Prediction of winter precipitation over northwest India using ocean heat fluxes

Nageswararao, M. M.; Mohanty, U. C.; Osuri, Krishna K.; Ramakrishna, S. S. V. S.

doi:10.1007/s00382-015-2962-x

Cited by 17 publications

(15 citation statements)

References 45 publications

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“…Upper Myanmar has unseasonably a lack of meteorological data that permits long-term patterns to be examined and dependable prognostications made of climate changes in the future (N. Sen Roy & Kaur, 2000). Currently, the coupled or atmospheric general rotation models are being extensively used for generating seasonal vaticinations (Nageswararao et al, 2016). (Bjerknes, 1969;Horel, J. D., 1981).…”

Section: Introductionmentioning

confidence: 99%

Interannual Variability of Winter Rainfall in Upper Myanmar

Oo¹

2022

J. Stabil. Environ. Mgt

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Upper Myanmar region, roughly located between 21° 00' N and 28° 30' N latitude and 92° 10' E and 101° 11' E longitude, is the place where the winter cold season contributes ~2% of the annual total rainfall. The rainfall associated with Western disturbances is small in quantum but veritably important for the cold season crops, maintaining the glaciers over the Putao region, hydropower generation for the whole country and hazard of Jade mining of the Upper Myanmar area. This study aims to find interannual variability and related ocean-atmospheric pattern link with Upper Myanmar cold season rainfall by using great-resolution reanalysis data (ERA5) during 1990-2020. Correlation analysis to test the validation of ERA5 gridded data with the observed data from 25 stations across Myanmar, showed a strong correlation value in the same period that enough reliable for best analysis results. An anomalous anticyclonic (cyclonic) circulation persists over the southern part of the Bay of Bengal and South China sea during wet (dry) years. Also, the warming over the Indian Ocean and the cooling over the Tibetan plateau region correspond to south-north transport of moisture, ensuing in positive rainfall anomalies over the study region during winter. The wide patches of strong negative (positive) correlation are found over the Pacific Ocean, the Atlantic Ocean, Mediterranean Sea (MED), Arabian Sea (ARS), and Red Sea (RED) during wet (dry) years. The link implies that NPO, SPO, and MED have an impact on the winter rainfall inter-annual variability. In addition, the cooling (warming) over the Indochina and western Pacific regions influences the Hadley and Walker circulation bringing above (below) normal rainfall, respectively, over Upper Myanmar. The reply of indices (PO, MED, NINO3.4, IOD, and WDs) on winter rainfall, necessary to further investigation. The complete analysis of winter rainfall aids in the understanding of past extreme events as well as the forecasting and monitoring of drought and floods in Upper Myanmar.

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Section: Introductionmentioning

confidence: 99%

Interannual Variability of Winter Rainfall in Upper Myanmar

Oo¹

2022

J. Stabil. Environ. Mgt

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“…It is also noticed that during winter and pre-monsoon, the frequency relatively increases toward the coast with increase in the rainfall intensity category. During the summer monsoon, the frequency of MR events (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) in most parts of Odisha is more followed by VLR (18-32), LR (17-28), RHR (2-7), HR (1-3), VHR and EHR events. The frequency relatively increases from east to western parts of Odisha with increase in the intensity of rainfall category.…”

Section: Statistical Characteristics Of Seasonal Rainfall At Grid Poimentioning

confidence: 99%

“…The El Nino-Southern Oscillation (ENSO) phenomenon plays a significant role on variations of various seasonal variables of local climate in different parts of the globe, and in particular, its role has been crucial over the Northern Hemisphere and its influence varies from region to region as well as season to season. A plenty number of research studies [4,[25][26][27][28][29][30][31][32][33] enlightened the ENSO influence on various seasonal rainfall over the Indian sub-continent. The ENSO is negatively correlated with the Indian summer monsoon rainfall (ISMR) [30,34], while it is positively correlated with NWI winter precipitation, and northeast monsoon precipitation over India [4,[30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…A plenty number of research studies [4,[25][26][27][28][29][30][31][32][33] enlightened the ENSO influence on various seasonal rainfall over the Indian sub-continent. The ENSO is negatively correlated with the Indian summer monsoon rainfall (ISMR) [30,34], while it is positively correlated with NWI winter precipitation, and northeast monsoon precipitation over India [4,[30][31][32][33]. However, all the previous studies have been only intended to understand the variability and trends in the rainfall at the country scale of India and no such studies are available at district level using advanced high-resolution gridded rainfall analysis dataset of India Meteorological Department (IMD), which is more useful for planning and management in various sectors, viz.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of district-level rainfall characteristics over Odisha using high-resolution gridded dataset (1901–2013)

