Scientists at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin—Madison have made new updates to old technology that will enable weather forecasters to make improved predictions of severe weather.
The new capability is based on an algorithm CIMSS scientists Tony Wimmers and Chris Velden developed nearly a decade ago to better equip satellites to measure “total precipitable water” (TPW), the total amount of water vapor contained in a column of moist air from the Earth’s surface to the very top of the atmosphere. That algorithm allowed measurements solely above the sea, but now TPW values can be measured over land, too.
By measuring TPW, forecasters learn how much moisture in a given column of moist air could potentially become rain or snow. It is an especially important metric for predicting and tracking tropical cyclones and other severe weather in the humid tropics.
In 2003, Wimmers and Velden sought to fill in the time gaps in TPW data already collected by polar-orbiting satellites, which gather swaths of information from above the Earth’s surface roughly every one-to-18 hours. They wanted to create an algorithm that would apply an existing technique to TPW data, but the technique had only ever been used for flat values, not for volume, such as a column of TPW.
“I didn’t think it would work,” says Wimmers, the lead developer for the project, called the Morphed Integrated Microwave Imagery at CIMSS – Total Precipitable Water, or MIMIC-TPW. “But, it turns out, it provides a very accurate approximation over the time gaps that we were filling in … I think it surprised everyone.”
Wimmers and Velden launched the first MIMIC-TPW algorithm in 2007 and it has been an integral tool for tropical weather analysis ever since. Still, there was room for improvement, particularly to better serve coastal forecasters.
Some polar-orbiting satellites scan the Earth and collect data in a conical pattern, so while they are constantly changing position all over the globe, their retrievals are generated from the same scan angle. This allows them to take very precise measurements and stay well calibrated with other conical scanners, Wimmers explains, but it constrains TPW data collection to uniform surfaces like oceans. Land was simply too uneven, especially in locations with varied topography.
“It’s a very simple algorithm that gives you a very fast retrieval of TPW over oceanic areas, says Wimmers. “But it doesn’t generate retrievals over land.”
This was adequate for users like the U.S. Naval Research Laboratory — a major source of support for MIMIC–TPW — and other users generally interested in forecasting marine weather and tropical cyclone environments. But Wimmers and Velden hoped to find an alternative solution that could extend coverage to over land areas.
Then, last year, they got their opportunity.
The National Oceanic and Atmospheric Administration (NOAA) made significant improvements in the TPW retrieval from its microwave-scanning satellites, called the Microwave Integrated Retrieval System (MIRS). While lower in overall resolution, these satellites provide more comprehensive coverage than conical-scanning satellites.
“Since this is a full atmospheric retrieval system, it works over all surfaces, including land,” says Wimmers.
He and Velden made changes to the MIRS algorithm, a technique they refer to as “morphological compositing.” It uses TPW data from every available operational microwave-frequency satellite sensor, extending MIMIC-TPW’s coverage above and beyond the original design, providing TPW values over land and sea for the entire globe.
“We adapted the image-morphing algorithm to work over water and land, and applied it to a more formal coordinate system,” says Wimmers. “What we were doing before was essentially a shortcut — it only needed to apply to the tropics. This new method makes it work in global coordinates.”
The algorithm uses data from seven microwave instruments from U.S. military, NOAA, Japanese and European satellites. Then it incorporates wind values from the National Weather Service’s global weather model — the Global Forecast System (GFS) — and accounts for water vapor motion. The algorithm can “push” the data forward from the time of the measurement about one-to-10 hours or push it backward by the same time interval, Wimmers explains.
“That way, you can take one observation and make it apply to a long stretch of time,” he says, though he cautions the technique needs “special care.”
“You have to make sure you are not violating too many assumptions about how water vapor moves, but it is a pretty simple process,” Wimmers says.
While the new MIMIC-TPW version is not yet fully operational (it has only been online a few weeks) the team has already received requests for case study imagery, some of which predate its release, such as a severe flooding event in South Carolina last fall. The data should be more useful for a variety of interested users.
“This is very encouraging. It shows us we are on the right track,” Wimmers says. “It’s a very good sign that people (forecasters) are that interested in it, and we can start to take it in new directions.”
MIMIC-TPW 2.0 will most likely be operational in the fall of 2016.
Source: University of Wisconsin-Madison