Fog Detection Using Geostationary Satellite Data ... - Springer Link

Asia-Pacific J. Atmos. Sci., 47(2), 113-122, 2011 DOI:10.1007/s13143-011-0002-2

Fog Detection Using Geostationary Satellite Data: Temporally Continuous Algorithm Jung-Rim Lee1, Chu-Yong Chung1 and Mi-Lim Ou2 1

Satellite Planning Division, National Meteorological Satellite Center, Jincheon, Korea Global Environment System Research Laboratory, National Institute of Meteorological Research, Seoul, Korea

2

(Manuscript received 2 December 2009; revised 13 September 2010; accepted 13 September 2010) © The Korean Meteorological Society and Springer 2011

Abstract: A fog detection algorithm that uses geostationary satellite data has been developed and tested. This algorithm focuses on continuous fog detection since temporal discontinuities, especially at dawn and dusk, are a major problem with current fog detection algorithms that use satellite imagery data. This is because the spectral radiance at 3.7 µm contains overlapping emissive and reflectance components. In order to determine the radiance at 3.7 µm under fog conditions, radiative transfer model simulations were performed. The results showed that the radiance at 3.7 µm obviously varies with the solar zenith angle, and the brightness temperature differences between 3.7 µm and 10.8 µm are completely dissimilar between day and night (positive and varying with the angle during the daytime, but negative and constant at night). In this algorithm, a dynamic threshold is used as a function of the solar zenith angle. Moreover, additional criteria such as infrared, split-window channels, and a water vapor channel are used to remove high-level clouds. Also, the visible reflectance (0.67 µm) channel is used in the daytime algorithm because visible channel images are very practical for confirming a fog area with the high reflectivity and the smooth texture. The clear-sky visible reflectance for the previous 15 days was also employed to eliminate the surface effect that appeared during dawn and dusk. As the results, fog areas were estimated continuously, allowing the lifecycle of the fog system, from its development to decline, to appear obviously in the resulting images. Moreover, the estimated fog areas matched well with surface observations, except in a high latitude region that was covered by thin cirrus clouds. Key words: Fog, 3.7 µm radiance, dynamic threshold, clear sky visible reflectance, temporal consistency

1. Introduction Fog is obviously one of the worst weather condition factors for marine, air, and road traffic. However, the representativeness of the observation is uncertain since only the information near the station can be reported. In fact, there are about 80 observation stations for weather conditions in Korea, and the temporal interval is formally three hours. Furthermore, the information for sea conditions relies on reports from ships and lighthouses, which unfortunately are very irregular temporally and spatially. On the other hand, fog monitoring using a satellite has some advantages, especially since it is possible to monitor a large area at once. Corresponding Author: Jung-Rim Lee, Global Environment System Research Laboratory, National Institute of Meteorological Research, 45 Gisangcheong gil, Dongjak gu, Seoul, Korea. E-mail: [email protected]

Fog has some distinctive properties with other clouds and the surface in satellite images. First, a fog area appears brighter than the surface in visible images (around 0.67 µm), and the texture is smoother than other convective and cirrus clouds (Ahn et al., 2003; Gultepe et al., 2007). In addition, its development and the movement are very slow. On the other hand, it is not easily distinguishable in infrared images (around 10.8 µm) since fog is a meteorological phenomenon that occurs very near the surface. Thus, the temperature difference with the surface is generally not obvious (Ellord, 1995). The Multifunctional Transport Satellite1R (MTSAT-1R) images at 0200 UTC 7 November 2007 are shown in Fig. 1. The fog (circled in the images) is located over Beijing and the north-west part of Korea. The fog area is brighter than the surface in the visible image (Fig. 1a), and its homogeneous texture distinguishes it from other types of clouds. In the infrared image (Fig. 1b), the fog area is also brighter than the surface, but the contrast is not always visible, like in this case, because the height of the fog is generally very low. The emissivity of fog in shortwave infrared (around 3.7 µm) image is about 0.8~0.9, yet the emissivity in infrared image is almost 1 (Ellord, 1995; Wetzel et al., 1996; Lee et al., 1997). Therefore, the brightness temperature differences between the shortwave infrared and infrared images (SWIR-IR1) are the main key for nighttime fog detection (Eyre et al., 1984; Turner et al., 1986; Yamanouchi et al., 1987; Bendix, 2002). The brightness temperature differences between the two channels over fog are less than 0 K at night. On the other hand, the differences are greater than 0 K during the daytime since the shortwave infrared channel measures both emission and reflection (Turk et al., 1998; Schreiner et al., 2007), causing the day and night images to be dissimilar. The SWIR-IR1 images are shown in Fig. 2. The fog in the images is located on the East coast of China over the land through the sea (circled in the images). In the nighttime image (Fig. 2a), the fog area is darker than the surface (less than 0 K), but it is much brighter (higher than the surface and positive) in the daytime image (Fig. 2b). The large differences between the two channels are caused by the reflectivity, as mentioned above. Many researchers (Eyre et al., 1984; Lee et al., 1997; Cermak et al., 2008) have tried to derive fog areas from satellite images. However, despite the fact that many cloud classification methods exist and are in operation, it is said that the detection of fog is still obscure. Those algorithms mainly rely on the emissivity of the shortwave infrared channel, but this emissivity is very sensitive

