VALIDATION OF A DAYTIME OVER LAND ALGORITHM FOR
COMPUTING CONVECTIVE CLOUD COMPOSITES OVER THE
IBERIAN PENINSULA AND BALEARIC ISLANDS (SPAIN)
Cesar Azorin-Molina (1,2), Sergio-M. Vicente-Serrano (3), Rafael Baena-Calatrava (4),
Bernadette Connell (5)
(1) Laboratory of Meteorology-Climatology, Mixed Unity CEAM-UVEG, The CEAM Foundation
(Fundación Centro de Estudios Ambientales del Mediterráneo), Charles R. Darwin 14, 46980-Paterna
(Valencia), Spain; [email protected] ; http://www.ceam.es/
(2) Group of Climatology, University of Barcelona (UB), Barcelona, Spain
http://www.ub.edu/gc/menu.htm
(3) Pyrenean Institute of Ecology, CSIC (Spanish Scientific Research Council), Department of Global
Change and Environmental Processes, Zaragoza, Spain http://www.ipe.csic.es/
(4) Centro de Estudios Materiales y Control de Obra, S.A., Málaga, Spain
(5) Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins,
Colorado, USA http://www.cira.colostate.edu/
Abstract
In previous work (Azorin et al., 2007), a new daytime over land cloud detection algorithm, derived from
the Advanced Very High Resolution Radiometer (AVHRR) Processing scheme Over cLouds, Land
and Ocean (APOLLO), was used to compute convective cloud composites over the Iberian Peninsula
(IP) and Balearic Islands (BI), both in Spain. The modified scheme was used to process AVHRR
HRPT data (L1B ESA SHARP; 1.1 km) from NOAA-17 (0900-1200 UTC) and NOAA-16 (1200-1500
UTC) overpasses for the 6-month study-period May-October 2004. The algorithm consists of four
spectral tests to distinguish three categories: cloud, cloud-free, and snow-ice pixels. In this study, the
results of the algorithm are quantitatively compared with observations using two validation techniques:
(a) comparing satellite-observed earth cover with a manual subjective classification of pixels according
to the aforementioned categories, and (b) comparing side by side satellite-derived cloud cover with
corresponding synoptic observations of total cloud cover (TCC). For TCC, 20 stations with daily
observations at 13 UTC were evaluated. These stations were more or less evenly distributed over the
IP and BI. There was very good agreement between the subjective classification of pixels and satellite
detection of cloud, whereas there was less agreement between ground observations and satellitederived cloud cover. It was found that AVHRR observed 16.6% less than synoptic observations, which
is in accordance with previous satellite-surface comparisons. A detailed description of the cloud
detection algorithm, preliminary results and other interesting aspects of the comparison are briefly
presented in this short article.
INTRODUCTION
The spatial and temporal distribution of clouds observed from high-resolution satellite imagery
constitutes a powerful data for climatological and meteorological applications (Karlsson, 2003). For
instance, clouds play a complex role in the Earth´s radiation budget and therefore in long-term climate
change (Arking, 1991). Consequently, new cloud analysis tools should be the subject of increasingly
sophisticated research, due to the fact that they result in the observed global climate change.
The International Satellite Cloud Climatology Project (ISCCP; http://isccp.giss.nasa.gov/index.html;
Rossow and Garder, 1993) represents the international effort aimed to infer the global distribution of
clouds and radiance parameters, supporting many climate and modelling studies since 1982. On the
other hand, the EUMETSAT Satellite Application Facility on support to Nowcasting and Very ShortRange Forecasting (SAFNWC; http://www.nwcsaf.org; Derrien and Le Gléau, 2005) has been
developing a software package to extract various products (i.e. cloud masks, cloud types, and cloud
top temperatures and heights) from meteorological satellites (MSG SEVIRI and NOAA and EPS
AVHRR data) since 1997.
