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Meteorology
Meteorology, is the study of the atmosphere that focuses on weather processes and forecasting. Meteorological phenomena are observable weather events which illuminate and are explained by the science of meteorology.
Those events are bound by the variables that exist in Earth’s atmosphere. They are temperature, pressure, water vapor, and the gradients and interactions of each variable, and how they change in time. The majority of Earth’s observed weather is located in the troposphere.
Meteorology, climatology, and atmospheric physics are subsets of the atmospheric sciences.
History of Meteorology
Also refer to the Timeline of Meteorology
The term meteorology goes back to the book Meteorologica (about 340 BC) by Aristotle, who combined observations with speculation as to the origin of celestial phenomena. The greek word meteoron refers to things “high in the sky”, that is between Earth and the realm of the stars, while logos means “study”. A similar work, called “Book of Signs”, was published by Theophrastus, a pupil of Aristotle. It was centered more on predicting the weather by interpreting established celestial phenomena, such as a halo around the moon, without asking for explanations.
Further progress in the meteorological field had to wait until accurate instruments were available. Galileo constructed a thermometer in the 1500s, followed by Torricelli’s invention of the barometer in 1643. The dependence of atmospheric pressure on height was first shown by Blaise Pascal and RenŽ Descartes. The anemometer for measuring wind speed was constructed in 1667 by Robert Hooke, while Horace de Saussure completed this list of the most important meteorological instruments in 1780 with the hair hygrometer, which measures humidity.
Other advances that are usually thought of as part of the progression of physics were Robert Boyle’s investigation of the dependence of gas volume on pressure which lead to thermodynamics and Benjamin Franklin’s kite experiments with lightning.
The first essentially correct explanation of global circulation was the 1735 study by George Hadley about the trade winds, which gave rise to calling the tropical cell of zonal mean atmospheric circulation “Hadley cell”. In 1835, Gaspard de Coriolis recognized that the rotation of Earth causes a velocity-dependent force on bodies in the reference frame of a non-rotating Earth.
Synoptic weather observations were still hindered by the difficulty of establishing certain weather characteristics such as clouds or wind. These were solved when Luke Howard and Francis Beaufort introduced their systems for classifying clouds (1803) and wind speeds (1806), respectively. The real turning point however was the invention of the telegraph in 1843 that allowed exchange of weather information with unprecedented speed.
Early in the 20th century, theoretical studies of atmospheric phenomena usually were performed analytically, that is by taking the fluid-dynamical equations that govern atmosperic flow, simplifying them by neglecting lesser terms, and looking for solutions to these equations. For example, Vilhelm Bjerknes developed the model that explains the generation, intensification and ultimate decay (the lifecycle) of midlatitude cyclones, introducing the idea of fronts, that is, sharply defined boundaries between air masses.
Starting in the 1950s, numerical experiments with computers became feasible. The first weather forecasts derived this way used barotropic (that means, single-vertical-level) models, and could successfully predict the large-scale movement of midlatitude Rossby waves, that is, the pattern of atmosperic lows and highs.
In the 1960s, the chaotic nature of the atmosphere was first understood by Edward Lorenz, founding the field of chaos theory. The mathematical advances achieved here later filtered back to meteorology and made it possible to describe the limits of predictability inherent in atmospheric modelling. This is known as the butterfly effect, because the growth of disturbances over time means that even one as minute as the flapping of a butterfly’s wings could much later cause a large disturbance to form somewhere else.
In 1960, the launch of Tiros 1, the first weather satellite marked the beginning of the age where weather information is availabe globally. Weather satellites along with more general-purpose Earth-observing satellites circling the earth at various altitudes have become an indispensable tool for studying a wide range of phenomena from forest fires to El Ni–o.
In recent years, climate models have been developed that feature a resolution comparable to older weather prediction models. These climate models are used to investigate the effects of long-term climate shifts such as the one caused by human emission of greenhouse gases.
