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Statistical Approaches in Climatic Parameters Prediction

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 13432

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Special Issue Editors

Dr. Iqbal Hossain

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Guest Editor

Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne 3122, Australia
Interests: hydrology; extreme event modelling; water footprint; water quality modelling

Dr. Abdullah Gokhan Yilmaz

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Guest Editor

Senior Lecturer, La Trobe University, School of Engineering and Mathematical Sciences, Victoria 3552, Australia
Interests: climate change; rainfall–runoff modelling; water resources management; geographic information systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Statistical methods are commonly applied to obtain insight from underlying data and detecting useful evidence for making informed decisions. The current climate system and potential future changes are investigated through statistical analysis. Moreover, statistical methods are key tools for the production of effective and sustainable solutions to the climate crisis. As a result, numerous statistical techniques are developed and adopted to predict the climatic parameters.

To cover the progress in statistical techniques in forecasting climatic parameters, the open-access journal Atmosphere is hosting a Special Issue titled ‘Statistical Approaches in Climatic Parameters Prediction’. The aim of this Special Issue is to provide recent advances in statistical techniques for climatic parameters prediction. This topic comprises numerous probabilistic and statistical approaches and multivariate methods including extreme value analysis of climatic parameters (e.g., rainfall, temperature, bushfire). The topic is also highly relevant to different engineering applications, such as stormwater infrastructure design, flood and drought assessment, and the adoption of appropriate construction techniques.

Dr. Iqbal Hossain
Dr. Abdullah Gokhan Yilmaz
Guest Editors

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

statistical methods
climatic parameters
prediction
probabilistic approach
flood and drought analysis

Published Papers (7 papers)

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Research

22 pages, 1756 KiB

Open AccessEditor’s ChoiceArticle

Regionalization of the Onset and Offset of the Rainy Season in Senegal Using Kohonen Self-Organizing Maps

by Dioumacor Faye, François Kaly, Abdou Lahat Dieng, Dahirou Wane, Cheikh Modou Noreyni Fall, Juliette Mignot and Amadou Thierno Gaye

Atmosphere 2024, 15(3), 378; https://doi.org/10.3390/atmos15030378 - 20 Mar 2024

Viewed by 669

Abstract

This study explores the spatiotemporal variability of the onset, end, and duration of the rainy season in Senegal. These phenological parameters, crucial for agricultural planning in West Africa, exhibit high interannual and spatial variability linked to precipitation. The objective is to detect and spatially classify these indices across Senegal using different approaches. Daily precipitation data and ERA5 reanalyses from 1981 to 2018 were utilized. The employed method enables the detection of key dates. Subsequently, the Kohonen algorithm spatially classifies these indices on topological maps. The results indicate a meridional gradient of the onset, progressively later from the southeast to the northwest, whereas the end follows a north–south gradient. The duration varies from 45 days in the north to 150 days in the south. The use of self-organizing maps allows for classifying the onset, end, and duration of the season into four zones for the onset and end, and three zones for the duration of the season. They highlight the interannual irregularity of transitions, with both early and late years. The dynamic analysis underscores the complex influence of atmospheric circulation fields, notably emphasizing the importance of low-level monsoon flux. These findings have tangible implications for improving seasonal forecasts and agricultural activity planning in Senegal. They provide information on the onset, end, and duration classes for each specific zone, which can be valuable for planning crops adapted to each region. Full article

(This article belongs to the Special Issue Statistical Approaches in Climatic Parameters Prediction)

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18 pages, 6135 KiB

Open AccessArticle

Assessment of the Spatiotemporal Changes in the Extreme Precipitation Climate Indices over the Chungcheong Region of South Korea during 1973–2020

by Hyungon Cho, Bashir Adelodun, Hyo-Jeong Kim and Gwangseob Kim

Atmosphere 2023, 14(12), 1718; https://doi.org/10.3390/atmos14121718 - 22 Nov 2023

