Nick Stockton, Wired magazine
This image, made with NOAA’s newest weather model, shows ground temperature readings at a 2 mile resolution. Each pixel is shaded according to the temperature, ranging from 113 degrees F (the brightest yellow) to freezing (white). (c) NOAA
This colorful map of ground temperature shows the tapestry of American weather on September 30. Undeniably beautiful, it owes its rich color gradient to a powerful new scientific tool for modeling the weather for incredibly small chunks of both time and space.
After five years of work, NOAA unveiled the new model, called High Resolution Rapid Refresh (HRRR), on September 30. Like its predecessor, HRRR will update every hour. But, HRRR fine tunes the forecast every 15 minutes by constantly digesting radar reports, so that the hourly update is as accurate as possible. Each forecast starts with a 3-D radar snapshot of the atmosphere that it modifies with data from NOAA’s vast network of weather stations, balloons, and satellites.
The added timeliness is important, but where the HRRR really shines is in its spatial resolution. While the previous model could only model the weather in 8-mile chunks, HRRR has a 2-mile spatial resolution. This is important because even a small front can contain dozens of individual storms. Forecasters will have the ability to read the weather on a neighborhood scale, rather than a city-wide scale.
This will let meteorologists and disaster-planners focus on localized weather threats, like tornadoes, hail, or whether that rain-laden thundercloud is going to pass over a neighborhood that’s prone to flooding, or one with better drainage. It could also lead to smoother flights by helping pilots and air traffic controllers get a more detailed picture of where turbulence occurs.
The graphic below shows the same weather system in HRRR (right) and its predecessor (left). In the old model, the front appears to be one, big, splotchy storm. HRRR shows that the front is actually a patchy group of storm cells, a picture that is much closer to reality.