Fig. 2. Contour map showing position of collector sites on Saddleworth Moor. B, Rain collector only; l, rain and cloud collector; , rain and cloud collector and AWS; and v, rain collector and AWS.
important. In addition, the volume of rain in each event was large enough for liquid left on the funnel and not collected to be insignificant.
3. Computer simulations of wet deposition
The three-dimensional rainfall model, Rainstar is described by Dore and Choularton Ž
. Ž
. 1992 and is based on the model of Carruthers and Choularton 1983 . Terrain heights
and roughness lengths are input on a rectangular grid of 64 = 64 elements. Model output can be accessed at any grid point. The boundary layer is treated as eight horizontal
Ž layers that flow over the terrain as dictated by a computational airflow model Car-
. ruthers and Hunt, 1990 . In the discussion that follows, the layer closest to the surface is
referred to as layer 1. The vertical distribution of temperature, water vapour, and soluble ionic material is
defined at the upstream edge of the grid and is derived from data as is geostrophic wind
speed and direction. Orographic cloud may condense or evaporate in any layer according to adiabatic temperature changes as it flows over the terrain.
Raindrops at the top of the model grid can also contain dissolved ions corresponding to observations of unperturbed rain. As they fall through the layers, they are subject to
growth by accretion with cloud drops, evaporation in cloud-free layers and horizontal drift. The number and size of raindrops are defined by the rainfall rate and the assumed
Ž .
drop size distribution Marshall and Palmer, 1948 . Ionic deposition to a point at the surface is the sum of the dissolved ions in all drops arriving at that position.
3.1. Rain eÕent 3 Rain event 3 occurred between 12:00 and 20:00 GMT on 20 October 1993. Fig. 3a
shows the synoptic situation at 12:00 GMT. The rain is associated with a cold front that lies over southern Ireland, north Wales and northern England. Moderate precipitation
resulted in approximately 3 mm of rain at lowland sites close to Saddleworth Moor.
3.1.1. Model input parameters We are primarily interested in ions of anthropogenic origin of which the most
important include nitrate and sulphate. We choose to examine nitrate since its primary Ž
. source motor vehicles is more widely distributed than for sulphate, making a homoge-
neous input to the domain, as assumed by the model more likely. 3.1.1.1. Vertical temperature and water Õapour profiles. We take the radio-sonde profile
Ž .
recorded at Aughton at 12:00 GMT Fig. 4a as being representative of the boundary layer structure during the rain event. The temperature throughout the sounding closely
follows the saturated adiabatic curve. There is a layer between 850 and 900 mb in which mechanical lifting would result in the release of potential instability. However, this layer
is not considered deep enough to result in any convective rainfall. Throughout the sounding, deviations from saturation and the saturated adiabatic curve are such that
buoyancy forces acting on mechanically lifted air would be very small. Under these circumstances, we consider it most appropriate to assume the boundary layer tempera-
ture profile to be neutral. The model domain reaches to an altitude of 1500 m above the surface.
The freezing level is at 850 mb. However, for this case, simulations show that over 90 of precipitation enhancement occurs below this level. It is therefore unnecessary to
consider the ice phase in the simulation. 3.1.1.2. Nitrate loading of the orographic cloud. Of all model input parameters, the
ionic loading of the cloud is the most difficult to assess accurately. We consider the method described below to represent the best estimate possible. The uncertainty
associated with the loading dominates uncertainty in the model output.
Cloud water is sampled at site 2. This is close to the upstream edge of the orographic cloud, the ionic loading of which is depleted by raindrop scavenging further down-
stream. The concentration of nitrate ions in cloud water sampled at site 2 after correction for contamination by rain is 142 35 meq l
y1
. We take the collection efficiency for rain
Ž . Ž
. Ž . Fig. 3. a Surface chart for 12:00 GMT on 20 October 1993 rain event 3 . b Surface chart for 12:00 GMT
Ž .
on 9 November 1993 rain event 8 .
to be 97 30 as estimated in Section 2. This introduces the dominant source of uncertainty into the calculation of cloud loading. Estimates of the range of possible
model output are based on the forward propagation of this uncertainty. The cloud loading is the product of the concentration calculated above and the cloud
liquid water content. The model predicts the liquid water content of the cloud at the
Ž . Ž
. Ž . Fig. 4. a Radio-sonde ascent data from Aughton on 20 October 1993 at 12:00 GMT rain event 3 . b
Ž .