Viewed by 838

Abstract

This study analyzed the changes and trends in twelve extreme precipitation-based climate indices obtained using daily data from 10 synoptic stations in the Chungcheong region of South Korea during the 1973–2020 period. The climate indices were used to assess the trends in the extreme precipitation characteristics of duration, frequency, and intensity using the innovative trend analysis (ITA) method. The results of the ITA were further compared with two other non-parametric test methods such as Mann–Kendall (MK) and Spearman’s rho (SR). The results showed that most stations exhibited significant increasing trends in all the investigated climate indices at a 95% confidence level as indicated by the ITA method, with only a few stations indicating significant decreasing trends in R95p, R99p, Rx3day, and Rx5day. The sub-trend analysis further revealed the dominance of neutral behavior around the low-value cluster, especially for the extreme precipitation duration. At the same time, increasing trends dominate the high-value cluster at most stations. Meanwhile, only R10mm, R99p, and R95p exhibited monotonic trends in the Boeun and Seosan stations, respectively. Further, the ITA exhibited superior performance over the MK and SR methods by indicating the presence of more significant trends in the climate indices at most stations. The distribution of the extreme precipitation indices for duration, frequency, and intensity indicate the pronounced risk of flood conditions around the north–central and some parts of southern regions, while the western region indicates a potential drought risk, which could greatly impact the water resources and consequently agricultural activities in the study area. The results of this study provide essential information for addressing the climate-related problems of water resource management and agriculture in the study area and other related climatic regions. Full article

(This article belongs to the Special Issue Statistical Approaches in Climatic Parameters Prediction)

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26 pages, 13052 KiB

Open AccessArticle

Performance-Based Evaluation of CMIP5 and CMIP6 Global Climate Models and Their Multi-Model Ensembles to Simulate and Project Seasonal and Annual Climate Variables in the Chungcheong Region of South Korea

by Bashir Adelodun, Mirza Junaid Ahmad, Golden Odey, Qudus Adeyi and Kyung Sook Choi

Atmosphere 2023, 14(10), 1569; https://doi.org/10.3390/atmos14101569 - 16 Oct 2023

Cited by 4 | Viewed by 1341

Abstract

Extreme climate change events are major causes of devastating impacts on socioeconomic well-being and ecosystem damage. Therefore, understanding the performance of appropriate climate models representing local climate characteristics is critical for future projections. Thus, this study analyses the performance of 24 GCMs from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6) and their multi-model ensembles in simulating climate variables including average rainfall, maximum (Tmax), and minimum (Tmin) temperatures at annual and seasonal scales over the Chungcheong region of South Korea from 1975 to 2015. A trend analysis was conducted to estimate the future trends in climate variables in the 2060s (2021–2060) and 2080s (2061–2100). Inverse distance weighting and quantile delta mapping were applied to bias-correct the GCM data. Further, six major evaluating indices comprising temporal and spatial performance assessments were used, after which a comprehensive GCM ranking was applied. The results showed that CMIP6 models performed better in simulating rainfall, Tmax, and Tmin at both temporal and spatial scales. For CMIP5, the top three performing models were GISS, ACCESS1-3, and MRI-CGCM3 for rain; CanESM2, GISS, and MPI-ESM-L-R for Tmax; and GFDL, MRI-CGCM3, and CanESM2 for Tmin. However, the top three performing models in the CMIP6 were MRI-ESM2-0, BCC_CSM, and GFDL for rain; MIROC6, BCC_CSM, and MRI-ESM2-0 for Tmax, and GFDL, MPI_ESM_HR, and MRI-ESM2-0 for Tmin. The multi-model ensembles (an average of the top three GCMs) performed better in simulating rain and Tmin for both CMIP5 and CMIP6 compared with multi-model ensembles (an average of all the GCMs), which only performed slightly better in simulating Tmax. The trend analysis of future projection indicates an increase in rain, Tmax, and Tmin; however, with distinct changes under similar radiative forcing levels in both CMIP5 and CMIP6 models. The projections under RCP4.5 and RCP8.5 increase more than the SSP2-4.5 and SSP5-8.5 scenarios for most climate conditions but are more pronounced, especially for rain, under RCP8.5 than SSP5-8.5 in the far future (2080s). This study provides insightful findings on selecting appropriate GCMs to generate reliable climate projections for local climate conditions in the Chungcheong region of South Korea. Full article

(This article belongs to the Special Issue Statistical Approaches in Climatic Parameters Prediction)