et al. 2019

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In the present global warming era, exploring the regional/district-level changes in rainfall patterns/events is highly essential for understanding the diverse characteristics of rainfall, which will be value added for the policy planning for rainfed agricultural-based states like Odisha, a region that is also highly prone to flood and droughts. Since most of the global and continental scale climate studies rarely contribute for such planning of agro-economic states of India like Odisha, there is a colossal necessity for the analysis of rainfall characteristics at regional/district level. In the present study, the rainfall characteristics at district level over Odisha are extensively evaluated for winter (JF), pre-monsoon (MAM), monsoon (JJAS) and post-monsoon (OND) using India Meteorological Department high-resolution gridded rainfall analysis dataset for the period 1901-2013. The El Nino Southern Oscillation (ENSO) influence on the district-level seasonal rainfall during four distinctive seasons has also been studied. The results suggest that the frequency of various categorical rainfall events and associated seasonal rainfall patterns over Odisha is highly heterogeneous and varies from season to season in magnitude and similar statements can be highlighted for inter-annual variability and coefficient of variation. Although the seasonal rainfall time series is significantly persistent in most of the districts during all the seasons, it is manifested to the normal distribution in majority of districts only for JJAS. Statistical analysis infers that most of the districts possess reasonable abrupt changes in seasonal rainfall during mid-and late twentieth century. A significant increasing (decreasing) trend in the frequency of high-intensity (low-intensity and wet days) rainfall events over most parts of Odisha is observed for all the seasons. A notable decreasing trend in seasonal rainfall is perceived over southern districts during MAM (7-21 mm) and OND (23-49 mm), western districts during MAM (2-41 mm) and JJAS (6-309 mm) and most of the districts during JF (6-46 mm), while increasing trend is prominent over coastal and northern Odisha during MAM (7-46 mm), northern coastal Odisha during JJAS (25-175 mm), northern and northern coastal Odisha during OND (2-64 mm). It has been noticed that the ENSO influence on seasonal rainfall is insignificant during summer monsoon, while it is notable during the other seasons. This study is very useful in determining the effects and assists the risk management for various sectors in acclimating innovative technologies for a sustainable future in the present global warming era.

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“…Devi et al (2020) developed a statistical regression model for winter precipitation forecast over WH using North Atlantic Oscillation (NAO) and Outgoing Longwave Radiation (OLR) as predictors. Nageswararao et al (2016) constructed a MLR model using total downward ocean heat fluxes to predict winter precipitation over WH. None of these studies used CORDEX-SA RCMs to investigate the future changes in frequency and intensity of WDs.…”

Section: Introductionmentioning

confidence: 99%

Future frequency and intensity of Western Disturbance(s)

Midhuna

Dimri²,

Suneeth

et al. 2022

Intl Journal of Climatology

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Western Disturbances (WDs) are the primary contributor of winter precipitation over the Western Himalayas (WH). The precipitation falling as snow is essential for Himalayan glaciers accumulation, contributing to Himalayan rivers supporting downstream population of the Indian subcontinent. Climate change has affected the Himalayas, which is noticeable through lesser snowfall and higher melt leading to critical water resource stresses and managements. Available climate models are one of the best available tools to assess precipitation changes over and around the Himalayas. Inherent uncertainties and errors in these numerical models need a certain urge for improvement. Integration of statistical models and methods on top of dynamical models provide better insight and utility in associated processes. Thus, a statistical relationship between various predictors and precipitation over the region is investigated. Available observations from APHRODITE and ERA‐I and model fields from EC‐EARTH‐RCA4 (a CORDEX‐SA member) is used. Multiple linear regression (MLR) model is used to establish statistical estimates using meteorological variables as predictors and precipitation. The correlation between the predictors and precipitation is determined, and a contour depicting maximum correlation is used to estimate regression coefficients. The MLR model is trained during present (1987–2005) and tested during 2006–2007. Higher positive correlations between observations and MLR model are found. This developed MLR model is then implemented for assessing future WDs under RCP8.5 scenario. The intensity and frequency of WDs are calculated during the present and future. Model‐based WDs' intensities slightly deviate from the corresponding observations in present. But the model‐based WDs' frequencies match with the corresponding observations in present. The results indicate that WDs show a decreasing trend in frequency and intensity in the present and near‐future. But in far‐future intensity of WDs shows an increasing trend.

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Prediction of winter precipitation over northwest India using ocean heat fluxes

Cited by 17 publications

References 45 publications

Interannual Variability of Winter Rainfall in Upper Myanmar

Interannual Variability of Winter Rainfall in Upper Myanmar

Evaluation of district-level rainfall characteristics over Odisha using high-resolution gridded dataset (1901–2013)

Future frequency and intensity of Western Disturbance(s)

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