Large-scale dynamics of western disturbances caused extreme precipitation on 24–27 January 2017 over Jammu and Kashmir, India

Para, J. A.; Kesarkar, Amit P.; Bhate, Jyoti; Singh, Vikas; Panchal, Abhishek; Munsi, Arpita; Shivamurthy, Y.; Lotus, Sonam; Ahmad, Noor Azlinda; Sheikh, J. A.

doi:10.1007/s40808-019-00661-4

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…This increase in baroclinic response of the atmosphere can contribute to intensification of WDs and result in enhanced precipitation, provided sufficient atmospheric moisture is available. The growing baroclinicity can possibly influence the vertical component of relative vorticity and cause intense convection in the moist middle and upper troposphere, ultimately contributing to precipitation extremes (e.g., Para et al ., 2020).…”

Section: Resultsmentioning

confidence: 99%

Underlying physical mechanisms of winter precipitation extremes over India's high mountain region

Nischal,

Attada,

Hunt

et al. 2024

Quart J Royal Meteoro Soc

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Extreme precipitation events (EPEs) are among the most pervasive weather hazards in the western Himalayan region (WHR), posing widespread damage to life, infrastructure, and agriculture. This study investigates the synoptic and large‐scale characteristics linked to winter precipitation extremes over the WHR. EPEs are identified as events surpassing the 95th percentile threshold. A composite analysis is employed using two reanalyses—the fifth‐generation European Centre for Medium‐Range Weather Forecasts Reanalysis (ERA5) and the Indian Monsoon Data Assimilation and Analysis (IMDAA)—to elucidate the synoptic conditions conducive to EPEs. Our findings suggest that EPEs in the WHR are linked to an intensified subtropical westerly jet, characteristically shifted to south than normal. Enhanced kinetic energy in the upper troposphere, attributed to increased baroclinic instability, reinforces moisture convergence and strengthens synoptic‐scale circulation, triggering deep convection and supporting EPEs. Notably, the interplay of pronounced Rossby waves sinking over the WHR and regional orography significantly modulates the intensity of western disturbances (WDs). Employing clustering analysis, we observed that the strongest EPEs are linked to anomalous vorticity in the upper to middle troposphere, together with deep convection via strengthened WDs, suggesting the potential role of large‐scale influences. Using Lagrangian method, we identify that the Arabian Sea is the primary moisture source for EPEs in the WHR. We further delved into the role of large‐scale connections and EPEs through quasi‐resonant amplification (QRA) analysis. The findings unveil distinct QRA fingerprints in meridional temperature gradients along with notably magnified, quasi‐stationary midlatitude planetary waves characterized by zonal wave numbers 6/7/8 (baroclinic waves) contributing to EPEs. Overall, our results highlight the underlying physical mechanisms for winter precipitation extremes, emphasizing QRA's role in amplifying planetary waves and promoting EPEs, underscoring the WHR's vulnerability to evolving climatic conditions.

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Section: Resultsmentioning

confidence: 99%

Underlying physical mechanisms of winter precipitation extremes over India's high mountain region

Nischal,

Attada,

Hunt

et al. 2024

Quart J Royal Meteoro Soc

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“…During the past few years, the Kashmir valley has been impacted by intense WDs and has witnessed heavy snowfall events both on the plains as well as in the mountains. There was an extremely heavy snowfall event in 2017, which resulted in avalanches, killing many people (Rafiq and Mishra, 2018; Para et al ., 2020). In the winter season of 2019/2020, heavy snow again fell across much of Kashmir, which broke all the previous records.…”

Section: Extreme Snowfall Events Of 2017–2020mentioning

confidence: 99%

A study of extreme snowfall during 2019 and 2020 across the Kashmir Himalaya

Rafiq

Para

Kesarkar

et al. 2021

Weather

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here are in glorious technicolour and easily understandable.Although the mathematics and physics are sound, some readers may find them difficult to follow. Attempts to make things easier for the layperson result in some convoluted explanations that appear more complex than they really are. Terms such as 'an additional' or 'further rise of' are often used before a temperature increase, but it is unclear whether this refers to a gross increase or an 'additional' increase on top of the baseline temperature.I consider this a must-read for anyone wanting to understand better how Earth's climate operates and the effects we humans are having on it. What sets this book apart from others on the same subject is the way Krauss presents, weighs up and tests the information. I suspect this book might be read only by those who already accept climate change as fact, not by sceptics and policymakers. There are, however, plans to give a copy to each of the 600 members of Congress, and the book is certainly timely as November's UN Climate Change Conference 2021 (COP26) approaches.

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“…The unique topography of North India, particularly the Himalayas, plays a crucial role in shaping the Indian monsoon [17][18][19][20][21] . The interplay between the mountainous terrain and atmospheric disturbances, coupled with cold air intrusion from northern latitudes, creates conditions conducive to extreme precipitation events, as seen in Jammu and Kashmir during January 2017 22 . Western disturbances, embedded in the eastward-moving upper tropospheric Rossby wave train, contribute signi cantly to heavy precipitation in the Western Himalayas, especially during winter [23][24][25] .…”

Section: Introductionmentioning

confidence: 99%

Efficacy of Machine Learning in Simulating Precipitation and Its Extremes Over the Capital Cities in North Indian States

Tandon,

Awasthi,

Pattnayak

2024

Preprint

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Climate change-induced precipitation extremes have become a pressing global concern. This study investigate the predictability of precipitation patterns and its extremes using MERRA2 datasets across North Indian states for the period 1984 to 2022 utilizing machine learning (ML) models. A strong positive correlations of precipitation 0.4 was found with dew point temperature and relative humidity significant at 0.05. In simulating precipitation, Random Forest Classifier (RFC) achieved the highest accuracy (~ 83%) for Rajasthan and Uttar Pradesh, while Support Vector Classifier (SVC) performed best (79–83% accuracy) for other states. However, the ML models exhibited about 5% lower skill in higher elevated stations as compared to the lower elevated stations, its due to the different atmospheric mechanisms control differently over the lower and higher topography. For extreme precipitation events (10th and 95th percentiles of intensity), RFC consistently outperformed SVC across all states. It demonstrated superior ability to distinguish extreme from non-extreme events (Area under curve ~ 0.90) and better model calibration (Brier Scores ~ 0.01). The developed ML models successfully simulated precipitation and extreme patterns, with RFC excelling at predicting extreme precipitation events. These findings can contribute to disaster preparedness and water resource management efforts in the region with varied topography and complex terrain.

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Large-scale dynamics of western disturbances caused extreme precipitation on 24–27 January 2017 over Jammu and Kashmir, India

Cited by 8 publications

References 17 publications

Underlying physical mechanisms of winter precipitation extremes over India's high mountain region

Underlying physical mechanisms of winter precipitation extremes over India's high mountain region

A study of extreme snowfall during 2019 and 2020 across the Kashmir Himalaya

Efficacy of Machine Learning in Simulating Precipitation and Its Extremes Over the Capital Cities in North Indian States

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