Articles | Volume 14, issue 5
Atmos. Meas. Tech., 14, 3253–3276, 2021
https://doi.org/10.5194/amt-14-3253-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue: CALIPSO version 4 algorithms and data products
Atmos. Meas. Tech., 14, 3253–3276, 2021
https://doi.org/10.5194/amt-14-3253-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
04 May 2021
Research article
| 04 May 2021
Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part I: The retrieval algorithms
Anne Garnier et al.
Related authors
Thibault Vaillant de Guélis, Gérard Ancellet, Anne Garnier, Laurent C.-Labonnote, Jacques Pelon, Mark A. Vaughan, Zhaoyan Liu, and David M. Winker
Atmos. Meas. Tech., 15, 1931–1956, https://doi.org/10.5194/amt-15-1931-2022, https://doi.org/10.5194/amt-15-1931-2022, 2022
Short summary
Short summary
A new IIR-based cloud and aerosol discrimination (CAD) algorithm is developed using the IIR brightness temperature differences for cloud and aerosol features confidently identified by the CALIOP version 4 CAD algorithm. IIR classifications agree with the majority of V4 cloud identifications, reduce the ambiguity in a notable fraction of
not confidentV4 cloud classifications, and correct a few V4 misclassifications of cloud layers identified as dense dust or elevated smoke layers by CALIOP.
Anne Garnier, Jacques Pelon, Nicolas Pascal, Mark A. Vaughan, Philippe Dubuisson, Ping Yang, and David L. Mitchell
Atmos. Meas. Tech., 14, 3277–3299, https://doi.org/10.5194/amt-14-3277-2021, https://doi.org/10.5194/amt-14-3277-2021, 2021
Short summary
Short summary
The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and ice or liquid water path estimates. This paper (Part II) shows retrievals over ocean and describes the improvements made with respect to version 3 as a result of the significant changes implemented in the version 4 algorithms, which are presented in a companion paper (Part I).
Jason L. Tackett, Jayanta Kar, Mark A. Vaughan, Brian J. Getzewich, Man-Hae Kim, Jean-Paul Vernier, Ali H. Omar, Brian E. Magill, Michael C. Pitts, and David M. Winker
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-289, https://doi.org/10.5194/amt-2022-289, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
The accurate identification of aerosol types in the stratosphere is important to characterize their impacts on the Earth climate system. The space-borne lidar on board CALIPSO is well-posed to identify aerosol in the stratosphere from volcanic eruptions and major wildfire events. This manuscript describes improvements implemented in the version 4.5 CALIPSO data release to more accurately discriminate between volcanic ash, sulfate, and smoke within the stratosphere.
Meryl Wimmer, Gwendal Rivière, Philippe Arbogast, Jean-Marcel Piriou, Julien Delanoë, Carole Labadie, Quitterie Cazenave, and Jacques Pelon
Weather Clim. Dynam., 3, 863–882, https://doi.org/10.5194/wcd-3-863-2022, https://doi.org/10.5194/wcd-3-863-2022, 2022
Short summary
Short summary
The effect of deep convection representation on the jet stream above the cold front of an extratropical cyclone is investigated in the global numerical weather prediction model ARPEGE. Two simulations using different deep convection schemes are compared with (re)analysis datasets and NAWDEX airborne observations. A deeper jet stream is observed with the less active scheme. The diabatic origin of this difference is interpreted by backward Lagrangian trajectories and potential vorticity budgets.
Zhujun Li, David Painemal, Gregory Schuster, Marian Clayton, Richard Ferrare, Mark Vaughan, Damien Josset, Jayanta Kar, and Charles Trepte
Atmos. Meas. Tech., 15, 2745–2766, https://doi.org/10.5194/amt-15-2745-2022, https://doi.org/10.5194/amt-15-2745-2022, 2022
Short summary
Short summary
For more than 15 years, CALIPSO has revolutionized our understanding of the role of aerosols in climate. Here we evaluate CALIPSO aerosol typing over the ocean using an independent CALIPSO–CloudSat product. The analysis suggests that CALIPSO correctly categorizes clean marine aerosol over the open ocean, elevated smoke over the SE Atlantic, and dust over the tropical Atlantic. Similarities between clean and dusty marine over the open ocean implies that algorithm modifications are warranted.
Thibault Vaillant de Guélis, Gérard Ancellet, Anne Garnier, Laurent C.-Labonnote, Jacques Pelon, Mark A. Vaughan, Zhaoyan Liu, and David M. Winker
Atmos. Meas. Tech., 15, 1931–1956, https://doi.org/10.5194/amt-15-1931-2022, https://doi.org/10.5194/amt-15-1931-2022, 2022
Short summary
Short summary
A new IIR-based cloud and aerosol discrimination (CAD) algorithm is developed using the IIR brightness temperature differences for cloud and aerosol features confidently identified by the CALIOP version 4 CAD algorithm. IIR classifications agree with the majority of V4 cloud identifications, reduce the ambiguity in a notable fraction of
not confidentV4 cloud classifications, and correct a few V4 misclassifications of cloud layers identified as dense dust or elevated smoke layers by CALIOP.
Lilian Loyer, Jean-Christophe Raut, Claudia Di Biagio, Julia Maillard, Vincent Mariage, and Jacques Pelon
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-326, https://doi.org/10.5194/amt-2021-326, 2021
Revised manuscript not accepted
Short summary
Short summary
The Arctic is facing drastic climate changes, and more observations are needed to better understand what is happening. Unfortunately observations are limited in the High Arctic. To obtain more observations, multiples buoys equipped with lidar, have been deployed in this region. This paper presents an approach to estimate the optical properties of clouds, and solar plus terrestrial energies from lidar measurements in the Arctic.
Gwendal Rivière, Meryl Wimmer, Philippe Arbogast, Jean-Marcel Piriou, Julien Delanoë, Carole Labadie, Quitterie Cazenave, and Jacques Pelon
Weather Clim. Dynam., 2, 1011–1031, https://doi.org/10.5194/wcd-2-1011-2021, https://doi.org/10.5194/wcd-2-1011-2021, 2021
Short summary
Short summary
Inacurracies in representing processes occurring at spatial scales smaller than the grid scales of the weather forecast models are important sources of forecast errors. This is the case of deep convection representation in models with 10 km grid spacing. We performed simulations of a real extratropical cyclone using a model with different representations of deep convection. These forecasts lead to different behaviors in the ascending air masses of the cyclone and the jet stream aloft.
Didier Bruneau and Jacques Pelon
Atmos. Meas. Tech., 14, 4375–4402, https://doi.org/10.5194/amt-14-4375-2021, https://doi.org/10.5194/amt-14-4375-2021, 2021
Short summary
Short summary
Taking advantage of Aeolus success and of our airborne lidar system expertise, we present a new spaceborne wind lidar design for operational Aeolus follow-on missions, keeping most of the initial lidar system but relying on a single Mach–Zehnder interferometer to relax operational constraints and reduce measurement bias. System parameters are optimized. Random and systematic errors are shown to be compliant with the initial mission requirements. In addition, the system allows unbiased retrieval.
Anne Garnier, Jacques Pelon, Nicolas Pascal, Mark A. Vaughan, Philippe Dubuisson, Ping Yang, and David L. Mitchell
Atmos. Meas. Tech., 14, 3277–3299, https://doi.org/10.5194/amt-14-3277-2021, https://doi.org/10.5194/amt-14-3277-2021, 2021
Short summary
Short summary
The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and ice or liquid water path estimates. This paper (Part II) shows retrievals over ocean and describes the improvements made with respect to version 3 as a result of the significant changes implemented in the version 4 algorithms, which are presented in a companion paper (Part I).
David L. A. Flack, Gwendal Rivière, Ionela Musat, Romain Roehrig, Sandrine Bony, Julien Delanoë, Quitterie Cazenave, and Jacques Pelon
Weather Clim. Dynam., 2, 233–253, https://doi.org/10.5194/wcd-2-233-2021, https://doi.org/10.5194/wcd-2-233-2021, 2021
Short summary
Short summary
The representation of an extratropical cyclone in simulations of two climate models is studied by comparing them to observations of the international field campaign NAWDEX. We show that the current resolution used to run climate model projections (more than 100 km) is not enough to represent the life cycle accurately, but the use of 50 km resolution is good enough. Despite these encouraging results, cloud properties (partitioning liquid and solid) are found to be far from the observations.
Julia Maillard, François Ravetta, Jean-Christophe Raut, Vincent Mariage, and Jacques Pelon
Atmos. Chem. Phys., 21, 4079–4101, https://doi.org/10.5194/acp-21-4079-2021, https://doi.org/10.5194/acp-21-4079-2021, 2021
Short summary
Short summary
Clouds remain a major source of uncertainty in understanding the Arctic climate, due in part to the lack of measurements over the sea ice. In this paper, we exploit a series of lidar profiles acquired from autonomous drifting buoys deployed in the Arctic Ocean and derive a statistic of low cloud frequency and macrophysical properties. We also show that clouds contribute to warm the surface in the shoulder seasons but not significantly from May to September.
Thibault Vaillant de Guélis, Mark A. Vaughan, David M. Winker, and Zhaoyan Liu
Atmos. Meas. Tech., 14, 1593–1613, https://doi.org/10.5194/amt-14-1593-2021, https://doi.org/10.5194/amt-14-1593-2021, 2021
Short summary
Short summary
We introduce a new lidar feature detection algorithm that dramatically improves the fine details of layers identified in the CALIOP data. By applying our two-dimensional scanning technique to the measurements in all three channels, we minimize false positives while accurately identifying previously undetected features such as subvisible cirrus and the full vertical extent of dense smoke plumes. Multiple comparisons to version 4.2 CALIOP retrievals illustrate the scope of the improvements made.
Setigui Aboubacar Keita, Eric Girard, Jean-Christophe Raut, Maud Leriche, Jean-Pierre Blanchet, Jacques Pelon, Tatsuo Onishi, and Ana Cirisan
Geosci. Model Dev., 13, 5737–5755, https://doi.org/10.5194/gmd-13-5737-2020, https://doi.org/10.5194/gmd-13-5737-2020, 2020
David L. Mitchell, John Mejia, Anne Garnier, Yuta Tomii, Martina Krämer, and Farnaz Hosseinpour
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-846, https://doi.org/10.5194/acp-2020-846, 2020
Publication in ACP not foreseen
Short summary
Short summary
This may be the first estimate of the radiative contribution of homogeneous ice nucleation in cirrus clouds on a global, regional and seasonal scale. This is achieved by constraining an atmospheric global climate model with measured cirrus cloud properties via satellite remote sensing. The results show that the overall radiative warming contributed by homogeneous ice nucleation at the top of the atmosphere is 2.4 W m-2 outside the ± 30° latitude zone during non-summer months (JJA).
Melody A. Avery, Robert A. Ryan, Brian J. Getzewich, Mark A. Vaughan, David M. Winker, Yongxiang Hu, Anne Garnier, Jacques Pelon, and Carolus A. Verhappen
Atmos. Meas. Tech., 13, 4539–4563, https://doi.org/10.5194/amt-13-4539-2020, https://doi.org/10.5194/amt-13-4539-2020, 2020
Short summary
Short summary
CALIOP data users will find more cloud layers detected in V4, with edges that extend further than in V3, for an increase in total atmospheric cloud volume of 6 %–9 % for high-confidence cloud phases and 1 %–2 % for all cloudy bins, including cloud fringes and unknown cloud phases. In V4 there are many fewer cloud layers identified as horizontally oriented ice, particularly in the 3° off-nadir view. Depolarization at 532 nm is the predominant parameter determining cloud thermodynamic phase.
Antonin Zabukovec, Gerard Ancellet, Iwan E. Penner, Mikhail Arshinov, Valery Kozlov, Jacques Pelon, Jean-Daniel Paris, Grigory Kokhanenko, Yuri S. Balin, Dmitry Chernov, and Boris D. Belan
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-195, https://doi.org/10.5194/acp-2020-195, 2020
Preprint withdrawn
Short summary
Short summary
Description of two aircraft campaigns results carried out over Siberia in 2013 and 2017 to characterize aerosol emission. A methodology is proposed to derive the aerosol types using transport model and satellite observations. The extinction to backscatter ratio for each aerosol types is reported as it is a key parameter to constrain their radiative impact. These results are compared to previous work conducted in other regions and to aerosol data products observed by spaceborne lidars.
