INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 33: 2113–2128 (2013) Published online 15 August 2012 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.3572
A daytime over land algorithm for computing AVHRR convective cloud climatologies for the Iberian Peninsula and the Balearic Islands Cesar Azorin-Molina,a * Rafael Baena-Calatrava,b Imanol Echave-Calvo,c Bernadette H. Connell,d Sergio M. Vicente-Serranoa and Juan I. L´opez-Morenoa a
Pyrenean Institute of Ecology, CSIC (Spanish Research Council), Department of Global Change and Environmental Processes, Zaragoza, Spain b Centro de Estudios Materiales y Control de Obra S.A., M´ alaga, Spain c ITT Visual Information Solutions, Paris, France d Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
ABSTRACT: A daytime over land multispectral cloud detection algorithm is presented to derive accurate convective cloud climatologies with high spatial resolution (1.1 km) over the Iberian Peninsula (IP) and the Balearic Islands (BI). The cloud detection scheme was designed to process Advanced Very High Resolution Radiometer (AVHRR) HRPT data and is tested here on NOAA-17 morning (0900–1200 UTC) and NOAA-16 afternoon (1200–1500 UTC) overpasses for the warm 6-month study period May–October. The algorithm consists of four spectral threshold tests applied to each pixel. Test 1 corresponds to the snow-ice removal, test 2 is the thermal infrared test, test 3 is the albedo or visible test and test 4 is the ratio between near-infrared and visible channels. The algorithm discretizes all AVHRR data into four groups called cloud-filled, cloud-free, snow-ice and snow-free radiances. The high-resolution convective cloud masks are obtained by subtracting snow-ice pixels from cloudy ones. In this article, a detailed description of the convective cloud detection scheme and the sources of error detected for each test are given, and the first seasonal and monthly regional convective cloud frequency composites are presented. Future applications of the newly proposed threshold algorithm in climate and meteorology are also discussed in this article, particularly the production of convective cloud composites for climate monitoring of storms over the IP and BI. KEY WORDS
cloud algorithm; NOAA AVHRR; convective cloud climatologies; Iberian Peninsula and Balearic Islands
Received 14 February 2012; Revised 3 July 2012; Accepted 14 July 2012
1. Introduction The spatial and temporal distribution of clouds observed from high-resolution satellite imagery constitutes a powerful tool for climatological and meteorological applications (Karlsson, 2003). On the one hand, it is widely recognized that clouds play a complex role in the Earth’s radiation budget (i.e. absorption and scattering of solar radiation, and absorption and emission of terrestrial radiation), and therefore in determining climate (Ramanathan et al., 1989; Arking, 1991) and long-term climate change, e.g. in the Mediterranean region (Giorgi and Lionello, 2008). As a consequence, clouds and associated precipitation in the form of rain, snow and ice crystals regulate the hydrological cycle on Earth. On the other hand, the response of clouds to the greenhouse effect (global warming) represents the major uncertainty in the numerical climate predictions aimed at determining the magnitude and distribution of climate change worldwide (Trenberth ∗ Correspondence to: C. Azorin-Molina, Pyrenean Institute of Ecology (Spanish Research Council), Department of Global Change and Environmental Processes, Avenida Monta˜nana 1005, 50059 Zaragoza, Spain. E-mail:
[email protected] 2012 Royal Meteorological Society
et al., 2007). Consequently, accurate global and regional cloud analysis tools should be the subject of increasingly sophisticated research (Karlsson, 2003), mainly due to the fact that they modulate the observed global climate change (IPCC, 2007). Most environmental research requires cloud-free fields of view because clouds and cloud shadows influence the quality of the electromagnetic signal measured by space-borne sensors. Cloud-free pixels from Advanced Very High Resolution Radiometer (AVHRR) data on board US National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites are used to quantitatively infer surface parameters such as sea surface temperature (SST) (May et al., 1998), land surface temperature (LST) (Oku and Ishikawa, 2004), normalized difference vegetation index (NDVI) (Chen et al., 2003) and snow cover (Parajka and Bl¨oschl, 2008). In contrast, cloud-filled radiances have been widely analysed for the retrieval of microphysical cloud properties (Ou and Liou, 1999; Perez et al., 2000) such as cloud fraction, cloud-top height, optical depth, cloud top temperature, liquid water content, and their cloud radiative effects. In addition, the classification of cloud types from satellite imagery has
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practical applications for improving the forecasting of meteorological risk situations, such as flooding in the Mediterranean region (Feidas et al., 2000). The International Satellite Cloud Climatology Project (ISCCP; http://isccp.giss.nasa.gov/index.html) (Rossow and Garder, 1993; Rossow and Shiffer, 1999) represents the international effort to infer the global distribution of clouds and radiance parameters, supporting many climate (Rossow and Duenas, 2004) and modelling (Weare, 2004) studies since 1982. The Pathfinder Atmospheres Extended (PATMOS-x; http://cimss.ssec.wisc.edu/pat mosx/), as an extension of the PATMOS project (1995– 1999), is a project aimed at deriving atmospheric and surface climate products (cloud, aerosol, surface and radiometric) from the roughly 30 years of NOAAAVHRR-collected data. With meteorological purposes, the EUMETSAT Satellite Application Facility in support of Nowcasting and Very Short-Range Forecasting (SAFNWC; http://www.nwcsaf.org) (Derrien and Le Gl´eau, 2005) has developed a software package to extract various cloud products (i.e. cloud masks, cloud types, and cloud top temperatures and heights) from meteorological satellite data (MSG SEVIRI and NOAA and EPS AVHRR data) since 1997. Focusing on regional climatological applications, high-resolution regional cloud climatologies computed from the Regional and Mesoscale Meteorology Branch (RAMMB) are available in the following link: http://rammb.cira.colostate.edu/research/ satellite climatologies/ In order to objectively quantify cloud amounts from satellite-registered radiances and compute global and regional cloud products as shown in the above URL links, advanced cloud-analysis tools have been developed over the past two decades: e.g., the SCANDIA model (Karlsson, 1996) and the CLAVR cloud mask algorithm (Stowe et al., 1999). Most of these 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 (1988). APOLLO was designed to process AVHRR High Resolution Picture Transmission (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; Kriebel et al., 2003). However, the majority of the cloud detection schemes developed cover wide areas (e.g. entire Europe) or regions other than the Iberian Peninsula (IP) and the Balearic Islands (BI). Therefore, algorithms and threshold tests found in the literature for locally prevailing conditions are not ideally suited for our regional study area and hence the purpose of our cloud processing tool: the production of convective cloud composites for studying the spatial and temporal distribution of storms. The IP and the BI have climatic conditions different from central Europe, being affected by geophysical variables such as mountainous and plain terrains, the Atlantic Ocean and the Mediterranean Sea. In addition, due to its location in the temperate mid-latitudes, it is influenced 2012 Royal Meteorological Society
