Case Study: A Better Way To Predict Weather

By Ed Sperling

Most of our weather predictions are developed from about 150 stationary government radar systems, which interlock and occasionally overlap to create a cohesive picture. The picture isn’t perfect—in fact, it’s probably the equivalent of looking at a large, grainy satellite photo—which creates plenty of wrong forecasts. But the system can track large storms across state borders and, in many cases, well into the ocean.

Getting insights into the inner workings of storms and how they are affected by a number of variables is generally left to amateurs, who have devised their own technology—sometimes crude, often innovative—to look into the center of hurricanes and tornadoes. But getting an up-close, crystal-clear look into the center of the beast, and being able to repeat that experience with consistency, has been impossible.

At least it was impossible until a piece of government radar fell into the hands of the Naval Postgraduate School in Monterey, Calif. The radar originally belonged to the U.S. Army and was being used for mobile air defense. While it was considered outdated for military purposes, it proved to be incredibly advanced for scientific research. Weather researchers don’t typically acquire a $2 million piece of military radar for chasing storms.

“What we’ve been doing is casting versus forecasting,” said Jeffrey Knorr, professor and chairman of the Naval Postgraduate School’s department of electrical and computer engineering. “We thought we could use this for atmospheric science. This is a phased array, and it’s the only mobile phased array in existence.”

It became mobile when Knorr and his team mounted it on the back of a flatbed truck, added a diesel generator and developed some software programs to take advantage of the radar in real time.

“The National Weather System radar is a high-power S-band system, which is a parabolic antenna that basically can scan 360 degrees. There’s a clear-air mode and a precipitation mode, but it takes time to develop an image in 360 degrees. It’s about 5 to 6 minutes for a precipitation scan and about 10 minutes for a clear-air scan. With mobile radar, you can get the same data but you don’t have to scan 360 degrees. It’s all programmable from a laptop, so you can take a phased area and make it frequency agile,” he said.

The shape of things to come?

Weather radar can measure how hard it is raining through reflectivity, which includes the number of raindrops and the average velocity. It also can measure spectral spread of the precipitation, which includes turbulence and wind sheer, which is useful in measuring rainfall rates and predicting flash floods. But the speed of updates is a problem for making fast and accurate predictions.

Knorr’s system allows updates every 5 to 10 seconds through the addition of a high-speed digital signal processor. But it does more than that. Most radar is horizontally or vertically polarized. His team added a third axis, so instead of just seeing how hard it is raining and how many raindrops there are, it can measure the size of the raindrops. The larger they are, the flatter they are, which makes it impossible to pick up using ordinary polarization.

“What we’re able to measure now is the storm velocity, reflectivity, motion toward or away from the radar, and the gray area, which is zero radio velocity,” he said. “We also get a higher-resolution picture. Radar spreads as it goes out, so a 1 degree beam width has a certain cross-range resolution at 1 mile. Shorter-range radar has higher resolution.”

This is particularly important in tracking the path of tornadoes, which have a signature characteristic on weather radar. When weather experts look at a radar image, they can identify this signature and predict that tornadoes will form. What they can’t do is refresh the image frequently enough and look inside with a better image. That requires radar to be much more mobile, quicker and much more accurate.