Heat wave and cold wave days in different States of India

Subbaramayya, I.; RAO, D.A. SURYA

doi:10.54302/mausam.v27i4.2631

Cited by 8 publications

(2 citation statements)

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“…Early studies by Raghavan (1966) identified West Bengal, Bihar plains, Madhya Pradesh, East and West Uttar Pradesh, and Punjab as the most vulnerable to persistent and prolonged heat waves. Subsequent studies delved into the characteristics and impacts of heat waves in India (Bedekar et al , 1974;Subbaramayya and Surya Rao, 1976;Pai et al , 2013;Kothawale et al , 2010), with regional vulnerability highlighted in coastal Andhra Pradesh, Odisha, East Madhya Pradesh, and Chhattisgarh (Pai et al , 2004). Anomalies in atmospheric circulation, soil moisture, temperature, and sea surface temperatures are linked to extreme heat wave formation and intensity (Perkins, 2015;Alghamdi and Harrington, 2019;.…”

Section: Introductionmentioning

confidence: 99%

An object-based approach to Forecasting and Verification of heat waves over India

Singh,

Dube,

Ashrit

et al. 2024

Preprint

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Heat waves are one of the most dangerous natural hazards in the world. Higher daily peak temperatures as well as duration, intensity and frequency of heat waves are increasing globally due to climate change. In India, the instances of heat waves have increased in recent years along with their intensity which has resulted in the increased number of casualties. For the purpose of disaster mitigation and reduction of losses due to heat waves, a timely and accurate forecast of these events is required. However, traditional verification methods (which rely on grid-wise comparisons) used for verification of forecasts from high resolution models have a limited utility. In order to assess the utility of these forecasts, spatial verification techniques that can differentiate between forecast and observed features are needed. In this study, we have tried to demonstrate the ability of the National Centre for Medium Range Weather Forecasting (NCMRWF) Unified Model (NCUM) in predicting maximum 2m temperature (Tmax), over the heat wave-prone zones of India. The state-of-art Object-Based Diagnostic Evaluation (MODE) was used for verification of the heat waves. Additionally, applying fuzzy logic enhances the discernment of similarities between forecasted and observed objects, offering a valuable tool for adapting to varying object types and leveraging human knowledge. For instance, in certain scenarios, emphasizing the matching of area sizes may be crucial, while in others, aligning object locations could be more pertinent. In both cases, achieving the desired outcome involves adjusting weights or revising interest maps. This study shows that NCUM forecasts have a southwesterly bias in the location of Tmax objects for values exceeding 43 and 45°C, indicating a potential lag in system propagation. When verified over a season, it is seen that the performance of the model in predicting forecast attributes is rather very good in terms of smaller variation in the median of centroid distance (~150-200 km upto 120 hr lead time), an 83% match in the internal structure of the forecasts with a near perfect curvature ratio (95-97%), a near perfect 50th percentile intensity ratio (98-99%) and the symmetric difference which is small enough to coincide with the observed heat wave zones. In summary, NCUM forecasts demonstrate accurate heat wave predictions in terms of geographical area, structure, shape, and size up to a 5-day lead time, showcasing their potential for effective disaster preparedness.

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Section: Introductionmentioning

confidence: 99%

An object-based approach to Forecasting and Verification of heat waves over India

Singh,

Dube,

Ashrit

et al. 2024

Preprint

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“…India being a country with about 65% rural population, consequences of extreme heat are scarcity of drinking water, crop failures, power outages, wild res, human illness as well as infrastructure disruption and damage (Garcia-Herrera, 2010). Some of the earliest studies over India have reported maximum temperatures exceeding the daily normal by 1°-1.2°C (Ramamurthy 1972) occurring mostly during March-June (Raghavan 1966, Subbaramayya and Rao 1976). Most of these studies were case-by-case over various parts of India, constrained by the nonavailability of quality controlled daily temperature data and hence, inconsistent in their results.…”

Section: Introductionmentioning

confidence: 99%

Recent Changes in Temperature Extremes and Heat Stress over Eastern India

Akhter,

Biswas,

Das

et al. 2024

Pure Appl. Geophys.

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The rise of extreme heat events and increase in heat stress under changing climate has been a major concern in recent periods. The present study has been undertaken to analyze temperature extremes and heat stress patterns over Eastern India from the period 1981-2020 for April, May, and June (AMJ). Six indices namely Monthly maximum value of daily maximum temperature (TXx), Monthly maximum value of daily minimum temperature (TNx), Monthly minimum value of daily maximum temperature (TXn), Monthly minimum value of daily minimum temperature (TNn), Percentage of days when maximum temperature > 90th percentile (TX90p) and minimum temperature > 90th percentile (TN90p) has been utilized to assess changes in temperatures extremes. Furthermore, three heat stress indices (HSIs) namely, Humidex, Simpli ed Wet-Bulb Globe Temperature, and Apparent Temperature have been used to estimate and categorize heat stress levels. Mixed results with both increasing and decreasing trends over different parts during three months have been observed in the case of TXx, TXn, TNx, and TNn. During AMJ, there has been ampli cation in the frequency of TX90p over coastal regions of West Bengal and Odisha season in the latest decades. Increasing TN90p frequency has been found over Sikkim, Bihar, and North Bengal while it has decreased over Chhattisgarh and Odisha. The frequency of strong heat stress has intensi ed over Gangetic West Bengal and Bihar during recent decades. Spatial coverage of strong stress has also gradually extended and the area under no stress has decreased during AMJ season.

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