Rebecca M. Pauly, John E. Yorks, Dennis L. Hlavka, Matthew J. McGill, Vassilis Amiridis, Stephen P. Palm, Sharon D. Rodier, Mark A. Vaughan, Patrick A. Selmer, Andrew W. Kupchock, Holger Baars, and Anna Gialitaki
Atmos. Meas. Tech., 12, 6241–6258, https://doi.org/10.5194/amt-12-6241-2019, https://doi.org/10.5194/amt-12-6241-2019, 2019
Short summary
Short summary
The Cloud Aerosol Transport System (CATS) demonstrated that direct calibration of 1064 nm lidar data from a spaceborne platform is possible. By normalizing the CATS signal to a modeled molecular backscatter profile the CATS data were calibrated, enabling the derivation of optical properties of clouds and aerosols. Comparisons of the calibrated signal with airborne lidar, ground-based lidar, and spaceborne lidar all show agreement within the estimated error bars of the respective instruments.
Jayanta Kar, Kam-Pui Lee, Mark A. Vaughan, Jason L. Tackett, Charles R. Trepte, David M. Winker, Patricia L. Lucker, and Brian J. Getzewich
Atmos. Meas. Tech., 12, 6173–6191, https://doi.org/10.5194/amt-12-6173-2019, https://doi.org/10.5194/amt-12-6173-2019, 2019
Short summary
Short summary
This work describes the science algorithm for the recently released CALIPSO level 3 stratospheric aerosol product. It is shown that the retrieved extinction profiles capture the major stratospheric perturbations over the last decade resulting from volcanic eruptions, pyroCb smoke events, and signatures of stratospheric dynamics. An initial assessment is also provided by intercomparison with the latest aerosol retrievals from the SAGE III instrument aboard the International Space Station.
Quitterie Cazenave, Marie Ceccaldi, Julien Delanoë, Jacques Pelon, Silke Groß, and Andrew Heymsfield
Atmos. Meas. Tech., 12, 2819–2835, https://doi.org/10.5194/amt-12-2819-2019, https://doi.org/10.5194/amt-12-2819-2019, 2019
Short summary
Short summary
The impact of ice clouds on the water cycle and radiative budget is still uncertain due to the complexity of cloud processes that makes it difficult to acquire adequate observations of ice cloud properties and parameterize them into climate and weather prediction models. In this paper we present the latest refinements brought to the DARDAR-CLOUD product, which contains ice cloud microphysical properties retrieved from the cloud radar and lidar measurements from the A-Train space mission.
Shan Zeng, Mark Vaughan, Zhaoyan Liu, Charles Trepte, Jayanta Kar, Ali Omar, David Winker, Patricia Lucker, Yongxiang Hu, Brian Getzewich, and Melody Avery
Atmos. Meas. Tech., 12, 2261–2285, https://doi.org/10.5194/amt-12-2261-2019, https://doi.org/10.5194/amt-12-2261-2019, 2019
Short summary
Short summary
We use a fuzzy k-means (FKM) classifier to assess the ability of the CALIPSO cloud–aerosol discrimination (CAD) algorithm to correctly distinguish between clouds and aerosols detected in the CALIPSO lidar backscatter signals. FKM is an unsupervised learning algorithm, so the classifications it derives are wholly independent from those reported by the CAD scheme. For a full month of measurements, the two techniques agree in ~ 95 % of all cases, providing strong evidence for CAD correctness.
Meloë S. Kacenelenbogen, Mark A. Vaughan, Jens Redemann, Stuart A. Young, Zhaoyan Liu, Yongxiang Hu, Ali H. Omar, Samuel LeBlanc, Yohei Shinozuka, John Livingston, Qin Zhang, and Kathleen A. Powell
Atmos. Chem. Phys., 19, 4933–4962, https://doi.org/10.5194/acp-19-4933-2019, https://doi.org/10.5194/acp-19-4933-2019, 2019
Short summary
Short summary
Significant efforts are required to estimate the direct radiative effects of aerosols above clouds (DAREcloudy). We have used a combination of passive and active A-Train satellite sensors and derive mainly positive global and regional DAREcloudy values (e.g., global seasonal values between 0.13 and 0.26 W m-2). Despite differences in methods and sensors, the DAREcloudy values in this study are generally higher than previously reported. We discuss the primary reasons for these higher estimates.
David Painemal, Marian Clayton, Richard Ferrare, Sharon Burton, Damien Josset, and Mark Vaughan
Atmos. Meas. Tech., 12, 2201–2217, https://doi.org/10.5194/amt-12-2201-2019, https://doi.org/10.5194/amt-12-2201-2019, 2019
Short summary
Short summary
We present 1 year of a new CALIOP-based aerosol extinction coefficient and lidar ratio over the ocean, with the goal of providing a flexible dataset for climate research as well as independent retrievals that can be helpful for refining CALIPSO Science Team algorithms. The retrievals are derived by constraining the lidar equation with an aerosol optical depth estimated from cross-calibrated CALIOP and CloudSat surface echos.
Travis D. Toth, Jianglong Zhang, Jeffrey S. Reid, and Mark A. Vaughan
Atmos. Meas. Tech., 12, 1739–1754, https://doi.org/10.5194/amt-12-1739-2019, https://doi.org/10.5194/amt-12-1739-2019, 2019
Short summary
Short summary
An innovative method is presented for deriving particulate matter (PM) concentrations using CALIOP measurements. Deviating from conventional approaches of relying on passive satellite column-integrated aerosol measurements, PM concentrations are derived from near-surface CALIOP measurements through a bulk-mass-modeling method. This proof-of-concept study shows that, while limited in spatial and temporal coverage, CALIOP exhibits reasonable skill for PM applications.
Zhaoyan Liu, Jayanta Kar, Shan Zeng, Jason Tackett, Mark Vaughan, Melody Avery, Jacques Pelon, Brian Getzewich, Kam-Pui Lee, Brian Magill, Ali Omar, Patricia Lucker, Charles Trepte, and David Winker
Atmos. Meas. Tech., 12, 703–734, https://doi.org/10.5194/amt-12-703-2019, https://doi.org/10.5194/amt-12-703-2019, 2019
Short summary
Short summary
We describe the enhancements made to the cloud–aerosol discrimination (CAD) algorithms used to produce the CALIPSO version 4 (V4) data products. Revisions to the CAD probability distribution functions have greatly improved the recognition of aerosol layers lofted into the upper troposphere, and CAD is now applied to all layers detected in the stratosphere and all layers detected at single-shot resolution. Detailed comparisons show significant improvements relative to previous versions.
Gerard Ancellet, Iogannes E. Penner, Jacques Pelon, Vincent Mariage, Antonin Zabukovec, Jean Christophe Raut, Grigorii Kokhanenko, and Yuri S. Balin
Atmos. Meas. Tech., 12, 147–168, https://doi.org/10.5194/amt-12-147-2019, https://doi.org/10.5194/amt-12-147-2019, 2019
Short summary
Short summary
Aerosol type seasonal variability and sources in Siberia are obtained from an automatic 808 nm micropulse lidar. A total of 540 aerosol backscatter vertical profiles have been retrieved using careful lidar calibration. Aerosol optical depth is retrieved using sun-photometer complementary observations and an aerosol source apportionment based on aerosol transport model simulations. Comparisons with satellite observations are discussed for three case studies.
Mark Vaughan, Anne Garnier, Damien Josset, Melody Avery, Kam-Pui Lee, Zhaoyan Liu, William Hunt, Jacques Pelon, Yongxiang Hu, Sharon Burton, Johnathan Hair, Jason L. Tackett, Brian Getzewich, Jayanta Kar, and Sharon Rodier
Atmos. Meas. Tech., 12, 51–82, https://doi.org/10.5194/amt-12-51-2019, https://doi.org/10.5194/amt-12-51-2019, 2019
Short summary
Short summary
The version 4 (V4) release of the CALIPSO data products includes substantial improvements to the calibration of the CALIOP 1064 nm channel. In this paper we review the fundamentals of 1064 nm lidar calibration, explain the motivations for the changes made to the algorithm, and describe the mechanics of the V4 calibration technique. Internal consistency checks and comparisons to collocated high spectral resolution lidar measurements show the V4 1064 nm calibration coefficients to within ~ 3 %.
David L. Mitchell, Anne Garnier, Jacques Pelon, and Ehsan Erfani
Atmos. Chem. Phys., 18, 17325–17354, https://doi.org/10.5194/acp-18-17325-2018, https://doi.org/10.5194/acp-18-17325-2018, 2018
Short summary
Short summary
To realistically model a changing climate, global measurements of cirrus cloud ice-particle number concentration (N) and size (De) are needed, through which one may infer the general mechanism of ice formation. A satellite remote sensing method was developed to measure N and De. It was found that N was highest and De lowest at high latitudes. In the Arctic, cirrus clouds occurred much more often during winter, which may have an impact on mid-latitude winter weather.
Brian J. Getzewich, Mark A. Vaughan, William H. Hunt, Melody A. Avery, Kathleen A. Powell, Jason L. Tackett, David M. Winker, Jayanta Kar, Kam-Pui Lee, and Travis D. Toth
Atmos. Meas. Tech., 11, 6309–6326, https://doi.org/10.5194/amt-11-6309-2018, https://doi.org/10.5194/amt-11-6309-2018, 2018
Short summary
Short summary
We describe the new architecture of the version 4 (V4) CALIOP 532 nm daytime calibration procedures. Critical differences from the versions include moving the night-to-day calibration transfer region into the lower stratosphere coupled to a multi-dimensional data averaging scheme. Comparisons to collocated high spectral resolution lidar (HSRL) measurements shows that the V4 532 nm daytime attenuated backscatter coefficients replicate the HSRL data to within 1.0 % ± 3.5 %.
Man-Hae Kim, Ali H. Omar, Jason L. Tackett, Mark A. Vaughan, David M. Winker, Charles R. Trepte, Yongxiang Hu, Zhaoyan Liu, Lamont R. Poole, Michael C. Pitts, Jayanta Kar, and Brian E. Magill
Atmos. Meas. Tech., 11, 6107–6135, https://doi.org/10.5194/amt-11-6107-2018, https://doi.org/10.5194/amt-11-6107-2018, 2018
Short summary
Short summary
This paper discusses recent advances made in distinguishing among different aerosols species detected in the CALIPSO lidar measurements. A new classification algorithm now classifies four different aerosol types in the stratosphere, and the number of aerosol types recognized in the troposphere has increased from six to seven. The lidar ratios characterizing each type have been updated and the effects of these changes on CALIPSO retrievals of aerosol optical depth are examined in detail.
Stuart A. Young, Mark A. Vaughan, Anne Garnier, Jason L. Tackett, James D. Lambeth, and Kathleen A. Powell
Atmos. Meas. Tech., 11, 5701–5727, https://doi.org/10.5194/amt-11-5701-2018, https://doi.org/10.5194/amt-11-5701-2018, 2018
Short summary
Short summary
This paper describes comprehensive upgrades to the algorithms used to retrieve altitude-resolved profiles of cloud and aerosol extinction coefficients from the elastic backscatter measurements made by the space-based CALIPSO lidar. The CALIPSO version 4 data products generated by these new algorithms are explored in detail, and the many areas of improvement are highlighted using extensive comparisons both to previous versions and to collocated measurements made by space-based passive sensors.
Jason L. Tackett, David M. Winker, Brian J. Getzewich, Mark A. Vaughan, Stuart A. Young, and Jayanta Kar
Atmos. Meas. Tech., 11, 4129–4152, https://doi.org/10.5194/amt-11-4129-2018, https://doi.org/10.5194/amt-11-4129-2018, 2018
Short summary
Short summary
The CALIPSO level 3 aerosol profile product reports globally gridded, quality-screened monthly mean aerosol extinction profiles retrieved by the spaceborne lidar, CALIOP. This paper describes the quality screening and averaging methods used to generate the product. Impacts of quality screening on reported quantities are evaluated, in particular the change in aerosol extinction profiles and aerosol optical depth. The paper thereby provides guidance on the use of CALIOP aerosol data.
