Algorithm for determination of cloud cover from downwelling
infrared radiation for use as part of an integrated automatic
weather station
M J Molyneux
Easthampstead
Wokingham
Berks
RG40 3DN
United Kingdom
Tel +44(0)1344 85 5803
Fax +44(0)1344 85 5897 E-mail:
mike.molyneux@metoffice.com
Met Office
Beaufort Park
Abstract
The Met Office has developed an automatic meteorological observing system known
as SAMOS. When no observers are available, SAMOS is required to output values
of total cloud cover.
At the moment this is derived from Laser Cloud Base
Recorder (LCBR) data with a method known as the Exponential Decay Algorithm.
This is similar to the more widely used SMHI "Larsson" algorithm.
The LCBRs used have been the Belfort 7013C and the recently introduced Vaisala
CT25K.
Early generations of LCBR suffered from problems during precipitation
which degrades the cloud cover output, but whatever the quality of LCBR used,
this method of determining cloud cover has limits. For example, it can only use
the cloud information that has passed through its single measurement point.
This makes it necessary to consider the introduction of other methods of
measuring of cloud cover.
However, these should not be seen as stand-alone
systems but an additional input to a supervisory “cloud arbiter” software module
which will be capable of improving the output. The arbiter concept is already
in use in SAMOS - It is used to increase the quality of "Present Weather"
output.
Most complex instruments have features in their operation that have
been well-established in trials and these can be improved by an arbiter.
For
example, if an instrument is known to give false alarms at low visibility and
low temperatures - the arbiter can filter out the false alarms since it can
monitor both these parameters.
The work below describes an additional method of measuring cloud cover using
downwelling infrared radiation from a pyrgeometer. This paper will be presented
as a poster at TECO 2002.
Description of the data
The data from Camborne Principal Radiation Station (PRS) data was analysed for
2000. (Camborne is in Cornwall, Lat=50.133 degrees, Long=-5.100 degrees). For
each hour the figure used is the average from minutes 51 to 60, which was
thought to give the best correspondence to the human observer looking at the sky
at observation time.
These were merged into an Excel spreadsheet with full
human observations using a template and a macro.
The PRS has two pyrgeometers - labelled one and two - both are Eppley Precision
Infrared Radiometers.
The raw data used was uncorrected irradiance from
pyrgeometer two in Wm-2, referred to as downwelling long wave (dwlw).
Analysis
Processing initially followed that suggested in the “Clear Sky Index” method
(Marty 2000).
This is based on detailed physics and modelling.
However, it
quickly became clear the equation used was not removing all the baseline
variation of the signal. This may be because Marty uses data from Swiss sites
at relatively high altitude.
It is suggested that the lower altitude and
frequent changes in air mass at Camborne have a large effect.
Since the first attempt at processing was not successful, other investigations
were made. Figure 1 shows a simple plot of the dwlw data against time for the
whole year, two features are immediately clear. Firstly, there is a broad band
of high frequency variability that should to be related to the amount of cloud.
Secondly, there is a longer-term variability that is approximately seasonal.
Tests were therefore carried out to determine if the baseline could be made flat
and how well the short-term variability is correlated with cloud cover.
Figure 1 - Camborne 2000 - 10 minute dwlw average
450
dwlw irradiance (Wm-2)
400
350
300
250
200
0
1000
2000
3000
4000
5000
6000
7000
8000
time in 1hr sections for 1 year
Following Marty, the first parameters checked were screen temperature and
dewpoint.
This quickly revealed that the dwlw signal seasonality could be
largely removed with a simple correction for dewpoint.
This method is
comparatively crude but it does seem to give results of sufficient quality.
To demonstrate the relationship between dwlw and dewpoint Figure 2 was plotted.
It shows dwlw against dewpoint for observations of 0&1 and 7&8 oktas of cloud clear and overcast occasions as reported by the human observer. There is a good
relationship, but there may be some non-linearity. It appears that there may be
a “knuckle” at around 5 C dewpoint with linear sections above and below,
although this may not be entirely clear due to the small size of figure 2. It
doesn’t quite look like a curve.
The correction used applies 2 straight-line
fits, one above and one below a 5C dewpoint.
