Articles | Volume 13, issue 7
https://doi.org/10.5194/amt-13-3609-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-3609-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jul 2020
Research article |
| 07 Jul 2020
Three-dimensional wind profiles using a stabilized shipborne cloud radar in wind profiler mode
Australian Bureau of Meteorology, Melbourne, Victoria, Australia
Ian McRobert
Engineering and Technology Program, CSIRO Oceans and Atmosphere,
Hobart, Tasmania, Australia
Related authors
McKenna W. Stanford, Ann M. Fridlind, Israel Silber, Andrew S. Ackerman, Greg Cesana, Johannes Mülmenstädt, Alain Protat, Simon Alexander, and Adrian McDonald
Atmos. Chem. Phys., 23, 9037–9069, https://doi.org/10.5194/acp-23-9037-2023, https://doi.org/10.5194/acp-23-9037-2023, 2023
Short summary
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Clouds play an important role in the Earth’s climate system as they modulate the amount of radiation that either reaches the surface or is reflected back to space. This study demonstrates an approach to robustly evaluate surface-based observations against a large-scale model. We find that the large-scale model precipitates too infrequently relative to observations, contrary to literature documentation suggesting otherwise based on satellite measurements.
Luis Ackermann, Joshua Soderholm, Alain Protat, Rhys Whitley, Lisa Ye, and Nina Ridder
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-161, https://doi.org/10.5194/amt-2023-161, 2023
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The manuscript addresses the crucial topic of hail damage quantification using radar observations. We propose a new radar-derived hail product that utilises a large dataset of insurance hail damage claims and radar observations. A deep neural network was employed, trained with local meteorological variables and the radar observations, to better quantify hail damage. Key meteorological variables were identified to have the most predictive capability in this regards.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
EGUsphere, https://doi.org/10.5194/egusphere-2023-531, https://doi.org/10.5194/egusphere-2023-531, 2023
Short summary
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This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model. We used cloud property biases within ACCESS as predictors, and can explain up to 55 % of the variance in the shortwave radiation bias. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundations for better understanding future developments of Earth System Models.
Ruhi S. Humphries, Melita D. Keywood, Jason P. Ward, James Harnwell, Simon P. Alexander, Andrew R. Klekociuk, Keiichiro Hara, Ian M. McRobert, Alain Protat, Joel Alroe, Luke T. Cravigan, Branka Miljevic, Zoran D. Ristovski, Robyn Schofield, Stephen R. Wilson, Connor J. Flynn, Gourihar R. Kulkarni, Gerald G. Mace, Greg M. McFarquhar, Scott D. Chambers, Alastair G. Williams, and Alan D. Griffiths
Atmos. Chem. Phys., 23, 3749–3777, https://doi.org/10.5194/acp-23-3749-2023, https://doi.org/10.5194/acp-23-3749-2023, 2023
Short summary
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Observations of aerosols in pristine regions are rare but are vital to constraining the natural baseline from which climate simulations are calculated. Here we present recent seasonal observations of aerosols from the Southern Ocean and contrast them with measurements from Antarctica, Australia and regionally relevant voyages. Strong seasonal cycles persist, but striking differences occur at different latitudes. This study highlights the need for more long-term observations in remote regions.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
EGUsphere, https://doi.org/10.5194/egusphere-2023-181, https://doi.org/10.5194/egusphere-2023-181, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Sonya L. Fiddes, Alain Protat, Marc D. Mallet, Simon P. Alexander, and Matthew T. Woodhouse
Atmos. Chem. Phys., 22, 14603–14630, https://doi.org/10.5194/acp-22-14603-2022, https://doi.org/10.5194/acp-22-14603-2022, 2022
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Climate models have difficulty simulating Southern Ocean clouds, impacting how much sunlight reaches the surface. We use machine learning to group different cloud types observed from satellites and simulated in a climate model. We find the model does a poor job of simulating the same cloud type as what the satellite shows and, even when it does, the cloud properties and amount of reflected sunlight are incorrect. We have a lot of work to do to model clouds correctly over the Southern Ocean.
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
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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.
Sonya L. Fiddes, Matthew T. Woodhouse, Steve Utembe, Robyn Schofield, Simon P. Alexander, Joel Alroe, Scott D. Chambers, Zhenyi Chen, Luke Cravigan, Erin Dunne, Ruhi S. Humphries, Graham Johnson, Melita D. Keywood, Todd P. Lane, Branka Miljevic, Yuko Omori, Alain Protat, Zoran Ristovski, Paul Selleck, Hilton B. Swan, Hiroshi Tanimoto, Jason P. Ward, and Alastair G. Williams
Atmos. Chem. Phys., 22, 2419–2445, https://doi.org/10.5194/acp-22-2419-2022, https://doi.org/10.5194/acp-22-2419-2022, 2022
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Coral reefs have been found to produce the climatically relevant chemical compound dimethyl sulfide (DMS). It has been suggested that corals can modify their environment via the production of DMS. We use an atmospheric chemistry model to test this theory at a regional scale for the first time. We find that it is unlikely that coral-reef-derived DMS has an influence over local climate, in part due to the proximity to terrestrial and anthropogenic aerosol sources.
