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

2012 ◽  
Vol 51 (6) ◽  
pp. 1129-1144 ◽  
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.

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

2004 ◽  
Vol 17 (24) ◽  
pp. 4805-4822 ◽  
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.

scholarly journals The Continuity MODIS-VIIRS Cloud Mask

2020 ◽  
Vol 12 (20) ◽  
pp. 3334 ◽  
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.

scholarly journals Calibration of global MODIS cloud amount using CALIOP cloud profiles

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.

scholarly journals Arctic Cloud Characteristics as Derived from MODIS, CALIPSO, and CloudSat

2013 ◽  
Vol 26 (10) ◽  
pp. 3285-3306 ◽  
Author(s):  
Mark Aaron Chan ◽  
Josefino C. Comiso
Keyword(s):  
Sea Ice ◽  
Cloud Cover ◽  
Arctic Region ◽  
The Arctic ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
Percentage Difference ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
High Level ◽  
The Arctic Region

Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), and CloudSat Cloud Profiling Radar (CPR) set of sensors, all in the Afternoon Constellation (A-Train), has been regarded as among the most powerful tools for characterizing the cloud cover. While providing good complementary information, the authors also observed that, at least for the Arctic region, the different sensors provide significantly different statistics about cloud cover characteristics. Data in 2007 and 2010 were analyzed, and the annual averages of cloud cover in the Arctic region were found to be 66.8%, 78.4%, and 63.3% as derived from MODIS, CALIOP, and CPR, respectively. A large disagreement between MODIS and CALIOP over sea ice and Greenland is observed, with a cloud percentage difference of 30.9% and 31.5%, respectively. In the entire Arctic, the average disagreement between MODIS and CALIOP increased from 13.1% during daytime to 26.7% during nighttime. Furthermore, the MODIS cloud mask accuracy has a high seasonal dependence, in that MODIS–CALIOP disagreement is the lowest during summertime at 10.7% and worst during winter at 28.0%. During nighttime the magnitude of the bias is higher because cloud detection is limited to the use of infrared bands. The clouds not detected by MODIS are typically low-level (top height <2 km) and high-level clouds (top height >6 km) and, especially, those that are geometrically thin (<2 km). Geometrically thin clouds (<2 km) accounted for about 95.5% of all clouds that CPR misses. As reported in a similar study, very low and thin clouds (<0.3 km) over sea ice that are detected by MODIS are sometimes not observed by CPR and misclassified by CALIOP.

scholarly journals Investigating the frequency and interannual variability in global above-cloud aerosol characteristics with CALIOP and OMI

2016 ◽  
Vol 16 (1) ◽  
pp. 47-69 ◽  
Author(s):  
R. Alfaro-Contreras ◽  
J. Zhang ◽  
J. R. Campbell ◽  
J. S. Reid
Keyword(s):  
Interannual Variability ◽  
Orthogonal Polarization ◽  
Cloud Detection ◽  
Ozone Monitoring Instrument ◽  
Data Set ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
First Time ◽  
Aerosol Index

Abstract. Seven and a half years (June 2006 to November 2013) of Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol and cloud layer products are compared with collocated Ozone Monitoring Instrument (OMI) aerosol index (AI) data and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products in order to investigate variability in estimates of biannual and monthly above-cloud aerosol (ACA) events globally. The active- (CALIOP) and passive-based (OMI-MODIS) techniques have their advantages and caveats for ACA detection, and thus both are used to derive a thorough and robust comparison of daytime cloudy-sky ACA distribution and climatology. For the first time, baseline above-cloud aerosol optical depth (ACAOD) and AI thresholds are derived and examined (AI  =  1.0, ACAOD  =  0.015) for each sensor. Both OMI-MODIS and CALIOP-based daytime spatial distributions of ACA events show similar patterns during both study periods (December–May) and (June–November). Divergence exists in some regions, however, such as Southeast Asia during June through November, where daytime cloudy-sky ACA frequencies of up to 10 % are found from CALIOP yet are non-existent from the OMI-based method. Conversely, annual cloudy-sky ACA frequencies of 20–30 % are reported over northern Africa from the OMI-based method yet are largely undetected by the CALIOP-based method. Using a collocated OMI-MODIS-CALIOP data set, our study suggests that the cloudy-sky ACA frequency differences between the OMI-MODIS- and CALIOP-based methods are mostly due to differences in cloud detection capability between MODIS and CALIOP as well as QA flags used. An increasing interannual variability of  ∼  0.3–0.4 % per year (since 2009) in global monthly cloudy-sky ACA daytime frequency of occurrence is found using the OMI-MODIS-based method. Yet, CALIOP-based global daytime ACA frequencies exhibit a near-zero interannual variability. Further analysis suggests that the OMI-derived interannual variability in cloudy-sky ACA frequency may be affected by OMI row anomalies in later years. A few regions are found to have increasing slopes in interannual variability in cloudy-sky ACA frequency, including the Middle East and India. Regions with slightly negative slopes of the interannual variability in cloudy-sky ACA frequencies are found over South America and China, while remaining regions in the study show nearly zero change in ACA frequencies over time. The interannual variability in ACA frequency is not, however, statistically significant on both global and regional scales, given the relatively limited sample sizes. A longer data record of ACA events is needed in order to establish significant trends of ACA frequency regionally and globally.

On the Use of Naive Bayesian Classifiers for Detecting Elementary and Coordinated Attacks

2010 ◽  
Vol 105 (4) ◽  
pp. 435-466 ◽  
Author(s):  
Tayeb Kenaza ◽  
Karim Tabia ◽  
Salem Benferhat
Keyword(s):  
Bayesian Classifiers ◽  
Naive Bayesian ◽  
Naïve Bayesian

Thin cirrus clouds that extinct supercooled water clouds in the Arctics

2021 ◽  
Author(s):  
Suginori Iwasaki ◽  
Hajime Okamoto ◽  
Kaori Sato
Keyword(s):  
Radiative Forcing ◽  
Supercooled Water ◽  
Terminal Velocity ◽  
Cirrus Clouds ◽  
The Arctic ◽  
Significant Feature ◽  
Orthogonal Polarization ◽  
Cloud Particle ◽  
Particle Clouds ◽  
Moderate Resolution Imaging Spectroradiometer

<p><span><span>We show that thin cirrus clouds, whose particle radius is greater than 50 μm and number concentration is less than 10 /L, extinct supercooled water clouds (SC) by use of the data of the space-borne lidar, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and the space-borne 94-GHz cloud profiling radar (CPR). We call the cirrus Large-and-Sparse-particle Clouds (LSC). </span></span></p><p><span><span>The space-borne imagers, such as Moderate Resolution Imaging Spectroradiometer (MODIS), cannot measure LSC; hence, LSC had been difficult to be found by satellites. CALIOP is less sensitive to LSC than CPR though CALIOP is usually more sensitive to clouds than CPR because of the cloud particle size distribution of LSC.</span></span></p><p><span><span>The most significant feature of LSC is that LSC extinct SC and cloud particles of SC are changed into pristine ice particles. This is because (1) SC and LSC do not tend to coexist while horizontally oriented ice particle clouds (2D) and LSC tend to coexist, (2) the cloud top height of LSC is higher than that of SC, and (3) the terminal velocity of LSC particles is about 1 km/h.</span></span></p><p><span><span>Because 10-20% of clouds in the Arctic are LSC, LSC would indirectly impact on radiative forcing in the Arctics.</span></span></p>

scholarly journals MODIS Cloud Detection Evaluation Using CALIOP over Polluted Eastern China

Atmosphere ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 333 ◽  
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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