Articles | Volume 15, issue 7
https://doi.org/10.5194/amt-15-2099-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-2099-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Apr 2022
Research article |
| 07 Apr 2022
| 07 Apr 2022
Evaluating the consistency and continuity of pixel-scale cloud property data records from Aqua and SNPP (Suomi National Polar-orbiting Partnership)
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Eric J. Fetzer
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Likun Wang
Earth System Science Interdisciplinary Center, University of
Maryland, 5825 University Research Court, Suite 4001, College Park, MD
20740, USA
Brian H. Kahn
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Nadia Smith
Science and Technology Corporation, 10015 Old Columbia Road, Columbia, MD 21046, USA
John M. Blaisdell
Science Applications International Corporation, 12010 Sunset
Hills Road, Reston, VA 20190, USA
Kerry G. Meyer
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Mathias Schreier
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Bjorn Lambrigtsen
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Irina Tkatcheva
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Related authors
Wan Wu, Xu Liu, Liqiao Lei, Xiaozhen Xiong, Qiguang Yang, Qing Yue, Daniel K. Zhou, and Allen M. Larar
Atmos. Meas. Tech., 16, 4807–4832, https://doi.org/10.5194/amt-16-4807-2023, https://doi.org/10.5194/amt-16-4807-2023, 2023
Short summary
Short summary
We present a new operational physical retrieval algorithm that is used to retrieve atmospheric properties for each single field-of-view measurement of hyper-spectral IR sounders. The physical scheme includes a cloud-scattering calculation in its forward-simulation part. The data product generated using this algorithm has an advantage over traditional IR sounder data production algorithms in terms of improved spatial resolution and minimized error due to cloud contamination.
Adeleke S. Ademakinwa, Zhibo Zhang, Daniel Miller, Kerry G. Meyer, Steven Platnick, Zahid H. Tushar, Sanjay Purushotham, and Jianwu Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-4169, https://doi.org/10.5194/egusphere-2025-4169, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Many satellites measure cloud properties using reflected light from droplets, but most assume simple cloud structures, which can reduce accuracy. Using cloud simulations, we tested how these errors affect droplet number in a given volume and climate studies. We found that while they strongly affect small scales, at the larger scales used by satellites the errors mostly cancel out, meaning satellite data remain reliable for climate research.
Nadia Smith and Christopher D. Barnet
Atmos. Meas. Tech., 18, 1823–1839, https://doi.org/10.5194/amt-18-1823-2025, https://doi.org/10.5194/amt-18-1823-2025, 2025
Short summary
Short summary
CLIMCAPS extends the Aqua AIRS+AMSU record with retrievals from CrIS+ATMS on Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System series (JPSS-1 to JPSS-4). With “continuous”, we mean a data record that is consistent in its characterization of natural variation despite changes in source instrumentation. Here we investigate how sounding continuity can improve across the full CLIMCAPS record (2002 to the present day), spanning multiple instruments and satellites.
Nadia Smith, Michelle L. Santee, and Christopher D. Barnet
EGUsphere, https://doi.org/10.5194/egusphere-2025-1569, https://doi.org/10.5194/egusphere-2025-1569, 2025
Short summary
Short summary
Once Aura is decommissioned, the multi-decadal MLS record of stratospheric HNO3 will end. This paper presents the retrieval of HNO3 from nadir IR sounders, AIRS and CrIS. We show how the CLIMCAPS approach allows HNO3 to be reported as a partial stratospheric column that is largely independent of tropospheric noise and reflects the variation captured by MLS. This novel retrieval approach improves upon the status quo and lays the foundation for validation studies and product roll-out in future.
Meloë S. F. Kacenelenbogen, Ralph Kuehn, Nandana Amarasinghe, Kerry Meyer, Edward Nowottnick, Mark Vaughan, Hong Chen, Sebastian Schmidt, Richard Ferrare, John Hair, Robert Levy, Hongbin Yu, Paquita Zuidema, Robert Holz, and Willem Marais
EGUsphere, https://doi.org/10.5194/egusphere-2025-1403, https://doi.org/10.5194/egusphere-2025-1403, 2025
Short summary
Short summary
Aerosols perturb the radiation balance of the Earth-atmosphere system. To reduce the uncertainty in quantifying present-day climate change, we combine two satellite sensors and a model to assess the aerosol effects on radiation in all-sky conditions. Satellite-based and coincident aircraft measurements of aerosol radiative effects agree well over the Southeast Atlantic. This constitutes a crucial first evaluation before we apply our method to more years and regions of the world.
Kerry Meyer, Steven Platnick, G. Thomas Arnold, Nandana Amarasinghe, Daniel Miller, Jennifer Small-Griswold, Mikael Witte, Brian Cairns, Siddhant Gupta, Greg McFarquhar, and Joseph O'Brien
Atmos. Meas. Tech., 18, 981–1011, https://doi.org/10.5194/amt-18-981-2025, https://doi.org/10.5194/amt-18-981-2025, 2025
Short summary
Short summary
Satellite remote sensing retrievals of cloud droplet size are used to understand clouds and their interactions with aerosols and radiation but require many simplifying assumptions. Evaluation of these retrievals is typically done by comparing against direct measurements of droplets from airborne cloud probes. This paper details an evaluation of proxy airborne remote sensing droplet size retrievals against several cloud probes and explores the impact of key assumptions on retrieval agreement.
