A novel approach to fog/low stratus detection using ... · A novel approach to fog/low stratus detection using Meteosat 8 data Jan Cermak⁎, Jörg Bendix Laboratory for Climatology

7 (2008) 279–292www.elsevier.com/locate/atmos

Atmospheric Research 8

A novel approach to fog/low stratus detection using Meteosat 8 data

Jan Cermak ⁎, Jörg Bendix

Laboratory for Climatology and Remote Sensing, Faculty of Geography, University of Marburg,Deutschhausstr 10, 35037 Marburg, Germany

Abstract

A method is presented for fog and low stratus detection from daytime satellite imagery based on Meteosat 8 SEVIRI(Spinning-Enhanced Visible and Infra-Red Imager) data. With its excellent spatial, spectral and temporal resolutions, thisimagery is an ideal basis for operational fog monitoring. The scheme utilizes a range of pixel-based and novel object-orientedtechniques to separate fog and low stratus clouds from other cloud types. Fog and low stratus are identified by a number of testswhich explicitly and implicitly address fog/low stratus spectral, spatial and microphysical properties. The scheme's performanceis evaluated using ground-based measurements of cloud height over Europe. The algorithm is found to detect low clouds veryaccurately, with probabilities of detection (POD) ranging from 0.632 to 0.834 (for different inter-comparison approaches), andfalse alarm ratios (FAR) between 0.059 and 0.021. The retrieval of sub-pixel and temporal effects remain issues for furtherinvestigation.© 2007 Elsevier B.V. All rights reserved.

Keywords: Fog; Stratiform clouds; Remote sensing; Nowcasting; Meteosat Second Generation

1. Introduction

1.1. Background

Fog and low stratus are phenomena of significantsocietal and economic importance, especially as a majorobstacle to road and air traffic. While the former is onlyaffected by low stratus with ground contact (‘fog’), thelatter is impaired by any low stratus cloud, i.e. a low cloudbase. While meteorological station data does not yield aspatially coherent picture of fog/low stratus distribution,satellite imagery can be used to compensate for thisdeficiency.

⁎ Corresponding author. Tel.: +49 6421 2824227; fax: +49 64212828950.

E-mail addresses: [email protected] (J. Cermak), [email protected](J. Bendix).

0169-8095/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.atmosres.2007.11.009

The detection of fog and very low stratus fromsatellite imagery has presented a challenge for severaldecades. Many extensive cloud classification schemesexist, but these either do not expressly address fog/lowstratus, or they rely on ancillary (model or observation)data (e.g. Saunders and Kriebel, 1988; Ackerman et al.,1998; Derrien and LeGleau, 2005). Many approacheshave been developed to address fog detection at night(e.g. Eyre et al., 1984; Turner et al., 1986; Dybbroe,1993; Bendix, 2002). Most of these rely on theparticular emissive properties of fog droplets at 3.9and 10.8 μm (cf. Bendix and Bachmann, 1991): thesmall droplets found in fog are less emissive at 3.9 μmthan at 10.8 μm, whereas both emissivities are roughlythe same for larger droplets (Hunt, 1973). The dif-ference between the brightness temperatures measuredat both wavelengths is tested against a threshold valueand attributed to the categories low stratus/clear/other

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http://dx.doi.org/10.1016/j.atmosres.2007.11.009

280 J. Cermak, J. Bendix / Atmospheric Research 87 (2008) 279–292

cloud. While historically this technique has been used onpolar orbiting systems, it has now been ported to geo-stationary platforms. The algorithm has been successfullyimplemented on the newGeostationary Operational Envi-ronmental Satellite (GOES) imager (GOES-8+), whichprovides the required spectral bands (cf. Ellrod, 1995;Wetzel et al., 1996; Lee et al., 1997; Greenwald andChristopher, 2000; Underwood et al., 2004). More re-cently, the same algorithm has been applied to data of theSpinning Enhanced Visible and Infrared Imager(SEVIRI) on board Meteosat Second Generation (MSG),where it can be appliedwith some constraints (Cermak andBendix, 2007).

During daytime, however, the situation is entirelydifferent. The solar signal that mixes into the 3.9 μmradiation renders the method useless after sunrise, as thesmall fog droplets reflect at this wavelength. Thereforean altogether different approach is needed for daytimefog detection.

Only few quantitative schemes have been developedin the past to address this issue. Bendix and Bachmann(1991) implemented an algorithm on the NationalOceanic and Atmospheric Administration (NOAA)Advanced Very High Resolution Radiometer (AVHRR)based on the assumption that the 10.8–3.9 μm brightnesstemperature difference is smaller for fog than for clear skypixels. However, this also applies to many other cloudtypes so that an additional cloud exclusion scheme isneeded. More recently, Bendix et al. (2006) developed aradiative transfer-based classification scheme for theModerate Resolution Imaging Spectroradiometer(MODIS) instrument aboard the Terra and Aqua plat-forms. In this approach, the authors derive the minimumand maximum albedo values for modelled fog layers in anumber of spectral channels using the STREAMER ra-diative transfer code (Key and Schweiger, 1998). Theseextremevalues are then applied as thresholds in each of thechannels, with all values in between classified as fog. Thisapproach has already been ported to MSG SEVIRI(Cermak and Bendix, 2007). Although this techniqueyields a good probability of detection for low clouds, it hasa high false alarm ratio, since other cloud types displaysimilar optical properties in many situations. Furthermore,the scheme was developed for a polar orbiting platformand with a regional focus. It assumes a well-definedoverpass time (at or after 11 am local time) with goodillumination conditions of little variation. In operationalprocessing on a geostationary platform the complete rangeof solar elevations are encountered and must be taken intoconsideration. This robustness is of crucial importance toany geostationary satellite daytime fog/low stratus detec-tion scheme.

