A NEW APPROACH TO FOG DETECTION USING SEVIRI AND MODIS DATA
J. Cermak, B. Thies and J. Bendix Laboratory for Climatology and Remote Sensing University of Marburg, Germany Deutschhausstr 10, 35037 Marburg, Germany www.lcrs.de,
[email protected]
ABSTRACT
Meteosat 8 SEVIRI with its good spectral, spatial and temporal resolutions provides an excellent basis for the monitoring and nowcasting of fog. Based on this the present study outlines a method for fog detection using SEVIRI data, with algorithms for both, night and daytime. The night algorithm is ported from NOAA AVHRR and relies on brightness temperature differences between the 10.8 and 3.9 μm channels. The daytime method relies on the spatial evaluation cloud cluster features: Based on a gross water cloud check, spatially discrete and homogeneous cloud patches are tested for their elevation and homogeneity. Where a set of criteria is met, a cloud cluster is identified as fog / low stratus. In first analyses the method shows encouraging results. A number of sample classifications are introduced and discussed.
1. ORIENTATION Fog is a phenomenon of some pertinence to air hygiene, air and ground traffic. Its accurate description in space in time therefore is of considerable importance economic, ecological and health aspects. Satellite data provides a good basis for the spatial description of weather phenomena. Indeed, many approaches to fog and fog property retrieval have been made using satellite data. Most of these focus on night-time fog detection (e.g. EYRE et al. 1984, TURNER et al. 1986, DYBBROE 1993), many relying on NOAA AVHRR data. The most common approach is based on the differences of the brightness temperatures in the 10.8 μm and 3.7 μm channels. Daytime detection of fog is less straightforward and has been less frequently attempted. A method based on brightness temperature differences has been developed for NOAA AVHRR (up to NOAA 14) (BENDIX & BACHMANN 1991). Recently, a new technique was devised for Terra/Aqua MODIS, using albedo thresholds derived from radiative transfer calculations (BENDIX et al. 2003).
With the advent of Meteosat 8 SEVIRI, a suitable spectral resolution (in 12 channels) has become available paired with a good spatial resolution (11 channels at 3 km / HRV at 1 km) on a geostationary platform. At a repeat cycle length of 15 minutes, the basis for a temporally more precise detection of fog is thus set. Against this background the main aim of the current study is to develop a set of stable algorithms for the detection of fog using Meteosat 8 SEVIRI data, for both night and daytime. With the above-mentioned advantages of the new platform, this is meant to provide a basis for a spatially and temporally precise fog monitoring scheme. This may then not only serve as a foundation for fog nowcasting but also to deepen the understanding of fog formation and dissolution processes.
2. APPROACH 2.1 Fog Detection at Night The difference between the 10.8 μm and 3.9 μm channel brightness temperatures is used for low stratus identification, in analogy to the method applied to NOAA data. This technique relies on fog pixels displaying higher brightness temperature differences as compared to land pixels and those covered by other clouds. This algorithm shows remarkably stable results. However, the thresholds identified for NOAA AVHRR could not be applied to Meteosat 8 SEVIRI. Instead of the usual 2.5 Kelvin cut-off value, 6 K seems a more appropriate threshold on Meteosat 8 SEVIRI data (see figure 1). A sample image is shown in figure 6.
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Figure 1: The distribution of 10.8 – 3.9 μm brightness temperature differences at night for a typical SEVIRI scene (2003-11-05, 05:00)
2.2 Daytime Fog Detection While the night-time brightness temperature difference-based fog retrieval technique could be ported from NOAA AVHRR, this method does not work during daytime. This is due to the solar component in the 3.9 μm channel that increases during the morning hours and shifts the brightness temperature difference of fog pixels below that of cloud-free land pixels (see histogram in figure 2).
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Figure 2: The distribution of 10.8 - 3.9 μm brightness temperature differences in the morning for a typical SEVIRI scene (2003-11-05, 09:00)
The daytime fog detection technique based on radiative transfer-derived albedo thresholds (BENDIX et al. 2003) has therefore been ported to Meteosat 8 SEVIRI. This method uses albedo values computed for a supposed minimum and maximum thickness fog layer in all visible channels as cut-off values for fog identification. While this approach yields good results at most times, it is very unreliable in the morning hours, when solar elevation is still low. Since this is a time of great importance in fog research, a new method was developed. The new daytime algorithm is intended to adapt to each situation dynamically instead of relying on fixed absolute thresholds. An overview of the algorithm is presented in figure 3.