Audrey Gaudel, Ilann Bourgeois, Meng Li, Kai-Lan Chang, Jerald Ziemke, Bastien Sauvage, Ryan M. Stauffer, Anne M. Thompson, Debra E. Kollonige, Nadia Smith, Daan Hubert, Arno Keppens, Juan Cuesta, Klaus-Peter Heue, Pepijn Veefkind, Kenneth Aikin, Jeff Peischl, Chelsea R. Thompson, Thomas B. Ryerson, Gregory J. Frost, Brian C. McDonald, and Owen R. Cooper
Atmos. Chem. Phys., 24, 9975–10000, https://doi.org/10.5194/acp-24-9975-2024, https://doi.org/10.5194/acp-24-9975-2024, 2024
Short summary
Short summary
The study examines tropical tropospheric ozone changes. In situ data from 1994–2019 display increased ozone, notably over India, Southeast Asia, and Malaysia and Indonesia. Sparse in situ data limit trend detection for the 15-year period. In situ and satellite data, with limited sampling, struggle to consistently detect trends. Continuous observations are vital over the tropical Pacific Ocean, Indian Ocean, western Africa, and South Asia for accurate ozone trend estimation in these regions.
Brian Kahn, Cameron Bertossa, Xiuhong Chen, Brian Drouin, Erin Hokanson, Xianglei Huang, Tristan L'Ecuyer, Kyle Mattingly, Aronne Merrelli, Tim Michaels, Nate Miller, Federico Donat, Tiziano Maestri, and Michele Martinazzo
EGUsphere, https://doi.org/10.5194/egusphere-2023-2463, https://doi.org/10.5194/egusphere-2023-2463, 2023
Preprint archived
Short summary
Short summary
A cloud detection mask algorithm is developed for the upcoming Polar Radiant Energy in the Far Infrared Experiment (PREFIRE) satellite mission to be launched by NASA in May 2024. The cloud mask is compared to "truth" and is capable of detecting over 90 % of all clouds globally tested with simulated data, and about 87 % of all clouds in the Arctic region.
Wan Wu, Xu Liu, Liqiao Lei, Xiaozhen Xiong, Qiguang Yang, Qing Yue, Daniel K. Zhou, and Allen M. Larar
Atmos. Meas. Tech., 16, 4807–4832, https://doi.org/10.5194/amt-16-4807-2023, https://doi.org/10.5194/amt-16-4807-2023, 2023
Short summary
Short summary
We present a new operational physical retrieval algorithm that is used to retrieve atmospheric properties for each single field-of-view measurement of hyper-spectral IR sounders. The physical scheme includes a cloud-scattering calculation in its forward-simulation part. The data product generated using this algorithm has an advantage over traditional IR sounder data production algorithms in terms of improved spatial resolution and minimized error due to cloud contamination.
Mark T. Richardson, Brian H. Kahn, and Peter Kalmus
Atmos. Chem. Phys., 23, 7699–7717, https://doi.org/10.5194/acp-23-7699-2023, https://doi.org/10.5194/acp-23-7699-2023, 2023
Short summary
Short summary
Convection over land often triggers hours after a satellite last passed overhead and measured the state of the atmosphere, and during those hours the atmosphere can change greatly. Here we show that it is possible to reconstruct most of those changes by using weather forecast winds to predict where warm and moist air parcels will travel. The results can be used to better predict where precipitation is likely to happen in the hours after satellite measurements.
Robert Pincus, Paul A. Hubanks, Steven Platnick, Kerry Meyer, Robert E. Holz, Denis Botambekov, and Casey J. Wall
Earth Syst. Sci. Data, 15, 2483–2497, https://doi.org/10.5194/essd-15-2483-2023, https://doi.org/10.5194/essd-15-2483-2023, 2023
Short summary
Short summary
This paper describes a new global dataset of cloud properties observed by a specific satellite program created to facilitate comparison with a matching observational proxy used in climate models. Statistics are accumulated over daily and monthly timescales on an equal-angle grid. Statistics include cloud detection, cloud-top pressure, and cloud optical properties. Joint histograms of several variable pairs are also available.
Ian Chang, Lan Gao, Connor J. Flynn, Yohei Shinozuka, Sarah J. Doherty, Michael S. Diamond, Karla M. Longo, Gonzalo A. Ferrada, Gregory R. Carmichael, Patricia Castellanos, Arlindo M. da Silva, Pablo E. Saide, Calvin Howes, Zhixin Xue, Marc Mallet, Ravi Govindaraju, Qiaoqiao Wang, Yafang Cheng, Yan Feng, Sharon P. Burton, Richard A. Ferrare, Samuel E. LeBlanc, Meloë S. Kacenelenbogen, Kristina Pistone, Michal Segal-Rozenhaimer, Kerry G. Meyer, Ju-Mee Ryoo, Leonhard Pfister, Adeyemi A. Adebiyi, Robert Wood, Paquita Zuidema, Sundar A. Christopher, and Jens Redemann
Atmos. Chem. Phys., 23, 4283–4309, https://doi.org/10.5194/acp-23-4283-2023, https://doi.org/10.5194/acp-23-4283-2023, 2023
Short summary
Short summary
Abundant aerosols are present above low-level liquid clouds over the southeastern Atlantic during late austral spring. The model simulation differences in the proportion of aerosol residing in the planetary boundary layer and in the free troposphere can greatly affect the regional aerosol radiative effects. This study examines the aerosol loading and fractional aerosol loading in the free troposphere among various models and evaluates them against measurements from the NASA ORACLES campaign.