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21 pages, 933 KiB

Open AccessArticle

Long Memory Cointegration in the Analysis of Maximum, Minimum and Range Temperatures in Africa: Implications for Climate Change

by OlaOluwa S. Yaya, Oluwaseun A. Adesina, Hammed A. Olayinka, Oluseyi E. Ogunsola and Luis A. Gil-Alana

Atmosphere 2023, 14(8), 1299; https://doi.org/10.3390/atmos14081299 - 16 Aug 2023

Cited by 2 | Viewed by 907

Abstract

This paper deals with the analysis of the temperatures in a group of 36 African countries. By looking at the maximum, minimum and the range (the difference between the maximum and the minimum) and using a long memory model based on fractional integration and cointegration, we first show that all series display a long memory pattern, with a significant positive time trend in 29 countries for the maximum temperatures and in 33 for the minimum ones. Looking at the range, the estimated value for the order of integration is smaller than the one based on maximum or minimum temperatures in 17 countries. Performing fractional cointegration tests between the maximum and minimum temperatures, our results indicate that the two series cointegrate in the classical sense (i.e., with a short memory equilibrium relationship) in a group of 11 countries, and there is another group of eight countries displaying cointegration in the fractional sense. The remaining 17 countries with no evidence of cointegration are therefore at a very high risk of climate change due to the absence of long-term co-movement in their maximum and minimum temperatures. Findings in this paper are of tremendous interpretations and relevance for the analysis and climate projections in Africa. Full article

(This article belongs to the Special Issue Statistical Approaches in Climatic Parameters Prediction)

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18 pages, 11933 KiB

Open AccessArticle

Adaptive Parameter Estimation of the Generalized Extreme Value Distribution Using Artificial Neural Network Approach

by Tossapol Phoophiwfa, Teerawong Laosuwan, Andrei Volodin, Nipada Papukdee, Sujitta Suraphee and Piyapatr Busababodhin

Atmosphere 2023, 14(8), 1197; https://doi.org/10.3390/atmos14081197 - 25 Jul 2023

Cited by 3 | Viewed by 1312

Abstract

Parameter estimation strategies have long been a focal point in research due to their significant implications for understanding data behavior, including the dynamics of big data. This study offers an advancement in these strategies by proposing an adaptive parameter estimation approach for the Generalized Extreme Value distribution (GEVD) using an artificial neural network (ANN). Through the proposed adaptive parameter estimation approach, based on ANNs, this study addresses the parameter estimation challenges associated with the GEVD. By harnessing the power of ANNs, the proposed methodology provides an innovative and effective solution for estimating the parameters of the GEVD, enhancing our understanding of extreme value analysis. To predict the flood risk areas in the Chi river watershed in Thailand, we first determine the variables that are significant in estimation of the three GEVD parameters

μ, σ

, and

ξ

by considering the respective correlation coefficient and then estimating these parameters. The data were compiled from satellite and meteorological data in the Chi watershed gathered from the Meteorological Department and 92 meteorological stations from 2010 to 2021, and consist of such variables as the Normalized Difference Vegetation Index (NDVI), climate, rainfall, runoff, and so on. The parameter estimation focuses on the GEVD. Taking into consideration that the processes could be stationary (parameters are constant over time, S) or non-stationary (parameters change over time, NS), maximum likelihood estimation and ANN approaches are applied, respectively. Both cases are modeled with the GEVD for the monthly maximum rainfall. The Nash-Sutcliffe coefficient (NSE), is used to compare the performance and accuracy of the models. The results illustrate that the non-stationary model was suitable for 82 stations, while the stationary model was suitable for only 10 stations. The NSE values in each model range from 0.6 to 0.9. This indicated that all 92 models were highly accurate. Furthermore, it is found that meteorological variables, geographical coordinates, and NDVI, that are correlated with the shape parameter in the ANN model, are more significant than others. Finally, two-dimensional maps of the return levels in the 2, 5, 10, 20, 50, and 100-year return periods are presented for further application. Overall, this study contributes to the advancement of parameter estimation strategies in the context of extreme value analysis and offers practical implications for water resource management and flood risk mitigation. Full article

(This article belongs to the Special Issue Statistical Approaches in Climatic Parameters Prediction)

