Optical Thickness and Effective Radius Retrievals of Low Stratus and Fog from MTSAT Daytime Data as a Prerequisite for Yellow Sea Fog Detection

Li, Yi; Thies, Boris; Zhang, Suping; Shi, Xiaomeng; Bendix, Jörg

doi:10.3390/rs8010008

Cited by 17 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The IR1 channel (from 10.3 µm to 11.3 µm), which detects surface or cloud-top temperature, was used in this study. The data was distributed on a 0.05 • × 0.05 • grid with a time interval of one hour and can be downloaded from the website of Kochi University, Japan [52,53].…”

Section: Satellite Datamentioning

confidence: 99%

Importance of Precipitation on the Upper Ocean Salinity Response to Typhoon Kalmaegi (2014)

Zhang

Ming

et al. 2020

Water

View full text Add to dashboard Cite

Using multiple-satellite datasets, in situ observations, and numerical simulations, the influence of typhoon-induced precipitation on the oceanic response to Typhoon Kalmaegi has been discussed. It is found that the convective system and precipitation distribution of Kalmaegi was asymmetric, which leaded to the asymmetric rainfall at observational stations. The sea surface salinity (SSS) of the buoy to the right of storm track increased with a 0.176 practical salinity units (psu) maximal positive anomaly, while the two buoys on the left side underwent several desalination processes, with a maximum decreases of 0.145 psu and 0.278 psu. Numerical simulations with and without precipitation forcing were also performed. Model results showed that typhoon-induced precipitation can weaken sea surface cooling by approximately 0.03–0.40 °C and suppress the SSS increase by approximately 0.074–0.152 psu. The effect of precipitation can be divided into the direct effect and indirect effect. On one hand, freshwater from precipitation directly dilutes the salinity. On the other hand, when salinity decreases, the ocean stratification will be enhanced, the vertical mixing will be restrained, and then the temperature and salinity can be further affected by weakened vertical mixing.

show abstract

Section: Satellite Datamentioning

confidence: 99%

Importance of Precipitation on the Upper Ocean Salinity Response to Typhoon Kalmaegi (2014)

Zhang

Ming

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…From satellite perspectives, one detects fog from a top-down approach. Using spectral infrared and visible measurements, Bendix et al (2005), Cermak and Bendix (2011), and Yi et al (2016Yi et al ( , 2015 attempted to identify fog among other cloud types in the satellite images using the zero-cloud-base-height definition, where the cloud-base height was defined by cloud-top height minus cloud thickness. The cloud thickness was estimated indirectly based on some extra knowledge such as cloud optical depth.…”

Section: Introductionmentioning

confidence: 99%

Arctic Fog Detection Using Infrared Spectral Measurements

Chen

et al. 2019

Journal of Atmospheric and Oceanic Technology

Self Cite

View full text Add to dashboard Cite

The rapid increase in open-water surface area in the Arctic, resulting from sea ice melting during the summer likely as a result of global warming, may lead to an increase in fog [defined as a cloud with a base height below 1000 ft (~304 m)], which may imperil ships and small aircraft transportation in the region. There is a need for monitoring fog formation over the Arctic. Given that ground-based observations of fog over Arctic open water are very sparse, satellite observations may become the most effective way for Arctic fog monitoring. We developed a fog detection algorithm using the temperature difference between the cloud top and the surface, called ∂ T in this work. A fog event is said to be detected if ∂ T is greater than a threshold, which is typically between −6 and −12 K, depending on the time of the day (day or night) and the surface types (open water or sea ice). We applied this method to the coastal regions of Chukchi Sea and Beaufort Sea near Barrow, Alaska (now known as Utqiaġvik), during the months of March–October. Training with satellite observations between 2007 and 2014 over this region, the ∂ T method can detect Arctic fog with an optimal probability of detection (POD) between 74% and 90% and false alarm rate (FAR) between 5% and 17%. These statistics are validated with data between 2015 and 2016 and are shown to be robust from one subperiod to another.

show abstract

“…The geostationary-orbit satellite imageries, e.g., the products of the MTSAT (2005MTSAT ( -2015 and its replacement Himawari-8 of Japan (since 2016), and the Fengyun-4 of China (since 2018), have already been widely adopted for sea fog detection over the Yellow Sea [10,[27][28][29][30][31].…”

Section: Derivation and Assimilation Of Satellite-derived Humiditymentioning

confidence: 99%

Impact of Feature-Dependent Static Background Error Covariances for Satellite-Derived Humidity Assimilation on Analyses and Forecasts of Multiple Sea Fog Cases over the Yellow Sea

et al. 2022

View full text Add to dashboard Cite

Assimilation of satellite-derived humidity with a homogenous static background error covariance (B) matrix computed over the entire computational domain (Full-B) tends to overpredict sea fog coverage. A feature-dependent B (Fog-B) is proposed to address this issue. In Fog-B, the static error statistics for clear air and foggy areas are calculated separately using a feature-dependent binning method. The resultant error statistics are used simultaneously at appropriate locations guided by the satellite-derived sea fog. Diagnostics show that Full-B generally has broader horizontal and vertical length scales and larger error variances than Fog-B below ~300 m except for the vertical length scale near the surface. Experiments on three sea fog cases over the Yellow Sea are conducted to understand and examine the impact of Fog-B on sea fog analyses and forecasts. Results show that using Full-B produces greater and broader water vapor mixing ratio increments and thus predicts larger sea fog coverage than using Fog-B. Further evaluations suggest that using Fog-B has greater forecast skills in sea fog coverage and more accurate moisture conditions than using Full-B.

show abstract

Optical Thickness and Effective Radius Retrievals of Low Stratus and Fog from MTSAT Daytime Data as a Prerequisite for Yellow Sea Fog Detection

Cited by 17 publications

References 33 publications

Importance of Precipitation on the Upper Ocean Salinity Response to Typhoon Kalmaegi (2014)

Importance of Precipitation on the Upper Ocean Salinity Response to Typhoon Kalmaegi (2014)

Arctic Fog Detection Using Infrared Spectral Measurements

Impact of Feature-Dependent Static Background Error Covariances for Satellite-Derived Humidity Assimilation on Analyses and Forecasts of Multiple Sea Fog Cases over the Yellow Sea

Contact Info

Product

Resources

About