by both Polar and Tropical air masses, all of which explain the diversity of climates (Martin-Vide and OlcinaCantos, 2001) over the region (Figure 1). Thunderstorms are common from May to October and sometimes challenging to predict (Azorin-Molina et al., 2011). Spring and summer convection is primarily associated with sea breeze and local winds and modulated by terrain (AzorinMolina et al., 2009). During dry summer months this is typically the only source of precipitation (Millan et al., 2005a), even though thunderstorms can produce extreme rainfall and flooding, and large hail can damage agriculture crops. With this in mind the overall aim of this study is to design a daytime over land multichannel algorithm for computing daily convective cloud masks of high spatial resolution (1.1 km) over the IP and the BI during the warm months of the year (May to October). The current manuscript is structured into five sections: Section 2 summarizes a background review of cloud masking algorithms for the IP and BI; Section 3 gives a basic description of the AVHRR data processing; Section 4 describes the new daytime over land multispectral algorithm for computing AVHRR convective cloud masks; Section 5 presents regional convective cloud frequency composites for the 6-month study period May–October for both NOAA-17 (2002–2010) and NOAA-16 (2001–2005) satellites; and finally Section 6 discusses the future applications of this cloud detection scheme. 2. Background review of cloud masking algorithms for the Iberian Peninsula and Balearic Islands Cloud detection is based on the spectral response of clouds that are generally brighter in the visible (VIS) channels and colder in the infrared (IR) spectrums than the underlying surface, which enhances the spatial variability of measured radiance. The definition of cloud screening algorithms represents an extremely complex task in remote sensing studies because cloud features are strongly conditioned by different factors such as sun elevation, variable path length, atmospheric water vapour, aerosol concentrations, variable reflectance, and subpixel clouds (G´omez-Chova, 2008). Postprocessing radiometric and atmospheric corrections can minimize all these errors, even though this task is very difficult to handle as justified (Karlsson, 2003). The cloud detection methods found in the literature can be classified into three categories (Derrien et al., 1993): (1) statistical histogram analysis (Phulpin et al., 1983), (2) threshold methods (Saunders and Kriebel, 1988), and (3) pattern recognition methods of large-scale texture (100 km or more) (Garand and Wwinman, 1986). In the last decade sophisticated cloud screening algorithms have been designed to improve limitations introduced by these three most common cloud detection algorithms (G´omezChova, 2008). The new cloud masking techniques have been based on Bayesian methods (Merchant et al., 2005), fuzzy logic (Ghosh et al., 2006), artificial neural networks Int. J. Climatol. 33: 2113–2128 (2013)
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Figure 1. Map of the study area showing locations mentioned in the text.
(Arriaga et al., 2003), and kernel procedures (Mazzoni et al., 2007). In contrast to the large number of studies dealing with cloud detection algorithms, few efforts to use satellitemeasured radiances for the estimations of cloud distributions over the IP and the BI have been made until recently. Flores-Tovar and Baldasano (2001) applied a cloud masking scheme previously developed by Laine et al. (1999) for mapping solar radiation from NOAAAVHRR over the north-eastern region of the IP. Sospedra et al. (2004) proposed an algorithm for cloud cover assessment at night-time. Casanova et al. (2005, 2010) developed a cloud detection and classification algorithm for the IP using Meteosat VIS-IR, NOAA-A/TOVS, MSG and MODIS AQUA-AIRS temperature profiles, to be used in real time as a support tool in air navigation. G´omez-Chova (2008) conducted research on cloud screening in multispectral (MERIS; MEdium Resolution Imaging Spectrometer) and hyperspectral (CHRIS; Compact High Resolution Imaging Spectrometer) satellite images in order to provide new operational cloud masking tools based on advanced pattern recognition and machine learning techniques. Finally, Costa and Bortoli (2009) developed a satellite remote sensing method for the detection and classification of clouds over the IP based on the combination of nine distinct spectral bands of Meteosat-8. The knowledge of clouds over the IP is partially limited because it has been based on empirical-visual ground observations performed by qualified observers from the Spanish Meteorological Agency (AEMET; http://www.aemet.es/en/portada). Sanchez-Lorenzo et al. (2009) constructed a high-quality homogenized total cloud cover (TCC) data set consisting of 69 series across the whole IP for the period 1961–2004 with the goal of 2012 Royal Meteorological Society
analyzing regional dimming and brightening. The significant subjective component in the cloud coverage assessment, the restricted precision of the TCC data measured in oktas, and the limited spatial (69 locations) and temporal (three daily observations taken at 7, 13 and 18 h UTC) coverage justify the need for improving cloud screening methods using remote sensing data. This will help to infer the regional distribution of clouds and consequently to increase our knowledge about the spatial and temporal trends in cloudiness. In any case, a detailed discussion about the advantages and limitations of using empirical visual and remote sensing data can be found in Warren and Hahn (2002). In this article, a daytime over land thresholding algorithm is presented with the motivation of identifying convective clouds in AVHRR scenes and producing accurate monthly and seasonal composites for monitoring the prominent convergence zones associated with intensified convection and storm activity over the IP and the BI. This is the novelty of this cloud analysis tool in comparison with the aforementioned schemes; i.e., the definition of a new cloud detection scheme which is exclusively aimed at dealing with the climatology of convective clouds and storms in future studies.
