Ew. O'Brien et al., Relationships between condensation nuclei number concentration, tides, andstandard meteorological variables at Mace Head, Ireland, J GEO RES-A, 105(D2), 2000, pp. 1973-1986
The set of hourly averaged condensation nuclei (CN) data collected at Mace
Head during 1991-1994 was examined for relationships that might exist betwe
en CN number concentrations and the more commonly measured meteorological v
ariables, including tides. CN number concentrations at Mace Head can be cha
racterized by typically low "background" levels (less than about 700 partic
les cm(-3)) when the wind is from the west, somewhat higher "background" le
vels (1000-4000 particles cm(-3)) when the wind is from the east, with spor
adic bursts of short-lived discrete "events" of more than 10,000 cm (-3) fo
r several hours. These events occur typically during early afternoon and ar
e normally associated with slack winds and anomalously warm, dry air. They
appear to be independent of pressure, wind direction and precipitation. The
y can occur any time during the year, although the strongest events tend to
occur during spring and autumn. Large-amplitude low tides also occur predo
minantly in the early afternoon during this observing period. We present ev
idence that large CN concentration events occur preferentially after except
ionally low tides during daylight. A neural network was employed to train t
he standard meteorological variables to predict CN concentrations. Baseline
forecasts of CN counts for the final 180 days of the observing period were
made using lagged values of all other variables. Further forecasts were ma
de with some variables removed from the predictor set. The best correlation
between the predicted values and the verifying data over the 180 days was
0.67, which was obtained from a 1-hour forecast using knowledge of all vari
ables except temperature. Other variables whose removal improved the foreca
st (or whose presence degraded it) were pressure and wind speed. The best p
redictors of CN values were wind direction, relative humidity, and time of
day. An elementary "nearest neighbor," or "historical analogue" approach to
predicting the same set of CN values generated lower correlations with the
verifying data but generated a much more accurate probability distribution
function.