IMD Reports Temperature Data for Delhi, Notes Outlier Reading and Potential Sensor Error

Temperature variations in urban areas can be attributed to local factors such as proximity to water bodies, barren land, concrete structures, dense urban clusters, and green areas.

The Indian Meteorological Department (IMD) has released temperature data for Delhi, based on readings from its Automatic Weather Stations (AWS) network, which has been operational since the summer of 2022. This includes temperature and rainfall observations for 15 new locations across Delhi and NCR, supplementing the existing manual departmental stations.

As per IMD's report, maximum temperatures recorded on May 29, 2024, by five departmental observatories (Safdarjung, Palam, Ayanagar, Ridge, and Lodi Road) and 15 AWS locations are listed in Table 1. The maximum temperatures across Delhi NCR ranged from 45.2°C to 49.1°C. However, Mungeshpur reported an outlier temperature of 52.9°C, which could be due to a sensor error or local factors. IMD is currently examining the data and sensors to determine the cause of this anomaly.

Compared to the previous day, the maximum temperatures across many parts of Delhi have decreased today. Rainfall in several areas during the afternoon has further contributed to the temperature drop. IMD forecasts that heatwave conditions will diminish over the next 2-3 days due to a gradual fall in temperature, influenced by an approaching western disturbance, rainfall, thunderstorms, and southwesterly winds from the Arabian Sea to northwest India.

Temperature variations in urban areas can be attributed to local factors such as proximity to water bodies, barren land, concrete structures, dense urban clusters, and green areas.

IMD emphasizes that data from its five major stations (Safdarjung, Palam, Ayanagar, Ridge, and Lodi Road), which have been consistently reporting observations over a long period, should be considered for identifying trends and extremes in temperature. These stations are equipped to meet all exposure conditions, making their data reliable for long-term climate analysis.