3.
AVHRR data processing
AVHRR data on board NOAA polar-orbiting satellites were used to develop this multispectral algorithm and derive reliable daily convective cloud masks. AVHRR full horizontal resolution data (in L1B ESA SHARP format) from NOAA-17 morning (900–1200 UTC) and NOAA-16 afternoon (1200–1500 UTC) orbits were collected from the High Resolution Picture Transmission Int. J. Climatol. 33: 2113–2128 (2013)
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Table I. Number of NOAA-17 and NOAA-16 images for the periods 2002–2010 and 2001–2005, respectively, during the warm semester May–October. Satellite
May
June
July
August
September
October
Total
NOAA-17 NOAA-16
150 91
158 95
160 118
165 116
166 92
155 103
954 615
(HRPT) receiving ground station at the National Institute for Aerospace Technology (INTA, Maspalomas, Canary Islands, Spain http://www.inta.es/index.asp). The testing period comprised the warm/convective season May–October. The 1.1 km spatial resolution at nadir of the AVHRR imagery is useful for detecting convective clouds because it is close to the Cumulus (Cu) and Cumulonimbus (Cb) cloud scale. In addition, the morning and afternoon orbits observe the early convective stages (clouds from shallow convection, i.e. small scale Cu clouds like Cumulus humilis and Cumulus mediocris) and mature development (clouds from deep convection, i.e. Cumulus congestus and Cumulonimbus clouds), respectively. A set of 954 (NOAA-17; operated during the period 2002–2010) and 615 (NOAA-16; operated during the period 2001–2005) images covering the entire IP and BI were used (Table I). The remaining NOAA-AVHRR data contained only partial portions of the IP or the BI, and therefore were not used for computing regional convective cloud composites shown in Section 5. A fully automated AVHRR data processing routine (Baena-Calatrava, 2002) included radiometric calibration for five channels, satellite zenith and solar zenith angles for each point, and first and second geometric corrections (using a set of 97 Ground Control Points; Ho and Asem, 1986) to the European-1979-UTMzone 30° N over an area bounded between 34° 22 N and 44° 12 N and 11° 7 W and 4° 16 E. The solar channels 1 (0.58–0.68 µm; VIS) and 2 (0.72–1.10 µm; NIR) were calibrated from digital counts (DNs) to reflectance (%) and the thermal IR spectrum 4 (10.30–11.30 µm) and 5 (11.50–12.50 µm) to brightness temperature (K). Since channel 3a (1.58–1.64 um) from NOAA-16 satellite was on during daylight (3a switching) it was calibrated to reflectance (%) until 30 April 2003, and to brightness temperature (K) since channel 3b (3.55–3.93 um) was always on (no 3a switching) from 1 May 2003. However, the NOAA-17 satellite switches channels at the terminator such that 3a (1.58–1.64 µm) is on during daylight and 3b is on during night. Therefore, channel 3a from NOAA-17 was calibrated to reflectance (%). The daytime over land multispectral algorithm described below uses all five spectral channels of the AVHRR sensor to better delineate between cloud, snowice, and clear ground pixels to compute the daily cloud masks. The visible and near infrared channels (channels 1 and 2) enable the detection of Cu clouds because their high reflectivity and low absorption in these spectral bands (Hunt, 1973). In contrast, the channels in the thermal infrared (channels 3, 4 and 5) range enable the 2012 Royal Meteorological Society
Figure 2. A new daytime over land multispectral cloud detection algorithm applied to (a) NOAA-17 and (b) NOAA-16 AVHRR data over the Iberian Peninsula and Balearic Islands during the 6-month warm period May–October. Option (1) on Test 1 is also applied to NOAA-16 images prior to 1 May 2003.
detection of Cb clouds based on the thermal contrast (Papin et al., 2002) between cold cloud top-temperatures and warmer underlying surfaces.
4. Daytime over land multispectral cloud masking algorithm The selection of the type of automated cloud detection algorithm depends not only on the spectral range and spatial resolution of the radiometric sensor but also on the remote sensing application (G´omez-Chova, 2008). The daytime over land cloud masking algorithm described in detail here corresponds to a multispectral thresholding scheme and has been fundamentally based on the APOLLO model (Saunders, 1986; Saunders and Kriebel 1988). Figure 2 shows the flow diagram of the proposed multispectral algorithm, which consists of four spectral threshold tests that are applied to each pixel. The fixed or Int. J. Climatol. 33: 2113–2128 (2013)
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constant thresholds have been meticulously tested during the 6-month study period May–October, ensuring that the quality characteristics are the same for the entire data set (Karlsson, 2003). Test 1 corresponds to the snowice detection; test 2 is the thermal infrared test; test 3 is the albedo or visible test and test 4 is the quota ratio NIR/VIS. The algorithm discretizes all AVHRR data into four groups called cloud-free, cloudy, snow-ice and snow-ice free radiances. The algorithm identifies a pixel as cloudy if the three cloud tests (tests 2, 3 or 4) prove positive, a condition which ensures that pixels tagged as cloudy correctly correspond to cloud contaminated radiances. The high-resolution (1.1 km) daily convective cloud mask is obtained by subtracting snow-ice pixels from cloudy ones. A cloud mask is a raster image with pixels indicating the presence or absence of cloud. The convective cloud frequency composites presented in Section 5 are based on the cloud mask count on a pixelby-pixel basis and were calculated as follows: fi =
ni × 100 N
(1)
