Currently, the people working in weather forecast offices use a variety of tools to monitor flood conditions. They rely on rain and stream gauges to tell them how much water is in a certain place at any given time, Laber says.

They track developing storms using satellites and weather radars. The staff monitors rainfall, but their computers also alert them when it rains more than what is considered safe for a specific area.

”— Ivory SmallAll of that, plus the years of expertise inside those buildings, determines whether or not someone presses a button to issue a flood alert for their area, Laber says. A watch is the least serious alert.

It means that there’s a chance a flash flood could happen. The watch might be issued anywhere from 12 to 48 hours in advance, Laber explains.

He says local emergency teams should start working on “flood protection measures” when the flood watch is issued, so there’s enough time to prepare before people are stuck and forced to find higher ground. Response teams might review evacuation plans, set up sandbags and road barriers, and identify the highest risk areas ahead of a potential flood.

An advisory is the next level up. It might be flooding, “but it’s more nuisance-level.

It’s not going to threaten life or property,” Laber says. A warning is the most serious alert.

“That’s a life-and-death possibility of being swept off your feet [or] being run over by a car floating down the stream,” Small says. The NWS differentiates a regular flood from a flash flood based on time, says Small.

If an area floods in under six hours, it’s a flash flood. The NWS has a lot of different ways to determine whether or not it needs to issue a flash flood warning, but its tech has limitations, says Yehuda Bock, TACLS project lead and research geodesist at the Scripps Institution of Oceanography at the University of California, San Diego.

The satellites the NWS uses are more detailed over oceans than land, he says. And tracking rainfall as it’s happening doesn’t give you enough lead time, “because once there’s precipitation, you’re already in the event itself,” he explains.

Deserts and other less populated areas typically have fewer rain gauges, Laber says. “We have a fairly dense network [of rain gauges], but it doesn’t cover every inch of the Earth.”

Up in the airTACLS uses satellites and machine learning to identify places on a map — for now, just in California — that risk transitioning from rain to dangerous flash floods. But soon, the technology will be available to every NWS office.

Scientists from UCSD, the NWS, and NASA worked together to create TACLS. NASA’s Earth Science Technology Office funded the project through its Advanced Information Systems Technology program.

TACLS relies on the Global Navigation Satellite System (GNSS), which is primarily used to predict earthquakes, says Bock. But the GNSS network, which consists of satellites and ground sensors, also happens to provide data that can help forecasters decide if they need to issue a flash flood warning.

The more water vapor, or moisture, there is in the atmosphere, the longer the delay in communication between the GNSS satellites and the sensors on the ground. The amount of moisture, or “precipitable water,” in the atmosphere tells the NWS what is actually happening in the skies leading up to a storm, Laber says.

TACLS uses satellites and machine learning to identify places that risk transitioning from rain to dangerous flash floods. Before TACLS, Laber and his colleagues had to rely on the forecast, which wasn’t always accurate.

TACLS provides real-time data about the precipitable water in the atmosphere to compare to the forecast. This helps them determine whether the storm is actually moving at the speed predicted by the weather models.

“It helps us to make better decisions in our warning decision process,” Laber says. Even with the GNSS moisture data, forecasters still need something to analyze all of it.

Bhavik Chandna, a UCSD graduate student, spent about a year developing the machine learning side of TACLS using what’s called long short-term memory architecture. According to Chandna, this specific machine learning system works especially well for things like developing storms and other weather patterns that change over time.

Years of atmospheric measurements from the GNSS network, as well as data about atmospheric rivers, precipitation, and flash flood warnings, were used to train the machine learning model to understand moisture changes in the atmosphere “as a storm develops,” Chandna explains. “Machine learning sucks at some things and excels at some things,” says Joel Johnson, associate professor in the department of earth and planetary sciences at the University of Texas at Austin.

Johnson, who was not involved in the TACLS project, knows firsth and the challenges of working with these types of tools. “In general, it’s very easy to have artifacts in your data, false positives.

A sensor goes funky, and things like that,” Johnson says. Still, he says TACLS seems “well suited” to the task of predicting flash floods.

False positives do happen, Chandna says. “A real weather system usually affects an area, not just one isolated GNSS station.

If one station shows a strong signal but its neighbors do not, that detection can be suppressed. If several nearby stations see the same pattern, we have much more confidence that it represents a real atmospheric signal,” he adds.

Chandna says TACLS is not intended to replace the forecasters who issue weather alerts. It’s just another piece of information that can assist in their decision-making, he says.

Finding a better wayTACLS software is already being used in the LA and San Diego weather forecast offices where flash flooding is a common occurrence, says Laber. “In the West, we deal with a lot more flash flooding, where it happens a lot quicker,” he explains.

Today, TACLS shows them a graph with various points telling them what is going on in real time. A newer version of TACLS with improved graphics is currently being loaded onto the NWS system and is nearly done, Laber says.

It will be available to all of the NWS offices in the second half of October, he adds. This iteration has a map with detailed graphics showing how much it’s raining and whether an extreme weather event is occurring.

According to Laber, having more tools — like this latest update to TACLS — will further improve their decision-making when it comes to issuing flash flood alerts, and, in turn, maybe speed up alert times so people know there’s a problem sooner. Laber says TACLS will likely be used the most in the Western US because the majority of the GNSS sensors are located in places known for earthquake activity.

But Chandna thinks TACLS could work well elsewhere too. As long as there are some GNSS sensors and available local weather data, “we could use the same ideology that we have in our system around different parts of the world,” he says.

Small is thinking beyond TACLS. He believes ongoing research is crucial to improving how the NWS issues flash flood warnings.

“We learn what’s going on a little more every time we do a storm because they’re all different,” he says. “It’s imperative that you do the research after, especially big events, and see what caused it to do what it did, and why, if it was different than what we already knew, did this occur.”

Lin’s concern is Lanesville and her home. “It would have been helpful to know [water was coming] before I was locked in.

I couldn’t go anywhere,” Lin says. “If I got an alert that was like, ‘You need to get out now if you’re in lower-lying ground, this is coming.’

Yeah, that would have [been helpful]. But it came in so fast.”

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