In addition to the numerous global and regional cloud products provided by the ISCCP and the
SAFNWC, advanced analysis tools have also been developed during the last decade to objectively
quantify cloud amounts (high-resolution regional cloud climatologies; Regional and Mesoscale
Meteorology Branch; http://rammb.cira.colostate.edu/research/satellite_climatologies/ ) from satelliteregistered radiances. Most of the cloud detection algorithms have been based on the AVHRR
Processing scheme Over cLouds, Land and Ocean (APOLLO), which was developed by Saunders
(1986) and Saunders and Kriebel (1989). APOLLO was designed to process AVHRR HRPT data as
well as Local Area Coverage (LAC) and Global Area Coverage (GAC) data over Western Europe and
North Atlantic. This cloud analysis tool has been modified and extended by others (Derrien et al.,
1993; Karlsson, 1996; Stowe et al., 1999; Kriebel et al., 2003).
The validation of satellite-derived cloud data is often carried out by comparing with visually estimated
cloud cover reported by observers at meteorological weather stations (Kotarba, 2009), a procedure
which is crucial for studies on long-term cloud amount trends. Other approaches have used radar,
lidar and photographic surface-based observations. This short paper presents the validation of a
daytime over land algorithm (Azorin et al., 2007) for computing convective cloud composites over the
IP and the BI (Spain) for both NOAA-17 (morning pass) and NOAA-16 (afternoon pass) AVHRR data.
The satellite-derived cloud cover is compared with (a) a manual subjective classification of pixels and
(b) with corresponding synoptic cloud observations (hereafter called Synops) of Total Cloud Cover
(TCC).
DAYTIME OVER LAND CLOUD DETECTION ALGORITHM
Figure 1 shows the flow diagram of the daytime over land cloud detection algorithm (solar elevation
greater than 5º), which was used to process AVHRR HRPT data (L1B ESA SHARP; 1.1 km) from
NOAA-17 (0900-1200 UTC) and NOAA-16 (1200-1500 UTC) overpasses for the 6-month study-period
May-October 2004. The daytime over land algorithm consists of four spectral tests applied to each
pixel. The constant thresholds have been successfully tested to be functional during the 6-month study
period. Therefore, the quality characteristics are the same for the entire data set (Karlsson, 2003).
Figure 1: An algorithm for computing convective cloud composites applied to (a) NOAA-17 and (b) NOAA-16 AVHRR
data during the daytime over land
Test 1 corresponds to snow-ice detection (Figure 2). Snow can be discriminated from cloud using
visible and short wave infrared channels. For NOAA-17, a quota ratio (Q1) is defined as the ratio of
reflectance of 1.6 µm divided by the reflectance of 0.6 µm. Since snow is considerably less reflective
at 1.6 µm, values of Q1 ≤0.2 designate a snow/ice pixel. We used a threshold brightness temperature
difference (BTD) of ≤15 K between 3.7 µm and 11 µm for detecting snow-ice pixels for NOAA-16. May,
June and October have snow-ice pixels over the Pyrenees, the Iberian System and the Betic
mountains within the study area.
(a)
(b)
(c)
(d)
(e)
Figure 2: (a) Channel 3b (3.55-3.93 µm); (b) Channel 4 (10.30-11.30 µm); (c) BTD T3.7 µm -T11 µm; (d) Cloud mask BTD
≤15 K;(e) Histogram of a 50x50 pixel scene used for snow-ice detection (Test 1, NOAA-16). The black line represents
the threshold value (15 K) found for computing cloud mask.
(a)
(b)
(c)
(d)
(e)
(f)
Figure 3: (a) Channel 5 (11.50-12.50 µm) NOAA-16, IP; (b) Channel 5 (11.50-12.50 µm) NOAA-16, BI; (c) Cloud mask T12
µm ≤290 K, IP; (d) Cloud mask T12 µm ≤290 K, BI; (e) Histogram of a 515x485 pixel scene used for cloud detection in
the IP; (f) Histogram of a 33x33 pixel scene used for cloud detection in the BI. The black line represents the threshold
value (290 K) found for computing cloud mask.