Radio-sonde ascent data from Aughton on 9 November 1993 at 18:00 GMT rain event 8 .
surface at site 2 to be 0.33 g m
y3
. This is achieved by initialising the model with the observed water vapour profile as described above. We are confident in this value since
the model also correctly simulates the relative humidity at site 1 to be 90. The cloud loading can now be calculated as 2.9 0.8 mg NO m
y3
.
3
Direct chemical observations of the boundary layer upstream of cloud formation would certainly have added valuable information to this estimate for the model input.
However, the method applied here does have the advantage of directly reflecting the ionic content of the orographic cloud that is available for scavenging rather than its
precursors. Having fully considered the uncertainties that arise from our method and having followed these through to the comparison with data, we still consider it possible
to reach firm conclusions using this approach.
Hourly meteorological observations from Manchester Ringway represent upstream conditions during this rain event. The height of the lowest significant cloud layer with
cover of four or more oktas is recorded to range from 400 to 1500 m during the rain
event, however, cloud layers of less than four oktas are recorded as low as 200 m. In this cloud, it is reasonable to assume that all available nitrate ions are already dissolved in
cloud drops and contribute to the concentration of ions in seeder rain through collision and coalescence. When these cloudy layers are lifted orographically, excess water
vapour will condense onto the existing cloud drops and scavenging efficiency is affected only marginally. This is in contrast to cloud-free layers where the formation of
orographic cloud dramatically increases the scavenging efficiency for material that becomes dissolved in the cloud drops. This means that it is logical to apply the ion
loading calculated above to cloud-free layers only. The depth of the cloud-free layer is clearly variable, and the structure of the cloud base is complex during the event.
However, we simplify the situation by assuming that only the lowest two layers in the
Ž .
model below 530 m are cloud-free at the upstream edge of the model domain. 3.1.1.3. Concentration of ions in upstream rain. The concentration of ions in upstream
rain is set to reproduce the observed concentration at site 1. The model predicts that no scavenged material is received at site 1 but that some evaporation takes place in the
lowest layers. Taking account of this, a value of 22 meq l
y1
reproduces the observed value.
3.1.2. Comparison of model results to data for rain eÕent 3 Table 1 compares data to model predictions for the observational sites during rain
event 3. Fig. 5 shows model simulations near the collector sites. Fig. 5c and Table 1 show that the predicted rain depth simulates many aspects of the
observed rainfall well. Both exhibit a rainfall peak in the centre of the moor at site 4 with rain amounts decreasing in both directions. However, the increase in rain amount is
somewhat underestimated. Typically, the rain amount is enhanced by a factor of between 1.75 and 2.0, whereas the model indicates enhancement factors of between 1.4
and 1.6. At the downstream site 8, the model accurately predicts a return to a rain
Table 1 Comparison of model results to data for rain event 3
Site 1
2 3
4 5
6 7
8 9
[ ]
Rainfall depth mm Data
3.1 5.6
5.4 6.3
5.7 4.5
8.0 3.7
4.3 Model
3.1 4.5
4.9 5.0
4.8 4.1
4.0 3.3
4.7
y 1
[ ]
NO concentration m eq l
3
Data 24.7
40.1 40.2
32.5 26.6
24.5 19.5
34.3 12.9
Model 24.7
43.8 40.1
32.1 27.8
29.0 30.5
31.1 22.8
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
5.8 4.9
4.6 3.0
3.0 3.5
3.1 2.1
y 2
[ ]
NO – N deposition mg m
3
Data 1.07
3.14 3.04
2.87 2.12
1.54 2.18
1.78 0.78
Model 1.07
2.76 2.75
2.25 1.87
1.66 1.71
1.44 1.50
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
0.37 0.34
0.32 0.2
0.17 0.2
0.14 0.14
amount close to the upstream value and the prediction of rainfall at the summit site 9 is also accurate. However, there is disagreement on the downstream edge of the moor at
site 7. The data show a large increase in rainfall amount up to 8.0 mm. This is not reproduced by the model, which shows only a small increase in rainfall at site 7 as
compared to site 6. This is associated with the small hill positioned 2 km upstream from
Ž .
