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: 6 January 2025 | Viewed by 4901

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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, AI tools
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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

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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

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Published Papers (5 papers)

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Research

24 pages, 2872 KiB  
Article
Climatology of Cirrus Clouds over Observatory of Haute-Provence (France) Using Multivariate Analyses on Lidar Profiles
by Florian Mandija, Philippe Keckhut, Dunya Alraddawi, Sergey Khaykin and Alain Sarkissian
Atmosphere 2024, 15(10), 1261; https://doi.org/10.3390/atmos15101261 - 21 Oct 2024
Viewed by 468
Abstract
This study aims to achieve the classification of the cirrus clouds over the Observatory of Haute-Provence (OHP) in France. Rayleigh–Mie–Raman lidar measurements, in conjunction with the ERA5 dataset, are analyzed to provide geometrical morphology and optical cirrus properties over the site. The method [...] Read more.
This study aims to achieve the classification of the cirrus clouds over the Observatory of Haute-Provence (OHP) in France. Rayleigh–Mie–Raman lidar measurements, in conjunction with the ERA5 dataset, are analyzed to provide geometrical morphology and optical cirrus properties over the site. The method of cirrus cloud climatology presented here is based on a threefold classification scheme based on the cirrus geometrical and optical properties and their formation history. Principal component analysis (PCA) and subsequent clustering provide four morphological cirrus classes, three optical groups, and two origin-related categories. Cirrus clouds occur approximately 37% of the time, with most being single-layered (66.7%). The mean cloud optical depth (COD) is 0.39 ± 0.46, and the mean heights range around 10.8 ± 1.35 km. Thicker tropospheric cirrus are observed under higher temperature and humidity conditions than cirrus observed in the vicinity of the tropopause level. Monthly cirrus occurrences fluctuate irregularly, whereas seasonal patterns peak in spring. Concerning the mechanism of the formation, it is found that the majority of cirrus clouds are of in situ origin. The liquid-origin cirrus category consists nearly entirely of thick cirrus. Overall results suggest that in situ origin thin cirrus, located in the upper tropospheric and tropopause regions, have the most noteworthy occurrence over the site. Full article
(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies (2nd Edition))
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28 pages, 6583 KiB  
Article
Artificial Intelligence-Based Detection of Light Points: An Aid for Night-Time Visibility Observations
by Zuzana Gáborčíková, Juraj Bartok, Irina Malkin Ondík, Wanda Benešová, Lukáš Ivica, Silvia Hnilicová and Ladislav Gaál
Atmosphere 2024, 15(8), 890; https://doi.org/10.3390/atmos15080890 - 25 Jul 2024
Viewed by 740
Abstract
Visibility is one of the key meteorological parameters with special importance in aviation meteorology and the transportation industry. Nevertheless, it is not a straightforward task to automatize visibility observations, since the assistance of trained human observers is still inevitable. The current paper attempts [...] Read more.
Visibility is one of the key meteorological parameters with special importance in aviation meteorology and the transportation industry. Nevertheless, it is not a straightforward task to automatize visibility observations, since the assistance of trained human observers is still inevitable. The current paper attempts to make the first step in the process of automated visibility observations: it examines, by the approaches of artificial intelligence (AI), whether light points in the target area can or cannot be automatically detected for the purposes of night-time visibility observations. From a technical point of view, our approach mimics human visibility observation of the whole circular horizon by the usage of camera imagery. We evaluated the detectability of light points in the camera images (1) based on an AI approach (convolutional neural network, CNN) and (2) based on a traditional approach using simple binary thresholding (BT). The models based on trained CNN achieved remarkably better results in terms of higher values of statistical metrics, and less susceptibility to errors than the BT-based method. Compared to BT, the CNN classification method indicated greater stability since the accuracy of these models grew with increasing pixel size around the key points. This fundamental difference between the approaches was also confirmed through the Mann–Whitney U test. Thus, the presented AI-based determination of key points’ detectability in the night with decent accuracy has great potential in the objectivization of everyday routines of professional meteorology. Full article
(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies (2nd Edition))
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24 pages, 4737 KiB  
Article
COAT Project: Intercomparison of Thermometer Radiation Shields in the Arctic
by Carmen García Izquierdo, Sonia Hernandez, Marina Parrondo, Alberto Casas, Angelo Viola, Mauro Mazzola, Andrea Merlone and Yves-Alain Roulet
Atmosphere 2024, 15(7), 841; https://doi.org/10.3390/atmos15070841 - 16 Jul 2024
Cited by 1 | Viewed by 685
Abstract
A metrological field intercomparison of thermometer radiation shields in the Arctic was conducted with the aim of obtaining information to increase the worldwide comparability of air temperature measurements. Air temperature measurements are performed by different combinations of thermometers and shields. The response of [...] Read more.
A metrological field intercomparison of thermometer radiation shields in the Arctic was conducted with the aim of obtaining information to increase the worldwide comparability of air temperature measurements. Air temperature measurements are performed by different combinations of thermometers and shields. The response of each system (thermometer + shield) to local meteorological conditions depends on the system itself, limiting the comparability of air temperature measurements. Ten different models of radiation shields were included in the intercomparison, involving two campaigns: (1) the laboratory campaign, where all the instrumentation was calibrated just before and just after the field campaign, and (2) the field campaign that lasted 14 months where 41 thermometers were sampled every 2 min. All the delivered data were subjected to quality control to assure the robustness of the conclusions. A reference shield was defined, and the other shields were compared to the reference one for the conditions where maximum divergences were expected, solar irradiance being the highest impact factor. A maximum divergence value of 1.29 °C was derived for one of the shields and, for all the shields, the difference from the reference one decreases with wind speed. Finally, the uncertainties associated with the shields intercomparison were calculated. Full article
(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies (2nd Edition))
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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 952
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 1374
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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