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Atmosphere
Special Issues
Problems of Meteorological Measurements and Studies (2nd Edition)
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Problems of Meteorological Measurements and Studies (2nd Edition)

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1746

Special Issue Editor

Dr. Agnieszka Krzyżewska

E-Mail Website
Guest Editor
Department of Hydrology and Climatology, Institute of Earth and Environmental Sciences, Faculty of Earth Sciences and Spatial Management, University of Maria Curie Sklodowska, 20-400 Lublin, Poland
Interests: heatwaves; biometeorology; extreme weather and climate events; climatology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a follow-up to the Special Issue entitled “Problems of Meteorological Measurements and Studies” (https://www.mdpi.com/journal/atmosphere/special_issues/Meteorological_Measurements_Studies) published in Atmosphere, and it will cover all aspects of the methodology of meteorological observations and data analysis.

One of the foundations of atmospheric science is the proper methodology of research, starting with the “standard” of meteorological measurements, through automatic sensors and the visual assessment of meteorological phenomena, and ending with the incorporation of satellites, drones, and other aviation data. After data collection, there are multiple methods and applications of statistical analysis and machine learning techniques that can be used. There are also multiple databases with different temporal and spatial resolutions for different applications in climatology. There are also some problems with climate regionalization, applying different criteria for determining extreme events, or some issues of weather typology and atmospheric circulation. We invite you to submit a paper to this Special Issue concerning the methodology of meteorological observations and data analysis.

Dr. Agnieszka Krzyżewska
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

  • standard of meteorological measurements
  • visual assessment of meteorological phenomena
  • satellite data
  • drones
  • aviation as a source of information
  • methods of climatological analysis
  • application of statistical methods
  • climatological databases and their quality—possibilities of use
  • issues of weather typology and atmospheric circulation
  • criteria for determining extreme events
  • problems of climate regionalization

Published Papers (2 papers)

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Research

22 pages, 5927 KiB  
Article
Adjustment Methods Applied to Precipitation Series with Different Starting Times of the Observation Day
by Francesca Becherini, Claudio Stefanini, Antonio della Valle, Francesco Rech, Fabio Zecchini and Dario Camuffo
Atmosphere 2024, 15(4), 412; https://doi.org/10.3390/atmos15040412 - 26 Mar 2024
Viewed by 526
Abstract
The study of long precipitation series constitutes an important issue in climate research and risk assessment. However, long datasets are affected by inhomogeneities that can lead to biased results. A frequent but sometimes underestimated problem is the definition of the climatological day. The [...] Read more.
The study of long precipitation series constitutes an important issue in climate research and risk assessment. However, long datasets are affected by inhomogeneities that can lead to biased results. A frequent but sometimes underestimated problem is the definition of the climatological day. The choice of different starting times may lead to inhomogeneity within the same station and misalignment with other stations. In this work, the problem of temporal misalignment between precipitation datasets characterized by different starting times of the observation day is analyzed. The most widely used adjustment methods (1 day and uniform shift) and two new methods based on reanalysis (NOAA and ERA5) are evaluated in terms of temporal alignment, precipitation statistics, and percentile distributions. As test series, the hourly precipitation series of Padua and nearby stations in the period of 1993–2022 are selected. The results show that the reanalysis-based methods, in particular ERA5, outperform the others in temporal alignment, regardless of the station. But, for the periods in which reanalysis data are not available, 1-day and uniform shift methods can be considered viable alternatives. On the other hand, the reanalysis-based methods are not always the best option in terms of precipitation statistics, as they increase the precipitation frequency and reduce the mean value over wet days, NOAA much more than ERA5. The use of the series of a station near the target one, which is mandatory in case of missing data, can sometimes give comparable or even better results than any adjustment method. For the Padua series, the analysis is repeated at monthly and seasonal resolutions. In the tested series, the adjustment methods do not provide good results in summer and autumn, the two seasons mainly affected by heavy rains in Padua. Finally, the percentile distribution indicates that any adjustment method underestimates the percentile values, except ERA5, and that only the nearby station most correlated with Padua gives results comparable to ERA5. Full article
(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies (2nd Edition))
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20 pages, 7088 KiB  
Article
Explainable Boosting Machine: A Contemporary Glass-Box Strategy for the Assessment of Wind Shear Severity in the Runway Vicinity Based on the Doppler Light Detection and Ranging Data
by Afaq Khattak, Jianping Zhang, Pak-Wai Chan, Feng Chen and Hamad Almujibah
Atmosphere 2024, 15(1), 20; https://doi.org/10.3390/atmos15010020 - 23 Dec 2023
Viewed by 923
Abstract
Pilots commonly undergo training to effectively manage instances of wind shear (WS) during both the landing and takeoff stages. Nevertheless, in exceptional circumstances, there may be instances of severe wind shear (SWS) surpassing a magnitude of 30 knots, leading to adverse effects on [...] Read more.
Pilots commonly undergo training to effectively manage instances of wind shear (WS) during both the landing and takeoff stages. Nevertheless, in exceptional circumstances, there may be instances of severe wind shear (SWS) surpassing a magnitude of 30 knots, leading to adverse effects on the operation of taking off and landing aircraft. This phenomenon can lead to the execution of aborted landing maneuvers and deviations from the intended glide path. This study utilized the explainable boosting machine (EBM), an advanced machine learning (ML) model known for its transparency, to predict the severity of WS occurrences and analyze the underlying factors. The dataset consisted of 21,392 data points from 2018 to 2022 acquired from two Doppler light detection and ranging (LiDAR) systems installed at Hong Kong International Airport (HKIA). Initially, the Doppler LiDAR data received data treatment in order to address the issue of data imbalance. Subsequently, utilizing the processed data, the hyperparameters of EBM were optimized using the Bayesian optimization technique. The EBM model underwent subsequent training and evaluation, wherein its performance metrics were computed and compared with those of an alternative glass-box model including decision tree (DT) and counterpart black-box models, namely, random forest (RF) and extreme gradient boosting (XGBoost). The EBM model trained on synthetic minority oversampling technique (SMOTE)-treated data demonstrated superior performance in comparison with the alternative models, as indicated by its higher geometric mean (0.77), balanced accuracy (0.78), and Matthews’ correlation coefficient (0.169). Furthermore, the EBM exhibited enhanced predictive performance and facilitated a comprehensive analysis of individual and pairwise factor interactions in the prediction of WS severity. This enabled the assessment of the factors that contributed to the instances of SWS in the proximity of airport runways. Full article
(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies (2nd Edition))
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