Calibration of global MODIS cloud amount using CALIOP cloud profiles

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOVs) as either “confident clear”, “probably clear”, “probably cloudy”, or “confident cloudy”. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by the MODIS Science Team assumes that confident clear and probably clear IFOVs are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this “best-guess” approach is unreliable, especially on a regional/local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with Aqua MODIS IFOV. Based on 33 793 648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in polar regions at night, and in selected locations over the Northern Hemisphere (e.g. China, the north-west coast of Africa, and eastern parts of the United States). Consequently, applications of MODIS data on a regional/local scale should first assess the extent of the uncertainty. We suggest using CALIPSO-based cloud fractions to improve MODIS cloud amount estimates. This approach can also be used for Terra MODIS data, and other passive cloud imagers, where the footprint is collocated with CALIPSO.

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Calibration of global MODIS cloud amount using CALIOP cloud profiles

10.5194/amt-2020-111 ◽

2020 ◽

Author(s):

Andrzej Z. Kotarba

Keyword(s):

Cloud Amount ◽

Satellite Observation ◽

Cloud Fraction ◽

Orthogonal Polarization ◽

Polar Regions ◽

Cloud Detection ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cloud Mask

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOV) as either confident cloudy, probably cloudy, probably clear, or confident clear. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by NASA assumes that confident clear and probably clear IFOV are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this best guess approach is unreliable, especially on a regional/ local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with MODIS/ Aqua IFOV. Based on 33,793,648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in the polar regions at night, and in selected locations over the northern hemisphere. Applications of MODIS data at ~10 degrees resolution (or finer) should first assess the extent of the error. Uncertainties were related to the efficiency of the cloud masking algorithm. Until the algorithm can be significantly modified, our method is a robust way to calibrate (correct) MODIS estimates. It can be also used for MODIS/ Terra data, and other missions where the footprint is collocated with CALIPSO.

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On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset

Atmospheric Measurement Techniques ◽

10.5194/amt-6-1271-2013 ◽

2013 ◽

Vol 6 (5) ◽

pp. 1271-1286 ◽

Cited By ~ 19

Author(s):

K.-G. Karlsson ◽

E. Johansson

Keyword(s):

Satellite Imagery ◽

Winter Season ◽

Satellite Observation ◽

Orthogonal Polarization ◽

Polar Regions ◽

Cloud Detection ◽

Optimal Method ◽

Tropical Regions ◽

Boundary Layer Clouds ◽

High Level

Abstract. A method for detailed evaluation of a new satellite-derived global 28 yr cloud and radiation climatology (Climate Monitoring SAF Clouds, Albedo and Radiation from AVHRR data, named CLARA-A1) from polar-orbiting NOAA and Metop satellites is presented. The method combines 1 km and 5 km resolution cloud datasets from the CALIPSO-CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation – Cloud-Aerosol Lidar with Orthogonal Polarization) cloud lidar for estimating cloud detection limitations and the accuracy of cloud top height estimations. Cloud detection is shown to work efficiently for clouds with optical thicknesses above 0.30 except for at twilight conditions when this value increases to 0.45. Some misclassifications of cloud-free surfaces during daytime were revealed for semi-arid land areas in the sub-tropical and tropical regions leading to up to 20% overestimated cloud amounts. In addition, a substantial fraction (at least 20–30%) of all clouds remains undetected in the polar regions during the polar winter season due to the lack of or an inverted temperature contrast between Earth surfaces and clouds. Subsequent cloud top height evaluation took into account the derived information about the cloud detection limits. It was shown that this has fundamental importance for the achieved results. An overall bias of −274 m was achieved compared to a bias of −2762 m when no measures were taken to compensate for cloud detection limitations. Despite this improvement it was concluded that high-level clouds still suffer from substantial height underestimations, while the opposite is true for low-level (boundary layer) clouds. The validation method and the specifically collected satellite dataset with optimal matching in time and space are suggested for a wider use in the future for evaluation of other cloud retrieval methods based on passive satellite imagery.

