Dramatic Contrast between Low Clouds and Snow Cover if Daytime 3.7 Imagery

Kidder, Stanley Q.; Wu, Huey-Tzu

doi:10.1175/1520-0493(1984)112<2345:dcblca>2.0.co;2

Cited by 43 publications

(18 citation statements)

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“…The dual-channel technique also could be useful for fog/low stratus detection during daylight (KIDDER and WU, 1984). Because the 3.7 lm channel was not available for the first generation of geostationary systems (e.g., Meteosat MVIRI), in the past, special schemes including structural parameters (e.g., the spatial standard deviation) were used to detect fog/low stratus layers with a limited accuracy (e.g., GÜ LS and BENDIX, 1996;KARLSSON, 1989).…”

Section: Remote Sensing Applicationsmentioning

confidence: 99%

Fog Research: A Review of Past Achievements and Future Perspectives

Gültepe

Tardif

Michaelides³

et al. 2007

Pure appl. geophys.

559

348

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The scientific community that includes meteorologists, physical scientists, engineers, medical doctors, biologists, and environmentalists has shown interest in a better understanding of fog for years because of its effects on, directly or indirectly, the daily life of human beings. The total economic losses associated with the impact of the presence of fog on aviation, marine and land transportation can be comparable to those of tornadoes or, in some cases, winter storms and hurricanes. The number of articles including the word ''fog'' in Journals of American Meteorological Society alone was found to be about 4700, indicating that there is substantial interest in this subject. In spite of this extensive body of work, our ability to accurately forecast/nowcast fog remains limited due to our incomplete understanding of the fog processes over various time and space scales. Fog processes involve droplet microphysics, aerosol chemistry, radiation, turbulence, large/small-scale dynamics, and surface conditions (e.g., partaining to the presence of ice, snow, liquid, plants, and various types of soil). This review paper summarizes past achievements related to the understanding of fog formation, development and decay, and in this respect, the analysis of observations and the development of forecasting models and remote sensing methods are discussed in detail. Finally, future perspectives for fog-related research are highlighted.

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Section: Remote Sensing Applicationsmentioning

confidence: 99%

Fog Research: A Review of Past Achievements and Future Perspectives

Gültepe

Tardif

Michaelides³

et al. 2007

Pure appl. geophys.

559

348

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“…Also, Inoue (1985) used the split window channels (11 and 12*m) to retrieve cloud temperature and emissivity of cirrus clouds. Kidder and Wu (1984), Tanaka et al (1984), and Jacobs (1986) have found the efficiency of the 3.7*m channel to detect clouds over the snow field. In this paper, possibilities to detect clouds using the brightness temperature difference between infrared channels are described and several problems are pointed out to use this method for the inland cloud lying over the low temperature snow surface.…”

Section: Introductionmentioning

confidence: 99%

Detection of Clouds in Antarctica from Infrared Multispectral Data of AVHRR

Yamanouchi

Suzuki

Kawaguchi

1987

Journal of the Meteorological Society of Japan

109

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A method to detect cloud cover in the Antarctic using only the infrared channels of AVHRR is discussed. From the data of NOAA-7 received at Syowa Station, the difference in the brightness temperature of each channel appeared to be useful for the identification of clouds. The brightness temperature of the channels 3 (3.7*m) and 4 (11*m) shows the positive difference when the thickness of clouds are in some particular range, and then tends to show negative difference for the thick cloud. Thin clouds have the difference in the brightness temperature between channels 4 and 5 (12 * m). These tendencies are explained by the radiative properties of model clouds theoretically calculated. On the graph of these temperature difference against the channel 4 brightness temperature, pixels of the same cloud distribute on the particular arch starting from the clear pixel. From the arch, clouds can be distinguished from the ground surface. The particle size, temperature and thickness of the cloud can also be inferred. At the low temperature over the inland snow surface, many troubles arise. The channel 3 brightness temperature accompanies poor resolution and large noise at the low temperature. The brightness temperature difference between channels 4 and 5 shows strong dependence on temperature and viewing angle at the low temperature due to the nonlinearity error and variation of snow surface emissivity. An empirical correction is applied to the low temperature data for the automatic cloud detection.

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“…Additionally, we use the brightness temperature difference (BTD) between MODIS bands 21 (3.9 m) and 31 (11 m), as well as NDSI criteria, to remove thin clouds over snow cover. These criteria have been widely used in previous studies to distinguish between snow cover and clouds [28,46]. The last step was used to detect snow in dense forests.…”

Section: Mod021kmmentioning

confidence: 99%

New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data

et al. 2017

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Snow cover plays an important role in climate and hydrology, at both global and regional scales. Most previous studies have used static threshold techniques to detect snow cover, which can lead to errors such as misclassification of snow and clouds, because the reflectance of snow cover exhibits variability and is affected by several factors. Therefore, we present a simple new algorithm for mapping snow cover from Moderate Resolution Imaging Spectroradiometer (MODIS) data using dynamic wavelength warping (DWW), which is based on dynamic time warping (DTW). DTW is a pattern recognition technique that is widely used in various fields such as human action recognition, anomaly detection, and clustering. Before performing DWW, we constructed 49 snow reflectance spectral libraries as reference data for various solar zenith angle and digital elevation model conditions using approximately 1.6 million sampled data. To verify the algorithm, we compared our results with the MODIS swath snow cover product (MOD10 L2). Producer's accuracy, user's accuracy, and overall accuracy values were 92.92%, 78.41%, and 92.24%, respectively, indicating good overall classification accuracy. The proposed algorithm is more useful for discriminating between snow cover and clouds than threshold techniques in some areas, such as those with a high viewing zenith angle.

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Dramatic Contrast between Low Clouds and Snow Cover if Daytime 3.7 Imagery

Cited by 43 publications

References 0 publications

Fog Research: A Review of Past Achievements and Future Perspectives

Fog Research: A Review of Past Achievements and Future Perspectives

Detection of Clouds in Antarctica from Infrared Multispectral Data of AVHRR

New Approach for Snow Cover Detection through Spectral Pattern Recognition with MODIS Data

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