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

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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Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products

Atmospheric Measurement Techniques ◽

10.5194/amt-13-3263-2020 ◽

2020 ◽

Vol 13 (6) ◽

pp. 3263-3275 ◽

Cited By ~ 3

Author(s):

Benjamin Marchant ◽

Steven Platnick ◽

Kerry Meyer ◽

Galina Wind

Keyword(s):

Optical Thickness ◽

Detection Algorithm ◽

Separation Distance ◽

Orthogonal Polarization ◽

Cloud Detection ◽

Ice Cloud ◽

Active Sensor ◽

Vertical Distributions ◽

Sensor Cloud ◽

The Impact

Abstract. Since multilayer cloud scenes are common in the atmosphere and can be an important source of uncertainty in passive satellite sensor cloud retrievals, the MODIS MOD06 and MYD06 standard cloud optical property products include a multilayer cloud detection algorithm to assist with data quality assessment. This paper presents an evaluation of the Aqua MODIS MYD06 Collection 6 multilayer cloud detection algorithm through comparisons with active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products that have the ability to provide cloud vertical distributions and directly classify multilayer cloud scenes and layer properties. To compare active sensor products with an imager such as MODIS, it is first necessary to define multilayer clouds in the context of their radiative impact on cloud retrievals. Three main parameters have thus been considered in this evaluation: (1) the maximum separation distance between two cloud layers, (2) the thermodynamic phase of those layers and (3) the upper-layer cloud optical thickness. The impact of including the Pavolonis–Heidinger multilayer cloud detection algorithm, introduced in Collection 6, to assist with multilayer cloud detection has also been assessed. For the year 2008, the MYD06 C6 multilayer cloud detection algorithm identifies roughly 20 % of all cloudy pixels as multilayer (decreasing to about 13 % if the Pavolonis–Heidinger algorithm output is not used). Evaluation against the merged CPR and CALIOP 2B-CLDCLASS-lidar product shows that the MODIS multilayer detection results are quite sensitive to how multilayer clouds are defined in the radar and lidar product and that the algorithm performs better when the optical thickness of the upper cloud layer is greater than about 1.2 with a minimum layer separation distance of 1 km. Finally, we find that filtering the MYD06 cloud optical properties retrievals using the multilayer cloud flag improves aggregated statistics, particularly for ice cloud effective radius.

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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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Development of CO2 Band-Based Cloud Emission and Scattering Indices and Their Applications to FY-3D Hyperspectral Infrared Atmospheric Sounder

Remote Sensing ◽

10.3390/rs12244171 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4171

Author(s):

Xinlu Xia ◽

Xiaolei Zou

Keyword(s):

Brightness Temperature ◽

Optical Depth ◽

Co2 Absorption ◽

Cloud Detection ◽

Detection Scheme ◽

Cloud Optical Depth ◽

Ice Cloud ◽

Clear Sky ◽

Temperature Observations ◽

Cloud Top Pressure

The Hyperspectral Infrared Atmospheric Sounder (HIRAS) onboard the Feng Yun-3D (FY-3D) satellite is the first Chinese hyperspectral infrared instrument. In this study, an improved cloud detection scheme using brightness temperature observations from paired HIRAS long-wave infrared (LWIR) and short-wave infrared (SWIR) channels at CO2 absorption bands (15-μm and 4.3-μm) is developed. The weighting function broadness and a set of height-dependent thresholds of cloud-sensitive-level differences are incorporated into pairing LWIR and SWIR channels. HIRAS brightness temperature observations made under clear-sky conditions during a training period are used to develop a set of linear regression equations between paired LWIR and SWIR channels. Moderate-resolution Imaging Spectroradiometer (MODIS) cloud mask data are used for selecting HIRAS clear-sky observations. Cloud Emission and Scattering Indices (CESIs) are defined as the differences in SWIR channels between HIRAS observations and regression simulations from LWIR observations. The cloud retrieval products of ice cloud optical depth and cloud-top pressure from the Atmospheric Infrared Sounder (AIRS) are used to illustrate the effectiveness of the proposed cloud detection scheme for FY-3D HIRAS observations. Results show that the distributions of modified CESIs at different altitudes can capture features in the distributions of AIRS-retrieved ice cloud optical depth and cloud-top pressure better than the CESIs obtained by the original method.

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Advancing Maritime Transparent Cirrus Detection Using the Advanced Baseline Imager “Cirrus” Band

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-20-0130.1 ◽

2021 ◽

Author(s):

Theodore M. McHardy ◽

James R. Campbell ◽

David A. Peterson ◽

Simone Lolli ◽

Richard L. Bankert ◽

...

Keyword(s):

Precipitable Water ◽

Detection Algorithm ◽

Machine Learning Techniques ◽

Orthogonal Polarization ◽

Cirrus Cloud ◽

Cloud Detection ◽

Cloud Optical Depth ◽

Clear Sky ◽

Learning Techniques ◽

Microphysical Property

AbstractWe describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite – 16 (GOES-16) and developed with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) Channel 4 (1.378 μm) radiance and CALIOP 0.532 μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378 μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the Ch. 4 radiance as a function of AMF. The algorithm detects nearly 50% of sub-visual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semi-quantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378 μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an over-land algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

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Retrieval and Validation of Cloud Top Temperature from the Geostationary Satellite INSAT-3D

Remote Sensing ◽

10.3390/rs11232811 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2811 ◽

Cited By ~ 1

Author(s):

Lima ◽

Prijith ◽

Sesha Sai ◽

Rao ◽

Niranjan ◽

...