Xiaomei Lu, Yongxiang Hu, Yuekui Yang, Mark Vaughan, Zhaoyan Liu, Sharon Rodier, William Hunt, Kathy Powell, Patricia Lucker, and Charles Trepte
Atmos. Meas. Tech., 11, 3281–3296, https://doi.org/10.5194/amt-11-3281-2018, https://doi.org/10.5194/amt-11-3281-2018, 2018
Short summary
Short summary
This paper presents an innovative retrieval method that translates the CALIOP land surface laser pulse returns into the surface bidirectional reflectance. The surface bidirectional reflectances retrieved from CALIOP measurements contribute complementary data for existing MODIS standard data products and could be used to detect and monitor seasonal surface reflectance changes in high latitude regions where passive MODIS measurements are limited.
Anne Garnier, Thierry Trémas, Jacques Pelon, Kam-Pui Lee, Delphine Nobileau, Lydwine Gross-Colzy, Nicolas Pascal, Pascale Ferrage, and Noëlle A. Scott
Atmos. Meas. Tech., 11, 2485–2500, https://doi.org/10.5194/amt-11-2485-2018, https://doi.org/10.5194/amt-11-2485-2018, 2018
Short summary
Short summary
Residual calibration biases affecting CALIPSO IIR Version 1 calibrated radiances in the Northern Hemisphere are analyzed and reduced through in-depth analysis of the IIR internal calibration procedure in conjunction with observations such as statistical comparisons with similar MODIS/Aqua channels.
Jayanta Kar, Mark A. Vaughan, Kam-Pui Lee, Jason L. Tackett, Melody A. Avery, Anne Garnier, Brian J. Getzewich, William H. Hunt, Damien Josset, Zhaoyan Liu, Patricia L. Lucker, Brian Magill, Ali H. Omar, Jacques Pelon, Raymond R. Rogers, Travis D. Toth, Charles R. Trepte, Jean-Paul Vernier, David M. Winker, and Stuart A. Young
Atmos. Meas. Tech., 11, 1459–1479, https://doi.org/10.5194/amt-11-1459-2018, https://doi.org/10.5194/amt-11-1459-2018, 2018
Short summary
Short summary
We present the motivation for and the implementation of the version 4.1 nighttime 532 nm parallel-channel calibration of the CALIOP lidar. The accuracy of calibration is significantly improved by raising the molecular normalization altitude from 30–34 km to 36–39 km to substantially reduce stratospheric aerosol contamination. The new calibration procedure eliminates biases in earlier versions and leads to an improved representation of stratospheric aerosols.
Travis D. Toth, James R. Campbell, Jeffrey S. Reid, Jason L. Tackett, Mark A. Vaughan, Jianglong Zhang, and Jared W. Marquis
Atmos. Meas. Tech., 11, 499–514, https://doi.org/10.5194/amt-11-499-2018, https://doi.org/10.5194/amt-11-499-2018, 2018
Lucia T. Deaconu, Fabien Waquet, Damien Josset, Nicolas Ferlay, Fanny Peers, François Thieuleux, Fabrice Ducos, Nicolas Pascal, Didier Tanré, Jacques Pelon, and Philippe Goloub
Atmos. Meas. Tech., 10, 3499–3523, https://doi.org/10.5194/amt-10-3499-2017, https://doi.org/10.5194/amt-10-3499-2017, 2017
Short summary
Short summary
This study presents a comparison between active (CALIOP) and passive (POLDER) remote sensing methods, developed for retrieving aerosol above-cloud optical and microphysical properties. Main results show a good agreement when the aerosol microphysics is dominated by fine-mode particles or coarse-mode dust or when the aerosol layer is well separated from the cloud below. The paper is also focused on understanding the differences between the retrievals and the limitations of each method.
Leslie David, Olivier Bock, Christian Thom, Pierre Bosser, and Jacques Pelon
Atmos. Meas. Tech., 10, 2745–2758, https://doi.org/10.5194/amt-10-2745-2017, https://doi.org/10.5194/amt-10-2745-2017, 2017
Short summary
Short summary
The Raman lidar ability to retrieve atmospheric water vapor with high accuracy makes it a premium instrument in different research fields such as climatology, meteorology, or calibration of GNSS altimetry data. In order to achieve long-term stability of the measurements, the system has to be carefully calibrated. In this work we strove to investigate and mitigate the error and instability sources through numerical simulations as well as experimental tests.
Anne Garnier, Noëlle A. Scott, Jacques Pelon, Raymond Armante, Laurent Crépeau, Bruno Six, and Nicolas Pascal
Atmos. Meas. Tech., 10, 1403–1424, https://doi.org/10.5194/amt-10-1403-2017, https://doi.org/10.5194/amt-10-1403-2017, 2017
Short summary
Short summary
An assessment of IIR radiances after 9.5 years of nearly continuous operation since June 2006 is presented. First, IIR is compared with similar MODIS or SEVIRI channels in various conditions. Second, clear sky measurements in each channel are compared with simulations. The first approach detects biases and/or trends, and the second approach contributes to identifying which channel deviates from the other. The analyses are based on simulations using the 4A/OP radiative transfer model.
Ehsan Erfani and David L. Mitchell
Atmos. Chem. Phys., 17, 1241–1257, https://doi.org/10.5194/acp-17-1241-2017, https://doi.org/10.5194/acp-17-1241-2017, 2017
Short summary
Short summary
Rimed particle projected area- and mass-dimension expressions are developed and validated.
A convenient means of relating the unrimed and rimed m – D and A – D expressions was developed.
Equations are provided to calculate collision efficiency for use in models.
David L. Mitchell, Anne Garnier, Melody Avery, and Ehsan Erfani
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-1062, https://doi.org/10.5194/acp-2016-1062, 2016
Revised manuscript not accepted
Short summary
Short summary
Although the main objective of our DOE/Atmospheric Systems Research project was to use aircraft measurements to determine the formation (i.e. ice nucleation) mechanism of cirrus clouds, it soon became evident that the formation mechanism will depend on latitude, season and surface topography. A new satellite remote sensing method was developed to discover this dependency, which shows that roughly half or more of the cirrus clouds at high latitudes form through homogeneous ice nucleation.
Jean-Pierre Chaboureau, Cyrille Flamant, Thibaut Dauhut, Cécile Kocha, Jean-Philippe Lafore, Chistophe Lavaysse, Fabien Marnas, Mohamed Mokhtari, Jacques Pelon, Irene Reinares Martínez, Kerstin Schepanski, and Pierre Tulet
Atmos. Chem. Phys., 16, 6977–6995, https://doi.org/10.5194/acp-16-6977-2016, https://doi.org/10.5194/acp-16-6977-2016, 2016
Short summary
Short summary
The Fennec field campaign conducted in June 2011 led to the first observational data set ever obtained that documents the Saharan atmospheric boundary layer under the influence of the heat low. In addition to the aircraft operation, four dust forecasts were run at low and high resolutions with convection-parameterizing and convection-permitting models, respectively. The unique airborne and ground-based data sets allowed the first ever intercomparison of dust forecasts over the western Sahara.
Robert E. Holz, Steven Platnick, Kerry Meyer, Mark Vaughan, Andrew Heidinger, Ping Yang, Gala Wind, Steven Dutcher, Steven Ackerman, Nandana Amarasinghe, Fredrick Nagle, and Chenxi Wang
Atmos. Chem. Phys., 16, 5075–5090, https://doi.org/10.5194/acp-16-5075-2016, https://doi.org/10.5194/acp-16-5075-2016, 2016
Gerard Ancellet, Jacques Pelon, Julien Totems, Patrick Chazette, Ariane Bazureau, Michaël Sicard, Tatiana Di Iorio, Francois Dulac, and Marc Mallet
Atmos. Chem. Phys., 16, 4725–4742, https://doi.org/10.5194/acp-16-4725-2016, https://doi.org/10.5194/acp-16-4725-2016, 2016
Short summary
Short summary
A multi-lidar analysis conducted in the Mediterranean basin compares the impact of the long-range transport of North American biomass burning aerosols with the role of frequently observed Saharan dust outbreaks. This paper provides a detailed analysis of the potential North American aerosol sources, their transport to Europe and the mixing of different aerosol sources, using simulations of a particle dispersion model and lidar measurements of the aerosol optical properties.
Ehsan Erfani and David L. Mitchell
Atmos. Chem. Phys., 16, 4379–4400, https://doi.org/10.5194/acp-16-4379-2016, https://doi.org/10.5194/acp-16-4379-2016, 2016
Short summary
Short summary
Ice particle projected area- and mass-dimension expressions are developed and validated, and uncertainties for power laws derived from these expressions are determined.
Patrick Chazette, Julien Totems, Gérard Ancellet, Jacques Pelon, and Michaël Sicard
Atmos. Chem. Phys., 16, 2863–2875, https://doi.org/10.5194/acp-16-2863-2016, https://doi.org/10.5194/acp-16-2863-2016, 2016
Short summary
Short summary
We performed synergetic active and passive remote-sensing observations at Minorca (Spain), over more than 3 weeks in spring 2013. We characterized the aerosol optical properties and type using a combination of Rayleigh–Mie–Raman lidar and sun-photometer data. Results show a high variability due to changing atmospheric transport regimes and aerosol sources. Such variability significantly influences the radiative balance through the entire atmosphere and then the climate of the Mediterranean area.
M. Mallet, F. Dulac, P. Formenti, P. Nabat, J. Sciare, G. Roberts, J. Pelon, G. Ancellet, D. Tanré, F. Parol, C. Denjean, G. Brogniez, A. di Sarra, L. Alados-Arboledas, J. Arndt, F. Auriol, L. Blarel, T. Bourrianne, P. Chazette, S. Chevaillier, M. Claeys, B. D'Anna, Y. Derimian, K. Desboeufs, T. Di Iorio, J.-F. Doussin, P. Durand, A. Féron, E. Freney, C. Gaimoz, P. Goloub, J. L. Gómez-Amo, M. J. Granados-Muñoz, N. Grand, E. Hamonou, I. Jankowiak, M. Jeannot, J.-F. Léon, M. Maillé, S. Mailler, D. Meloni, L. Menut, G. Momboisse, J. Nicolas, T. Podvin, V. Pont, G. Rea, J.-B. Renard, L. Roblou, K. Schepanski, A. Schwarzenboeck, K. Sellegri, M. Sicard, F. Solmon, S. Somot, B Torres, J. Totems, S. Triquet, N. Verdier, C. Verwaerde, F. Waquet, J. Wenger, and P. Zapf
Atmos. Chem. Phys., 16, 455–504, https://doi.org/10.5194/acp-16-455-2016, https://doi.org/10.5194/acp-16-455-2016, 2016
Short summary
Short summary
The aim of this article is to present an experimental campaign over the Mediterranean focused on aerosol-radiation measurements and modeling. Results indicate an important atmospheric loading associated with a moderate absorbing ability of mineral dust. Observations suggest a complex vertical structure and size distributions characterized by large aerosols within dust plumes. The radiative effect is highly variable, with negative forcing over the Mediterranean and positive over northern Africa.
C. He, K.-N. Liou, Y. Takano, R. Zhang, M. Levy Zamora, P. Yang, Q. Li, and L. R. Leung
Atmos. Chem. Phys., 15, 11967–11980, https://doi.org/10.5194/acp-15-11967-2015, https://doi.org/10.5194/acp-15-11967-2015, 2015
A. Garnier, J. Pelon, M. A. Vaughan, D. M. Winker, C. R. Trepte, and P. Dubuisson
Atmos. Meas. Tech., 8, 2759–2774, https://doi.org/10.5194/amt-8-2759-2015, https://doi.org/10.5194/amt-8-2759-2015, 2015
Short summary
Short summary
Cloud absorption optical depths retrieved at 12.05 microns are compared to extinction optical depths retrieved at 0.532 microns from perfectly co-located observations of single-layered semi-transparent cirrus over oceans made by the space-borne CALIPSO IIR infrared radiometer and CALIOP lidar. A new relationship describing the temperature-dependent effect of multiple scattering in the CALIOP retrievals is derived and discussed.
