Fog detection using geostationary satellite data: Temporally continuous algorithm

Lee, Jung‐Rim; Chung, Chu-Yong; Ou, Mi-Lim

doi:10.1007/s13143-011-0002-2

Cited by 30 publications

(17 citation statements)

References 21 publications

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“…Fog is commonly perceived as a hazardous weather situation that can impact traffic systems as well as the economy (Cermak and Knutti, 2009;Egli et al, 2018). In arid environments like the Namib desert, fog can act as a critical source of water that enables life for diverse species and helps to sustain ecosystems (e.g., Seely, 1979;Shanyengana, 2002;Ebner et al, 2011;Azúa-Bustos et al, 2011;Roth-Nebelsick et al, 2012;Eckardt et al, 2013;McHugh et al, 2015). As such, knowledge on the exact occurrence and spatiotemporal patterns of fog holds potential, ranging from socioeconomic benefits to a better understanding of fog processes and fogdriven ecosystems.…”

Section: Introductionmentioning

confidence: 99%

“…As previous studies (e.g., Cermak and Knutti, 2009;Lee et al, 2011;Egli et al, 2016;Nilo et al, 2018) have shown, geostationary satellites have the potential to draw a spatiotemporally coherent picture of the occurrence of fog and low clouds (FLCs). However, information on FLCs from satellites is typically inferred using separate daytime (e.g., Bendix et al, 2006;Cermak andBendix, 2008, 2011;Nilo et al, 2018) and night-time (e.g., Ellrod, 1995;Cermak and Bendix, 2007) algorithms, disrupting our view of fog development at a critical time of its life cycle, as typically, shortwave radiative heating starts the dissipation of fog shortly after sunrise (Tardif and Rasmussen, 2007;Haeffelin et al, 2010;Waersted et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

Andersen

Čermák

2018

Atmos. Meas. Tech.

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Abstract. Fog and low clouds (FLCs) are a typical feature along the southwestern African coast, especially in the central Namib, where fog constitutes a valuable resource of water for many ecosystems. In this study, a novel algorithm is presented to detect FLCs over land from geostationary satellite data using only infrared observations. The algorithm is the first of its kind as it is stationary in time and thus able to reveal a detailed view of the diurnal and spatial patterns of FLCs in the Namib region. A validation against net radiation measurements from a station network in the central Namib reveals a high overall accuracy with a probability of detection of 94 %, a false-alarm rate of 12 % and an overall correctness of classification of 97 %. The average timing and persistence of FLCs seem to depend on the distance to the coast, suggesting that the region is dominated by advection-driven FLCs. While the algorithm is applied to study Namib-region fog and low clouds, it is designed to be transferable to other regions and can be used to retrieve long-term data sets.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

Andersen

Čermák

2018

Atmos. Meas. Tech.

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“…For night time fog detection, the brightness temperature difference between TIR and MIR channels is considered. The emissivity of fog in MIR channel is about 0.8-0.9 and in TIR channel it is almost 1 (Ellrod, 1995;Lee et al, 2011). Therefore TIR minus MIR is greater than 0 K over foggy region.…”

Section: Corresponding Authormentioning

confidence: 90%

“…The study gives us an idea about the association of prolonged fog episode of 2010 with western disturbances over the northern plains of India. Similar kind of study based on BTD threshold technique was done to develop an algorithm on temporally continuous fog detection (Lee et al, 2011). The study included one of the major problem associated with algorithms using satellite imagery to detect fog, i.e.…”

Section: Related Workmentioning

confidence: 99%

Radiative Transfer Model Simulations to Determine the Night Time Fog Detection Threshold

Gaurav

Jindal

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Every winter the Indo-Gangetic plains (IGP) of northern India are severely impacted both socially and economically by fog. For night time fog detection, visible imagery cannot be used. Also, as emissions from ground and fog is almost similar in thermal infrared (TIR, 10.8&mu;m) channel, TIR channel cannot help in identifying fog. However, emission in middle infrared (MIR, 3.9&mu;m) channel is less than emission in TIR channel over foggy area. Therefore, brightness temperature difference (BTD) between TIR and MIR is positive during night time over fog area. This BTD technique cannot be directly used during day time as MIR channel is contaminated by solar radiations. In the present work, a spectral sensitivity analysis study has been done for these two spectral channels using radiative transfer model (RTM) simulations to determine a threshold BTD for night time fog detection. SBDART (Santa Barbara DISORT Radiative Transfer) model was used for this study to simulate brightness temperatures (BT). The RTM simulations of BT of the two spectral channels was carried out for different fog microphysical characteristics like fog optical depth (FOD) and fog droplet size (Re). The fog episode of January 2018 over IGP was studied by applying threshold BTD obtained from simulation results for INSAT-3D data. A threshold BTD value >5K detected night time fog over IGP with good accuracy. The threshold BTD obtained from satellite image is compared with different cases established from simulation result which gave idea about microphysical properties of fog over IGP during winter seasons.

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“…However, this method was unable to essentially improve the fog detection at the time zone, because of the SNR limitation [8]. Indeed, most of the single-satellite LSF sensing methods was inaccurate at sunrise in previous studies [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Probability Index of Low Stratus and Fog at Dawn using Dual Geostationary Satellite Observations from COMS and FY-2D near the Korean Peninsula

et al. 2019

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We developed a new remote sensing method for detecting low stratus and fog (LSF) at dawn in terms of probability index (PI) of LSF from simultaneous stereo observations of two geostationary-orbit satellites; the Korean Communication, Ocean, and Meteorological Satellite (COMS; 128.2°E); and the Chinese FengYun satellite (FY-2D; 86.5°E). The algorithm was validated near the Korean Peninsula between the months of April and August from April 2012 to June 2015, by using surface observations at 45 meteorological stations in South Korea. The optical features of LSF were estimated by using satellite retrievals and simulated data from the radiative transfer model. The PI was calculated using the combination of three satellite-observed variables: 1) the reflectance at 0.67 μm (R0.67) from COMS, and 2) the FY-2D R0.67 minus the COMS R0.67 (△R0.67) and 3) the FY-2D-COMS difference in the brightness temperature difference between 3.7 and 11.0 μm (ΔBTD3.7-11). The three variables, adopted from the top three probability of detection (POD) scores for their fog detection thresholds: △R0.67 (0.82) > ΔBTD3.7-11 (0.73) > R0.67 (0.70) > BTD3.7-11 (0.51). The LSF PI for this algorithm was significantly better in the two case studies compared to that using COMS only (i.e., R0.67 or BTD3.7-11), so that this improvement was due to △R0.67 and ΔBTD3.7-11. Overall, PI in the LSF spatial distribution has the merits of a high detection rate, a specific probability display, and a low rate of seasonality and variability in detection accuracy. Therefore, PI would be useful for monitoring LSF in near-real-time, and to further its forecast ability, using next-generation satellites.

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Fog detection using geostationary satellite data: Temporally continuous algorithm

Cited by 30 publications

References 21 publications

First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib

Radiative Transfer Model Simulations to Determine the Night Time Fog Detection Threshold

Probability Index of Low Stratus and Fog at Dawn using Dual Geostationary Satellite Observations from COMS and FY-2D near the Korean Peninsula

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