In the light of this, the aim of the present study was todevelop a new, solid daytime scheme for the detection offog and low stratus from a geostationary platform.

1.2. Meteosat Second Generation

Meteosat 8 became operational early in 2004, as thefirst of four planned MSG satellites (Schmetz et al.,2002). The SEVIRI sensor (Aminou, 2002) aboard eachof these provides full disk scans in twelve spectralchannels, at a repeat rate of 15 min. Eleven of the chan-nels are spread over the visible, water vapour and infra-red wavelengths, each at a spatial resolution of 3 km atsub-satellite point (ssp). The remaining channel is a HighResolution Visible (HRV) channel with a nominal (ssp)resolution of 1 km.

With this payload, Meteosat 8 presents a significantspectral improvement over previous generations of geo-stationary satellites while at the same time providing aspatial resolution comparable to that ofmany polar orbitingsystems. It is for these reasons that this platform waschosen in the present study.

1.3. The need for automation

The present paper stresses the operationality of thescheme presented. The aim is to develop a fog/lowstratus nowcasting system, i.e., a near-real-time assess-ment of the fog cover situation, with further applicationto statistical and climatological evaluations and fogforecasting.

For statistical evaluations, fog and low stratus fre-quency and persistence averaged at SEVIRI's 15 minutetemporal resolution can hardly be obtained economicallyin retrospect unless products have already been computedbefore in an operational fashion. Fog/low stratus now-casting and very-short term forecasting are done withinthe framework of the European Science Foundation (ESF)COST Action 722 (Short Range Forecasting of Fog andLow Clouds). Most numerical fog models have a time-consuming spin-up phase and are thus not suitable fornowcasting. Satellite retrievals may be used to bridge thisgap (Jacobs et al., 2005). Moreover, a continually gen-erated satellite fog and low stratus product can be used inmodel intervalidations.

With these factors in mind, the present algorithm wasdeveloped for the automated real-time processing ofSEVIRI data. It is implemented in Fortran computer codeand runs on a standard personal computer (PC), fullyintegrated into the operational processing of satellite dataat the Laboratory for Climatology and Remote Sensing(LCRS).

281J. Cermak, J. Bendix / Atmospheric Research 87 (2008) 279–292

2. The algorithm

The detection of fog and low stratus is implementedas a chain of complementary spectral and spatial fog/lowstratus property tests. Pixels which do not meet theprescribed criteria are excluded from further processing.While initially cloudy areas are separated from clear

Fig. 1. An overview of the fog/low stratus detection algorithm. After preproceof some confirmed non-fog pixels, the remaining areas undergo a series of tepixel or entity of pixels will no longer be considered in succeeding steps. ‘F

areas, the tests increasingly become more fine-grainedand specific, each building on the results of the pre-ceding tests. The procedure is highlighted in some detailin the following subsections.

For the spectral tests, the following SEVIRI channelsare used: 0.6 µm, 0.8 µm, 1.6 µm, 3.9 µm, 8.7 µm,10.8 µm and 12.0 µm. The scheme is applied up to a

ssing, the satellite data enters the classification scheme. After exclusionsts for spatial and microphysical fog properties. ‘Stop’ indicates that a/ls’ means fog/low stratus. See text for details.

Table 1Channels used in the spectral tests

Test Channels used (µm)

Gross cloud 3.9, 10.8Snow 0.6, 0.8, 1.6, 10.8Ice cloud 8.7, 10.8, 12.0Small droplets 3.9


satellite zenith angle of 70° and a solar zenith angle of80°. Information on surface elevation is extracted fromthe GTOPO30 digital elevation model (USGS, 1993).

The tests and thresholds are based on theoreticalconsiderations, radiative transfer calculations and con-tinuous fine-tuning in operational processing. Forradiative transfer computations, the STREAMERmodel (Key and Schweiger, 1998) was used.

Fig. 1 gives an overview of the entire fog/low stratusdetection scheme described in detail in the followingsub-chapters and Table 1 summarizes the channels usedin the spectral tests.

2.1. Tests

2.1.1. Removal of cloud-free areasIn a first step, a rough separation of clear and cloud-

contaminated pixels is achieved using the radiances at 10.8and 3.9 μm.Given the combination of a solar and a thermal

Fig. 2. Distribution of 10.8 μm–3.9 μm brightness temperature difference09:00 UTC. Clear land pixels peak at around 3 K, clear sea pixels slightly abaround −2 to −3 K.

signal at 3.9 μm, the difference in radiances must be largerfor a cloud-contaminated pixel than for a clear pixel:

Rcc 10:8ð Þ � Rcc 3:9ð ÞbRcf 10:8ð Þ � Rcf 3:9ð Þ ð1Þ

with Rcc(λ) the radiance of a cloud-contaminated pixel ata wavelength of λ μm, and Rcf(λ) the same for a cloud-free pixel.