L. Marelle, J.-C. Raut, J. L. Thomas, K. S. Law, B. Quennehen, G. Ancellet, J. Pelon, A. Schwarzenboeck, and J. D. Fast
Atmos. Chem. Phys., 15, 3831–3850, https://doi.org/10.5194/acp-15-3831-2015, https://doi.org/10.5194/acp-15-3831-2015, 2015
N. Bègue, P. Tulet, J. Pelon, B. Aouizerats, A. Berger, and A. Schwarzenboeck
Atmos. Chem. Phys., 15, 3497–3516, https://doi.org/10.5194/acp-15-3497-2015, https://doi.org/10.5194/acp-15-3497-2015, 2015
T. Fauchez, P. Dubuisson, C. Cornet, F. Szczap, A. Garnier, J. Pelon, and K. Meyer
Atmos. Meas. Tech., 8, 633–647, https://doi.org/10.5194/amt-8-633-2015, https://doi.org/10.5194/amt-8-633-2015, 2015
Z. Liu, D. Winker, A. Omar, M. Vaughan, J. Kar, C. Trepte, Y. Hu, and G. Schuster
Atmos. Chem. Phys., 15, 1265–1288, https://doi.org/10.5194/acp-15-1265-2015, https://doi.org/10.5194/acp-15-1265-2015, 2015
J. R. Campbell, M. A. Vaughan, M. Oo, R. E. Holz, J. R. Lewis, and E. J. Welton
Atmos. Meas. Tech., 8, 435–449, https://doi.org/10.5194/amt-8-435-2015, https://doi.org/10.5194/amt-8-435-2015, 2015
Short summary
Short summary
Digital thresholds based on 2012 CALIOP satellite lidar measurements are investigated for distinguishing cirrus cloud presence, including cloud top temperatures and heights combined with layer depolarization and phase and optical depths. A cloud top temperature of -37 C is found to exhibit the most stable performance, owing to it being the point of homogeneous liquid-water freezing. Depolarization and phase help but are mostly ambiguous at warmer temperatures where mixed-phase clouds propagate.
R. R. Rogers, M. A. Vaughan, C. A. Hostetler, S. P. Burton, R. A. Ferrare, S. A. Young, J. W. Hair, M. D. Obland, D. B. Harper, A. L. Cook, and D. M. Winker
Atmos. Meas. Tech., 7, 4317–4340, https://doi.org/10.5194/amt-7-4317-2014, https://doi.org/10.5194/amt-7-4317-2014, 2014
F. Marenco, V. Amiridis, E. Marinou, A. Tsekeri, and J. Pelon
Atmos. Chem. Phys., 14, 11871–11881, https://doi.org/10.5194/acp-14-11871-2014, https://doi.org/10.5194/acp-14-11871-2014, 2014
G. Ancellet, J. Pelon, Y. Blanchard, B. Quennehen, A. Bazureau, K. S. Law, and A. Schwarzenboeck
Atmos. Chem. Phys., 14, 8235–8254, https://doi.org/10.5194/acp-14-8235-2014, https://doi.org/10.5194/acp-14-8235-2014, 2014
S. P. Burton, M. A. Vaughan, R. A. Ferrare, and C. A. Hostetler
Atmos. Meas. Tech., 7, 419–436, https://doi.org/10.5194/amt-7-419-2014, https://doi.org/10.5194/amt-7-419-2014, 2014
C. Jouan, J. Pelon, E. Girard, G. Ancellet, J. P. Blanchet, and J. Delanoë
Atmos. Chem. Phys., 14, 1205–1224, https://doi.org/10.5194/acp-14-1205-2014, https://doi.org/10.5194/acp-14-1205-2014, 2014
P. Dubuisson, H. Herbin, F. Minvielle, M. Compiègne, F. Thieuleux, F. Parol, and J. Pelon
Atmos. Meas. Tech., 7, 359–371, https://doi.org/10.5194/amt-7-359-2014, https://doi.org/10.5194/amt-7-359-2014, 2014
J.-F. Gayet, V. Shcherbakov, L. Bugliaro, A. Protat, J. Delanoë, J. Pelon, and A. Garnier
Atmos. Chem. Phys., 14, 899–912, https://doi.org/10.5194/acp-14-899-2014, https://doi.org/10.5194/acp-14-899-2014, 2014
F. J. S. Lopes, E. Landulfo, and M. A. Vaughan
Atmos. Meas. Tech., 6, 3281–3299, https://doi.org/10.5194/amt-6-3281-2013, https://doi.org/10.5194/amt-6-3281-2013, 2013
C. Tsamalis, A. Chédin, J. Pelon, and V. Capelle
Atmos. Chem. Phys., 13, 11235–11257, https://doi.org/10.5194/acp-13-11235-2013, https://doi.org/10.5194/acp-13-11235-2013, 2013
O. Bock, P. Bosser, T. Bourcy, L. David, F. Goutail, C. Hoareau, P. Keckhut, D. Legain, A. Pazmino, J. Pelon, K. Pipis, G. Poujol, A. Sarkissian, C. Thom, G. Tournois, and D. Tzanos
Atmos. Meas. Tech., 6, 2777–2802, https://doi.org/10.5194/amt-6-2777-2013, https://doi.org/10.5194/amt-6-2777-2013, 2013
S. Rodier, Y. Hu, and M. Vaughan
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-4681-2013, https://doi.org/10.5194/tcd-7-4681-2013, 2013
Revised manuscript has not been submitted
O. Sourdeval, L. C. -Labonnote, G. Brogniez, O. Jourdan, J. Pelon, and A. Garnier
Atmos. Chem. Phys., 13, 8229–8244, https://doi.org/10.5194/acp-13-8229-2013, https://doi.org/10.5194/acp-13-8229-2013, 2013
S. P. Burton, R. A. Ferrare, M. A. Vaughan, A. H. Omar, R. R. Rogers, C. A. Hostetler, and J. W. Hair
Atmos. Meas. Tech., 6, 1397–1412, https://doi.org/10.5194/amt-6-1397-2013, https://doi.org/10.5194/amt-6-1397-2013, 2013
D. M. Winker, J. L. Tackett, B. J. Getzewich, Z. Liu, M. A. Vaughan, and R. R. Rogers
Atmos. Chem. Phys., 13, 3345–3361, https://doi.org/10.5194/acp-13-3345-2013, https://doi.org/10.5194/acp-13-3345-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Latent heating profiles from GOES-16 and its impacts on precipitation forecasts
A CO2-independent cloud mask from Infrared Atmospheric Sounding Interferometer (IASI) radiances for climate applications
Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network
Intercomparison of Sentinel-5P TROPOMI cloud products for tropospheric trace gas retrievals
Improved spectral processing for a multi-mode pulse compression Ka–Ku-band cloud radar system
Uncertainty-bounded estimates of ash cloud properties using the ORAC algorithm: application to the 2019 Raikoke eruption
Ice water path retrievals from Meteosat-9 using quantile regression neural networks
High spatial resolution retrieval of cloud droplet size distribution from polarized observations of the cloudbow
An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties
Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks
Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network
Retrieval of Terahertz Ice Cloud Properties from airborne measurements based on the irregularly shaped Voronoi ice scattering models
Top-of-the-atmosphere reflected shortwave radiative fluxes from GOES-R
Optimizing radar scan strategies for tracking isolated deep convection using observing system simulation experiments
Evaluation of the smile effect on the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE)/Multi-Spectral Imager (MSI) cloud product
A kriging-based analysis of cloud liquid water content using CloudSat data
High-resolution satellite-based cloud detection for the analysis of land surface effects on boundary layer clouds
Retrievals of ice microphysical properties using dual-wavelength polarimetric radar observations during stratiform precipitation events
The surface longwave cloud radiative effect derived from space lidar observations
Cloud phase and macrophysical properties over the Southern Ocean during the MARCUS field campaign
Detection of supercooled liquid water containing clouds with ceilometers: development and evaluation of deterministic and data-driven retrievals
An all-sky camera image classification method using cloud cover features
Determination of atmospheric column condensate using active and passive remote sensing technology
Improving discrimination between clouds and optically thick aerosol plumes in geostationary satellite data
Towards the use of conservative thermodynamic variables in data assimilation: a case study using ground-based microwave radiometer measurements
Empirical model of multiple-scattering effect on single-wavelength lidar data of aerosols and clouds
Analytic characterization of random errors in spectral dual-polarized cloud radar observations
Assessing synergistic radar and radiometer capability in retrieving ice cloud microphysics based on hybrid Bayesian algorithms
A Semi-Lagrangian Method for Detecting and Tracking Deep Convective Clouds in Geostationary Satellite Observations
Applying self-supervised learning for semantic cloud segmentation of all-sky images
Coincident in situ and triple-frequency radar airborne observations in the Arctic
Analysis of improvements in MOPITT observational coverage over Canada
Climatology of estimated LWC and scaling factor for warm clouds using radar – microwave radiometer synergy
Using artificial neural networks to predict riming from Doppler cloud radar observations
Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network
Cloud optical properties retrieval and associated uncertainties using multi-angular and multi-spectral measurements of the airborne radiometer OSIRIS
PARAFOG v2.0: a near-real-time decision tool to support nowcasting fog formation events at local scales
Inpainting radar missing data regions with deep learning
Improved cloud detection for the Aura Microwave Limb Sounder (MLS): training an artificial neural network on colocated MLS and Aqua MODIS data
Triple-frequency radar retrieval of microphysical properties of snow
Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations
Comparison of mid-latitude single- and mixed-phase cloud optical depth from co-located infrared spectrometer and backscatter lidar measurements
Physical characteristics of frozen hydrometeors inferred with parameter estimation
Cloud height measurement by a network of all-sky imagers
Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: implementation and examples
Why we need radar, lidar, and solar radiance observations to constrain ice cloud microphysics
Estimating the optical extinction of liquid water clouds in the cloud base region
W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors
Identification of snowfall microphysical processes from Eulerian vertical gradients of polarimetric radar variables
Identifying insects, clouds, and precipitation using vertically pointing polarimetric radar Doppler velocity spectra
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 15, 7119–7136, https://doi.org/10.5194/amt-15-7119-2022, https://doi.org/10.5194/amt-15-7119-2022, 2022
Short summary
Short summary
Vertical profiles of latent heating are derived from GOES-16 to be used in convective initialization. They are compared with other latent heating products derived from NEXRAD and GPM satellites, and the results show that their values are very similar to the radar-derived products. Finally, using latent heating derived from GOES-16 for convective initialization shows improvements in precipitation forecasts, which are comparable to the results using latent heating derived from NEXRAD.
Simon Whitburn, Lieven Clarisse, Marc Crapeau, Thomas August, Tim Hultberg, Pierre François Coheur, and Cathy Clerbaux
Atmos. Meas. Tech., 15, 6653–6668, https://doi.org/10.5194/amt-15-6653-2022, https://doi.org/10.5194/amt-15-6653-2022, 2022
Short summary
Short summary
With more than 15 years of measurements, the IASI radiance dataset is becoming a reference climate data record. Its exploitation for satellite applications requires an accurate and unbiased detection of cloud scenes. Here, we present a new cloud detection algorithm for IASI that is both sensitive and consistent over time. It is based on the use of a neural network, relying on IASI radiance information only and taking as a reference the last version of the operational IASI L2 cloud product.
Wenyu Wang, Zhenzhan Wang, Qiurui He, and Lanjie Zhang
Atmos. Meas. Tech., 15, 6489–6506, https://doi.org/10.5194/amt-15-6489-2022, https://doi.org/10.5194/amt-15-6489-2022, 2022
Short summary
Short summary
This paper uses a neural network approach to retrieve the ice water path from FY-3B/MWHS polarimetric measurements, focusing on its unique 150 GHz quasi-polarized channels. The Level 2 product of CloudSat is used as the reference value for the neural network. The results show that the polarization information is helpful for the retrieval in scenes with thicker cloud ice, and the 150 GHz channels give a significant improvement compared to using only 183 GHz channels.
Miriam Latsch, Andreas Richter, Henk Eskes, Maarten Sneep, Ping Wang, Pepijn Veefkind, Ronny Lutz, Diego Loyola, Athina Argyrouli, Pieter Valks, Thomas Wagner, Holger Sihler, Michel van Roozendael, Nicolas Theys, Huan Yu, Richard Siddans, and John P. Burrows
Atmos. Meas. Tech., 15, 6257–6283, https://doi.org/10.5194/amt-15-6257-2022, https://doi.org/10.5194/amt-15-6257-2022, 2022
Short summary
Short summary
The article investigates different S5P TROPOMI cloud retrieval algorithms for tropospheric trace gas retrievals. The cloud products show differences primarily over snow and ice and for scenes under sun glint. Some issues regarding across-track dependence are found for the cloud fractions as well as for the cloud heights.
