Heat waves are recognized as one of the world’s most hazardous natural phenomena. It is well known that in recent times, both the intensity and frequency of heat waves have been increasing globally and in India, resulting in increased casualties. Timely and accurate forecasts of these events can help mitigate disasters and reduce losses due to heat waves. Usually, high-resolution numerical weather prediction (NWP) models can predict the intensity of extreme events accurately. However, these forecasts often suffer from a location mismatch and show limited reliability when verified using traditional methods relying on a grid-wise comparison. New and advanced spatial verification techniques allow the comparison of different aspects of the forecast-observation pair and highlight the actual value and utility of the forecasts. This study utilizes the method for object-based evaluation (MODE) to evaluate the 2 m maximum temperature forecasts from the NCMRWF Unified Model (NCUM) over the Indian land region for three summer seasons (2022–2024). The main objectives of this study are (a) to quantify the errors in the location, intensity, area, and structure of the forecasted objects obtained by using MODE and (b) based on the distribution of the displacement and intensity errors of the forecasted objects, decide if output from MODE can be used for forecasting heat waves over larger areas, which will be highly beneficial for the forecasters to generate an impact-based forecast for a region. To assess the skill of NCUM in predicting Tmax, we compared attributes like centroid distance, area, intensity, and structure of the forecasted and observed objects. It is found that (i) for lower Tmax values, the centroid distance between the forecast and observed objects is small (≤200 km, up to day-5 lead time), and for higher Tmax, this distance is ~250 km. (ii) for lower Tmax values, most of the forecasted objects have an area ratio that is closer to 1, indicating a similar spatial extent of the forecasts and observations; (iii) the intensity of the forecasted and observed objects is quite similar, which is seen from high values of the percentile intensity ratio (~0.99), (iv) the structure of the forecasted objects is predicted with reasonable accuracy which the higher values can see of the complexity ratio. These results demonstrate that the NCUM model has the ability to provide valuable heat wave forecasts in terms of location, area, intensity, and structure up to 5-days in advance, highlighting its potential for use in disaster preparedness and response efforts.

Object-based forecasting of heat waves over India: A novel approach

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发表日期：

2025年6月14日

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Harvir Singh,
Anumeha Dube,
Raghavendra Ashrit,
Prashant Kumar Srivastava,
V S Prasad

摘要

Heat waves are recognized as one of the world’s most hazardous natural phenomena. It is well known that in recent times, both the intensity and frequency of heat waves have been increasing globally and in India, resulting in increased casualties. Timely and accurate forecasts of these events can help mitigate disasters and reduce losses due to heat waves. Usually, high-resolution numerical weather prediction (NWP) models can predict the intensity of extreme events accurately. However, these forecasts often suffer from a location mismatch and show limited reliability when verified using traditional methods relying on a grid-wise comparison. New and advanced spatial verification techniques allow the comparison of different aspects of the forecast-observation pair and highlight the actual value and utility of the forecasts. This study utilizes the method for object-based evaluation (MODE) to evaluate the 2 m maximum temperature forecasts from the NCMRWF Unified Model (NCUM) over the Indian land region for three summer seasons (2022–2024). The main objectives of this study are (a) to quantify the errors in the location, intensity, area, and structure of the forecasted objects obtained by using MODE and (b) based on the distribution of the displacement and intensity errors of the forecasted objects, decide if output from MODE can be used for forecasting heat waves over larger areas, which will be highly beneficial for the forecasters to generate an impact-based forecast for a region. To assess the skill of NCUM in predicting T_max, we compared attributes like centroid distance, area, intensity, and structure of the forecasted and observed objects. It is found that (i) for lower T_max values, the centroid distance between the forecast and observed objects is small (≤200 km, up to day-5 lead time), and for higher T_max, this distance is ~250 km. (ii) for lower T_max values, most of the forecasted objects have an area ratio that is closer to 1, indicating a similar spatial extent of the forecasts and observations; (iii) the intensity of the forecasted and observed objects is quite similar, which is seen from high values of the percentile intensity ratio (~0.99), (iv) the structure of the forecasted objects is predicted with reasonable accuracy which the higher values can see of the complexity ratio. These results demonstrate that the NCUM model has the ability to provide valuable heat wave forecasts in terms of location, area, intensity, and structure up to 5-days in advance, highlighting its potential for use in disaster preparedness and response efforts.