Using the NASA EOS A-Train to Probe the Performance of the NOAA PATMOS-x Cloud Fraction CDR
Abstract
:1. Introduction
2. Materials and Methods
2.1. CALIPSO/CALIOP Data
2.2. MODIS Data
2.3. PATMOS-x Data
2.4. Colocation of MODIS and CALIPSO/CALIOP
3. Results and Discussion
3.1. Comparison of MODIS PATMOS-x to NASA MODIS MYD35
3.2. Comparison of MODIS PATMOS-x to NASA CALIPSO CALIOP
3.3. Verification of the PATMOS-x Cloud Fraction Uncertainty
3.4. Sensitivity of PATMOS-x Cloud Fraction to Prior Cloud Amount Assumptions
3.5. Sensitivity Based on Spectral Content
4. Conclusions
- For regions where the reported PATMOS-x cloud fraction uncertainty is less than 5%, the PATMOS-x and MYD35 cloud fraction annual anomaly correlations are high and the linear trends over 2003 to 2014 agree well.
- Relative to CALIPSO, PATMOS-x and MYD35 global cloud fractions generally agree within 2%, except for snow-covered land and nighttime Arctic and nighttime Antarctic surface types.
- Comparisons of the reported PATMOS-x cloud fraction uncertainty to direct estimates of error relative to CALIPSO or MYD35 reveal that the PATMOS-x cloud fraction uncertainties are 1.6 times too small but show a linear relationship.
- Being a naïve Bayesian technique, the PATMOS-x cloud fraction is dependent on the assumed surface-type-dependent climatological cloud fraction. Regions with low cloud and small cloud fractions over the ocean showed the most sensitivity to the climatological clouds, as did regions where the cloud fraction uncertainty was high (>10%).
- The cloud fraction trends from the naïve Bayesian PATMOS-x approach agreed well with those from the non-Bayesian MYD35 approach over most regions. This supports the idea that naïve Bayesian cloud detection approaches are suitable for multi-decadal satellite climate research.
- The PATMOS-x AVHRR results show little sensitivity to the spectral switch from AVHRR Ch3a to Ch3b. The PATMOS-x AVHRR cloud fractions are higher than those from MODIS and VIIRS in oceanic regions with low cloud amounts. The PC values also show little impact, except for the Antarctica region where the additional MODIS spectral information adds skill.
- In general, the PATMOS-x MYD02SSH/AVHRR results agree better with CALIPSO than the PATMOS-x AVHRR generated from NOAA-19/AVHRR/GAC data. These differences were small for most surface types. The larger differences occurred over snow and ice-covered surfaces where the radiometric differences between the AVHRR and the MODIS sensors are the largest.
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
AVHRR | Advanced Very High Resolution Radiometer |
AMSU | Advanced Microwave Sounding Unit |
C6 | Collection 6 of the MODIS Science Team Products |
CALIOP | Cloud–Aerosol Lidar with Orthogonal Polarization |
CALIPSO | Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations |
CDR | Climate Data Record |
DNB | Day Night Band |
EOS | Earth Observing System |
EUMETSAT | European Organization for the Exploitation of Meteorological Satellites |
HIRS | High Resolution Infrared Sounder |
JPSS | Joint Polar Satellite System |
LAADSWEB | L1 and Atmosphere Archive and Distribution System |
MODIS | Moderate Resolution Imaging Spectroradiometer |
MYD021KM | Aqua MODIS 1-km Level-1b |
MYD02SSH | Aqua MODIS 5-km sub-sampled Level-1b |
NASA | National Aeronautics and Space Administration |
NCEI | National Centers for Environmental Information |
NESDIS | National Environmental Satellite Data and Information Service |
NOAA | National Oceanic and Atmospheric Administration |