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Fig. 1. Fog in satellite images at 0200 UTC 7 November 2007. (a) and (b) represent visible and infrared images, respectively, and the brighter parts in the circles are fog.

Fig. 2. SWIR-IR1 images in (a) nighttime and (b) daytime. Fog exists in the circle, and the brightness temperature difference is lower (dark) than the surface at night, and higher (bright) during daytime.

to the microphysical parameters. Moreover, the brightness temperature of shortwave infrared is increased in daytime. Therefore, a different algorithm, such as different threshold values, visible reflectance and skin temperature, should be applied during the daytime. Moreover, the brightness temperature of shortwave infrared channel largely changes during dawn and dusk. As the results, temporal discontinuities generally appear between day and night, and large contaminations by the surface or underestimations are caused during the periods of dawn and dusk. In order to overcome the temporal discontinuity, dynamic threshold values are adopted in this study. The dynamic threshold values are derived in many ways such as the brightness temperature differences, infrared channel differences and clear-sky visible reflectance.

This paper introduces a fog detection algorithm that uses a geostationary satellite. This algorithm was developed focusing on the continuous detection of fog areas using a dynamic threshold based on the solar zenith angle. Then, other channels are used to erase the contamination from high-level clouds and the surface. Finally, the fog detection results are compared with the surface observation data. Explanation of the data and method is described in Section 2. In Section 3, a flowchart and explanation of the algorithm are presented. Section 4 shows fog detection results for three fog cases. The paper is concluded in Section 5.

2. Data and method The geostationary satellite data, MTSAT-1R, is used in this

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study. The MTSAT-1R operates at 140oE, and makes observation almost every 30 minutes. The MTSAT-1R has 5 channels, including visible (VIS, centered at 0.675 µm), shortwave infrared (SWIR, centered at 3.75 µm), and 3 infrared channels (WV, centered at 6.75 µm; IR1, at 10.8 µm; and IR2, at 12.0 µm). The spatial resolutions are 1 km for the visible and 4 km for the other 4 channels. To compare the results, the surface observation data of WMO’s Global Telecommunication System (GTS) is utilized. GTS data is available at an interval of three hours, and has 555 stations in Asia. To see the variation of SWIR-IR1 with a fog condition, RTM simulations using the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART, Ricchiazzi et al., 1998) model were conducted. A microphysical definition for fog has been studied (Tampieri and Tomasi, 1976; Pinnick et al., 1978; Chourlaton et al., 1981; Steward and Essenwanger, 1982; Minnis et al., 1992; Roach, 1994; Wetzel et al., 1996; Miles et al., 2000; Brenguier et al., 2000; Bendix, 2002). Then, Cermak and Bendix (2008) defined that the cloud optical thickness (COT) ranges from 0.5 to 30, and the effective radius (Re) varies between 3 and 12 µm with a maximum of 20 µm in coastal fog. With this previous definition about fog optical properties, different conditions of microphysical parameters such as COT and Re were simulated. The values were changed from 2 to 16 (2, 4, 8, and 16) to see the various fog conditions. In addition, the solar zenith angles are changed from 0o to 110o at 10o intervals to see the reflectivity effect. This algorithm mainly relies on the threshold tests for fog detection. Fog cases including continental and marine types were

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selected for determination of the threshold values. Fog in the satellite images were decided by interpretation of the satellite images and the surface observation data. Then, the threshold values are determined empirically.

