Journal of Atmospheric Science Research

Projected Changes in the Characteristics of Dry and Wet Episodes over Côte d’Ivoire

Stella Todzo — 2026-01-13

This study analyses the persistence and frequency of multi-day dry and wet rainfall episodes over Côte d’Ivoire, which are quantified using CPC observations (1979–2022) and a 14-member Coordinated Regional Climate Downscaling Experiment (CORDEX)-Africa ensemble under RCP4.5 and RCP8.5. Rather than focusing solely on total rainfall, we quantify rainfall by duration and frequency of dry spells and wet episodes to capture duration-driven drought and flood risks. Historically, dry episodes last 8–15 days, with maximum annual dry spells of 25–40 days, while wet episodes persist for 3–6 days and occur 48 times per year. Projections show substantial changes in rainfall sequencing. Under RCP4.5, mean dry-episode durations increase by +1 to +2 days by mid-century and +2 to +4 days by late-century; under RCP8.5, increases reach +2 to +3 days and +3 to +6 days, respectively, with maximum dry-spell extensions of +15 to +25 days in northern regions. Wet episodes become 1–4 events per year, less frequent but lengthen by +1 to +4 days, especially along the coast under RCP8.5. These shifts suggest fewer but more persistent rainfall events, which heighten drought-related crop-water stress and multi-day flood accumulation risks. The results provide actionable insights for agriculture, hydrology and climate-risk planning by highlighting rainfall sequencing and persistence metrics not captured by traditional rainfall totals. This nuanced perspective enhances understanding of drought, flood accumulation, and agricultural risk under changing climate conditions.

Comparative Analysis of Traditional and Machine Learning Models for Rainfall Forecasting in Barishal District of Bangladesh

Shawrab Chandra — 2026-01-05

Rainfall prediction is crucial for agricultural planning and water resource management, as Bangladesh’s agriculture heavily depends on rainfed irrigation. Existing forecasting models are complex and costly, both budgetarily and computationally. As a result, our study evaluates the comparative performance of forecasting models, comprising two traditional time series models (Exponential Smoothing (ES) and Seasonal Autoregressive Integrated Moving Average (SARIMA)), and one machine learning model (Long Short-Term Memory (LSTM)). The monthly rainfall data for Barishal, Bangladesh, spanning the period from 1970 to 2022, were obtained from the Bangladesh Meteorological Department. The models' performance was assessed using root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Nash-Sutcliffe efficiency coefficient (NSEC), and Kling-Gupta Efficiency (KGE). The ES and SARIMA models perform closely. With RMSE, MAE, R, NSEC, and KGE values of 109.35, 73.60, 0.79, 0.62, and 0.74, respectively, the ES model performs better than the SARIMA model. On the other hand, the machine learning model LSTM struggled with the test data, resulting in a higher RMSE (150.34), MAE (100.95), and lower R (0.60), NSEC (0.27), and KGE (0.60) values. This indicates that for the small dataset, the LSTM machine learning model is less effective. Therefore, our suggestion is to use a statistical model, especially the ES model, to forecast monthly rainfall in the Barishal division, as it is effective and computationally efficient. These findings are beneficial for policy development, the pesticide industry, tourism, event management, water conservation, and predicting floods and droughts.

Seasonal Patterns and Forecasting of CO and Ozone Using Singular Spectrum Analysis in a Tropical Urban Environment

Amaury de Souza — 2026-01-02

Singular Spectrum Analysis (SSA) was applied to daily time series of carbon monoxide (CO) and ozone (O₃) observed between 2000 and 2018 in Campo Grande, MS, Brazil, to identify seasonal patterns, long-term variability, and evaluating the predictive capacity of the technique. The methodology involved the decomposition of the series into structural components and subsequent prediction using the Linear Recurrence Formula (LRF). The analysis revealed strong and persistent annual seasonality for both pollutants, particularly for CO, whose maximum concentrations occur between August and October, coinciding with the dry season and intensified biomass-burning activity. SSA proved effective in extracting low-frequency components, including trend and seasonal cycles, providing a clear representation of the dominant temporal structure of both pollutants. Forecasting results indicated that SSA-LRF successfully reproduced the main seasonal behavior of O₃, while daily prediction skill remained limited, as reflected by negative R² values during the validation period. For CO, the highly irregular and episodic nature of fire-related peaks resulted in larger forecast errors and reduced predictive skill. These results highlight that univariate SSA is more suitable for reconstructing and predicting low-frequency pollutant dynamics than short-term daily variability. The findings demonstrate that SSA is a robust exploratory and decomposition tool for air-quality time series in tropical environments, particularly for identifying seasonal and structural patterns. For operational forecasting of pollutants with strong volatility, such as CO, hybrid approaches combining SSA with statistical or machine-learning models are recommended to improve predictive performance.

