Articles | Volume 15, issue 16
https://doi.org/10.5194/amt-15-4931-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-4931-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Aug 2022
Research article |
| 30 Aug 2022
| 30 Aug 2022
Optimizing radar scan strategies for tracking isolated deep convection using observing system simulation experiments
School of Marine and Atmospheric Sciences, Stony Brook University,
Stony Brook, NY, USA
Stephen M. Saleeby
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO, USA
Peter J. Marinescu
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO, USA
Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
Pavlos Kollias
School of Marine and Atmospheric Sciences, Stony Brook University,
Stony Brook, NY, USA
Environmental and Climate Sciences Department, Brookhaven National
Laboratory, Upton, NY, USA
Susan C. van den Heever
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO, USA
Related authors
Sarah Wugofski, Matthew R. Kumjian, Mariko Oue, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-671, https://doi.org/10.5194/egusphere-2025-671, 2025
Short summary
Short summary
Doppler spectral data inform on how particles of varying vertical velocities contribute to total backscattered power observed. Through examining three case studies, consistent features in radar moment data were found to be characteristic of multi-modal spectra. We quantified how spectrum width and mean Doppler velocity can be used to determine whether or not a layer is multi-modal. The identification criteria and methods are described in Part 1 and assessed in Part 2.
Laura M. Tomkins, Sandra E. Yuter, Matthew A. Miller, Mariko Oue, and Charles N. Helms
EGUsphere, https://doi.org/10.5194/egusphere-2025-6, https://doi.org/10.5194/egusphere-2025-6, 2025
Short summary
Short summary
This study investigates how radar-detected snow bands relate to snowfall rates during winter storms in the northeastern U.S. Using over a decade of data, we found that snow bands are not consistently linked to heavy snowfall at the surface, as snow particles are often dispersed by wind before reaching the ground. These findings highlight limitations of using radar reflectivity for predicting snow rates and suggest focusing on radar echo duration to better understand snowfall patterns.
Zackary Mages, Pavlos Kollias, Bernat Puigdomenech Treserras, Paloma Borque, and Mariko Oue
EGUsphere, https://doi.org/10.5194/egusphere-2024-2984, https://doi.org/10.5194/egusphere-2024-2984, 2024
Short summary
Short summary
Convective clouds are a key component of the climate system. Using remote sensing observations during two field experiments in Houston, Texas, we identify four diurnal patterns of shallow convective clouds. We find areas more frequently experiencing shallow convective clouds, and we find areas where the vertical extent of shallow convective clouds is higher and where they are more likely to precipitate. This provides insight into the complicated environment that forms these clouds in Houston.
Kristofer S. Tuftedal, Bernat Puigdomènech Treserras, Mariko Oue, and Pavlos Kollias
Atmos. Chem. Phys., 24, 5637–5657, https://doi.org/10.5194/acp-24-5637-2024, https://doi.org/10.5194/acp-24-5637-2024, 2024
Short summary
Short summary
This study analyzed coastal convective cells from June through September 2018–2021. The cells were classified and their lifecycles were analyzed to better understand their characteristics. Features such as convective-core growth, for example, are shown. The study found differences in the initiation location of shallow convection and in the aerosol loading in deep convective environments. This work provides a foundation for future analyses of convection or other tracked events elsewhere.
Zeen Zhu, Fan Yang, Pavlos Kollias, Raymond A. Shaw, Alex B. Kostinski, Steve Krueger, Katia Lamer, Nithin Allwayin, and Mariko Oue
Atmos. Meas. Tech., 17, 1133–1143, https://doi.org/10.5194/amt-17-1133-2024, https://doi.org/10.5194/amt-17-1133-2024, 2024
Short summary
Short summary
In this article, we demonstrate the feasibility of applying advanced radar technology to detect liquid droplets generated in the cloud chamber. Specifically, we show that using radar with centimeter-scale resolution, single drizzle drops with a diameter larger than 40 µm can be detected. This study demonstrates the applicability of remote sensing instruments in laboratory experiments and suggests new applications of ultrahigh-resolution radar for atmospheric sensing.
Mariko Oue, Pavlos Kollias, Sergey Y. Matrosov, Alessandro Battaglia, and Alexander V. Ryzhkov
Atmos. Meas. Tech., 14, 4893–4913, https://doi.org/10.5194/amt-14-4893-2021, https://doi.org/10.5194/amt-14-4893-2021, 2021
Short summary
Short summary
Multi-wavelength radar measurements provide capabilities to identify ice particle types and growth processes in clouds beyond the capabilities of single-frequency radar measurements. This study introduces Doppler velocity and polarimetric radar observables into the multi-wavelength radar reflectivity measurement to improve identification analysis. The analysis clearly discerns snowflake aggregation and riming processes and even early stages of riming.
Katia Lamer, Mariko Oue, Alessandro Battaglia, Richard J. Roy, Ken B. Cooper, Ranvir Dhillon, and Pavlos Kollias
Atmos. Meas. Tech., 14, 3615–3629, https://doi.org/10.5194/amt-14-3615-2021, https://doi.org/10.5194/amt-14-3615-2021, 2021
Short summary
Short summary
Observations collected during the 25 February 2020 deployment of the VIPR at the Stony Brook Radar Observatory clearly demonstrate the potential of G-band radars for cloud and precipitation research. The field experiment, which coordinated an X-, Ka-, W- and G-band radar, revealed that the differential reflectivity from Ka–G band pair provides larger signals than the traditional Ka–W pairing underpinning an increased sensitivity to smaller amounts of liquid and ice water mass and sizes.
Mariko Oue, Aleksandra Tatarevic, Pavlos Kollias, Dié Wang, Kwangmin Yu, and Andrew M. Vogelmann
Geosci. Model Dev., 13, 1975–1998, https://doi.org/10.5194/gmd-13-1975-2020, https://doi.org/10.5194/gmd-13-1975-2020, 2020
Short summary
Short summary
We developed the Cloud-resolving model Radar SIMulator (CR-SIM) capable of apples-to-apples comparisons between the multiwavelength, zenith-pointing, and scanning radar and multi-remote-sensing (radar and lidar) observations and the high-resolution atmospheric model output. Applications of CR-SIM as a virtual observatory operator aid interpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-based measurements.
Mariko Oue, Pavlos Kollias, Alan Shapiro, Aleksandra Tatarevic, and Toshihisa Matsui
Atmos. Meas. Tech., 12, 1999–2018, https://doi.org/10.5194/amt-12-1999-2019, https://doi.org/10.5194/amt-12-1999-2019, 2019
Short summary
Short summary
This study investigated impacts of the selected radar volume coverage pattern, the sampling time period, the number of radars used, and the added value of advection correction on the retrieval of vertical air motion from a multi-Doppler-radar technique. The results suggest that the use of rapid-scan radars can substantially improve the quality of wind retrievals and that the retrieved wind field needs to be carefully used considering the limitations of the radar observing system.
Kirk W. North, Mariko Oue, Pavlos Kollias, Scott E. Giangrande, Scott M. Collis, and Corey K. Potvin
Atmos. Meas. Tech., 10, 2785–2806, https://doi.org/10.5194/amt-10-2785-2017, https://doi.org/10.5194/amt-10-2785-2017, 2017
Short summary
Short summary
Vertical air motion retrievals from 3DVAR multiple distributed scanning Doppler radars are compared against collocated profiling radars and retrieved from an upward iteration integration iterative technique to characterize their veracity. The retrieved vertical air motions are generally within 1–2 m s−1 of agreement with profiling radars and better solution than the upward integration technique, and therefore can be used as a means to improve parameterizations in numerical models moving forward.
Nitika Yadlapalli Yurk, Matthew Lebsock, Juan Socuellamos, Raquel Rodriguez Monje, Ken Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-618, https://doi.org/10.5194/egusphere-2025-618, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Current knowledge of the link between clouds and climate is limited by lack of observations of the drop size distribution (DSD) within clouds, especially for the smallest drops. We demonstrate a method of retrieving DSDs down to small drop sizes using observations of drizzling marine layer clouds captured by the CloudCube millimeter-wave Doppler radar. We compare the shape of the observed spectra to theoretical expectations of radar echoes to solve for DSDs at each time and elevation.
Sarah Wugofski, Matthew R. Kumjian, Mariko Oue, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-671, https://doi.org/10.5194/egusphere-2025-671, 2025
Short summary
Short summary
Doppler spectral data inform on how particles of varying vertical velocities contribute to total backscattered power observed. Through examining three case studies, consistent features in radar moment data were found to be characteristic of multi-modal spectra. We quantified how spectrum width and mean Doppler velocity can be used to determine whether or not a layer is multi-modal. The identification criteria and methods are described in Part 1 and assessed in Part 2.