Nora Mettig, Mark Weber, Alexei Rozanov, John P. Burrows, Pepijn Veefkind, Anne M. Thompson, Ryan M. Stauffer, Thierry Leblanc, Gerard Ancellet, Michael J. Newchurch, Shi Kuang, Rigel Kivi, Matthew B. Tully, Roeland Van Malderen, Ankie Piters, Bogumil Kois, René Stübi, and Pavla Skrivankova
Atmos. Meas. Tech., 15, 2955–2978, https://doi.org/10.5194/amt-15-2955-2022, https://doi.org/10.5194/amt-15-2955-2022, 2022
Short summary
Short summary
Vertical ozone profiles from combined spectral measurements in the UV and IR spectral ranges were retrieved by using data from TROPOMI/S5P and CrIS/Suomi-NPP. The vertical resolution and accuracy of the ozone profiles are improved by combining both wavelength ranges compared to retrievals limited to UV or IR spectral data only. The advancement of our TOPAS algorithm for combined measurements is required because in the UV-only retrieval the vertical resolution in the troposphere is very limited.
Galina Wind, Arlindo M. da Silva, Kerry G. Meyer, Steven Platnick, and Peter M. Norris
Geosci. Model Dev., 15, 1–14, https://doi.org/10.5194/gmd-15-1-2022, https://doi.org/10.5194/gmd-15-1-2022, 2022
Short summary
Short summary
This is the third paper in series about the Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS). In this paper we use MCARS to create a set of constraints that might be used to assimilate a new above-cloud aerosol retrieval product developed for the MODIS instrument into a general circulation model. We executed the above-cloud aerosol retrieval over a series of synthetic MODIS granules and found the product to be of excellent quality.
Sarah J. Doherty, Pablo E. Saide, Paquita Zuidema, Yohei Shinozuka, Gonzalo A. Ferrada, Hamish Gordon, Marc Mallet, Kerry Meyer, David Painemal, Steven G. Howell, Steffen Freitag, Amie Dobracki, James R. Podolske, Sharon P. Burton, Richard A. Ferrare, Calvin Howes, Pierre Nabat, Gregory R. Carmichael, Arlindo da Silva, Kristina Pistone, Ian Chang, Lan Gao, Robert Wood, and Jens Redemann
Atmos. Chem. Phys., 22, 1–46, https://doi.org/10.5194/acp-22-1-2022, https://doi.org/10.5194/acp-22-1-2022, 2022
Short summary
Short summary
Between July and October, biomass burning smoke is advected over the southeastern Atlantic Ocean, leading to climate forcing. Model calculations of forcing by this plume vary significantly in both magnitude and sign. This paper compares aerosol and cloud properties observed during three NASA ORACLES field campaigns to the same in four models. It quantifies modeled biases in properties key to aerosol direct radiative forcing and evaluates how these biases propagate to biases in forcing.
David R. Thompson, Brian H. Kahn, Philip G. Brodrick, Matthew D. Lebsock, Mark Richardson, and Robert O. Green
Atmos. Meas. Tech., 14, 2827–2840, https://doi.org/10.5194/amt-14-2827-2021, https://doi.org/10.5194/amt-14-2827-2021, 2021
Short summary
Short summary
Concentrations of water vapor in the atmosphere vary dramatically over space and time. Mapping this variability can provide insights into atmospheric processes that help us understand atmospheric processes in the Earth system. Here we use a new measurement strategy based on imaging spectroscopy to map atmospheric water vapor concentrations at very small spatial scales. Experiments demonstrate the accuracy of this technique and some initial results from an airborne remote sensing experiment.
Macey W. Sandford, David R. Thompson, Robert O. Green, Brian H. Kahn, Raffaele Vitulli, Steve Chien, Amruta Yelamanchili, and Winston Olson-Duvall
Atmos. Meas. Tech., 13, 7047–7057, https://doi.org/10.5194/amt-13-7047-2020, https://doi.org/10.5194/amt-13-7047-2020, 2020
Short summary
Short summary
We demonstrate an onboard cloud-screening approach to significantly reduce the amount of cloud-contaminated data transmitted from orbit. We have produced location-specific models that improve performance by taking into account the unique cloud statistics in different latitudes. We have shown that screening clouds based on their location or surface type will improve the ability for a cloud-screening tool to improve the volume of usable science data.
Tianle Yuan, Hua Song, Robert Wood, Johannes Mohrmann, Kerry Meyer, Lazaros Oreopoulos, and Steven Platnick
Atmos. Meas. Tech., 13, 6989–6997, https://doi.org/10.5194/amt-13-6989-2020, https://doi.org/10.5194/amt-13-6989-2020, 2020
Short summary
Short summary
We use deep transfer learning techniques to classify satellite cloud images into different morphology types. It achieves the state-of-the-art results and can automatically process a large amount of satellite data. The algorithm will help low-cloud researchers to better understand their mesoscale organizations.