Figure 2 - dwlw as function of dewpoint
values selected of observed 0+1 oktas and 7+8 oktas
450
400
dwlw Wm-2
350
dwlw average 0 or 1 okta
dwlw average 7 or 8 okta
300
250
200
-10
-5
0
5
10
15
20
Dewpoint (C)
The corrections were applied to the full timeseries and the results are shown in
figure 3 - the seasonal signal is no longer visible. This is a good result; it
seems easy to remove the first problem with the data by using the dewpoint - a
readily available measurement.
Figure 3 - dwlw averages corrected by dewpoint
120
100
adwlw
80
60
40
20
0
0
1000
2000
3000
4000
5000
6000
7000
8000
-20
Time in 1 hr sections for 1 year
Figure 4 shows the range of values of adjusted dwlw that were measured for
occasions of 0 and 8 oktas. There are now clear groups of values of adjusted
down welling long wave (adwlw) that show little sign of correlation with
dewpoint.
Figure 4 - adjusted dwlw as function of dewpoint
values selected for observed 0 and 8 oktas only
100
80
60
adwlw
40
adwlw 8 oktas
adwlw 0 oktas
20
0
-10
-5
0
5
10
15
20
-20
-40
Dewpoint
There is still some scatter in the results. Investigations were carried out to
find any further correlations that might be used to sharpen the relationship.
Correlations were sought with air temperature, wind speed and direction (for air
mass), pressure, visibility, cloud height and if the sensor was wet
(present
weather). None of these tests gave a clear improvement and the results are not
presented.
For the time being a degree of scatter will have to be accepted.
However, the cases that showed a large "error" during overcast conditions were
plotted against time of day. This made it clear that most of the worst cases
occurred in darkness.
This may be a problem with the sensing method, but is
just as likely to reflect the problems that the observers suffer in darkness.
Using adwlw it is possible to identify clear and overcast conditions reliably.
However there is more uncertainty when there are intermediate amounts of cloud.
Radiometer signal level and variability may be used to identify cloud type
(Duchon 1999). Although type identification is not attempted, it does suggest
another parameter to examine for cloud cover information.
The standard
deviations (SD) for each preceding hour were used. Theoretically, most of the 0
and 8 okta reports will have very low SD since the signal should not vary much.
Other amounts of cloud cover should have more variability. A further plot
figure 5 - shows the total number of occasions in each category of SD for 0&1,
8&9 and 2-7 oktas.
This shows well that for the lowest SD it is most likely
that there will be 0&1 or 8&9 oktas, for SD level 2 and above it is much more
likely that there will be 2-7 oktas.
Figure 5 - 1 hour sd frequency split as 0+1 oktas, 8+9 oktas and remainder
1800
1600
1400
Frequency N
1200
0 and 1 oktas combined
1000
2-7 oktas
800
8 and 9 oktas
600
400
200
0
1
2
3
4
5
6
7
8
9
category (linear bins) of sd
Discussion and Conclusions
It is shown that Infrared radiometer signal level and variability, corrected by
screen dewpoint can give useful information on total cloud cover.
The
information is most reliable at high and low oktas of cloud cover, but this is
aimed at improving the overall cloud cover.
The choice of the "best"
measurement will be carried out by "arbiter" software. As long as the arbiter
can weight the measurements appropriately, there should be an improvement in
output quality.
The next phases of the work will involve processing data for other years and at
another site to check wider applicability.
(Preliminary results seem
promising). For the cloud arbiter to improve results, two sensing methods with
complimentary strengths and weaknesses are required.
At the time of writing
this is untested but some preliminary results may be available in the Poster.
References
Duchon D. and O'Malley M. Estimating Cloud Type form pyranometer Observations.
J. App Met. Volume 38. P132. 1999
Marty C. and Philipona R.
Index to separate Clear-Sky from Cloudy Sky
Situations in Climate Research.
Geophysical Research Letters. Vol. 27, No 17 p2649-2652. 2000.
Acknowledgements
Thanks to Aidan Green and Patrick Fishwick of the Met Office Radiation Section
for the pyrgeometer data and Met Office Camborne station staff for the
observations.