3. Algorithm The flowchart for this algorithm is shown in Fig. 3. This algorithm follows the different flow according to the observation period (day, dawn/dusk and night), and the periods are classified by the solar zenith angle (night is when the angle is greater than 90o; dawn/dusk is when the angle ranges from 60o to 90o; day is when the angle is less than 60o). In Fig. 3, the night algorithm follows solid line arrows, and dawn/dusk and day algorithm follow dash-dotted line and dotted line, respectively. First, this algorithm checks the solar zenith angle, and then applies SWIR-IR1 threshold values that are different for each observation period. Second, the algorithm goes through threshold test composed of IR, IR1-IR2, and IR1-WV. Third, temporal consistency check and clear-sky visible reflectance test (CSR test) are executed for the dawn/dusk algorithm and the day algorithm. Fourth, the threshold test for visible reflectance is added only for the day algorithm. The night algorithm is most simple, and goes through the first and the second steps. The dawn/dusk algorithm passes through three steps including the first, the second, and the third. VIS channel is available during daytime, so that the fourth step is added to the day algorithm. The day algorithm goes through all the four steps. Each test in the process will be described in detail in this section.

Fig. 3. The flowchart of the fog detection algorithm. The arrows with solid lines explain the nighttime algorithm, and the dash-dotted lines and dotted lines explain the dawn/dusk and daytime algorithm, respectively. The threshold values of SWIR-IR1 are different for each period. The values are greater than 0 K in the day algorithm, and less than 0 K in the night algorithm. Meanwhile, the values change as a function of solar zenith angle in the dawn/dusk algorithm.

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Fig. 4. Simulated SWIR-IR1 as a function of the solar zenith angle. The graphs show different cloud optical depths (COT) of 2, 4, 8, and 16, and the different line styles in the graphs represent the different effective radius (Re) values of 2, 4, 8, and 16.

a. SWIR-IR1 Radiative transfer model (RTM) simulations were conducted to see the variations of SWIR-IR1 over fog. The results for a total of 192 simulations are shown in Fig. 4. The COT values in every graph are different, and the different line styles in the graphs represent different Re values. The SWIR-IR1 varies with the solar zenith angle obviously under any conditions when the angle is less than 90o. The differences change with the angle when the angle ranges from 60o to 90o. Then, it does not change much when the angle is less than about 60o. Since the brightness temperature difference arises from the reflectance of SWIR during the daytime, the difference is invariable at night. The variation of SWIR-IR1 is bigger when the COT value is larger. The lower Re values (Re = 2, 4) are more sensitive to the SWIR-IR1 that the difference is lower at night, and higher during the daytime than the higher Re values (Re = 16). As the results, the small size of droplet like fog shows lower values during the night, and higher values during the day rather than other types of clouds. In the fog detection algorithms using shortwave infrared such as 3.7 µm (MTSAT-1R) or 3.9 µm (GOES and Meteosat), individual application methods for day and night are necessary because the

SWIR channel is affected by the sun. We knew that the variation of SWIR-IR1 can be considered as a function of solar zenith angle from the RTM simulation results in Fig. 4. Even though there are many other features affecting the values of SWIR-IR1 such as azimuth angle, surface types, and the atmospheric profiles, solar zenith angle is simply considered in this algorithm. Figure 5 shows the variation of observed SWIR-IR1 as a function of the solar zenith angle over fog (plots). The satellite data was observed from MTSAT-1R, and this case is the same as that shown in Fig. 2. The data period is from 1633 UTC on 8 to 0533 UTC on 9 January 2008. In this period, the fog system started to generate, developed, and declined from the night through the day. Similarly to the simulation results in Fig. 4, SWIR-IR1 varies with the solar zenith angle when the angle is less than about 90o (dawn through dusk), while it is constant when the angle is larger than 90o (night). Meanwhile, the plots apart from the main plots (the lower when the angle is less than about 80o, and the higher when the angle is greater than 90o) are the contaminated pixels by cloud. The shaded range in Fig. 5 shows the threshold values of SWIR-IR1 used in this algorithm. The range of the values are constant during day and night (day: SZA < 60o, night: SZA > 90o), and changed during dawn/dusk (60o < SZA < 90o).

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Fig. 5. The variation of the observed brightness temperature difference (SWIR-IR1) as a function of the solar zenith angle over fog. This fog case is the same as Fig. 2, and the period of the data is from 1633 UTC on 8 to 0533 UTC on 9 January 2008. The shaded ranges in the graph represent the threshold values used in this algorithm.