Spectral Transmission Properties (0.35–25 µm) of Some Anthropogenic and Natural Atmospheric Aerosol Constituents

Edward Cloutis — 2026-01-07

Atmospheric aerosols can have wide-ranging effects on the Earth’s atmosphere, hydrosphere, and biosphere. To help improve our understanding of these effects, we have conducted spectral reflectance and transmission properties of a suite of anthropogenic and natural aerosols across a wide wavelength range (0.35–25 microns). Our sample suite included rock and mineral dusts, sulfates and nitrates, ocean water precipitates, and a variety of carbonaceous and organic materials. These data are useful to identify species that may be present in the atmosphere from spectroscopic measurements. Different aerosol/dust species can have unique spectroscopic properties in terms of diagnostic absorption bands and spectral shapes, and diagnostic absorption bands, can be present in multiple wavelength regions, including within atmospheric transmission windows. The composition of aerosol species can be determined with varying degrees of specificity from their spectroscopic properties. Increases in aerosol abundance, which may lead to saturation of strong absorption bands can be partially compensated for by accompanying strengthening of “secondary” absorption bands that are normally weak. Grain size variations mostly affect absorption/transmission intensities, but do not lead to the appearance of any new absorption bands. Empirical laboratory studies, where grain size and concentration are varied, can provide information that can be used to determine atmospheric aerosol composition and to help constrain measurements and models of atmospheric optical depth.

Convergence at the Horizon towards Predictive Resilience in a Non-Stationary World

Qiang Zhang — 2026-01-28

Climate change and technological advancement are driving a paradigm shift in the atmospheric and hydrometeorological sciences. This transformation is reflected in the complex interactions between ocean and atmospheric dynamics, the role of climate phenomena such as El Niño and La Niña, and the consequent effects on global water resources. This editorial argues that the critical task for the coming decade is to advance from hazard-focused forecasting toward the robust prediction of systemic impacts and societal resilience. We highlight three convergent frontiers of innovation: the integration of artificial intelligence and machine learning across the observational-modeling-prediction chain; the explicit coupling of human and water-energy-food systems within Earth system models; and the development of a “digital twin” framework for the Earth system. Success will require transcending disciplinary boundaries, promoting open science and data democratization, and fostering a new “convergence science” that interweaves physical dynamics, data science, socio-economics, and governance. The ultimate aim is to deliver actionable intelligence for anticipatory adaptation and enhanced climate resilience.

Atmospheric Non-Methane Hydrocarbons over Indian Region: From Initial Measurements to Latest Results

Soniya Yadav — 2026-01-20

Increased non-methane hydrocarbons (NMHCs) emissions brought on by urbanisation, industrialisation, and a warming planet have significantly modulated regional atmospheric photochemistry, ground-level ozone formation, oxidant levels, and secondary organic aerosol production. India, being a country with fast-paced changes with excessive and variable emissions like in the Indo-Gangetic Plains, a comprehensive understanding of the temporal and spatial evolution of NMHCs over this region is crucial to decipher contributing sources and quantify their impacts. In this context, we have evaluated peer-reviewed observational studies employing established analytical techniques on the detection of NMHCs over the Indian subcontinent, classified into major geographical realms (West, North, IGP, East, South, and Central). The study accesses measurement techniques, concentration levels, spatio-temporal variability, NMHC ratios and inter-relationships among species over multiple Indian locations representing diverse topographical, meteorological, and urbanization regimes. The reviewed research consistently shows that major urban regions like Delhi, Mumbai, Hyderabad, and Kanpur experience significantly higher NMHC levels in comparison to background and rural locations like Mount Abu, Nainital and Ajmer. Background regions also exhibit an increase under burgeoning emissions. Increasing contributions to the atmospheric NMHCs from the solvent usage sector has been observed in urbanized locations. The study emphasises how improvements in NMHC measurement methods—from conventional canister samplers to contemporary real-time devices like PTR-ToF-MS have improved evaluations of air quality. This review, based on over 15 study locations over India, underscores the need for expanded and sustained NMHC monitoring networks, adoption of cleaner fuels, and stricter emission control strategies to effectively mitigate NMHC-driven air quality degradation in India.