Northeast monsoon rainfall variability over south peninsular India and its teleconnections

Sreekala, P. P.; Rao, S. Vijaya Bhaskara; Rajeevan, M.

doi:10.1007/s00704-011-0513-x

Cited by 60 publications

(40 citation statements)

References 9 publications

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“…In contrast, the negative phase reveals winds diverging and transporting moisture away from the south Indian region. The analysis by Sreekala et al (2011) shows that the probability of an El Niño year being a deficient NEMR year is small (0.22) compared to the year being an excess (0.41) monsoon year. With the additional information on IOD, more confidence on excess or deficient monsoons can be obtained.…”

Section: Inter-annual Variability Of the Nemr And Its Secular Variationmentioning

confidence: 99%

“…The increasing trend in the NEMR during the recent years could be related to the increasing trend in the positive phase of the Indian Ocean Dipole Mode (Kripalani and Kumar, 2004). The inter-annual variability of the NEMR is linked to the El Niño/Southern Oscillation (ENSO), Indian Ocean Dipole and the EQUINOO (De and Mukhopadhyay, 1999;Bhanu Kumar et al, 2004;Kripalani and Kumar, 2004;Zubair and Ropelewski, 2006;Kumar et al, 2007;Sreekala et al, 2011). The NEMR and IOD are directly related suggesting that the positive (negative) phase enhances (suppresses) the northeast monsoon activity.…”

Section: Inter-annual Variability Of the Nemr And Its Secular Variationmentioning

confidence: 99%

“…With the additional information on IOD, more confidence on excess or deficient monsoons can be obtained. The study by Sreekala et al (2011) reveals that the positive phases of ENSO, IOD and EQUINOO are thus …”

Section: Inter-annual Variability Of the Nemr And Its Secular Variationmentioning

confidence: 99%

“…They derived a zonal temperature gradient index between the eastern and western equatorial Pacific, which shows a stable significant inverse relationship with the the NEMR. Recently Sreekala et al (2011) examined the inter-annual variability of the NEMR and its teleconnections. They found that the positive phase of ENSO, IOD and the Equatorial Indian Ocean Oscillation (EQUINOO) (Gadgil et al, 2004(Gadgil et al, , 2007 favour the NEMR to be normal or above normal over south peninsular India.…”

Section: Introductionmentioning

confidence: 99%

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Northeast monsoon over India: variability and prediction

Rajeevan

Unnikrishnan

Bhate

et al. 2012

Meteorological Applications

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173

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South peninsular India experiences a large portion of the annual rainfall during the northeast monsoon season (October to December). In this study, the facets of diurnal, intra-seasonal and inter-annual variability of the northeast monsoon rainfall (the NEMR) over India have been examined. The analysis of satellite derived hourly rainfall reveals that there are distinct features of diurnal variation over the land and oceans during the season. Over the land, rainfall peaks during the late afternoon/evening, while over the oceans an early morning peak is observed. The harmonic analysis of hourly data reveals that the amplitude and variance are the largest over south peninsular India. The NEMR also exhibits significant intra-seasonal variability on a 20-40 day time scale. Analysis also shows significant northward propagation of the maximum cloud zone from south of equator to the south peninsula during the season. The NEMR exhibits large inter-annual variability with the co-efficient of variation (CV) of 25%. The positive phases of ENSO and the Indian Ocean Dipole (IOD) are conducive for normal to above normal rainfall activity during the northeast monsoon. There are multi-decadal variations in the statistical relationship between ENSO and the NEMR. During the period 2001-2010 the statistical relationship between ENSO and the NEMR has significantly weakened. The analysis of seasonal rainfall hindcasts for the period 1960-2005 produced by the state-of-the-art coupled climate models, ENSEMBLES, reveals that the coupled models have very poor skill in predicting the inter-annual variability of the NEMR. This is mainly due to the inability of the ENSEMBLES models to simulate the positive relationship between ENSO and the NEMR correctly.

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Section: Inter-annual Variability Of the Nemr And Its Secular Variationmentioning

confidence: 99%

Section: Inter-annual Variability Of the Nemr And Its Secular Variationmentioning

confidence: 99%

Section: Inter-annual Variability Of the Nemr And Its Secular Variationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Northeast monsoon over India: variability and prediction

Rajeevan

Unnikrishnan

Bhate

et al. 2012

Meteorological Applications

Self Cite

173

161

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show abstract

“…Recent results suggest that the path of propagation, i.e. geomagnetic field lines connecting the conjugate points should lie in the ionosphere, and ducted or pro-longitudinal mode of propagation is more likely to occur [18][19][20] . Studies are pursued and the results obtained will enhance the existing knowledge on the intricacies involved with low latitude whistlers.…”

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confidence: 99%

A Synoptic-Scale Perspective of Heavy Rainfall over Chennai in November 2015

Chakraborty

2016

Current Science

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Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques

2018

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The socio‐economic growth of India is adversely affected by the abnormal meteorological phenomena of floods and droughts. Thus, rainfall prediction is highly desirable for livelihood and sustainability. In this study, northeast monsoon rainfall (NEMR) is predicted over the Indian peninsular region for the months of October, November, and December using a global sea surface temperature (SST) anomaly as a predictor by linear regression (LR), artificial neural network (ANN), and extreme learning machine (ELM) techniques. The predictions are made by LR, ANN, and ELM models for the period 1990–2016 using the training input (1871–1989) of different time series samples of global SST anomaly data collected from Hadley Centre SST data set (HadSST3). Principal component analysis was used for dimensionality reduction of the data sets, and its application substantially improved the predictive ability of the machine learning techniques. The performance of the PC‐ELM technique with the ensemble method (ESM 8–9) training window is found to be more accurate than the LR and ANN techniques and provides minimal error scores as per the statistical analysis. This study concludes that the global SST anomaly has the potential to be used as a predictor for northeast monsoon rainfall and useful for long‐range climatic projections.

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Northeast monsoon rainfall variability over south peninsular India and its teleconnections

Cited by 60 publications

References 9 publications

Northeast monsoon over India: variability and prediction

Northeast monsoon over India: variability and prediction

A Synoptic-Scale Perspective of Heavy Rainfall over Chennai in November 2015

Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques

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