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

Deadline for manuscript submissions: closed (14 June 2023) | Viewed by 15897

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Dr. Agnieszka Krzyżewska

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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,

One of the foundations of atmospheric science is proper methodology of research, starting with the “standard” of meteorological measurements, through automatic sensors and visual assessment of meteorological phenomena, ending with incorporation of satellite, drone, and other aviation data. After data collection, there are multiple methods and applications of statistical analysis and machine learning techniques which can be used. There are also multiple databases, with different temporal and spatial resolution of a different application 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.

These topics will be presented and discussed during the 5th Methodological Conference on “Problems of Meteorological Measurements and Studies” which will be held in Lublin, Poland, on 30 June–2 July 2022. We invite you to submit a paper to this Special Issue on the methodology of meteorological observations and data analysis.

Dr. Agnieszka Krzyżewska
Guest Editor

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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

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Published Papers (7 papers)

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Research

17 pages, 3380 KiB

Open AccessArticle

Hybrid Deep Learning Model for Mean Hourly Irradiance Probabilistic Forecasting

by Vateanui Sansine, Pascal Ortega, Daniel Hissel and Franco Ferrucci

Atmosphere 2023, 14(7), 1192; https://doi.org/10.3390/atmos14071192 - 24 Jul 2023

Cited by 4 | Viewed by 1450

Abstract

For grid stability, operation, and planning, solar irradiance forecasting is crucial. In this paper, we provide a method for predicting the Global Horizontal Irradiance (GHI) mean values one hour in advance. Sky images are utilized for training the various forecasting models along with measured meteorological data in order to account for the short-term variability of solar irradiance, which is mostly caused by the presence of clouds in the sky. Additionally, deep learning models like the multilayer perceptron (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), or their hybridized forms are widely used for deterministic solar irradiance forecasting. The implementation of probabilistic solar irradiance forecasting, which is gaining prominence in grid management since it offers information on the likelihood of different outcomes, is another task we carry out using quantile regression. The novelty of this paper lies in the combination of a hybrid deep learning model (CNN-LSTM) with quantile regression for the computation of prediction intervals at different confidence levels. The training of the different machine learning algorithms is performed over a year’s worth of sky images and meteorological data from the years 2019 to 2020. The data were measured at the University of French Polynesia (17.5770° S, 149.6092° W), on the island of Tahiti, which has a tropical climate. Overall, the hybrid model (CNN-LSTM) is the best performing and most accurate in terms of deterministic and probabilistic metrics. In addition, it was found that the CNN, LSTM, and ANN show good results against persistence. Full article

(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies)

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22 pages, 14103 KiB

Open AccessArticle

Quantifying the Spatio-Temporal Pattern Differences in Climate Change before and after the Turning Year in Southwest China over the Past 120 Years

by Meng Wang, Shouyan Wang and Zhengfeng An

Atmosphere 2023, 14(6), 940; https://doi.org/10.3390/atmos14060940 - 27 May 2023

Viewed by 1512

Abstract

In conjunction with Earth’s ongoing global warming, the Southwest China (SWC) region has become a fascinating case study on the control of local climate change. Moreover, an entire period of climate change may partially mask the patterns in some stages. Therefore, in this research, we investigated the spatial patterns of the significant turning years of climatic factor change, and determined the heterogeneity of the spatial patterns of climate change before and after the significant turning years. We used the long time-series of the CRU datasets (CRU_TS4.02) from 1901 to 2017 with a piecewise linear regression model to explore the significant turning-year distribution characteristics of inter-annual and inter-seasonal climate factor changes, and further describe and quantize the differences in the spatio-temporal patterns of climate factors before and after the significant turning years on the grid scale in SWC. Overall, the trends in temperature and precipitation factors in SWC were segmented over the last 120 years, with significant turning years with different regional and stepwise characteristics. In terms of timing, temperature and precipitation factors changed significantly in 1954 and 1928, respectively, and overall temporal variability (0.04 °C/(10 a) (p < 0.05), −0.48 mm/(10 a)) masked the magnitude or direction of variability (0.13 °C/(10 a) and 0.16 °C/(10 a) both at the level of p < 0.05 before the turning year, 19.56 mm/(10 a) (p < 0.05) and 1.19 mm/(10 a) after the turning year) around the watershed years. Spatially, the significant turning years were concentrated in the periods 1940–1993 (temperature) and 1910–2008 (precipitation), and the distribution pattern of the turning years was patchy and concentrated. The turning years of temperature factors were gradually delayed from east to west, and the variability of climate factors before and after the turning years exhibited significant shifts in location (e.g., temperature decreased from southeast to northwest before the turning year and increased after the turning year). After the turning year, the warming variability of the temperature factor increased, while the increasing variability of the precipitation factor decreased. Further integrated analysis revealed that the increase in variability of the climate factor after the turning year was mainly due to the increase in winter and autumn variability (0.05 °C/(10 a), 7.30 mm/(10 a) in autumn; and 0.12 °C/(10 a), 1.97 mm/(10 a) in winter). To the extent that this study provides a necessary academic foundation for efficiently unveiling the spatio-temporal variability properties of climate factors against the background of modern global climate change, more attention should be paid to the location and phase of the study. Full article