Marc Mallet, Fabien Solmon, Pierre Nabat, Nellie Elguindi, Fabien Waquet, Dominique Bouniol, Andrew Mark Sayer, Kerry Meyer, Romain Roehrig, Martine Michou, Paquita Zuidema, Cyrille Flamant, Jens Redemann, and Paola Formenti
Atmos. Chem. Phys., 20, 13191–13216, https://doi.org/10.5194/acp-20-13191-2020, https://doi.org/10.5194/acp-20-13191-2020, 2020
Short summary
Short summary
This paper presents numerical simulations using two regional climate models to study the impact of biomass fire plumes from central Africa on the radiative balance of this region. The results indicate that biomass fires can either warm the regional climate when they are located above low clouds or cool it when they are located above land. They can also alter sea and land surface temperatures by decreasing solar radiation at the surface. Finally, they can also modify the atmospheric dynamics.
Cited articles
Ackerman, S., Menzel, P., Frey, R., and Baum, B.: MODIS Atmosphere L2 Cloud Mask Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], https://doi.org/10.5067/MODIS/MOD35_L2.061, 2017.
Ackerman, S., et al.: MODIS/Aqua Cloud Mask and Spectral Test Results 5-Min L2 Swath 1km, Version-1, NASA Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC) [data set], Goddard Space Flight Center, USA, https://doi.org/10.5067/MODIS/CLDMSK_L2_MODIS_Aqua.001, 2019a.
Ackerman, S., et al.: VIIRS/SNPP Cloud Mask and Spectral Test Results 6-Min L2 Swath 750m, Version-1. NASA Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC) [data set], Goddard Space Flight Center, USA, https://doi.org/10.5067/VIIRS/CLDMSK_L2_VIIRS_SNPP.001, 2019b.
AIRS project: Aqua/AIRS L2 Support Retrieval (AIRS-only) V7.0, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/APJ6EEN0PD0Z, 2019.
Barnet, C.: Sounder SIPS: Suomi NPP CrIMSS Level 2 CLIMCAPS Full Spectral Resolution: Atmosphere cloud and surface geophysical state V2, Goddard Earth Sciences Data and Information Services Center [data set] (GES DISC), Greenbelt, MD, USA, https://doi.org/10.5067/62SPJFQW5Q9B, 2019a.
Barnet, C.: Sounder SIPS: Suomi NPP CrIMSS Level 2 CLIMCAPS Normal Spectral Resolution: Atmosphere, cloud and surface geophysical state V1, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/8RUZI1F8U1UX, 2019b.
Barnet, C: Sounder SIPS: AQUA AIRS IR + MW Level 2 CLIMCAPS: Atmosphere, cloud and surface geophysical state V2, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/JZMYK5SMYM86, 2020.
Baum, B. A., Menzel, W. P., Frey, R. A., Tobin, D. C., Holz, R. E.,
Ackerman, S. A., Heidinger, A. K., and Yang, P.: MODIS Cloud-Top Property
Refinements for Collection 6, J. Appl. Meteorol. Clim., 51, 1145–1163, 2012.
Bony, S, Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 261–268, https://doi.org/10.1038/ngeo2398, 2015.
Borbas, E. E., Hulley, G., Feltz, M., Knuteson, R., and Hook, S.: The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 1: Methodology and High Spectral Resolution Application, Remote Sensing, 10, 643, https://doi.org/10.3390/rs10040643, 2018.
Chahine, M. T.: Remote sounding of cloudy atmospheres. I. The single cloud
layer, J. Atmos. Sci., 31, 233–243, 1974.
Chan, M. A. and Comiso, J. C.: Arctic Cloud Characteristics as Derived from
MODIS, CALIPSO, and CloudSat, J. Climate, 26, 3285–3306, 2013.
Eresmaa, R.: Imager-assisted cloud detection for assimilation of infrared
atmospheric sounding interferometer radiances, Q. J. Roy. Meteor. Soc., 140, 2342–2352, 2014.
Feltz, M., Borbas, E., Knuteson, R., Hulley, G., and Hook, S.: The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation,
Remote Sensing, 10, 664, https://doi.org/10.3390/rs10050664, 2018.
Fetzer, E. J., Lambrigtsen, B. H., Eldering, A., Aumann, H. H., and Chahine,
M. T.: Biases in total precipitable water vapor climatologies from
Atmospheric Infrared Sounder and Advanced Microwave Scanning Radiometer, J.
Geophys. Res., 111, D09S16, https://doi.org/10.1029/2005JD006598, 2006.
Fetzer, E. J., Yue, Q., Thrastarson, H. Th., and Ruzmaikin, A. (Eds.):
ALGORITHM THEORETICAL BASIS DOCUMENT, AIRS-Team Retrieval For Core Products
and Geophysical Parameters: Versions 6 and 7 Level2, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, https://docserver.gesdisc.eosdis.nasa.gov/public/project/AIRS/L2_ATBD.pdf (last access: 11 January 2021), 2020.
Fishbein, E., Lee, S.-Y., and Fetzer, E. J.: Atmospheric Infrared Sounder (AIRS) Level 2 Simulation System Description Document, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA,
http://asl.umbc.edu/pub/airs/jpldocs/sim/AIRS_L2_Simulation_Desc.pdf (last access: 10 January 2021), 2001.
Frey, R. A., Ackerman, S. A., Holz, R. E., Steven, D., and Griffith, Z.: The
Continuity MODISVIIRS Cloud Mask, Remote Sensing, 12, 3334, https://doi.org/10.3390/rs12203334, 2020.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective analysis for research and applications, Version 2 (MERRA-2), 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gong, X., Li, Z., Li, J., Moeller, C. C., Cao, C., Wang, W., and Menzel, W.
