Although the concept of automated observations is relatively new, for years observers have used instruments to measure the height of clouds. From ceiling balloons and ceiling lights to the modern laser beam ceilometers, human observers have depended on measuring devices to determine the height of clouds above the ground. Observers have used the most recent tool, the laser beam ceilometer, for nearly 10 years. The new Automated Surface Observing System (ASOS) employs an identical laser beam ceilometer to determine sky conditions.
In the past, human observers evaluated a trace on a graphical chart depicting the signal return of the ceilometer to measure a cloud height. They then viewed the sky to determine the number of cloud layers and amount of clouds. ASOS processes the ceilometer data through computers and employs mathematical logic, called algorithms, to ascertain the cloud height, the number of layers, and amount of coverage. How do these differences in measurement affect the way you interpret and apply the information?
The cloud height indicator (CHI) transmits skyward approximately 9,240 pulses in 12 seconds. ASOS then assigns the returned signals, cloud base hits (CHIs), to one of 252 50-foot interval bins. After the 12 seconds, ASOS produces a profile of the back-scattered signal to help determine if the returned signals were from cloud bases. The system reports cloud layers up to an altitude of 12,000 feet.
ASOS processes the sensor signals into 30-second samples of cloud "hits." Each minute the algorithm processes 30 minutes of the 30- second data samples to create values for sky coverage and cloud height for the observation. By processing 30 minutes of data, the observation becomes more representative of an area 3-5 miles around the sensor site. To be more responsive to the latest changes in the weather, the last 10 minutes of the data are double-weighted in the algorithm calculations. ASOS identifies the recorded "hits" by height and processes them into layers. It may create up to three layers. The system assigns a coverage value of FEW (few), SCT (scattered), BKN (broken), or OVC (overcast). If no clouds are detected, ASOS transmits CLR in the observation.
The computer algorithm also tests the sensor return for obscurations and variable ceilings. An obscuration occurs when fog or precipitation masks the ability of a surface observer to clearly see the base of the lowest clouds. These same elements can mask the ceilometer from determining clear cloud "hits" in the signal return. The observation will carry a totally obscured sky condition (VV001, VV002, etc.) if enough of these hits persist, the visibility is 1 mile or less, and there is a cloud layer at 2,000 feet or less. ASOS also will determine variable ceilings when a ceiling is below 3,000 feet. If the variability tests are met, the observation will contain a remark such as CIG 008V016.
Day-to-day performance of ASOS is above average. The system reports sky conditions accurately most of the time. A study by the Hughes STX Corp. found that when ceilings were under 5,000 feet, ASOS observations agreed with the human observer 78% of the time. With fog, the comparability was 84%, with rain it was 69%, and when snowing 74%. During rain, ASOS reported more Special observations than the human observer. The lower agreement rate may be due to ASOS reporting more changes than the human observer.
ASOS reports all cloud layers as opaque. Thus when ASOS detects high moisture layers or very thin layers of clouds, the algorithm must process the signal return and decide whether to ignore the data or report the condition as a cloud layer. At rare times, ASOS may report a dense moisture layer as opaque clouds before the layer becomes totally visible to the human eye. This kind of report occurs before a cold front when the sensitive laser beam ceilometer detects the pre-frontal large scale lifting of moisture. There have been cases where ASOS reported a layer of clouds 20 minutes before an observer.
Rain and snow will contaminate both the transmission and return signal of the CHI. To compensate, ASOS incorporates an algorithm to evaluate the quality of the CHI signal. If ASOS cannot confirm a cloud base, it will transmit a height value similar to that which human observers report when fog, rain, or snow obscures the base of clouds. This value more accurately reflects how far you can see into the phenomena than exactly where the base of the clouds will be. Therefore the value is sometimes perceived as a "phantom" cloud layer.
Pilots have reported this "phantom" scattered deck of clouds near
the altitude where virga was evaporating. Other times, the "phantom"
appeared while rain or snow was falling. A pilot remarked that there were times
when automated observations reported cloud decks lower than actual conditions.
But stay alert. The lower height often indicated the altitude below which a
pilot had to fly before gaining enough forward visibility to see an airport
and land (see Figure 1).