Marco Coppola, Alessandro Battaglia, Frederic Tridon, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-416, https://doi.org/10.5194/egusphere-2025-416, 2025
Short summary
Short summary
The WIVERN conically scanning Doppler W-band radar, has the potential, for the first time, to map the mesoscale and synoptic variability of cloud dynamics, and precipitation microphysics. This study shows that the oblique angle of incidence will be advantageous compared to standard nadir-looking radars due to substantial clutter suppression over ocean surface. This feature will enable the detection and quantification of light and moderate precipitation, with improved proximity to the surface.
Laura M. Tomkins, Sandra E. Yuter, Matthew A. Miller, Mariko Oue, and Charles N. Helms
EGUsphere, https://doi.org/10.5194/egusphere-2025-6, https://doi.org/10.5194/egusphere-2025-6, 2025
Short summary
Short summary
This study investigates how radar-detected snow bands relate to snowfall rates during winter storms in the northeastern U.S. Using over a decade of data, we found that snow bands are not consistently linked to heavy snowfall at the surface, as snow particles are often dispersed by wind before reaching the ground. These findings highlight limitations of using radar reflectivity for predicting snow rates and suggest focusing on radar echo duration to better understand snowfall patterns.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
Short summary
Short summary
The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Sean W. Freeman, Jennie Bukowski, Leah D. Grant, Peter J. Marinescu, J. Minnie Park, Stacey M. Hitchcock, Christine A. Neumaier, and Susan C. van den Heever
EGUsphere, https://doi.org/10.5194/egusphere-2024-2425, https://doi.org/10.5194/egusphere-2024-2425, 2025
Short summary
Short summary
In this work, we tested different placements of a temperature and humidity sensor onboard a drone to understand what the relative errors are. Understanding these errors is critical as we want to collect more meteorological data from non-specialized platforms, such as drone swarms and drone package delivery.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
Short summary
Short summary
This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Bernat Puigdomènech Treserras and Pavlos Kollias
Atmos. Meas. Tech., 17, 6301–6314, https://doi.org/10.5194/amt-17-6301-2024, https://doi.org/10.5194/amt-17-6301-2024, 2024
Short summary
Short summary
The paper presents a comprehensive approach to improve the geolocation accuracy of spaceborne radar and lidar systems, crucial for the successful interpretation of data from the upcoming EarthCARE mission. The paper details the technical background of the presented methods and various examples of geolocation analyses, including a short period of CloudSat observations when the star tracker was not operating properly and lifetime statistics from the CloudSat and CALIPSO missions.
Francesco Manconi, Alessandro Battaglia, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2024-2779, https://doi.org/10.5194/egusphere-2024-2779, 2024
Short summary
Short summary
The paper aims at studying the ground reflection, or clutter, of the signal from a spaceborne radar in the context of ESA's WIVERN mission, which will observe in-cloud winds. Using topography and land type data, with a model of the satellite orbit and rotating antenna, simulations of scans have been run over Italy's Piedmont region. These measurements cover the full range of the ground clutter over land for WIVERN, and allowed for analyses on the precision and accuracy of velocity observations.
Zackary Mages, Pavlos Kollias, Bernat Puigdomenech Treserras, Paloma Borque, and Mariko Oue
EGUsphere, https://doi.org/10.5194/egusphere-2024-2984, https://doi.org/10.5194/egusphere-2024-2984, 2024
Short summary
Short summary
Convective clouds are a key component of the climate system. Using remote sensing observations during two field experiments in Houston, Texas, we identify four diurnal patterns of shallow convective clouds. We find areas more frequently experiencing shallow convective clouds, and we find areas where the vertical extent of shallow convective clouds is higher and where they are more likely to precipitate. This provides insight into the complicated environment that forms these clouds in Houston.
Corey G. Amiot, Timothy J. Lang, Susan C. van den Heever, Richard A. Ferrare, Ousmane O. Sy, Lawrence D. Carey, Sundar A. Christopher, John R. Mecikalski, Sean W. Freeman, George Alexander Sokolowsky, Chris A. Hostetler, and Simone Tanelli
EGUsphere, https://doi.org/10.5194/egusphere-2024-2384, https://doi.org/10.5194/egusphere-2024-2384, 2024
Short summary
Short summary
Decoupling aerosol and environmental impacts on convection is challenging. Using airborne data, we correlated microwave-frequency convective metrics with aerosol concentrations in several different environments. Medium-to-high aerosol concentrations were often strongly and positively correlated with convective intensity and frequency, especially in favorable environments based on temperature lapse rates and K-Index. Storm environment is important to consider when evaluating aerosol effects.
Dié Wang, Roni Kobrosly, Tao Zhang, Tamanna Subba, Susan van den Heever, Siddhant Gupta, and Michael Jensen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2436, https://doi.org/10.5194/egusphere-2024-2436, 2024
Short summary
Short summary
We use a new method to understand how tiny particles in the air, called aerosols, affect rain clouds in the Houston-Galveston area. Aerosols generally do not make these clouds grow much taller, with an average height increase of about 1 km under certain conditions. However, their effects on rainfall strength and cloud expansion are less certain. Clouds influenced by sea breezes show a stronger aerosol impact, possibly due to unaccounted factors like vertical winds in near-surface layers.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
Short summary
Short summary
Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Robin J. Hogan, Anthony J. Illingworth, Pavlos Kollias, Hajime Okamoto, and Ulla Wandinger
Atmos. Meas. Tech., 17, 3081–3083, https://doi.org/10.5194/amt-17-3081-2024, https://doi.org/10.5194/amt-17-3081-2024, 2024
Kristofer S. Tuftedal, Bernat Puigdomènech Treserras, Mariko Oue, and Pavlos Kollias
Atmos. Chem. Phys., 24, 5637–5657, https://doi.org/10.5194/acp-24-5637-2024, https://doi.org/10.5194/acp-24-5637-2024, 2024
Short summary
Short summary
This study analyzed coastal convective cells from June through September 2018–2021. The cells were classified and their lifecycles were analyzed to better understand their characteristics. Features such as convective-core growth, for example, are shown. The study found differences in the initiation location of shallow convection and in the aerosol loading in deep convective environments. This work provides a foundation for future analyses of convection or other tracked events elsewhere.
Zeen Zhu, Fan Yang, Pavlos Kollias, Raymond A. Shaw, Alex B. Kostinski, Steve Krueger, Katia Lamer, Nithin Allwayin, and Mariko Oue
Atmos. Meas. Tech., 17, 1133–1143, https://doi.org/10.5194/amt-17-1133-2024, https://doi.org/10.5194/amt-17-1133-2024, 2024
Short summary
Short summary
In this article, we demonstrate the feasibility of applying advanced radar technology to detect liquid droplets generated in the cloud chamber. Specifically, we show that using radar with centimeter-scale resolution, single drizzle drops with a diameter larger than 40 µm can be detected. This study demonstrates the applicability of remote sensing instruments in laboratory experiments and suggests new applications of ultrahigh-resolution radar for atmospheric sensing.
Shannon L. Mason, Howard W. Barker, Jason N. S. Cole, Nicole Docter, David P. Donovan, Robin J. Hogan, Anja Hünerbein, Pavlos Kollias, Bernat Puigdomènech Treserras, Zhipeng Qu, Ulla Wandinger, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, https://doi.org/10.5194/amt-17-875-2024, 2024
Short summary
Short summary
When the EarthCARE mission enters its operational phase, many retrieval data products will be available, which will overlap both in terms of the measurements they use and the geophysical quantities they report. In this pre-launch study, we use simulated EarthCARE scenes to compare the coverage and performance of many data products from the European Space Agency production model, with the intention of better understanding the relation between products and providing a compact guide to users.
David P. Donovan, Pavlos Kollias, Almudena Velázquez Blázquez, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5327–5356, https://doi.org/10.5194/amt-16-5327-2023, https://doi.org/10.5194/amt-16-5327-2023, 2023
Short summary
Short summary
The Earth Cloud, Aerosol and Radiation Explorer mission (EarthCARE) is a multi-instrument cloud–aerosol–radiation-oriented satellite for climate and weather applications. For this satellite mission to be successful, the development and implementation of new techniques for turning the measured raw signals into useful data is required. This paper describes how atmospheric model data were used as the basis for creating realistic high-resolution simulated data sets to facilitate this process.