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MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China

Atmosphere ◽

10.3390/atmos10060333 ◽

2019 ◽

Vol 10 (6) ◽

pp. 333 ◽

Cited By ~ 3

Author(s):

Saichun Tan ◽

Xiao Zhang ◽

Guangyu Shi

Keyword(s):

Eastern China ◽

Cloud Fraction ◽

Orthogonal Polarization ◽

Aerosol Layer ◽

Cloud Detection ◽

Haze Pollution ◽

Moderate Resolution ◽

Resolution Imaging ◽

Cloud Optical Thickness ◽

Moderate Resolution Imaging Spectroradiometer

Haze pollution has frequently occurred in winter over Eastern China in recent years. Over Eastern China, Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection data were compared with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) for three years (2013–2016) for three kinds of underlying surface types (dark, bright, and water). We found that MODIS and CALIOP agree most of the time (82% on average), but discrepancies occurred at low CALIOP cloud optical thickness (COT < 0.4) and low MODIS cloud top height (CTH < 1.5 km). In spring and summer, the CALIOP cloud fraction was higher by more than 0.1 than MODIS due to MODIS’s incapability of observing clouds with a lower COT. The discrepancy increased significantly with a decrease in MODIS CTH and an increase in aerosol optical depth (AOD, about 2–4 times), and MODIS observed more clouds that were undetected by CALIOP over PM2.5 > 75 μg m−3 regions in autumn and particularly in winter, suggesting that polluted weather over Eastern China may contaminate MODIS cloud detections because MODIS will misclassify a heavy aerosol layer as cloudy under intense haze conditions. Besides aerosols, the high solar zenith angle (SZA) in winter also affects MODIS cloud detection, and the ratio of MODIS cloud pixel numbers to CALIOP cloud-free pixel numbers at a high SZA increased a great deal (about 4–21 times) relative to that at low SZA for the three surfaces. As a result of the effects of aerosol and SZA, MODIS cloud fraction was 0.08 higher than CALIOP, and MODIS CTH was more than 2 km lower than CALIOP CTH in winter. As for the cloud phases and types, the results showed that most of the discrepancies could be attributed to water clouds and low clouds (cumulus and stratocumulus), which is consistent with most of the discrepancies at low MODIS CTH.

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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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Cloud Detection with MODIS. Part II: Validation

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2007jtecha1053.1 ◽

2008 ◽

Vol 25 (7) ◽

pp. 1073-1086 ◽

Cited By ~ 250

Author(s):

S. A. Ackerman ◽

R. E. Holz ◽

R. Frey ◽

E. W. Eloranta ◽

B. C. Maddux ◽

...

Keyword(s):

Optical Depth ◽

Great Plains ◽

Cloud Amount ◽

Department Of Energy ◽

Field Of View ◽

High Spectral Resolution ◽

Polar Regions ◽

Cloud Detection ◽

Laser Altimeter ◽

Cloud Mask

Abstract An assessment of the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm for Terra and Aqua satellites is presented. The MODIS cloud mask algorithm output is compared with lidar observations from ground [Arctic High-Spectral Resolution Lidar (AHSRL)], aircraft [Cloud Physics Lidar (CPL)], and satellite-borne [Geoscience Laser Altimeter System (GLAS)] platforms. The comparison with 3 yr of coincident observations of MODIS and combined radar and lidar cloud product from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site in Lamont, Oklahoma, indicates that the MODIS algorithm agrees with the lidar about 85% of the time. A comparison with the CPL and AHSRL indicates that the optical depth limitation of the MODIS cloud mask is approximately 0.4. While MODIS algorithm flags scenes with a cloud optical depth of 0.4 as cloudy, approximately 90% of the mislabeled scenes have optical depths less than 0.4. A comparison with the GLAS cloud dataset indicates that cloud detection in polar regions at night remains challenging with the passive infrared imager approach. In anticipation of comparisons with other satellite instruments, the sensitivity of the cloud mask algorithm to instrument characteristics (e.g., instantaneous field of view and viewing geometry) and thresholds is demonstrated. As expected, cloud amount generally increases with scan angle and instantaneous field of view (IFOV). Nadir sampling represents zonal monthly mean cloud amounts but can have large differences for regional studies when compared to full-swath-width analysis.