where ni is the count of cloudy pixels and N the total number of scenes. The results were multiplied by 100. 4.1. Test 1: snow-ice detection The IP and particularly Spain has an average altitude of 650 m (18.4% is above 1000 m). The highest mountain chains of the IP are extensive and generally have continuous snow-cover from December through April (Lopez-Moreno et al., 2008) with around 90–120 snow days each year above 1500 m.a.s.l. The highest peaks in the Betic Mountains (Mulhac´en, 3,478 m), the Pyrenees (Aneto, 3,404 m), the Cantabrian Mountains (Torre Cerredo 2,648 m), the Central System (Almanzor, 2,592 m) and the Iberian System (Moncayo, 2,314 m) can also have snow fields in May, June, October and November. In addition, small glacierettes can persist all year in shaded areas of the Betic Mountains and the Cantabrian Mountains, particularly on north-facing cirque glaciers above 2800 m in the Pyrenees. The BI has no snow during the study period. Cloud-free snow-ice pixels dominate the AVHRR scenes in mountainous areas, and the removal of snow-ice pixels is crucial for computing high-quality daily convective cloud masks, especially for May, June and October within the warm 6-month study period. Test 1 corresponds to snow-ice detection. The snow-ice pixels share similar spectral particularities with cloudy pixels, i.e. a high reflectance value in VIS (R0.6 µm) and NIR (R0.9 µm) channels (Derrien et al., 1993; Baum and Trepte, 1999), and low brightness temperature in the infrared at T11 µm and T12 µm. However, snow-ice can be discriminated from clouds using visible and short wave infrared channels. For instance, snow and ice are separated from clouds by their low reflectance at 1.6 µm or at 3.7 µm, as discussed by Yamanouchi et al. (1987), 2012 Royal Meteorological Society
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Gesell (1989) and Hall et al. (1995). The snow-ice detection test is based on solar reflection at 0.6, 1.6 and 3.7 µm (Derrien et al., 1993; Lavanant, 2002) and is applied if the solar elevation is greater than 20° (10° for the channel 3a). It is usually applied before the rest of cloud tests (Derrien and Le Gl´eau, 2005). Both tests described below for NOAA-17 and NOAA-16 data employ features which are used for the snow-ice/cloud discrimination task in the EUMETSAT Nowcasting SAF (NWCSAF; online at http://www.nwcsaf.org/HD/MainNS.jsp) and Climate Monitoring SAF software (online at http://www.cmsaf.eu/ bvbw/appmanager/bvbw/cmsafInternet) to create cloud climatologies over the entire European area, the African continent and the inner Arctic area. For the NOAA-17 (and NOAA-16 until 30 April 2003), a quota ratio (Q1, Figure 3(d)) is defined as the reflectance of 1.6 µm (Figure 3(b)) divided by the reflectance of 0.6 µm (Figure 3(a)), as channel 3a is on during the daylight and the 1.6 µm wavelength allows significantly improved discrimination between snow and clouds (Hall et al., 1995). Snow and ice are separated from cloud free continental surfaces by their higher visible reflectance at 0.6 µm, and large difference in snow and cloud reflectance at 1.6 µm. Snow-ice is considerably less reflective and hence darker at 1.6 µm than Cirrus (Ci) and optically thick clouds (Warren, 1982). We found from the quota ratio histograms of 50 × 50 pixel arrays over different mountainous areas of the IP that ratio values of Q1 ≤ 0.2 designate snowice pixels (Figure 3(g)). This threshold value does vary slightly according to the character of snow (e.g. moist or dry or old) and the different snow grain radius. After examining different AVHRR images we also found that values of Q1 ≤ 0.2 representing snow-ice (Figure 3(e)) have two main sources of error: convective clouds such as Cu and Cb and some lakes, lagoons and ponds. These two features can exhibit ratio values of Q1 ≤ 0.2 and be wrongly designated as snow/ice and hence removed from cloud masks. In the case of Cu and Cb clouds, which are crucial for the purpose of this cloud detection scheme, they present relatively low reflectance in channel 3a at 1.6 µm (Figure 3(b); e.g, darkest areas south of the IP similar to the reflectance of snow-ice over the Pyrenees) and very high reflectance in channel 1 at 0.6 µm (Figure 3(a)). Both of these characteristics indicate ice clouds. Lakes, lagoons and ponds present very low reflectance at both wavelengths, being lower in channel 3a (Figure 3(b)), which means that they can be confused with snow-ice pixels (Figure 3(e)). In order to remove these errors the proposed cloud algorithm added a supplementary threshold test based on the thermal infrared portion of the spectrum at T12 µm (Figure 3(c)). Snow-ice pixels exhibit surface brightness temperatures markedly warmer than Cu and Cb cloud tops (opaque clouds have much cooler temperatures) and slighltly colder than cloud free continental surfaces (e.g. lakes, lagoons and ponds) (Derrien and Le Gl´eau, 2005). Even though snow-ice surface temperature varies significantly in relation to air temperature fluctuations Int. J. Climatol. 33: 2113–2128 (2013)
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Figure 3. (a) Channel 1 (0.58–0.68 µm); (b) channel 3a (1.58–1.64 µm); (c) channel 5 (11.50–12.50 µm); (d) ratio R1.6 µm/R0.6 µm; (e) mapping of Q1 ratio values of ≤0.2; (f) snow-ice mask (Q1 ≤ 0.2 and T between 265 and 285 K) on 13 May 2004 at 1113 h UTC; (g) Q1 ratio histogram of a 50 × 50 pixel scene centered over the Pyrenees (black square in (c)) used for snow-ice detection (Test 1a. NOAA-17). The dark grey line represents the threshold value (Q1 ≤ 0.2) found for computing daily snow-ice masks; (h) thermal infrared histogram of a 50 × 50 pixel scene centered over the Pyrenees (black square in (d)) used for snow-ice detection (test 1a. NOAA-17). The dark grey lines represent the threshold values (between 265 and 285 K) found for computing snow-ice masks.