Figure 3: Daytime algorithm for fog / low stratus detection
1. The basis of the algorithm is a gross cloud check. This is carried out using a dynamically identified threshold in the brightness temperature differences between the 10.8 and 3.9 μm channels. This test is based on the assumption that the brightness temperature difference for clouds will generally be below that of cloud-free pixels during the day. 2. The areas identified as clouds in the gross cloud check are then further analysed in a number of spectral tests to exclude any ice clouds. These tests are in part based on the APOLLO cloud classification scheme introduced by SAUNDERS & KRIEBEL (1988). 3. The resulting binary water cloud mask is then used as input to a spatial clustering. All spatially discrete and homogeneous cloud patches are assigned a unique ID in this step and are henceforth treated as a distinct entity. 4. For each of the cloud clusters identified in the previous step, the brightness temperature of the marginal pixels is compared to that of the directly adjacent pixels not covered by cloud. The maximum temperature difference is located for each cluster margin. 5. Based on the temperature difference identified above, and the ground elevation of the respective pixels, it is then estimated whether a certain cloud cluster can be below 2000 m ground height or not. Where this is the case, the cloud is consecutively treated as a 'low cloud'. 6. For all low cloud clusters the homogeneity of the cloud top height is tested for. 10.8 μm brightness temperature is taken as a proxy for altitude. Where its variation within a cloud cluster remains below a pre-defined maximum standard deviation, the cloud cluster is identified as fog / low stratus. As a final test, the mean surface elevation below a fog / low stratus cluster is compared with that of the surroundings. If the difference in sea altitude exceeds a certain threshold, the corresponding patch is addressed as valley fog.
3. RESULTS Figures 4, 5, 6 and 7 show a typical fog situation on both sides of the alps. Figure 4 is the MSG high resolution visible (HRV) 5 November 2003, 9:00. Figure 5 shows the fog classification for the same time. Figure 6 shows the fog / low stratus mask derived using the night-time algorithm, earlier on the same day.
Figure 4: Meteosat 8 HRV channel, 2003-11-05, 9:00
Figure 5: SEVIRI fog classification over Digital Elevation Model, 2003-11-05, 9:00.Red: Valley fog, Yellow: Other low stratus, Blue: Other water clouds
Figure 6: SEVIRI fog classification, night algorithm, 2003-11-05, 5:00
The daytime fog detection algorithm has been ported to Terra/Aqua MODIS. Given the better spatial resolution available from the MODIS instruments, slight adjustments in the spatial-test threshold were necessary. On the whole, the algorithm seems to perform well on this platform, too. A sample classification can be seen in figure 7.
Figure 7: MODIS fog classification over Digital Elevation Model, 2003-11-05, 9:00.Yellow: Fog / low stratus
Validation of the algorithms is currently taking place. First intercomparisons with data obtained from a profiling intercomparison experiment during the last winter show very encouraging results.
4. OUTLOOK Apart from further validation the next steps will be: • • •
Improvements in the spatial resolution of the fog / low stratus mask by including high resolution channels (HRV at 1 km for Meteosat 8 SEVIRI, 250 m channels for Terra / Aqua MODIS) in the scheme. Distinction between ground fog and elevated fog patches using microphysical information Modelling of fog dissolution and formation
Based on the sensor's potential and the first results obtained in the present study it seems likely that good progress can be made in these fields in the near future.
5. ACKNOWLEDGEMENTS The study presented is part of the project NEKAMM and the MSG-RAO PI programme (ID 141). The authors would like to thank the Deutsche Forschungsgemeinschaft (DFG) for the generous funding of the work.
6. REFERENCES BENDIX, J.; BACHMANN , M. (1991): Ein operationell einsetzbares Verfahren zur Nebelerkennung auf der Basis von AVHRR-Daten der NOAA-Satelliten. Meteorologische Rundschau 43, 169-178. BENDIX, J.; THIES, B.; CERMAK, J. (2004): Fog Detection With Terra-MODIS and MSG-SEVIRI. Proceedings of the 2003 Meteorological Satellite Conference, Darmstadt, 427-435. DYBBROE, A. (1993): Automatic Detection of Fog at Night Using AVHRR Data. Proceedings of the 6th AVHRR Data Users' Meeting, 254-252. EYRE, J.R.; BROWNSCOMBE, J.L.; ALLAM, R.J. (1984): Detection of Fog at Night Using Advanced Very High Resolution Radiometer. Meteorological Magazine 113, 266-271. SAUNDERS, R.W. & KRIEBEL, K.T. (1988): An Improved Method for Detecting Clear Sky and Cloudy Radiances from AVHRR Data. International Journal of Remote Sensing 9, 123-150. TURNER, J.; ALLAM, R.J.; MAINE, D.R. (1986): A Case Study of the Detection of Fog at Night Using Channel 3 and 4 On the Advanced Very High Resolution Radiometer (AVHRR). Meteorological Magazine 115, 285290.