(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies)

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14 pages, 4423 KiB

Open AccessArticle

Features of Multiannual Air Temperature Variability in Poland (1951–2021)

by Michał Marosz, Mirosław Miętus and Dawid Biernacik

Atmosphere 2023, 14(2), 282; https://doi.org/10.3390/atmos14020282 - 31 Jan 2023

Cited by 9 | Viewed by 1724

Abstract

Over the last 71 years, the air temperature in Poland has increased on average by 0.28 °C per decade—which gives a total change in this period exceeding 2 °C. The subject of this study was an analysis of the long-term variability of the Polish climate in terms of thermal characteristics. The aim of the research was to verify the hypothesis on the lack of homogeneity of this change and to identify points of significant acceleration of the observed tendencies. The analysis utilized the average monthly air temperature at selected synoptic stations in Poland over the period 1951–2021. The values were then processed into a reference series using Alexandersson’s method, which provided synthetic information on the variability in thermal conditions in the country. The analyses were carried out on an annual and seasonal basis. The values of the trend coefficients (and their statistical significance) were also calculated in shorter periods (minimum 30 years), which enabled determination of the stability of the observed changes’ tendencies. In addition to the analysis of the basic characteristics, non-parametric tests (Wilcoxon, Kruskal–Wallis) were used to verify shifts between decades. The annual and seasonal analyses showed the existence of sub-periods with different directions and scales of the observed tendencies. Additionally, statistically significant changes in decadal characteristics were noted, e.g., in the decades 2001–2010 and 2011–2020 in the case of annual temperature, and 1961–1970 and 1971–1980 in the case of the winter season. Full article

(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies)

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26 pages, 5237 KiB

Open AccessArticle

Comparison of Radiosonde Measurements of Meteorological Variables with Drone, Satellite Products, and WRF Simulations in the Tropical Andes: The Case of Quito, Ecuador

by Luis Eduardo Muñoz, Lenin Vladimir Campozano, Daniela Carolina Guevara, René Parra, David Tonato, Andrés Suntaxi, Luis Maisincho, Carlos Páez, Marcos Villacís, Jenry Córdova and Nathalia Valencia

Atmosphere 2023, 14(2), 264; https://doi.org/10.3390/atmos14020264 - 28 Jan 2023

Cited by 5 | Viewed by 3666

Abstract

Radiosondes are the most widely used method for studies of vertical atmospheric behavior, but the high costs associated, and the logistic limitations have forced researchers to look for alternative methods for atmospheric profiling, such as lidar and satellite measurements, or modeling. However, the assessment of the accuracy of alternative methods is recommended, especially in complex terrain, such as the tropical Andes. In this research, the atmospheric profiling of satellite data from AIRS and MODIS products, simulations of the Weather Research and Forecasting model, WRF, and drone measurements are evaluated for a campaign of 10 radio soundings, between August 2021 and January 2022. Additionally, the capability to capture the planetary boundary layer height, hPBL, is studied. The measurements were conducted at Izobamba station near Quito, Ecuador. Temperature, T, Dew Point Temperature, TD, Mixing Ratio, Q, and Potential Temperature, PT, were evaluated from 0 to 300 m above ground level (magl.) for satellite, WRF, and drone data, and from 0 km to 15 km for satellite and WRF data. Additionally, the capability to capture the planetary boundary layer height, HPBL, was assessed. The results show that drone profiles best represented the magnitude of the analyzed variables showing mean RMSE of 0.79 for T, but the noise of the measurements caused a low correlation with radio sounding profiles, which was partially corrected with a quadratic fit on the profile. The WRF results achieved a positive representation in terms of correlation, but error metrics show that there are remarkable differences in magnitude in the first 300 magl., up to the tropopause height, which surpasses satellite representations for all variables. The MODIS profiles do not generally perform well due to their low vertical resolution and limitations with cloud coverage. However, AIRS data, despite its low resolution, show a better representation of vertical profiles than MODIS, for T and TD, surpassing WRF simulations in some dates. For the HPBL, the WRF results show that physical and atmospheric conditions limit its determination, and the methods and conditioning factors should be further analyzed. Full article

