Korean fog probability retrieval using remote sensing combined with machine-learning

Lee, Han-Byul; Heo, Jun-Hyung; Sohn, Eun-Ha

doi:10.1080/15481603.2021.1995973

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…The spectral range of IR1 corresponds to the IRW band, which has been widely used for TC observations [21][22][23]. Since the WV channel responds to the amount of mid-to high-altitude WV and the IR1 channel responds to the cloud top's coldness, these two channels have been widely used for quantifying convective cloud regions [16,[24][25][26][27][28][29]. Because of the obscurity of the cirrus canopy, the combination of longwave infrared and WV channels contributes to the determination of TC centers [30].…”

Section: Communication Ocean and Meteorological Satellite Meteorologi...mentioning

confidence: 99%

An Advanced Operational Approach for Tropical Cyclone Center Estimation Using Geostationary-Satellite-Based Water Vapor and Infrared Channels

Shin

Lee

et al. 2022

Remote Sensing

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Tropical cyclones (TCs) are destructive natural disasters. Accurate prediction and monitoring are important to mitigate the effects of natural disasters. Although remarkable efforts have been made to understand TCs, operational monitoring information still depends on the experience and knowledge of forecasters. In this study, a fully automated geostationary-satellite-based TC center estimation approach is proposed. The proposed approach consists of two improved methods: the setting of regions of interest (ROI) using a score matrix (SCM) and a TC center determination method using an enhanced logarithmic spiral band (LSB) and SCM. The former enables prescreening of the regions that may be misidentified as TC centers during the ROI setting step, and the latter contributes to the determination of an accurate TC center, considering the size and length of the TC rainband in relation to its intensity. Two schemes, schemes A and B, were examined depending on whether the forecasting data or real-time observations were used to determine the initial guess of the TC centers. For each scheme, two models were evaluated to discern whether SCM was combined with LSB for TC center determination. The results were investigated based on TC intensity and phase to determine the impact of TC structural characteristics on TC center determination. While both proposed models improved the detection performance over the existing approach, the best-performing model (i.e., LSB combined with SCM) achieved skill scores (SSs) of +17.4% and +20.8% for the two schemes. In particular, the model resulted in a significant improvement for strong TCs (categories 4 and 5), with SSs of +47.8% and +72.8% and +41.2% and +72.3% for schemes A and B, respectively. The research findings provide an improved understanding of the intensity- and phase-wise spatial characteristics of TCs, which contributes to objective TC center estimation.

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Section: Communication Ocean and Meteorological Satellite Meteorologi...mentioning

confidence: 99%

An Advanced Operational Approach for Tropical Cyclone Center Estimation Using Geostationary-Satellite-Based Water Vapor and Infrared Channels

Shin

Lee

et al. 2022

Remote Sensing

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“…A wide variety of fog models, ranging from simple 1D models (Bari, 2019;Bergot et al, 2007) to complex 3D models have been applied to regional forecasting of fog events (Pithani et al, 2020;Szintai et al, 2019). Recently machine learning techniques (Lee et al, 2021) and statistical methods, as well as combined numerical and statistical model approaches (Menut et al, 2014;Tuba & Bottya ´n, 2018) are also being used to improve the numerical weather prediction (NWP). However, 1D models don't consider dynamical processes like horizontal advection and large-scale subsidence (Gultepe, 2008).…”

Section: Introductionmentioning

confidence: 99%

An Observational Case Study of a Radiation Fog Event

Gandhi,

Geresdi,

Gyöngyösi

et al. 2024

Pure Appl. Geophys.

