1. INTRODUCING A cloud detector USING global horizontal irradiances in UV and PAR in Thessaloniki, Greece.
Zempila M.M.1,2, Taylor M. 2,*, Fountoulakis I.2,
Bais A.2, Kazadzis S. 3, Fragkos K.2
1 UVMRP, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, USA.
2 Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki, Greece.
3 Physikalisch-Meteorologisches Observatorium Davos, World Radiation Center, Switzerland. *

2. ONE QUESTION, MANY REASONS…
Why detect cloud presence with solar irriadiance measurements?
Geostationary meteorological satellites capture cloud info (optical depths, type etc) BUT their temporal resolution is lower (e.g. 15 minutes compared to 1 minute).
Cloud octas are available at stations… BUT are observer-dependent and sensitive to weather conditions.
Lidar measurements can provide detailed information about clouds… BUT
From space, their temporal resolution is low.
From ground, their operation depend on weather conditions
Total sky imagers (TSI) can provide adequate information… BUT their temporal resolution is still typically >> 1 minute due to large storage requirements of high res camera images.
This slide poses the reasonable question why to detect clouds through solar ground-based measurements. We do have space born sources of cloud properties as well as a variety of ground-based instrumentation that can provide cloud information. But all of them experience temporal frequency limitations while in some cases the storage requirements are quite demanding.

3. THE MAIN POINT IS… (WITH ONE BUT)
Solar irradiance measurements can be obtained at high temporal rates, every 1-minute
Solar irradiance measurements are performed under all weather conditions
BUT…
The detection of the clouds via solar irradiance measurements depends on the wavelength measured, since clouds (especially the thin ones) have different spectral attributes.
On the other hand solar UV and Visible measurements are performed at several sites around the world with a temporal analysis of few minutes. It should be mentioned though that solar irradiance measurements experience some limitations regarding their cloud detection ability. Clouds have different spectral features thus can have different impact at different spectral regions based on their type and composition. Thus measurements at shorter wavelengths can miss the detection of thin cirrus clouds because these cloud types do not a significant impact at this low signal wavelengths.

4. Solar irradiance measurements at Thessaloniki, greece
One instrument = NILU-UV Multifilter Radiometer
5 UV irradiance measurements: 302, 312, 319, 340 and 376 nm
1 broadband channel: Photosynthetically Active Radiation (PAR)
1-minute solar measurements and corresponding standard deviation
10 year time measurement series ( ).
A co-located YES total sky imager (TSI) camera
In this study, both UV and Visible irradiance measurements were analyzed towards their ability to detect clouds over Thessaloniki. The NILU-UV multifilter radiometer has 5 channels in the UV spectral region while it is also equipped with a sixth channel that measures the Photosynthetically active radiation. All measurements are performed every 1 minute and the data are stored in a central server along with the corresponding standard deviation. For our research collocated sky images were used in order to evaluate the performance of the proposed methodology.

5. KEy: Singular spectrum analysis
Segregation of the signal to 3 main components:
Trend
Seasonal / Quasi-Periodic
Noise
The first step of the cloud detection algorithm uses singular spectrum analysis, a strong mathematical tool that can distinguish our timeseries to 3 basic components: the long term trend, the daily seasonal pattern and the random noise part. An example of the application to the PAR data series is shown in the figure. With black line we present the trend, with pink the seasonal and with blue the noise component. The capability of the reconstruction of the timeseries when combining the 3 SSA (singular Spectrum Analysis) components is also depicted.