P.: Intercomparison between VIIRS and CrIS by taking into account the CrIS
subpixel cloudiness and viewing geometry, J. Geophys. Res.-Atmos., 123, 5335–5345, https://doi.org/10.1029/2017JD027849, 2018.
Heidinger, A. K., Evan, A. T., Foster, M. J., and Walther, A.: A naive
Bayesian cloud detection scheme derived from CALIPSO and applied with
PATMOS-x, J. Appl. Meteorol. Clim., 51, 1129–1144, 2012.
Heidinger, A. K., Foster, M. J., Walther, A., and Zhao, X.: The Pathfinder
AtmospheresExtended AVHRR climate dataset, B. Am. Meteorol. Soc., 95,
909–922, https://doi.org/10.1175/BAMS-D-12-00246.1, 2014.
Heidinger, A. K., Bearson, N., Foster, M. J., Li, Y., Wanzong, S., Ackerman, S., Holz, R. E., Platnick, S., and Meyer, K.: Using Sounder Data to Improve Cirrus Cloud Height Estimation from Satellite Imagers, J. Atmos. Ocean. Tech., 36, 1331–1342, https://doi.org/10.1175/JTECH-D-18-0079.1, 2019.
Holz, R. E., Ackerman, S. A., Nagle, F. W., Frey, R., Dutcher, S., Kuehn, R.
E., Vaughan, M. A., and Baum, B.: Global Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection and height evaluation using CALIOP, J. Geophys. Res., 113, D00A19, https://doi.org/10.1029/2008JD009837, 2008.
Hook, S.: Combined ASTER and MODIS Emissivity database over Land (CAMEL)
Emissivity Monthly Global 0.05Deg V002, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MEASURES/LSTE/CAM5K30EM.002, 2019.
IPCC: Climate Change 2013: The Physical Science Basis. Contribution
of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp., https://doi.org/10.1017/CBO9781107415324, 2013.
Irion, F. W., Kahn, B. H., Schreier, M. M., Fetzer, E. J., Fishbein, E., Fu, D., Kalmus, P., Wilson, R. C., Wong, S., and Yue, Q.: Single-footprint retrievals of temperature, water vapor and cloud properties from AIRS, Atmos. Meas. Tech., 11, 971–995, https://doi.org/10.5194/amt-11-971-2018, 2018.
Jin, H. C. and Nasiri, S. L.: Evaluation of AIRS cloud-thermodynamic-phase
determination with CALIPSO, J. Appl. Meteorol. Clim., 53, 1012–1027, https://doi.org/10.1175/JAMC-D-13-0137.1, 2014.
Kahn, B. H., Irion, F. W., Dang, V. T., Manning, E. M., Nasiri, S. L., Naud, C. M., Blaisdell, J. M., Schreier, M. M., Yue, Q., Bowman, K. W., Fetzer, E. J., Hulley, G. C., Liou, K. N., Lubin, D., Ou, S. C., Susskind, J., Takano, Y., Tian, B., and Worden, J. R.: The Atmospheric Infrared Sounder version 6 cloud products, Atmos. Chem. Phys., 14, 399–426, https://doi.org/10.5194/acp-14-399-2014, 2014.
Kahn, B. H., Schreier, M. M., Yue, Q., Fetzer, E. J., Irion, F. W., Platnick, S., Wang, C., Nasiri, S. L., and L'Ecuyer, T. S.: Pixel-scale assessment and uncertainty analysis of AIRS and MODIS ice cloud optical thickness and effective radius, J. Geophys. Res.-Atmos., 120, 11669–11689, https://doi.org/10.1002/2015JD023950, 2015.
Kahn, B. H., Matheou, G., Yue, Q., Fauchez, T., Fetzer, E. J., Lebsock, M., Martins, J., Schreier, M. M., Suzuki, K., and Teixeira, J.: An A-train and MERRA view of cloud, thermodynamic, and dynamic variability within the subtropical marine boundary layer, Atmos. Chem. Phys., 17, 9451–9468, https://doi.org/10.5194/acp-17-9451-2017, 2017.
Kawai, H. and Teixeira, J.: Probability density functions of liquid water
path and cloud amount of marine boundary layer clouds: Geographical and
seasonal variations and controlling meteorological factors, J. Climate, 23,
2079–2092, 2010.
Kawai, H. and Teixeira, J.: Probability Density Functions of Liquid Water Path and Total Water Content of Marine Boundary Layer Clouds: Implications for Cloud Parameterization, J. Climate, 25, 2162–2177, 2012.
Kou, L., Labrie, D., and Chylek, P.: Refractive-indexes of water and ice in
the 0.65- to 2.5-µm spectral range, Appl. Optics, 32, 3531–3540, 1993.
Li, J., Menzel, W. P., Sun, F., Schmit, T. J., and Gurka, J.: AIRS Subpixel
Cloud Characterization Using MODIS Cloud Products, J. Appl. Meteorol., 43, 1083–1094, 2004.
Manning, E. M. and Aumann H. H: Tropical simultaneous nadir observations
for IR sounder evaluation and comparison, Proc. SPIE, Earth Observing
Systems XX, 96070L, https://doi.org/10.1117/12.2187151, 2015.
Marchant, B., Platnick, S., Meyer, K., Arnold, G. T., and Riedi, J.: MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP, Atmos. Meas. Tech., 9, 1587–1599, https://doi.org/10.5194/amt-9-1587-2016, 2016.
