Improved cloud mask algorithm for FY-3A/VIRR data over the northwest region of China

Abstract. The existence of various land surfaces has always been a difficult problem for researchers who study cloud detection using satellite observations, especially over bright surfaces such as snow and desert. To improve the cloud mask result over complex terrain, an unbiased daytime cloud detection algorithm for the Visible and InfRared Radiometer (VIRR) on board the Chinese FengYun-3A polar-orbiting meteorological satellite is applied over the northwest region of China. Based on the statistical seasonal threshold tests, the algorithm consists of six main channels centered on the wavelengths of 0.63, 0.865, 10.8, 1.595, 0.455, and 1.36 μm. The combination of the unbiased algorithm and the specific threshold tests for special surfaces has effectively improved the cloud mask results over complex terrain and decreased the false identifications of clouds. The visual images over snow and desert adopting the proposed scheme exhibit better correlations with true-color images than do the VIRR official cloud mask results. The validation with the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product shows that the probability of detection for clear-sky regions over snow of the new scheme has increased nearly five times over the official method, and the false-alarm ratio for cloudy areas over desert has reduced by half compared with the official result. With regard to comparisons between ground measurements and cloud mask results, this approach also provides acceptable correspondence with the ground observations except for some cases, which are mainly obscured by cirrus clouds.

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Improved cloud mask algorithm for FY-3A/VIRR data over the northwest region of China

Atmospheric Measurement Techniques ◽

10.5194/amt-6-549-2013 ◽

2013 ◽

Vol 6 (3) ◽

pp. 549-563 ◽

Cited By ~ 5

Author(s):

X. Wang ◽

W. Li ◽

Y. Zhu ◽

B. Zhao

Keyword(s):

Confidence Level ◽

Detection Algorithm ◽

Cloud Detection ◽

Data Set ◽

Four Seasons ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Land Surfaces ◽

Northwest Region ◽

Cloud Mask

Abstract. The existence of various land surfaces always leads to more difficulties in cloud detection based on satellite observations, especially over bright surfaces such as snow and deserts. To improve the cloud mask result over complex terrain, an unbiased, daytime cloud detection algorithm for the Visible and InfRared Radiometer (VIRR) on board the Chinese FengYun-3A polar-orbiting meteorological satellite is applied over the northwest region of China. The algorithm refers to the concept of the clear confidence level from Moderate Resolution Imaging Spectroradiometer (MODIS) and the unbiased structure of the CLoud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA). Six main channels of VIRR centered at the wavelengths of 0.455, 0.63, 0.865, 1.595, 1.36, and 10.8 μm are designed to estimate the degree of a pixel's cloud contamination judged by the clear confidence level. Based on the statistical data set during four months (January, April, July, and October) in 2010, seasonal thresholds are applied to improve the accuracy of the cloud detection results. Flags depicting snow and water are also generated by the specific threshold tests for special surfaces. As shown in image inspections, the cloud detection results over snow and deserts, adopting the proposed scheme, exhibit better correlations with true-color images than the VIRR official cloud mask results do. The performance of the proposed algorithm has been evaluated in detail for four seasons in 2011, using cloud mask products from MODIS and the ground-based observations. The evaluation is based on, overall, 47 scenes collocated with MODIS and 96 individual matchups between VIRR and the ground-based observations from two weather stations located in the research region. The quantitative validations suggest that the estimations of clear-sky regions have been greatly improved by the proposed algorithm, while a poor identification of the cirrus clouds occurs over deserts.

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Enhanced MOPITT data coverage through cloud detection improvement

10.5194/amt-2021-112 ◽

2021 ◽

Author(s):

Heba S. Marey ◽

James R. Drummond ◽

Dylan B. A. Jones ◽

Helen Worden ◽

Merritt N. Deeter ◽

...