A threshold value of observed brightness temperaturedifference between cloud and cloud-free areas must bedetermined dynamically. Since this threshold depends onviewing geometry and season, no fixed value can be used.Therefore, a procedure for the dynamic retrieval of aproper threshold was developed. It is applied to eachsingle slot and works as follows. Over an area of about250,000 pixels (size depending on solar geometry) thehistogram of the radiance differences is analysed (seeFig. 2 for a representative example). In this histogram theclear sky peak is identified within a certain range. Thenearest significant relative minimum in the histogramtowards more negative values is detected and used as athreshold to separate clear fromcloudy pixels in the image.The advantage of this approach over the use of a channelin the visible range is that a certain amount of snow-covered pixels is already rejected as clear, due to the lowerreflectivity of snow as compared to clouds in the mediuminfrared (N1.4 µm, Wiscombe and Warren, 1980).

for a typical low stratus scene during daytime (5 November 2003,ove this level, and cloudy pixels remain well below, with a transition at


2.1.2. Removal of remaining snow pixelsNext, snow-covered pixels that have accidentally

been classified as clouds are singled out. This step isnecessary as the previous test does not exclude allsnow-covered pixels. Snow-testing is based on theinsights that snow a) has a certain minimum reflec-tance (0.11 at 0.8 μm), b) has a certain minimum tem-perature (256 K), and c) displays a lower reflectivitythan water clouds at 1.6 μm, combined with a slightlyhigher level of absorption (Wiscombe and Warren,1980). The first two criteria are tested against pre-defined thresholds before the third factor is testedusing the Normalized Difference Snow Index (NDSI),(Eq. (2)) (Dozier and Painter, 2004; Dozier, 1989).Where the NDSI exceeds a certain threshold (0.4) andthe two other criteria are met, a pixel is rejected assnow-covered.

NDSI ¼ q 0:6ð Þ � q 1:6ð Þq 0:6ð Þ þ q 1:6ð Þ ð2Þ

with ρ(λ) the reflectance at wavelength λ μm.

2.1.3. Ice cloud exclusionOnce the cloud area has been delineated with some

confidence, cloud properties are investigated in moredetail. A number of tests for cloud phase are implemen-ted. They have slightly different foci (e.g. thin cirrus) andcomplement each other. For the purpose of this study,only warm fog (i.e. clouds in the water phase) are con-sidered. The cloud phase tests are based on the assump-tions that ice clouds and water clouds display distinctlydifferent refractive behaviour in various parts of theinfrared. In addition, low brightness temperature is usedas a criterion.

Initially, cloud phase is addressed in a positive watercloud test. The difference of brightness temperatures inthe 12.0 and 8.7 µm channels is used as an indicator ofcloud phase (Strabala et al., 1994). Where it exceeds2.5K, awater-cloud-covered pixel is assumedwith a largedegree of certainty.

In order to identify any less obvious potential watercloud pixels missed by this test, ice clouds are excludedexplicitly from the remaining pixels in the following.They are identified in a number of ways. The simplest ofthese is a straightforward temperature test, cutting off atvery low 10.8 µm brightness temperatures (250 K). Thincirrus clouds are identified by two different techniques.Both of these make use of the infrared (IR) windowchannels and the different sensitivities each of the threeavailable channels has to thin cirrus clouds: a) Cirrus isdetected by means of the split-window IR channel

brightness temperature difference (T10.8–T12.0). This dif-ference is compared to a threshold dynamically inter-polated from a lookup table based on satellite zenith angleand brightness temperature at 10.8 µm (Saunders andKriebel, 1988), b) The second approach uses thedifference between the 8.7 and 10.8 µm brightnesstemperatures (T8.7–T10.8), founded on the relatively strongcirrus signal at the former wavelength (Wiegner et al.,1998). Where the difference is greater than 0 K, cirrus isassumed to be present. Those pixels whose cloud phasestill remains undefined after these ice cloud exclusions aresubjected to amuchweaker cloud phase test in order to getan estimate regarding their phase. This test uses the NDSIintroduced above. Where it falls below 0.1, a water cloudis assumed to be present.

2.1.4. Small droplet proxy testAfter the removal of snow pixels and ice clouds, the

presence of small droplets is tested for. This is doneimplicitly using radiances in the 3.9 µm channel. Theunderlying assumption relates to the specific radiativeproperties of fog and stratus as compared to other cloudsand clear areas at this wavelength (Hunt, 1973). The smalldroplets usually found in fog and stratus (usually no largerthan 20 µm, Roach, 1994) are slightly reflective at thiswavelength. The solar component in the signal receivedfrom fog areas exceeds that from both land and otherclouds. At the same time, land normally has a largerthermal signal than non-fog clouds, the latter being athigher altitudes.

Due to these principles, fog generally has a strongersignal at 3.9 μm than clear ground, which in turn radiatesmore than other clouds. A sample 3.9 μm radiance imagecan be seen in Fig. 3. The vertical line indicates theprofile whose values are plotted in Fig. 4 (north to southdirection).

This insight is used in the small droplet proxy test. The3.9 μm radiances for cloud-free land areas are averagedover 50 rows at a time to obtain an approximately lati-tudinal value. Wherever a cloud-covered pixel exceedsthis value, it is flagged ‘small droplet cloud’.

2.1.5. Spatial testsThe analysis of a set of spatial features is at the centre

of the fog and low stratus detection scheme (see Fig. 1).These tests explicitly address fog/low stratus propertiesin space and thus yield a significant advantage overconventional spectral tests. The spatial analysis con-siders all pixels which have been identified as watercloud-contaminated and have not previously been re-jected as obviously non-fog. The focus is shifted fromthe pixel level to an entity-oriented approach, with cloud

Fig. 4. A profile of radiances registered in the 3.9 μm channel overcentral France, 5 December 2003, 13:00 UTC (see Fig. 3 for preciselocation). The profile extends from line 110 (N) to line 170 (S) andencompasses different types of cloud cover: fog/low stratus (‘Lowstratus’, 110–145), clear land area (‘Clear’, 146–159), undifferentiatedmedium and high clouds (‘Medium/high clouds’, 160–166, 174–180),and cirrus (‘Cirrus’, 167–173).

Fig. 3. A 3.9 μm radiance image for 5 December 2003, 13:00 UTC. The vertical line over central France indicates the location of the profile shown inFig. 4.


and fog patches observed as units in space. These aretested for two essential fog properties:

• Low altitude: Fog is expected to be a cloud close tothe ground.

