A mesoscale observation network can be regarded as a network that observes microscale phenomena with mesoscale resolution. Thus, it is important to understand how microscale land features impact the observations of surface layer phenomena. To isolate the microscale signal of a particular variable, averaged values of that variable across the network can be used to remove meso- and synoptic scale signals from a set of observations.

Observations of wind speed, wind direction, temperature, and dewpoint from 114 Oklahoma Mesonet sites collected between 1994-2002 were compared with statewide averages of these variables. First, the observations were stratified by wind direction. Next, the statewide mean values of wind speed, temperature, and dewpoint were subtracted from corresponding values at individual sites. The result was a meteorological “fingerprint” of each site that indicated anomalies of atmospheric variables based on wind direction.

For example, wind speed anomalies of 50% or greater were detected at approximately half of the Oklahoma Mesonet sites. In turn, the wind speed anomalies created wind direction-dependent anomalies of nighttime temperature – a variable that is strongly impacted by wind speed.

In other cases, anomalies of particular meteorological variables did not correspond with the wind speed anomalies, but were consistent with influences from larger scale features such as nearby cities and lakes. Thus, the stratification of anomalies by wind direction was useful to quantify the downwind impact of meso-gamma scale surface features as well as the local impact of microscale features near each site.