Keyword(s):

Lower Troposphere ◽

Detection Algorithm ◽

Cloud Detection ◽

Detection Scheme ◽

Cloud Height ◽

Standard Product ◽

Low Cloud ◽

Data Coverage ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cloud Mask

Abstract. The Measurements of Pollution in the Troposphere (MOPITT) satellite instrument has been measuring global tropospheric carbon monoxide (CO) since March 2000, providing the longest nearly continuous record of CO from space. During its long mission the data processing algorithms have been updated to improve the quality of CO retrievals and the sensitivity to the lower troposphere. Currently, MOPITT retrievals are only performed for clear-sky observations or over low clouds for ocean scenes. Compared to all observed radiances, successful retrieval rates are about 30 % and 40 % between 90° S–90° N and 60° S–60° N, respectively. Spatial seasonal variations show that while MOPITT data coverage in some places reaches 30 % in summer, this number can drop to less than 10 % in winter due to significantly increased cloud cover. Therefore, we investigate the current MOPITT cloud detection algorithm and consider approaches to increase the data coverage. The MOPITT CO total column (TC) data were modified by turning off the cloud detection scheme to allow a CO retrieval result regardless of their cloud status. Analyses of the standard CO TC product (cloud filtered) and non-standard product (non-cloud masked) were conducted for selected days. Results showed some coherent structures that were observed frequently in the non-masked CO product that were not present in the standard product and could potentially be actual CO features. A corresponding analysis of Moderate Resolution Imaging Spectroradiometer(MODIS) cloud height and cloud mask products along with MOPITT cloud flag descriptors was conducted in order to understand the cloud conditions present for these apparently physical CO features. Results show that a significant number of low cloud CO retrievals were rejected in the standard product. Those missing areas match the coherent patterns that were detected in the non-masked CO product. Many times, these structures were also seen in the Infrared Atmospheric Sounding Interferometer (IASI) CO TC product indicating actual CO plumes. Multi-angle Imaging SpectroRadiometer (MISR) data on the Terra satellite were also employed for cloud height comparison with MODIS. Comparisons of MODIS and MISR cloud height data indicate remarkable agreement which is encouraging for the possibility of incorporating MODIS cloud height in the MOPITT cloud detection scheme. Statistics of the global assessment of the potential use of MODIS cloud height shows that MOPITT data increases significantly when cloud heights less than 2 km in height are incorporated in the retrievals. However quality indices should be defined and produced to ensure sufficient retrieval quality.

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An automated cloud detection method based on the green channel of total-sky visible images

Atmospheric Measurement Techniques ◽

10.5194/amt-8-4671-2015 ◽

2015 ◽

Vol 8 (11) ◽

pp. 4671-4679 ◽

Cited By ~ 17

Author(s):

J. Yang ◽

Q. Min ◽

W. Lu ◽

W. Yao ◽

Y. Ma ◽

...

Keyword(s):

Detection Algorithm ◽

Mie Scattering ◽

Brightness Distribution ◽

Adaptive Threshold ◽

Cloud Detection ◽

Blue Band ◽

Green Channel ◽

The Past ◽

Visible Images ◽

Cloud Mask

Abstract. Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total-sky images. By analyzing the imaging principle of cameras, the green channel has been selected to replace the 2-D red-to-blue band for detecting cloud pixels from partly cloudy total-sky images in this study. The brightness distribution in a total-sky image is usually nonuniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, an adaptive threshold, and binarization. Five experimental cases show that the GBSAT algorithm produces more accurate retrieval results for all these test total-sky images.

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Multilayer Cloud Detection with the MODIS Near-Infrared Water Vapor Absorption Band

Journal of Applied Meteorology and Climatology ◽

10.1175/2010jamc2364.1 ◽

2010 ◽

Vol 49 (11) ◽

pp. 2315-2333 ◽

Cited By ~ 59

Author(s):

Galina Wind ◽

Steven Platnick ◽

Michael D. King ◽

Paul A. Hubanks ◽

Michael J. Pavolonis ◽

...

Keyword(s):

