Transcript On the Generation of an Optimized Fractional Cloudiness Time Series using a Multi-Sensor Approach Wiel Wauben*, Marijn de Haij Reinout Boers, Henk Klein Baltink, Bert van Ulft, Mark.

On the Generation of
an Optimized
Fractional Cloudiness
Time Series using a
Multi-Sensor Approach
Wiel Wauben*, Marijn de Haij
Reinout Boers,
Henk Klein Baltink, Bert van Ulft,
Mark Savenije
*R&D
Information and Observation Technology,
Climate Observations Dept,
Regional Climate Dept,
Weather Research and Development Dept
TECO-2010, Helsinki | 31 August 2010
Contents
• Introduction
• Instruments
• Combination algorithm
• Cabauw Fractional
Cloudiness
• Conclusions and outlook
TECO-2010, Helsinki | 31 August 2010
TECO-2010, Helsinki | 31 August 2010
3
CESAR
Cabauw Experimental Site for Atmospheric Research
• Five remote sensing techniques for cloud observations
• Active and passive
• Column and hemispheric (integrated and resolved/scanning)
• 1 year data sets of 10-minute cloud data (15 May 2008 - 14 May
2009, total cloud cover & base)
• Generation of optimized & continuous cloudiness time series
• Evaluation of different techniques
TECO-2010, Helsinki | 31 August 2010
4
Instruments
35 GHz cloud radar & CT75K
• Sensitive to detect high cirrus
• CLOUDNET procedure
Ceilometer (operational SYNOP/METAR cloud product)
• column techniques
• including cloud base height
TECO-2010, Helsinki | 31 August 2010
5
Instruments
Pyrgeometer (BSRN)
•
•
•
long-wave downward radiation
integrated hemispheric
APCADA algorithm
NubiScope
•
•
•
thermal infrared
scanning
cloud mask
Total sky imager (TSI)
•
•
•
visual digital camera
cloud mask
day-time only
TECO-2010, Helsinki | 31 August 2010
6
Combination
Goal: construction of optimized & continuous cloudiness time series
Manual approach
•
•
•
•
•
•
strong / weak points
situation dependent
subjective
no reference!
complex algorithm
not generic
Hence
• “simple” weighted
average based on
experiences
• checked with climatology
of manual observations (1970-2000)
TECO-2010, Helsinki | 31 August 2010
7
Combination
“Reference” algorithm
• R
j
 H W  C

 H W
i, j
i, j
i, j
•
•
•
•
i, j
i
i, j
i
Rj is the reference cloudiness (in percentage) at time j
Wi,j is the weighting value at time j for the i-th instrument
Hi,j=1 when the i-th instrument has a valid output at time j, else =0
Ci,j is the cloudiness (in percentage) measured by the i-th instrument
at time j
• WNUB,j = WTSI,j = 1
• Wi , j  exp(DCLOUDNET , j / 1.3km) for APCADA, CLOUDNET, LD40
• DCLOUDNET,j is the observed minimum CLOUDNET cloud base height in
the 10-minute period at time j
• uncertainty E   1
( R C ) 2 1 / 2 for all R j  Ci , j  0
i
N

j,I 
j
i, j
TECO-2010, Helsinki | 31 August 2010
8
Cabauw fractional cloudiness
Cloud cover histogram
• Column
n=0, 8 high
n=2-7 low
• CLOUDNET
60% n=8
mainly due
to cirrus
• “reference” is
good
compromise
• low n=2-6 (higher during day time)
TECO-2010, Helsinki | 31 August 2010
9
Cabauw fractional cloudiness
Cloudiness versus cloud base height
• NubiScope & TSI
generally best
agreement
• ACPADA &
LD40 lower
• CLOUDNET
too high
TECO-2010, Helsinki | 31 August 2010
10
Cabauw fractional cloudiness
Contingency matrix LD40 () versus Reference ()
0
1
2
3
4
5
6
7
8
%
#
0
10.5
6.1
1.2
0.8
0.4
0.3
0.4
0.2
0.1
20.1
10309
1
2.1
3.8
1.1
0.7
0.4
0.3
0.4
0.3
0.1
9.3
4769
2
0.3
0.8
0.8
0.5
0.3
0.2
0.2
0.3
0.0
3.3
1700
3
0.1
0.4
0.6
0.6
0.3
0.2
0.2
0.3
0.0
2.8
1445
4
0.1
0.2
0.3
0.8
0.6
0.4
0.4
0.5
0.0
3.3
1706
5
0.0
0.1
0.1
0.3
0.7
0.6
0.5
0.7
0.0
3.1
1574
6
0.0
0.1
0.1
0.1
0.4
0.7
0.7
1.2
0.0
3.3
1712
7
0.0
0.0
0.1
0.1
0.3
0.8
1.8
6.0
1.8
10.9
5575
8
0.0
0.0
0.0
0.1
0.1
0.3
0.9
10.6
32.0
44.0
22592
%
13.2
11.6
4.3
3.9
3.5
3.8
5.4
20.2
34.0
100.0
#
6777
5981
2201
2024
1812
1972
2790
10377
17448
Band 0: 55.6%
Band 1: 85.6%
Band 2: 92.4%
Over: 2.0%
Under: 5.5%
51382
Δ: -0.1
|Δ|: 0.7
• 8 % with differences > 2 okta; fraction clear sky & overcast
TECO-2010, Helsinki | 31 August 2010
11
Cabauw fractional cloudiness
Reference data set cloudiness
• 98%
availability
10-minute
cloudiness
• e.g. daily with uncertainty
TECO-2010, Helsinki | 31 August 2010
12
Conclusions & Outlook
Conclusions
• Reference is weighted combination of individual instruments
• Not a true reference, but general and robust approach that
produces useful results
• Compromise whereby the NubiScope and TSI are considered to
be a higher quality product (weight 1) than the others (height
dependent weight)
• Uncertainty range of reference cloudiness determined from the
negative and positive differences between the reference and the
cloudiness reported by each instrument over the time period
under consideration
• Findings for instruments see paper
• OBS also has limitations so 100% similarity not expected
• Automated cloudiness using ceilometer introduced changes in
climatological cloud observations records
TECO-2010, Helsinki | 31 August 2010
13
Conclusions & Outlook
Outlook
• APCADA and TSI are being / have been optimised as a result of
this study
• Physical definition of cloud/cloudiness, threshold possibly
dependent on application
• Usage of hemispheric method to overcome changes in
climatological cloud observations records should be considered
• Towards scanning reference system?
TECO-2010, Helsinki | 31 August 2010
14
Thank you for your attention!
Lookup conference paper for more information
Boers, R., M.J. de Haij, W.M.F. Wauben, H. Klein Baltink, L.H. van
Ulft, M. Savenije and C.N. Long (2010),
Optimized Fractional Cloudiness Determination from Five Ground based Remote Sensing Techniques,
submitted to J. Geophys. Res.