6. The cloud screening algorithm
Does not need model estimates of the clear sky reference curve
Works by applying limitations:
On the SSA noise component
Deviations from the cloud-free adjusted SSA seasonal component
Differences between the slopes of the real and adjusted cloudless seasonal component
Uses a sliding 3-minute window for the seasonal criteria
Checks for existence of 5 consecutive clear minutes
Detects clear sky days automatically with reference to NILU irradiance timeseries
Detects overcast days by screening the daily maxima of cloud free detected cases
Limitations can be easily altered based on the type of instrument, station characteristics and desired sensitivity
The second part of the cloud screening algorithm takes advantage of the sensitivity of the two out of three ssa constituents, the seasonal and the noise. The user defines the pattern that will be used as the definition of the cloudless day. The first check is performed on the noise part – the noise data should not exceed the 1σ value of the cloudless noise ssa component. After this limitation is fulfilled, the seasonal component is tested for its similarity with the adjusted cloud-free one. (I adjust the cloud free seasonal component to the maximum of each day’s seasonal component). Both the absolute differences from the adjusted seasonal component are checked (up to 30% difference is accepted) as well as the slopes of the two compared patterns. For the latest a moving 3 minute time window is applied and differences less than 30% in the observed slopes are accepted. It also uses a limitation since it requires at least 5 minutes of consecutive cloud free cases in order to accept cloudless periods. A last check is performed to the daily seasonal maxima in order to exclude overcast days.

7. The PERFORMANCE
DOY = 188
On this slide an example of a rather cloud free day is provided. The daily course of solar irradiances at 302, 380 and PAR are presented on July, 7th All data are processed for sza<=80 degrees. With blue dots are all skies measurements while red dots indicate the cloud free instances as reported from our algorithm. The influence of a rather thin cloud around noon time is depicted in different ways for different wavelengths.

8. THE PERFORMANCE
DOY = 167
As well the ability to detect broken clouds is presented on this slide. Measurements reveal the different spectral patterns in different spectral regions. One could combine these measurements in order to absolute detect the presence of clouds or not.

9. Mike do we need this case?
We have overcast as a snapshot on the second day
If we need it I can prepare the slide as the followings.
DOY = 167
As well the ability to detect broken clouds is presented on this slide. Measurements reveal the different spectral patterns in different spectral regions. One could combine these measurements in order to absolute detect the presence of clouds or not.

10. Validation DOY = 209 MI: 375 MI: 525 MI: 540 MI: 545 MI: 585 MI: 600
July 28, 2014
Comparisons with sky camera images
PAR based cloud screening
MI: 545
MI: 585
MI: 600
The evaluation of the presented cloud detection algorithm was performed via sky camera images with a temporal analysis of 15 minutes. In blue frames we provide the cases where the algorithm detected the presence of clouds, while I red are the cloud free instances. In the last capture, based on PAR measurements, the algorithm fails to observe the presence of clouds low into the horizon. Probably this miss capture is due to sun’s low elevation where the signal becomes the 1/3 of the noon values and the algorithm strives to detect any clouds at this rather difficult period of the day.
MI: 645
MI: 780
MI: 930

11. Validation DOY = 267 MI: 390 MI: 405 MI: 435 MI: 495 MI: 510 MI: 645
September 24, 2014
Comparisons with sky camera images
PAR based cloud screening0
MI: 495
MI: 510
MI: 645
Another example that proves the ability of the cloud detection. When sun is rather high, the algorithm is able to detect the presences of small clouds. The PAR measurements are also presented on the right hand side.
MI: 735
MI: 810
MI: 915

12. CONCLUSIONS The cloud detection algorithm is very sensitive
High temporal resolution (≈1-minute)
Validated with a TSI in Thessaloniki for 134 days
Day Type
Days Categorized
Mean measurements per day
Mean clear instances (%)
Standard deviation (%)
Clear Sky 68
727
87.6
4.2
Overcast 14
687
4.87
6.39
Broken Cloud 52
715
47.53
17.9
The SSA based cloud detection algorithm has proven to be efficient, while its user determination features make it of general use in other applications. The advantage of no use of model estimations gives it an also unique characteristic. On this slide, a sampling of 134 days in 2014 were analyzed in order to reveal the general cloud situation over Thessaloniki for this period while these results were cross-checked with sky camera images. A sample of this algorithm usefulness was analyzed during this presentation, while on going efforts aim to better improve its response on low elevations.

13. THANK YOU! Zempila M.M.1,2, Taylor M. 2,*, Fountoulakis I.2,
Bais A.2, Kazadzis S. 3, Fragkos K.2
1 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, USA.
2 Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki, Greece.
3 Physikalisch-Meteorologisches Observatorium Davos, World Radiation Center, Switzerland. *