Ž site 7 Fig. 5f that generates increased liquid water content in the orographic cloud Fig.
. 5d and e .
The physical explanation for the large sample volume at site 7 is not clear but it may be associated with possible flow separation close to site 7. In the direction of the flow,
site 7 lies on a downslope of approximately 1 in 10. This may be steep enough to cause
Fig. 5. Contour plots showing model output corresponding to box A in Fig. 2 for rain event 3. Collector sites Ž
. Ž . w
y2
x Ž . shown v . a Deposition of nitrate ions in rain mg NO N m
. b Concentration of nitrate ions in rain
3
w
y1
x Ž . w
x Ž . w
y3
x Ž . m
eq l . c Rain depth mm . d Orographic cloud liquid water content for model layer 2 g m
. e w
y3
x Ž . w
Ž .x
Orographic cloud liquid water content for model layer 1 g m . f Terrain height m asl .
Ž .
Fig. 5 continued .
flow separation close to the surface in this region. This represents a large and abrupt change to the wind velocity field and would have a significant effect on the horizontal
wind drift of raindrops allowing the possibility of sudden changes to the rainfall amount in this region. A linearized airflow model generates the wind field used for calculating
Ž .
the horizontal drift of raindrops. This is known from previous work Inglis, 1992 to be least accurate in precisely the circumstances described above. Where the perturbation to
the upstream flow is large, as in a region of flow separation, the model will underesti- mate the perturbation, and the effects of flow separation will not be simulated. Although
in the absence of supporting data, this argument remains speculative, we believe it offers the best explanation of both the large sample volume at site 7 and the failure of the
model to reproduce it.
Ž .
More generally, the model predictions of rainfall depth Fig. 5c are seen to be Ž
. closely related to the liquid water content of the orographic cloud Fig. 5d and e .
Maximum rainfall depths are observed approximately 1 km downstream of the maxi- mum cloud liquid water content. This indicates the scale of the average horizontal drift
of raindrops through the cloud. Primarily, the vertical motion of the model layers over the terrain controls the cloud liquid water content. However, the water content can be
seen to decline well before the terrain descends. This is a result of the progressive washout of liquid water by raindrop scavenging.
The concentration of nitrate ions in the rain is reproduced within the uncertainty limits of this simulation at sites 2, 3, 4, 5 and 8. Thus, the enhancement to the
concentration between site 1 and the western end of the moor is accurately simulated, as is the decline in concentration observed over the central region of the moor. The model
significantly underestimates the concentration at sites 7 and 9. If the concentration of ions in individual raindrops is dependent on raindrop size, then the result at site 7 may
be explained by the same process, which is suggested for the discrepancy in the rain amount at this site. For this rain event, site 9 is also in the lee of higher terrain and so
flow separation may also explain the results at this site.
Between sites 1 and 2, the terrain rises enough to generate thin cloud in layers 1 and Ž
. 2 Fig. 5d and e . The concentration of ions in this cloud is high since its liquid water
content is low and scavenging has not yet seriously depleted it. It is in this region that the model predicts the highest concentrations of ions in rain as the thin cloud is
scavenged. Unfortunately, there are no datapoints in this region so we cannot comment confidently on this important feature.
