Why weather forecasts are role models

The reasons for such an increase are not simply related to the climatic changes, but also to the intensive exploitation of the land areas, like unauthorized building, diffuse urbanization, river canalization, intensive agriculture etc. In this context, the use of advanced meteorological instruments could surely give help in the forecasting and in the prevention of extreme phenomena and of their consequences.

However, the greatest improvement in extreme events prediction can be achieved by the use of numerical models, which constitute an essential means in the aim of improving both the forecast of the extreme events and the correlated hydrogeological risk assessment.

The paper, after a short introduction on the numerical models, contains some considerations on the importance of land surface conditions and of surface layer parameterizations in order to produce a reasonably good prediction of some extreme dry droughts or heat waves and wet floods events. Among the surface layer parameterizations, the representation of the orographic influences on the atmospheric flow is quite important for estimating the quantity and the intensity of the precipitation.

A detailed discussion will examine the role of the seasonal meteorological predictions, with a part dedicated to the Ensemble Prediction System, and to their utility for forecasting the hydrological variables. Finally, a reflection in the last section is dedicated to the problem of the communication of meteorological and hydrological risks to the public.

Unable to display preview. Download preview PDF. Skip to main content. This service is more advanced with JavaScript available. Advertisement Hide. Authors Authors and affiliations C. Conference paper. This is a preview of subscription content, log in to check access. Balsamo, G. Google Scholar. Barnston, A. Bulletin of the American Meteorological Society , 86 : 59— CrossRef Google Scholar. Bartholmes, J. But twenty-eight years later, in , the first modern electrical computer, ENIAC , made use of his methods and generated a weather forecast.

The Richardsonian method proved to be remarkably accurate. The only downside: the twenty-four-hour forecast took about twenty-four hours to produce. The math, even when aided by an electronic brain, could only just keep pace with the weather.

It was a long time before we could build machines that were capable of scrolling ahead to the future faster than time can progress. Blum, in his book, recounts a visit to the E. Blum describes the process of prediction as though there were two parallel worlds running in sequence: the real one, our own blue marble, and the simulated one, which lives inside the machine.

Periodically, it pauses for real Earth to catch up, checks its answers, corrects anything it got wrong, makes adjustments, and then gallops off into the future again. The supercomputers have brought improved accuracy, too. In , the E. In , its computers correctly foresaw Hurricane Sandy at least six days in advance.

By , they are expected to be able to detect high-impact events two weeks into the future. The E. Notably, it had Hurricane Sandy turning out to sea until just four days before it made landfall. The two systems differ in the way they take observations into account, and there is no shortage of people who are vehement proponents of one model over the other.

And the distance between the model world and the real world—the discrepancy between what we can predict and what will happen—widens as we spin forward further into the future. That puts a limit on how good weather forecasts will ever be. The atmosphere is too complicated, too fragile, too sensitive to small perturbations, to submit to the equations with the precision of planets or stars. All this is compounded by the fact that the big forecasting models typically stick to general patterns of pressure, air speed, precipitation, and temperature.

They pull together the results from the various global models, occasionally add their own data, and often spit out a much more local, tangible prediction. The apps differ wildly in how they handle all that uncertainty, and some will be much more pessimistic than others. Blum recalls a conversation with Tim Palmer, an Oxford professor and a key figure at the E. Was it going to rain? Or not? All forecasters can offer is their best guess at the atmosphere of the future, whispered by the simulated blue marble and wrapped up in uncertainty.

There will always be a gap between the weather and the forecast. Unfortunately, that gap becomes critical in the case of extreme-weather events. Even if we manage to achieve the goal set by the Paris Agreement and global warming is kept to under two degrees centigrade, the impact on our weather will be dramatic.

Two degrees might not sound like much; it can be the difference between a nice day and a slightly nicer day. But that shift in temperature makes extremes more likely. Our long-range predictions—especially those which anticipate extreme-weather events—rely on an assumption that the future will be similar to the past.

Lose that, and we lose the tools that have allowed us to prepare for such eventualities. By Kathryn Schulz. The book, like this blog, is detail-oriented. In fact, one of the arguments that it advances is that we are sometimes too willing to take elegantly written narratives as substitutes for a more uncertain truth.

But there is a healthy balance between computer modeling and human judgment in weather forecasting that is lacking in many other disciplines. Weather forecasters, however, have an unusually good sense of the strengths and weaknesses of these approaches:. But there are literally countless other areas in which weather models fail in more subtle ways and rely on human correction.

Nate Silver is the founder and editor in chief of FiveThirtyEight. Sorry, your blog cannot share posts by email.