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The lowdown on vertical visibility

Updated: Nov 3, 2021

A pilot will encounter several different flavors of visibility. This includes flight visibility, ground visibility, prevailing visibility, runway visual range and vertical visibility just to name a few. But wait, is vertical visibility even a legitimate visibility? Actually it’s not a true measure of visibility at all. Vertical visibility is more of a close cousin to ceiling than it is to visibility. That is, it represents the distance in feet a person can see vertically from the surface of the earth into an obscuring phenomena or indefinite ceiling. Clear as mud?


There’s no doubt that an indefinite ceiling is perhaps the most misunderstood phenomenon reported in a surface observation (METAR). You’ll also find it mentioned in a terminal aerodrome forecast (TAF) as shown below in the EZAirport page of the EZWxBrief progressive web app. Whether in a METAR or TAF, vertical visibility is coded as VV followed by a three digit height in hundreds of feet (e.g., VV002). So let’s explore the difference between a definite and indefinite ceiling.


Automated observations

Human observers have used rotating beam and laser beam ceilometers for many years to measure the height of clouds. Today, the task of walking outside and assessing the height of clouds is generally a thing of the past given that this technology is incorporated into the Automated Surface Observing System (ASOS) located at many airports throughout the U.S. The trained observer simply logs into the ASOS and makes his or her observation based on the data gathered and reported by the automated system. Then the observation is edited and augmented by the observer as necessary.

Making an estimate of the height of the cloud base isn’t the difficult part. What’s difficult is to provide a representative description of the amount of cloud coverage (e.g. few, scattered, broken or overcast) in the airport’s terminal area. A laser beam that points straight up may easily miss a scattered or broken cloud deck. To alleviate this issue, the automated systems process the data over a period of time since clouds are generally moving through the sensor array most of the time. It was found that a 30-minute time period provided a representative and responsive observation similar to that created by a trained observer. The most recent 10 minutes are double-weighted using a harmonic mean.