Imke Schirmacher, Pavlos Kollias, Katia Lamer, Mario Mech, Lukas Pfitzenmaier, Manfred Wendisch, and Susanne Crewell
Atmos. Meas. Tech., 16, 4081–4100, https://doi.org/10.5194/amt-16-4081-2023, https://doi.org/10.5194/amt-16-4081-2023, 2023
Short summary
Short summary
CloudSat’s relatively coarse spatial resolution, low sensitivity, and blind zone limit its assessment of Arctic low-level clouds, which affect the surface energy balance. We compare cloud fractions from CloudSat and finely resolved airborne radar observations to determine CloudSat’s limitations. Cloudsat overestimates cloud fractions above its blind zone, especially during cold-air outbreaks over open water, and misses a cloud fraction of 32 % and half of the precipitation inside its blind zone.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Abdanour Irbah, Julien Delanoë, Gerd-Jan van Zadelhoff, David P. Donovan, Pavlos Kollias, Bernat Puigdomènech Treserras, Shannon Mason, Robin J. Hogan, and Aleksandra Tatarevic
Atmos. Meas. Tech., 16, 2795–2820, https://doi.org/10.5194/amt-16-2795-2023, https://doi.org/10.5194/amt-16-2795-2023, 2023
Short summary
Short summary
The Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID) aboard the EarthCARE satellite are used to probe the Earth's atmosphere by measuring cloud and aerosol profiles. ATLID is sensitive to aerosols and small cloud particles and CPR to large ice particles, snowflakes and raindrops. It is the synergy of the measurements of these two instruments that allows a better classification of the atmospheric targets and the description of the associated products, which are the subject of this paper.
Gabrielle R. Leung, Stephen M. Saleeby, G. Alexander Sokolowsky, Sean W. Freeman, and Susan C. van den Heever
Atmos. Chem. Phys., 23, 5263–5278, https://doi.org/10.5194/acp-23-5263-2023, https://doi.org/10.5194/acp-23-5263-2023, 2023
Short summary
Short summary
This study uses a suite of high-resolution simulations to explore how the concentration and type of aerosol particles impact shallow tropical clouds and the overall aerosol budget. Under more-polluted conditions, there are more aerosol particles present, but we also find that clouds are less able to remove those aerosol particles via rainout. Instead, those aerosol particles are more likely to be detrained aloft and remain in the atmosphere for further aerosol–cloud interactions.
Pavlos Kollias, Bernat Puidgomènech Treserras, Alessandro Battaglia, Paloma C. Borque, and Aleksandra Tatarevic
Atmos. Meas. Tech., 16, 1901–1914, https://doi.org/10.5194/amt-16-1901-2023, https://doi.org/10.5194/amt-16-1901-2023, 2023
Short summary
Short summary
The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission developed by the European Space Agency (ESA) and Japan Aerospace Exploration Agency (JAXA) features the first spaceborne 94 GHz Doppler cloud-profiling radar (CPR) with Doppler capability. Here, we describe the post-processing algorithms that apply quality control and corrections to CPR measurements and derive key geophysical variables such as hydrometeor locations and best estimates of particle sedimentation fall velocities.
Zackary Mages, Pavlos Kollias, Zeen Zhu, and Edward P. Luke
Atmos. Chem. Phys., 23, 3561–3574, https://doi.org/10.5194/acp-23-3561-2023, https://doi.org/10.5194/acp-23-3561-2023, 2023
Short summary
Short summary
Cold-air outbreaks (when cold air is advected over warm water and creates low-level convection) are a dominant cloud regime in the Arctic, and we capitalized on ground-based observations, which did not previously exist, from the COMBLE field campaign to study them. We characterized the extent and strength of the convection and turbulence and found evidence of secondary ice production. This information is useful for model intercomparison studies that will represent cold-air outbreak processes.
Ewan Crosbie, Luke D. Ziemba, Michael A. Shook, Claire E. Robinson, Edward L. Winstead, K. Lee Thornhill, Rachel A. Braun, Alexander B. MacDonald, Connor Stahl, Armin Sorooshian, Susan C. van den Heever, Joshua P. DiGangi, Glenn S. Diskin, Sarah Woods, Paola Bañaga, Matthew D. Brown, Francesca Gallo, Miguel Ricardo A. Hilario, Carolyn E. Jordan, Gabrielle R. Leung, Richard H. Moore, Kevin J. Sanchez, Taylor J. Shingler, and Elizabeth B. Wiggins
Atmos. Chem. Phys., 22, 13269–13302, https://doi.org/10.5194/acp-22-13269-2022, https://doi.org/10.5194/acp-22-13269-2022, 2022
Short summary
Short summary
The linkage between cloud droplet and aerosol particle chemical composition was analyzed using samples collected in a polluted tropical marine environment. Variations in the droplet composition were related to physical and dynamical processes in clouds to assess their relative significance across three cases that spanned a range of rainfall amounts. In spite of the pollution, sea salt still remained a major contributor to the droplet composition and was preferentially enhanced in rainwater.
Eva-Lou Edwards, Jeffrey S. Reid, Peng Xian, Sharon P. Burton, Anthony L. Cook, Ewan C. Crosbie, Marta A. Fenn, Richard A. Ferrare, Sean W. Freeman, John W. Hair, David B. Harper, Chris A. Hostetler, Claire E. Robinson, Amy Jo Scarino, Michael A. Shook, G. Alexander Sokolowsky, Susan C. van den Heever, Edward L. Winstead, Sarah Woods, Luke D. Ziemba, and Armin Sorooshian
Atmos. Chem. Phys., 22, 12961–12983, https://doi.org/10.5194/acp-22-12961-2022, https://doi.org/10.5194/acp-22-12961-2022, 2022
Short summary
Short summary
This study compares NAAPS-RA model simulations of aerosol optical thickness (AOT) and extinction to those retrieved with a high spectral resolution lidar near the Philippines. Agreement for AOT was good, and extinction agreement was strongest below 1500 m. Substituting dropsonde relative humidities into NAAPS-RA did not drastically improve agreement, and we discuss potential reasons why. Accurately modeling future conditions in this region is crucial due to its susceptibility to climate change.
J. Minnie Park and Susan C. van den Heever
Atmos. Chem. Phys., 22, 10527–10549, https://doi.org/10.5194/acp-22-10527-2022, https://doi.org/10.5194/acp-22-10527-2022, 2022
Short summary
Short summary
This study explores how increased aerosol particles impact tropical sea breeze cloud systems under different environments and how a range of environments modulate these cloud responses. Overall, sea breeze flows and clouds that develop therein become weaker due to interactions between aerosols, sunlight, and land surface. In addition, surface rainfall also decreases with more aerosol particles. Weakening of cloud and rain with more aerosols is found irrespective of 130 different environments.
Zeen Zhu, Pavlos Kollias, Edward Luke, and Fan Yang
Atmos. Chem. Phys., 22, 7405–7416, https://doi.org/10.5194/acp-22-7405-2022, https://doi.org/10.5194/acp-22-7405-2022, 2022
Short summary
Short summary
Drizzle (small rain droplets) is an important component of warm clouds; however, its existence is poorly understood. In this study, we capitalized on a machine-learning algorithm to develop a drizzle detection method. We applied this algorithm to investigate drizzle occurrence and found out that drizzle is far more ubiquitous than previously thought. This study demonstrates the ubiquitous nature of drizzle in clouds and will improve understanding of the associated microphysical process.
Alessandro Battaglia, Paolo Martire, Eric Caubet, Laurent Phalippou, Fabrizio Stesina, Pavlos Kollias, and Anthony Illingworth
Atmos. Meas. Tech., 15, 3011–3030, https://doi.org/10.5194/amt-15-3011-2022, https://doi.org/10.5194/amt-15-3011-2022, 2022
Short summary
Short summary
We present an instrument simulator for a new sensor, WIVERN (WInd VElocity Radar Nephoscope), a conically scanning radar payload with Doppler capabilities, recently down-selected as one of the four candidates for the European Space Agency Earth Explorer 11 program. The mission aims at measuring horizontal winds in cloudy areas. The simulator is instrumental in the definition and consolidation of the mission requirements and the evaluation of mission performances.
Russell J. Perkins, Peter J. Marinescu, Ezra J. T. Levin, Don R. Collins, and Sonia M. Kreidenweis
Atmos. Chem. Phys., 22, 6197–6215, https://doi.org/10.5194/acp-22-6197-2022, https://doi.org/10.5194/acp-22-6197-2022, 2022
Short summary
Short summary
We used 5 years (2009–2013) of aerosol and cloud condensation nuclei (CCN) data from a total of seven instruments housed at the Southern Great Plains site, which were merged into a quality-controlled, continuous dataset of CCN spectra at ~45 min resolution. The data cover all seasons, are representative of a rural, agricultural mid-continental site, and are useful for model initialization and validation. Our analysis of this dataset focuses on seasonal and hourly variability.