(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies)

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14 pages, 7163 KiB

Open AccessArticle

A Practical Approach for Determining Multi-Dimensional Spatial Rainfall Scaling Relations Using High-Resolution Time–Height Doppler Data from a Single Mobile Vertical Pointing Radar

by Arthur R. Jameson

Atmosphere 2023, 14(2), 252; https://doi.org/10.3390/atmos14020252 - 27 Jan 2023

Viewed by 1082

Abstract

The rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 s over intervals varying from 15 up to 41 min in two different locations and in two different years using two different micro-rain radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor “frozen” turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations but not the need of those radars for surveillance. Full article

(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies)

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19 pages, 3224 KiB

Open AccessArticle

Manual and Automatic Measurements of Sunshine Duration in Cassubian Lakeland (Northern Poland)

by Małgorzata Owczarek and Mirosława Malinowska

Atmosphere 2023, 14(2), 244; https://doi.org/10.3390/atmos14020244 - 26 Jan 2023

Cited by 2 | Viewed by 3200

Abstract

The aim of this research is to compare daily sunshine duration data measured using a Campbell–Stokes sunshine recorder (CS) and a CSD3 sunshine duration sensor. This was undertaken because in recent decades automatic sunshine duration sensors have been systematically replacing traditional sunshine recorder. This replacement created a problem with the data quality, and the continuity of homogeneous series. The study material consisted of the daily sunshine duration sums derived from synchronous manual and automated measurements at the Borucino station (northern Poland) for the years 2015–2021. Comparison covered the daily and monthly sums and their statistical distributions. In most cases, the daily sum recorded by the sensor CSD3 was higher than that measured by the CS. On average, higher values of sums were obtained from CSD3 for all months of the year, with the exception of June. This can be explained by the higher sensitivity threshold of the CS, as well as by the difference in height of both instruments above the station’s level. Higher daily totals recorded by the CS than by the CSD3 occurred not only in June. The cause was most likely the so-called “overburning effect”. Monthly regression equations were determined, allowing for substitution of the CS measurement results with the values recorded automatically. Full article

(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies)

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27 pages, 8123 KiB

Open AccessArticle

Evaluation of Surface Data Simulation Performance with the Brazilian Global Atmospheric Model (BAM)

by Dirceu Luis Herdies, Fabrício Daniel dos Santos Silva, Helber Barros Gomes, Maria Cristina Lemos da Silva, Heliofábio Barros Gomes, Rafaela Lisboa Costa, Mayara Christine Correia Lins, Jean Souza dos Reis, Paulo Yoshio Kubota, Dayana Castilho de Souza, Maria Luciene Dias de Melo and Glauber Lopes Mariano

Atmosphere 2023, 14(1), 125; https://doi.org/10.3390/atmos14010125 - 6 Jan 2023

Cited by 3 | Viewed by 2070

Abstract

In this study, we evaluated the performance of the Brazilian Global Atmospheric Model (BAM), in its version 2.2.1, in the representation of the surface variables solar radiation, temperature (maximum, minimum, and average), and wind speed. Three experiments were carried out for the period from 2016 to 2022 under three different aerosol conditions (constant (CTE), climatological (CLIM), and equal to zero (ZERO)), discarding the first year as a spin-up period. The observations came from a high-resolution gridded analysis that provides Brazil with robust data based on observations from surface stations on a daily scale from 1961 to 2020; therefore, combining the BAM outputs with the observations, our intercomparison period took place from 2017 to 2020, for three timescales: daily, 10-day average, and monthly, targeting different applications. In its different simulations, BAM overestimated solar radiation throughout Brazil, especially in the Amazon; underestimated temperature in most of the northeast, southeast, and south regions; and overestimated in parts of the north and mid-west; while wind speed was only not overestimated in the Amazon region. In relative terms, the simulations with constant aerosol showed better performance than the others, followed by climatological conditions and zero aerosol. The dexterity indices applied in the intercomparison between BAM and observations indicate that BAM needs adjustments and calibration to better represent these surface variables. Where model deficiencies have been identified, these can be used to drive model development and further improve the predictive capabilities. Full article

(This article belongs to the Special Issue Problems of Meteorological Measurements and Studies)

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