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Fig. 6. Fog detection result after the SWIR-IR1 test at 2333 UTC 24 October 2007. The land surface over China is contaminated by the surface.

b. Cloud mask (IR1, IR1-IR2, IR1-WV) d. Temporal consistency check The range of threshold values should be quite broad to cover all the different kinds of fog since the spectral properties of fog are various. As the results, high-level clouds, as well as low stratus, are often detected as fog after the SWIR-IR1 test. In order to remove those errors, the surface temperature (IR1) and brightness temperature differences (IR1-IR2 and IR1-WV) are utilized in this algorithm. The threshold value of IR1 for fog is greater than 270 K in this study. Generally, high-level cloud exists when IR1-WV is less than 15, or IR1-IR2 is greater than 0 K. However, the values are not always valid because the atmospheric condition is different. In this study, the threshold values for IR1-IR2 and IR1-WV are changed dynamically with the IR1 brightness temperature. c. Clear-sky visible reflectance Even though this algorithm uses a dynamic threshold for SWIR-IR1, large contamination by the surface exists during dawn and dusk. It is caused by overlapping the properties of SWIR-IR1 between fog and the surface. Over fog, the emission is smaller, but the reflectance is higher than the surface. However, the amount of reflectance is very small in this period since the SWIR channel is just starting to be affected by the sun. Then, the SWIR-IR1 is not sufficient to distinguish fog from the surface. As a result, surface contamination arises, as shown in Fig. 6. The figure is the fog detection result after the SWIR-IR1 test at 2333 UTC 24 October 2007. Large contamination exists over China. In order to eliminate this error, clear-sky visible reflectance (CSR) is utilized. The CSR is the second minimum composite values of the previous 15 days VIS images. The algorithm checks the reflectance difference between VIS and CSR, and it helps to define fog area from the clear sky surface. The difference values are bigger when the fog or other clouds exist.

Fog usually generates slowly, and its shape follows terrain features such as a mountain or river. Moreover, it hardly moves, especially in land, and its movement is very slow even in the ocean. In addition, in the fog detection results using this algorithm, fog signals occasionally disappeared during dawn and dusk because of the correction in Section 3c. To make up the signal, this algorithm checks the former fog detection result. When a fog pixel is detected at this time by the test of Section 3a-b, this algorithm checks the former result. If the fog exists in the previous time, then the pixel is determined as fog. e. Visible reflectance in daytime As mentioned in Section 2a, the visible reflectance is high over fog, like over other clouds. The VIS data is normalized in this study using the solar zenith angle, and then the normalized values are used for the daytime detection. Since the reflectance changes according to the sun’s location, a normalization process is needed to apply constant threshold values anytime, and it can be written as Eq. (1): Mod_VIS ≈ VIS/cos(θ0)

(1)

where θ0 is the solar zenith angle. The threshold range for daytime fog detection is 25 < Mod_VIS < 55 in this algorithm.

4. Results a. A continental fog case (6-7 November 2007) The first case is a continental fog that occurred from 6 to 7 November 2007. This fog was located near Beijing, and along

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Fig. 7. Fog detection results with IR1 images and the GTS stations that reported fog at the time. (a) 1733 UTC on 6, (b) 2033 UTC on 6, (c) 2333 UTC on 6, (d) 0233 UTC on 7, and (e) 0533 UTC on 7 November 2007. The boxes in the images represent the GTS stations that reported fog at that time, and the color shadings are the pixels detected as fog.

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the West coast of North Korea (Fig. 7). The former is close to a mountain range, so it followed the terrain. The latter, over North Korea, was mixed with clouds related to a low-pressure system. The figures represent the fog detection results on the infrared images. The boxes in the figures represent the GTS stations where the fog was reported at the time indicated. The images are the results from 1733 UTC on 6 to 0533 UTC on 7 November 2007. The fog area in the first image (Fig. 7a, at night) is just starting to grow, and it becomes larger at 2033 UTC (Fig. 7b). Then, the fog has a maximum spatial distribution at 2333 UTC (Fig. 7c, in the morning). After the sun rises, the fog area decreases at 0233 UTC (Fig. 7d, during the day), and it has almost disappeared at 0533 UTC (Fig. 7e). In a comparison with the GTS reports (boxes in the images), the fog detection results seem to match well with the GTS observations, except in the high latitude region (in Figs. 7c and

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7d). In the high latitude region, cirrus clouds seem to exist in the IR1 image. Therefore, the algorithm could not detect fog in this situation since those optical sensors cannot penetrate clouds. In addition, the fog near the west part of North Korea is mixed with a cloud system, as can be seen in the images, so it was not possible to detect it with these sensors. Contaminations from the surface are partly shown in the northern part of China in Figs. 7d and 7e. These errors arose from the surface since the surface is a desert area where visible reflectance is high. b. A marine fog case (11-12 June 2008) The second case is a marine fog from 11 to 12 in June 2008. This fog was located over the east coast of China (Fig. 8). The fog was a marine type, so the microphysical properties were not

Fig. 8. The same as in Fig. 6, except for (a) 1733 UTC on 11, (b) 2033 UTC on 11, (c) 0033 UTC on 12, and (d) 0233 UTC on 12 June 2008.