Alain Protat, Valentin Louf, Joshua Soderholm, Jordan Brook, and William Ponsonby
Atmos. Meas. Tech., 15, 915–926, https://doi.org/10.5194/amt-15-915-2022, https://doi.org/10.5194/amt-15-915-2022, 2022
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This study uses collocated ship-based, ground-based, and spaceborne radar observations to validate the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks to the accuracy required for operational severe weather applications such as rainfall and hail nowcasting.
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
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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.
Ruhi S. Humphries, Melita D. Keywood, Sean Gribben, Ian M. McRobert, Jason P. Ward, Paul Selleck, Sally Taylor, James Harnwell, Connor Flynn, Gourihar R. Kulkarni, Gerald G. Mace, Alain Protat, Simon P. Alexander, and Greg McFarquhar
Atmos. Chem. Phys., 21, 12757–12782, https://doi.org/10.5194/acp-21-12757-2021, https://doi.org/10.5194/acp-21-12757-2021, 2021
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The Southern Ocean region is one of the most pristine in the world and serves as an important proxy for the pre-industrial atmosphere. Improving our understanding of the natural processes in this region is likely to result in the largest reductions in the uncertainty of climate and earth system models. In this paper we present a statistical summary of the latitudinal gradient of aerosol and cloud condensation nuclei concentrations obtained from five voyages spanning the Southern Ocean.
Robert Jackson, Scott Collis, Valentin Louf, Alain Protat, Die Wang, Scott Giangrande, Elizabeth J. Thompson, Brenda Dolan, and Scott W. Powell
Atmos. Meas. Tech., 14, 53–69, https://doi.org/10.5194/amt-14-53-2021, https://doi.org/10.5194/amt-14-53-2021, 2021
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About 4 years of 2D video disdrometer data in Darwin are used to develop and validate rainfall retrievals for tropical convection in C- and X-band radars in Darwin. Using blended techniques previously used for Colorado and Manus and Gan islands, with modified coefficients in each estimator, provided the most optimal results. Using multiple radar observables to develop a rainfall retrieval provided a greater advantage than using a single observable, including using specific attenuation.
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We characterised for the first time the rainfall microphysics for Southern Hemisphere temperate latitudes. Co-located instruments were deployed to provide information on the sampling effect and spatio-temporal variabilities at micro scales. Substantial differences were found across the instruments, increasing with increasing values of the rain rate. Specific relations for reflectivity–rainfall are presented together with related uncertainties for drizzle and stratiform and convective rainfall.
Ingo Wohltmann, Ralph Lehmann, Georg A. Gottwald, Karsten Peters, Alain Protat, Valentin Louf, Christopher Williams, Wuhu Feng, and Markus Rex
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We present a trajectory-based model for simulating the transport of air parcels by convection. Our model extends the approach of existing models by explicitly simulating vertical updraft velocities inside the clouds and the time that an air parcel spends inside the convective event.
Robert C. Jackson, Scott M. Collis, Valentin Louf, Alain Protat, and Leon Majewski
Atmos. Chem. Phys., 18, 17687–17704, https://doi.org/10.5194/acp-18-17687-2018, https://doi.org/10.5194/acp-18-17687-2018, 2018
Short summary
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This paper looks at a 17 year database of echo top heights of thunderstorms in Darwin retrieved by CPOL. We find that the echo top heights are generally bimodal, corresponding to cumulus congestus and deep convection, and show a greater bimodality during an inactive MJO. Furthermore, we find that convective cell areas are larger in break conditions compared to monsoon conditions, but only during MJO-inactive conditions.
Christopher R. Yost, Kristopher M. Bedka, Patrick Minnis, Louis Nguyen, J. Walter Strapp, Rabindra Palikonda, Konstantin Khlopenkov, Douglas Spangenberg, William L. Smith Jr., Alain Protat, and Julien Delanoe
Atmos. Meas. Tech., 11, 1615–1637, https://doi.org/10.5194/amt-11-1615-2018, https://doi.org/10.5194/amt-11-1615-2018, 2018
Short summary
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Accretion of cloud ice particles upon engine or instrument probe surfaces can cause engine malfunction or even power loss, and therefore it is important for aircraft to avoid flight through clouds that may have produced large quantities of ice particles. This study introduces a method by which potentially hazardous conditions can be detected using satellite imagery. It was found that potentially hazardous conditions were often located near or beneath very cold clouds and thunderstorm updrafts.