Sonja Drueke, Daniel J. Kirshbaum, and Pavlos Kollias
Atmos. Chem. Phys., 21, 14039–14058, https://doi.org/10.5194/acp-21-14039-2021, https://doi.org/10.5194/acp-21-14039-2021, 2021
Short summary
Short summary
This numerical study provides insights into the sensitivity of shallow-cumulus dilution to geostrophic vertical wind profile. The cumulus dilution is strongly sensitive to vertical wind shear in the cloud layer, with shallow cumuli being more diluted in sheared environments. On the other hand, wind shear in the subcloud layer leads to less diluted cumuli. The sensitivities are explained by jointly considering the impacts of vertical velocity and the properties of the entrained air.
Mariko Oue, Pavlos Kollias, Sergey Y. Matrosov, Alessandro Battaglia, and Alexander V. Ryzhkov
Atmos. Meas. Tech., 14, 4893–4913, https://doi.org/10.5194/amt-14-4893-2021, https://doi.org/10.5194/amt-14-4893-2021, 2021
Short summary
Short summary
Multi-wavelength radar measurements provide capabilities to identify ice particle types and growth processes in clouds beyond the capabilities of single-frequency radar measurements. This study introduces Doppler velocity and polarimetric radar observables into the multi-wavelength radar reflectivity measurement to improve identification analysis. The analysis clearly discerns snowflake aggregation and riming processes and even early stages of riming.
Katia Lamer, Mariko Oue, Alessandro Battaglia, Richard J. Roy, Ken B. Cooper, Ranvir Dhillon, and Pavlos Kollias
Atmos. Meas. Tech., 14, 3615–3629, https://doi.org/10.5194/amt-14-3615-2021, https://doi.org/10.5194/amt-14-3615-2021, 2021
Short summary
Short summary
Observations collected during the 25 February 2020 deployment of the VIPR at the Stony Brook Radar Observatory clearly demonstrate the potential of G-band radars for cloud and precipitation research. The field experiment, which coordinated an X-, Ka-, W- and G-band radar, revealed that the differential reflectivity from Ka–G band pair provides larger signals than the traditional Ka–W pairing underpinning an increased sensitivity to smaller amounts of liquid and ice water mass and sizes.
Marek Jacob, Pavlos Kollias, Felix Ament, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 5757–5777, https://doi.org/10.5194/gmd-13-5757-2020, https://doi.org/10.5194/gmd-13-5757-2020, 2020
Short summary
Short summary
We compare clouds in different cloud-resolving atmosphere simulations with airborne remote sensing observations. The focus is on warm shallow clouds in the Atlantic trade wind region. Those clouds are climatologically important but challenging for climate models. We use forward operators to apply instrument-specific thresholds for cloud detection to model outputs. In this comparison, the higher-resolution model better reproduces the layered cloud structure.
Sonja Drueke, Daniel J. Kirshbaum, and Pavlos Kollias
Atmos. Chem. Phys., 20, 13217–13239, https://doi.org/10.5194/acp-20-13217-2020, https://doi.org/10.5194/acp-20-13217-2020, 2020
Short summary
Short summary
This numerical study provides insights into selected environmental sensitivities of shallow-cumulus dilution. Among the parameters under consideration, the dilution of the cloud cores is strongly sensitive to continentality and cloud-layer relative humidity and weakly sensitive to subcloud- and cloud-layer depths. The impacts of all four parameters are interpreted using a similarity theory of shallow cumulus and buoyancy-sorting arguments.
Alessandro Battaglia, Pavlos Kollias, Ranvir Dhillon, Katia Lamer, Marat Khairoutdinov, and Daniel Watters
Atmos. Meas. Tech., 13, 4865–4883, https://doi.org/10.5194/amt-13-4865-2020, https://doi.org/10.5194/amt-13-4865-2020, 2020
Short summary
Short summary
Warm rain accounts for slightly more than 30 % of the total rain amount and 70 % of the total rain area in the tropical belt and usually appears in kilometer-size cells. Spaceborne radars adopting millimeter wavelengths are excellent tools for detecting such precipitation types and for separating between the cloud and rain components. Our work highlights the benefits of operating multifrequency radars and discusses the impact of antenna footprints in quantitative estimates of liquid water paths.
Mario Mech, Maximilian Maahn, Stefan Kneifel, Davide Ori, Emiliano Orlandi, Pavlos Kollias, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, https://doi.org/10.5194/gmd-13-4229-2020, 2020
Short summary
Short summary
The Passive and Active Microwave TRAnsfer tool (PAMTRA) is a public domain software package written in Python and Fortran for the simulation of microwave remote sensing observations. PAMTRA models the interaction of radiation with gases, clouds, precipitation, and the surface using either in situ observations or model output as input parameters. The wide range of applications is demonstrated for passive (radiometer) and active (radar) instruments on ground, airborne, and satellite platforms.
Steven Albers, Stephen M. Saleeby, Sonia Kreidenweis, Qijing Bian, Peng Xian, Zoltan Toth, Ravan Ahmadov, Eric James, and Steven D. Miller
Atmos. Meas. Tech., 13, 3235–3261, https://doi.org/10.5194/amt-13-3235-2020, https://doi.org/10.5194/amt-13-3235-2020, 2020
Short summary
Short summary
A fast 3D visible-light forward operator is used to realistically visualize, validate, and potentially assimilate ground- and space-based camera and satellite imagery with NWP models. Three-dimensional fields of hydrometeors, aerosols, and 2D land surface variables are considered in the generation of radiance fields and RGB imagery from a variety of vantage points.
Katia Lamer, Pavlos Kollias, Alessandro Battaglia, and Simon Preval
Atmos. Meas. Tech., 13, 2363–2379, https://doi.org/10.5194/amt-13-2363-2020, https://doi.org/10.5194/amt-13-2363-2020, 2020
Short summary
Short summary
According to ground-based radar observations, 50 % of liquid low-level clouds over the Atlantic extend below 1.2 km and are thinner than 400 m, thus limiting their detection from space. Using an emulator, we estimate that a 250 m resolution radar would capture cloud base better than the CloudSat radar which misses about 52 %. The more sensitive EarthCARE radar is expected to capture cloud cover but stretch cloud. This calls for the operation of interlaced pulse modes for future space missions.
Mariko Oue, Aleksandra Tatarevic, Pavlos Kollias, Dié Wang, Kwangmin Yu, and Andrew M. Vogelmann
Geosci. Model Dev., 13, 1975–1998, https://doi.org/10.5194/gmd-13-1975-2020, https://doi.org/10.5194/gmd-13-1975-2020, 2020
Short summary
Short summary
We developed the Cloud-resolving model Radar SIMulator (CR-SIM) capable of apples-to-apples comparisons between the multiwavelength, zenith-pointing, and scanning radar and multi-remote-sensing (radar and lidar) observations and the high-resolution atmospheric model output. Applications of CR-SIM as a virtual observatory operator aid interpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-based measurements.
Jennie Bukowski and Susan C. van den Heever
Atmos. Chem. Phys., 20, 2967–2986, https://doi.org/10.5194/acp-20-2967-2020, https://doi.org/10.5194/acp-20-2967-2020, 2020
Short summary
Short summary
This paper seeks to better our understanding of how dust storms are represented in a weather model. Depending on how well the model can represent the storm, it can change the dust forecast significantly. This is important for predictions of air quality and visibility; as dust can heat and cool the air in its environment, it is also crucial for calculating the Earth's energy budget. Here, we communicate the uncertainty in a dust model and the effect that it may have on dust forecasts.
Fan Yang, Robert McGraw, Edward P. Luke, Damao Zhang, Pavlos Kollias, and Andrew M. Vogelmann
Atmos. Meas. Tech., 12, 5817–5828, https://doi.org/10.5194/amt-12-5817-2019, https://doi.org/10.5194/amt-12-5817-2019, 2019
Short summary
Short summary
In-cloud supersaturation is crucial for droplet activation, growth, and drizzle initiation but is poorly known and hardly measured. Here we provide a novel method to estimate supersaturation fluctuation in stratocumulus clouds using remote-sensing measurements, and results show that our estimated supersaturation agrees reasonably well with in situ measurements. Our method provides a unique way to estimate supersaturation in stratocumulus clouds from long-term ground-based observations.
Max Heikenfeld, Peter J. Marinescu, Matthew Christensen, Duncan Watson-Parris, Fabian Senf, Susan C. van den Heever, and Philip Stier
Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, https://doi.org/10.5194/gmd-12-4551-2019, 2019
Short summary
Short summary
We present tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing clouds in different types of datasets. It provides a flexible new way to include the evolution of individual clouds in a wide range of analyses. It is developed as a community project to provide a common basis for the inclusion of existing tracking algorithms and the development of new analyses that involve tracking clouds and other features in geoscientific research.