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A micrometeorological fog experiment was carried out in Budapest, Hungary during the winter half year of 2020–2021. The field observation involved (i) standard meteorological and radiosonde measurements; (ii) surface radiation balance and energy budget components, and (iii) ceilometer measurements. 23 fog events occurred during the whole campaign. Foggy events were categorized based on two different methods suggested by Tardif and Rasmussen (2007) and Lin et al. (2022). Using the Present Weather Detector and Visibility sensor (PWD12), duration of foggy periods are approximately shorter (~ 9%) compared to ceilometer measurements. The categorization of fog based on two different methods suggests that duration of radiation fogs is lower compared to that of cloud base lowering (CBL) fogs. The results of analysis of observed data about the longest fog event suggest that (i) it was a radiation fog that developed from the surface upwards with condition of a near neutral temperature profile. Near the surface the turbulent kinetic energy and turbulent momentum fluxes remained smaller than 0.4 m2 s–2 and 0.06 kg m–1 s–2, respectively. In the surface layer the vertical profile of the sensible heat flux was near constant (it changes with height ~ 10%), and during the evolution of the fog, its maximum value was smaller than 25 W m–2, (ii) the dissipation of the fog occurred due to increase of turbulence, (iii) longwave energy budget was close to zero during fog, and a significant increase of virtual potential temperature with height was observed before fog onset. The complete dataset gives an opportunity to quantify local effects, such as tracking the effect of strengthening of wind for modification of stability, surface layer profiles and visibility. Fog formation, development and dissipation are quantified based on the micrometeorological observations performed in suburb area of Budapest, providing a processing algorithm for investigating various fog events for synoptic analysis and for optimization of numerical model parameterizations.

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“…As a common weather phenomenon, fog tends to occur during traffic peaks at dawn and dusk, which has a significant adverse impact on both respiratory health and traffic safety, and it has become an important priority for monitoring by meteorological and environmental departments [1][2][3]. Traditional fog detection relies on ground observations, which have difficulty reflecting the formation and evolution of fog in a large area [4]. With the rapid development of meteorological satellites, remote sensing technology has been widely used in fog detection due to its large observation range, high temporal resolution and low cost [1,[5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Novel ST-ViBe Algorithm for Satellite Fog Detection at Dawn and Dusk

Liu

Jiang

et al. 2023

Remote Sensing

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Satellite remote sensing provides a potential technology for detecting fog at dawn and dusk on a large scale. However, the spectral characteristics of fog at dawn and dusk are similar to those of the ground surface, which makes satellite-based fog detection difficult. With the aid of time-series datasets from the Himawari-8 (H8)/AHI, this study proposed a novel algorithm of the self-adaptive threshold of visual background extractor (ST-ViBe) model for satellite fog detection at dawn and dusk. Methodologically, the background model was first built using the difference between MIR and TIR (BTD) and the local binary similarity patterns (LBSP) operator. Second, BTD and scale invariant local ternary pattern (SILTP) texture features were coupled to form scene factors, and the detection threshold of each pixel was determined adaptively to eliminate the influence of the solar zenith angles. The background model was updated rapidly by accelerating the updating rate and increasing the updating quantity. Finally, the residual clouds were removed with the traditional cloud removal method to achieve accurate detection of fog at dawn and dusk over a large area. The validation results demonstrated that the ST-ViBe algorithm could detect fog at dawn and dusk precisely, and on a large scale. The probability of detection, false alarm ratio, and critical success index were 72.5%, 18.5%, 62.4% at dawn (8:00) and 70.6%, 33.6%, 52.3% at dusk (17:00), respectively. Meanwhile, the algorithm mitigated the limitations of the traditional algorithms, such as illumination mutation, missing detection, and residual shadow. The results of this study could guide satellite fog detection at dawn and dusk and improve the detection of similar targets.

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Korean fog probability retrieval using remote sensing combined with machine-learning

Cited by 7 publications

References 24 publications

An Advanced Operational Approach for Tropical Cyclone Center Estimation Using Geostationary-Satellite-Based Water Vapor and Infrared Channels

An Advanced Operational Approach for Tropical Cyclone Center Estimation Using Geostationary-Satellite-Based Water Vapor and Infrared Channels

An Observational Case Study of a Radiation Fog Event

A Novel ST-ViBe Algorithm for Satellite Fog Detection at Dawn and Dusk

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