Keyword(s):

Geostationary Satellite ◽

Probability Of Detection ◽

Quality Data ◽

Orthogonal Polarization ◽

Cloud Detection ◽

Pixel Resolution ◽

Clear Sky ◽

Hit Rate ◽

Cloud Mask ◽

Cloud Top Temperature

Investigation of cloud top temperature (CTT) and its diurnal variation is highly reliant on high spatial and temporal resolution satellite data, which is lacking over the Indian region. An algorithm has been developed for detection of clouds and retrieval of CTT from the geostationary satellite INSAT-3D. These retrievals are validated (inter-compared) with collocated in-situ (satellite) measurements with specific intent to generate climate-quality data. The cloud detection algorithm employs nine different tests, in accordance with solar illumination, satellite angle and surface type conditions to generate pixel-resolution cloud mask. Validation of cloud mask with cloud-aerosol lidar with orthogonal polarization (CALIOP) shows that probability of detection (POD) of cloudy (clear) sky is 81% (85%), with 83% hit rate. The algorithm is also implemented on similar channels of moderate resolution imaging spectroradiometer (MODIS), which provides 88% (83%) POD of cloudy (clear) sky, with 86% hit rate. CTT retrieval is done at the pixel level, for all cloud pixels, by employing appropriate methods for various types of clouds. Comparison of CTT with radiosonde and cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) shows mean absolute error less than 3%. The study also examines sensitivity of retrieved CTT to the cloud classification scheme and retrieval criteria. Validation results and their close agreements with those of similar satellites demonstrate the reliability of the retrieved product for climate studies.

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CALIPSO polar stratospheric cloud observations: second-generation detection algorithm and composition discrimination

Atmospheric Chemistry and Physics ◽

10.5194/acp-9-7577-2009 ◽

2009 ◽

Vol 9 (19) ◽

pp. 7577-7589 ◽

Cited By ~ 112

Author(s):

M. C. Pitts ◽

L. R. Poole ◽

L. W. Thomason

Keyword(s):

Second Generation ◽

Particle Surface ◽

Detection Algorithm ◽

Airborne Lidar ◽

Orthogonal Polarization ◽

Cloud Detection ◽

Original Algorithm ◽

Areal Coverage ◽

Polar Stratospheric Cloud ◽

532 Nm

Abstract. This paper focuses on polar stratospheric cloud (PSC) measurements by the CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) lidar system onboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) spacecraft, which has been operating since June 2006. We describe a second-generation PSC detection algorithm that utilizes both the CALIOP 532-nm scattering ratio (ratio of total-to-molecular backscatter coefficients) and 532-nm perpendicular backscatter coefficient measurements for cloud detection. The inclusion of the perpendicular backscatter measurements enhances the detection of tenuous PSC mixtures containing low number densities of solid (likely nitric acid trihydrate, NAT) particles and leads to about a 15% increase in PSC areal coverage compared with our original algorithm. Although these low number density NAT mixtures would have a minimal impact on chlorine activation due to their relatively small particle surface area, these particles may play a significant role in denitrification and therefore are an important component of our PSC detection. In addition, the new algorithm allows discrimination of PSCs by composition in terms of their ensemble backscatter and depolarization in a manner analogous to that used in previous ground-based and airborne lidar PSC studies. Based on theoretical optical calculations, we define four CALIPSO-based composition classes which we call supercooled ternary solution (STS), ice, and Mix1 and Mix2, denoting mixtures of STS with NAT particles in lower or higher number densities/volumes, respectively. We examine the evolution of PSCs for three Antarctic and two Arctic seasons and illustrate the unique attributes of the CALIPSO PSC database. These analyses show substantial interannual variability in PSC areal coverage and also the well-known contrast between the Antarctic and Arctic. The CALIPSO data also reveal seasonal and altitudinal variations in Antarctic PSC composition, which are related to changes in HNO3 and H2O observed by the Microwave Limb Sounder on the Aura satellite.

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

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-5-8189-2012 ◽

2012 ◽

Vol 5 (6) ◽

pp. 8189-8222 ◽

Cited By ~ 1

Author(s):

X. Wang ◽

W. Li ◽

Y. Zhu ◽

B. Zhao

Keyword(s):

Complex Terrain ◽

Detection Algorithm ◽

Difficult Problem ◽

Probability Of Detection ◽

Official Method ◽

Cloud Detection ◽

Moderate Resolution Imaging Spectroradiometer ◽

Land Surfaces ◽

Northwest Region ◽

Cloud Mask

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

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-9413-2014 ◽

2014 ◽

Vol 7 (9) ◽

pp. 9413-9452 ◽

Cited By ~ 1

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 a 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 the clear sky condition. It is designated as cloud free data only when both the spectral and temporal tests confirm a cloud free data. Overall, most of the thick and uniform clouds are successfully screened out by the spectral test, while the broken and fast-varying clouds are screened out by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for 6 months, from January 2013 to June 2013. The overall proportion correct 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 failures occur when the new algorithm detects clouds while the ceilometer does not detect, 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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Distinguishing cirrus cloud presence in autonomous lidar measurements

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-7207-2014 ◽

2014 ◽

Vol 7 (7) ◽

pp. 7207-7243

Author(s):

J. R. Campbell ◽

M. A. Vaughan ◽

M. Oo ◽

R. E. Holz ◽

J. R. Lewis ◽

...