Peter J. Marinescu, Ezra J. T. Levin, Don Collins, Sonia M. Kreidenweis, and Susan C. van den Heever
Atmos. Chem. Phys., 19, 11985–12006, https://doi.org/10.5194/acp-19-11985-2019, https://doi.org/10.5194/acp-19-11985-2019, 2019
Short summary
Short summary
We characterized and provided fits for the seasonal aerosol size distributions (7 nm–14 µm diameter) at a North American, long–term surface site (SGP), which can be applied to models. Key cycles on timescales of several hours to weeks were also assessed using power spectra for various aerosol size ranges. One key finding is the consistent presence of diurnal cycles in the smallest particles in each season, providing insights into the formation and roles of new particle formation at SGP.
Steven D. Miller, Louie D. Grasso, Qijing Bian, Sonia M. Kreidenweis, Jack F. Dostalek, Jeremy E. Solbrig, Jennifer Bukowski, Susan C. van den Heever, Yi Wang, Xiaoguang Xu, Jun Wang, Annette L. Walker, Ting-Chi Wu, Milija Zupanski, Christine Chiu, and Jeffrey S. Reid
Atmos. Meas. Tech., 12, 5101–5118, https://doi.org/10.5194/amt-12-5101-2019, https://doi.org/10.5194/amt-12-5101-2019, 2019
Short summary
Short summary
Satellite–based detection of lofted mineral via infrared–window channels, well established in the literature, faces significant challenges in the presence of atmospheric moisture. Here, we consider a case featuring the juxtaposition of two dust plumes embedded within dry and moist air masses. The case is considered from the vantage points of numerical modeling, multi–sensor observations, and radiative transfer theory arriving at a new method for mitigating the water vapor masking effect.
Mario Mech, Leif-Leonard Kliesch, Andreas Anhäuser, Thomas Rose, Pavlos Kollias, and Susanne Crewell
Atmos. Meas. Tech., 12, 5019–5037, https://doi.org/10.5194/amt-12-5019-2019, https://doi.org/10.5194/amt-12-5019-2019, 2019
Short summary
Short summary
An improved understanding of Arctic mixed-phase clouds and their contribution to Arctic warming can be achieved by observations from airborne platforms with remote sensing instruments. Such an instrument is MiRAC combining active and passive techniques to gain information on the distribution of clouds, the occurrence of precipitation, and the amount of liquid and ice within the cloud. Operated during a campaign in Arctic summer, it could observe lower clouds often not seen by spaceborne radars.
Pavlos Kollias, Bernat Puigdomènech Treserras, and Alain Protat
Atmos. Meas. Tech., 12, 4949–4964, https://doi.org/10.5194/amt-12-4949-2019, https://doi.org/10.5194/amt-12-4949-2019, 2019
Short summary
Short summary
Profiling millimeter-wavelength radars are the cornerstone instrument of surface-based observatories. Calibrating these radars is important for establishing a long record of observations suitable for model evaluation and improvement. Here, the CloudSat CPR is used to assess the calibration of a record over 10 years long of ARM cloud radar observations (a total of 44 years). The results indicate that correction coefficients are needed to improve record reliability and usability.
Katia Lamer, Bernat Puigdomènech Treserras, Zeen Zhu, Bradley Isom, Nitin Bharadwaj, and Pavlos Kollias
Atmos. Meas. Tech., 12, 4931–4947, https://doi.org/10.5194/amt-12-4931-2019, https://doi.org/10.5194/amt-12-4931-2019, 2019
Short summary
Short summary
This article describes the three newly deployed second-generation radar of the Atmospheric Radiation Measurement program. Techniques to retrieve precipitation rate from their measurements are presented: noise and clutter filtering, gas and liquid attenuation correction, and radar reflectivity calibration. Rain rate for a 40 km radius domain around Graciosa estimated from all three radar differ, which highlights the need to consider sensor capabilities when interpreting radar measurements.
Melville E. Nicholls, Warren P. Smith, Roger A. Pielke Sr., Stephen M. Saleeby, and Norman B. Wood
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-569, https://doi.org/10.5194/acp-2019-569, 2019
Preprint withdrawn
Short summary
Short summary
Numerical modeling simulations indicate that radiation significantly accelerates tropical cyclogenesis. This study provides evidence that the primary physical mechanism is nocturnal longwave cooling of the environment. This generates weak upward motion in the core of the system that over the course of a night promotes convective activity and is responsible for a diurnal cycle. Understanding the role of radiation is likely to lead to improved forecasting of these major weather events.
Stephen M. Saleeby, Susan C. van den Heever, Jennie Bukowski, Annette L. Walker, Jeremy E. Solbrig, Samuel A. Atwood, Qijing Bian, Sonia M. Kreidenweis, Yi Wang, Jun Wang, and Steven D. Miller
Atmos. Chem. Phys., 19, 10279–10301, https://doi.org/10.5194/acp-19-10279-2019, https://doi.org/10.5194/acp-19-10279-2019, 2019
Short summary
Short summary
This study seeks to understand how intense dust storms impact the heating and cooling of the land surface and atmosphere. Dust storms that are intense enough to substantially impact visibility can also alter how much sunlight reaches the surface during the day and how much heat is trapped in the atmosphere at night. These radiation changes can impact the temperature of the atmosphere and impact the weather in the vicinity.
Alessandro Battaglia and Pavlos Kollias
Atmos. Meas. Tech., 12, 3335–3349, https://doi.org/10.5194/amt-12-3335-2019, https://doi.org/10.5194/amt-12-3335-2019, 2019
Short summary
Short summary
This work investigates the potential of an innovative differential absorption radar for retrieving relative humidity inside ice clouds. The radar exploits the strong spectral dependence of the water vapour absorption for frequencies close to the 183 GHz water vapour band.
Results show that observations from a system with 4–6 frequencies can provide
novel information for understanding the formation and growth of ice crystals.
Stacey Kawecki and Susan van den Heever
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-399, https://doi.org/10.5194/acp-2019-399, 2019
Preprint withdrawn
Short summary
Short summary
This work examines how the topographic height and diameter of an island influence where and when precipitation falls, and why these patterns change. Using a numerical weather model, we systematically increased island orographic heights and diameters. We find that increasing orography increases precipitation amounts, regardless of island diameter size. Precipitation increases because changing the topography alters where moisture and lift occur, which are the prime ingredients for precipitation.
Mariko Oue, Pavlos Kollias, Alan Shapiro, Aleksandra Tatarevic, and Toshihisa Matsui
Atmos. Meas. Tech., 12, 1999–2018, https://doi.org/10.5194/amt-12-1999-2019, https://doi.org/10.5194/amt-12-1999-2019, 2019
Short summary
Short summary
This study investigated impacts of the selected radar volume coverage pattern, the sampling time period, the number of radars used, and the added value of advection correction on the retrieval of vertical air motion from a multi-Doppler-radar technique. The results suggest that the use of rapid-scan radars can substantially improve the quality of wind retrievals and that the retrieved wind field needs to be carefully used considering the limitations of the radar observing system.
Ting-Chi Wu, Milija Zupanski, Stephen Saleeby, Anton Kliewer, Lewis Grasso, Qijing Bian, Samuel A. Atwood, Yi Wang, and Jun Wang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-1249, https://doi.org/10.5194/acp-2018-1249, 2018
Revised manuscript not accepted
Guangjie Zheng, Yang Wang, Allison C. Aiken, Francesca Gallo, Michael P. Jensen, Pavlos Kollias, Chongai Kuang, Edward Luke, Stephen Springston, Janek Uin, Robert Wood, and Jian Wang
Atmos. Chem. Phys., 18, 17615–17635, https://doi.org/10.5194/acp-18-17615-2018, https://doi.org/10.5194/acp-18-17615-2018, 2018
Short summary
Short summary
Here, we elucidate the key processes that drive marine boundary layer (MBL) aerosol size distribution in the eastern North Atlantic (ENA) using long-term measurements. The governing equations of particle concentration are established for different modes. Particles entrained from the free troposphere represent the major source of MBL cloud condensation nuclei (CCN), contributing both directly to CCN population and indirectly by supplying Aitken-mode particles that grow to CCN in the MBL.
Katia Lamer, Ann M. Fridlind, Andrew S. Ackerman, Pavlos Kollias, Eugene E. Clothiaux, and Maxwell Kelley
Geosci. Model Dev., 11, 4195–4214, https://doi.org/10.5194/gmd-11-4195-2018, https://doi.org/10.5194/gmd-11-4195-2018, 2018
Short summary
Short summary
Weather and climate predictions of cloud, rain, and snow occurrence remain uncertain, in part because guidance from observation is incomplete. We present a tool that transforms predictions into observations from ground-based remote sensors. Liquid water and ice occurrence errors associated with the transformation are below 8 %, with ~ 3 % uncertainty. This (GO)2-SIM forward-simulator tool enables better evaluation of cloud, rain, and snow occurrence predictions using available observations.
