Scales of Meteorological Networks - How are Meteorological Networks Classified?
/While the world of meteorology is changing fast, it is important to use and maintain correct terminology. The following meteorological network classification promotes clear and concise communication between professionals, researchers, amateur enthusiasts, and the public.
Meteorological observation networks are classified by their sensor and weather station spacing (how far apart are observations and measuring points from each other). With the advent of the Internet-of-Things (IoT), there is a strong focus on creating dense city-scale and local-scale weather station networks even at the expense of conforming to even the loosest measurement standards.
Scientists seem to be forgetting that the promise of lower-cost wireless and sensor technologies does not, in fact, replace measurement know-how. This measurement know-how is a result of long-term experience and R&D, which takes time and, by default, drives up sensor costs of companies able to perform real measurements with scientific precision. Minimizing the "Observer Effect" of measurement systems takes know-how to avoid measuring unintended influences. One such sin is mounting a rain bucket or a radiation shield close to an ultrasonic wind sensor. Cheap solar radiation shielding for air temperature sensors is another common mistake of amateurs and professionals alike. It is described in detail in "Will the BARANI DESIGN MeteoShield® replace the Stevenson screen as the new reference for climate change measurements?"
| Types of meteorological networks and their density. | |||
|---|---|---|---|
| Spatial scale areal extent | Description | Atmospheric processes and applications | Network examples |
| Global-scale 1,000+ km | Global network of networks, internationally coordinated and facilitated | Semi-permanent pressure centers like the Polar Vortex and trade winds. Data is used from synoptic forecasting, global climate change monitoring and modeling, satellite sensor calibration and validation | Global surface temperature monitoring networks such as NOAA Global Historical Climate Network (GHCN) and Global Climate Observing System (GCOS) |
| Synoptic Macro-scale 100 km - 1,000 km | Networks of national meteorological monitoring stations located within countries, usually in rural areas. Used for examining regional and national synoptic events | National weather forecasting (extratropical cyclones, baroclinic troughs and ridges, frontal zones), modeling | US Automated Weather Observing System (AWOS), US Climate Reference Network (USCRN), AMeDAS, Japan, and the UK Met Office MIDAS network have stations in rural and urban areas that provide hourly surface weather data for weather forecasting, aviation. These datasets are also fed into global data networks |
| Meso-scale Mesonet Regional network10 km - 100 km | Monitor regional meso-scale weather events. Cover urban, peri-urban and rural areas. Meso-scale meteorological events are often hazardous and might go undetected without densely spaced weather observations. Individual monitoring equipment representative of the local or micro-scale climate meso-scale measurements from individual sensors is only now becoming possible with the advent of WMO precision micro-weather stations like the MeteoHelix. | Thunderstorms, downbursts, squall lines, temperature variations over urban and rural areas, sea circulations | Currently several relatively high-density Mesonets (meso-scale networks) exist in the US, China, Finland like the Oklahoma Mesonet network which was designed and implemented by scientists as the gold standard for mesonets by the University of Oklahoma (OU) and Oklahoma State University (OSU).For more information about Mesonets across the United States, visit the National Mesonet website. |
| City-scale 1 km - 10 km | Monitoring weather and climate at the scale of the whole city. Individual monitoring equipment representative of the local micro-climate. City-scale measurements from individual sensors are now becoming possible with the advent of WMO precision micro-weather stations like the MeteoHelix. | Urban heat island studies, urban climate studies, air pollution | Very high weather station density networks such as the Oklahoma City Micronet, installed to examine urban climate variability. |
| Local-scale Neighbourhood 100 meters - 1 km | Effects of minor landscape features (parks, ponds, small topographic features) neighbourhoods with similar types of urban development (surface cover, size and spacing of buildings, activity). Meteorological equipment can be mounted on street lamp posts and is sited to be representative of neighbourhood (i.e. a set height, representative surface cover, little obstructions, to avoid micro-climate effects) | Urban heat island, variations with land use, surface cover, air pollution, tornadoes and twisters | Few local-scale networks exists, since most individual climate stations within city-scale networks or meso-scale networks are often representative of the neighbourhood in which it is located (unless they are specifically examining micro-climates). Urban networks are usually city-scale or meso-scale since dense networks are not necessary to assess local-scale climate over similar land-use types |
| Micro-scale 100 meters or less | Micrometeorological phenomena. Influenced by urban areas, the dimensions of component elements: buildings, green roofs, trees, roads, streets, courtyards, and gardens. Equipment such as a micro-weather station can be located on street lamps or traffic light poststo be representative of the micro-climate | Urban canyon studies, turbulence and dispersion studies, human comfort and exposure, impact of buildings, agricultural meteorology | Some micro-scale networks such as uScan project, Tokyo, have been used to examine fine-scale temperature variations over complex infrastructure |
| Citation | Muller, C.L., Chapman, L., Grimmond, C.S.B., Young, D.T. and Cai, X. (2013), Sensors and the city: a review of urban meteorological networks. Int. J. Climatol., 33: 1585-1600. doi:10.1002/joc.3678 https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.3678 | ||
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