• Surface homogeneity: As a stratiform cloud and oftenlimited by a boundary layer inversion, a fog patchshould have a more or less homogeneous upper height.

Spatially coherent and discrete entities of similar pro-perties have to be identified before the spatial feature testscan be applied. To this end, all remaining cloud pixels aregrouped into spatially coherent and discrete entities. Anentity in this context is defined as a set of adjoined pixelsbelonging to the same cloud class. The latter is derivedfrom the tests described in the previous section. Eachentity is assigned a unique identifying common entitynumber (ID) so that it can be considered as a discrete unit infurther testing. Depending on the degree of segmentationderiving from the spatial distribution of cloud classes, theentity areas may vary in size. For maximum differentiation,entities should not be too large. Therefore, diagonalrelations between pixels (i.e. pixels bordering each otherat angles other than multiples of 90°) are excluded in theclustering process.

2.1.5.1. Altitude test. The purpose of the altitude test isto determine the relative height of a cloud entity abovethe underlying surface and thus to reach a rough estimate

as to whether a given cloud could potentially qualify as alow cloud or not.

In this test, the 10.8 μm brightness temperature ofevery fringe pixel of a given entity is compared to that ofany and all bordering clear pixels. In order tominimize therisk of underestimating cloud altitude where pixels withonly partial cloud cover are compared with clear pixels,themaximum brightness temperature difference is located


along the entity margin. Clear pixels covered by snow arenot considered.

Using the brightness temperature difference, theground elevation for both the cloudy and the clearpixel, and an assumed atmospheric temperature lapserate (0.65 K 100 m−1), cloud top altitude is estimated asfollows:

zct ¼ Tcf 10:8ð Þ � Tcc 10:8ð ÞC

� zcf � zccð Þ ð3Þ

where zct is the cloud top height, Tcf(10.8) the 10.8 μmbrightness temperature for a cloud-free pixel, Tcc(10.8)the same for a cloud-contaminated pixel, Γ the atmo-spheric temperature lapse rate [Temperature /Height], zcfthe surface elevation of the cloud-free pixel and zcc thatfor the cloud-contaminated pixel. The latter two aretaken from a digital elevation model (see above).

If zct falls below a threshold of 2000 m, the cloudentity is classified as a potentially low cloud. If the cloudappears to be higher, it is no longer considered a poten-tial fog or low stratus entity and disregarded in furthertests.

2.1.5.2. Surface homogeneity test. Assuming that fogis a cloud trapped under an inversion, a fairly homo-geneous cloud top surface height is expected. Sinceheight information is not directly available, brightnesstemperature measured in the 10.8 μm channel is taken asa proxy. As only relative height information is needed,this approach appears appropriate.

Fig. 5. Albedo image from SEVIRI 0.6 µm c

For each low cloud entity, the standard deviation of the10.8 μm brightness temperature is determined. Where itfalls below a certain threshold (2.5), the entity is identifiedas a low stratus patch. The threshold was determinedempirically, by continuous monitoring of operationalprocessing. This test concludes the initial fog/low stratusdetection scheme.

2.1.6. Microphysical property plausibility testOnly a well-defined range of droplet radii and cloud

optical depths are encountered in fog and low stratus.Unfortunately, some combinations of microphysicalproperties are encountered in both, fog/low stratus andother cloud types. Therefore, microphysical propertiescannot be used as sole indicators of fog presence.However, they may well be used to exclude areas withproperties outside the known range. This approach istaken here.

Microphysics computation times are significant. Sincethe fog detection scheme is run operationally, theseproperties are only retrieved for the areas previouslyflagged as fog/low stratus. For this reason the micro-physics test is the last one applied in the scheme.

Microphysical properties are retrieved based on ascheme developed by Kawamoto et al. (2001, based onNakajima and Nakajima, 1995).While this algorithmwasoriginally developed for application to NOAA AVHRRdata, it has now been ported to Terra/Aqua MODIS andMSG SEVIRI (Nauss et al., 2004). Its basis is the specificradiative behaviour displayed by clouds with differentdroplet sizes and optical depths at visible and near infrared

hannel, 5 December 2003, 13:00 UTC.

Fig. 6. Simplified fog/cloud mask for 5 December 2003, 13:00. Dark grey indicates non-fog clouds, medium grey fog/low stratus, and light grey areasare snow. Clear areas are shown in black, country borders given in white for reference.


wavelengths. While the radiation registered in the 0.6 μmchannel mostly depends on cloud optical depth, theradiation received at 3.9μmvarieswith cloud droplet size.In order to reduce errors caused by background (i.e.surface) temperature and albedo, especially in situationswith thin clouds, these parameters are included in themicrophysics retrieval scheme. They are estimated basedon a spatial interpolation for surface temperature andtemporal averaging for albedo. A certain degree ofuncertainty remains and thus a potential source of error,as the parameterizations may not be applicable to allpossible cloud situations. The algorithm's good overallperformance has, however, been ascertained (Nauss et al.,2005a, Nauss et al., 2005b).

Typical microphysical parameters for fog were takenfrom previous studies (e.g. Tampieri and Tomasi, 1976;Pinnick et al., 1978; Chourlaton et al., 1981; Stewardand Essenwanger, 1982; Minnis et al., 1992; Roach,1994; Wetzel et al., 1996; Miles et al., 2000; Brenguieret al., 2000; Bendix, 2002). According to these sources,fog optical depth normally ranges between 0.15 and 30while droplet effective radius varies between 3 and12 μm, with a maximum of 20 μm in coastal fog.

The respective maxima for optical depth (30) anddroplet radius (20 μm) are applied to the low stratus maskas cut-off levels. Where a pixel previously identified asfog/low stratus falls outside the range it will now beflagged as a non-fog pixel.