McKenna W. Stanford, Adam Varble, Ed Zipser, J. Walter Strapp, Delphine Leroy, Alfons Schwarzenboeck, Rodney Potts, and Alain Protat
Atmos. Chem. Phys., 17, 9599–9621, https://doi.org/10.5194/acp-17-9599-2017, https://doi.org/10.5194/acp-17-9599-2017, 2017
Short summary
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Radar reflectivity is a valuable observational tool used to guide numerical weather model improvement. Biases in simulated reflectivity help identify potential errors in physical process and property representation in models. This study uniquely compares simulated and observed tropical convective systems to establish that a commonly documented high bias in radar reflectivity values at least partially results from the production of simulated ice particle sizes that are larger than observed.
McKenna W. Stanford, Ann M. Fridlind, Israel Silber, Andrew S. Ackerman, Greg Cesana, Johannes Mülmenstädt, Alain Protat, Simon Alexander, and Adrian McDonald
Atmos. Chem. Phys., 23, 9037–9069, https://doi.org/10.5194/acp-23-9037-2023, https://doi.org/10.5194/acp-23-9037-2023, 2023
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Clouds play an important role in the Earth’s climate system as they modulate the amount of radiation that either reaches the surface or is reflected back to space. This study demonstrates an approach to robustly evaluate surface-based observations against a large-scale model. We find that the large-scale model precipitates too infrequently relative to observations, contrary to literature documentation suggesting otherwise based on satellite measurements.
Luis Ackermann, Joshua Soderholm, Alain Protat, Rhys Whitley, Lisa Ye, and Nina Ridder
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-161, https://doi.org/10.5194/amt-2023-161, 2023
Preprint under review for AMT
Short summary
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The manuscript addresses the crucial topic of hail damage quantification using radar observations. We propose a new radar-derived hail product that utilises a large dataset of insurance hail damage claims and radar observations. A deep neural network was employed, trained with local meteorological variables and the radar observations, to better quantify hail damage. Key meteorological variables were identified to have the most predictive capability in this regards.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
EGUsphere, https://doi.org/10.5194/egusphere-2023-531, https://doi.org/10.5194/egusphere-2023-531, 2023
Short summary
Short summary
This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model. We used cloud property biases within ACCESS as predictors, and can explain up to 55 % of the variance in the shortwave radiation bias. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundations for better understanding future developments of Earth System Models.
Ruhi S. Humphries, Melita D. Keywood, Jason P. Ward, James Harnwell, Simon P. Alexander, Andrew R. Klekociuk, Keiichiro Hara, Ian M. McRobert, Alain Protat, Joel Alroe, Luke T. Cravigan, Branka Miljevic, Zoran D. Ristovski, Robyn Schofield, Stephen R. Wilson, Connor J. Flynn, Gourihar R. Kulkarni, Gerald G. Mace, Greg M. McFarquhar, Scott D. Chambers, Alastair G. Williams, and Alan D. Griffiths
Atmos. Chem. Phys., 23, 3749–3777, https://doi.org/10.5194/acp-23-3749-2023, https://doi.org/10.5194/acp-23-3749-2023, 2023
Short summary
Short summary
Observations of aerosols in pristine regions are rare but are vital to constraining the natural baseline from which climate simulations are calculated. Here we present recent seasonal observations of aerosols from the Southern Ocean and contrast them with measurements from Antarctica, Australia and regionally relevant voyages. Strong seasonal cycles persist, but striking differences occur at different latitudes. This study highlights the need for more long-term observations in remote regions.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
EGUsphere, https://doi.org/10.5194/egusphere-2023-181, https://doi.org/10.5194/egusphere-2023-181, 2023
Short summary
Short summary
We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Sonya L. Fiddes, Alain Protat, Marc D. Mallet, Simon P. Alexander, and Matthew T. Woodhouse
Atmos. Chem. Phys., 22, 14603–14630, https://doi.org/10.5194/acp-22-14603-2022, https://doi.org/10.5194/acp-22-14603-2022, 2022
Short summary
Short summary
Climate models have difficulty simulating Southern Ocean clouds, impacting how much sunlight reaches the surface. We use machine learning to group different cloud types observed from satellites and simulated in a climate model. We find the model does a poor job of simulating the same cloud type as what the satellite shows and, even when it does, the cloud properties and amount of reflected sunlight are incorrect. We have a lot of work to do to model clouds correctly over the Southern Ocean.
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.
