The science of weather forecasting: What it takes and why it’s so hard to get right
Weather forecasting is an important science. Accurate forecasting can help to save lives and minimise property damage. It’s also crucial for agriculture, allowing farmers to track when it’s best to plant or helping them protect their crops.
And it will only become more vital in the coming years. Severe weather events are becoming more frequent and more intensebecause of climate change and variability.
I am a meteorologist with specialities in forecasting weather and climate change – who wants to improve the quality of weather products and their applications to spur socioeconomic development across Africa. Doing so matters: the World Bank has pointed out that better weather forecasts can bolster the continent’s development.
So, how does forecasting work? What does it take to produce accurate, reliable and timely forecasts? And how can African countries do better on this front?
A complex process
Weather forecasting is complex and challenging. The process entails three steps: observation, analysis and communication.
For observation, forecasters work with atmospheric models. These are sets of equations that depict the state of the atmosphere. The models use information on the initial state (observations) of the atmosphere, land and ocean to forecast weather. Data from the models is combined with information drawn from weather stations which are set up at key points across a region or country to give the actual state of the atmosphere. This data assimilation produces a better forecast since it optimises forecasters’ understanding of the evolving weather system.
It’s easier to be accurate when giving a short-range forecast – one that covers hours to days – than it is when interpreting long-range (months or seasons) data. The atmospheric system is dynamic; the more time that passes, the less certain forecasters can be of its state.
Technological advances have greatly improved the general quality of weather forecasting. For instance, more observations are possible because of automated weather stations. There’s also been an increase in the use of high performance computing. This allows for more data storage, faster processing, analysis, and visualisation of incoming data.
These datasets are key in diagnosing past and current weather to create a forecast. Unfortunately, the data observation network (both manual and automated stations) is still poor, especially in developing countries. That’s the result of limited investment into the sector. Forecasters in these countries are forced to use alternative datasets that are not very accurate.
One such alternative dataset is Numerical Weather Prediction. It uses global deterministic models that are normally not detailed enough to realistically represent convection at a local or regional level; forecasters using this data often can’t accurately predict rainfall, especially heavy rain. A lack of access to better historical data also means forecasters struggle to identify when an area’s seasonal rainfall will start and end because they can’t examine trends over years or decades.
It’s these variations in access to data and technology that mean some forecasts are more accurate than others.
Once forecasts have been collated, they are released in various forms. The way that weather products – apps, TV and radio bulletins or website updates – are packaged will differ depending on end users’ needs. Some people, like farmers, may be especially interested in seasonal forecasts and will seek these out. Athletes, for example, are more likely to use portals or services that focus on hourly and daily forecasts.
I would recommend that, whoever you are, you consider seasonal forecasts general information for broad planning purposes. But this should be interpreted together with monthly, weekly and daily forecasts for accuracy’s sake.
Some African countries also use another kind of data for their forecasts: indigenous ecological knowledge. This entails drawing from communities’ long held knowledge about their environments, and especially about long-term trends and shifts. Such knowledge can be blended with scientific processes during forecasting.
The “rainmakers” from the Nganyi community in western Kenya are a good example. These residents have deep historical knowledge about the area’s climate and weather patterns. They use plants and animals to understand what the weather is doing. They now work with meteorologists from Kenya’s Meteorological Department to produce seasonal weather forecasts.
Indigenous knowledge is under threat as the elders who are its custodians are perishing. Vital plants and animals used in their processes are going extinct, too. It would be a great pity if this resource were lost to forecasters. This knowledge plays an important role in local livelihoods and it supports efforts to forecast and make sense of seasonal climate state at local scale.
Some of the ways that weather is forecast today may change in the coming years. The World Meteorological Organisation is encouraging national meteorological services to move from what the weather will be (forecasting weather) to what the weather will do – impact based forecasting-and-warning.
There’s also a push to ensure forecasts reach the people who need them. A number of African countries, among them Malawiand Chad, have adopted what’s known as Participatory Scenario Planning. This collaborative approach designs and delivers user focused climate information services by taking the co-production process down to the sub-national level. It brings together producers and users of weather and climate information – meteorologists, indigenous knowledge experts, researchers, various sectors of local government, farmers, as well as NGOs and journalists.
Private firms that provide global weather forecasts are also emerging. This is commendable given that they supplement the services of countries with limited resources. But my advice is that, where the national meteorological and hydrological centres have capacity to produce weather forecasts, theirs should be considered first, ahead of those generated by private firms. This is because national bodies’ forecasts are based on the observed historical and observed data which they are custodians of rather than private institutions that rely mainly on model data.