The microphysics test concludes the fog/low stratusdetection scheme. Fig. 5 shows a 0.6 µm albedo image,a sample fog/low stratus mask product for the same

scene can be seen in Fig. 6. Both images belong to the13:00 UTC scene of 5 December 2003.

3. Validation and performance of the scheme

3.1. Preliminary thoughts

One of the greatest advantages of satellite-based fogretrieval is the spatial information. Satellite productsallow for statements regarding the extent and distribu-tion of fog, as opposed to ground-based point measure-ments. Operationally, the latter are most commonlyavailable in the form of SYNOP data at hourly intervals.

Unfortunately ground-based point measurements arethe only type of data available for validation of this kindof product. A few fundamental flaws are obvious in thissort of inter-comparison approach:

• Sub-pixel effects. Ground-based point measurementsdo not necessarily represent the state of parametersover the entire pixel as seen by the satellite, so themeasured effects may not be detectable in the pixel as awhole. In the case ofMSG SEVIRI the nominal size ofa pixel at sub-satellite point (0°N, 3.4°W) is 3×3 km,yielding an even larger area per pixel for Europe. Sub-pixel effects are of particular importance at themarginsof a fog patch.Moreover, a meteorological station maybe located at the intersection of two pixels, making itnearly impossible to attribute its data to either one.

• Timing. MSG SEVIRI takes 15 min for one scan of thehemisphere, scanning from south to north. Therefore


the nominal time assigned to each scene does notnecessarily represent the actual scan time at a givenlocation within that scene. For this reason the featuresobserved on the ground may not always match thoseseen in a supposedly corresponding satellite image.This holds true especially for fog in its final dissipationstages.

• Co-location issues.While the geo-location ofMeteosat8 is generally stable, slight changes may occur inindividual scenes. In operational processing, a manualfiltering of these scenes is impossible. One thereforehas to consider that features may be found in a pixelother than the expected one in some instances. On topof this, the observation angle is of importance: OverEurope, MSG's satellite zenith angle ranges betweenabout 45° and 65°. While this is no great problem forvery thin fog layers with a top height close to sea level,it becomes increasingly problematic at higher eleva-tions. The atmospheric profile observed verticallyfrom the ground does not match the profile seen fromthe satellite at the angle mentioned above.

• Data accuracy. Lastly, the quality of the ground-basedmeasurements is very variable. In many cases theparameters visibility and cloud height are not derivedfrom measurements (transmissometers, ceilometers)but estimated by a human observer. Besides divergingdefinitions of visibility in both cases, in the latter casethe data are only classified roughly and depend on thehighly subjective assessment of a person.

Despite all these significant draw-backs, ground-based point measurements remain as about the onlysource of validation data available. They have hencebeen used in an attempt to assess the performance of thescheme presented in the first chapters. The outcome ofthis inter-comparison has to be seen against thebackground of the issues outlined above.

3.2. Validation data and methodology

Inter-comparison data retrieved from meteorologicalstations has been made available for 449 stations inGermany and neighbouring countries by the DeutscherWetterdienst (DWD). The data set (henceforth referred toas reference data set) encompasses a total of 10 days withdifferent cloud type mixtures over Europe, at a temporalresolution of 1 h. These observations have been comparedto the products computed by use of the algorithm outlinedabove. Relevant parameters contained in the ground datasets are visibility and cloud base height.

The reference data set encompassed days in 2003 (30September, 12 October, 5 November, 10–11 November, 5

December, 8 December, 12 December) and 2004 (24January and 1 February). This selection includes a range ofvery different cloud situations over Europe. Some scenesfeature individual fog patches in valleys, others closedcovers of low stratus, and others contain no fog at all. In allscenes other cloud types are present, most of the timecovering fog to at least some extent where fog is present.

A range of statistical indicators was computed tonumerically express the algorithm's validity. Theparameters chosen are Probability of Detection (POD),False Alarm Ratio (FAR), and the Critical SuccessIndex (CSI). They are defined as follows:

POD ¼ NH

NH þ NMð4Þ

FAR ¼ NF

NH þ NFð5Þ

CSI ¼ NH

NH þ NM þ NFð6Þ

where NX is the number of H: Hits, M: Misses, F: FalseAlarms. A hit is defined as an instance where bothproduct and reference data show the sought feature, at amiss the reference data reports the feature, but not theproduct, and vice versa for a false alarm.

For each data point in the reference data set acorresponding value was extracted from the satelliteproduct based on the co-ordinates. To make sure onlydaytime scenes were used, the solar zenith angle waschecked for each data point. Where it was larger than80°, the data point was skipped. The statistical indicatorswere then computed for the entire reference data set.

In order to compensate for the co-location issuesoutlined in Section 3.1, all indices were computed notonly for individual pixels, but also for a 3×3 pixelenvironment. This approach was based on the assumptionthat the sought-after ground station might be representedin one of the pixels neighbouring the one identified as thetheoretical location. Each 3×3 pixel environment wastherefore tested for the presence of the feature found in thecorresponding ground station data. Where any one of thepixels agreed with the reference measurement, it wastaken to be the sought-after location. Since this approachis somewhat biased, both, the pixel-based and the 3×3approaches are considered in the following.

The satellite fog/low stratus mask product was testedagainst the presence of low clouds in ground data. Lowclouds are identified by means of cloud base height data.

Table 3Same as Table 2, but omitting high and medium level clouds

Case POD FAR CSI n

1×1 0.764 0.059 0.729 20,7423×3 0.834 0.021 0.819 20,914

In the ‘Case’ column, the size of the validation domain (in pixels) isgiven; n is the number of observations used.


Unfortunately, no information on cloud top height wasavailable. Thus a cloud base height of less than 1000 mwas taken to indicate a low cloud. With respect to theapplication (low cloud base) this approach appearsappropriate.