Test 2 is the IR gross threshold test at 12 µm (Figure 3). A unique threshold of ≤290 K was used to
select cloudy pixels representing all cloud types for both satellites. Diurnal convection develops under
high insolation and therefore land surface temperatures during the day (LST) are >290 K even during
the cooler months.
Test 3 is the albedo or visible test at 0.6 µm (Figure 4). A fixed threshold reflectance of ≥20% was
chosen to select cloudy pixels.
(a)
(b)
(c)
Figure 4: (a) Channel 1 (0.58-0.68 µm) NOAA-16, IP; (b) Cloud mask R0.6 µm ≥20%; (c) Histogram of a 50x50 pixel scene
used for cloud detection. The black line represents the threshold value (20%) found for computing cloud mask.
Test 4 is the reflectance quotient (Q2) of Near-IR (R0.9 µm) to VIS (R0.6 µm), (Figure 5). A Q2 threshold
from 0.7 to 1.3 identified cloudy pixels. A supplementary IR threshold test was added to discriminate
bare ground pixels from cloudy ones. We tagged as cloudy the pixels with a Q2 ratio between 0.7 and
1.3 and T12 ≤290 K.
(a)
(b)
(c)
Figure 5: (a) Q Ratio R0.9 µm/R0.6µm NOAA-16, IP; (b) Cloud mask Q 0.7-1.3 and T12µm ≤290K, IP; (c) Histogram of a
50x50 pixel scene used for cloud detection. The black line represents the threshold value found for computing cloud
mask
The algorithm discretizes all AVHRR data into three groups: cloud-free, cloudy and snow-ice. The
algorithm identifies a pixel as cloudy if at least two tests (Test 2, 3 or 4) prove positive. The final
AVHRR convective cloud mask with a 1.1 km resolution is obtained by subtracting snow-ice pixels
from cloudy ones.
VALIDATION OF THE CLOUD DETECTION ALGORITHM
Two validation procedures were used in order to validate the daytime over land cloud detection
algorithm presented here. The high resolution cloud masks for the NOAA-16 and the NOAA-17
satellites were quantitatively compared with: (a) a manual subjective classification of pixels, and (b) 20
Synoptic stations with daily TCC made by observers at 13 UTC.
COMPARING SATELLITE-OBSERVED EARTH COVER WITH SUBJECTIVE CLASSIFICATION
A sample of each month was analyzed for the NOAA-16 and the NOAA-17 satellites during the 6month study period May-October 2004, and thirty random points were visually classified into two
categories (clear and cloudy pixels) and compared with the cloud mask for each image. A total of 360
instantaneous visual AVHRR and cloud mask observations were used for the subjective validation.
Figures 6 and 7 represent channel 1 (0.58-0.68 µm) with the corresponding cloud masks for the
eastern region of the IP and BI, respectively. Most of the pixels with high reflectance in the VIS (R0.6
µm) channel were tagged as cloudy pixels by both the visual and the algorithm classification.
Therefore, we found a very good match between the subjective classification of pixels and satellite
detection of clouds. The statistical results of the manual validation have been tabulated in Table 1,
which corresponds with a 2x2 contingency table (observation by algorithm) for (a) NOAA-17 and (b)
NOAA-16 satellites. The observation and algorithm frequencies (in %) for clear-clear and cloudycloudy categories are greater than 85.0% for both satellites. In contrast, these frequencies for clearcloudy and cloudy-clear categories are lower than 15.0%. The contingency coefficient statistically
confirms this good agreement for the NOAA-17 (0.609; sig.= 0.000 < α = 0.05) and the NOAA-16
(0.651; sig.= 0.000 < α = 0.05).
(a)
(b)
(c)
(d)
Figure 6: (a) Channel 1 (0.58-0.68 µm); (b) Cloud mask; (c) Channel 1 (zoom); and (d) Cloud mask (zoom), Eastern of the
Iberian Peninsula, NOAA-16, 17/06/2004, 1312 UTC.