(Armstrong and Brun, 2008), we found that only pure snow-ice pixels have brightness temperature ≤273 K (De Ruyter de Wildt et al., 2006). In contrast, due to the spatial resolution of the AVHRR sensor (1.1 km) and the heterogeneous topography, snow-ice surfaces within a pixel can contain dark patches made of bare soil, rock, or trees (Oesch et al., 2002). These non-snow-ice surfaces absorb the solar irradiation and therefore emit high amounts of longwave radiation. Therefore, non-snow-ice surfaces within a mixed pixel distort the snow-ice signal exhibiting brightness temperatures >273 K. After examining several AVHRR scenes we found the existence of pure and mixed snow-ice cover pixels with temperatures ranging between 265 and 285 K for the 6-month study period at 12 µm wavelength region (Figure 3(h)). The effectiveness of test 1a is shown in the snow-ice cover mask (Figure 3(f)), where we tagged as snow-ice pixels 2012 Royal Meteorological Society
(pure and mixed) those with Q1 ratio values ≤0.2 and brightness temperatures ranging between 265 and 285 K at 12 µm wavelength. For the NOAA-16 images later than 30 April 2003, we used the brightness temperature difference (BTD; Figure 4(c)) between 3.7 µm (Figure 4(a)) and 11 µm (Figure 4(b)) wavelengths for discriminating snow-ice pixels. The 3a channel is not operational after the aforementioned date and the short wave infrared (3b) channel is on day and night. A snow-ice surface during daytime reflects sunlight relatively weakly in the 3b channel and is in contrast to water clouds. Therefore snow-ice pixels tend to have lower brightness temperature at 3.7 µm and BTD values (Baum and Trepte, 1999) in comparison to cloudy pixels, which present higher brightness temperatures at this wavelength and BTD values. A threshold BTD of ≤10 K was chosen to reject Int. J. Climatol. 33: 2113–2128 (2013)
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Figure 4. (a) Channel 3b (3.55–3.93 µm); (b) channel 4 (10.30–11.30 µm); (c) BTD T3.7 µm–T11 µm; (d) Snow-ice and land mask (BTD ≤10.0 K); (e) cloud mask (BTD >10.0 K) on 5 June 2004 at 1346 h UTC; (f) BTD histogram of a 50 × 50 pixel scene centered over the Pyrenees (black square in (c)) used for snow-ice detection (test 1b. NOAA-16). The dark grey line represents the threshold value (BTD ≤10.0 K) found for computing daily snow-ice and land masks.
snow-ice contamination below this value (Figure 4(f)), since almost all clouds with few exceptions (e.g., Cirrus, Ci; Stratus, St; Stratocumulus, Sc) have BTD >10 K. An example of the snow-ice removal is provided in Figures 4(d) (mask of BTD pixels ≤10 K) and 4e (mask of BTD pixels >10 K). The mask with BTD pixels ≤10 K corresponds to the snow-ice and also cloud free continental surfaces that will be substracted from the convective cloud masks in the algorithm. Note that snowice pixels are correctly discriminated in this mask over the IP. In contrast, the mask with BTD pixels >10 K separates almost all the cloudy pixels, whereas snow-ice pixels have been removed. This BTD test is applicable only during daylight hours. To conclude, separating clouds from snow-covered ground is a very difficult task in cloud screening (Karlsson, 1997). Therefore, test 1 for both NOAA-17 and NOAA-16 satellites aims to best discriminate snow-ice pixels from cloudy ones to reduce non-cloud noise in the daily convective cloud masks. 2012 Royal Meteorological Society
4.2.
Test 2: infrared gross test
Test 2 is the IR gross threshold test at 12 µm wavelength (channel 5) for both morning NOAA-17 (Figure 5(a)) and afternoon NOAA-16 (Figure 5(c)) orbits. The first assumption is that clouds normally are colder than the LST, with few exceptions as described below. This first cloud test is based on the brightness temperature at 12 µm because clouds have a greater optical depth at 12 µm than at 11 µm (Olesen and Grassl, 1985). Radiance at this wavelength is very useful for identifying thin Ci clouds and thin mid-level clouds (Inoue, 1987), and other cloud genera as well. For instance, Cu and Cb cloud-tops are significantly colder than land surfaces not contaminated by snow-ice (Mart´ınez et al., 2000) or affected by tropospheric low-level temperature inversions. The threshold was determined from a brightness temperature histogram of 515 × 485 pixel arrays for the IP and 33 × 33 pixel arrays for the isle of Mallorca, this last one as representative of the BI. In addition, various scenes for each month Int. J. Climatol. 33: 2113–2128 (2013)
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Figure 5. (a) Channel 5 (11.50–12.50 µm) NOAA-17; (b) cloud mask (BT ≤290.0 K) on 17 May 2004 at 1122 h UTC; (c) channel 5 (11.50–12.50 µm) NOAA-16; (d) cloud mask (BT ≤290.0 K) on 17 May 2004 at 1400 h UTC; (e) BT histogram of a 515 × 485 pixel scene (black square in (a)). The dark grey line represents the threshold value (BT ≤290.0 K) found for computing daily cloud masks for NOAA-17; (f) BT histogram of a 515 × 485 pixel scene (black square in (c)). The dark grey line represents the threshold value (BT ≤290.0 K) found for computing daily cloud masks for NOAA-16.
were analysed empirically. The large day-to-day variability in LST linked to daily changes in atmospheric conditions made it difficult to set a static threshold. A threshold temperature of ≤290 K was used to select cloudy pixels representing all cloud types (Derrien et al., 1993) for data from both satellites (Figure 5(e) and (f)). Cloud masks from the IR gross threshold test designate those pixels equal to or colder than 290 K as cloudy, whereas pixels warmer than this value are tagged as clear background regions (Figure 5(b) and (d)). Under high insolation, the LSTs during the afternoon NOAA16 orbits are >290 K even during the cooler months. However, we noted that cold clear nights and subsequent 2012 Royal Meteorological Society
low-level inversions are responsible for pixels that are colder than this threshold in inland areas or mountain valleys of the IP during the morning NOAA-17 orbits, particularly in May, September and October. Additionally, secondary sources of error for this test are lakes, reservoirs and coastal areas where pixels can exhibit brightness temperatures ≤290 K and be misidentified as clouds. Tests 3 and 4 are designed to clarify if these pixels are truly cloudy. The algorithm identifies a pixel as cloudy if the three cloud tests (test 2, 3 or 4) are positive. For instance, low-level inversions, lakes, reservoirs and coastal fringes exhibit low reflectance in the VIS spectrum and therefore do not pass the albedo or visible test Int. J. Climatol. 33: 2113–2128 (2013)
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Figure 6. (a) Channel 1 (0.58–0.68 µm) NOAA-17; (b) mapping of VIS values of ≥20% on 4 June 2004 1113 h UTC; (c) channel 1 (0.58–0.68 µm) NOAA-16; (d) mapping of VIS values of ≥20% on 04 June 2004 1356 h UTC; (e) histogram of a 50 × 50 pixel (white square in (a)). used for cloud detection (test 3. NOAA-17). The dark grey line represents the threshold value (≥20%) found for computing daily cloud masks; (f) histogram of a 50 × 50 pixel (white square in (c)) used for cloud detection (test 3. NOAA-16). The dark grey line represents the threshold value (≥20%) found for computing daily cloud masks.