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A Naive Bayesian Cloud-Detection Scheme Derived from CALIPSO and Applied within PATMOS-x

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-02.1 ◽

2012 ◽

Vol 51 (6) ◽

pp. 1129-1144 ◽

Cited By ~ 122

Author(s):

Andrew K. Heidinger ◽

Amato T. Evan ◽

Michael J. Foster ◽

Andi Walther

Keyword(s):

Cloud Amount ◽

The Arctic ◽

Orthogonal Polarization ◽

Bayesian Classifiers ◽

Cloud Detection ◽

Detection Scheme ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Moderate Resolution Imaging Spectroradiometer ◽

Mean Differences

AbstractThe naive Bayesian methodology has been applied to the challenging problem of cloud detection with NOAA’s Advanced Very High Resolution Radiometer (AVHRR). An analysis of collocated NOAA-18/AVHRR and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations was used to automatically and globally derive the Bayesian classifiers. The resulting algorithm used six Bayesian classifiers computed separately for seven surface types. Relative to CALIPSO, the final results show a probability of correct detection of roughly 90% over water, deserts, and snow-free land; 82% over the Arctic; and below 80% over the Antarctic. This technique is applied within the NOAA Pathfinder Atmosphere’s Extended (PATMOS-x) climate dataset and the Clouds from AVHRR Extended (CLAVR-x) real-time product generation system. Comparisons of the PATMOS-x results with those from International Satellite Cloud Climatology Project (ISCCP) and Moderate Resolution Imaging Spectroradiometer (MODIS) indicate close agreement with zonal mean differences in cloud amount being less than 5% over most zones. Most areas of difference coincided with regions where the Bayesian cloud mask reported elevated uncertainties. The ability to report uncertainties is a critical component of this approach.

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Comparing scattering ratio products retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations: sensitivity, comparability, and temporal evolution

10.5194/amt-2021-96 ◽

2021 ◽

Author(s):

Artem Feofilov ◽

Hélène Chepfer ◽

Vincent Noel ◽

Rodrigo Guzman ◽

Cyprien Gindre ◽

...

Keyword(s):

Temporal Evolution ◽

Signal To Noise Ratio ◽

Low Frequency ◽

Satellite Observation ◽

Middle Level ◽

Orthogonal Polarization ◽

Lower Sensitivity ◽

Global Changes ◽

Cloud Detection ◽

Scattering Ratio

Abstract. The spaceborne active sounders have been contributing invaluable vertically resolved information of atmospheric optical properties since the launch of CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) in 2006. To ensure the continuity of climate studies and monitoring the global changes, one has to understand the differences between lidars operating at different wavelengths, flying at different orbits, and utilizing different observation geometries, receiving paths, and detectors. In this article, we show the results of an intercomparison study of ALADIN (Atmospheric Laser Doppler INstrument) and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidars using their scattering ratio (SR) products for the period of 28/06/2019−31/12/2019. We suggest an optimal set of collocation criteria (Δdist < 1º; Δtime < 6 h), which would give a representative set of collocated profiles and we show that for such a pair of instruments the theoretically achievable cloud detection agreement for the data collocated with aforementioned criteria is 0.77 ± 0.17. The analysis of a collocated database consisting of ~78000 pairs of collocated nighttime SR profiles revealed the following: (a) in the cloud-free area, the agreement is good indicating low frequency of false positive cloud detections by both instruments; (b) the cloud detection agreement is better for the lower layers. Above ~7 km, the ALADIN product demonstrates lower sensitivity because of lower backscatter at 355 nm and because of lower signal-to-noise ratio; (c) in 50 % of the analyzed cases when ALADIN reported a low cloud not detected by CALIOP, the middle level cloud hindered the observations and perturbed the ALADIN’s retrieval indicating the need for quality flag refining for such scenarios; (d) large sensitivity to lower clouds leads to skewing the ALADIN’s cloud peaks down by ~0.5 ± 0.4 km, but this effect does not alter the polar stratospheric cloud peak heights; (e) temporal evolution of cloud agreement quality does not reveal any anomaly for the considered period, indicating that hot pixels and laser degradation effects in ALADIN have been mitigated at least down to the uncertainties in the following cloud detection agreement values: 61 ± 16 %, 34 ± 18 % 24 ± 10 %, 26 ± 10 %, and 22 ± 12 % at 0.75 km, 2.25 km, 6.75 km, 8.75 km, and 10.25 km, respectively.

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Strong aerosol effects on cloud amount based on long-term satellite observations over the East Coast of the United States

10.5194/egusphere-egu21-2457 ◽

2021 ◽

Author(s):

Yang Cao ◽

Minghuai Wang ◽

Daniel Rosenfeld ◽

Yannian Zhu ◽

Yuan Liang ◽

...

