scholarly journals Improved Dust Detection over East Asia Using Geostationary Satellite Data

2021 ◽  
Author(s):  
Yu-Rim Shin ◽  
Eun-Ha Sohn ◽  
Ki-Hong Park ◽  
Geun-Hyeok Ryu ◽  
Soobong Lee ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Asian Dust ◽  
Observation Data ◽  
Threshold Values ◽  
Clear Sky ◽  
East Asian Region ◽  
Data Comparison ◽  
Dust Detection ◽  
Rgb Images ◽  
Dust Dispersion

AbstractThis paper presents an improved algorithm, based on the D*-parameter, for dust detection over the East Asian region using brightness temperature differences (BTDs) between the infrared channels of the Advanced Himawari Imager (AHI) onboard Himawari-8. The developed algorithm defines a dust index in the form of the ratio of BTDs: BTD between the 10.4 μm and 12.4 μm channels (BTD10.4–12.4) to that between the 8.6 μm and 10.4 μm channels (BTD8.6–10.4). To identify dust with this index, threshold values were determined empirically. A masking technique using the BTD8.6–10.4 was utilized in the dust index to mitigate the problem of detecting clear-sky deserts and fog over the ocean as dust. BTD8.6–10.4 was analyzed for dust, clear-sky desert, and fog over the ocean cases during 2017 and 2018 with this method. Fog over the ocean and clear-sky desert were distinguished by the criteria of BTD8.6–10.4 > −1.1 K and BTD8.6–10.4 > −1 K, respectively. Based on these thresholds, the influence of fog over the ocean and clear-sky desert was filtered out. The results showed that the dust area was qualitatively consistent with RGB images and ground observation data. Comparison with the AERONET aerosol optical depth (AOD) demonstrated that the D*-parameter was exponentially proportional to AOD, and the correlation coefficient between them was approximately 0.6. The improved Asian Dust detection algorithm can be applied to the monitoring of dust dispersion and movement and also serve as a quantitative indicator of Asian Dust.

scholarly journals A RGB channel operation for removal of the difference of atmospheric scattering and its application on total sky cloud detection

2016 ◽  
Author(s):  
Jun Yang ◽  
Qilong Min ◽  
Weitao Lu ◽  
Ying Ma ◽  
Wen Yao ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Qualitative Assessment ◽  
The Sun ◽  
Cloud Detection ◽  
Recognition Error ◽  
Atmospheric Scattering ◽  
Clear Sky ◽  
Different Types ◽  
Rgb Images ◽  
The Difference

Abstract. The inhomogeneous sky background presents a great challenge for accurate cloud recognition from the total sky images. A channel operation was introduced in this study to produce a new composite channel in which the difference of atmospheric scattering has been removed and a homogeneous sky background can be obtained. Following this, a new cloud detection algorithm was proposed, which combined the merits of the differencing and threshold methods and named "differencing and threshold combination algorithm (DTCA)". Firstly, the channel operation was applied to transform 3-D RGB images to the new channel, then the circumsolar saturated pixels and its circularity were used to judge whether the sun is visible or not in the image. When the sun is obscured, a single threshold can be used to identify cloud pixels, and, when the sun is visible in the image, the true clear sky background differencing algorithm is adopted to detect clouds. The qualitative assessment for eight different total sky images shows the DTCA algorithm obtained satisfactory cloud identification effectiveness for thin clouds and in the circumsolar and near-horizon regions. Quantitative evaluation also shows the DTCA algorithm achieved the highest cloud recognition precision for five different types of clouds, with an average recognition error rate of 8.7 %.

scholarly journals A Sentinel-2 Dataset for Uganda

Data ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 35
Author(s):  
Jonas Ardö
Keyword(s):  
Environmental Monitoring ◽  
Input Data ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Observation Data ◽  
Output Data ◽  
Radiometric Correction ◽  
Clear Sky ◽  
Earth Observation Data ◽  
Sentinel 2