Han Ding, Haoran Li, and Liping Liu
Atmos. Meas. Tech., 15, 6181–6200, https://doi.org/10.5194/amt-15-6181-2022, https://doi.org/10.5194/amt-15-6181-2022, 2022
Short summary
Short summary
In this study, a framework for processing the Doppler spectra observations of a multi-mode pulse compression Ka–Ku cloud radar system is presented. We first proposed an approach to identify and remove the clutter signals in the Doppler spectrum. Then, we developed a new algorithm to remove the range sidelobe at the modes implementing the pulse compression technique. The radar observations from different modes were then merged using the shift-then-average method.
Andrew T. Prata, Roy G. Grainger, Isabelle A. Taylor, Adam C. Povey, Simon R. Proud, and Caroline A. Poulsen
Atmos. Meas. Tech., 15, 5985–6010, https://doi.org/10.5194/amt-15-5985-2022, https://doi.org/10.5194/amt-15-5985-2022, 2022
Short summary
Short summary
Satellite observations are often used to track ash clouds and estimate their height, particle sizes and mass; however, satellite-based techniques are always associated with some uncertainty. We describe advances in a satellite-based technique that is used to estimate ash cloud properties for the June 2019 Raikoke (Russia) eruption. Our results are significant because ash warning centres increasingly require uncertainty information to correctly interpret,
aggregate and utilise the data.
Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, https://doi.org/10.5194/amt-15-5701-2022, 2022
Short summary
Short summary
Geostationary satellites continuously image a given location on Earth, a feature that satellites designed to characterize atmospheric ice lack. However, the relationship between geostationary images and atmospheric ice is complex. Machine learning is used here to leverage such images to characterize atmospheric ice throughout the day in a probabilistic manner. Using structural information from the image improves the characterization, and this approach compares favourably to traditional methods.
Veronika Pörtge, Tobias Kölling, Anna Weber, Lea Volkmer, Claudia Emde, Tobias Zinner, and Bernhard Mayer
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-245, https://doi.org/10.5194/amt-2022-245, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
In this work, we analyze polarized cloudbow observations of the airborne camera system specMACS to retrieve the cloud droplet size distribution, defined by the effective radius (reff) and the effective variance (veff). Two case studies of trade wind cumulus clouds measured during the EUREC4A field campaign are presented. The results are combined into maps of reff and veff with a very high spatial resolution (100 m by 100 m), that give new insights into the spatial distribution of the parameters.
Alistair Bell, Pauline Martinet, Olivier Caumont, Frédéric Burnet, Julien Delanoë, Susana Jorquera, Yann Seity, and Vinciane Unger
Atmos. Meas. Tech., 15, 5415–5438, https://doi.org/10.5194/amt-15-5415-2022, https://doi.org/10.5194/amt-15-5415-2022, 2022
Short summary
Short summary
Cloud radars and microwave radiometers offer the potential to improve fog forecasts when assimilated into a high-resolution model. As this process can be complex, a retrieval of model variables is sometimes made as a first step. In this work, results from a 1D-Var algorithm for the retrieval of temperature, humidity and cloud liquid water content are presented. The algorithm is applied first to a synthetic dataset and then to a dataset of real measurements from a recent field campaign.
Willi Schimmel, Heike Kalesse-Los, Maximilian Maahn, Teresa Vogl, Andreas Foth, Pablo Saavedra Garfias, and Patric Seifert
Atmos. Meas. Tech., 15, 5343–5366, https://doi.org/10.5194/amt-15-5343-2022, https://doi.org/10.5194/amt-15-5343-2022, 2022
Short summary
Short summary
This study introduces the novel Doppler radar spectra-based machine learning approach VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn). VOODOO is a powerful probability-based extension to the existing Cloudnet hydrometeor target classification, enabling the detection of liquid-bearing cloud layers beyond complete lidar attenuation via user-defined p* threshold. VOODOO performs best for (multi-layer) stratiform and deep mixed-phase clouds with liquid water path > 100 g m−2.
Vikas Nataraja, Sebastian Schmidt, Hong Chen, Takanobu Yamaguchi, Jan Kazil, Graham Feingold, Kevin Wolf, and Hironobu Iwabuchi
Atmos. Meas. Tech., 15, 5181–5205, https://doi.org/10.5194/amt-15-5181-2022, https://doi.org/10.5194/amt-15-5181-2022, 2022
Short summary
Short summary
A convolutional neural network (CNN) is introduced to retrieve cloud optical thickness (COT) from passive cloud imagery. The CNN, trained on large eddy simulations from the Sulu Sea, learns from spatial information at multiple scales to reduce cloud inhomogeneity effects. By considering the spatial context of a pixel, the CNN outperforms the traditional independent pixel approximation (IPA) across several cloud morphology metrics.
Ming Li, Husi Letu, Hiroshi Ishimoto, Shulei Li, Lei Liu, Takashi Y. Nakajima, Dabin Ji, Huazhe Shang, and Chong Shi
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-247, https://doi.org/10.5194/amt-2022-247, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
Influenced by the representativeness of ice crystal scattering models, the existing terahertz ice cloud remote sensing inversion algorithms still have significant uncertainties. We developed an ice cloud remote sensing retrieval algorithm of the ice water path and particle size from aircraft-based terahertz radiation measurements based on the Voronoi model. Validation revealed that the Voronoi model performs better than the Sphere model.
Rachel T. Pinker, Yingtao Ma, Wen Chen, Istvan Laszlo, Hongqing Liu, Hye-Yun Kim, and Jaime Daniels
Atmos. Meas. Tech., 15, 5077–5094, https://doi.org/10.5194/amt-15-5077-2022, https://doi.org/10.5194/amt-15-5077-2022, 2022
Short summary
Short summary
Scene-dependent narrow-to-broadband transformations are developed to facilitate the use of observations from the Advanced Baseline Imager (ABI), the primary instrument on GOES-R, to derive surface shortwave radiative fluxes. This is a first NOAA product at the high resolution of about 5 k over the contiguous United States (CONUS) region. The product is archived and can be downloaded from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).
Mariko Oue, Stephen M. Saleeby, Peter J. Marinescu, Pavlos Kollias, and Susan C. van den Heever
Atmos. Meas. Tech., 15, 4931–4950, https://doi.org/10.5194/amt-15-4931-2022, https://doi.org/10.5194/amt-15-4931-2022, 2022
Short summary
Short summary
This study provides an optimization of radar observation strategies to better capture convective cell evolution in clean and polluted environments as well as a technique for the optimization. The suggested optimized radar observation strategy is to better capture updrafts at middle and upper altitudes and precipitation particle evolution of isolated deep convective clouds. This study sheds light on the challenge of designing remote sensing observation strategies in pre-field campaign periods.
Minrui Wang, Takashi Y. Nakajima, Woosub Roh, Masaki Satoh, Kentaroh Suzuki, Takuji Kubota, and Mayumi Yoshida
EGUsphere, https://doi.org/10.5194/egusphere-2022-736, https://doi.org/10.5194/egusphere-2022-736, 2022
Short summary
Short summary
Smile effect (an effect in which a shift in the center wavelength appears as a distortion in the spectral image) was detected during our recent work. To evaluate how it affects the cloud retrieval product, we did a simulation of MSI forward radiation, then evaluated the error in simulated scenes from a global cloud system resolving model and a satellite simulator. Our results indicated that the error from the smile effect was generally small and could be seen as negligible for oceanic scenes.
Jean-Marie Lalande, Guillaume Bourmaud, Pierre Minvielle, and Jean-François Giovannelli
Atmos. Meas. Tech., 15, 4411–4429, https://doi.org/10.5194/amt-15-4411-2022, https://doi.org/10.5194/amt-15-4411-2022, 2022
Short summary
Short summary
In this paper we describe the implementation of an interpolation–prediction estimator applied to cloud properties derived from CloudSat observations. The objective is to evaluate the uncertainty associated with the estimated quantity. The model developed in this study can be valuable for satellite applications (GPS, telecommunication) as well as for cloud product comparisons. This paper is didactic and beneficial for anyone interested in kriging estimators.
Julia Fuchs, Hendrik Andersen, Jan Cermak, Eva Pauli, and Rob Roebeling
Atmos. Meas. Tech., 15, 4257–4270, https://doi.org/10.5194/amt-15-4257-2022, https://doi.org/10.5194/amt-15-4257-2022, 2022
Short summary
Short summary
Two cloud-masking approaches, a local and a regional approach, using high-resolution satellite data are developed and validated for the region of Paris to improve applicability for analyses of urban effects on low clouds. We found that cloud masks obtained from the regional approach are more appropriate for the high-resolution analysis of locally induced cloud processes. Its applicability is tested for the analysis of typical fog conditions over different surface types.
Eleni Tetoni, Florian Ewald, Martin Hagen, Gregor Köcher, Tobias Zinner, and Silke Groß
Atmos. Meas. Tech., 15, 3969–3999, https://doi.org/10.5194/amt-15-3969-2022, https://doi.org/10.5194/amt-15-3969-2022, 2022
Short summary
Short summary
We use the C-band POLDIRAD and the Ka-band MIRA-35 to perform snowfall dual-wavelength polarimetric radar measurements. We develop an ice microphysics retrieval for mass, apparent shape, and median size of the particle size distribution by comparing observations to T-matrix ice spheroid simulations while varying the mass–size relationship. We furthermore show how the polarimetric measurements from POLDIRAD help to narrow down ambiguities between ice particle shape and size.
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, https://doi.org/10.5194/amt-15-3893-2022, 2022
Short summary
Short summary
We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from observations collected by a space-based lidar on board the CALIPSO satellite and radiative transfer computations. Our estimate appropriately captures the surface LW CRE annual variability over bright polar surfaces, and it provides a dataset more than 13 years long.
Baike Xi, Xiquan Dong, Xiaojian Zheng, and Peng Wu
Atmos. Meas. Tech., 15, 3761–3777, https://doi.org/10.5194/amt-15-3761-2022, https://doi.org/10.5194/amt-15-3761-2022, 2022
Short summary
Short summary
This study develops an innovative method to determine the cloud phases over the Southern Ocean (SO) using the combination of radar and lidar measurements during the ship-based field campaign of MARCUS. Results from our study show that the low-level, deep, and shallow cumuli are dominant, and the mixed-phase clouds occur more than single phases over the SO. The mixed-phase cloud properties are similar to liquid-phase (ice-phase) clouds in the midlatitudes (polar) region of the SO.
Adrien Guyot, Alain Protat, Simon P. Alexander, Andrew R. Klekociuk, Peter Kuma, and Adrian McDonald
Atmos. Meas. Tech., 15, 3663–3681, https://doi.org/10.5194/amt-15-3663-2022, https://doi.org/10.5194/amt-15-3663-2022, 2022
Short summary
Short summary
Ceilometers are instruments that are widely deployed as part of operational networks. They are usually not able to detect cloud phase. Here, we propose an evaluation of various methods to detect supercooled liquid water with ceilometer observations, using an extensive dataset from Davis, Antarctica. Our results highlight the possibility for ceilometers to detect supercooled liquid water in clouds.
Xiaotong Li, Baozhu Wang, Bo Qiu, and Chao Wu
Atmos. Meas. Tech., 15, 3629–3639, https://doi.org/10.5194/amt-15-3629-2022, https://doi.org/10.5194/amt-15-3629-2022, 2022
Short summary
Short summary
The all-sky camera images can reflect the local cloud cover, which is considerable for astronomical observatory site selection. Therefore, the realization of automatic classification of the images is very important. In this paper, three cloud cover features are proposed to classify the images. The proposed method is evaluated on a large dataset, and the method achieves an accuracy of 96.58 % and F1_score of 96.24 %, which greatly improves the efficiency of automatic processing of the images.
Huige Di, Yun Yuan, Qing Yan, Wenhui Xin, Shichun Li, Jun Wang, Yufeng Wang, Lei Zhang, and Dengxin Hua
Atmos. Meas. Tech., 15, 3555–3567, https://doi.org/10.5194/amt-15-3555-2022, https://doi.org/10.5194/amt-15-3555-2022, 2022
Short summary
Short summary
It is necessary to correctly evaluate the amount of cloud water resources in an area. Currently, there is a lack of effective observation methods for atmospheric column condensate evaluation. We propose a method for atmospheric column condensate by combining millimetre cloud radar, lidar and microwave radiometers. The method can realise determination of atmospheric column condensate. The variation of cloud before precipitation is considered, and the atmospheric column is deduced and obtained.
