Weather forecasts are based on computer models and numerical forecasts based on equations
When the majority of the population of Mexico was preparing to celebrate Independence on the night of September 15, 2013, the hurricane Manuel, formed from a tropical storm southwest of Guerrero, made landfall on the coast of Manzanillo. Fifteen hours later, another category 1 hurricane, which also formed from a tropical storm but on the Atlantic coast, entered the community of La Pesca, in the state of Veracruz. The combined force of both hurricanes unleashed intense rains for several days during which there were many landslides and flooding. 18 states were affected with an initial toll of more than 150 deaths.
The most affected state was Guerrero, where the loss of 615,000 hectares of crops was compounded by the drama of dozens of missing people in small towns whose precarious buildings were flooded with mud. The entity was declared a disaster area. Complaints to the authorities for the lack of preventive actions were immediate. Many people wondered why a timely warning was not given to evacuate people. However, meteorologists’ projections anticipated that from the 14th of that month there would be intense rainfall in the states of Guerrero and Tamaulipas. They did their job well and spread the information, but the subsequent tragic episode raised distrust and skepticism regarding the reliability of weather forecasts. Nothing more unjustified. The science of forecasting has changed a lot.
Torrents of data
We have all heard at some point the reports from the National Meteorological Service (SMN), the Mexican agency, dependent on the National Water Commission, in charge of monitoring the atmosphere to identify phenomena that could put the population or the economy at risk, such as storms. , hurricanes or cold fronts. But many of us do not know how that organization makes its forecasts. Instead of isolated observations of cloud patterns, the Sun’s path, or other stars as in ancient times, meteorologists use knowledge from many scientific disciplines to collect, organize, and interpret their data.
The first step is the collection of a large amount of quantitative information about the conditions of the atmosphere in a given space and time. To do this, instruments located both in earth stations and in space are used: barometers, anemometers, rain gauges and radiometers – which respectively measure atmospheric pressure, wind direction, rainfall and air temperature -, radars, satellites and even airplanes. hurricane hunters in countries with greater resources. The information obtained is analyzed and processed with mathematical and computing tools to forecast or project the probable evolution of the phenomenon being observed.
In the case of the National Meteorological Service, this work is carried out on an observation platform that includes a surface synoptic network, made up of 79 meteorological observatories, and a high-altitude synoptic network with 16 radiosonde stations (with balloon probes) for the upper layers of the atmosphere. Each of these stations performs daily measurements of pressure, temperature, humidity and wind.
In addition to this infrastructure, there is a set of 13 meteorological radars distributed throughout the national territory that monitor cloud systems and collect information on the intensity of precipitation, the height and density of clouds, their movements and the speed and direction. of the winds. The SMN also has a station that receives images captured by meteorological satellites from the US agencies NASA (space) and NOAA (ocean and atmosphere) that provide additional data, especially for the Atlantic area.
Forecasts and billiards
To describe the science behind a weather forecast, we can imagine a game of billiards: if we know the speed and position of the balls, then we can predict with a small margin of error where they will go. But to determine the trajectory and destination of the balls, several factors must be taken into account: the force with which they are hit with the cue, the friction of the air, if the table has any inclination with respect to the floor or if the cloth that covers it covers is completely smooth or rough. The latter would be equivalent, for example, to the orography of the area through which a hurricane passes.
Researcher Jorge Zavala Hidalgo, from the Center for Atmospheric Sciences (CCA) at , says: “You have to have information about what enters and leaves the system. It is a three-dimensional phenomenon where in addition to mechanics, thermodynamic aspects must be considered; that is, the interaction of solar energy with the atmosphere.” To know these factors, it is necessary to first describe the initial conditions of the system, but then global analysis models must be used to know the extremes to which a phenomenon could reach and for this other projections on a larger scale are used.