Ion deposition is the product of rainfall amount and ion concentration. The model reproduces the main features of the deposition pattern observed at the sites, although as
a result of the underestimation of rain amount, the deposition is generally somewhat low. However, in many cases, the observed value is close to the upper limit of the
modelled value. Both model and data indicate a maximum deposition at the upstream
Ž .
edge of the moor site 2 and a steady decline till site 6. Both then show an increase in deposition at site 7. However, it should be noted that the reasonably good agreement
seen at this site is an artefact brought about by the under-prediction of rainfall and the over-prediction of nitrate concentration.
The data support many of the model predictions, such as the location of the greatest deposition of nitrate ions, and the decreasing trend in deposition moving downstream
across the moor. The model reveals that this is due to the progressive washout of ions by rain. In general, the predicted deposition is somewhat low as a result of the underestima-
tion of rain amount at most of the sites. Discrepancies between the model and data at sites 7 and 9 may be a result of flow separation in the wake of high terrain, an area in
which the airflow model is known to be inaccurate.
3.2. Rain eÕent 8 Event 8 occurred during the afternoon and evening of 9 November. Fig. 3b shows the
synoptic situation at noon. Heavy precipitation associated with the vigorous occluded front resulted in approximately 15 mm of rain at lowland sites near Saddleworth Moor.
3.2.1. Model input parameters 3.2.1.1. Vertical temperature and water Õapour profiles. The radio-sonde temperature
profile recorded at Aughton at 18:00 GMT defines the temperature and water vapour profiles. From the surface to the 920 mb level, the atmosphere is stable with a potential
temperature gradient of 0.0025 K m
y1
. Above this, up to the 800 mb level, the atmosphere can be considered saturated, since only a small vertical uplift would result in
the formation of cloud. The environment curve shows a gradual increase in wet equivalent potential temperature, the gradient being approximately 0.0033 K m
y1
. The airflow model does not allow for a layered atmosphere of this type. However, it is a
good approximation to apply an average potential temperature gradient throughout the boundary layer. This produces an average value of the Brunt–Vaisala frequency,
N s 0.01 s
y1
, which is used as input to the model. To isolate and examine the effect of stratification on deposition, we present simulations produced using the observed atmo-
spheric temperature profile alongside simulations assuming an adiabatic temperature profile.
The vertical displacement of the lowest layer is not sensitive to the boundary layer temperature structure since it is close to the lower boundary condition for the flow.
However, in higher layers, the position and liquid water content of orographic cloud is influenced by the upstream temperature structure. The implications of this dependence
are examined in the analysis below.
3.2.1.2. Nitrate loading of the orographic cloud. The method used to calculate the nitrate cloud loading takes advantage of extra data available during this rain event. It is
important that the loading used as input to the model is representative of the orographic cloud that was actually available to be scavenged by rain. During this rain event, high
resolution cloud and rain sampling was carried out at site 9 during the period of maximum rainfall intensity. Sufficient sample for analysis could be collected in a
15-min period. Large variations in the concentration of ions in cloud water were observed before, during and after the rain event. To compute the most appropriate cloud
loading, we exclude cloud sampled when it was not raining.
For each 15-min observation period, we correct the concentration of ions in cloud samples for contamination by rain. Based on the 15-min sampling, the volume averaged
concentration of nitrate ions in the cloud, at site 9, during the period of rain, after correction for contamination by rain, was found to be 77 10 meq l
y1
. However, the rainfall rate varies considerably during the event and scavenging is most intense during
the heaviest rain. Accordingly, the cloud loading recorded during the most intense precipitation ought to be weighted more heavily than the loading during periods of
lighter rain. We therefore choose to use the average concentration of ions in cloud weighted with respect to the recorded rain amounts rather than cloud amounts. This
process reduces the concentration of the cloud to 63 13 meq l
y1
. This is our best estimate of the average concentration of nitrate ions in cloud that are available to be
scavenged by rain at site 9. The relative humidity is constant at site 1 throughout the rain event, representing a
constant input of water vapour to the orographic cloud. Model runs using the Aughton
ascent data that reproduce the observed humidity at site 1 produce cloud liquid water content at site 9 of 0.21 g m
y3
. This value is not sensitive to the input temperature profile. It is however, dependent on the seeder rainfall rate, which defines the rate at
which liquid water and nitrate ions are stripped from the cloud. The loading of nitrate ions in cloud at site 9 can now be evaluated as 0.82 0.16 mg NO m
y3
.
