Tamil Nadu’s lukewarm heatwave policy

From ‘Tamil Nadu heatwave policy is only a start’, The Hindu, November 21, 2024:

Estimates of a heatwave’s deadliness are typically based on the extent to which the ambient temperature deviates from the historical average at a specific location and the number of lives lost during and because of the heatwave. This is a tricky, even devious, combination as illustrated by the accompanying rider: “to the reasonable exclusion of other causes of hyperthermia”.

A heatwave injures and/or kills by first pushing more vulnerable people over the edge; the less vulnerable are further down the line. The new policy is presumably designed to help the State catch those whose risk exposure the State has not been able to mitigate in time. However, the goal should be to altogether reduce the number of people requiring such catching. The policy lacks the instruments to guide the State toward this outcome.

The cost of forgetting Ballia

In the day or so before June 1, 14 people died in Bihar of heat stroke. Ten of these people were election personnel deployed to oversee voting and associated activities in Bihar and Uttar Pradesh, and of them, five died in Bhojpur alone. On Friday, at least 17 people in Uttar Pradesh, 14 in Bihar, and four in Jharkhand had died of heat-related morbidity. And of the 17 in Uttar Pradesh, 13 deaths were reported from Mirzapur alone. This is a toll rendered all the more terrible by two other issues.

First, after the first phase of the polls, the Election Commission of India (ECI) recorded lower voter turnout than expected (from previous Lok Sabha polls) and blamed the heat. Srinivasan Ramani, my colleague at The Hindu, subsequently found “little correlation” between the maximum temperature recorded and turnouts in various constituencies, and in fact an anti-correlation in some places. By this time the ECI had said it would institute a raft of measures to incentivise voters to turn up. These were certainly welcome irrespective of there being a relationship between turnout and heat. However, did it put in place similar ‘special’ measures for electoral officials?

On March 16, the ECI forwarded an advisory that included guidelines by the National Disaster Management Authority to manage heat to the chief electoral officers of all states and Union territories. These guidelines had the following recommendations, among others: “avoid going out in the sun, especially between 12.00 noon and 3.00 pm”; “wear lightweight, light-coloured, loose, and porous cotton clothes. Use protective goggles, umbrella/hat, shoes or chappals while going out in sun”; and “avoid strenuous activities when Balliathe outside temperature is high”.

A question automatically arises: if poll officers are expected to avoid such activities, the polling process should have been set up such that those incidents requiring such activities wouldn’t arise in the first place. So were they? Because it’s just poka-yoke: if the process itself didn’t change, expecting poll officers to “avoid going out in the sun … between 12 pm and 3 pm” would have been almost laughable.

The second issue is worse. Heat wave deaths in India are often the product of little to no advance planning, even if the India Meteorological Department (IMD) has forecast excessive heat on certain dates. But to make matters worse, there was a deadly heat wave last year in the same region where many of these deaths have been reported now.

Recall that in the first half of June 2023, more than 30 people died of heat-related illnesses in Ballia village in Uttar Pradesh. After the chief medical superintendent of the local district hospital told mediapersons the people had indeed died of excessive heat, the state health department — led by deputy chief minister Brajesh Pathak — transferred him away, and his successors later denied heat had had anything to do with the deaths.

So even if the IMD hadn’t predicted a heat wave in this region for around May 30-31, the local and national governments and the ECI should have made preparations for heat exposure leading at least to morbidity. Did they? To the extent that people wouldn’t have had to be hospitalised or have died if they’d made effective preparations, they didn’t. Actively papering over the effects of extreme weather (and of adverse exposure) has to be the most self-destructive thing we’re capable of in the climate change era.

Featured image: Representative image of a tree whose leaves appear to have wilted in the heat. Credit: Zoltan Tasi/Unsplash.

Reading fog data from INSAT 3DR

At 7.57 am today, the India Meteorological Department’s Twitter handle posted this lovely image of fog over North India on January 21, as captured by the INSAT 3DR satellite. However, it didn’t bother explaining what the colours meant or how the satellite captured this information. So I dug a little.