Masuda, K., Takashima, T., and Takayama, Y.: Emissivity of pure and sea
waters for the model sea surface in the infrared window regions, Remote
Sens. Environ., 24, 313–329, https://doi.org/10.1016/0034-4257(88)90032-6, 1988.
McCoy, D. T., Eastman, R., Hartmann, D. L., and Wood, R.: The change in low
cloud cover in a warmed climate inferred from AIRS, MODIS, and ERA-interim,
J. Climate, 30, 3609–3620, https://doi.org/10.1175/JCLI-D-15-0734.1, 2017.
Milstein, A. B. and Blackwell, W. J.: Neural network temperature and
moisture retrieval algorithm validation for AIRS/AMSU and CrIS/ATMS, J. Geophys. Res.-Atmos., 121, 1414–1430, https://doi.org/10.1002/2015JD024008, 2016.
Monarrez, R. (Ed.): NASA-SNPP and NOAA-20 (JPSS-1) CLIMCAPS CrIS and ATMS
Level-2 Products User Guide: File Format and Definition, GES DISC,
https://docserver.gesdisc.eosdis.nasa.gov/public/project/Sounder/CLIMCAPS.V2.README.pdf (last access: 10 January 2021), 2020.
Nagle, F. W. and Holz, R. E.: Computationally Efficient Methods of
Collocating Satellite, Aircraft, and Ground Observations, J. Atmos. Ocean. Tech., 26, 1585–1595, 2009.
NASA Goddard Earth Sciences Data Information and Services Center (GESDISC): https://earthdata.nasa.gov/, last access: 1 November 2021.
Nasiri, S. L. and Kahn, B. H.: Limitations of bispectral infrared cloud
phase determination and potential for improvement, J. Appl. Meteorol. Clim., 47, 2895–2910, https://doi.org/10.1175/2008JAMC1879.1, 2008.
Nasiri, S. L., Dang, V. T., Kahn, B. H., Fetzer, E. J., Manning, E. M.,
Schreier, M. M., and Frey, R. A.: Comparing MODIS and AIRS Infrared-Based
Cloud Retrievals, J. Appl. Meteorol. Clim., 50, 1057–1072, 2011.
National Academies of Sciences, Engineering, and Medicine: Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space, The National Academies Press, Washington, DC, https://doi.org/10.17226/24938, 2018.
Oreopoulos, L., Cho, N., Lee, D., and Kato, S.: Radiative effects of global
MODIS cloud regimes, J. Geophys. Res.-Atmos., 121, 2299–2317, https://doi.org/10.1002/2015JD024502, 2016.
Oudrari, H., McIntire, J., Xiong, X., Butler, J., Lee, S., Lei, N.,
Schwarting, T., and Sun, J.: Prelaunch radiometric characterization and
calibration of the SNPP VIIRS sensor, IEEE T. Geosci. Remote, 53, 2195–2210, 2015.
Peterson, C. A., Yue, Q., Kahn, B. H., Fetzer, E., and Huang, X.: Evaluation
of AIRS Cloud Phase Classification over the Arctic Ocean against Combined
CloudSat–CALIPSO Observations, J. Appl. Meteorol. Clim., 59, 1277–1294, 2020.
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Patrick
Hofmann, R. J.: Reconciling Simulated and Observed Views of Clouds: MODIS,
ISCCP, and the Limits of Instrument Simulators, J. Climate, 25, 4699–4720, 2012.
Platnick, S., Ackerman, S., King, M., Wind, G., Meyer, K., Menzel, P., Frey, R., Holz, R., Baum, B., and Yang, P.: MODIS atmosphere L2 cloud product (06_L2), NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], https://doi.org/10.5067/MODIS/MOD06_L2.061,
2017a.
Platnick, S., Meyer, K. G., Yang, P., Ridgway, W. L., Riedi, J. C., King, M.
D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway, W. L., and Riedi, J.: The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples from Terra and Aqua, IEEE T. Geosci. Remote, 55, 502–525,
2017b.
Platnick, S., Meyer, K. G., Heidinger, A. K., and Holz, R.: VIIRS Atmosphere L2 Cloud Properties Product, Version-1, NASA Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC) [data set], Goddard Space Flight Center, USA, https://doi.org/10.5067/VIIRS/CLDPROP_L2_VIIRS_SNPP.001, 2017c (data
available at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/CLDPROP_L2_MODIS_Aqua/#product-information, last access: 1 November 2021).
Platnick, S., Meyer, K., Amarasinghe, N., Wind, G., Hubanks, P. A., and Holz,
R. E.: Sensitivity of Multispectral Imager Liquid Water Cloud Microphysical
Retrievals to the Index of Refraction, Remote Sensing, 12, 4165, https://doi.org/10.3390/rs12244165, 2020.
Platnick, S., Meyer, K., Wind, G., Holz, R. E., Amarasinghe, N., Hubanks, P.
A., Marchant, B., Dutcher, S., and Veglio, P.: The NASA MODIS-VIIRS
Continuity Cloud Optical Properties Products, Remote Sensing, 13, 2, https://doi.org/10.3390/rs13010002, 2021.
Rossow, W. B. and Schiffer, R. A.: Advances in understanding clouds from
ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2, 1999.
Schreier, M. M., Kahn, B. H., Eldering, A., Elliott, D. A., Fishbein, E., Irion, F. W., and Pagano, T. S.: Radiance comparisons of MODIS and AIRS using spatial response information, J. Atmos. Ocean. Tech., 27, 1331–1342, 2010.
