A meteogram is a graphical representation that displays meteorological variables—such as temperature, precipitation, wind speed and direction, atmospheric pressure, humidity, and cloud cover—over a specified time interval for a particular location. Typically plotted on a shared time axis, meteograms allow simultaneous visualization of multiple weather parameters, facilitating the analysis of temporal weather patterns and short‑term forecasting.
Description and Components
| Component | Typical Representation | Units |
|---|---|---|
| Temperature | Line or symbol plot | °C or °F |
| Precipitation | Bar chart (accumulated) or line (rate) | mm or in |
| Wind | Barbs or arrows indicating speed and direction | knots, m s⁻¹ |
| Atmospheric pressure | Line plot | hPa or mb |
| Relative humidity | Line or shaded area | % |
| Cloud cover | Symbolic icons or shaded regions | oktas or % |
Meteograms may incorporate additional data such as solar radiation, dew point, visibility, or model-derived parameters (e.g., probability of precipitation). The time axis can span from a few hours to several days, depending on the data source (observational records, numerical weather prediction outputs, or climatological averages).
Historical Development
The concept of visualizing weather data over time dates to the nineteenth‑century development of synoptic meteorology, when handwritten charts displayed temperature and pressure trends. With the advent of computerized plotting in the mid‑20th century, meteorologists began generating automated, multi‑parameter time series for specific stations. The term “meteogram” emerged in the late 20th century, primarily in academic literature and operational forecasting software, to describe integrated plots that combine several meteorological variables.
Data Sources
- Surface Observations – Real‑time data from Automated Weather Stations (AWS), airport METAR reports, or personal weather stations.
- Numerical Weather Prediction (NWP) Models – Output from models such as the Global Forecast System (GFS), European Centre for Medium‑Range Weather Forecasts (ECMWF), or high‑resolution regional models.
- Reanalysis Datasets – Retrospective, gridded products (e.g., ERA5) that blend observations with model output.
The selection of data source influences temporal resolution (e.g., hourly observations vs. 3‑hourly model forecasts) and the reliability of the displayed parameters.
Applications
- Operational Forecasting – Forecasters at national meteorological services and private weather firms use meteograms to assess short‑term trends for aviation, marine, and public safety contexts.
- Public Weather Services – Web portals and mobile applications provide meteograms for user‑friendly, location‑specific weather summaries.
- Research and Education – Atmospheric scientists employ meteograms to examine case studies of weather events, validate model performance, and teach meteorological concepts.
- Energy Sector – Wind and solar power operators use meteograms to anticipate resource availability.
Visualization Techniques
Meteograms are generated using various software libraries and platforms, including:
- Meteorological Plotting Packages – Python libraries such as Matplotlib with MetPy extensions, R’s ggplot2, or the JavaScript-based Highcharts for web deployment.
- Dedicated Forecast Tools – Commercial systems like Weather Decision Technologies (WDT) or open‑source platforms such as the Integrated Forecast System (IFS) viewer.
Customization options often allow users to select which variables to display, adjust color schemes, and set time ranges.
Standardization
While there is no single international standard governing meteogram design, the World Meteorological Organization (WMO) offers guidance on the presentation of time‑series weather data within its Guide to Meteorological Instruments and Methods of Observation (GMIMO). Consistency in axis labeling, unit specification, and legend usage is promoted to aid interpretability across agencies.
Limitations
- Data Gaps – Incomplete observational records can produce discontinuities.
- Model Uncertainty – Meteograms derived from forecast models inherit inherent uncertainties, especially beyond 48 hours.
- Interpretation Complexity – Overloading a single plot with many variables may reduce clarity for non‑expert audiences.
See Also
- Weather map
- Skew‑T log‑P diagram
- Time‑height cross‑section
References
- World Meteorological Organization (2022). Guide to Meteorological Instruments and Methods of Observation (GMIMO). Geneva: WMO.
- D. J. Stensrud (2021). Meteorology: Understanding the Atmosphere. 3rd ed. Wiley.
- H. G. Hensley & J. T. Velden (2015). “Automated generation of site‑specific meteograms from NWP output.” Journal of Applied Meteorology and Climatology, 54(6): 1395‑1408.
- MetPy Development Team (2024). “MetPy: A collection of tools for reading, visualizing, and performing calculations with weather data.” Zenodo.
This entry reflects the current understanding of the term “meteogram” as a recognized meteorological visualization tool.