Daniel Robbins, Caroline Poulsen, Steven Siems, and Simon Proud
Atmos. Meas. Tech., 15, 3031–3051, https://doi.org/10.5194/amt-15-3031-2022, https://doi.org/10.5194/amt-15-3031-2022, 2022
Short summary
Short summary
A neural network (NN)-based cloud mask for a geostationary satellite instrument, AHI, is developed using collocated data and is better at not classifying thick aerosols as clouds versus the Japanese Meteorological Association and the Bureau of Meteorology masks, identifying 1.13 and 1.29 times as many non-cloud pixels than each mask, respectively. The improvement during the day likely comes from including the shortest wavelength bands from AHI in the NN mask, which the other masks do not use.
Pascal Marquet, Pauline Martinet, Jean-François Mahfouf, Alina Lavinia Barbu, and Benjamin Ménétrier
Atmos. Meas. Tech., 15, 2021–2035, https://doi.org/10.5194/amt-15-2021-2022, https://doi.org/10.5194/amt-15-2021-2022, 2022
Short summary
Short summary
Two conservative thermodynamic variables (moist-air entropy potential temperature and total water content) are introduced into a one-dimensional EnVar data assimilation system to demonstrate their benefit for future operational assimilation schemes, with the use of microwave brightness temperatures from a ground-based radiometer installed during the field campaign SOFGO3D. Results show that the brightness temperatures analysed with the new variables are improved, including the liquid water.
Valery Shcherbakov, Frédéric Szczap, Alaa Alkasem, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 15, 1729–1754, https://doi.org/10.5194/amt-15-1729-2022, https://doi.org/10.5194/amt-15-1729-2022, 2022
Short summary
Short summary
We performed extensive Monte Carlo (MC) simulations of lidar signals and developed an empirical model to account for the multiple scattering in the lidar signals. The simulations have taken into consideration four types of lidar configurations (the ground based, the airborne, the CALIOP, and the ATLID) and four types of particles (coarse aerosol, water cloud, jet-stream cirrus, and cirrus).
The empirical model has very good quality of MC data fitting for all considered cases.
Alexander Myagkov and Davide Ori
Atmos. Meas. Tech., 15, 1333–1354, https://doi.org/10.5194/amt-15-1333-2022, https://doi.org/10.5194/amt-15-1333-2022, 2022
Short summary
Short summary
This study provides equations to characterize random errors of spectral polarimetric observations from cloud radars. The results can be used for a broad spectrum of applications. For instance, accurate error characterization is essential for advanced retrievals of microphysical properties of clouds and precipitation. Moreover, error characterization allows for the use of measurements from polarimetric cloud radars to potentially improve weather forecasts.
Yuli Liu and Gerald G. Mace
Atmos. Meas. Tech., 15, 927–944, https://doi.org/10.5194/amt-15-927-2022, https://doi.org/10.5194/amt-15-927-2022, 2022
Short summary
Short summary
We propose a suite of Bayesian algorithms for synergistic radar and radiometer retrievals to evaluate the next-generation NASA Cloud, Convection and Precipitation (CCP) observing system. The algorithms address pixel-level retrievals using active-only, passive-only, and synergistic active–passive observations. Novel techniques in developing synergistic algorithms are presented. Quantitative assessments of the CCP observing system's capability in retrieving ice cloud microphysics are provided.
William K. Jones, Matthew W. Christensen, and Philip Stier
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-31, https://doi.org/10.5194/amt-2022-31, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
Geostationary weather satellites have been used to detect storm clouds since their earliest applications. However, this task remains difficult as imaging satellites cannot observe the strong vertical winds that are characteristic of storm clouds. Here we introduce a new method that allows us to detect the early development of storms and continue to track them throughout their lifetime, allowing us to study how their early behaviour affects subsequent weather.
Yann Fabel, Bijan Nouri, Stefan Wilbert, Niklas Blum, Rudolph Triebel, Marcel Hasenbalg, Pascal Kuhn, Luis F. Zarzalejo, and Robert Pitz-Paal
Atmos. Meas. Tech., 15, 797–809, https://doi.org/10.5194/amt-15-797-2022, https://doi.org/10.5194/amt-15-797-2022, 2022
Short summary
Short summary
This work presents a new approach to exploit unlabeled image data from ground-based sky observations to train neural networks. We show that our model can detect cloud classes within images more accurately than models trained with conventional methods using small, labeled datasets only. Novel machine learning techniques as applied in this work enable training with much larger datasets, leading to improved accuracy in cloud detection and less need for manual image labeling.
Cuong M. Nguyen, Mengistu Wolde, Alessandro Battaglia, Leonid Nichman, Natalia Bliankinshtein, Samuel Haimov, Kenny Bala, and Dirk Schuettemeyer
Atmos. Meas. Tech., 15, 775–795, https://doi.org/10.5194/amt-15-775-2022, https://doi.org/10.5194/amt-15-775-2022, 2022
Short summary
Short summary
An analysis of airborne triple-frequency radar and almost perfectly co-located coincident in situ data from an Arctic storm confirms the main findings of modeling work with radar dual-frequency ratios (DFRs) at different zones of the DFR plane associated with different ice habits. High-resolution CPI images provide accurate identification of rimed particles within the DFR plane. The relationships between the triple-frequency signals and cloud microphysical properties are also presented.
Heba S. Marey, James R. Drummond, Dylan B. A. Jones, Helen Worden, Merritt N. Deeter, John Gille, and Debbie Mao
Atmos. Meas. Tech., 15, 701–719, https://doi.org/10.5194/amt-15-701-2022, https://doi.org/10.5194/amt-15-701-2022, 2022
Short summary
Short summary
In this study, an analysis has been performed to understand the improvements in observational coverage over Canada in the new MOPITT V9 product. Temporal and spatial analysis of V9 indicates a general coverage gain of 15–20 % relative to V8, which varies regionally and seasonally; e.g., the number of successful MOPITT retrievals in V9 was doubled over Canada in winter. Also, comparison with the corresponding IASI instrument indicated generally good agreement, with about a 5–10 % positive bias.
Pragya Vishwakarma, Julien Delanoë, Susana Jorquera, Pauline Martinet, Frederic Burnet, Alistair Bell, and Jean-Charles Dupont
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-3, https://doi.org/10.5194/amt-2022-3, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
Cloud observations are necessary to characterize the cloud properties at the local and global scales. These observations must be translated to cloud geophysical parameters. This paper presents the estimation of liquid water content (LWC) using radar and microwave radiometer (MWR) measurements. LWP from MWR scales the LWC and the scaling factor (lna) is retrieved. The retrievals are compared with in-situ observations. A climatology of lna is built to estimate LWC with only radar information.
Teresa Vogl, Maximilian Maahn, Stefan Kneifel, Willi Schimmel, Dmitri Moisseev, and Heike Kalesse-Los
Atmos. Meas. Tech., 15, 365–381, https://doi.org/10.5194/amt-15-365-2022, https://doi.org/10.5194/amt-15-365-2022, 2022
Short summary
Short summary
We are using machine learning techniques, a type of artificial intelligence, to detect graupel formation in clouds. The measurements used as input to the machine learning framework were performed by cloud radars. Cloud radars are instruments located at the ground, emitting radiation with wavelenghts of a few millimeters vertically into the cloud and measuring the back-scattered signal. Our novel technique can be applied to different radar systems and different weather conditions.
Heike Kalesse-Los, Willi Schimmel, Edward Luke, and Patric Seifert
Atmos. Meas. Tech., 15, 279–295, https://doi.org/10.5194/amt-15-279-2022, https://doi.org/10.5194/amt-15-279-2022, 2022
Short summary
Short summary
It is important to detect the vertical distribution of cloud droplets and ice in mixed-phase clouds. Here, an artificial neural network (ANN) previously developed for Arctic clouds is applied to a mid-latitudinal cloud radar data set. The performance of this technique is contrasted to the Cloudnet target classification. For thick/multi-layer clouds, the machine learning technique is better at detecting liquid than Cloudnet, but if lidar data are available Cloudnet is at least as good as the ANN.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Jean-Marc Nicolas
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-414, https://doi.org/10.5194/amt-2021-414, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
The uncertainties in cloud remote sensing can propagate to the retrieved cloud properties and they need to be quantified. We present the formalism of error extraction and we apply it on the cloud properties retrieved from the measurements of the airborne radiometer OSIRIS. We show that errors related to measurement uncertainties reach 10 %. Errors related to the simplified model assuming that the clouds are plane-parallel and homogeneous lead to uncertainties exceeding 10 %.
Jean-François Ribaud, Martial Haeffelin, Jean-Charles Dupont, Marc-Antoine Drouin, Felipe Toledo, and Simone Kotthaus
Atmos. Meas. Tech., 14, 7893–7907, https://doi.org/10.5194/amt-14-7893-2021, https://doi.org/10.5194/amt-14-7893-2021, 2021
Short summary
Short summary
PARAFOG is a near-real-time decision tool that aims to retrieve pre-fog alert levels minutes to hours prior to fog onset. The second version of PARAFOG allows us to discriminate between radiation and stratus lowering fog situations. It is based upon the combination of visibility observations and automatic lidar and ceilometer measurements. The overall performance of the second version of PARAFOG over more than 300 fog cases at five different locations presents a good perfomance.
Andrew Geiss and Joseph C. Hardin
Atmos. Meas. Tech., 14, 7729–7747, https://doi.org/10.5194/amt-14-7729-2021, https://doi.org/10.5194/amt-14-7729-2021, 2021
Short summary
Short summary
Radars can suffer from missing or poor-quality data regions for several reasons: beam blockage, instrument failure, and near-ground blind zones, etc. Here, we demonstrate how deep convolutional neural networks can be used for filling in radar-missing data regions and that they can significantly outperform conventional approaches in terms of realism and accuracy.
Frank Werner, Nathaniel J. Livesey, Michael J. Schwartz, William G. Read, Michelle L. Santee, and Galina Wind
Atmos. Meas. Tech., 14, 7749–7773, https://doi.org/10.5194/amt-14-7749-2021, https://doi.org/10.5194/amt-14-7749-2021, 2021
Short summary
Short summary
In this study we present an improved cloud detection scheme for the Microwave Limb Sounder, which is based on a feedforward artificial neural network. This new algorithm is shown not only to reliably detect high and mid-level convection containing even small amounts of cloud water but also to distinguish between high-reaching and mid-level to low convection.
Kamil Mroz, Alessandro Battaglia, Cuong Nguyen, Andrew Heymsfield, Alain Protat, and Mengistu Wolde
Atmos. Meas. Tech., 14, 7243–7254, https://doi.org/10.5194/amt-14-7243-2021, https://doi.org/10.5194/amt-14-7243-2021, 2021
Short summary
Short summary
A method for estimating microphysical properties of ice clouds based on radar measurements is presented. The algorithm exploits the information provided by differences in the radar response at different frequency bands in relation to changes in the snow morphology. The inversion scheme is based on a statistical relation between the radar simulations and the properties of snow calculated from in-cloud sampling.
Nicholas J. Kedzuf, J. Christine Chiu, V. Chandrasekar, Sounak Biswas, Shashank S. Joshil, Yinghui Lu, Peter Jan van Leeuwen, Christopher Westbrook, Yann Blanchard, and Sebastian O'Shea
Atmos. Meas. Tech., 14, 6885–6904, https://doi.org/10.5194/amt-14-6885-2021, https://doi.org/10.5194/amt-14-6885-2021, 2021
Short summary
Short summary
Ice clouds play a key role in our climate system due to their strong controls on precipitation and the radiation budget. However, it is difficult to characterize co-existing ice species using radar observations. We present a new method that separates the radar signals of pristine ice embedded in snow aggregates and retrieves their respective abundances and sizes for the first time. The ability to provide their quantitative microphysical properties will open up many research opportunities.
Gianluca Di Natale, Marco Barucci, Claudio Belotti, Giovanni Bianchini, Francesco D'Amato, Samuele Del Bianco, Marco Gai, Alessio Montori, Ralf Sussmann, Silvia Viciani, Hannes Vogelmann, and Luca Palchetti
Atmos. Meas. Tech., 14, 6749–6758, https://doi.org/10.5194/amt-14-6749-2021, https://doi.org/10.5194/amt-14-6749-2021, 2021
Short summary
Short summary
The importance of cirrus and mixed-phase clouds in the Earth radiation budget has been proven by many studies. In this paper the properties that characterize these clouds are retrieved from lidar and far-infrared spectral measurements performed in winter 2018/19 on the Zugspitze (Germany). The synergy of lidar and spectrometer measurements allowed us to assess the exponent k of the power-law relationship between the backscattering and the extinction coefficients.