Seemann, S. W., Borbas, E. E., Knuteson, R. O., Stephenson, G. R., and
Huang, H.-L.: Development of a Global Infrared Land Surface Emissivity Database for Application to Clear Sky Sounding Retrievals from Multi-spectral Satellite Radiance Measurements, J. Appl. Meteorol. Clim., 47, 108–123, 2008.
Smith, N. and Barnet, C. D.: Uncertainty Characterization and Propagation in
the Community Long-Term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS), Remote Sensing, 11, 1227, https://doi.org/10.3390/rs11101227, 2019.
Smith, N. and Barnet, C. D.: CLIMCAPS observing capability for temperature, moisture, and trace gases from AIRS/AMSU and CrIS/ATMS, Atmos. Meas. Tech., 13, 4437–4459, https://doi.org/10.5194/amt-13-4437-2020, 2020.
Smith, N., Esmaili, R., and Barnet, C. D.: Community Long-Term Infrared
Microwave Combined Atmospheric Product System (CLIMCAPS) Science Application
Guides, Science and Technology Corpoeration, Hampton, VA,
https://docserver.gesdisc.eosdis.nasa.gov/public/project/Sounder/CLIMCAPS_V2_L2_science_guides.pdf, last access: 1 October 2021.
Su, H., Jiang, J. H., Neelin, J. D., Shen, T. J., Zhai, C., Yue, Q., Wang, Z., Huang, L., Choi, Y.-S., Stephens, G. L., and Yung, Y. L.: Tightening of Hadley ascent and tropical high cloud region key to precipitation change in a warmer climate, Nat. Commun., 8, 15771, https://doi.org/10.1038/ncomms15771, 2017.
Susskind, J., Barnet, C. D., and Blaisdell, J. M.: Retrieval of atmospheric
and surface parameters from AIRS/AMSU/HSB data in the presence of clouds,
IEEE T. Geosci. Remote, 41, 390–409, 2003.
Susskind, J., Barnet, C., Blaisdell, J., Iredell, L., Keita, F., Kouvaris, L., Molnar, G., and Chahine, M.: Accuracy of geophysical parameters derived from Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit as a function of fractional cloud cover, J. Geophys. Res., 111, D09S17, https://doi.org/10.1029/2005JD006272, 2006.
Susskind, J., Blaisdell, J. M., and Iredell, L.: Improved methodology for
surface and atmospheric soundings, error estimates, and quality control
procedures: the atmospheric infrared sounder science team version-6
retrieval algorithm, Journal of Applied Remote Sensing, 8, 084994, https://doi.org/10.1117/1.JRS.8.084994, 2014.
Tian, B. and Hearty, T.: Estimating and removing the sampling biases of the
AIRS Obs4MIPs V2 data, Earth and Space Science, 7, e2020EA001438, https://doi.org/10.1029/2020EA001438, 2020.
Thrastarson, H. Th. (Ed.): AIRS/AMSU/HSB Version 7 Level 2 Product User
Guide, Jet Propulsion Laboratory California Institute of Technology, Pasadena, CA,
https://docserver.gesdisc.eosdis.nasa.gov/public/project/AIRS/V7_L2_Product_User_Guide.pdf, last access: 1 October 2021a.
Thrastarson, H. Th., Fetzer, E. F., Ray, S., Hearty, T., and Smith, N.: Overview of the AIRS Mission: Instruments, Processing Algorithms,
Products, and Documentation, 2nd edn., Jet Propulsion Laboratory
California Institute of Technology, Pasadena, CA,
https://docserver.gesdisc.eosdis.nasa.gov/public/project/AIRS/Overview_of_the_AIRS_Mission.pdf, last access: 1 October 2021b.
Tobin, D. C., Revercomb, H. E., Moeller, C. C., and Pagano, T. S.: Use of
atmospheric infrared sounder high–spectral resolution spectra to assess the
calibration of moderate resolution imaging spectroradiometer on EOS Aqua, J. Geophys. Res., 111, D09S05, https://doi.org/10.1029/2005JD006095, 2006.
Wagner, R., Benz, S., Möhler, O., Saathoff, H., Schnaiter, M., and Schurath, U.: Mid-infrared Extinction Spectra and Optical Constants of Supercooled Water Droplets, J. Phys. Chem. A, 109, 7099–7112, 2005.
Wang, L.: wanglikun1973/CrIS_VIIRS_collocation: VIIRS and CrIS collocation code, Version v0.1, Zenodo [code], https://doi.org/10.5281/zenodo.6369192, 2022.
Wang, L., Tremblay, D. A., Han, Y., Esplin, M., Hagan, D. E., Predina, J.,
Suwinski, L., Jin, X., and Chen, Y.: Geolocation assessment for CrIS sensor data records, J. Geophys. Res.-Atmos., 118, 12690–12704, 2013.
Wang, L., Tremblay, D., Zhang, B., and Han, Y.: Fast and Accurate Collocation of the Visible Infrared Imaging Radiometer Suite Measurements with Cross-Track Infrared Sounder, Remote Sens., 8, 76, https://doi.org/10.3390/rs8010076, 2016.