Water Vapor ◽

Near Infrared ◽

Single Layer ◽

Precipitable Water ◽

Detection Algorithm ◽

Cloud Detection ◽

Water Vapor Absorption ◽

Cloud Properties ◽

Cloud Models ◽

Moderate Resolution Imaging Spectroradiometer

Abstract Data Collection 5 processing for the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Earth Observing System (EOS) Terra and Aqua spacecraft includes an algorithm for detecting multilayered clouds in daytime. The main objective of this algorithm is to detect multilayered cloud scenes, specifically optically thin ice cloud overlying a lower-level water cloud, that present difficulties for retrieving cloud effective radius using single-layer plane-parallel cloud models. The algorithm uses the MODIS 0.94-μm water vapor band along with CO2 bands to obtain two above-cloud precipitable water retrievals, the difference of which, in conjunction with additional tests, provides a map of where multilayered clouds might potentially exist. The presence of a multilayered cloud results in a large difference in retrievals of above-cloud properties between the CO2 and the 0.94-μm methods. In this paper the MODIS multilayered cloud algorithm is described, results of using the algorithm over example scenes are shown, and global statistics for multilayered clouds as observed by MODIS are discussed. A theoretical study of the algorithm behavior for simulated multilayered clouds is also given. Results are compared to two other comparable passive imager methods. A set of standard cloudy atmospheric profiles developed during the course of this investigation is also presented. The results lead to the conclusion that the MODIS multilayer cloud detection algorithm has some skill in identifying multilayered clouds with different thermodynamic phases.

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Comparison of NOAA's Operational AVHRR-Derived Cloud Amount to Other Satellite-Derived Cloud Climatologies

Journal of Climate ◽

10.1175/jcli-3242.1 ◽

2004 ◽

Vol 17 (24) ◽

pp. 4805-4822 ◽

Cited By ~ 42

Author(s):

Sarah M. Thomas ◽

Andrew K. Heidinger ◽

Michael J. Pavolonis

Keyword(s):

High Resolution ◽

Phase 1 ◽

Cloud Amount ◽

Cloud Detection ◽

Detection Scheme ◽

Noaa Avhrr ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Individual ◽

Cloud Mask

Abstract A comparison is made between a new operational NOAA Advanced Very High Resolution Radiometer (AVHRR) global cloud amount product to those from established satellite-derived cloud climatologies. The new operational NOAA AVHRR cloud amount is derived using the cloud detection scheme in the extended Clouds from AVHRR (CLAVR-x) system. The cloud mask within CLAVR-x is a replacement for the Clouds from AVHRR phase 1 (CLAVR-1) cloud mask. Previous analysis of the CLAVR-1 cloud climatologies reveals that its utility for climate studies is reduced by poor high-latitude performance and the inability to include data from the morning orbiting satellites. This study demonstrates, through comparison with established satellite-derived cloud climatologies, the ability of CLAVR-x to overcome the two main shortcomings of the CLAVR-1-derived cloud climatologies. While systematic differences remain in the cloud amounts from CLAVR-x and other climatologies, no evidence is seen that these differences represent a failure of the CLAVR-x cloud detection scheme. Comparisons for July 1995 and January 1996 indicate that for most latitude zones, CLAVR-x produces less cloud than the International Satellite Cloud Climatology Project (ISCCP) and the University of Wisconsin High Resolution Infrared Radiation Sounder (UW HIRS). Comparisons to the Moderate Resolution Imaging Spectroradiometer (MODIS) for 1–8 April 2003 also reveal that CLAVR-x tends to produce less cloud. Comparison of the seasonal cycle (July–January) of cloud difference with ISCCP, however, indicates close agreement. It is argued that these differences may be due to the methodology used to construct a cloud amount from the individual pixel-level cloud detection results. Overall, the global cloud amounts from CLAVR-x appear to be an improvement over those from CLAVR-1 and compare well to those from established satellite cloud climatologies. The CLAVR-x cloud detection results have been operational since late 2003 and are available in real time from NOAA.

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

Atmospheric Measurement Techniques ◽

10.5194/amt-8-553-2015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 553-566 ◽

Cited By ~ 5

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.

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Cloud Detection with MODIS. Part I: Improvements in the MODIS Cloud Mask for Collection 5

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2008jtecha1052.1 ◽

2008 ◽

Vol 25 (7) ◽

pp. 1057-1072 ◽

Cited By ~ 265

Author(s):

Richard A. Frey ◽

Steven A. Ackerman ◽

Yinghui Liu ◽

Kathleen I. Strabala ◽

Hong Zhang ◽

...

