Partial wavelet coherence analysis for understanding the standalone relationship between Indian Precipitation and Teleconnection patterns
Maheswaran Rathinasamy, Ankit Agarwal, Vilakshna Parmar, Rakesh Khosa, and Arvind Bairwa

TL;DR
This study uses partial wavelet coherence analysis to isolate and understand the independent effects of specific climate indices on Indian precipitation, revealing that Ni extasciitilde{n}o 3.4 and IOD have significant standalone impacts.
Contribution
It introduces the application of partial wavelet analysis to distinguish the independent influence of climate indices on precipitation, accounting for interdependencies among indices.
Findings
Precipitation is significantly affected by Ni extasciitilde{n}o 3.4 and IOD.
SOI and PDO do not have a significant standalone effect on precipitation.
Partial wavelet analysis effectively isolates the independent relationships between climate factors and precipitation.
Abstract
Hydro-meteorological variables, like precipitation, streamflow are significantly influenced by various climatic factors and large-scale atmospheric circulation patterns. Efficient water resources management requires an understanding of the effects of climate indices on the accurate predictability of precipitation. This study aims at understanding the standalone teleconnection between precipitation across India and the four climate indices, namely, Ni\~no 3.4, PDO, SOI, and IOD using partial wavelet analysis. The analysis considers the cross correlation between the climate indices while estimating the relationship with precipitation. Previous studies have overlooked the interdependence between these climate indices while analysing their effect on precipitation. The results of the study reveal that precipitation is only affected by Ni\~no 3.4 and IOD and a non-stationary relationship…
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Taxonomy
TopicsClimate variability and models · Meteorological Phenomena and Simulations · Energy Load and Power Forecasting