At the bottom right of the image is a useful clue: “Night Microphysics”. According to this paper, the INSAT 3D satellite has an RGB (red, green, blue) imager whose colours are determined by two factors: solar reflectance and brightness temperature. Solar reflectance is a ratio of the amount of solar energy reflected by a surface and the amount of solar energy incident on it. Brightness temperature has to do with the relationship between the temperature of an object and the corresponding brightness of its surface. It is different from temperature as we usually understand it – by touching a glass of hot tea, say – because brightness temperature also has to do with how the tea glass emits the thermal radiation: at different frequencies in different directions.

INSAT 3D’s ‘day microphysics’ data component studies solar reflectance at three wavelengths: 0.5 µm (visible), 1.6 µm (shortwave infrared) and 10.8 µm (thermal infrared). The strength of the visible signal determines the amount of green colour; the strength of the shortwave infrared signal, the amount of red colour; and the strength of the thermal infrared signal, the amount of blue colour. This way, the INSAT 3D computer determines the colour on each point of the screen.

‘CB’ stands for ‘cumulonimbus’

According to the paper:

The major applications of this colour scheme are an analysis of different cloud types, initial stages of convection, maturing stages of a thunderstorm, identification of snow area and the detection of fires.

The authors also note that the INSAT 3D is useful to image snow: while the solar reflectance of snow and the clouds is similar in the visible part of the spectrum, snow absorbs radiation of 1.6 µm strongly. As a result, when the satellite is imaging snow, the red component of the colour scheme becomes very weak.

The night microphysics is a little more involved. Here, two colours are determined not by a single signal but by the strength of the difference between two signals. The computer determines the amount of red colour according to the difference between two thermal infrared signals: 12 µm and 10 µm. The amount of green colour varies according to the difference between a thermal infrared and a middle infrared signal: 10.8 µm and 3.9 µm. The amount of blue colour is not a difference but is determined by the strength of a thermal infrared signal of wavelength 10.8 µm.

For example, in the image above, the data indicates three kinds of clouds. (‘K’ denotes the temperature differences in kelvin.) A mature cumulonimbus cell, possibly part of a tropical storm, hangs over West Bengal and is visible mostly in red, but whose blue component indicates it is also very cold. Somewhere north of Delhi, flecks of green dominate, indicating a preponderance of lower clouds. Further north, a the sky is dominated by a heavy, high cloud system that encompasses lower clouds as well.

By combining day and night microphysics data, atmospheric scientists can elucidate the presence of moisture droplets of different shapes and temperature differences over time, and in turn track the formation, evolution and depletion of cyclones and other weather events.

For example, taking advantage of the fact that INSAT 3D can produce images based on signals of multiple wavelengths, the authors of the paper have proposed day and night microphysics data that they say would indicate a thunderstorm impending in one to three hours.

Both INSAT 3D and INSAT 3DR use radiometers to make their spectral measurements. A radiometer is a device that measures various useful properties of radiation, typically by taking advantage of radiation’s interaction with matter (e.g. in the form of temperature or electrical activity).

Both satellites also carry atmospheric sounders. They measure temperature and humidity and study water vapour as a function of their heights from the ground.

Scientists combine the radiometer and sounder measurements to understand various atmospheric characteristics.

According to the INSAT 3DR brochure, its radiometer is an upgraded version of the very-high-resolution radiometer (VHRR) that the Kalpana 1 and INSAT 3A satellites used (launched in 2002 and 2003, respectively).

The Space Application Centre’s brief for INSAT 3A states: “For meteorological observation, INSAT-3A carries a three channel Very High Resolution Radiometer (VHRR) with 2 km resolution in the visible band and 8 km resolution in thermal infrared and water vapour bands.” The radiometers onboard 3D and 3DR have “significant improvements in spatial resolution, number of spectral channels and functionality”.

The Kalpana 1 and INSATs 3A, 3D and 3DR satellites aided India’s weather monitoring and warning services with the best technology available in the country at the time, and with each new satellite being an improved as well as better-equipped version of the previous one. So while Kalpana 1 had a launch mass of 1,060 kg and carried a early VHRR and a data-relay transponder, INSAT 3DR had a launch mass of 2,211 kg – in 2016 – and carried an upgraded VHRR, a sounder, a data-relay transponder and a search-and-rescue transponder.

India deactivated Kalpana 1 in September 2017, after 15 years in orbit. The INSAT 3A, 3D and 3DR satellites are currently active in a geostationary orbit around Earth, at inclinations respectively of 93.5º, 82º and 74º E longitudes.