3
We know that nitrate ions are progressively removed from the cloud by seeder rain at a rate that depends on the rainfall rate. Because of this, the loading calculated for site 9
is lower than that which should be used as an upstream input. Again, model runs are used to simulate the rate at which nitrate is removed. Using the model inputs described
above, we find that the loading at site 9 is reproduced if a value of 2.5 mg NO m
y3
is
3
used as an upstream loading. As for rain event 3, we use cloud layer data from Ringway to determine the
appropriate vertical distribution of the nitrate loading. These indicate that the nitrate loading should be applied to the lowest three model layers only.
3.2.1.3. Concentration of ions in upstream rain. It should be noted that the model input for this parameter refers to the concentration of ions in the rain when it begins to fall
from the top of the model domain. The model simulation using the observed temperature structure predicts the formation of orographic cloud upstream of site 1 in layers 3 and
above. Thus, the rain collected at site 1 contains a proportion of scavenged material. We use as input the concentration of ions in the seeder rain that reproduces the observed
concentration at site 1 taking the scavenging, and also any evaporation in the lowest layer, into account. This results in an input concentration of 20.0 meq l
y1
.
Fig. 6. Model output for orographic cloud liquid water content for stratified and neutral temperature profiles in layer 3 for rain event 8. Flow transect begins at point A as shown in Fig. 2.
For this scenario, the model predicts site 1 to be free from the effects of scavenging and so we may use an upstream concentration that takes account of evaporation only.
This results in a concentration of 28.0 meq l
y1
. 3.2.2. Comparison of model results to data for rain eÕent 8
Modifications to the flow caused by the stratification affect the position and shape of the cap cloud, and hence, may affect the pattern of deposition. Fig. 6 illustrates this
point with model simulations of orographic cloud in layer 3 which is far enough from the ground for the model streamlines to respond freely to the temperature profile. For the
stratified case, cloud condenses approximately 12 km further upstream than for the neutral case and then evaporates about 2 km further upstream. Closer to the surface, the
position of the cloud for the two model runs shows a much greater similarity.
This modification to the position of orographic cloud affects the wet deposition since layer 3 is one of the layers in which the orographic cloud nitrate loading is applied.
Above this, modifications to the cloud do not affect ion deposition but do affect rainfall amount since the orographic cloud acts as a source of liquid water.
Model simulations are produced using the initialising parameters discussed above and compared to data in Table 2. It should be noted that the data used to assess model
performance from sites 2 to 7 are entirely independent to that of sites 1 and 9 which are used to derive the inputs. Fig. 7 shows contour plots of model output for the stratified
run for the area labelled box A in Fig. 2.
Differences to the modelled rain depths for the two model runs are relatively small but are consistent with modifications to the flow as discussed above. The formation of
Table 2 Comparison of model output to data for rain event 8
Site 1
2 3
4 5
6 7
8 9
[ ]
Rainfall depth mm Data
14.7 19.4
22.6 25.6
22.5 25.7
22.6 12.3
20.0 Model
14.7 20.5
19.6 21.5
25.0 22.8
21.2 12.5
23.0 Ž
. stratified
Model 14.7
20.1 19.1
20.6 23.8
22.9 21.9
15.5 24.3
Ž .