(a)
(b)
(c)
(d)
Figure 7: (a) Channel 1 (0.58-0.68 µm); (b) Cloud mask; (c) Channel 1 (zoom); and (d) Cloud mask (zoom), Balearic
Islands, NOAA-16, 17/06/2004, 1312 UTC.
(a)
Algorithm
Observation
NOAA-17
Clear
Cloudy
Absolute frequency (nº)
Expected frequency (nº)
Frequency-Observ. (%)
Frequency-Algorithm (%)
Absolute frequency (nº)
Expected frequency (nº)
Frequency-Observ. (%)
Frequency-Algorithm (%)
Clear
82.0
47.5
91.1
86.3
13.0
47.5
14.4
13.7
Cloudy
8.0
42.5
8.9
9.4
77.0
42.5
85.6
90.6
Total
90.0
90.0
100.0
50.0
90.0
90.0
100.0
50.0
(b)
Algorithm
NOAA-16
Clear
Cloudy
Total
Observation
Absolute frequency (nº)
87.0
3.0
90.0
Expected frequency (nº)
48.5
41.5
90.0
Frequency-Observ. (%)
96.7
3.3
100.0
Frequency-Algorithm (%)
89.7
3.6
50.0
Absolute frequency (nº)
10.0
80.0
90.0
Expected frequency (nº)
48.5
41.5
90.0
Cloudy
Frequency-Observ. (%)
11.1
88.9
100.0
Frequency-Algorithm (%)
10.3
96.4
50.0
Table 1: Contingency table 2x2 (observation by algorithm) for (a) NOAA-17 and (b) NOAA-16. The absolute and
expected frequencies (in nº of cases) and observation and algorithm frequencies (in %) are tabulated here.
Clear
Figure 8 displays two discrepancies detected between both cloud detection methods. Cloud-free land
reflectance depends on the atmosphere (scattering and absorption), land cover and the viewing
geometry (Derrien et al., 1993). Non-vegetated pixels with reflectance values ≥20% at 0.6 µm and
brightness temperatures ≤290 K at 12 µm wavelength were classified as cloudy pixels by the cloud
detection algorithm, whereas the visual observation correctly tagged these barren pixels as cloud-free
ones (Figures 8a and 8b). On the other hand, cloud tops with brightness temperature >290 K at 12 µm
were classified as cloud-free pixels by the algorithm, whereas the visual procedure correctly detects
them as cloudy ones (Figures 8c and 8d).
(a)
(b)
(c)
(d)
Figure 8: (a) Channel 1 (0.58-0.68 µm), and (b) Cloud mask – NOAA17, 17/06/2004, 1124 UTC; (c) Channel 5 (11.50-12.50
µm), and (d) Cloud mask - NOAA16, 26/05/2004, 1350 UTC.
COMPARING SIDE BY SIDE SATELLITE-DERIVED CLOUD COVER WITH SYNOPS OF TCC
20 Synop stations with daily observations at 13 UTC (coded on an octa scale; 0 corresponds to a clear
sky and 8 to an overcast sky) were used in this second validation procedure. The Synop stations are
more or less evenly distributed over the IP and the BI and cloud data were converted into fractional
cloud amounts (1 octa equal to 12.5%). Synop cloud data were obtained from the homogenised data
set developed by Sanchez-Lorenzo et al. (2009).
AVHRR cloud amounts were calculated as a percentage of clouded pixels within a 30 km radius
around the Synop stations (Kotarba, 2009), a procedure which has been applied in previous satellitesurface comparisons. This covers most low clouds that are observed but also includes some of the
mid- and high-level clouds at quite distant locations that most likely also are observed at the surface
station. To minimize the time discrepancy between AVHRR and Synop observations, 76 AVHRR
NOAA-16 overpasses were selected for the comparison. A total of 1480 individual AVHRR-Synop
cloud amount observation pairs were compared here. AVHRR overpasses differ from surface
observation by no more than 01h 33’.
