Journal of Atmospheric Science Research

Role of Resolution on Atmospheric Dynamics during Fani Cyclone in the Indian Subcontinent

2026-06-26T09:30:05+08:00

A persistent discrepancy between mathematically modeled atmospheric circulation and observed precipitation has become increasingly evident over recent decades. High-resolution numerical weather prediction (NWP) models offer a pathway to reduce this gap, yet their practical deployment is constrained by limited data availability, high computational cost, and reliance on traditional model verification measures. This study proposes an efficient NWP framework implemented on small-scale parallel computing systems using an adaptive grid-resolution strategy within the well-known Nonhydrostatic Icosahedral Atmospheric Model (NICAM), enabling global weather analysis. By systematically varying horizontal grid spacing, the approach identifies the minimum resolution required to reliably capture mesoscale variations in wind, temperature, and precipitation while maintaining computational efficiency for targeted forecasting objectives. Global cloud-resolving simulations using NICAM are conducted under low-pressure conditions characteristic of cyclone formation over the Indian subcontinent, with specific focus on Cyclone Fani. Results show that even at reduced resolution, the model reproduces key atmospheric dynamics, including temperature, pressure, wind velocity, and precipitation at both near-surface and upper-atmospheric levels. These findings indicate that reasonable coarse-resolution NICAM simulations executed on small-scale parallel computing systems can effectively capture the essential atmospheric circulation associated with Cyclone Fani, providing a computationally efficient alternative for high-resolution numerical weather prediction.

The Hydrospheric Solar Correlation Dipole: Mapping, Interpretations, and Forecasting

2026-07-21T11:33:06+08:00

A meridional dipole, centered near 180° E, characterizes solar correlations to oceanic and atmospheric parameters—salinity, ocean vertical velocities, temperatures, atmospheric moisture, winds, cloud cover, geopotential heights, carbon dioxide (CO₂) fluxes, and ozone (O₃)—over several years of lag, with opposing correlation signs on either side of the dipole axis. Ocean correlations are stronger and more persistent than atmospheric ones; most fields intensify equatorward through the third lag-year before decaying over subsequent years. We propose, as a hypothesis for future testing, that this pattern traces global energy transport along an expanded Hadley Cell framework, with opposing correlation zones arising from latent heat exchange at the ocean surface and tropopause. Statistical significance is assessed using methods that account for serial autocorrelation (AC) and correct for multiple comparisons across variables and lags. Where correlations are highest, we propose solar activity as a useful baseline hypothesis for evaluating regional climate trends—an improvement, in these specific regions, over the null hypothesis of random states; we do not extend this claim more broadly. We further examine predictive value: reviewing an earlier cross-regression-moving-average (CRMA) case study and presenting a new forecast of five-year trailing rainfall at an Upper Colorado Basin (US) site, three years ahead. Hindcast results show the solar-regressed exercise is more skillful than the standard auto-regression-moving-average (ARMA) techniques, suggesting value in anticipating climate extremes (drought vs. fluvial) in high-correlation regions.

Spatiotemporal Assessment of Atmospheric Trace Gases over Singrauli Coal-Industrial Cluster Using Sentinel-5P TROPOMI and Google Earth Engine (2019–2024)

2026-05-18T14:22:19+08:00

This study presents a multi-year satellite-based assessment of atmospheric pollution over Singrauli, Madhya Pradesh, India's largest coal-industrial cluster using Sentinel-5P (TROPOMI) retrievals for 2019–2024. Concentrations of CO, NO₂, SO₂, O₃, HCHO, Cloud Fraction, UV Aerosol Absorbing Index (UV-AAI), and surface solar irradiance (NASA POWER) were analysed using Google Earth Engine (GEE), with spatial visualization performed in ArcGIS Pro. The study characterizes seasonal behaviour, identifies persistent emission hotspots, quantifies long-term pollutant trends, and explores pollutant interdependencies in a critically polluted industrial region. Results indicate strong winter and post-monsoon enhancement of CO, NO₂, and SO₂, attributed to suppressed boundary-layer mixing and intensified coal combustion, while O₃ and HCHO peaked during pre-monsoon under elevated photochemical activity driven by high solar irradiance. Industrial hotspots were consistently identified over Anpara, Vindhyachal, Renusagar, and the Mahan thermal power plant zones. Mann–Kendall trend analysis indicated gradual, non-significant directional tendencies (p > 0.05) toward increasing CO, SO₂, and O₃, alongside slight NO₂ reductions. Spearman correlation revealed strong combustion-linked coupling (CO–NO₂ ρ = 0.78; NO₂–SO₂ ρ = 0.72), photochemical associations (Solar–O₃ ρ = 0.75; Solar–HCHO ρ = 0.61), and cloud-mediated suppression of UV-AAI and O₃. The Sentinel-5P and GEE framework demonstrates a scalable, cost-effective approach applicable to data-scarce industrial regions, supporting National Clean Air Programme (NCAP)-aligned air quality management in coal-dependent clusters across India.