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Meteorology

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Meteorology is the scientific study of the atmosphere, with particular attention to the processes that produce weather and to the practice of forecasting it. Meteorologists observe the air, describe its behavior with equations, and turn that understanding into forecasts and warnings used in agriculture, transport, energy and public safety.


What meteorology studies

Nearly all weather takes place in the lowest layer of the atmosphere, the troposphere. Meteorology examines how sunlight, water vapor, temperature contrasts and the rotation of the Earth combine to drive winds, clouds and precipitation. Weather describes the short term state of the atmosphere at a given place and time, while climate describes average weather over an extended period; NOAA summarizes the idea as "climate is what you expect, weather is what you get."[1] Meteorology concentrates on minutes to weeks, while climatology studies longer periods.

History

The mercury barometer devised by Evangelista Torricelli made air pressure measurable, and in 1648 Blaise Pascal and Florin Perier carried one up the Puy de Dome to show that pressure falls with altitude. Later observers used portable barometers and thermometers to record how temperature decreases with height.[2] The telegraph then turned scattered readings into networks. In the United States the Army Signal Service equipped 25 stations with barometers, thermometers, hygrometers, anemometers and rain gauges, and on 1 November 1870 their synchronized observations were first telegraphed to Washington.[3]

In 1904 the Norwegian scientist Vilhelm Bjerknes proposed predicting weather by solving the equations of fluid dynamics and thermodynamics. Lewis Fry Richardson tried this by hand and published the method in 1922 as Weather Prediction by Numerical Process; his six hour forecast took weeks to compute and was badly wrong, yet it set out the procedure computers would later follow.[4] In 1950 a group led by Jule Charney used the ENIAC computer to produce four 24 hour forecasts with a simplified model, showing that numerical weather prediction was practical.[5][4]

Key concepts

Air pressure is the weight of the air above a point, and wind blows from higher toward lower pressure. Because the Earth rotates, moving air is deflected to the right in the Northern Hemisphere and to the left in the Southern Hemisphere, the Coriolis effect, which shapes wind belts such as the trade winds.[6]

An air mass is a large body of air with fairly uniform temperature and humidity, classed as continental or maritime and as arctic, polar or tropical. Where air masses meet they form fronts. At a cold front, dense cold air undercuts warmer air and lifts it abruptly, often into a narrow band of showers or thunderstorms; at a warm front, warm air rises gently over cooler air, giving broad layered cloud and steady precipitation.[7] Humidity, the water vapor content of the air, governs cloud formation.

How forecasts are made today

Surface stations measure temperature, pressure, humidity, wind and precipitation. Radiosondes carried aloft by balloons sample the upper air; the National Weather Service launches them twice daily from 92 stations, and more than 800 upper air stations operate worldwide.[8] Doppler radars such as the 160 radar NEXRAD network track precipitation and wind inside storms,[9] while satellites, aircraft, ships and buoys add many more measurements.[10]

Numerical weather prediction models represent the atmosphere on a three dimensional grid and use its governing equations to step the state forward in time.[4] Because small errors in the starting conditions grow, forecast centers run ensembles of slightly varied simulations whose spread indicates confidence; ECMWF has issued ensemble forecasts since 1992 and runs 51 members.[11]

Machine learning has recently entered operational practice. GraphCast, developed at Google DeepMind and described in Science in 2023, was trained on decades of reanalysis data and produces a ten day global forecast in under a minute.[12] On 25 February 2025 ECMWF placed its Artificial Intelligence Forecasting System (AIFS) into operational service alongside its physics based model, with both starting from the same initial conditions.[10]

Branches

Synoptic meteorology analyzes large weather systems such as cyclones and fronts. Dynamic meteorology studies the equations of motion and thermodynamics behind atmospheric flow. Boundary layer meteorology examines the lowest part of the atmosphere, where friction and surface heating drive turbulence. Mesoscale meteorology covers features such as thunderstorms and sea breezes. Applied branches include agricultural meteorology, concerned with crops, soil moisture and frost, and aviation meteorology, which addresses turbulence, icing, visibility and wind shear.

Independent and community weather observation

Individuals and communities can run their own weather stations, keep their own records and contribute to shared networks.

Siting matters more than the price of the equipment. National Weather Service guidance recommends placing the temperature and humidity sensor in a ventilated radiation shield 1.25 to 2.0 meters above representative ground, away from pavement and roofs, at a distance of at least twice the height of the nearest tree or building. The anemometer ideally sits 10 meters up, and the rain gauge should be level and clear of obstructions.[13] After installation, comparing readings with nearby official observations over several days reveals biases that need correction.