Table 2 presents the difference states results of Synop data substracted from AVHRR. The average
AVHRR-Synop difference was -16.6%, i.e. the AVHRR sensor observed less clouds than visual
surface observations. The greater differences were found in May (-23.9%), October (-20.5%) and
September (-17.6%), whereas the lower differences occurred in central summer months: June (14.0%), July (-14.7%) and August (-17.6%). These results agree well with those found by Meerkötter
et al. (2004) who reported -15.3% for Europe during the period May-July 1990-2003. Correlations
between AVHRR and Synop cloud amounts were significant at p<0.0001, with values ranging between
0.62 and 0.70, also indicating a good agreement.
NOAA-16
May
June
July
August
September
October
Median
-15.0
-12.5
-12.4
-12.5
-12.5
-14.8
Mean
-23.9
-14.0
-14.7
-15.4
-17.6
-20.5
Maximum
15.6
69.5
61.4
89.7
38.8
40.7
Minimum
-81.5
-87.5
-99.0
-100.0
-76.0
-87.5
St.desv.
23.9
22.0
20.6
22.7
20.4
22.5
R-Pearson
0.68*
0.69*
0.66*
0.62*
0.70*
0.68*
R2
0.46
0.48
0.44
0.39
0.49
0.47
Nº of observations
140
300
380
260
240
160
Table 2: Difference states results of Synop data substracted from AVHRR. * Significant at p<0.0001
Total
-12.5
-16.6
89.7
-100.0
21.9
0.68*
0.46
1480
The histogram presented in Figure 9 is clear evidence that the AVHRR sensor observed fewer clouds
than Synop observations. Despite the highest difference (27.8%) occurred within the -5% to 5% range,
the negative AVHRR-Synop bias dominates. For instance, 26.0% of all differences take place within
the range from -5% to -15%, and 13.7% within the range from -15% to -25%. However, few Synop
cloud amounts were greater than AVHRR observations: 2.9% within the range from 5% to 15% and
1.6% within the range from 15% to 25%.
Figure 9: Frequency of distribution for AVHRR-Synop cloud amount differences. Differences in cloud amount were
calculated by substracting Synop cloud amount from AVHRR cloud amount. Negative results mean AVHRR to report
smaller cloud amount than Synop.
CONCLUSIONS AND DISCUSSION
Analyses of the validation dataset reported the following main findings:
(a) We detected a very good agreement between the subjective classification of pixels and
satellite detection of cloud, even though different detections between both methods were
noticed.
(b) We found that the polar-orbiting AVHRR sensor observed 16.6% less than visual surface
observations and correlations ranged between 0.62 and 0.70, which is in agreement with other
seasonal mean differences. These differences are mainly controlled by the different viewing
perspective of an earth-cover observed from the AVHRR or a sky-cover reported from the
surface: i.e. the AVHRR sensor capability (number of spectral bands; spatial-temporal
resolution); the cloud detection algorithm (cloud masks) and the amount of cloud detected by
surface observers.
In conclusion, the convective cloud composites (Azorin-Molina et al., 2009) derived from the new
daytime over land cloud detection algorithm become a valuable tool for forecasters to use in detecting
the location of convection (http://rammb.cira.colostate.edu/research/satellite_climatologies/spain/ ).
The cloud frequency composites obtained could have practical applications to improve convective
short-term forecasting.
Acknowledgements. The Fundacion CEAM is financed by the Generalitat Valenciana and BANCAIXA.
This research has been undertaken in the frame of the CONSOLIDER-INGENIO 2010 Programme
(GRACCIE project) and by the Torres Quevedo Program (PTQ-08-02-06762). This study was partially
supported by a grant from the Aragon Regional Government GA-LC-019/2008. The authors would like
to thank the INTA (CREPAD project; assistance provided by Angel Garcia-Sevilla) for AVHRR data;
and to Imanol Echave from ITT (Spain) for some ENVI4.2/IDL6.2 assistance. Contributions from the
fifth author were supported by NOAA Grant NA17RJ1228.
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