Keyword(s):

United States ◽

Regression Model ◽

Multivariate Regression ◽

East Coast ◽

Cloud Amount ◽

The United States ◽

Cloud Fraction ◽

Multivariate Regression Model ◽

Aerosol Effects

Here we use 16-year satellite and reanalysis data in combination with a multivariate regression model to investigate how aerosols affect cloud fraction (CF) over the East Coast of the United States. Cloud droplet number concentrations (Nd), cloud geometrical thickness, lower tropospheric stability, and relative humidity at 950 hPa (RH950) are identified as major cloud controlling parameters that explain 97% of the variability in CF. Nd is shown to play an important role in regulating the dependence of cloud fraction on RH950. The observed annual-mean CF shows no significant trend due to the cancelation from the opposite trends in Nd and RH950. The multivariate regression model revealed that the decline in Nd alone would lead to a about 20% relative decline in CF. Our results indicate the significant aerosol effects on CF and suggest the need to account for pollution-induced cloud changes in quantifying cloud feedback based on long-term observations.

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Cloud Coverage in the Joint OSSE Nature Run

Monthly Weather Review ◽

10.1175/mwr-d-11-00131.1 ◽

2012 ◽

Vol 140 (6) ◽

pp. 1863-1871 ◽

Cited By ~ 15

Author(s):

Will McCarty ◽

Ronald M. Errico ◽

Ronald Gelaro

Keyword(s):

Simulation Experiment ◽

Model Simulation ◽

System Simulation ◽

Cloud Amount ◽

Satellite Observation ◽

Cloud Fraction ◽

Validation Data ◽

Cloud Coverage ◽

Free Running ◽

Observing System Simulation Experiment

Abstract A successful observing system simulation experiment (OSSE) is fundamentally dependent on the simulation of the global observing system used in the experiment. In many applications, a free-running numerical model simulation, called a nature run, is used as the meteorological truth from which the observations are simulated. To accurately and realistically simulate observations from any nature run, the simulated observations must contain realistic cloud effects representative of the meteorological regimes being sampled. This study provides a validation of the clouds in the Joint OSSE nature run generated at ECMWF. Presented is the methodology used to validate the nature run cloud fraction fields with seasonally aggregated combined CloudSat/Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud geometric profile retrievals and the Wisconsin High Resolution Infrared Radiation Sounder (HIRS) cloud climatology. The results show that the Joint OSSE nature run has a correct vertical distribution of clouds but lacks globally in cloud amount compared to the validation data. The differences between the nature run and validation datasets shown in this study should be considered and accounted for in the generation of the global observing system for use in full OSSE studies.

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Cloud fraction determined by thermal infrared and visible all-sky cameras

10.5194/amt-2018-68 ◽

2018 ◽

Author(s):

Christine Aebi ◽

Julian Gröbner ◽

Niklaus Kämpfer

Keyword(s):

Cloud Amount ◽

Visible Spectrum ◽

Thermal Infrared ◽

Detection Algorithm ◽

Cloud Fraction ◽

High Temporal Resolution ◽

Cloud Detection ◽

Fractional Cloud ◽

Significant Difference ◽

High Level

Abstract. The thermal infrared cloud camera (IRCCAM) is a prototype instrument that determines cloud fraction continuously during day and nighttime with high temporal resolution. It has been developed and tested at Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC) in Davos, Switzerland. The IRCCAM consists of a commercial microbolometer camera sensitive in the 8 μm–14 μm wavelength range. Over a time period of two years, the fractional cloud coverage obtained by the IRCCAM is compared with two other commercial cameras sensitive in the visible spectrum (Mobotix Q24M and Schreder VIS-J1006) as well as with the automated partial cloud amount detection algorithm (APCADA) using pyrgeometer data. In comparison to the visible cloud detection algorithms, the IRCCAM shows median difference values of 0.01 to 0.07 cloud fraction wherein around 90 % of the data are within ±0.25 (±2 oktas) cloud fraction. Thus there is no significant difference in the cloud fraction determination of the IRCCAM in comparison to the other study instruments. Analysis indicates no significant difference in the performance of the IRCCAM during day or nighttime and also not in different seasons. The cloud types where all algorithms are in closest agreement are low-level clouds (with median differences in cloud fraction of −0.01 to 0.02), followed by mid-level (0.00) and high-level clouds (−0.13).

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