3.3. Inter-comparison results

The results of the inter-comparison run are summar-ized in Table 2.

The POD is fairly high for all cases, for both fog andlow clouds, indicating that low clouds seem to be de-tected well by the satellite algorithm. The FAR figuresare well below 0.1, indicating that hardly a feature de-tected as fog/low stratus turns out to be something otherthan a low cloud. The differences between the individualpixel approach and the 3×3 pixel environment arenoticeable but small.

A natural limitation of satellite data is the detection ofunderlying cloud layers: In cases of overlapping clouds,only the uppermost of these can be detected reliably.Therefore fog layers underneath other clouds will notshow in the satellite product (reduced POD). This situa-tion occurred in several locations on the days used forvalidation. If, in the light of this, one recalculates thecontingency indices leaving aside all cases where highand medium level clouds are registered in the satelliteproduct, the POD and CSI improve on those presentedabove. They are summarized in Table 3.

The POD and CSI values have improved visibly on theprevious run. Like all the other figures computed in thisground-satellite inter-comparison, these indices musthowever be interpreted with great care. The eliminationof high andmedium level cloudsmaywell have removed anumber of true misclassifications. On the other hand aslight increase in POD and CSI seems plausible, as anumber of overlap situations were indeed present in thescenes considered.

3.4. Individual test assessment

A comparison of the individual tests constituting thenew method presented in this paper is not possible

Table 2Results of the statistical comparison of ground-based measurementswith satellite products

Case POD FAR CSI N

1×1 0.632 0.059 0.608 22,8023×3 0.701 0.021 0.691 22,802

In the ‘Case’ column, the size of the validation domain (in pixels) isgiven; n is the number of observations used.

because they do not test the same property, but only intheir sum yield low stratus detection. Each test refines thecumulative result of all preceding tests in the wayoutlined in the previous chapter. The individualcontributions of the tests to the final result thereforevary for each scene depending on the distribution ofclouds and cloud properties in each scene. For one of thetest cases used in the validation study, the most importantclasses identified are shown in Fig. 7. A quantification ofthe test results for this and two other scenes is given inTable 4. The figures clearly underline that the contribu-tions by individual tests to the final classification resultvary significantly in dependence on the cloud situationencountered in a given scene. In the examples given, thegross cloud check rejects a greater number of pixels asclear on 5 November 2003 than on 10 November 2003,ice cloud testing is successful for a larger share of pixelson 10November 2003 than on 8December 2003, and thestratiformity test identifies larger areas on 10 November2003 than on 5 November 2003. While these figures aredifferent for each scene, the incremental character of thescheme becomes clear: the identification of low strati-form water clouds depends on the exclusion of othersurfaces, which is achieved by the spectral and spatialtests described above.

As stated above, the thresholds applied in the testsstem from a number of trusted sources, including valuesfound in other studies (thin cirrus, snow), radiativetransfer calculations (cloud phase), and dynamical sceneanalysis (gross cloud check, small droplet test). Thethresholds used in the spatial tests have been fine-tunedby visually evaluating several months worth of scenes inoperational processing. In order to highlight the effect ofchanges in spatial test thresholds, contingency statisticshave been computed for two selected scenes. Fig. 8shows classification performance for a range oftemperature lapse rate (Γ) values used in the altitudetest (Section 2.1.5.1) on two selected scenes. Thereceiver operating characteristic (ROC) representation(cf. Marzban, 2004) shows FAR versus POD; the skill ofa classification is indicated by proximity to the upper leftcorner of the plot. Next to each data point, thecorresponding lapse rate [K 100 m−1] is given. In thisgraph, two features are of interest: 1) several values of Γ

Fig. 7. Classification of a test scene (5 November 2003, 11:00 UTC) showing the most important classes (left) and the corresponding 0.6 µm albedo image (right).

289J.

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ak,J.

Bendix

/Atmospheric

Research

87(2008)

279–292

Fig. 8. Classification skill for some assumed temperature lapse rates(K/100 m, given next to the data points) in terms of probability ofdetection (POD) and false alarm ratio (FAR) for two selected scenes.

Table 4Portion of pixels identified in various steps (tests) of the scheme forselected scenes

Class 5 Nov 2003,11:00

8 Dec 2003,10:00

10 Nov 2003,10:00

Clear 91.43 80.86 32.92– Snow 2.73 5.03 0.56Ice clouds 1.12 0.97 20.83Medium/high waterclouds

0.36 2.78 3.82

– Small droplets 0.20 0.01 0.93Low water clouds 7.07 15.39 42.43– Stratiform lowwater clouds

3.28 14.65 20.38

Values are in percent. Classes included in the previous category areindented.


share the same FAR/POD combinations (e.g. 0.55 and0.65 K 100 m−1 on 5 November 2003) or POD values(0.45 and 0.55/0.65 K 100 m−1 on the same day). As thecomputations are applied to entities rather than indivi-dual pixels, changes in classification results are notgradual but occur in abrupt shifts. 2) POD increases withΓ, while FAR remains more or less constant. The formeris easily explained by the fact that a larger lapse ratemeans that more entities will be identified as low cloud,reducing the number of missed low cloud situations (NM

in Eq. (4)). With this in mind, the expected developmentof FAR with increasing Γ would be an increase as well,as medium and high clouds are more likely to bemisclassified as low. However, the portion of mediumand high water clouds not rejected by the cloud phaseand droplet size tests is obviously very low in bothscenes (see Table 4), so no large FAR values can bereached here. While the threshold of 0.65 K 100 m−1 isnot obvious from this two-scene analysis, it is physicallyrealistic and supported by long-time monitoring as statedabove.