Alan J. Geer
Atmos. Meas. Tech., 14, 5369–5395, https://doi.org/10.5194/amt-14-5369-2021, https://doi.org/10.5194/amt-14-5369-2021, 2021
Short summary
Short summary
Satellite observations sensitive to cloud and precipitation help improve the quality of weather forecasts. However, they are sensitive to things that models do not forecast, such as the shapes and sizes of snow and ice particles. These details can be estimated from the observations themselves and then incorporated in the satellite simulators used in weather forecasting. This approach, known as parameter estimation, will be increasingly useful to build models of poorly known physical processes.
Niklas Benedikt Blum, Bijan Nouri, Stefan Wilbert, Thomas Schmidt, Ontje Lünsdorf, Jonas Stührenberg, Detlev Heinemann, Andreas Kazantzidis, and Robert Pitz-Paal
Atmos. Meas. Tech., 14, 5199–5224, https://doi.org/10.5194/amt-14-5199-2021, https://doi.org/10.5194/amt-14-5199-2021, 2021
Short summary
Short summary
Cloud base height (CBH) is important, e.g., to forecast solar irradiance and, with it, photovoltaic production. All-sky imagers (ASIs), cameras monitoring the sky above their point of installation, can provide such forecasts and also measure CBH. We present a network of ASIs to measure CBH. The network provides numerous readings of CBH simultaneously. We combine these with a statistical procedure. Validation attests to significantly higher accuracy of the combination compared to two ASIs alone.
Hartwig Deneke, Carola Barrientos-Velasco, Sebastian Bley, Anja Hünerbein, Stephan Lenk, Andreas Macke, Jan Fokke Meirink, Marion Schroedter-Homscheidt, Fabian Senf, Ping Wang, Frank Werner, and Jonas Witthuhn
Atmos. Meas. Tech., 14, 5107–5126, https://doi.org/10.5194/amt-14-5107-2021, https://doi.org/10.5194/amt-14-5107-2021, 2021
Short summary
Short summary
The SEVIRI instrument flown on the European geostationary Meteosat satellites acquires multi-spectral images at a relatively coarse pixel resolution of 3 × 3 km2, but it also has a broadband high-resolution visible channel with 1 × 1 km2 spatial resolution. In this study, the modification of an existing cloud property and solar irradiance retrieval to use this channel to improve the spatial resolution of its output products as well as the resulting benefits for applications are described.
Florian Ewald, Silke Groß, Martin Wirth, Julien Delanoë, Stuart Fox, and Bernhard Mayer
Atmos. Meas. Tech., 14, 5029–5047, https://doi.org/10.5194/amt-14-5029-2021, https://doi.org/10.5194/amt-14-5029-2021, 2021
Short summary
Short summary
In this study, we show how solar radiance observations can be used to validate and further constrain ice cloud microphysics retrieved from the synergy of radar–lidar measurements. Since most radar–lidar retrievals rely on a global assumption about the ice particle shape, ice water content and particle size biases are to be expected in individual cloud regimes. In this work, we identify and correct these biases by reconciling simulated and measured solar radiation reflected from these clouds.
Karolina Sarna, David P. Donovan, and Herman W. J. Russchenberg
Atmos. Meas. Tech., 14, 4959–4970, https://doi.org/10.5194/amt-14-4959-2021, https://doi.org/10.5194/amt-14-4959-2021, 2021
Short summary
Short summary
We show a method for obtaining cloud optical extinction with a lidar system. We use a scheme in which a lidar signal is inverted based on the estimated value of cloud extinction at the far end of the cloud and apply a correction for multiple scattering within the cloud and a range resolution correction. By applying our technique, we show that it is possible to obtain the cloud optical extinction with an error better than 5 % up to 90 m within the cloud.
Alistair Bell, Pauline Martinet, Olivier Caumont, Benoît Vié, Julien Delanoë, Jean-Charles Dupont, and Mary Borderies
Atmos. Meas. Tech., 14, 4929–4946, https://doi.org/10.5194/amt-14-4929-2021, https://doi.org/10.5194/amt-14-4929-2021, 2021
Short summary
Short summary
This paper presents work towards making retrievals on the liquid water content in fog and low clouds. Future retrievals will rely on a radar simulator and high-resolution forecast. In this work, real observations are used to assess the errors associated with the simulator and forecast. A selection method to reduce errors associated with the forecast is proposed. It is concluded that the distribution of errors matches the requirements for future retrievals.
Noémie Planat, Josué Gehring, Étienne Vignon, and Alexis Berne
Atmos. Meas. Tech., 14, 4543–4564, https://doi.org/10.5194/amt-14-4543-2021, https://doi.org/10.5194/amt-14-4543-2021, 2021
Short summary
Short summary
We implement a new method to identify microphysical processes during cold precipitation events based on the sign of the vertical gradient of polarimetric radar variables. We analytically asses the meteorological conditions for this vertical analysis to hold, apply it on two study cases and successfully compare it with other methods informing about the microphysics. Finally, we are able to obtain the main vertical structure and characteristics of the different processes during these study cases.
Christopher R. Williams, Karen L. Johnson, Scott E. Giangrande, Joseph C. Hardin, Ruşen Öktem, and David M. Romps
Atmos. Meas. Tech., 14, 4425–4444, https://doi.org/10.5194/amt-14-4425-2021, https://doi.org/10.5194/amt-14-4425-2021, 2021
Short summary
Short summary
In addition to detecting clouds, vertically pointing cloud radars detect individual insects passing over head. If these insects are not identified and removed from raw observations, then radar-derived cloud properties will be contaminated. This work identifies clouds in radar observations due to their continuous and smooth structure in time, height, and velocity. Cloud masks are produced that identify cloud vertical structure that are free of insect contamination.
Cited articles
AERIS/ICARE: Homepage, available at: http://www.icare.univ-lille.fr, last access: 22 April 2021.
Avery, M. A., Ryan, R. A., Getzewich, B. J., Vaughan, M. A., Winker, D. M., Hu, Y., Garnier, A., Pelon, J., and Verhappen, C. A.: CALIOP V4 cloud thermodynamic phase assignment and the impact of near-nadir viewing angles, Atmos. Meas. Tech., 13, 4539–4563, https://doi.org/10.5194/amt-13-4539-2020, 2020.
Baum, B. A., Yang, P., Heymsfield, A. J., Schmitt, C. G., Xie, Y., Bansemer,
A., Hu, Y.-X., and Zhang, Z.: Improvements in shortwave bulk scattering and
absorption models for the remote sensing of ice clouds, J. Appl. Meteorol.
Climatol., 50, 1037–1056, https://doi.org/10.1175/2010JAMC2608.1, 2011.
Bi, L. and Yang, P.: Improved ice particle optical property simulations in
the ultraviolet to far-infrared regime, J. Quant. Spectrosc. Radiat.
Transfer, 189, 228–237, https://doi.org/10.1016/j.jqsrt.2016.12.007, 2017.
Bodas-Salcedo, A., Hill, P. G., Furtado, K., Williams, K. D., Field, P. R.,
Manners, J. C., Hyder, P., and Kato, S.: Large contribution of supercooled
liquid clouds to the solar radiation budget of the Southern Ocean, J.
Climate, 29, 4213–4228, https://doi.org/10.1175/JCLI-D-15-0564.1, 2016.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M.,
Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C.,
Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration
and performance of the data assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Delanoë, J. and Hogan, R. J.: A variational scheme for retrieving ice
cloud properties from combined radar, lidar, and infrared radiometer, J.
Geophys. Res., 113, D07204, https://doi.org/10.1029/2007jd009000, 2008.
Delanoë, J. and Hogan, R. J.: Combined CloudSat-CALIPSO-MODIS retrievals
of the properties of ice clouds, J. Geophys. Res., 115, D00H29,
https://doi.org/10.1029/2009JD012346, 2010.
Deng, M., Mace, G. G., Wang, Z., and Okamoto, H.: Tropical composition,
cloud and climate coupling experiment validation for cirrus cloud profiling
retrieval using CloudSat radar and CALIPSO lidar, J. Geophys. Res., 115,
D00J15, https://doi.org/10.1029/2009JD013104, 2010.
Dubuisson, P., Giraud V., Chomette, O., Chepfer, H., and Pelon, J.: Fast
radiative transfer modeling for infrared imaging radiometry, J. Quant.
Spectrosc. Radiat. Transfer, 95, 201–220,
https://doi.org/10.1016/j.jqsrt.2004.09.034, 2005.
Dubuisson, P, Pelon, J., Cadet, B., and Yang, P.: Sensitivity of thermal
infrared radiation at the top of the atmosphere and the surface to ice cloud
microphysics, J. Appl. Meteor. Climatol., 47, 2545–2560, 2008.
Duncan, D. I. and Eriksson, P.: An update on global atmospheric ice estimates from satellite observations and reanalyses, Atmos. Chem. Phys., 18, 11205–11219, https://doi.org/10.5194/acp-18-11205-2018, 2018.
Foot, J. S.: Some observations of the optical properties of clouds. Part II:
cirrus, Q. J. Roy. Meteor. Soc., 114, 145–164, 1988.
Garnier A., Pelon, J., Dubuisson, P., Faivre, M., Chomette, O., Pascal, N.,
and Kratz, D. P.: Retrieval of cloud properties using CALIPSO Imaging
Infrared Radiometer: Part I: effective emissivity and optical depth, J.
Appl. Meteor. Climatol., 51, 1407–1425,
https://doi.org/10.1175/JAMC-D-11-0220.1, 2012.
Garnier, A., Pelon, J., Dubuisson, P., Yang, P., Faivre, M., Chomette, O.,
Pascal, N., Lucker, P., and Murray, T.: Retrieval of cloud properties using
CALIPSO Imaging Infrared Radiometer: Part II: effective diameter and ice
water path, J. Appl. Meteor. Climatol., 52, 2582–2599,
https://doi.org/10.1175/JAMC-D-12-0328.1, 2013.
Garnier, A., Pelon, J., Vaughan, M. A., Winker, D. M., Trepte, C. R., and Dubuisson, P.: Lidar multiple scattering factors inferred from CALIPSO lidar and IIR retrievals of semi-transparent cirrus cloud optical depths over oceans, Atmos. Meas. Tech., 8, 2759–2774, https://doi.org/10.5194/amt-8-2759-2015, 2015.
Garnier, A., Scott, N. A., Pelon, J., Armante, R., Crépeau, L., Six, B., and Pascal, N.: Long-term assessment of the CALIPSO Imaging Infrared Radiometer (IIR) calibration and stability through simulated and observed comparisons with MODIS/Aqua and SEVIRI/Meteosat, Atmos. Meas. Tech., 10, 1403–1424, https://doi.org/10.5194/amt-10-1403-2017, 2017.
Garnier, A., Trémas, T., Pelon, J., Lee, K.-P., Nobileau, D., Gross-Colzy, L., Pascal, N., Ferrage, P., and Scott, N. A.: CALIPSO IIR Version 2 Level 1b calibrated radiances: analysis and reduction of residual biases in the Northern Hemisphere, Atmos. Meas. Tech., 11, 2485–2500, https://doi.org/10.5194/amt-11-2485-2018, 2018.
Garnier, A., Pelon, J., Pascal, N., Vaughan, M. A., Dubuisson, P., Yang, P.,
and Mitchell, D. L.: Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part II: Results over oceans, Atmos. Meas. Tech., 14, 3277–3299, https://doi.org/10.5194/amt-14-3277-2021, 2021.
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., 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), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Hale, G. M. and Querry, M. R.: Optical constants of water in the 200 nm to
200 µm wavelength region, Appl. Opt., 12, 555–563, 1973.
Hansen, J. E.: Multiple scattering of polarized light in planetary
atmospheres. Part II. sunlight reflected by terrestrial water clouds, J.
Atmos. Sci., 28, 1400–1426,
https://doi.org/10.1175/1520-0469(1971)028<1400:MSOPLI>2.0.CO;2, 1971.
Heidinger, A. K., Pavolonis, M. J., Holz, R. E., Baum, B. A., and Berthier,
S.: Using CALIPSO to explore the sensitivity to cirrus height in the
infrared observations from NPOESS/ VIIRS and GOES-R/ABI, J. Geophys. Res.,
115, D00H20, https://doi.org/10.1029/2009JD012152, 2010.
