On Different Types of Weather Observations: Part II

Source: SA Weather and Disaster Observation Service

Our last post on weather observations dealt with large scale or synoptic observations. However, in recent years driven by the demands of users and advances in research, operational meteorologists have become interested in monitoring weather events at a smaller scale.

Users interested in this type of weather information include farmers, agricultural insurers (agricultural meteorology); drivers of all sorts and traffic managers (road weather); electrical utilities and power grid managers (watershed hydrometeorology and air quality meteorology)

Smaller scale observations
Small scale observations include those conducted at a meso-beta scale of 20 to 200 kilometers. Observations at this scale include phenomena like sea breezes, and lake effect snow storms.

Small scale observations can also be made at a meso-gamma scale of 2-20 kilometers. Phenomena at this scale include thunderstorms (also known as the storm scale), large wildfires, pyrocumulonimbus clouds, fire storms as well as complex terrain flows over or around mountains and urban heat islands.

In contrast to the detailed guidance provided for synoptic and aviation observations, only general guidance is available for observations on these smaller scales. The quantities to be observed, the spatial distribution of the several observing systems (an "observing network") necessary to capture essential details of the phenomena of interest, the frequency at which observations are to be reported and how they are processed and displayed for use vary significantly from application to application. 

As an example, support to agriculture may require observations on a variety of scales from sub-synoptic (regional drought monitoring) to meso-gamma (forecasting pest emergence in fields in a small region). This requires the meteorologist planning observing systems to be creative in terms of instrument selection and exposure as well as in observing network design. Thus, some agrometeorological observations may be at the heights of the plant canopy and taken every hour to monitor crop conditions. Other measurements, such as daily maximum and minimum temperature to estimate growing degree days, may need to be taken only once per day.


In developing observing systems and networks to monitor events on any of these mesoscales a basic principle- that the observations must be representative for the spatial and time scales of interest- will always apply. In addition, quality assurance measures become extremely important to ensure that the data have the characteristics required for the application. Similarly, continually updated metadata documenting the who, what, when, where, why and how of data collection for each observing site in the network are essential for users to understand the measurements being made.

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