PATMOS-x | Pathfinder Atmospheres Extended |
PC | Proportion Correct |
POES | Polar Orbiting Environmental Satellites |
VIIRS | Visible Infrared Imaging Radiometer Suite |
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Sensor | Nominal Wavelengths (µm) of Channels Used in PATMOS-x Cloud Mask | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
0.65 | 0.86 | 1.38 | 1.6 | 3.75 | 6.7 | 8.5 | 11 | 12 | DNB | |
AVHRR/3a | ✓ | ✓ | ✓ (day) | ✓ (night) | ✓ | ✓ | ||||
AVHRR/3b | ✓ | ✓ | ✓ | ✓ | ✓ | |||||
MODIS | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
VIIRS | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
PATMOS-x or MYD35 | ||
---|---|---|
CALIPSO | Clear | Cloudy |
Clear | a | b |
Cloudy | c | d |
Region | Cloud Fractions | PC 0/100 Filter | PC 50/50 Filter | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
CAL | PM | MYD | A/C | P/C | M/C | P/M | A/C | P/C | M/C | P/M | |
Global | 66 | 63 | 64 | 98 | 99 | 99 | 99 | 89 | 91 | 90 | 95 |
Ocean | 70 | 67 | 70 | 99 | 100 | 100 | 100 | 89 | 91 | 91 | 97 |
Water | 67 | 65 | 65 | 99 | 100 | 99 | 100 | 91 | 93 | 93 | 97 |
Land | 60 | 54 | 55 | 98 | 99 | 99 | 99 | 86 | 88 | 88 | 95 |
Snow | 73 | 69 | 75 | 96 | 96 | 98 | 97 | 88 | 88 | 89 | 86 |
Arctic | 78 | 72 | 72 | 91 | 95 | 96 | 99 | 82 | 90 | 90 | 91 |
Antarctic | 79 | 78 | 74 | 98 | 99 | 98 | 98 | 94 | 91 | 89 | 90 |
Desert | 36 | 31 | 28 | 99 | 99 | 99 | 100 | 93 | 90 | 89 | 94 |
Region | Cloud Fraction | PC 0/100 Filter | PC 50/50 Filter | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
CAL | PAT | MYD | A/C | P/C | M/C | P/M | A/C | P/C | M/C | P/M | |
Global | 72 | 66 | 69 | 95 | 96 | 97 | 96 | 84 | 88 | 88 | 89 |
Ocean | 76 | 71 | 75 | 98 | 99 | 99 | 99 | 85 | 90 | 90 | 93 |
Water | 77 | 72 | 77 | 98 | 98 | 99 | 99 | 90 | 91 | 92 | 92 |
Land | 61 | 52 | 62 | 90 | 97 | 97 | 96 | 81 | 91 | 91 | 89 |
Snow | 77 | 60 | 67 | 86 | 90 | 91 | 90 | 76 | 79 | 81 | 78 |
Arctic | 77 | 66 | 67 | 89 | 95 | 90 | 92 | 78 | 85 | 81 | 84 |
Antarctic | 76 | 71 | 57 | 82 | 73 | 85 | 66 | 72 | 67 | 74 | 59 |
Desert | 23 | 17 | 24 | 96 | 97 | 97 | 96 | 89 | 93 | 90 | 90 |
Sensor | Proportional Correct (%) for P/C Using 50/50 Filter | |||||||
---|---|---|---|---|---|---|---|---|
Global | Ocean | Water | Land | Snow | Arctic | Antarctica | Desert | |
AVHRR/3a | 88 | 91 | 91 | 88 | 80 | 80 | 75 | 91 |
AVHRR/3b | 89 | 91 | 93 | 89 | 82 | 83 | 77 | 91 |
MODIS | 87 | 89 | 92 | 86 | 79 | 79 | 81 | 91 |
VIIRS | 87 | 90 | 93 | 87 | 76 | 76 | 80 | 93 |
NOAA-19-AVHRR/3b | 86 | 87 | 91 | 84 | 81 | 75 | 73 | 91 |
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Heidinger, A.; Foster, M.; Botambekov, D.; Hiley, M.; Walther, A.; Li, Y. Using the NASA EOS A-Train to Probe the Performance of the NOAA PATMOS-x Cloud Fraction CDR. Remote Sens. 2016, 8, 511. https://doi.org/10.3390/rs8060511
Heidinger A, Foster M, Botambekov D, Hiley M, Walther A, Li Y. Using the NASA EOS A-Train to Probe the Performance of the NOAA PATMOS-x Cloud Fraction CDR. Remote Sensing. 2016; 8(6):511. https://doi.org/10.3390/rs8060511
Chicago/Turabian StyleHeidinger, Andrew, Michael Foster, Denis Botambekov, Michael Hiley, Andi Walther, and Yue Li. 2016. "Using the NASA EOS A-Train to Probe the Performance of the NOAA PATMOS-x Cloud Fraction CDR" Remote Sensing 8, no. 6: 511. https://doi.org/10.3390/rs8060511
APA StyleHeidinger, A., Foster, M., Botambekov, D., Hiley, M., Walther, A., & Li, Y. (2016). Using the NASA EOS A-Train to Probe the Performance of the NOAA PATMOS-x Cloud Fraction CDR. Remote Sensing, 8(6), 511. https://doi.org/10.3390/rs8060511