Weather forecasting is impressively accurate given how changeable and chaotic Earth’s climate can be. It’s not unusual to get 10-day forecasts with a reasonable level of accuracy.
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But there is still much to be done. One challenge for meteorologists is to improve their “nowcasting,” the ability to forecast weather in the next six hours or so at a spatial resolution of a square kilometer or less.
In areas where the weather can change rapidly, that is difficult. And there is much at stake. Agricultural activity is increasingly dependent on nowcasting, and the safety of many sporting events depends on it too. Then there is the risk that sudden rainfall could lead to flash flooding, a growing problem in many areas because of climate change and urbanization. That has implications for infrastructure, such as sewage management, and for safety, since this kind of flooding can kill.
So meteorologists would dearly love to have a better way to make their nowcasts.
Enter Blandine Bianchi from EPFL in Lausanne, Switzerland, and a few colleagues, who have developed a method for combining meteorological data from several sources to produce nowcasts with improved accuracy. Their work has the potential to change the utility of this kind of forecasting for everyone from farmers and gardeners to emergency services and sewage engineers.
Current forecasting is limited by the data and the scale on which it is gathered and processed. For example, satellite data has a spatial resolution of 50 to 100 km and allows the tracking and forecasting of large cloud cells over a time scale of six to nine hours. By contrast, radar data is updated every five minutes, with a spatial resolution of about a kilometer, and leads to predictions on the time scale of one to three hours. Another source of data is the microwave links used by telecommunications companies, which are degraded by rainfall.
Clearly radar has great potential for nowcasting, and indeed, meteorologists have devoted significant resources to studying it. The simplest approach is to take a snapshot of the current pattern of rainfall with its speed and direction, and then translate this pattern in space.
This works over short time scales and a resolution of around 4 km. But after 40 minutes or so, any forecasting ability is lost, say Bianchi and co. And with a greater resolution of around 1 km, the forecasting ability drops to less than 15 minutes.
One way to improve these forecasts is to correlate the radar images with rainfall measurements on the ground. This provides an extra way to constrain the weather model and so improve it. This is essentially the technique that Bianchi and co have developed.
These folks have combined the data collected in 2009 from 14 rain gauges, 14 microwave links, and the radar rain measurements from MeteoSwiss in the 20×20-km area around Zurich in Switzerland. The team then use this data from a specific time period to forecast rainfall over time scales of up to 30 minutes.
Since the data is historical, they can compare the forecast with the actual rainfall to determine its accuracy.
The results are promising. One important assumption weather forecasters make is that the atmosphere will continue to change in the same way it does now. This is called Lagrangian persistence, and it is often spot on.
When this assumption is correct, Bianchi and co say, their nowcasts produce accurate forecasts more than 20 minutes into the future at a scale of as little as 500 meters. That’s impressive. It would have significant implications for the real-time management of water runoff in urban sewage systems and in agricultural activities.
But the assumption of Lagrangian persistence isn’t always true. Sometimes the atmosphere undergoes unexpected changes—sudden heating events that cause convection cells, for example. And when this happens, the accuracy of the forecasts drops dramatically. “In the case of convective events, the performance of the nowcast algorithm decreases rapidly after 15 min, due to rapid development and movement of rain cells,” say Bianchi and co.
So that imposes some important limits on what this kind of nowcasting can do. Nevertheless, even the ability to characterize the uncertainty of a nowcast is an important development, say Bianchi and co.
There’s clearly more work to be done. But in some areas, accurate nowcasts on the scale of hundreds of meters is a goal that meteorologists clearly have in their sights.
Ref: arxiv.org/abs/1810.11811 : Rainfall Nowcasting By Combining Radars, Microwave Links And Rain Gauges