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Real time extended range prediction of heat waves over India.

ABSTRACT: Heat waves over India occur during the months of March-June. This study aims at the real-time monitoring and prediction of heat waves using a multi-model dynamical ensemble prediction system developed at Indian Institute of Tropical Meteorology, India. For this, a criterion has been proposed based on the observed daily gridded maximum temperature (Tmax) datasets, which can be used for real-time prediction as well. A heat wave day is identified when either (1) Tmax (a)? its climatological 95th percentile (calculated from daily values during March-June and for 1981-2010), (b) >36?°C, and (c) its departure from normal is >3.5?°C, Or, (2) when the Tmax >44?°C. Three heat wave prone regions, namely, northwest, southeast and northwest-southeast regions are recognized and heat wave spells of minimum consecutive six days are identified objectively for each region during 1981-2018. It is noticed that the prediction system has reasonable skill in predicting the heat waves over heat wave prone regions of India. Forecast verification of heat wave spells during 2003-2018 reveals that the prediction system has great potential in providing overall indication about the onset, duration and demise of the forthcoming heat wave spell with sufficient lead time albeit with some spatio-temporal error.

SUBMITTER: Mandal R

PROVIDER: S-EPMC6588722 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Real time extended range prediction of heat waves over India.

Mandal Raju R Joseph Susmitha S Sahai A K AK Phani R R Dey A A Chattopadhyay R R Pattanaik D R DR

Scientific reports 20190621 1

Heat waves over India occur during the months of March-June. This study aims at the real-time monitoring and prediction of heat waves using a multi-model dynamical ensemble prediction system developed at Indian Institute of Tropical Meteorology, India. For this, a criterion has been proposed based on the observed daily gridded maximum temperature (Tmax) datasets, which can be used for real-time prediction as well. A heat wave day is identified when either (1) Tmax (a)≥ its climatological 95<sup> ...[more]

PMID: 31227766

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Project description:BackgroundThe coronavirus disease (COVID) pandemic caused disruption globally and was particularly distressing in low- and middle-income countries such as India. This study aimed to provide population representative estimates of COVID-related outcomes in India over time and characterize how COVID-related changes and impacts differ by key socioeconomic groups across the life course.MethodsThe sample was leveraged from an existing nationally representative study on cognition and dementia in India: Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD). The wave-1 of LASI-DAD enrolled 4096 older adults aged 60 years and older in 3316 households from 18 states and union territories of India. Out of the 3316 LASI-DAD households, 2704 with valid phone numbers were contacted and invited to participate in the Real-Time Insights COVID-19 in India (RTI COVID-India) study. RTI COVID-India was a bi-monthly phone survey that provided insight into the individual's knowledge, attitudes, and behaviour towards COVID-19 and changes in the household's economic and health conditions throughout the pandemic. The survey was started in May 2020 and 9 rounds of data have been collected.Findings till dateOut of the 2704 LASI-DAD households with valid phone numbers, 1766 households participated in the RTI COVID-India survey at least once. Participants were in the age range of 18-102 years, 49% were female, 66% resided in rural area. Across all rounds, there was a higher report of infection among respondents aged 60-69 years. There was a greater prevalence of COVID-19 diagnosis reported in urban (23.0%) compared to rural areas (9.8%). Respondents with higher education had a greater prevalence of COVID-19 diagnosis compared to those with lower or no formal education. Highest prevalence of COVID-19 diagnosis was reported from high economic status compared to middle and low economic status households. Comparing education gradients in experiencing COVID-19 symptoms and being diagnosed, we observe an opposite pattern: respondents with no formal schooling reported the highest level of experiencing COVID-19 symptoms, whereas the greatest proportion of the respondents with secondary school or higher education reported being diagnosed with COVID-19.Future plansThe study group will analyse the data collected showing the real-time changes throughout the pandemic and will make the data widely available for researchers to conduct further studies.

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Real time extended range prediction of heat waves over India.

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Real time extended range prediction of heat waves over India.

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