neutral
y 1
[ ]
NO concentration m eq l
3
Data 34.2
32.6 29.1
25.9 26.5
22.5 36.2
47.6 24.0
Model 34.2
39.2 33.9
29.0 25.8
26.1 24.2
23.0 23.5
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
stratified 4.3
3.3 2.5
2.2 2.0
1.7 0.0
1.5 Model
34.2 48.5
47.0 41.3
37.8 37.6
33.8 27.5
31.5 Ž
. neutral
y 2
[ ]
NO – N deposition mg m
3
Data 7.0
8.9 9.2
9.3 8.3
8.1 11.5
8.2 6.7
Model 7.0
11.3 9.3
8.7 9.0
8.3 7.2
4.0 7.6
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
Ž .
stratified 1.4
1.0 0.9
0.7 0.6
0.4 0.0
0.4 Model
7.0 13.6
12.6 11.9
12.6 12.1
10.4 6.0
10.7 Ž
. neutral
high altitude orographic cloud further upstream for the stratified case allows more scavenging and so produces somewhat larger rainfall amounts over the terrain upstream
of site 6. Downstream of this point, the neutral simulation predicts more orographic cloud than the stratified case and so the rainfall amount is correspondingly larger. Both
simulations reproduce the scale of the rainfall enhancement successfully and correctly place the rainfall peak close to the centre of the moor. The most significant difference
between the simulations can be seen at site 8, the neutral flow simulation over-predicts rain depth, whereas the stratified case correctly returns the rainfall rate to below the
upstream value. This is due to the greater persistence of cloud downstream of the moor for the neutral case.
Fig. 7. Contour plots showing model output corresponding to box A in Fig. 2 for rain event 8. Collector sites Ž
. Ž . w
y2
x Ž . shown v . a Deposition of nitrate ions in rain mg NO N m
. b Concentration of nitrate ions in rain
3
w
y1
x Ž . w
x Ž . w
y3
x Ž . m
eq l . c Rain depth mm . d Orographic cloud liquid water content for model layer 3 g m
. e w
y3
x Ž . w
Ž .x
Orographic cloud liquid water content for model layer 1 g m . f Terrain height m asl .
Ž .
Fig. 7 continued .
The simulations of ion concentration show much greater variation than the predic- tions of rain depth and these can also be explained by the position of orographic cloud.
Both simulations show a general decrease in concentration from the upstream to the downstream edges of the moor. However, the stratified case produces lower concentra-
tions overall and a sharper decline in concentration between sites 2 and 3, both features that more realistically reproduce the data than the neutral simulation. For sites 2, 4, 5, 6
and 9, the lower uncertainty limit of the stratified simulation generally lies close to the datapoints. There are two reasons for the lower concentrations in the stratified flows.
First, the earlier formation of orographic cloud allows a greater proportion of the dissolved material to be removed from the cloud upstream of the first collector site. Fig.
7b shows that the peak concentrations for the stratified case occur in the region between
sites 1 and 2 where cloud first forms. Second, for the stratified case, site 1 actually receives some scavenged material from cloud formed in layer 3 as indicated in Fig. 7d.
This means that in order to match the observed concentration at site 1, the concentration of ions in the seeder rain need not be as high as for the neutral case. Thus, the overall
availability of ions for deposition is greater for the neutral simulation.
Both model simulations under-predict nitrate ion concentration at sites 7 and 8. Numerically, the values computed by the neutral simulation are closer to the data.
However, we believe this to be co-incidental and not indicative of a more realistic interpretation of the physical processes that give rise to the large concentrations at these
sites than that of the stratified model run.
Although differences in rain volume for the model runs are small, we can conclude that the stratified run is more successful since it more accurately returns the rain volume
at the downstream site 8 to below the upstream value. For ion concentration and ion deposition, the stratified model runs clearly agrees more closely with the observations
for the moorland sites 2, 3, 4, 5, 6 and 9. The data generally lie close to the lower limit of the model uncertainty. The stratified run agrees more closely with both the absolute
values of the observed concentration and the gradient in the data. The main discrepancy in the neutral simulation is the consistent over-prediction of ion concentration at the
moorland sites. Neither model run succeeds in predicting the large increase in ion concentration observed at sites 7 and 8, which may be the result of lee-side flow effects.
4. Conclusions