4. Conclusions and outlook

The results of the statistical inter-comparison ofground-based measurements and the satellite-based fog/low stratus retrieval algorithm are encouraging: hardlyany fog/low stratus situations are missed by thealgorithm as indicated by the high POD values. Theremoval of high cloud contamination from the statisticssuggests that a significant number of missed fog casesmay be attributed to multi-layer cloud situations.

The very low FAR figures for low cloud detectionsuggest that almost no false alarms occur, i.e. a pixelclassified as ’fog/low stratus’ by the algorithmwill almostnever be covered by a different cloud type or be entirely

cloud-free. This finding is particularly encouraging as tothe validity of the approach taken in the fog/low stratusdetection algorithm and further underlines the goodresults in terms of detection probability.

The differences between results found for individualpixel comparisons and 3×3 pixel environments arerelatively small. The co-location of the satellite imagerywith the ground measurements may thus be regarded asfairly good; the appropriate pixel seems to be looked atin most cases. Nonetheless, some sub-pixel fog situa-tions, especially in small valleys, may be missed by thesatellite algorithm, reducing the POD.

Whatever the ‘true’ accuracy of the algorithm maybe, the validation statistics demonstrate the generalstrengths and weaknesses of the scheme. Based on theshortcomings identified above, sub-pixel classificationaccuracy is a major issue to be tackled in future research.A number of small-scale fog situations at sub-pixel levelmay be missed by the algorithm in its currentformulation. This may possibly be the major factorkeeping down POD figures. It is thus desirable to gainmore detailed information on these small-scale phenom-ena. This issue will be addressed in an upcoming projectplanning to incorporate SEVIRI's High ResolutionVisible (HRV) channel into the scheme.

Another promising thread to be pursued in futureresearchmay be the use of temporal information.AsMSGSEVIRI data is available at 15minute intervals, the spatialdevelopment over time displayed by cloud areas may beutilized in fog identification, both radiometrically (e.g.Kleespies, 1995) and geometrically. An approach of thiskind will greatly profit from the cloud entity conceptintroduced in this paper.


In this paper, the new algorithm has only been testedover Europe and on autumn and winter scenes. Theselection of this region and seasons is mostly due to dataavailability: fog and low stratus situations most fre-quently occur within this time frame, and validation datais most readily accessible in Europe (within the spatialdomain of MSG). Since the scheme, and especially thespatial components are however diagnostic of fog andlow stratus properties, a good transferability is to beexpected. This remains to be tested.

Although certain issues remain to be tackled in futureresearch, the present scheme displayed very satisfactoryperformance in the validation experiment. The unavail-ability of a suitable spatial data set for validation oncemore illuminates the great potential of spatially coherentfog/low stratus information retrieved from satelliteimagery.

Acknowledgments

This research was generously funded by the GermanResearch Council DFG (BE 1780/8-1; 8-3) within theNEKAMM project. The study is part of the ESF COSTaction 722 (Short Range Forecasting Methods of Fog,Visibility and Low Clouds) and the ESA/EUMETSATMSG RAO PI programme (ID 141). The authors wish tothank theDWD (P. Bissolli) for the provision of validationdata.

References

Ackerman, S.A., Strabala, K.I., Menzel, W.P., Frey, R.A., Moeller, C.C.,Gumley, L.E., 1998. Discriminating clear sky from clouds withMODIS. J. Geophys. Res. 103, 32141–32157.

Aminou, D.M.A., 2002. MSG's SEVIRI Instrument. ESA Bull. 111,15–17.

Bendix, J., 2002. A satellite-based climatology of fog and low-levelstratus in Germany and adjacent areas. Atmos. Res. 64, 3–18.

Bendix, J., Bachmann, M., 1991. Ein operationell einsetzbaresVerfahren zur Nebelerkennung auf der Basis von AVHRR-Datender NOAA-Satelliten. Meteorol. Rundsch. 43, 169–178.

Bendix, J., Thies, B., Nauss, T., Cermak, J., 2006. A feasibility studyof daytime fog and low stratus detection with TERRA/AQUA-MODIS over land. Met. Appl. 13, 111–125.

Brenguier, J.-L., Pawlowska, H., Schüller, L., Preusker, R., Fischer, J.,Fouquart, Y., 2000. Radiative properties of boundary layer clouds:droplet effective radius versus number concentration. J. Atmos.Sci. 57, 803–821.

Cermak, J., Bendix, J., 2007. Dynamical nighttime fog/low stratusdetection based on Meteosat SEVIRI data - a feasibility study. PureAppl. Geophys. 164, 1179–1192.

Chourlaton, T.W., Fullarton, G., Lathamm, J., Mill, C.S., Smith, M.,Stromberg, I.M., 1981. A field study of radiation fog in Meppen,West Germany. Q. J. R. Meteorol. Soc. 107, 381–394.

Derrien, M., LeGleau, H., 2005. MSG/SEVIRI cloud mask and typefrom SAFNWC. Int. J. Remote Sens. 26, 4707–4732.

Dozier, J., 1989. Spectral signature of alpine snow cover from theLandsat Thematic Mapper. Remote Sens. Environ. 28, 9–22.

Dozier, J., Painter, T.H., 2004. Multispectral and hyperspectral remotesensing of alpine snow properties. Annu. Rev. Earth Planet. Sci.32, 465–494.

Dybbroe, A., 1993. Automatic detection of fog at night using AVHRRdata. Proc. 6th AVHRR Data Users' Meeting, pp. 245–252.

Ellrod, G.P., 1995. Advances in the detection and analysis of fog at nightusing GOES multispectral infrared imagery. Weather Forecast. 10,606–619.

Eyre, J.R., Brownscombe, J.L., Allam, R.J., 1984. Detection of fog atnight usingAdvancedVery High Resolution Radiometer.Meteorol.Mag. 113, 266–271.

