SearcharxivSearch

arXiv · 2512.22499

Emerging trend in the east-west Dipole Pattern in Indian Summer Monsoon Rainfall and the associated impact on Regional Dynamics

Abstract

Traditionally, during the monsoon season, more rainfall is received along the Western Ghats, the Northern Gangetic plains, the central belt, and northeast India. However, recently, there has been a shift in this canonical monsoon rainfall pattern on the monthly to seasonal scale. In this study, we quantify an east-west asymmetric trend in monthly to seasonal rainfall due to the increased rainfall over the northwestern part of the country. An Empirical Orthogonal Function (EOF) analysis has been performed to understand the spatial and temporal variation of the monsoon. EOF mode 3 shows such a distinct east-west dipole pattern, highlighting the existence of a modal feature representing the recent trend in the rainfall distribution. The physical nature of this mode is also established. The regression pattern of the rainfall anomalies to the Webster-Yang Index (Webster and Yang, 1992) exhibits a similar east-west pattern that further confirms the physical existence of this east-west rainfall modal dipole pattern. Since rainfall across the northwest is directly linked to the Arabian Sea and rainfall over the eastern region to the Bay of Bengal, the characteristics of these two regions are studied separately. Over the Arabian Sea, there is a significant negative trend in the Sea Level Pressure (SLP) anomalies and an increase in the specific humidity, causing greater moisture convergence. In contrast, over the Bay of Bengal, the SLP shows an increasing trend. The SST warming over the Arabian Sea is higher than that of the Bay of Bengal. Further, while investigating the zonal wind(u) at 850hPa, it shows an increasing trend along the northern branch that is more directed towards the northwestern part of the country. These factors together create dynamically favorable conditions for enhanced convection and thus receive more rainfall across the northwest compared to the northeast India.

Explore related subjects

Keep this discovery

BibTeXRIS

Akshara Satheesh, Rajib Chattopadhyay. 2025-12-27. Emerging trend in the east-west Dipole Pattern in Indian Summer Monsoon Rainfall and the associated impact on Regional Dynamics. https://arxiv.org/abs/2512.22499

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph