1 km fog and low stratus detection using pan-sharpened MSG  SEVIRI  data

Schulz, H. M.; Thies, Boris; Čermák, Jan; Bendix, Jörg

doi:10.5194/amt-5-2469-2012

Cited by 11 publications

(11 citation statements)

References 13 publications

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“…re f l HRV, min_thres = re f l HRV,CS + D min (9) re f l HRV, max_thres = re f l HRV,CS + D max (10) where the re f l HRV,CS is the typical HRV reflectance in clear sky at a certain time calculated as the minimum HRV reflectance over the 10 days before at the same time of the day. This approach takes into account solar and satellite angle variations.…”

Section: Hrv Reflectance Fog Testsmentioning

confidence: 99%

“…Research in satellite meteorology has shown that satellite imagery is useful for detecting fog in areas where there are no other observations. Fog detection with satellite imagery still represents a challenge and different approaches can be found in the literature [3][4][5][6][7][8][9][10][11]. A first method implemented to detect fog and low clouds was the assessment of their different reflectance characteristics compared to that of other clouds in the visible band [4].…”

Section: Introductionmentioning

confidence: 99%

“…For daytime, in [9] it has been implemented a satellite fog detection scheme (SOFOS) using MSG-SEVIRI infrared and visible (IR/VIS) channels. The requirement for higher spatial resolution led to the development of a pan-sharpening technique for the detection of FLS [10]. In this approach, the 3 km-resolution narrow band channels have been pan-sharpened using the 1 km High Resolution Visible (HRV) channel; a 1 km resolution FLS mask is then derived applying the SOFOS algorithm to the sharpened channels.…”

Section: Introductionmentioning

confidence: 99%

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Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel

et al. 2018

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In this study, the Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI) High Resolution Visible channel (HRV) is used in synergy with the narrow band MSG-SEVIRI channels for daytime fog detection. A new algorithm, named MSG-SEVIRI SatFog, has been designed and implemented. MSG-SEVIRI SatFog provides the indication of the presence of fog in near real time and at the high spatial resolution of the HRV channel. The HRV resolution is useful for detecting small scale daytime fog that would be missed in the MSG-SEVIRI low spatial resolution channels. By combining textural, physical and tonal tests, a distinction between fog and low stratus is performed for pixels identified as low/middle clouds or clear by the Classification-MAsk Coupling of Statistical and Physical Methods (C-MACSP) cloud detection algorithm. Suitable thresholds have been determined using a specific dataset covering different geographical areas, seasons and time of the day. MSG-SEVIRI SatFog is evaluated against METeorological Aerodrome Reports (METAR) data observations. Evaluation results in an accuracy of 69.9%, a probability of detection of 68.7%, a false alarm ratio of 31.3% and a probability of false detection of 30.0%.

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Section: Hrv Reflectance Fog Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel

et al. 2018

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“…For many areas, METeorological Aerodrome Reports (METARs) are ideally suited to validate ground fog detection schemes (cf., e.g., Cermak and Bendix, 2011;Schulz et al, 2012). These reports are based on weather observations from airports and include cloud base heights as well as information about the visibility at ground level.…”

Section: Methodology Of Validationmentioning

confidence: 99%

“…Some are, however, required to sharpen other MODIS products needed as an input. Therefore several infrared channels are transferred to the 250 m resolution of MODIS channels 1 and 2 using a suitable pan-sharpening technique by Schulz et al (2012). According to this method a highresolution satellite channel is degraded to match the resolution of the low-resolution channel that is to be sharpened.…”

Section: Pan Sharpening Of Modis Channelsmentioning

confidence: 99%

Detection of ground fog in mountainous areas from MODIS (Collection 051) daytime data using a statistical approach

et al. 2016

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Abstract. The mountain cloud forest of Taiwan can be delimited from other forest types using a map of the ground fog frequency. In order to create such a frequency map from remotely sensed data, an algorithm able to detect ground fog is necessary. Common techniques for ground fog detection based on weather satellite data cannot be applied to fog occurrences in Taiwan as they rely on several assumptions regarding cloud properties. Therefore a new statistical method for the detection of ground fog in mountainous terrain from MODIS Collection 051 data is presented. Due to the sharpening of input data using MODIS bands 1 and 2, the method provides fog masks in a resolution of 250 m per pixel. The new technique is based on negative correlations between optical thickness and terrain height that can be observed if a cloud that is relatively plane-parallel is truncated by the terrain. A validation of the new technique using camera data has shown that the quality of fog detection is comparable to that of another modern fog detection scheme developed and validated for the temperate zones. The method is particularly applicable to optically thinner water clouds. Beyond a cloud optical thickness of ≈ 40, classification errors significantly increase.

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Enhanced short‐range forecasting of sub‐inversion cloudiness in complex terrain

Kann

Schellander-Gorgas

Wittmann

2014

Atmospheric Science Letters

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Accurate spatial and temporal forecasts of fog and low cloud occurrence are still a challenging research topic due to the complexity of its physics. In the nowcasting range, observation-based techniques are often superior to numerical weather prediction (NWP) models and provide useful guidelines for operational forecasters. This article proposes a low stratus nowcasting scheme which merges station measurements, satellite data, a nowcasting technique and NWP data at a very high horizontal resolution (1 km). Case studies and a comprehensive validation over Austria reveal that the proposed approach of parameterizing sub-inversion cloudiness adds value especially in complex terrain and even for longer lead times due to the dynamic design of the method.

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1 km fog and low stratus detection using pan-sharpened MSG SEVIRI data

Cited by 11 publications

References 13 publications

Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel

Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel

Detection of ground fog in mountainous areas from MODIS (Collection 051) daytime data using a statistical approach

Enhanced short‐range forecasting of sub‐inversion cloudiness in complex terrain

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