Yes, but we can still precisely calculate what the average temperature will be 100 years out within a couple tenths of a degree. Not only that, but predict what it will be a century after you scrap your SUV.
Here in San Diego, the most stable weather pattern on planet earth, the forecasts will swing 15 degrees for just a 24 hour forecast. I give them crap all the time and their only answer is a shrug of the shoulders.
US forecast models have been pretty terrible during Hurricane Irma
NOAA's best weather model seems to be getting worse with hurricanes, not better.
We have written a fair amount at Ars recently about the superiority of the European forecast model, suggesting to readers that they focus on the ensemble runs of this system to get a good handle on track forecasts for Hurricane Irma. Then we checked out some of the preliminary data on model performance during this major hurricane, and it was truly eye-opening.
Brian Tang, an atmospheric scientist at the University of Albany, tabulates data on "mean absolute error" for the location of a storm's center at a given time and where it was forecast to be at that time. Hurricane Irma has been a thing for about a week now, so we have started to get a decent sample size—at least 10 model runs—to assess performance.
The model data
The chart below is extremely busy, but when you understand how to read it, the data is striking. It shows the average position error (in kilometers) at forecast lead times of 12, 24, 48, 72, 96, and 120 hours (so, out to five days). It compares several different classes of models, including global models that forecast conditions around the planet, nested models focused on hurricanes, and consensus forecasts.
Forecast models typically show their skill with three-, four-, and five-day forecasts. For simplicity's sake, we will focus on 120-hour forecasts. At this lead time, the average error of the European model with respect to Irma has been about 175km in its position forecast. The next best forecast is from the hurricane center, which is slightly more than 300km. An automated model, then, has so far beaten human forecasters at the National Hurricane Center (looking at all of this model data) by a wide margin. That's pretty astounding.
What is particularly embarrassing for NOAA, however, is the comparison between the European model and the various US forecast modeling efforts. The average 120-hour error of the GFS model is about 475km. The operational, hurricane-specific model, HWRF, does better, with an average error of 325km. But the experimental HMON model does terribly, at nearly 550km of error. A similar disparity in quality goes all the way down to 24-hour forecasts.
US forecast models have been pretty terrible during Hurricane Irma
Here in San Diego, the most stable weather pattern on planet earth, the forecasts will swing 15 degrees for just a 24 hour forecast. I give them crap all the time and their only answer is a shrug of the shoulders.
US forecast models have been pretty terrible during Hurricane Irma
NOAA's best weather model seems to be getting worse with hurricanes, not better.
We have written a fair amount at Ars recently about the superiority of the European forecast model, suggesting to readers that they focus on the ensemble runs of this system to get a good handle on track forecasts for Hurricane Irma. Then we checked out some of the preliminary data on model performance during this major hurricane, and it was truly eye-opening.
Brian Tang, an atmospheric scientist at the University of Albany, tabulates data on "mean absolute error" for the location of a storm's center at a given time and where it was forecast to be at that time. Hurricane Irma has been a thing for about a week now, so we have started to get a decent sample size—at least 10 model runs—to assess performance.
The model data
The chart below is extremely busy, but when you understand how to read it, the data is striking. It shows the average position error (in kilometers) at forecast lead times of 12, 24, 48, 72, 96, and 120 hours (so, out to five days). It compares several different classes of models, including global models that forecast conditions around the planet, nested models focused on hurricanes, and consensus forecasts.
Forecast models typically show their skill with three-, four-, and five-day forecasts. For simplicity's sake, we will focus on 120-hour forecasts. At this lead time, the average error of the European model with respect to Irma has been about 175km in its position forecast. The next best forecast is from the hurricane center, which is slightly more than 300km. An automated model, then, has so far beaten human forecasters at the National Hurricane Center (looking at all of this model data) by a wide margin. That's pretty astounding.
What is particularly embarrassing for NOAA, however, is the comparison between the European model and the various US forecast modeling efforts. The average 120-hour error of the GFS model is about 475km. The operational, hurricane-specific model, HWRF, does better, with an average error of 325km. But the experimental HMON model does terribly, at nearly 550km of error. A similar disparity in quality goes all the way down to 24-hour forecasts.
US forecast models have been pretty terrible during Hurricane Irma