Meteorology and climatology: Some challenges for this century
With the development of powerful new supercomputers like the Earth Simulator in Japan, numerical modelling of the atmosphere can reach unprecedented accuracy. This is not only due to the enhanced spatial and temporal resolution of the grids employed, but also because these more powerful machines can model the Earth as an integrated climate system, where atmosphere, ocean, vegetation, and man-made influences depend on each other realistically.
The goal in global meteorological modelling can thus currently be termed Earth System Modelling, with a growing number of submodels of various processes coupled to each other. Predictions for global effects like Global Warming and El Ni–o are expected to benefit substantially from these advancements.
Regional models are also becoming more interesting as the resolution of global models increases and with the observed increase in regional weather disasters such as the Elbe flooding in 2002 and the European heat wave in 2003. Decisionmakers expect from these models accurate assessments about the possible increase of these natural hazards in specific regions and countermeasures (such as dikes or areas that are flooded by purpose to decrease the flooding somewhere else) that might be effective in preventing or at least attenuating them.
For models at all scales, increased model resolution means less reliance on parameterizations , which are empirically derived expressions for processes that cannot be resolved on the model grid. For example, in mesoscale models individual clouds can now be resolved removing the need for formulations that average over a grid box. In global modelling, atmospheric waves such as gravity waves with short temporal and spatial scales can be represented without resorting to often overly simplified parameterizations.
With model output approaching oberservational data (e.g. from satellite soundings) in resolution, the sheer size of the datasets means that data mining and data management will become equally important considerations in meteorological computing. In light of the decrease in density of surface and rawinsonde observations, new algorithms have to be developed to extract similarly accurate information from satellite data, for example about cloud type and distribution.
Data management will become more global in nature, with some central archives storing a large number of numerical experiments from various institutions. This data needs to have a sufficient amount of metadata attached and can then be conveniently retrieved by a WWW interface from anywhere.
These new archives will alleviate the important task of comparing experiments conducted with different models, which is instrumental for their further improvement. Also, grid computing may be a interesting way to harness the power of meteorological supercomputers more effectively. Of course, international cooperation is nothing unusual in modelling, but grid computing might automate the process of running a model where the right amount of computing resources are currently available and leave scientists more time for analyzing the results.
Meteorological instrumentation that is used at the surface or in airplanes also has room for improvement. Radar and Lidar show precipitation and clouds by their effects on emitted monospectral electromagnetic waves. If radar measurements can be used to accurately determine the amount of precipitation (which as of now is only possible with rain gauges), this would be beneficial for numerical weather prediction.
Lidar can be used to study clouds that are so thin that they cannot be seen by the naked eye such as certain types of cirrus filaments. The high-altitude clouds that can form from contrails may well be the key to understanding the effect of the increasing amount of air travel on global warming.
Aside from weather and climate prediction, weather modification has been (often covertly) attempted sinces the 1950s—often by the military, but also at airports. But even without consideration of anecdotal evidence of trying to use weather modification as a “weapon” (such as the supposed cloud seeding by US troops during the Vietnam conflict), it is clear that unilateral weather modification may lead to political tensions.
Especially in the Middle East, the possibility of wars about water supply looms for this century. While many of the proposed systems for modification of the water cycle belong more to the domain of engineering than to meteorology, it is clear that meteorology may take on additional political dimensions beyond the battle between Europe and the US over the throttling of greenhouse gas emission.
Finally, meteorologists must educate the public more about weather and climate in general. Scientifically accurate and understandable information about topics like the ozone hole, global warming, the effects of rainforest deforestation, or sea level rise must be disseminated and misinformation by industry lobbyists be countered.
Particularly in Europe, which may see an increase in extreme weather events as it already has in the 1990s, the population must be educated to pay closer attention to severe weather warnings or information about other detrimental health factors such as high tropospheric ozone concentration or high levels of UV radiation.
Similarly, a better infrastructure to deal with natural desasters must be developed akin to similar services in the US. It is clear that political decisionmakers in Europe will rely on scientific assessment to validate the necessity for such spending.