Sonya L. Fiddes, Matthew T. Woodhouse, Steve Utembe, Robyn Schofield, Simon P. Alexander, Joel Alroe, Scott D. Chambers, Zhenyi Chen, Luke Cravigan, Erin Dunne, Ruhi S. Humphries, Graham Johnson, Melita D. Keywood, Todd P. Lane, Branka Miljevic, Yuko Omori, Alain Protat, Zoran Ristovski, Paul Selleck, Hilton B. Swan, Hiroshi Tanimoto, Jason P. Ward, and Alastair G. Williams
Atmos. Chem. Phys., 22, 2419–2445, https://doi.org/10.5194/acp-22-2419-2022, https://doi.org/10.5194/acp-22-2419-2022, 2022
Short summary
Short summary
Coral reefs have been found to produce the climatically relevant chemical compound dimethyl sulfide (DMS). It has been suggested that corals can modify their environment via the production of DMS. We use an atmospheric chemistry model to test this theory at a regional scale for the first time. We find that it is unlikely that coral-reef-derived DMS has an influence over local climate, in part due to the proximity to terrestrial and anthropogenic aerosol sources.
Alain Protat, Valentin Louf, Joshua Soderholm, Jordan Brook, and William Ponsonby
Atmos. Meas. Tech., 15, 915–926, https://doi.org/10.5194/amt-15-915-2022, https://doi.org/10.5194/amt-15-915-2022, 2022
Short summary
Short summary
This study uses collocated ship-based, ground-based, and spaceborne radar observations to validate the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks to the accuracy required for operational severe weather applications such as rainfall and hail nowcasting.
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.
Ruhi S. Humphries, Melita D. Keywood, Sean Gribben, Ian M. McRobert, Jason P. Ward, Paul Selleck, Sally Taylor, James Harnwell, Connor Flynn, Gourihar R. Kulkarni, Gerald G. Mace, Alain Protat, Simon P. Alexander, and Greg McFarquhar
Atmos. Chem. Phys., 21, 12757–12782, https://doi.org/10.5194/acp-21-12757-2021, https://doi.org/10.5194/acp-21-12757-2021, 2021
Short summary
Short summary
The Southern Ocean region is one of the most pristine in the world and serves as an important proxy for the pre-industrial atmosphere. Improving our understanding of the natural processes in this region is likely to result in the largest reductions in the uncertainty of climate and earth system models. In this paper we present a statistical summary of the latitudinal gradient of aerosol and cloud condensation nuclei concentrations obtained from five voyages spanning the Southern Ocean.
Jack B. Simmons, Ruhi S. Humphries, Stephen R. Wilson, Scott D. Chambers, Alastair G. Williams, Alan D. Griffiths, Ian M. McRobert, Jason P. Ward, Melita D. Keywood, and Sean Gribben
Atmos. Chem. Phys., 21, 9497–9513, https://doi.org/10.5194/acp-21-9497-2021, https://doi.org/10.5194/acp-21-9497-2021, 2021
Short summary
Short summary
Aerosols have a climate forcing effect in the Earth's atmosphere. Few measurements exist of aerosols in the Southern Ocean, a region key to our understanding of this effect. In this study, aerosol measurements from a summer 2017 campaign in the East Antarctic seasonal ice zone are examined. Higher concentrations of aerosols were found in dry air with origins from above the Antarctic continent compared to other periods of the voyage.
Kevin J. Sanchez, Gregory C. Roberts, Georges Saliba, Lynn M. Russell, Cynthia Twohy, J. Michael Reeves, Ruhi S. Humphries, Melita D. Keywood, Jason P. Ward, and Ian M. McRobert
Atmos. Chem. Phys., 21, 3427–3446, https://doi.org/10.5194/acp-21-3427-2021, https://doi.org/10.5194/acp-21-3427-2021, 2021
Short summary
Short summary
Measurements of particles and their properties were made from aircraft over the Southern Ocean. Aerosol transported from the Antarctic coast is shown to greatly enhance particle concentrations over the Southern Ocean. The occurrence of precipitation was shown to be associated with the lowest particle concentrations over the Southern Ocean. These particles are important due to their ability to enhance cloud droplet concentrations, resulting in more sunlight being reflected by the clouds.
Robert Jackson, Scott Collis, Valentin Louf, Alain Protat, Die Wang, Scott Giangrande, Elizabeth J. Thompson, Brenda Dolan, and Scott W. Powell
Atmos. Meas. Tech., 14, 53–69, https://doi.org/10.5194/amt-14-53-2021, https://doi.org/10.5194/amt-14-53-2021, 2021
Short summary
Short summary
About 4 years of 2D video disdrometer data in Darwin are used to develop and validate rainfall retrievals for tropical convection in C- and X-band radars in Darwin. Using blended techniques previously used for Colorado and Manus and Gan islands, with modified coefficients in each estimator, provided the most optimal results. Using multiple radar observables to develop a rainfall retrieval provided a greater advantage than using a single observable, including using specific attenuation.