Earth observation data provide useful information for the monitoring and management of vegetation- and land-related resources. The Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) was used to download, process and composite Sentinel-2 data from 2018–2020 for Uganda. Over 16,500 Sentinel-2 data granules were downloaded and processed from top of the atmosphere reflectance to bottom of the atmosphere reflectance and higher-level products, totalling > 9 TB of input data. The output data include the number of clear sky observations per year, the best available pixel composite per year and vegetation indices (mean of EVI and NDVI) per quarter. The study intention was to provide analysis-ready data for all of Uganda from Sentinel-2 at 10 m spatial resolution, allowing users to bypass some basic processing and, hence, facilitate environmental monitoring.

scholarly journals A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

2015 ◽  
Vol 8 (2) ◽  
pp. 553-566 ◽  
Author(s):  
M.-H. Ahn ◽  
D. Han ◽  
H. Y. Won ◽  
V. Morris
Keyword(s):  
Water Vapor Pressure ◽  
Microwave Radiometer ◽  
Surface Air Temperature ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Cloud Base ◽  
Cloud Detection ◽  
Clear Sky ◽  
Spectral Test ◽  
Free Data

Abstract. For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

Validation of GEMS L2 products using ground-based remote sensing data including PANDORA measurements

2020 ◽  
Author(s):  
KangHo Bae ◽  
Chang-Keun Song ◽  
Sang-Seo Park ◽  
Sang-Woo Kim ◽  
Jhoon Kim ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Public Service ◽  
Trace Gases ◽  
Remote Sensing Data ◽  
Observation Data ◽  
South East Asian Region ◽  
Sensing Data ◽  
East Asian Region ◽  
Asian Region ◽  
Ambient Surface

<p>Launch of the Geostationary Environmental Monitoring Spectrometer (GEMS) is scheduled in early 2020 to support public service and science related to air quality and climate by providing diurnal variation of concentrations of trace gases and aerosols with high spatial/temporal resolution over Asian region. We will introduce GEMS validation methodology in parallel with a strategy for integration of existed independent measurements like as low-orbit satellite, ground-based remote sensing, and ambient surface observation data. As collections of nearly real-time and quality-assured data from existing ground-based networks are still in great needs for GEMS validation, efforts to expand observational infra-structure have been going on. Currently, two PANDORA instruments started to be in operation at Seoul and Ulsan in Korea, and PANDORA Asian Network initiated by NIER, Korea will be expanded into South East Asian region beyond Korea, China and Japan in addition. In this study, we especially try to validate the initial L2 product of GEMS gathered during IOT period by utilizing PANDORA data and other ground remote sensing data as well so that availability and feasibility of those ground observations could be assessed for GEMS validation.</p><p> </p><p>Keywords: GEMS validation, ground-based remote sensing data, PANDORA</p>

scholarly journals Correction of UVS-AB-T UV Radiometers Using a Radiative Transfer Model

2014 ◽  
Vol 31 (5) ◽  
pp. 1098-1103 ◽  
Author(s):  
Dong Xia ◽  
Haobo Tan ◽  
Ling Chen ◽  
Weiqiang Mo ◽  
Zhiyang Yuan ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Quadratic Function ◽  
Reaction Rates ◽  
Correction Method ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Observation Data ◽  
Clear Sky ◽  
Corrected Data ◽  
Decrement Rate

AbstractObservation of UV radiation is of major importance to human health and to the calculation of photochemical reaction rates. However, the sensitivity of UV radiometers decays because of equipment aging. A correction method is therefore proposed by using a decrement formula that is approximately a quadratic function of time and is obtained by fitting the clear-sky observation data from an aged UVS-AB-T UV radiometer with the data simulated by the Tropospheric Ultraviolet and Visible (TUV) radiative transfer model. The corrected data from the older radiometer are verified by the data from another newer radiometer on selected clear-sky days. The results show a high correlation and a low bias between the radiometers, and the mean of the corrected data from the older radiometer is 94.5% of that from the newer radiometer. After a long time of use, the decrement of the observation data would increase dramatically and errors of the data after correction would still be significant. In Dongguan, China, a recommendation is made that a UV radiometer should not be used for more than 5 years when the decrement rate reaches 50%.

scholarly journals Detection of Optically Thin Mineral Dust Aerosol Layers over the Ocean Using MODIS