Greenwald, T.J., Christopher, S.A., 2000. The GOES I-M imagers:new tools for studying microphysical properties of boundary layerstratiform clouds. Bull. Am. Meteorol. Soc. 81, 2607–2619.

Hunt, G.E., 1973. Radiative properties of terrestrial clouds at visibleand infra-red thermal window wavelengths. Q. J. R. Meteorol. Soc.99, 346–369.

Jacobs, W., Nietosvaara, V., Michaelides, S.C., Gmoser, H. (Eds.),2005. Short-Range ForecastingMethods of Fog, Visibility and LowClouds — COST Action 722 Phase I Report. Office for OfficialPublications of the European Communities.

Kawamoto, K., Nakajima, T., Nakajima, T.Y., 2001. A globaldetermination of cloud microphysics with AVHRR remote sensing.J. Clim. 14, 2054–2068.

Key, J., Schweiger, A.J., 1998. Tools for atmospheric radiativetransfer: Streamer and FluxNet. Comp. & Geosci. 24 (5), 443–451.

Kleespies, T.J., 1995. The retrieval of marine stratiform cloud propertiesfrommultiple observations in the 3.9 µmwindowunder conditions ofvarying solar illumination. J. Appl. Meteorol. 34, 1512–1524.

Lee, T.F., Turk, F.J., Richardson, K., 1997. Stratus and fog productsusing GOES-8–9 3.9 μm data. Weather Forecast. 12, 664–677.

Marzban, C., 2004. The ROC curve and the area under it as performancemeasures. Weather Forecast. 19, 1106–1114.

Miles, N.L., Verlinde, J., Clothiaux, E.E., 2000. Cloud droplet sizedistribution in lowlevel stratiform clouds. J. Atmos. Sci. 57, 298–311.

Minnis, P., Heck, P.W., Young, D.F., Fairall, C.W., Snider, J.B., 1992.Stratocumulus cloud properties from simultaneous satellite and island-based instrumentation during FIRE. J. Appl. Meteorol. 31, 317–339.

Nakajima, T.Y., Nakajima, T., 1995. Wide-area determination of cloudmicrophysical properties from NOAA AVHRR measurements forFIRE and ASTEX regions. J. Atmos. Sci. 52, 4043–4059.

Nauss, T., Wagner, M., Bendix, J., 2004. Operational retrieval of micro-physical properties from day- and night-time MSG/MODIS data.Proc. 2003 EUMETSAT Met. Sat. Conf. Darmstadt, pp. 281–285.

Nauss, T., Cermak, J., Kokhanovsky, A., Reudenbach, C., Bendix, J.,2005a. Satellite based retrieval of cloud properties and their use inrainfall retrievals and fog detection. Photogram. Fernerkund.Geoinf. 3, 209–218.

Nauss, T., Kokhanovsky, A.A., Nakajima, T.Y., Reudenbach, C.,Bendix, J., 2005b. The intercomparison of selected cloud retrievalalgorithms. Atmos. Res. 78, 45–78.

Pinnick, R.G., Hoihjelle, D.L., Fernandez, G., Stenmark, E.B., Lindberg,J.D., Hoidale, G.B., Jenninges, S.G., 1978. Vertical structure inatmospheric fog and haze and its effect on visibility and infraredextinction. J. Atmos. Sci. 35, 2020–2032.

Roach, W.T., 1994. Back to the basics: fog, part 1 — definitions andbasic physics. Weather 49, 411–415.

Saunders, R.W., Kriebel, K.T., 1988. An improved method fordetecting clear sky and cloudy radiances from AVHRR data. Int. J.Remote Sens. 9, 123–150.


Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S.,Ratier, A., 2002. An introduction to Meteosat Second Generation(MSG). Bull. Am. Meteorol. Soc. 83, 977–992.

Steward, D.A., Essenwanger, O.M., 1982. A survey of fog and relatedoptical propagation characteristics. Rev. Geophys. Space Phys. 20,481–495.

Strabala, K.I., Ackerman, S.A., Menzel, W.P., 1994. Cloud propertiesinferred from 8–12 µm data. J. Appl. Meteorol. 33, 212–229.

Tampieri, F., Tomasi, C., 1976. Size distribution of fog and cloud dropletsin terms of the modified gamma function. Tellus 28, 333–347.

Turner, J., Allam, R.J., Maine, D.R., 1986. A case study of thedetection of fog at night using channel 3 and 4 on the AdvancedVery High Resolution Radiometer (AVHRR). Meteorol. Mag. 115,285–290.

Underwood, S.J., Ellrod, G.P., Kuhnert, A.L., 2004. A multiple-caseanalysis of nocturnal radiation-fog development off Californiautilizing GOES nighttime fog product. J. Appl. Meteorol. 43,297–311.

United States Geological Survey (USGS), 1993. Digital ElevationModels. Data users' guide 5. USGS (48 pp.).

Wetzel, M.A., Borys, R.D., Xu, L.E., 1996. Satellite microphysicalretrievals for land-based fog with validation by balloon profiling.J. Appl. Meteorol. 35, 810–829.

Wiegner, M., Seifert, R., Schlüssel, P., 1998. Radiative effect of cirrusclouds in Meteosat Second Generation Spinning Enhanced Visibleand Infrared Imager channels. J. Geoph. Res. 103, 23,217–23,230.

Wiscombe, W.J., Warren, S.G., 1980. A model for the spectral albedoof snow. I: Pure snow. J. Atmos. Sci. 37, 2712–2733.

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A novel approach to fog/low stratus detection using ... · A novel approach to fog/low stratus detection using Meteosat 8 data Jan Cermak⁎, Jörg Bendix Laboratory for Climatology