Heidinger, A. K., Li, Y., Baum, B. A., Holz, R. E., Platnick, S., and Yang,
P.: Retrieval of cirrus cloud optical depth under day and night conditions
from MODIS Collection 6 cloud property data, Remote Sens. 2015, 7,
7257–7271, 2015.
Holz, R. E., Platnick, S., Meyer, K., Vaughan, M., Heidinger, A., Yang, P., Wind, G., Dutcher, S., Ackerman, S., Amarasinghe, N., Nagle, F., and Wang, C.: Resolving ice cloud optical thickness biases between CALIOP and MODIS using infrared retrievals, Atmos. Chem. Phys., 16, 5075–5090, https://doi.org/10.5194/acp-16-5075-2016, 2016.
Hu, Y., Rodier, S., Xu, K., Sun, W., Huang, J., Lin, B., Zhai, P., and
Josset, D.: Occurrence, liquid water content, and fraction of supercooled
water clouds from combined CALIOP/IIR/MODIS measurements, J. Geophys.
Res., 115, D00H34, https://doi.org/10.1029/2009JD012384, 2010.
Inoue, T.: On the temperature and effective emissivity determination of
semitransparent cirrus clouds by bi-spectral measurements in the 10 µm
window region, J. Meteor. Soc. Japan, 63, 88–98, 1985.
Kahn, B. H., Takahashi, H., Stephens, G. L., Yue, Q., Delanoë, J., Manipon, G., Manning, E. M., and Heymsfield, A. J.: Ice cloud microphysical trends observed by the Atmospheric Infrared Sounder, Atmos. Chem. Phys., 18, 10715–10739, https://doi.org/10.5194/acp-18-10715-2018, 2018.
Kim, M.-H., Omar, A. H., Tackett, J. L., Vaughan, M. A., Winker, D. M., Trepte, C. R., Hu, Y., Liu, Z., Poole, L. R., Pitts, M. C., Kar, J., and Magill, B. E.: The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm, Atmos. Meas. Tech., 11, 6107–6135, https://doi.org/10.5194/amt-11-6107-2018, 2018.
Liu, Z., Kar, J., Zeng, S., Tackett, J., Vaughan, M., Avery, M., Pelon, J., Getzewich, B., Lee, K.-P., Magill, B., Omar, A., Lucker, P., Trepte, C., and Winker, D.: Discriminating between clouds and aerosols in the CALIOP version 4.1 data products, Atmos. Meas. Tech., 12, 703–734, https://doi.org/10.5194/amt-12-703-2019, 2019.
Martin, G. M., Johnson, D. W., and Spice, A.: The measurement and
parameterization of effective radius of droplets in warm stratocumulus
clouds, J. Amos. Sci., 51, 1823–1842, 1994.
Mitchell, D. L.: Effective diameter in radiation transfer: General
definition, applications, and limitations, J. Atmos. Sci., 59, 2330–2346,
2002.
Mitchell, D. L., d'Entremont, R. P., and Lawson, R. P.: Inferring cirrus
size distributions through satellite remote sensing and microphysical
databases, J. Atmos. Sci., 67, 1106–1125,
https://doi.org/10.1175/2009jas3150.1, 2010.
Mitchell, D. L., Garnier, A., Pelon, J., and Erfani, E.: CALIPSO (IIR–CALIOP) retrievals of cirrus cloud ice-particle concentrations, Atmos. Chem. Phys., 18, 17325–17354, https://doi.org/10.5194/acp-18-17325-2018, 2018.
Muhlbauer, A., McCoy, I. L., and Wood, R.: Climatology of stratocumulus cloud morphologies: microphysical properties and radiative effects, Atmos. Chem. Phys., 14, 6695–6716, https://doi.org/10.5194/acp-14-6695-2014, 2014.
NASA: CALIPSO Imaging Infrared Radiometer (IIR) Level 2 Track data, Beta V3-01, NASA Langley Research Center Atmospheric Science Data Center [data set], https://doi.org/10.5067/IIR/CALIPSO/L2_Track-Beta-V3-01, 2011.
NASA: CALIPSO Infrared Imaging Radiometer (IIR) Level 2 Track, V4-20, NASA Langley Research Center Atmospheric Science Data Center [data set], https://doi.org/10.5067/CALIOP/CALIPSO/CAL_IIR_L2_Track-Standard-V4-20, 2020.
Newman, S. M., Smith, J. A., Glew, M. D., Rogers, S. M., and Taylor, J. P.:
Temperature and salinity dependence of sea surface emissivity in the thermal
infrared, Q. J. R. Meteorol. Soc. 131, 2539–2557, 2005.
Parol, F., Buriez, J. C., Brogniez, G., and Fouquart, Y.: Information
content of AVHRR channels 4 and 5 with respect to the effective radius of
cirrus cloud particles, J. Appl. Meteor., 30, 973–984, 1991.
Pinnick, R. G., Jennings, S. G., Chylek, P., and Auvermann, H. J.:
Verification of a linear relation between IR extinction, absorption and
liquid water content of fogs, J. Atmos. Sci., 36, 1577–1586, 1979.
Platnick S., Meyer, K. G., 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
Transactions on Geoscience and Remote Sensing, 55, 502–525,
https://doi.org/10.1109/TGRS.2016.2610522, 2017.
Platt, C. M. R.: Lidar and radiometric observations of cirrus clouds, J.
Atmos. Sci., 30, 1191–1204, 1973.
Platt, C. M. R.: Infrared absorption and liquid water content in
stratocumulus clouds, Q. J. Roy. Meteor. Soc., 102, 553–561, 1976.
Platt, C. M. R. and Gambling, D. J.: Emissivity of high layer clouds by
combined lidar and radiometric techniques, Q. J. Roy. Meteor. Soc., 97,
322–325, 1971.
Rothman, L. S., Gordon, I. E., Babikov, Y., Barbe, A., Chris Benner , D.,
Bernath, P. F., Birk, M., Bizzocchi, L., Boudon, V., Brown, L. R.,
Campargue, A., Chance, K., Cohen, E. A., Coudert, L. H., Devi, V. M.,
Drouin, B. J., Fayt, A., Flaud, J.-M., Gamache, R. R., Harrison, J. J.,
Hartmann, J.-M. , Hill, C., Hodges, J. T., Jacquemart, D., Jolly, A.,
Lamouroux, J., Le Roy, R. J., Li, G., Long, D. A., Lyulin, O. M., Mackie, C.
J., Massie, S. T., Mikhailenko, S., Müller, H. S. P., Naumenko, O. V.,
Nikitin, A. V., Orphal, J., Perevalov, V., Perrin, A., Polovtseva, E. R.,
Richard, C., Smith, M. A. H., Starikova, E., Sung, K., Tashkun, S.,
Tennyson, J., Toon, G. C., Tyuterev, Vl. G., and Wagner, G.: The HITRAN 2012
molecular spectroscopic database, J. Quant. Spectrosc. Radiat. Transfer,
130, 4–50, https://doi.org/10.1016/j.jqsrt.2013.07.002, 2013.
Scott, N. A. and Chedin, A.: A fast line-by-line method for atmospheric
absorption computations: The Automatized Atmospheric Absorption Atlas, J.
Appl. Meteorol., 20, 802–812, 1981.
Stephens, G. L.: Radiation profiles in extended water clouds. II:
Parameterization schemes, J. Atmos. Sci., 35, 2123–2132, 1978.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang,
Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L.,
Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and the CloudSat
science team: The CloudSat mission and the A-train: A new dimension of
space-based observations of clouds and precipitation, B. Am. Meteorol.
Soc., 83, 1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002.
Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C.,
L'Ecuyer, T., and Lebsock, M.: CloudSat and CALIPSO within the A-Train: Ten
years of actively observing the Earth system, B. Am. Meteorol. Soc.,
99, 569–581, https://doi.org/10.1175/BAMS-D-16-0324.1, 2018.
Stubenrauch, C. J., Cros, S., Guignard, A., and Lamquin, N.: A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat, Atmos. Chem. Phys., 10, 7197–7214, https://doi.org/10.5194/acp-10-7197-2010, 2010.
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G.,
Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A.,
Maddux, B. C., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen,
C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao,
G.: Assessment of global cloud datasets from satellites: project and
database initiated by the GEWEX radiation panel, B. Am. Meteorol. Soc.,
94, 1031–1049, https://doi.org/10.1175/BAMS-D-12-00117.1, 2013.
Stubenrauch, C. J., Feofilov, A. G., Protopapadaki, S. E., and Armante, R.: Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications, Atmos. Chem. Phys., 17, 13625–13644, https://doi.org/10.5194/acp-17-13625-2017, 2017.
Stubenrauch, C. J., Caria, G., Protopapadaki, S. E., and Hemmer, F.: 3D radiative heating of tropical upper tropospheric cloud systems derived from synergistic A-Train observations and machine learning, Atmos. Chem. Phys., 21, 1015–1034, https://doi.org/10.5194/acp-21-1015-2021, 2021.
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8,
1251–1256, 1974.
Vaughan, M. A., Winker, D. M., and Powell, K. A.: CALIOP Algorithm
Theoretical Basis Document, Part 2: Feature Detection and Layers Properties
Algorithms. PC-SCI-202 Part 2, Release 1.01, available at:
https://www-calipso.larc.nasa.gov/resources/pdfs/PC-SCI-202_Part2_rev1x01.pdf (last access: 14 September 2020), 2005.
Vaughan, M., Powell, K., Kuehn, R., Young, S., Winker, D., Hostetler, C.,
Hunt, W., Liu, Z., McGill, M., and Getzewich, B.: Fully automated detection
of cloud and aerosol layers in the CALIPSO lidar measurements, J. Atmos.
Oceanic Technol., 26, 2034–2050, https://doi.org/10.1175/2009JTECHA1228.1,
2009.
Vaughan, M., Pitts, M., Trepte, C., Winker, D., Detweiler, P., Garnier, A.,
Getzewich, B., Hunt, W., Lambeth, J., Lee, K.-P., Lucker, P., Murray, T.,
Rodier, S., Tremas, T., Bazureau, A., and Pelon, J.: Cloud-Aerosol LIDAR
Infrared Pathfinder Satellite Observations (CALIPSO) data management system
data products catalog, Release 4.92, NASA Langley Research Center Document
PC-SCI-503, available at:
https://www-calipso.larc.nasa.gov/products/CALIPSO_DPC_Rev4x92.pdf, last access: 14 September 2020, 225 pp., 2020.
Winker, D. M., Pelon, J., Coakley Jr., J. A., Ackerman, S. A., Charlson, R.
J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T.
L., Le Treut, H., McCormick, M. P., Mégie, G., Poole, L., Powell, K.,
Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO mission: a
global 3D view of aerosols and clouds, B. Am. Meteorol. Soc., 91,
1211–1229, https://doi.org/10.1175/2010BAMS3009.1, 2010.
Yang, P., Wei, H., Huang, H. L., Baum, B. A., Hu, Y. X., Kattawar, G. W.,
Mishchenko, M. I., and Fu, Q.: Scattering and absorption property database
for non-spherical ice particles in the near-through far-infrared spectral
region, Appl. Opt., 44, 5512–5523, https://doi.org/10.1364/AO.44.005512,
2005.
Yang, P., Bi, L., Baum, B. A., Liou, K.-N., Kattawar, G. W., Mishchenko, M.
I., and Cole, B.: Spectrally consistent scattering, absorption, and
polarization properties of atmospheric ice crystals at wavelengths from 0.2 µm to 100 µm, J. Atmos. Sci., 70, 330–347, 2013.
Yang, P., Hioki, S., Saito, M., Kuo, C.-P., Baum, B.A., and Liou, K.-N. A:
Review of ice cloud optical property models for passive satellite remote
sensing, Atmosphere, 9, 499, https://doi.org/10.3390/atmos9120499, 2018.
Young, S. A., Vaughan, M. A., Garnier, A., Tackett, J. L., Lambeth, J. D., and Powell, K. A.: Extinction and optical depth retrievals for CALIPSO's Version 4 data release, Atmos. Meas. Tech., 11, 5701–5727, https://doi.org/10.5194/amt-11-5701-2018, 2018.
Short summary
The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and ice or liquid water path estimates. This paper (Part I) describes the improvements in the V4 algorithms compared to those used in the version 3 (V3) release, while results are presented in a companion paper (Part II).
The IIR Level 2 data products include cloud effective emissivities and cloud microphysical...
Special issue