Fan Yang, Pavlos Kollias, Raymond A. Shaw, and Andrew M. Vogelmann
Atmos. Chem. Phys., 18, 7313–7328, https://doi.org/10.5194/acp-18-7313-2018, https://doi.org/10.5194/acp-18-7313-2018, 2018
Short summary
Short summary
Cloud droplet size distribution (CDSD), which is related to cloud albedo and lifetime, is usually observed broader than predicted from adiabatic parcel calculations. Results in this study show that the CDSD can be broadened during condensational growth as a result of Ostwald ripening amplified by droplet deactivation and reactivation. Our results suggest that it is important to consider both curvature and solute effects before and after cloud droplet activation in a 3-D cloud model.
Damao Zhang, Zhien Wang, Pavlos Kollias, Andrew M. Vogelmann, Kang Yang, and Tao Luo
Atmos. Chem. Phys., 18, 4317–4327, https://doi.org/10.5194/acp-18-4317-2018, https://doi.org/10.5194/acp-18-4317-2018, 2018
Short summary
Short summary
Ice production in atmospheric clouds is important for global water cycle and radiation budget. Active satellite remote sensing measurements are analyzed to quantitatively study primary ice particle production in stratiform mixed-phase clouds on a global scale. We quantify the geographic and seasonal variations of ice production and their correlations with aerosol, especially mineral dust activities. The results can be used to evaluate mixed-phased clouds simulations by global climate models.
Xiaoli Zhou, Andrew S. Ackerman, Ann M. Fridlind, Robert Wood, and Pavlos Kollias
Atmos. Chem. Phys., 17, 12725–12742, https://doi.org/10.5194/acp-17-12725-2017, https://doi.org/10.5194/acp-17-12725-2017, 2017
Short summary
Short summary
Shallow maritime clouds make a well-known transition from stratocumulus to trade cumulus with flow from the subtropics equatorward. Three-day large-eddy simulations that investigate the potential influence of overlying African biomass burning plumes during that transition indicate that cloud-related impacts are likely substantially cooling to negligible at the top of the atmosphere, with magnitude sensitive to background and perturbation aerosol and cloud properties.
Kirk W. North, Mariko Oue, Pavlos Kollias, Scott E. Giangrande, Scott M. Collis, and Corey K. Potvin
Atmos. Meas. Tech., 10, 2785–2806, https://doi.org/10.5194/amt-10-2785-2017, https://doi.org/10.5194/amt-10-2785-2017, 2017
Short summary
Short summary
Vertical air motion retrievals from 3DVAR multiple distributed scanning Doppler radars are compared against collocated profiling radars and retrieved from an upward iteration integration iterative technique to characterize their veracity. The retrieved vertical air motions are generally within 1–2 m s−1 of agreement with profiling radars and better solution than the upward integration technique, and therefore can be used as a means to improve parameterizations in numerical models moving forward.
Claudia Acquistapace, Stefan Kneifel, Ulrich Löhnert, Pavlos Kollias, Maximilian Maahn, and Matthias Bauer-Pfundstein
Atmos. Meas. Tech., 10, 1783–1802, https://doi.org/10.5194/amt-10-1783-2017, https://doi.org/10.5194/amt-10-1783-2017, 2017
Short summary
Short summary
The goal of the paper is to understand what the optimal cloud radar settings for drizzle detection are. The number of cloud radars in the world has increased in the last 10 years and it is important to develop strategies to derive optimal settings which can be applied to all radar systems. The study is part of broader research focused on better understanding the microphysical process of drizzle growth using ground-based observations.
R. B. Seigel, S. C. van den Heever, and S. M. Saleeby
Atmos. Chem. Phys., 13, 4467–4485, https://doi.org/10.5194/acp-13-4467-2013, https://doi.org/10.5194/acp-13-4467-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Algorithm for continual monitoring of fog based on geostationary satellite imagery
Mitigation of satellite OCO-2 CO2 biases in the vicinity of clouds with 3D calculations using the Education and Research 3D Radiative Transfer Toolbox (EaR3T)
Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Peering into the heart of thunderstorm clouds: insights from cloud radar and spectral polarimetry
Improved Simulation of Thunderstorm Characteristics and Polarimetric Signatures with LIMA 2-Moment Microphysics in AROME
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Extension of AVHRR-based climate data records: Exploring ways to simulate AVHRR radiances from Suomi-NPP VIIRS data
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Satellite-based detection of deep convective clouds: the sensitivity of infrared methods, and implications for cloud climatology
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Riming-dependent Snowfall Rate and Ice Water Content Retrievals for W-band cloud radar
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
3D cloud masking across a broad swath using multi-angle polarimetry and deep learning
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Retrieval of cloud fraction and optical thickness of liquid water clouds over the ocean from multi-angle polarization observations
Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks
Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations
PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI
Contrail altitude estimation using GOES-16 ABI data and deep learning
The Ice Cloud Imager: retrieval of frozen water column properties
Supercooled liquid water cloud classification using lidar backscatter peak properties
Marine cloud base height retrieval from MODIS cloud properties using machine learning
How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties
Radiative Closure Assessment of Retrieved Cloud and Aerosol Properties for the EarthCARE Mission: The ACMB-DF Product
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data
Bayesian cloud-top phase determination for Meteosat Second Generation
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Infrared Radiometric Image Classification and Segmentation of Cloud Structure Using Deep-learning Framework for Ground-based Infrared Thermal Camera Observations
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
The EarthCARE mission: science data processing chain overview
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products
Raman lidar-derived optical and microphysical properties of ice crystals within thin Arctic clouds during PARCS campaign
Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements
Deep convective cloud system size and structure across the global tropics and subtropics
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
Atmos. Meas. Tech., 18, 1927–1941, https://doi.org/10.5194/amt-18-1927-2025, https://doi.org/10.5194/amt-18-1927-2025, 2025
Short summary
Short summary
Fog and low stratus (FLS) are both persistent clouds close to the Earth's surface. This study introduces a new machine-learning-based algorithm developed for the Meteosat Second Generation geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle.
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
Atmos. Meas. Tech., 18, 1859–1884, https://doi.org/10.5194/amt-18-1859-2025, https://doi.org/10.5194/amt-18-1859-2025, 2025
Short summary
Short summary
CO2 column-averaged dry-air mole fractions can be retrieved from space using spectrometers like OCO-2. However, nearby clouds induce spectral distortions that bias these retrievals beyond the accuracy needed for global CO2 source and sink assessments. This study employs a physics-based linearization approach to represent 3D cloud effects and introduces radiance-level mitigation techniques for actual OCO-2 data, enabling the operational implementation of these corrections.