Adrien Guyot, Jayaram Pudashine, Alain Protat, Remko Uijlenhoet, Valentijn R. N. Pauwels, Alan Seed, and Jeffrey P. Walker
Hydrol. Earth Syst. Sci., 23, 4737–4761, https://doi.org/10.5194/hess-23-4737-2019, https://doi.org/10.5194/hess-23-4737-2019, 2019
Short summary
Short summary
We characterised for the first time the rainfall microphysics for Southern Hemisphere temperate latitudes. Co-located instruments were deployed to provide information on the sampling effect and spatio-temporal variabilities at micro scales. Substantial differences were found across the instruments, increasing with increasing values of the rain rate. Specific relations for reflectivity–rainfall are presented together with related uncertainties for drizzle and stratiform and convective rainfall.
Ingo Wohltmann, Ralph Lehmann, Georg A. Gottwald, Karsten Peters, Alain Protat, Valentin Louf, Christopher Williams, Wuhu Feng, and Markus Rex
Geosci. Model Dev., 12, 4387–4407, https://doi.org/10.5194/gmd-12-4387-2019, https://doi.org/10.5194/gmd-12-4387-2019, 2019
Short summary
Short summary
We present a trajectory-based model for simulating the transport of air parcels by convection. Our model extends the approach of existing models by explicitly simulating vertical updraft velocities inside the clouds and the time that an air parcel spends inside the convective event.
Ruhi S. Humphries, Ian M. McRobert, Will A. Ponsonby, Jason P. Ward, Melita D. Keywood, Zoe M. Loh, Paul B. Krummel, and James Harnwell
Atmos. Meas. Tech., 12, 3019–3038, https://doi.org/10.5194/amt-12-3019-2019, https://doi.org/10.5194/amt-12-3019-2019, 2019
Short summary
Short summary
Undertaking atmospheric observations from ships provides important data in regions where measurements are impossible by other means. However, making measurements so close to a diesel exhaust plume is difficult. In this paper, we describe an algorithm that utilises ongoing measurements of aerosol number concentrations, black carbon mass concentrations, and mixing ratios of carbon monoxide and carbon dioxide to accurately distinguish between exhaust and background data periods.
Robert C. Jackson, Scott M. Collis, Valentin Louf, Alain Protat, and Leon Majewski
Atmos. Chem. Phys., 18, 17687–17704, https://doi.org/10.5194/acp-18-17687-2018, https://doi.org/10.5194/acp-18-17687-2018, 2018
Short summary
Short summary
This paper looks at a 17 year database of echo top heights of thunderstorms in Darwin retrieved by CPOL. We find that the echo top heights are generally bimodal, corresponding to cumulus congestus and deep convection, and show a greater bimodality during an inactive MJO. Furthermore, we find that convective cell areas are larger in break conditions compared to monsoon conditions, but only during MJO-inactive conditions.
Christopher R. Yost, Kristopher M. Bedka, Patrick Minnis, Louis Nguyen, J. Walter Strapp, Rabindra Palikonda, Konstantin Khlopenkov, Douglas Spangenberg, William L. Smith Jr., Alain Protat, and Julien Delanoe
Atmos. Meas. Tech., 11, 1615–1637, https://doi.org/10.5194/amt-11-1615-2018, https://doi.org/10.5194/amt-11-1615-2018, 2018
Short summary
Short summary
Accretion of cloud ice particles upon engine or instrument probe surfaces can cause engine malfunction or even power loss, and therefore it is important for aircraft to avoid flight through clouds that may have produced large quantities of ice particles. This study introduces a method by which potentially hazardous conditions can be detected using satellite imagery. It was found that potentially hazardous conditions were often located near or beneath very cold clouds and thunderstorm updrafts.
McKenna W. Stanford, Adam Varble, Ed Zipser, J. Walter Strapp, Delphine Leroy, Alfons Schwarzenboeck, Rodney Potts, and Alain Protat
Atmos. Chem. Phys., 17, 9599–9621, https://doi.org/10.5194/acp-17-9599-2017, https://doi.org/10.5194/acp-17-9599-2017, 2017
Short summary
Short summary
Radar reflectivity is a valuable observational tool used to guide numerical weather model improvement. Biases in simulated reflectivity help identify potential errors in physical process and property representation in models. This study uniquely compares simulated and observed tropical convective systems to establish that a commonly documented high bias in radar reflectivity values at least partially results from the production of simulated ice particle sizes that are larger than observed.
