooding. Despite their recurrence, there is a notable lack of sub-daily studies in the country. This research aims to assess patterns related to the structure and microphysics of clouds driving intense rainfall [...] Read more.
Intense rainfall events frequently occur in Brazil, often leading to rapid flooding. Despite their recurrence, there is a notable lack of sub-daily studies in the country. This research aims to assess patterns related to the structure and microphysics of clouds driving intense rainfall in Brazil, resulting in high accumulation within 1 h. Employing a 40 mm/h threshold and validation criteria, 83 events were selected for study, observed by both single and dual-polarization radars. Contoured Frequency by Altitude Diagrams (CFADs) of reflectivity, Vertical Integrated Liquid (VIL), and Vertical Integrated Ice (VII) are employed to scrutinize the vertical cloud characteristics in each region. To address limitations arising from the absence of polarimetric coverage in some events, one case study focusing on polarimetric variables is included. The results reveal that the generating system (synoptic or mesoscale) of intense rain events significantly influences the rainfall pattern, mainly in the South, Southeast, and Midwest regions. Regional CFADs unveil primary convective columns with 40–50 dBZ reflectivity, extending to approximately 6 km. The microphysical analysis highlights the rapid structural intensification, challenging the event predictability and the issuance of timely, specific warnings. Full article
(This article belongs to the Special Issue Early Career Scientists' (ECS) Contributions to Meteorology (2024))
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19 pages, 4653 KiB  
Article
Assessing Drought Vulnerability in the Brazilian Atlantic Forest Using High-Frequency Data
by Mahelvson Bazilio Chaves, Fábio Farias Pereira, Claudia Rivera Escorcia and Nathacha Cavalcante
Meteorology 2024, 3(3), 262-280; https://doi.org/10.3390/meteorology3030014 - 16 Jul 2024
Cited by 1 | Viewed by 1348
Abstract
This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation and maximum and minimum air [...] Read more.
This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation and maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from using imputation methods due to potential bias. Drought quantification involved the computation of the aridity index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann–Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting that geographical location and elevation do not strongly influence observed dryness trends. Full article
(This article belongs to the Special Issue Early Career Scientists' (ECS) Contributions to Meteorology (2024))
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