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025, https://doi.org/10.5194/amt-18-1641-2025, 2025
Short summary
Short summary
A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Yuli Liu and Ian Stuart Adams
Atmos. Meas. Tech., 18, 1659–1674, https://doi.org/10.5194/amt-18-1659-2025, https://doi.org/10.5194/amt-18-1659-2025, 2025
Short summary
Short summary
This paper presents our latest development in tomographic reconstruction algorithms that use multi-angle (sub)millimeter-wave brightness temperature to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomography technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the technique effectively increases detection sensitivity for small ice cloud particles.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025, https://doi.org/10.5194/amt-18-1621-2025, 2025
Short summary
Short summary
The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (ESM) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025, https://doi.org/10.5194/amt-18-1325-2025, 2025
Short summary
Short summary
This study introduces the JAXA EarthCARE Level 2 (L2) cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation are quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025, https://doi.org/10.5194/amt-18-1209-2025, 2025
Short summary
Short summary
The dynamics of thunderclouds are studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
Cloé David, Clotilde Augros, Benoît Vié, François Bouttier, and Tony Le Bastard
EGUsphere, https://doi.org/10.5194/egusphere-2025-685, https://doi.org/10.5194/egusphere-2025-685, 2025
Short summary
Short summary
Simulations of storm characteristics and associated radar signatures were improved, especially under the freezing level, using an advanced cloud scheme. Discrepancies between observations and forecasts at and above the melting layer highlighted issues in both the radar forward operator and the microphysics. To overcome part of these issues, different parametrizations of the operator were suggested. This work aligns with the future integration of polarimetric data into assimilation systems.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025, https://doi.org/10.5194/amt-18-773-2025, 2025
Short summary
Short summary
In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Karl-Göran Karlsson, Nina Håkansson, Salomon Eliasson, Erwin Wolters, and Ronald Scheirer
EGUsphere, https://doi.org/10.5194/egusphere-2025-379, https://doi.org/10.5194/egusphere-2025-379, 2025
Short summary
Short summary
The topic is finding methods to extend climate data records from single-instrument satellite observations, in this case the Advanced Very High Resolution Radiometer (AVHRR). Several modern instruments include AVHRR-heritage channels but some corrections are necessary to account for some differences. We have simulated AVHRR data from the VIIIRS sensor on NOAA polar satellites. We find that methods based on machine learning are capable of performing these corrections.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
Short summary
Short summary
We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
Andrzej Zbigniew Kotarba and Izabela Wojciechowska
EGUsphere, https://doi.org/10.5194/egusphere-2024-3693, https://doi.org/10.5194/egusphere-2024-3693, 2025
Short summary
Short summary
The research investigates methods for detecting deep convective clouds (DCCs) using satellite infrared data, essential for understanding long-term climate trends. By validating three popular detection methods against lidar-radar data, it found moderate accuracy (below 75 %), emphasizing the importance of fine-tuning thresholds regionally. The study discovers how small threshold changes significantly affect climatology of severe storms.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
Short summary
Short summary
Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Nina Maherndl, Alessandro Battaglia, Anton Kötsche, and Maximilian Maahn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3916, https://doi.org/10.5194/egusphere-2024-3916, 2025
Short summary
Short summary
Accurate measurements of cloud water content IWC and snowfall rate SR are challenging due to high spatial variability and limitations of our measurement techniques. Here we present a novel method to derive IWC and SR from W-band cloud radar observations, considering the degree of riming. We also investigate the use of the liquid water path as a proxy for the occurrence of riming, which is easier to measure, so that the method can be applied to both ground-based and space-based instruments.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
Short summary
Short summary
This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 2024
Short summary
Short summary
Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
Short summary
Short summary
This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
Short summary
Short summary
We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, https://doi.org/10.5194/amt-17-6707-2024, 2024
Short summary
Short summary
This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
Short summary
Short summary
This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
Short summary
Short summary
In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
Short summary
Short summary
This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
Short summary
Short summary
Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
Short summary
Short summary
The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
Short summary
Short summary
Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
Short summary
Short summary
Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
Short summary
Short summary
This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
EGUsphere, https://doi.org/10.5194/egusphere-2024-1651, https://doi.org/10.5194/egusphere-2024-1651, 2024
Short summary
Short summary
Measurements made by three instruments aboard EarthCARE are used to retrieve estimates of cloud and aerosol properties. A radiative closure assessment of these retrievals is performed by the ACMB-DF processor. Radiative transfer models acting on retrieved information produce broadband radiances commensurate with measurements made by EarthCARE’s broadband radiometer. Measured and modelled radiances for small domains are compared and the likelihood of them differing by 10 W/m2 defines the closure.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
Short summary
Short summary
ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
Short summary
Short summary
Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
Short summary
Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
Short summary
Short summary
This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
Short summary
Short summary
ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
Short summary
Short summary
Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
Short summary
Short summary
The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
Short summary
Short summary
This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
Short summary
Short summary
Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
Short summary
Short summary
This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
Short summary
Short summary
We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
Short summary
Short summary
We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
Short summary
Short summary
Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
Short summary
Short summary
In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
Short summary
Short summary
The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
Short summary
Short summary
We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
Short summary
Short summary
The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
Short summary
Short summary
The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
Short summary
Short summary
The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
Short summary
Short summary
The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
Short summary
Short summary
Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
Short summary
Short summary
A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying 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.
Cited articles
Adachi, T. and Mashiko, W.: High temporal-spatial resolution observation of tornadogenesis in a shallow supercell associated with Typhoon Hagibis (2019) using phased array weather radar, Geophys. Res. Lett., 47, e2020GL089635, https://doi.org/10.1029/2020GL089635, 2020.
Barnes, S. L.: A Technique for maximizing details in numerical weather map
analysis, J. Appl. Meteorol., 3, 396–409, 1964.
Billam, E. R. and Harvey, D. H.: MESAR – An advanced experimental phased array radar, Proceedings of the IEEE International Radar Conference, 19–21 October 1987, London, UK, 37–40, 1987.
Bousquet, O., Tabary, P., and Parent du Chtelet, J.: Operational
multiple-Doppler wind retrieval inferred from long-range radial velocity
measurements, J. Appl. Meteor. Climatol., 47, 2929–2945,
https://doi.org/10.1175/2008JAMC1878.1, 2008.
Bryan, G. H. and Fritsch, J. M.: A benchmark simulation for moist
nonhydrostatic numerical models, Mon. Weather Rev., 130, 2917–2928,
2002.
Clark, T. L., Harris, F. I., and Mohr, C. G.: Errors in wind fields derived
from multiple-Doppler radars: Random errors and temporal errors associated
with advection and evolution, J. Appl. Meteorol., 19, 1273–1284, 1980.
Collis, C., Protat, A., May, P. T., and Williams, C.: Statistics of storm
updraft velocities from TWP-ICE including verification with profiling
measurements, J. Appl. Meteor. Climatol., 52, 1909–1922,
https://doi.org/10.1175/JAMC-D-12-0230.1, 2013.
Collis, S., Protat, A., and Chung, K.-S.: The effect of radial velocity
gridding artifacts on variationally retrieved vertical velocities, J. Atmos.
Ocean. Tech., 27, 1239–1246, 2010.
Cotton, W. R., Pielke Sr., R. A., Walko, R. L., Liston, G. E., Tremback, C. J., Jiang, H., McAnelly, R. L., Harrington, J. Y., Nicholls, M. E., Carrio, G. G., and McFadden, J. P.: RAMS 2001: Current status and future
directions, Meteor. Atmos. Phys., 82, 5–29, https://doi.org/10.1007/s00703-001-0584-9, 2003.
Fridlind, A. M., van Lier-Walqui, M., Collis, S., Giangrande, S. E., Jackson, R. C., Li, X., Matsui, T., Orville, R., Picel, M. H., Rosenfeld, D., Ryzhkov, A., Weitz, R., and Zhang, P.: Use of polarimetric radar measurements to constrain simulated convective cell evolution: a pilot study with Lagrangian tracking, Atmos. Meas. Tech., 12, 2979–3000, https://doi.org/10.5194/amt-12-2979-2019, 2019.
Given, T. and Ray, P. S.: Response of a two-dimensional dual-Doppler radar
wind synthesis, J. Atmos. Ocean. Tech., 11, 239–255,
https://doi.org/10.1175/1520-0426(1994)011<0239:ROATDD>2.0.CO;2, 1994.
Grant, L. D. and van den Heever, S. C.: Cold pool and precipitation responses to aerosol loading: modulation by dry layers, J. Atmos. Sci., 72, 1398–1408, 2015.
Griffin, C. B., Bodine, D. J., Kurdzo, J. M., Mahre, A., and Palmer, R. D.:
High-temporal resolution observations of the 27 May 2015 Canadian, Texas
tornado using the Atmospheric Imaging Radar, Mon. Weather Rev., 147, 873–891, 2019.
Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P.: tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, 2019a.
Heikenfeld, M., Jones, W. K., Senf, F., and Marinescu, P. J.: Tobac v1.2: Tracking and Object-Based Analysis of Clouds, GitHub [code], https://github.com/climate-processes/tobac (last access: 26 August 2022), 2019b.
Heinselman, P. L. and Torres, S. M.: High-temporal-resolution capabilities of the national weather radar testbed phased-array radar, J. Appl. Meteor. Climatol., 50, 579–593, 2011.
Hu, J., Rosenfeld, D., Zrnic, D., Williams, E., Zhang, P., Snyder, J. C.,
Ryzhkov, A., Hashimshoni, E., Zhang, R., and Weitz, R.: Tracking and
characterization of convective cells through their maturation into
stratiform storm elements using polarimetric radar and lightning detection,
Atmos. Res., 226, 192–207, https://doi.org/10.1016/j.atmosres.2019.04.015, 2019.
Jensen, M., Bruning, E., Collins, D., Fridlind, A., Kollias, P., Kuang, C.,
Rosenfeld, D., Ryzhkov, A., Varble, A., Brooks, S.D., Collis, S., Defer, E.,
Fan, J., Flynn, J., Giangrande, S., Griffin, R., Hu, J., Jackson, R.,
Kumjian, M., Logan, T., Matsui, T., McFarquhar, G., Nowotarski, C., Quaas,
J., Oue, M., Sheesley, R., Snyder, J., Stier, P., Usenko, S., van den
Heever, S., van Lier Walqui, M., Wang, Y., Xu, Y., and Zhang, G.: Tracking
Aerosol Convection Interactions ExpeRiment (TRACER) Science Plan, edited by:
Stafford, R., U.S. Department of Energy, DOE/SC-ARM-19-017, 2019.