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Across-track extension of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-3D product
Insights into 3D cloud radiative transfer effects for the Orbiting Carbon Observatory
Evaluation of polarimetric ice microphysical retrievals with OLYMPEX campaign data
A neural network-based method for generating synthetic 1.6 μm near-infrared satellite images
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation
Simulation and sensitivity analysis for cloud and precipitation measurements via spaceborne millimeter-wave radar
The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations
Near-global distributions of overshooting tops derived from Terra and Aqua MODIS observations
Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy
Optimizing cloud motion estimation on the edge with phase correlation and optical flow
A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations
The CHROMA cloud-top pressure retrieval algorithm for the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission
Deep convective cloud system size and structure across the global tropics and subtropics
Segmentation of polarimetric radar imagery using statistical texture
High-spatial-resolution retrieval of cloud droplet size distribution from polarized observations of the cloudbow
Evaluation of the spectral misalignment on the Earth Clouds, Aerosols and Radiation Explorer/multi-spectral imager cloud product
Retrieval of terahertz ice cloud properties from airborne measurements based on the irregularly shaped Voronoi ice scattering models
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
Numerical Model Generation of Test Frames for Pre-launch Studies of EarthCARE’s Retrieval Algorithms and Data Management System
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
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
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
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
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
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Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
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We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023, https://doi.org/10.5194/amt-16-3459-2023, 2023
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We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
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The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech., 16, 3331–3344, https://doi.org/10.5194/amt-16-3331-2023, https://doi.org/10.5194/amt-16-3331-2023, 2023
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JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM (Nonhydrostatic ICosahedral
Atmospheric Model) simulation with 3.5 km horizontal resolution and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals.
Philipp Gregor, Tobias Zinner, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 16, 3257–3271, https://doi.org/10.5194/amt-16-3257-2023, https://doi.org/10.5194/amt-16-3257-2023, 2023
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This work introduces MACIN, a model for short-term forecasting of direct irradiance for solar energy applications. MACIN exploits cloud images of multiple cameras to predict irradiance. The model is applied to artificial images of clouds from a weather model. The artificial cloud data allow for a more in-depth evaluation and attribution of errors compared with real data. Good performance of derived cloud information and significant forecast improvements over a baseline forecast were found.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Marc Nicolas
Atmos. Meas. Tech., 16, 3221–3243, https://doi.org/10.5194/amt-16-3221-2023, https://doi.org/10.5194/amt-16-3221-2023, 2023
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The optimal estimation formalism is applied to OSIRIS airborne high-resolution multi-angular measurements to retrieve COT and Reff. The corresponding uncertainties related to measurement errors, which are up to 6 and 12 %, the non-retrieved parameters, which are less than 0.5 %, and the cloud model assumptions show that the heterogeneous vertical profiles and the 3D radiative transfer effects lead to average uncertainties of 5 and 4 % for COT and 13 and 9 % for Reff.
Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
Atmos. Meas. Tech., 16, 3211–3219, https://doi.org/10.5194/amt-16-3211-2023, https://doi.org/10.5194/amt-16-3211-2023, 2023
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The CPR on the EarthCARE satellite is the first satellite-borne Doppler radar. We evaluated the effectiveness of horizontal integration and the unfolding method for the reduction of the Doppler error (the standard deviation of the random error) in the CPR_ECO product. The error was higher in the tropics than in the other latitudes due to frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the errors become small enough.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
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We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
Anja Hünerbein, Sebastian Bley, Stefan Horn, Hartwig Deneke, and Andi Walther
Atmos. Meas. Tech., 16, 2821–2836, https://doi.org/10.5194/amt-16-2821-2023, https://doi.org/10.5194/amt-16-2821-2023, 2023
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The Multi-Spectral Imager (MSI) on board the EarthCARE satellite will provide the information needed for describing the cloud and aerosol properties in the cross-track direction, complementing the measurements from the Cloud Profiling Radar, Atmospheric Lidar and Broad-Band Radiometer. The accurate discrimination between clear and cloudy pixels is an essential first step. Therefore, the cloud mask algorithm provides a cloud flag, cloud phase and cloud type product for the MSI observations.
Zhipeng Qu, Howard W. Barker, Jason N. S. Cole, and Mark W. Shephard
Atmos. Meas. Tech., 16, 2319–2331, https://doi.org/10.5194/amt-16-2319-2023, https://doi.org/10.5194/amt-16-2319-2023, 2023
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This paper describes EarthCARE’s L2 product ACM-3D. It includes the scene construction algorithm (SCA) used to produce the indexes for reconstructing 3D atmospheric scene based on satellite nadir retrievals. It also provides the information about the buffer zone sizes of 3D assessment domains and the ranking scores for selecting the best 3D assessment domains. These output variables are needed to run 3D radiative transfer models for the radiative closure assessment of EarthCARE’s L2 retrievals.