Keyword(s):

Near Infrared ◽

Sea Surface ◽

Sea Surface Temperatures ◽

Cloud Detection ◽

Stream Data ◽

Water Vapor Absorption ◽

Brightness Temperatures ◽

Surface Temperatures ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cloud Mask

Abstract Significant improvements have been made to the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask (MOD35 and MYD35) for Collection 5 reprocessing and forward stream data production. Most of the modifications are realized for nighttime scenes where polar and oceanic regions will see marked improvement. For polar night scenes, two new spectral tests using the 7.2-μm water vapor absorption band have been added as well as updates to the 3.9–12- and 11–12-μm cloud tests. More non-MODIS ancillary input data have been added. Land and sea surface temperature maps provide crucial information for mid- and low-level cloud detection and lessen dependence on ocean brightness temperature variability tests. Sun-glint areas are also improved by use of sea surface temperatures to aid in resolving observations with conflicting cloud versus clear-sky signals, where visible and near-infrared (NIR) reflectances are high, but infrared brightness temperatures are relatively warm. Day and night Arctic cloud frequency results are compared to those created by the Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder-Extended (APP-X) algorithm. Day versus night sea surface temperatures derived from MODIS radiances and using only the MODIS cloud mask for cloud screening are contrasted. Frequencies of cloud from sun-glint regions are shown as a function of sun-glint angle to gain a sense of cloud mask quality in those regions. Continuing validation activities are described in Part II of this paper.

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Detailed characterisation of AVHRR global cloud detection performance of the CM SAF CLARA-A2 climate data record based on CALIPSO-CALIOP cloud information

10.5194/amt-2017-307 ◽

2017 ◽

Cited By ~ 1

Author(s):

Karl-Göran Karlsson ◽

Nina Håkansson

Keyword(s):

Optical Thickness ◽

Detection Performance ◽

Polar Regions ◽

Cloud Detection ◽

Climate Data ◽

Data Record ◽

Cloud Optical Thickness ◽

Land Surfaces ◽

Cloud Mask ◽

Climate Data Record

Abstract. The cloud detection performance of the cloud mask being used in the CM SAF cloud, albedo and surface radiation dataset from AVHRR data (CLARA-A2) cloud climate data record (CDR) has been evaluated in detail using cloud information from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the CALIPSO satellite. Validation results, including their global distribution, have been calculated from collocations of AVHRR and CALIOP measurements over a ten-year period (2006–2015). The sensitivity of the results to the cloud optical thicknesses of CALIOP-observed clouds were studied leading to the conclusion that the global cloud detection sensitivity (defined as the minimum cloud optical thickness for which 50 % of clouds could be detected) was estimated to 0.225. After applying this optical thickness threshold to the CALIOP cloud mask, results were found to be basically unbiased over most of the globe except over the polar regions where a considerably underestimation of cloudiness could be seen during the polar winter. The probability of detecting clouds in the polar winter could be as low as 50 % over the highest and coldest portions of Greenland and Antarctica, showing that also a large fraction of optically thick clouds remains undetected here. The study included an in-depth analysis of the probability of detecting a cloud as a function of the vertically integrated cloud optical thickness as well as of the cloud’s geographical position. Best results were achieved over oceanic surfaces at mid-to-high latitudes were at least 50 % of all clouds with an optical thickness down to a value of 0.075 were detected. Corresponding cloud detection sensitivities over land surfaces outside of the polar regions were generally larger than 0.2 with maximum values of approximately 0.5 over Sahara and the Arabian Peninsula. For polar land surfaces the values were close to 1 or higher with maximum values of 4.5 over the geographically highest parts of Greenland and Antarctica. The validation method is suggested to be applied also to other satellite-based CDRs and validation results are proposed to be used in Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulation Package (COSP) simulators for cloud detection characterisation of various cloud CDRs from passive imagery.

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The Continuity MODIS-VIIRS Cloud Mask

Remote Sensing ◽

10.3390/rs12203334 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3334 ◽

Cited By ~ 3

Author(s):

Richard A. Frey ◽

Steven A. Ackerman ◽

Robert E. Holz ◽

Steven Dutcher ◽

Zach Griffith

Keyword(s):