Keyword(s):

Orthogonal Polarization ◽

Cirrus Cloud ◽

Ice Clouds ◽

Ice Cloud ◽

Lidar Measurements ◽

Abstract Level ◽

Water Cloud ◽

Cloud Top Temperature ◽

Level 2 ◽

Global Mean

Abstract. Level 2 Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite-based cloud datasets from 2012 are investigated for metrics that help distinguish the cirrus cloud presence of in autonomous lidar measurements, using temperatures, heights, optical depth and phase. A thermal threshold, proposed by Sassen and Campbell (2001; SC2001) for cloud top temperature Ttop ≤ −37 °C, is evaluated vs. CALIOP algorithms that identify ice-phase cloud layers alone using depolarized backscatter. Global mean cloud top heights (11.15 vs. 10.07 km a.m.s.l.), base heights (8.76 vs. 7.95 km a.m.s.l.), temperatures (−58.48 °C vs. −52.18 °C and −42.40 °C vs. −38.13 °C, respectively for tops and bases) and optical depths (1.18 vs. 1.23) reflect the sensitivity to these competing constraints. Over 99% of all Ttop ≤ −37 °C clouds are classified as ice by CALIOP Level 2 algorithms. Over 81% of all ice clouds correspond with Ttop ≤ −37 °C. For instruments lacking polarized measurements, and thus practical phase estimates, Ttop ≤ −37 °C proves stable for distinguishing cirrus, as opposed to the risks of glaciated liquid water cloud contamination occurring in a given sample from clouds identified at warmer temperatures. Uncertainties in temperature profiles use to collocate with lidar data (i.e., model reanalyses/sondes) may justifiably relax the Ttop ≤ −37 °C threshold to include warmer cases. The ambiguity of "warm" (Ttop > −37 °C) ice cloud genus cannot be reconciled completely with available measurements, however, conspicuously including phase. Cloud top heights and optical depths are evaluated as potential constraints, as functions of CALIOP-retrieved phase. However, these data provide, at best, additional constraint in regional samples, compared with temperature alone, and may exacerbate classification uncertainties overall globally.

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Evaluation of the Aqua MODIS Collection 6.1 multilayer cloud detection algorithm through comparisons with CloudSat CPR and CALIPSO CALIOP products

10.5194/amt-2019-448 ◽

2020 ◽

Cited By ~ 1

Author(s):

Benjamin Marchant ◽

Steven Platnick ◽

Kerry Meyer ◽

Galina Wind

Keyword(s):

Optical Thickness ◽

Detection Algorithm ◽

Separation Distance ◽

Cloud Detection ◽

Ice Cloud ◽

Active Sensor ◽

Data Quality Assessment ◽

Vertical Distributions ◽

Sensor Cloud ◽

The Impact

Abstract. Since multilayer cloud scenes are common in the atmosphere and can be an important source of uncertainty in passive satellite sensor cloud retrievals, the MODIS MOD06/MYD06 standard cloud optical property products include a multilayer cloud detection algorithm to assist with data quality assessment. This paper presents an evaluation of the Aqua MODIS MYD06 Collection 6.1 (C6.1) multilayer cloud detection algorithm through comparisons with active CPR and CALIOP products that have the ability to provide cloud vertical distributions and directly classify multilayer cloud scenes and layer properties. To compare active sensor products with an imager such as MODIS, it is first necessary to define multilayer clouds in the context of their radiative impact on cloud retrievals. Three main parameters have thus been considered in this evaluation: (1) the maximum separation distance between two cloud layers, (2) the thermodynamic phase of those layers, and (3) the upper layer cloud optical thickness. The impact of including the Pavolonis-Heidinger multilayer cloud detection algorithm, introduced in Collection 6, to assist with multilayer cloud detection has also been assessed. For the year 2008, the MYD06 C6.1 multilayer cloud detection algorithm identifies roughly 20 percent of all cloudy pixels as multilayer (decreasing to about 13 percent if the Pavolonis-Heidinger algorithm output is not used). Evaluation against the merged CPR and CALIOP 2B-CLDCLASS-lidar product shows that the MODIS multilayer detection results are quite sensitive to how multilayer clouds are defined in the radar/lidar product, and that the algorithm performs better when the optical thickness of the upper cloud layer is greater than about 1.2 with a minimum layer separation distance of 1 km. Finally, we find that filtering the MYD06 cloud optical properties retrievals using the multilayer cloud flag improves aggregated statistics, particularly for ice cloud effective radius.

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