Steven T. Massie, Heather Cronk, Aronne Merrelli, Sebastian Schmidt, and Steffen Mauceri
Atmos. Meas. Tech., 16, 2145–2166, https://doi.org/10.5194/amt-16-2145-2023, https://doi.org/10.5194/amt-16-2145-2023, 2023
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This paper provides insights into the effects of clouds on Orbiting Carbon Observatory (OCO-2) measurements of CO2. Calculations are carried out that indicate the extent to which this satellite experiment underestimates CO2, due to these cloud effects, as a function of the distance between the surface observation footprint and the nearest cloud. The paper discusses how to lessen the influence of these cloud effects.
Armin Blanke, Andrew J. Heymsfield, Manuel Moser, and Silke Trömel
Atmos. Meas. Tech., 16, 2089–2106, https://doi.org/10.5194/amt-16-2089-2023, https://doi.org/10.5194/amt-16-2089-2023, 2023
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We present an evaluation of current retrieval techniques in the ice phase applied to polarimetric radar measurements with collocated in situ observations of aircraft conducted over the Olympic Mountains, Washington State, during winter 2015. Radar estimates of ice properties agreed most with aircraft observations in regions with pronounced radar signatures, but uncertainties were identified that indicate issues of some retrievals, particularly in warmer temperature regimes.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
EGUsphere, https://doi.org/10.5194/egusphere-2023-353, https://doi.org/10.5194/egusphere-2023-353, 2023
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This study extends MFASIS to simulate 1.6 μm NIR channel reflectances with a neural network, enabling its use in model evaluation and data assimilation. A two-layer model was developed for cloud structure with optimized reflectance errors using IFS forecasts and ICON-D2 hindcasts. Mean absolute reflectance error achieved was 0.01 or less, much smaller than typical differences between observations and models.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, https://doi.org/10.5194/amt-16-1803-2023, 2023
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We describe a new method for measuring the 3D spatial variations in water within clouds using the reflected light of the Sun viewed at multiple different angles by satellites. This is a great improvement over older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Leilei Kou, Zhengjian Lin, Haiyang Gao, Shujun Liao, and Piman Ding
Atmos. Meas. Tech., 16, 1723–1744, https://doi.org/10.5194/amt-16-1723-2023, https://doi.org/10.5194/amt-16-1723-2023, 2023
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Forward modeling of spaceborne millimeter-wave radar composed of eight submodules is presented. We quantify the uncertainties in radar reflectivity that may be caused by the physical model parameters via a sensitivity analysis. The simulations with improved and conventional settings are compared with CloudSat data, and the simulation results are evaluated and analyzed. The results are instructive to the optimization of forward modeling and microphysical parameter retrieval.
Heike Kalesse-Los, Anton Kötsche, Andreas Foth, Johannes Röttenbacher, Teresa Vogl, and Jonas Witthuhn
Atmos. Meas. Tech., 16, 1683–1704, https://doi.org/10.5194/amt-16-1683-2023, https://doi.org/10.5194/amt-16-1683-2023, 2023
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The Virga-Sniffer, a new modular open-source Python package tool to characterize full precipitation evaporation (so-called virga) from ceilometer cloud base height and vertically pointing cloud radar reflectivity time–height fields, is described. Results of its first application to RV Meteor observations during the EUREC4A field experiment in January–February 2020 are shown. About half of all detected clouds with bases below the trade inversion height were found to produce virga.
Yulan Hong, Stephen W. Nesbitt, Robert J. Trapp, and Larry Di Girolamo
Atmos. Meas. Tech., 16, 1391–1406, https://doi.org/10.5194/amt-16-1391-2023, https://doi.org/10.5194/amt-16-1391-2023, 2023
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Deep convective updrafts form overshooting tops (OTs) when they extend into the upper troposphere and lower stratosphere. An OT often indicates hazardous weather conditions. The global distribution of OTs is useful for understanding global severe weather conditions. The Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Terra satellites provides 2 decades of records on the Earth–atmosphere system with stable orbits, which are used in this study to derive 20-year OT climatology.
Pragya Vishwakarma, Julien Delanoë, Susana Jorquera, Pauline Martinet, Frederic Burnet, Alistair Bell, and Jean-Charles Dupont
Atmos. Meas. Tech., 16, 1211–1237, https://doi.org/10.5194/amt-16-1211-2023, https://doi.org/10.5194/amt-16-1211-2023, 2023
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Cloud observations are necessary to characterize the cloud properties at local and global scales. The 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. Liquid water path from MWR scales LWC and retrieves the scaling factor (ln a). The retrievals are compared with in situ observations. A climatology of ln a is built to estimate LWC using only radar information.