Wang, T., Roman, J., Yue, Q., and Wong, S. (Eds.): Test Report of Performance of CLIMCAPS-SNPP and CLIMCAPS-JPSS1 Retrievals, Jet Propulsion Laboratory, California Institute of Technology, CA, https://docserver.gesdisc.eosdis.nasa.gov/public/project/Sounder/CLIMCAPS.V2.Test.Report.pdf, last access: 1 October 2021.
Wong, S., Fetzer, E. J., Schreier, M., Manipon, G., Fishbein, E. F., Kahn,
B. H., Yue, Q., and Irion, F. W.: Cloud-induced uncertainties in AIRS and
ECMWF temperature and specific humidity, J. Geophys. Res.-Atmos., 120, 1880–1901, https://doi.org/10.1002/2014JD022440, 2015.
Wu, X. and Smith, W. L.: Emissivity of rough sea surface for 8–13 µm: modeling and verification, Appl. Optics, 36, 2609–2619, https://doi.org/10.1364/AO.36.002609, 1997.
Yao, Z., Li, J., and Zhao, Z.: Synergistic use of AIRS and MODIS for dust top
height retrieval over land, Adv. Atmos. Sci., 32, 470–476, https://doi.org/10.1007/s00376-014-4046-y, 2015.
Yue, Q.: Datasets for Yue et al. (2022), Atmospheric Measurement Techniques: “Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP”, Version V1, Zenodo [data set], https://doi.org/10.5281/zenodo.6368564, 2022.
Yue, Q. and Lambrigtsen, B. (Eds.): AIRS V6 Test Report Supplement:
Performance of AIRS+AMSU vs. AIRS-only Retrievals, Jet Propulsion Laboratory, California Institute of Technology, CA,
https://docserver.gesdisc.eosdis.nasa.gov/repository/Mission/AIRS/3.3_ScienceDataProduct_Documentation/3.3.5_ProductQuality/V6_Test_Report_Supplement_Performance_of_AIRS+AMSU_vs_AIRS-Only_Retrievals.pdf (last access: 1 October 2021), 2017.
Yue, Q. and Lambrigtsen, B. (Eds.): AIRS V7 L2 Performance Test and Validation Report, Jet Propulsion Laboratory, California Institute of Technology, CA, https://docserver.gesdisc.eosdis.nasa.gov/public/project/AIRS/V7_L2_Performance_Test_and_Validation_report.pdf (last access: 1 October 2021), 2020.
Yue, Q., Kahn, B. H., Fetzer, E. J., and Teixeira, J.: Relationship between
marine boundary layer clouds and lower tropospheric stability observed by
AIRS, CloudSat, and CALIOP, J. Geophys. Res., 116, D18212, https://doi.org/10.1029/2011JD016136, 2011.
Yue, Q., Kahn, B. H., Xiao, H., Schreier, M. M., Fetzer, E. J., Teixeira,
J., and Suselj, K.: Transitions of cloud-topped marine boundary layers
characterized by AIRS, MODIS, and a large eddy simulation model, J.
Geophys. Res.-Atmos., 118, 8598–8611, 2013.
Yue, Q., Kahn, B. H., Fetzer, E. J., Schreier, M., Wong, S., Chen, X., and Huang, X.: Observation-based Longwave Cloud Radiative Kernels Derived from the A-Train, J. Climate, 29, 2023–2040, https://doi.org/10.1175/JCLI-D-15-0257.1, 2016.
Yue, Q., Kahn, B. H., Fetzer, E. J., Wong, S., Frey, R., and Meyer, K. G.:
On the response of MODIS cloud coverage to global mean surface air
temperature, J. Geophys. Res.-Atmos., 122, 966–979, 2017.
Yue, Q., Fetzer, E. J., Kahn, B. H., Wong, S., Huang, X., and Schreier, M.:
Temporal and Spatial Characteristics of Short-term Cloud Feedback on Global
and Local Interannual Climate Fluctuations from A-Train Observations, J. Climate, 32, 1875–1893, https://doi.org/10.1175/JCLI-D-18-0335.1, 2019.
Yue, Q., Lambrigtsen, B., Wang, T., Roman, J. (Eds.): Version 2 CLIMCAPS-Aqua Retrieval Product Performance Test Report, Jet Propulsion Laboratory, California Institute of Technology, CA, https://docserver.gesdisc.eosdis.nasa.gov/public/project/Sounder/CLIMCAPS.V2.Test.Report.Aqua.pdf, last access: 1 October 2021.
Zelinka, M. D., Klein, S. A., and Hartmann D. L.: Computing and Partitioning
Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels, J. Climate, 25, 3715–3735, 2012.
Zhou, C., Zelinka, M. D. , Dessler, A. E., and Yang, P.: An analysis of the short-term cloud feedback using MODIS data, J. Climate, 26, 4803–4815, https://doi.org/10.1175/JCLI-D-12-00547.1, 2013.
Zhu, P. and Zuidema, P.: On the use of PDF schemes to parameterize sub-grid
clouds, Geophys. Res. Lett., 36, L05807, https://doi.org/10.1029/2008GL036817, 2009.
Short summary
The self-consistency and continuity of cloud retrievals from infrared sounders and imagers aboard Aqua and SNPP (Suomi National Polar-orbiting Partnership) are examined at the pixel scale. Cloud products are found to be consistent with each other. Differences between sounder products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric retrievals. The impact of algorithm and instrument differences is clearly seen in the imager cloud retrievals.
The self-consistency and continuity of cloud retrievals from infrared sounders and imagers...