(test 3). This IR threshold technique is the simplest of the three tests (Zavody et al., 2000; Connell et al., 2001) 4.3. Test 3: albedo or visible test The third test is the albedo or visible test which uses the reflectance (%) at 0.6 µm (Figure 6(a) and (c)). Land surfaces in this visible channel reflect much less than clouds resulting in increased contrast between clouds and background land surfaces (Saunders and Kriebel, 2012 Royal Meteorological Society
1988; Chen et al., 2002; Barreto Dos-Santos, 2003). The VIS test is only applied during the daytime when the solar elevation is greater than 10° (Saunders and Kriebel, 1988), which is true for the NOAA-17 morning and NOAA-16 afternoon orbits. Determining a fixed threshold value is complicated because the cloud-free land reflectance depends on the atmosphere (scattering and absorption), land cover, and the viewing geometry (Derrien et al., 1993). A Int. J. Climatol. 33: 2113–2128 (2013)
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mono-dimensional histogram of the reflectance values was constructed for a 50 × 50 pixel array to find the cloud-free reflectance peak which was then used to compute a threshold value (Saunders and Kriebel, 1988). Since the cloud-free reflectance peak was easily identified between 10 and 15% in the reflectance histograms for many AVHRR scenes, a fixed reflectance threshold of ≥20% was determined to represent cloudy pixels (Figure 6(e) and (f)). Chen et al. (2002) obtained similar results noting that cloud-free land surfaces have reflectance values from 0 to 30% for a continental area in Texas (USA), and Derrien et al. (1993) used a simple constant threshold value of 20% for solar zenith angles below 70° . The albedo or VIS test classifies as cloud all pixels with reflectance equal to or greater than 20% (Figure 6(b) and (d)). As explained above, snow-ice pixels can also exhibit reflectance values ≥20%. This source of error will be removed by the automated algorithm after test 4 when subtracting snow-ice pixels from cloudy ones. When checking the results of this threshold, we came across a non-natural region of reflectance values greater than 20% in Southeast Spain. These values corresponded to white greenhouse structures of the commercial operations of Campo de Dalias in Almeria. We also discovered that isolated barren pixels located in dry regions of the lower plateau of the IP and in the Ebro river valley can exhibit reflectance values in the VIS channel between 15 and 35% during the summer season. Plastic greenhouse and isolated barren pixels will be automatically removed because they do not accomplish the infrared gross test (test 2) exhibiting brightness temperatures warmer than 290 K at 12 µm wavelength. 4.4.
Test 4: ratio NIR/VIS
Test 4 is the ratio NIR (R0.9 µm)/VIS (R0.6 µm), (Figure 7(c)). The reflectance quotient (Q2) of NIR (Figure 7(b)) to VIS (Figure 7(a)) is close to unity over clouds, as the reflectance of cloudy pixels decreases slightly at NIR wavelengths. In contrast, vegetated and many desert surfaces have greater reflectance values at 0.9 µm than 0.6 µm (Swain and Davis, 1978; Saunders and Kriebel, 1988), except when covered by snow or ice, with resulting Q2 values greater than unity. This test particularly detects thin clouds (Franca and Cracknell, 1995) and has been used for identifying Ci cloud genera (Hutchison et al., 1997), e.g. formed from thunderstorms. The threshold range best representing cloud contaminated pixels was determined by examining Q2 histograms of 50 × 50 pixel arrays (Figure 7(f)). A best fit of many 50 × 50 pixel arrays led to setting the Q2 cloud detection range between 0.7 and 1.3. Well-defined cloud-free land peaks were identified in all the histograms as having a Q2 value between 1.5 and 2.0. We did experience discrepancies in properly identifying three cloud-free surfaces: non-vegetated, coastline, and urban areas. These discrepancies have been noted by others. Chen et al. (2002) and Kriebel et al. (2003) found that cloudy and non-vegetated 2012 Royal Meteorological Society
pixels can share Q2 values around 0.9, and Saunders and Kriebel (1988) set this threshold in ≤1.6. Figure 7(d) shows a mask of pixels exhibiting reflectance quotients ranging between 0.7 and 1.3 and highlights the problematic areas mentioned above. In order to remove these sources of error (coastal, barren, and urbanized pixels), a supplementary threshold value was added to test 4 based on T12 (Chen et al., 2002). Cloudy pixels were identified by a Q2 ratio between 0.7 and 1.3 and T12 ≤ 290 K (Figure 7(e)). The resulting cloud mask for test 4 is more realistic. 