Data extracted separately at first must then be integrated into three-dimensional models of what is happening in reality. At this stage, experts apply various mathematical tools to refine the models and resolve inconsistencies or contradicting data. They can, for example, describe with Isaac Newton’s classical physics equations, the forces that affect a mass of polar air or calculate whether a cyclone will move in a straight line when no other force acts on it to deviate it from its original path. . Returning to the analogy with the pool table, depending on the way the player hits it with the cue, a prediction will be made of the direction in which the ball will move, at what speed and whether it will bounce off some of the sides of the table. table. But it must be said that this analogy is very limited; in reality, atmospheric phenomena are extremely complex and the factors to be considered, and the interactions between them, are very numerous.
Alien atmospheres
If it is already complex to make a reliable weather forecast on Earth, what can we know about the climate and atmosphere of planets outside the Solar System? For astrophysicist Kevin Heng, from the Center for Space and Habitability at the University of Bern, Switzerland, the answer is not at all stormy. With the support of space telescopes such as NASA’s Kepler, thousands of exoplanets, planets orbiting other stars, have been discovered. The challenge is to find out if they could host life. Several conditions must be met for this, but it is essential to know their atmospheres. One way to achieve this is to observe the phenomenon known as a secondary eclipse: the passage of a planet behind its star. Astronomers compare the brightness of the system before and after, and thus can subtract the difference in luminosity. In the end, the faint light of the exoplanet and its atmosphere, if it exists, remains. By studying these eclipses and transits at different wavelengths, the spectrum of the exoplanet’s atmosphere can be constructed; that is, the pattern of its visible and invisible light emissions. By analyzing these spectral fingerprints, it is possible to determine what elements make it up and their abundance.
With these data on the exoplanet’s atmosphere, Heng creates theoretical and computational models to simulate the interactions between its components. These models can be applied to study gas giant planets, such as Jupiter, as well as other rocky Earth-like planets.
Predict chaotic phenomena
Unlike other scientists, such as chemists or astronomers—who can accurately predict the reactions they will get in the laboratory when mixing certain elements or anticipate the paths of planets in the sky—meteorologists cannot make completely accurate forecasts. But this does not diminish the validity of their work or make it less rigorous. Atmospheric phenomena have a chaotic nature (see As you see? No. 22). In physics they are approached as systems in which a small perturbation in the initial conditions can lead to large perturbations later. This was discovered in the case of meteorology by the American scientist Edward Lorenz in the early 1960s, when he was carrying out a computer simulation of weather patterns. For the simulation he entered data from 12 variables, including wind speed and temperature. By changing the value of a variable by a tiny amount, from 0.506127 to 0.506, that is, by rounding that value, the result of the simulation was very different. Lorenz published this result and the implications of it in a famous paper in 1963, in the Journal of Atmospheric Sciences. This very sensitive dependence on the initial values of atmospheric phenomena is one of the things that make meteorology not an exact science, the other, as already mentioned, is that the atmosphere is a system of enormous complexity. Lorenz’s discovery was popularized under the name “butterfly effect,” after the title of a lecture he gave in 1972 at the annual meeting of the American Association for the Advancement of Science: “Can the flapping of a butterfly’s wings in Brazil lead to a tornado breaking out in Texas?” Curiously, it was not Lorenz who chose this title, but a colleague of his. Lorenz had rather thought of the flapping of a seagull’s wings as a metaphor for his discovery.
Due to the nature of atmospheric phenomena, weather forecasts are not given as fixed or absolute data, but within a range of values and with a margin of error that increases according to the time scale. “If we have good information, we can predict how the phenomenon will evolve; but as time progresses, the errors grow,” acknowledges Dr. Zavala Hidalgo. The best example is the study of climate change: modeling the behavior of greenhouse gases that cause warming in the atmosphere over several years or decades is extremely complex due to the number of factors and variables that must be considered. This is the type of scientific work that is carried out daily at the CCA. On the other hand, making day-to-day forecasts is relatively simpler, but no less important. However, even if there is a well-defined methodology, in practice it is impossible to know with absolute certainty the initial conditions (temperature, humidity, pressure) at each point in the atmosphere. In addition, there are other factors that influence the degree of uncertainty in the forecasts: errors in the measurement of initial conditions, lack of a complete understanding of atmospheric phenomena and even lack of supercomputing equipment to process the torrents of data that are collected. . “Ideally I would like to state that…