Is ASOS wrong to attribute a scattered cloud layer to conditions such as virga, thin lower clouds (scud), or falling precipitation? A human observer often ignores, averages out, or misses these conditions when they have limited the forward visibility for pilots. In the February 1994 issue of Flying Magazine, an article titled "IFR Insight, Flying the Good Approach" contained the following remark: "Scud is frequently not reported because it is far less obvious to the observer looking up from the ground than it is to a pilot looking down from an airplane. When you're looking straight down, scud may hardly appear to be a problem, but when the necessary slant range viewing path to the airport is considered, a little scud can obliterate the view of the runway completely if the arrangement of the scud clouds is just so. The visual illusions can be disorienting when flying through scud, and it can be quite difficult to divide your attention between looking outside for the runway and inside at the instruments."
ASOS will report only weather that passes over the ceilometer. It will not measure cap clouds over distant mountains or low clouds anchored over bodies of water near an airport. In the more tropical regions, where winds aloft are often very light, such as Florida, afternoon cumulus moves slowly. Observers have reported up to four tenths of the sky covered by fair weather cumulus when ASOS reported CLR BLO 120.
At the transition between scattered and broken cloud coverage (five-tenths)
humans often report too much cloud coverage. This is attributed to the "packing
effect;" a condition where an observer does not see the openings in the
cloud decks near the horizon due to the viewing angle (see Figure 2). Pilots
tend to overestimate the coverage even more than ground observers because of
visual compression. When flying on top of a deck of clouds at speeds of 300
to 400 kph, those breaks in the clouds appear even smaller as they flash past
at 83 to 111 meters per second!
ASOS is not biased by the "packing effect" because it measures only the sky conditions passing over the sensor. ASOS does not view the sky at an angle. Thus human observers and pilots may feel that ASOS does not report enough cloud coverage.
Because the cloud coverage appears differently to pilots than to ground observers or automated systems, pilot reports (PIREPS) are vital. Pilots remain the "eyes of the skies," and their reports add an element of flight-level perception that complements automated and human surface observations.
Every minute the ASOS algorithm evaluates 30 minutes of data to create ceiling and cloud cover values. The last 10 minutes of data is double-weighted to reflect more accurately the most current conditions. Yet in rapidly changing conditions, the automated observations will lag slightly behind the actual weather. If skies are clear and a sudden overcast appears on the sensors, ASOS will take 2 minutes to report a scattered deck of clouds. Within 10 minutes, the system will show a broken layer.
Recognizing this characteristic in ASOS, a user should look at a sequence of weather observations to properly evaluate the changes in weather. You cannot determine trends or changes in the weather if you base all your decisions on a single observation. For example, if the observations indicate unchanging weather, you can anticipate the weather to remain nearly the same in the vicinity of the ASOS site.
If the observations show weather is deteriorating, expect worsening conditions as you approach the site. When weather is changing rapidly, observations 5 to 10 minutes old are unlikely to be accurate. Check the minute-by-minute observations or the latest Special observations to evaluate the weather around the ASOS site.
Overall, the ASOS observation remains timely and accurate. Only during the most rapidly changing conditions does the system present a noticeable lag. This lag is no greater than when human observers see a change in conditions, create the observation, and transmit it onto the various data networks. If you are a pilot using the ground-to-air radio broadcast at non-towered airports, the 1-minute observation, will show little if any lag.
The sky conditions ASOS presents are different from human observations. In the past, human observers would ignore cursory returns indicated on the ceilometer chart if they could not visually confirm the clouds. Today, the computer evaluates all the signal returns and creates height and coverage values based on strict mathematical logic. As sensor processing improves and users gain experience with automated observations, the ASOS observation will become the standard for all operations.
Bradley, J. T. and Imbembo, S.M., 1985: "Automated Visibility Measurements for Airports," American Institute of Aeronautics and Astronautics: Preprints, Reno, NV.
Bradley, J. T. and Lewis, Richard, 1993: "Representativeness and Responsiveness of Automated Weather Systems," Fifth International Conference on Aviation Weather Systems, AMS, Vienna, VA, pp 163-167.
Clark, P., 1995: "Automated Surface Observations, New ChallengesþNew Tools," Sixth Conference on Aviation Weather Systems: Preprints, Dallas,TX.
Flying Magazine, IFR Insight, Flying the Good Approach, Feb 1994, Hachette Filipacchi Magazines, Greenwich, CT.
Ramsay, Allen, and Burgas, Barbra, 1994: "Comparability Between Human and ASOS Ceiling/Visibility Observations."
U.S. Dept. of Commerce, NOAA, 1992: ASOS Users Guide, Government Printing Office.