5. Product: regional convective cloud frequency composites Figures 8 and 9 display the final product from the proposed threshold scheme in the form of seasonal and monthly convective cloud frequency composites. These composites represent the first climatology of convective clouds over the IP and the BI. The pioneering studies on cloud composites stressed that frequency maps can reveal the regional distribution of clouds in relation to topographical features such as altitude, and land/water boundaries (Klitch et al., 1985), and also measure the persistence/development of convective clouds under different atmospheric patterns. Both seasonal maps presented in Figure 8 show a marked latitudinal gradient in cloudiness with overall high cloud amounts ranging from 60 to 70% (NOAA-17; Figure 8(a)) and from 70 to 80% (NOAA-16; Figure 8(b)) over the northern fringe of the IP, and an overall minimum ranging from 0 to 10% in the southern part of the region. The maximum in cloudiness found over the Atlantic-Cantabrian coastal area and the Pyrenees is mainly associated with large-scale northwesterly winds and the influence of late season cold fronts. In contrast, the minimum in cloudiness over the southern region of the IP is caused by the strong influence of the Azores high pressure system, which suppresses convection and cloudiness during the summer months. Other preferential areas with high cloud activity are related to increased mean convective cloud over the mountainous areas described in Figure 1. For example, notice the increase in cloud cover over the Pyrenees or the eastern region of the Iberian system mountains between the morning and the afternoon orbits (Table IIb). Offshore, on the isle of Mallorca, a convective area with afternoon maximum cloud frequency amounts of 30–40% represents cloud formed as a result of the low level convergence of sea breezes. Large cloudfree regions are evident around coastal areas during the afternoon hours, bays (e.g. Gulf of Valencia) and river valleys (e.g. Ebro and Guadalquivir rivers) in relation to subsident divergent flows over nearby water bodies. Figure 9 shows an example of the pronounced intermonthly cycle in cloudiness. The statistics computed in Table IIa confirm that May (Figure 9(a)) is the third cloudiest month with a mean cloud frequency of 30.4% (NOAA-17) and 30.3% (NOAA-16); due to the development of widespread clouds associated Int. J. Climatol. 33: 2113–2128 (2013)
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Figure 7. (a) Channel 1 (0.58–0.68 µm) NOAA-17; (b) channel 2 (0.72–1.10 µm) NOAA-17; (c) Q2 ratio R0.9 µm/R0.6 µm; (d) mapping of Q2 values between 0.7 and 1.3 on 17 June 2004 1117 h UTC; (e) mapping of Q2 values between 0.7 and 1.3 and BT ≤290.0 K on 17 June 2004 1117 h UTC; (f) histogram of a 50 × 50 pixel (white square in (c)) used for cloud detection (test 4. NOAA-17). The dark grey line represents the threshold range (between 0.7 and 1.3) found for computing daily cloud masks.
Figure 8. Regional convective cloud frequency composites for (a) morning (left: NOAA-17; 2002–2010 period) and (b) afternoon (right: NOAA-16; 2001–2005 period) orbits during the 6-month study period May–October. The number of images averaged is shown in the lower-left corner of each image (n = sample size). 2012 Royal Meteorological Society
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Figure 9. As in Figure 8 except for morning and afternoon composites during (a) May, (b) June, (c) July, (d) August, (e) September and (f) October.
with convective processes operating during the spring months. The mean cloud frequency sharply decreases in June (Figure 9(b)) with 19.7% (NOAA-17) and 16.1% (NOAA-16). Even though convective clouds developed over the mountainous areas, particularly during the afternoon orbit in the eastern region, the 2012 Royal Meteorological Society
subsident influence of the Azores high pressure systems resulted in a decline in cloudiness. A similar, more dramatic reduction in cloudiness occurred in July (Figure 9(c)) resulting in overall low mean cloud frequency of 12.5% (NOAA-17) and 17.2% (NOAA-16). A gradual increase of convective activity was found in Int. J. Climatol. 33: 2113–2128 (2013)
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Figure 9. (Continued ).
August (Figure 9(d)), with mean cloud frequencies of 18.5% (NOAA-17) and 18.7% (NOAA-16). September is the second cloudiest month with a mean cloud frequency of 31.6% (NOAA-17) and 32.4% (NOAA-16), 2012 Royal Meteorological Society
(Figure 9(e)). Finally, clouds amounts are quite high in October (Figure 9(f)), representing the cloudiest month with a mean cloud frequency of 39.3% (NOAA-17) and 41.7% (NOAA-16). This is linked to the increasing
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Table II. Mean cloud frequency (in %) from the convective composites shown in Figures 8 and 9 for (a) the Iberian Peninsula and the Balearic Islands, and (b) the Pyrenees and the Iberian System Mountains during the warm semester May–October. (a) May
IP BI Mean
June
July
August
September
October
May–October
N17
N16
N17
N16
N17
N16
N17
N16
N17
N16
N17
N16
N17
N16
32.6 28.2 30.4
34.3 26.4 30.3
22.2 17.2 19.7
20.7 11.6 16.1
13.7 11.3 12.5
16.3 18.1 17.2
16.0 20.9 18.5
20.2 17.3 18.7
27.5 35.6 31.6
26.4 38.3 32.4
38.0 40.6 39.3
46.1 37.3 41.7
24.8 25.6 25.2
26.9 24.4 25.6
(b) May
P ISM Mean
June
July
August
October
May–October
N17
N16
N17
N16
N17
N16
N17
N16
N17
N16
N17
N16
N17
N16
53.3 36.0 44.7
64.7 36.7 50.7
43.9 21.3 32.6
52.6 26.7 39.7
35.2 17.4 26.3
50.6 24.4 37.5
35.3 20.5 27.9
49.5 30.2 39.9
40.6 32.7 36.6
41.3 36.1 38.7
37.3 36.9 37.1
47.8 41.6 44.7
40.8 27.4 34.1
50.9 32.3 41.6
occurrence of cold fronts approaching from the Atlantic Ocean in the autumn. This is clearly discernible in both morning and afternoon cloud composites showing the highest cloud frequency values in the northwestern part of the IP.
6.