For logging, the free and open source program WeeWX reads many common station models, stores the record in a local SQLite or MySQL database and generates graphs and web pages. It runs on Linux computers including the Raspberry Pi, so the full archive stays on hardware the observer controls.[14][15]

The Citizen Weather Observer Program (CWOP) gives each registered station an identifier, and its data flow into the NOAA MADIS system, where they are quality controlled, made public and returned to contributors as feedback.[16][17] CoCoRaHS, the Community Collaborative Rain, Hail and Snow Network, has more than 28,000 volunteers measuring precipitation with low cost gauges.[18] The mPING app collects anonymous reports of precipitation type that are relayed to local forecast offices.[19]

The National Weather Service API provides forecasts, alerts and observations free to use for any purpose,[20] and ECMWF publishes a subset of its IFS and AIFS forecasts under a CC BY 4.0 licence.[21]

  • Why did Richardson's 1922 forecast fail, and what changes made the 1950 ENIAC forecasts succeed?
  • Explain the difference between weather and climate using an example from your own region.
  • Compare the strengths and limits of radiosondes, radar and satellites as sources of atmospheric data.
  • Essay idea: how did the telegraph change the way societies prepared for storms in the nineteenth century?
  • What does the spread of an ensemble forecast tell a user, and how should it shape decisions?
  • Discuss whether machine learning forecast models can replace physics based models, or whether the two depend on each other.
  • AI prompt idea: "Walk me through siting a personal weather station on a small suburban lot, step by step, and list common sources of error."
  • AI prompt idea: "Explain how a cold front differs from a warm front, then quiz me with five questions."
  • Design a small community project that compares backyard rain gauge totals with official observations over one season.

Readings from Wikipedia

See also

References

  1. ↑ "What is the difference between weather and climate?". NOAA National Ocean Service. https://oceanservice.noaa.gov/facts/weather_climate.html. 
  2. ↑ "The History of Atmospheric Discovery". UCAR Center for Science Education. https://scied.ucar.edu/learning-zone/atmosphere/history-discovery-atmosphere. 
  3. ↑ "The First Synchronized Weather Observations". NOAA National Weather Service Heritage. https://vlab.noaa.gov/web/nws-heritage/-/the-first-synchronized-weather-observations. 
  4. ↑ 4.0 4.1 4.2 Geelhart, Chris. "Numerical Weather Prediction". NOAA National Weather Service Heritage. https://vlab.noaa.gov/web/nws-heritage/-/numerical-weather-prediction. 
  5. ↑ Charney, J. G.; Fjortoft, R.; von Neumann, J. (1950). "Numerical Integration of the Barotropic Vorticity Equation". Tellus 2 (4). doi:10.3402/tellusa.v2i4.8607. https://tellusjournal.org/articles/10.3402/tellusa.v2i4.8607. 
  6. ↑ "Wind". UCAR Center for Science Education. https://scied.ucar.edu/learning-zone/how-weather-works/wind. 
  7. ↑ "Air Masses". NOAA JetStream. https://www.noaa.gov/jetstream/synoptic/air-masses. 
  8. ↑ "Radiosonde Observation". NOAA National Weather Service. https://www.weather.gov/upperair/factsheet. 
  9. ↑ "Next Generation Weather Radar (NEXRAD)". NOAA National Centers for Environmental Information. https://www.ncei.noaa.gov/products/radar/next-generation-weather-radar. 
  10. ↑ 10.0 10.1 "ECMWF's AI forecasts become operational". ECMWF. 25 February 2025. https://www.ecmwf.int/en/about/media-centre/news/2025/ecmwfs-ai-forecasts-become-operational. 
  11. ↑ "Fact sheet: Ensemble weather forecasting". ECMWF. https://www.ecmwf.int/en/about/media-centre/focus/2017/fact-sheet-ensemble-weather-forecasting. 
  12. ↑ Lam, Remi (2023). "Learning skillful medium-range global weather forecasting". Science 382 (6677): 1416-1421. doi:10.1126/science.adi2336. https://arxiv.org/abs/2212.12794. 
  13. ↑ "Personal Weather Station: Siting". NOAA National Weather Service. https://www.weather.gov/media/epz/mesonet/CWOP-Siting.pdf. 
  14. ↑ "WeeWX: open source software for your weather station". https://weewx.com/. 
  15. ↑ "weewx/weewx". GitHub. https://github.com/weewx/weewx. 
  16. ↑ "Citizen Weather Observer Program". NOAA National Weather Service. https://www.weather.gov/fgz/cwop. 
  17. ↑ "MADIS: Citizen Weather Observer Program". NOAA. https://madis.ncep.noaa.gov/madis_cwop.shtml. 
  18. ↑ "CoCoRaHS: Community Collaborative Rain, Hail and Snow Network". https://www.cocorahs.org/. 
  19. ↑ "Weather Spotter's Field Guide: More Ways to Help". NOAA National Weather Service. https://www.weather.gov/spotterguide/morehelp. 
  20. ↑ "API Web Service". NOAA National Weather Service. https://www.weather.gov/documentation/services-web-api. 
  21. ↑ "Open data". ECMWF. https://www.ecmwf.int/en/forecasts/datasets/open-data. 
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