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21 pages, 28395 KiB

Open AccessArticle

Machine-Learning-Based Downscaling of Hourly ERA5-Land Air Temperature over Mountainous Regions

by Badr-eddine Sebbar, Saïd Khabba, Olivier Merlin, Vincent Simonneaux, Chouaib El Hachimi, Mohamed Hakim Kharrou and Abdelghani Chehbouni

Atmosphere 2023, 14(4), 610; https://doi.org/10.3390/atmos14040610 - 23 Mar 2023

Cited by 4 | Viewed by 3214

Abstract

In mountainous regions, the scarcity of air temperature (Ta) measurements is a major limitation for hydrological and crop monitoring. An alternative to in situ measurements could be to downscale the reanalysis Ta data provided at high-temporal resolution. However, the relatively coarse spatial resolution of these products (i.e., 9 km for ERA5-Land) is unlikely to be directly representative of actual local Ta patterns. To address this issue, this study presents a new spatial downscaling strategy of hourly ERA5-Land Ta data with a three-step procedure. First, the 9 km resolution ERA5 Ta is corrected at its original resolution by using a reference Ta derived from the elevation of the 9 km resolution grid and an in situ estimate over the area of the hourly Environmental Lapse Rate (ELR). Such a correction of 9 km resolution ERA5 Ta is trained using several machine learning techniques, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Extreme Gradient Boosting (Xgboost), as well as ancillary ERA5 data (daily mean, standard deviation, hourly ELR, and grid elevation). Next, the trained correction algorithms are run to correct 9 km resolution ERA5 Ta, and the corrected ERA5 Ta data are used to derive an updated ELR over the area (without using in situ Ta measurements). Third, the updated hourly ELR is used to disaggregate 9 km resolution corrected ERA5 Ta data at the 30-meter resolution of SRTM’s Digital Elevation Model (DEM). The effectiveness of this method is assessed across the northern part of the High Atlas Mountains in central Morocco through (1) k-fold cross-validation against five years (2016 to 2020) of in situ hourly temperature readings and (2) comparison with classical downscaling methods based on a constant ELR. Our results indicate a significant enhancement in the spatial distribution of hourly local Ta. By comparing our model, which included Xgboost, SVR, and MLR, with the constant ELR-based downscaling approach, we were able to decrease the regional root mean square error from approximately 3

^{\circ}

C to 1.61

^{\circ}

C, 1.75

^{\circ}

C, and 1.8

^{\circ}

C, reduce the mean bias error from −0.5

^{\circ}

C to null, and increase the coefficient of determination from 0.88 to 0.97, 0.96, and 0.96 for Xgboost, SVR, and MLR, respectively. Full article

(This article belongs to the Special Issue Statistical Approaches in Climatic Parameters Prediction)

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20 pages, 583 KiB

Open AccessArticle

Emissions and CO₂ Concentration—An Evidence Based Approach

by Joachim Dengler and John Reid

Atmosphere 2023, 14(3), 566; https://doi.org/10.3390/atmos14030566 - 16 Mar 2023

Cited by 2 | Viewed by 4001

Abstract

The relation between CO₂ emissions and atmospheric CO₂ concentration has traditionally been treated with more or less complex models with several boxes. Our approach is motivated by the question of how much CO₂ must necessarily be absorbed by sinks. This is determined by accepted measurements and the global carbon budget. Observations lead to the model assumption that carbon sinks, similar to oceans or the biosphere, are linearly dependent on CO₂ concentration on a decadal scale. In particular, this implies the falsifiable hypothesis that oceanic and biological CO₂ buffers have not significantly changed in the past 70 years and are not saturated in the foreseeable future. A statistical model with two parameters is built from the global carbon budget and two testable assumptions. This model explains the relation between CO₂ emission and historical CO₂ concentration data very well. The model gives estimates of the natural emissions, the pre-industrial CO₂ equilibrium concentration levels, the half-life time of an emission pulse, and the future CO₂ concentration level from a given emission scenario. It is validated by an ex-post forecast of the last 20 years. The important result is that, with the stated polices emission scenario of the International Energy Agency (IEA), the future CO₂ concentrations will not rise above 475 ppm. The model is compared with the carbon module of the Bern model, mapping their complex impulse response functions (IRFs) to a single time variant absorption parameter. Full article

(This article belongs to the Special Issue Statistical Approaches in Climatic Parameters Prediction)

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