Infrared Imaging ◽

Cloud Fraction ◽

The Arctic ◽

Orthogonal Polarization ◽

Cloud Detection ◽

Moderate Resolution ◽

Resolution Imaging ◽

Hit Rates ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cloud Mask

This paper introduces the Continuity Moderate Resolution Imaging Spectroradiometer (MODIS)-Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Mask (MVCM), a cloud detection algorithm designed to facilitate continuity in cloud detection between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Soumi National Polar-orbiting Partnership (SNPP) spacecraft. It is based on the MODIS cloud mask that has been operating since 2000 with the launch of the Terra spacecraft (MOD35) and continuing in 2002 with Aqua (MYD35). The MVCM makes use of fourteen spectral bands that are common to both MODIS and VIIRS so as to create consistent cloud detection between the two instruments and across the years 2000–2020 and beyond. Through comparison data sets, including collocated Aqua MODIS and Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) from the A-Train, this study was designed to assign statistical consistency benchmarks between the MYD35 and MVCM cloud masks. It is shown that the MVCM produces consistent cloud detection results between Aqua MODIS, SNPP VIIRS, and NOAA-20 VIIRS and that the quality is comparable to the standard Aqua MODIS cloud mask. Globally, comparisons with collocated CALIOP lidar show combined clear and cloudy sky hit rates of 88.2%, 87.5%, 86.8%, and 86.8% for MYD35, MVCM Aqua MODIS, MVCM SNPP VIIRS, and MVCM NOAA-20 VIIRS, respectively, for June through until August, 2018. For the same months and in the same order for 60S–60N, hit rates are 90.7%, 90.5%, 90.1%, and 90.3%. From the time series constructed from gridded daily means of 60S–60N cloud fractions, we found that the mean day-to-day cloud fraction differences/standard deviations in percent to be 0.68/0.55, 0.94/0.64, −0.20/0.50, and 0.44/0.82 for MVCM Aqua MODIS-MVCM SNPP VIIRS day and night, and MVCM NOAA-20 VIIRS-MVCM SNPP VIIRS day and night, respectively. It is seen that the MODIS and VIIRS 1.38 µm cirrus detection bands perform similarly but with MODIS detecting slightly more clouds in the middle to high levels of the troposphere and the VIIRS detecting more in the upper troposphere above 16 km. In the Arctic, MVCM Aqua MODIS and SNPP VIIRS reported cloud fraction differences of 0–3% during the mid-summer season and −3–4% during the mid-winter.

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Combination of AIRS Dual CO2 Absorption Bands to Develop an Ice Clouds Detection Algorithm in Different Atmospheric Layers

Remote Sensing ◽

10.3390/rs12010006 ◽

2019 ◽

Vol 12 (1) ◽

pp. 6 ◽

Cited By ~ 1

Author(s):

Liwen Wang ◽

Youfei Zheng ◽

Chao Liu ◽

Zeyi Niu ◽

Jingxin Xu ◽

...

Keyword(s):

Linear Relationship ◽

Weighting Function ◽

Detection Algorithm ◽

Probability Of Detection ◽

Orthogonal Polarization ◽

Co2 Absorption ◽

Cloud Detection ◽

Absorption Bands ◽

Ice Clouds ◽

Ice Cloud

The use of infrared (IR) sensors to detect clouds in different layers of the atmosphere is a big challenge, especially for ice clouds. This study aims to improve ice cloud detection using Lin’s algorithm and apply it to Atmospheric Infrared Sounder (AIRS). To achieve these objectives, the scattering and emission characteristics of clouds as perceived by AIRS longwave infrared (LWIR, ~15 μm) and shortwave infrared (SWIR, ~4.3 μm) CO2 absorption bands are applied for ice cloud detection. Hence, the weighting function peak (WFP), cut-off pressure, and correlation coefficients between the brightness temperatures (BTs) of LWIR and SWIR channels are used to pair the LWIR and SWIR channels. After that, the linear relationship between the clear-sky BTs of the paired LWIR and SWIR channels is established by the cloud scattering and emission Index (CESI). However, the linear relationship fails in the presence of ice clouds. Comparing these results with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations show that the probability of detection of ice clouds for Pair-8 (WFP~330hPa), Pair-19 (WFP~555hPa), and Pair-24 (WFP~866hPa) are 0.63, 0.71, and 0.73 in the daytime and 0.46, 0.62, and 0.7 in the nighttime at a false alarm rate of 0.1 when ice clouds top pressure above 330 hPa, 555 hPa, and 866 hPa, respectively. Furthermore, the thresholds of the three pairs are 2.4 K, 3 K, and 8.7 K in the daytime and 1.7 K, 1.7 K, and 4.4 K in the nighttime at the highest Heike Skill Score (HSS). The error of HSS values based on thresholds of ice clouds is between 0.01 and 0.02 which is comparable with the ice cloud detection results in both day and night conditions. It is shown that Pair-8 (WFP~330hPa) can detect opaque and thick ice clouds above its WFP altitude over the tropical areas but it is unable to observe ice clouds over the mid-latitude while Pair-19 and Pair-24 can identify ice clouds above their WFP altitude.

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