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images, just as in Fig. 7. The images are the results from 0033 UTC to 1533 UTC on 19 March 2009. The results show that the fog lasted through the day, and moved slowly toward the southeast. This algorithm continuously detected the fog area for the day including dawn (0033 UTC) and dusk (0933 UTC) scenes. Generally, it is hard to detect fog with those scenes in the periods of dawn and dusk for current fog detection algorithms using satellite data because the reflectivity changes a lot in those periods. Nevertheless, this algorithm estimated the fog area continuously and stably throughout the whole period. However, there seems to be some contamination pixels by the surface near Beijing at 0033 UTC, and false signals caused by cloud edge over the East sea of Korean peninsula. Otherwise, the results seem to match well with GTS reports over Korea and China except under the cloud system in the south.

5. Conclusions Fig. 9. SWIR image at 0033 UTC 12 June 2008.

like the first case. Sea fog generally spreads across a broad area, and lasts longer (Roach, 1994). The figures represent the fog detection results on the infrared images, just as in Fig. 7. The images are the results from 1733 UTC on 11 to 0233 UTC on 12 June 2008. The fog broadly spreads out over the Yellow Sea, and other clouds are mixed with the fog. Unlike the first case, the fog area declined very little after the sun rose since the fog was very thick and wide. The detected fog area is not as clear as the fog in case 1, for various reasons. First, other clouds are mixed in with and wrapped over the fog. Fig. 9 shows the SWIR image at 0033 UTC 12 June 2008 (at the same time as Fig. 8c). As mentioned in Section 2b, fog and low-level stratus clouds appear to be very dark, while high level clouds are brighter in SWIR images. From those facts, we can confirm that clouds exist with the fog. The other reason is that sea fog is usually thicker and wider than ground fog, so that its spectral properties would be more like cloud, such as larger Re and COT values. For the latter reason, it is easier to confirm a fog area visually in a satellite image, but is much harder to determine threshold values for automatic detection. In a comparison with GTS reports, the results seem to match well with observations, except under the cloud system in the north. There are some observation sites in the coastal region and islands in China and Korea, and the reports from those sites are overlapped with the detected fog area. c. A long-lasting fog case (19 March 2009) The third fog case includes continental and marine fog in 19 June 2009. This fog was located from the east part of China to the west coast of the Korean peninsula (Fig. 10). The fog existed over the land and the sea, and lasted for very long time from 18 to 20. The figures represent the fog detection results on the infrared

Various methods to derive fog areas using satellite data have been developed, but there are many limitations when using these algorithms for actual detection. The microphysical parameters vary considerably based on fog conditions such as the season, surface type, temperature, and the generation mechanism. In addition, one critical limitation of the current algorithm is the temporal discontinuities in the detection results, especially at dawn and dusk. In this study, we focused on developing an operational fog detection algorithm. In order to provide forecasters with information about fog, the results should not fluctuate significantly in time and space. Using an application combined a dynamic threshold, clear-sky visible reflectance and temporal consistency check, this algorithm results in temporally continuous fog detection even during the periods of dawn and dusk. In addition, infrared threshold tests like IR1, IR1-IR2, and IR1-WV helped to filter out other clouds. Using composite images of GTS reported fog sites and satellite detected fog areas, we found that the algorithm performed well, both in continental and marine fog cases. It appears that this algorithm can detect a fog area stably and continuously even during the periods of dawn and dusk as long as the fog area is broad enough. In the case studies, the algorithm proved to be comparatively weaker in marine fog detection because marine fog properties are more like clouds. Fogs have diverse spectral properties, and it is not possible to detect all kinds of fog with a threshold test, so that this study utilized sparsely determined threshold ranges, then, went through many steps. This algorithm shows very satisfactory performance for operational use although certain limitations remain. In the future, more case studies for recognizing the characteristics of the detection results will be executed in order to give forecaster guideline interpreting the results. In addition, the algorithm presented here will be implemented in the operational processing of the COMS Data Processing System (CMDPS).

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Fig. 10. The same as in Fig. 6, except for (a) 0033 UTC, (b) 0333 UTC, (c) 0633 UTC, (d) 0933 UTC, (e) 1233 UTC, and (f) 1533 UTC on 19 March 2009.

Acknowledgments. This research was supported by the NIMR/ KMA project “Development of Meteorological Data Processing System of Communication, Oceans and Meteorological Satellite

(COMS)” and “Research for the Meteorological Observation Technology and Its Application”.

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