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Summary and future work
This article has presented a new daytime over land multispectral cloud detection algorithm to derive daily AVHRR cloud masks with high spatial resolution (1.1 km) and consequently compute regional convective cloud frequency composites over the IP and BI. The algorithm consists of four static spectral threshold tests designed to be functional during the warm convective season (May–October). The manuscript describes in detail this fast and simple high-resolution convective cloud algorithm, discusses the sources of error for each test and shows the first monthly and seasonal regional convective cloud climatology for both morning (NOAA-17; 2002–2010 period) and afternoon (NOAA16; 2001–2005 period) overpasses. In spite of the small dynamic range of channels and low temporal resolution of the NOAA polar orbiting satellites, the long-term AVHRR datasets (1979–2012) will enable to quantitatively study the spatial-temporal variability of the most prominent convergence zones associated with intense convection and storm activity over the IP and the BI. One of the most interesting applications of the proposed detection scheme is to test the hypothesis about the rise of cloud condensation level above coastal mountains and consequent decline of summer storms observed in the Iberian Mediterranean area during the last few decades. Millan et al. (2005a) infer that land-use perturbations that have occurred during the last three decades may have induced less frequent summer convective episodes resulting in enhanced heating of SST, and increased autumn and winter Mediterranean cyclogenesis. All these feedback processes are of great interest for the European 2012 Royal Meteorological Society
Union (EU) water policies in southern Europe as pointed out by Millan et al. (2005b). Furthermore, daily convective cloud frequency information can be stratified by local weather and large-scale synoptic flow (Connell et al., 2001). This will help investigate cloud patterns in relation to local and large scale atmospheric forcing and have potential real-time meteorological applications such as improving convective short-term forecasting. Likewise, future research of convective cloud frequency composites will involve a combination thereof with lightning and radar data in order to study the intensity of storms. For studies on long-term cloud amount trends the results from the proposed algorithm should be quantitatively compared with observations using two validation techniques: (1) comparing satellite-observed earth cover with a manual subjective classification of pixels according to the aforementioned categories, and (2) comparing side by side satellite-derived cloud cover with corresponding synoptic observations of TCC. Additionally, convective cloud amounts extracted from this new detection scheme will be also compared with other climatologies (ISCCP, PATMOS-x, etc.), or related studies (Roebeling and van Meijgaard, 2009). Both validation procedures will strengthen the quality results of the new detection algorithm. Recent advances on remote sensing offer new ways to study convection. One of the future aims is to improve our knowledge of convective hot spots by using advantages from the Meteosat Second Generation (MSG), and the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellites. The proposed algorithm can be easily adapted using the spectral information from both satellites. In the case of the MSG data, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) scans the Earth every 15 min, which is a crucial advantage for tracking the evolution life-time of convective cells. The use of MSG data will help to create convective cloud composites with higher temporal resolution, ensuring the observation of convective clouds from the cumulus, Int. J. Climatol. 33: 2113–2128 (2013)
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mature and dissipating or anvil stages. In addition, the higher spectral resolution imaging capabilities on, e.g., MSG offers the chance for multispectral approaches to cloud detection so that many of the errors/concerns (e.g. masking of thin Cirrus clouds, small Cumulus, etc.) discussed here could be mitigated. To conclude, the use of high spectral and frequency observations afforded by the MSG SEVIRI sensor is the next step in order to improve our knowledge about the convective hot spots over the IP and the BI. Acknowledgements This study was supported by a JAE-DOC043 grant from the Spanish Research Council (CSIC) and the projects CGL2011-27536 and CGL2011-27574-C02-02 financed by the Spanish Commission of Science and Technology and FEDER. The authors thank the CREPAD project (assistance provided by Maria-de-los-Angeles Dominguez-Duran and Angel Garcia-Sevilla) involved within the INTA for supplying AVHRR data. The authors wish to acknowledge the anonymous reviewers for their detailed and helpful comments to the original manuscript. References Armstrong RL, Brun E. 2008. Snow and Climate: Physical Processes, Surface Energy Exchange and Modeling. Cambridge University Press: New York. Arking A. 1991. The radiative effects of clouds and their impact on climate. Bulletin of the American Meteorological Society 72(6): 795–813. Arriaga JAT, Rojas FG, L´opez MP, Canton M. 2003. An automatic cloud-masking system using backpro neural nets for AVHRR scenes. IEEE Transactions on Geoscience and Remote Sensing 41(4): 826–831. Azorin-Molina C, Connell BH, Baena-Calatrava R. 2009. Sea-breeze convergence zones from AVHRR over the Iberian Mediterranean Area and the Isle of Mallorca, Spain. Journal of Applied Meteorology and Climatology 48(10): 2069–2085. Azorin-Molina C, Tijm S, Ebert EE, Vicente-Serrano SM, EstrelaNavarro MJ. 2011. Numerical study of a non-forecasted sea breeze thunderstorm in the Eastern Iberian Peninsula. Part I. HIRLAM and HARMONIE precipitation performance. In 11th EMS Annual Meeting/10th European Conference on Applications of Meteorology, 12–16 September 2011, Berlin (Germany), Abstract EMS2011-3971. Baena-Calatrava R. 2002. Georreferenciaci´on autom´atica de im´agenes NOAA-AVHRR, Master thesis, University of Jaen, Spain. Barreto Dos-Santos D. 2003. Evaluaci´on de algoritmos multiumbral para la detecci´on de nubes en im´agenes AVHRR. Desarrollo de algoritmos para la detecci´on autom´atica de nubes en im´agenes multisensoriales. University of Las Palmas de Gran Canaria: Spain. Baum BA, Trepte Q. 1999. A grouped threshold approach for scene identification in AVHRR imagery. Journal of Atmospheric and Oceanic Technology 16(6): 793–800. Casanova C, Romo A, Hern´andez E, Casanova JL, Sanz J. 2005. Rapid response for cloud monitoring through Meteosat VIS-IR and NOAA-A/TOVS image fusion: civil aviation application. A first approach to MSG-SEVIRI. International Journal of Remote Sensing 26(8): 1699–1716. Casanova C, Romo A, Hern´andez E, Casanova JL. 2010. Operational cloud classification for the Iberian Peninsula using Meteosat Second Generation and AQUA-AIRS image fusion. International Journal of Remote Sensing 31(1): 93–115. Chen PY, Srinivasan R, Fedosejevs G, Narasimhan B. 2002. An automated cloud detection method for daily NOAA-14 AVHRR data for Texas, USA. International Journal of Remote Sensing 23(15): 2939–2950. 2012 Royal Meteorological Society
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