Bhupendra A. Raut, Paytsar Muradyan, Rajesh Sankaran, Robert C. Jackson, Seongha Park, Sean A. Shahkarami, Dario Dematties, Yongho Kim, Joseph Swantek, Neal Conrad, Wolfgang Gerlach, Sergey Shemyakin, Pete Beckman, Nicola J. Ferrier, and Scott M. Collis
Atmos. Meas. Tech., 16, 1195–1209, https://doi.org/10.5194/amt-16-1195-2023, https://doi.org/10.5194/amt-16-1195-2023, 2023
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We studied the stability of a blockwise phase correlation (PC) method to estimate cloud motion using a total sky imager (TSI). Shorter frame intervals and larger block sizes improve stability, while image resolution and color channels have minor effects. Raindrop contamination can be identified by the rotational motion of the TSI mirror. The correlations of cloud motion vectors (CMVs) from the PC method with wind data vary from 0.38 to 0.59. Optical flow vectors are more stable than PC vectors.
William K. Jones, Matthew W. Christensen, and Philip Stier
Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, https://doi.org/10.5194/amt-16-1043-2023, 2023
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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.
Andrew M. Sayer, Luca Lelli, Brian Cairns, Bastiaan van Diedenhoven, Amir Ibrahim, Kirk D. Knobelspiesse, Sergey Korkin, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 969–996, https://doi.org/10.5194/amt-16-969-2023, https://doi.org/10.5194/amt-16-969-2023, 2023
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This paper presents a method to estimate the height of the top of clouds above Earth's surface using satellite measurements. It is based on light absorption by oxygen in Earth's atmosphere, which darkens the signal that a satellite will see at certain wavelengths of light. Clouds "shield" the satellite from some of this darkening, dependent on cloud height (and other factors), because clouds scatter light at these wavelengths. The method will be applied to the future NASA PACE mission.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-6, https://doi.org/10.5194/amt-2023-6, 2023
Revised manuscript accepted for AMT
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A new database is constructed from satellites comprising millions of deep convective clouds that spans the global tropics and subtropics and greater than 20 years. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirical study of the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
EGUsphere, https://doi.org/10.5194/egusphere-2023-181, https://doi.org/10.5194/egusphere-2023-181, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Veronika Pörtge, Tobias Kölling, Anna Weber, Lea Volkmer, Claudia Emde, Tobias Zinner, Linda Forster, and Bernhard Mayer
Atmos. Meas. Tech., 16, 645–667, https://doi.org/10.5194/amt-16-645-2023, https://doi.org/10.5194/amt-16-645-2023, 2023
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In this work, we analyze polarized cloudbow observations by 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 observed 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 × 100 m) that allow new insights into cloud microphysics.
Minrui Wang, Takashi Y. Nakajima, Woosub Roh, Masaki Satoh, Kentaroh Suzuki, Takuji Kubota, and Mayumi Yoshida
Atmos. Meas. Tech., 16, 603–623, https://doi.org/10.5194/amt-16-603-2023, https://doi.org/10.5194/amt-16-603-2023, 2023
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SMILE (a spectral misalignment 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 products, we did a simulation of EarthCARE-MSI forward radiation, evaluating the error in simulated scenes from a global cloud system-resolving model and a satellite simulator. Our results indicated that the error from SMILE was generally small and negligible for oceanic scenes.
Ming Li, Husi Letu, Hiroshi Ishimoto, Shulei Li, Lei Liu, Takashi Y. Nakajima, Dabin Ji, Huazhe Shang, and Chong Shi
Atmos. Meas. Tech., 16, 331–353, https://doi.org/10.5194/amt-16-331-2023, https://doi.org/10.5194/amt-16-331-2023, 2023
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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 and hexagonal column models.
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
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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
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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.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-300, https://doi.org/10.5194/amt-2022-300, 2022
Revised manuscript accepted for AMT
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The Level 2 algorithms development of EarthCARE satellite mission requires realistic three-dimensional cloud and aerosols scenes along the satellite orbits. One of the best way to produce these scenes is to use high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This manuscript describes the production and validation of three EarthCARE test scenes.
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
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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
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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
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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
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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
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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.
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
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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
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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
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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.
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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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.
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Short summary
Three-dimensional (3D) wind motions play a major role in driving the life cycle of clouds. In this pilot study we have developed a technique to measure the 3D winds in clouds, using a shipborne Doppler cloud radar on a stabilized platform. The stabilized platform is driven to point in a series of predefined directions to collect the required measurements. Comparisons with radiosondes demonstrate that accurate 1 min resolution 3D wind motions can be obtained from this instrumental setup.
Three-dimensional (3D) wind motions play a major role in driving the life cycle of clouds. In...
