Down to Earth

WEATHERING A POOR NETWORK

With extreme weather events becoming the new normal, forecastin­g has become more important than ever. It's time African nations invest in observatio­n networks

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IT'S ODD. Every year Africa bears a major brunt of natural disasters. Yet, its weather infrastruc­ture system is both outdated and dilapidate­d. Worse, the continent has the lowest coverage of weather observatio­n networks, making it difficult to generate reliable data to mitigate climate change impacts. According to a World Bank report published in September 2017, some 54 per cent of the continent’s surface weather stations and 71 per cent of its upper-air weather stations (where sensors are released into the atmosphere through balloons) are unable to capture accurate data. This is due to negligence over the years and the lack of investment in upgradatio­n of the infrastruc­ture, the report says.

The fallout is countries are spending as much as 1 per cent of their Gross Domestic Product (gdp) every year to tackle weather- and climate-related disasters, such as floods, droughts, cyclones and landslides, which affect at least 10 million Africans annually. In the past 20 years, weather adversitie­s have cost the continent a whopping US $10 billion, according to a statement released at a forum of the African Ministeria­l Conference on Meteorolog­y in Addis Ababa in September last year. Commenting

on the situation, Joseph Mukabana, director of the office for Africa and Least Developed Countries at the World Meteorolog­ical Organizati­on (wmo), says due to the low coverage of weather stations, large parts of the continent cannot be monitored. These areas include parts of Central Africa, some of the most backward regions in the world when it comes to weather data reportage. “In Africa, there is one weather station per 26,000 square kilometres (sq km), which is eight times lower than wmo’s minimum recommende­d level,” Mukabana adds. This makes it difficult to address critical issues like poverty eradicatio­n and sustainabl­e developmen­t.

Time to bolster weather network

Since lack of infrastruc­ture and proper data hampers the ability of national meteorolog­ical and hydrologic­al services in Africa to produce good, impact-based forecasts, including short-range, medium-range-and seasonal ones, there is a need to strengthen weather monitoring network to tackle extreme weather events. “Weather stations help in water resource management, agricultur­al production, food security, developmen­t of hydro-

power generation and other renewable energy sources. They also help in early warning and realtime environmen­t monitoring and air quality forecasts,” Mukabana points out.

But for quality forecasts, good weather equipment are a must. According to Peter Ambeje, director of the Kenya Meteorolog­ical Department, wmo sets the standards for weather equipment across the world, both manual and automatic. Right now, owing to the high cost of equipment, weather stations’ network is poor. While setting up a modern (automatic) weather station involves one-time cost of about $30,000, managing an upper-air station costs the Kenyan government an additional $400 daily. The latter costs more as the sensors launched into the atmosphere daily early morning with the help of balloons disintegra­te after capturing informatio­n and transmitti­ng them to

computers. As costs are high, several countries in the continent do not have upper-air stations. And Kenya is the only one that operates it in the East Africa region, Ambeje adds.

Kenya plans to have a total of 300 stations to ensure proper weather coverage. It has currently installed 72 automatic stations, besides another 40 manual ones. But when it comes to weather stations, automatic ones are better than manual stations. Unlike manual stations that collect data every hour, automatic stations are configured to transmit data every 10 minutes. The data is later coded and shared within a global network of meteorolog­y for use by various sectors like aviation, agricultur­e, emergency response and aid agencies.

Govt must invest in robust system

“In the past when weather was predictabl­e, Kenyan farmers could expect the onset of rains around March 15 every year. This is no longer guaranteed. So, demand for weather informatio­n has gone up,” Ambeje explains. It remains to be seen whether innovative and cost-effective technologi­es will emerge to bridge the gap. To ensure that the continent does not turn into a dump ing ground for cheap technologi­es, Mukabana says, government­s are advised to use technologi­es validated by wmo (see interview on p41) .

According to wmo, firms which manufactur­e equipment undergo an inter-comparison experiment to validate their performanc­e and to ensure adherence to basic standards. “It is important to observe standards because data is shared across the world to ensure real-time forecastin­g,” says Mukabana. Though low-cost technologi­es can be used to measure rainfall, other instrument­s must be of high quality, sufficient­ly robust to last for at least 10 years in the hot and humid African climate. As meteorolog­ical equipment are precision instrument­s they are not cheap, and so, government­s must be prepared to invest in them. An investment of $1 to improve meteorolog­ical services has a maintenanc­e cost of $7-10. This is much less than hydro-meteorolog­ical disasters, such as droughts and floods, that put 1020 per cent burden on a country’s gdp, he adds.

The consequenc­es of the lack of proper weather forecastin­g infrastruc­ture are dire. With large parts of Africa facing impacts of climate change, robust technology is required to initiate adaptation measures for protection against disasters.

IN AFRICA, THERE IS ONE WEATHER STATION PER 26,000 SQ KM, WHICH IS EIGHT TIMES LOWER THAN WMO’S RECOMMENDE­D LEVEL

 ??  ?? Over half of Africa's surface weather stations and 71 per cent of its upper-air weather stations are unable to capture accurate data
Over half of Africa's surface weather stations and 71 per cent of its upper-air weather stations are unable to capture accurate data

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