2013 ◽  
Vol 30 (5) ◽  
pp. 896-916 ◽  
Author(s):  
Hyoun-Myoung Cho ◽  
Shaima L. Nasiri ◽  
Ping Yang ◽  
Istvan Laszlo ◽  
Xuepeng “Tom” Zhao
Keyword(s):  
Standard Deviation ◽  
Brightness Temperature ◽  
Ocean Surface ◽  
Training Dataset ◽  
Parameter Method ◽  
Detection Techniques ◽  
Clear Sky ◽  
Dust Detection ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Analyses show that several existing Moderate Resolution Imaging Spectroradiometer (MODIS) dust detection techniques, including an approach based on simple brightness temperature difference thresholds, the D-parameter method, and the multichannel image (MCI) algorithm, may be more effective for detection of highly concentrated dust plumes than for thin dust layers. Using the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud and aerosol classification as a reference, the sensitivities of six MODIS radiative parameters (including brightness temperature differences, and standard deviation and ratios of reflectances) to cloud, clear sky, and dust layers are examined in this paper. Reflectance ratios and the standard deviation of reflectances were confirmed to be useful in the discrimination of dust from cloud and underlying ocean surface, while brightness temperature differences alone were not sufficient to separate dust from cloud and clear sky over the ocean surface. Using a collocated MODIS and CALIPSO training dataset from 2008, visible and infrared MODIS radiative parameters from six latitude bands and four seasons were combined using linear and quadratic discriminant analyses to develop a new algorithm for the detection of optically thin dust over the ocean. The validation using collocated MODIS and CALIPSO data from 2009 shows that the present algorithm is effective in detecting thin dust layers having optical thicknesses between 0.1 and 2.0, but that it tends to misclassify optically thicker dust layers as clouds.

scholarly journals Combined dust detection algorithm by using MODIS infrared channels over East Asia

2014 ◽  
Vol 141 ◽  
pp. 24-39 ◽  
Author(s):  
Sang Seo Park ◽  
Jhoon Kim ◽  
Jaehwa Lee ◽  
Sukjo Lee ◽  
Jeong Soo Kim ◽  
...  
Keyword(s):  
East Asia ◽  
Detection Algorithm ◽  
Dust Detection

Development of a new over‐water Advanced Very High Resolution Radiometer dust detection algorithm

2006 ◽  
Vol 27 (18) ◽  
pp. 3903-3924 ◽  
Author(s):  
Amato T. Evan ◽  
Andrew K. Heidinger ◽  
Michael J. Pavolonis
Keyword(s):  
High Resolution ◽  
Detection Algorithm ◽  
Dust Detection ◽  
Very High

scholarly journals Investigation of dust storms in Ilam and the performance analysis of simulation of 6 numerical prediction models at a severe dust storm in west of Iran

2019 ◽  
Author(s):  
Sara Karami ◽  
Nasim Hossein Hamzeh ◽  
Faezeh Noori ◽  
Abbas Ranjbar
Keyword(s):  
Performance Analysis ◽  
Dust Storm ◽  
Satellite Images ◽  
Prediction Models ◽  
Statistical Investigation ◽  
Dust Storms ◽  
Observation Data ◽  
Surface Dust ◽  
Dust Dispersion ◽  
Meteorological Problems

Introduction: Many countries encounter dust storms phenomenon that is one of the meteorological problems leading to many disturbances. Materials and methods: Although the dust storm is historically recorded as an old event in some provinces of Iran, but it becomes a new event in some parts such as Ilam province. Results: After statistical investigation of dust storms in Ilam province, the dust storm from 3td to 6th July 2016 are studied. The source of this dust storm was the eastern areas of Syria and central Iraq base on the satellite images, the outputs of HYSPLIT and WRF-Chem models. Conclusion: Model outputs in intensity of surface dust concentration of MACC-ECMWF, NASA-GEOS, NCEP-NGAC, NMMB-BSC, and BSCDREAM8b models are compared to the observation data in Ilam city and results show that NASA-GEOS model has better performance. In display of dust dispersion on Iran, the middle of all models is more compatible with reality.

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