Jensen, M. P., Petersen, W. A., Bansemer, A., Bharadwaj, N., Carey, L. D.,
Cecil, D. J., Collis, S. M., Del Genio, A. D., Dolan, B., Gerlach, J.,
Giangrande, S. E., Heymsfield, A., Heymsfield, G., Kollias, P., Lang, T. J.,
Nesbitt, S. W., Neumann, A., Poellot, M., Rutledge, S. A., Schwaller, M.,
Tokay, A., Williams, C. R., Wolff, D. B., Xie, S., and Zipser, E. J.: The Midlatitude Continental Convective Clouds Experiment (MC3E),
B. Am. Meteorol. Soc., 97, 1667–1686, 2016.
Jensen, M. P., Flynn, J. H., Judd, L. M., Kollias, P., Kuang, C.,
Mcfarquhar, G., Nadkarni, R., Powers, H., and Sullivan, J.: A
Succession of Cloud, Precipitation, Aerosol, and Air Quality Field
Experiments in the Coastal Urban Environment, B. Am. Meteorol. Soc., 103, 103–105, 2022.
Kang, C., Giangrande, S. E., Serbin, S. P., Campbell, P., Hickmon, N., and
Ritsche, M.: Science and Deployment Plan for the DOE 3rd Atmospheric
Radiation Measurement Mobile Facility in the Southeastern United States,
2021 AGU Fall Meeting, New Orleans and online, 13–17 December 2021,
https://www.arm.gov/uploads/2021_AGU_SE_US_TownHall_Slides.pdf (last access: 18 August 2022), 2021.
Khairoutdinov, M. F. and Randall, D.A.: Cloud-resolving modeling of the ARM
summer 1997 IOP: Model formulation, results, uncertainties and
sensitivities, J. Atmos. Sci., 60, 607–625, 2003.
Kollias, P., Luke, E., Oue, M., and Lamer, K.: Agile adaptive radar
sampling of fast-evolving atmospheric phenomena guided by satellite imagery
and surface cameras, Geophys. Res. Lett., 45, e2020GL088440,
https://doi.org/10.1029/2020GL088440, 2020.
Kollias, P., Luke, E., Tuftedal, K., Dubois, M., and Knapp, E. J.: Agile Weather Observations using a Dual-Polarization X-band Phased Array Radar, IEEE Radar Conference, 21–25 March 2022, New York, NY, USA, https://doi.org/10.1109/RadarConf2248738.2022.9764308, 2022.
Kumjian, M. R. and Ryzhkov, A. V.: Polarimetric signatures in supercell
thunderstorms, J. Appl. Meteor. Climatol., 47, 1940–1961, https://doi.org/10.1175/2007JAMC1874.1, 2008.
Kumjian, M. R., Khain, A. P., Benmoshe, N., Ilotoviz, E., Ryzhkov, A. V.,
and Phillips V. T. J.: The anatomy and physics of ZDR columns: Investigating a polarimetric radar signature with a spectral bin microphysical model, J. Appl. Meteor. Climatol.,
53, 1820–1843, https://doi.org/10.1175/JAMC-D-13-0354.1, 2014.
Lamer, K., Tatarevic, A., Jo, I., and Kollias, P.: Evaluation of gridded scanning ARM cloud radar reflectivity observations and vertical doppler velocity retrievals, Atmos. Meas. Tech., 7, 1089–1103, https://doi.org/10.5194/amt-7-1089-2014, 2014.
L'Ecuyer, T., Petersen, W., and Moiseev, D.: Light Precipitation Validation
Experiment (LPVEx) Science Plan, NASA, https://ghrc.nsstc.nasa.gov/home/sites/default/files/lpvex_science_plan_June2010.pdf (last access: 11 July 2022), 2010.
Mahre, A., Kurdzo, J. M., Bodine, D. J., Griffin, C. B., and Palmer, R. D.:
Analysis of the 16 May 2015 Tipton, Oklahoma, EF-3 tornado at high
spatiotemporal resolution using the Atmospheric Imaging Radar, Mon. Weather
Rev., 146, 2103–2124, 2018.
Marinescu, P. J., Kennedy, P. C., Bell, M. M., Drager, A. J., Grant, L. D.,
Freeman, S. W., and van den Heever, S. C.: Updraft vertical velocity
observations and uncertainties in High Plains supercells using radiosondes
and radars, Mon. Weather Rev., 148, 4435–4452, https://doi.org/10.1175/MWR-D-20-0071.1, 2020.
Marinescu, P. J., van den Heever, S. C., Heikenfeld, M., Barrett, A. I.,
Barthlott, C., Hoose, C., Fan, J., Fridlind, A. M., Matsui, T.,
Miltenberger, A. K., Stier, P., Vie, B., White, B. A., and Zhang, Y.:
Impacts of Varying Concentrations of Cloud Condensation Nuclei on Deep
Convective Cloud Updrafts – A Multimodel Assessment, J. Atmos. Sci., 78, 1147–1172, 2021.
McLaughlin, D. J., Chandrasekar, V., Droegemeier, K., Frasier, S., Kurose, K., Junyent, F., Philips, B., Cruz-Pol, S., and Colom, J.: Distributed Collaborative Adaptive Sensing (DCAS) for Improved Detection, Understanding, and Prediction of Atmospheric Hazards, Ninth Symposium on
Integrated Observing and Assimilation Systems for the Atmosphere, Oceans,
and Land Surface (IOAS-AOLS), 10–13 January 2005, Boston, MA, USA, American Meteorological Society, https://ams.confex.com/ams/Annual2005/webprogram/Paper87890.html (last access: 18 August 2022), 2005.
Mishra, K. V., Krajewski, W. F., Goska, R., Ceynar, D., Seo, B., Kruger, A.,
Niemeier, J. J., Galvez, M. B., Thurai, M., Bringi, V. N., Tolstoy, L.,
Kucera, P. A., Petersen, W. A., Grazioli, J., and Pazmany, A. L.: Deployment
and Performance Analyses of High-Resolution Iowa XPOL Radar System during
the NASA IFloodS Campaign, J. Hydrometeorol., 17, 455–479,
2016.
ModEx Approach: Environmental System Science Program, U.S. DOE
Environmental System Science Program, https://ess.science.energy.gov,
last access: 11 May 2022.
Moroda, Y., Tsuboki, K., Satoh, S., Nakagawa, K., Ushio, T., and Shimizu, S.: Structure and Evolution of Precipitation Cores in an Isolated Convective Storm Observed by Phased Array Weather Radar, J. Meteorol. Soc. Jpn. Ser. II, 99, 765–784, https://doi.org/10.2151/jmsj.2021-038, 2021.
North, K. W., Oue, M., Kollias, P., Giangrande, S. E., Collis, S. M., and Potvin, C. K.: Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E, Atmos. Meas. Tech., 10, 2785–2806, https://doi.org/10.5194/amt-10-2785-2017, 2017.
Oue, M.: Radar simulator output used for tracking isolated convections, Stony Brook University Academin Commons, maintained by Stony Brook University Libraries Center for Scholarly Communication [data set],
https://commons.library.stonybrook.edu/somasdata/16, last access: 18 August 2022.
Oue, M., Inagaki, K., Shinoda, T., Ohigashi, T., Kouketsu, T., Kato, M., Tsuboki, K., and Uyeda, H.: Polarimetric Doppler radar analysis of orientation of a stationary rainband with changing orientations in July 2010, J. Meteorol. Soc. Jpn., 92, 457–481, https://doi.org/10.2151/jmsj.2014-503, 2014.
Oue, M., Kollias, P., Shapiro, A., Tatarevic, A., and Matsui, T.: Investigation of observational error sources in multi-Doppler-radar three-dimensional variational vertical air motion retrievals, Atmos. Meas. Tech., 12, 1999–2018, https://doi.org/10.5194/amt-12-1999-2019, 2019a.
Oue, M., Tatarevic, A., Kollias, P., Wang, D., and Yu, K.-M.: The Cloud Resolving Model Radar Simulator (CR-SIM) Version 3.3, SoMAS Research Data [code], https://commons.library.stonybrook.edu/somasdata/4/ (last access: 18 August 2022), 2019b.
Oue, M., Tatarevic, A., Kollias, P., Wang, D., Yu, K., and Vogelmann, A. M.: The Cloud-resolving model Radar SIMulator (CR-SIM) Version 3.3: description and applications of a virtual observatory, Geosci. Model Dev., 13, 1975–1998, https://doi.org/10.5194/gmd-13-1975-2020, 2020.
Potvin, C. K., Betten, D., Wicker, L. J., Elmore, K. L., and Biggerstaff, M.
I.: 3DVAR versus traditional dual-Doppler wind retrievals of a simulated
supercell thunderstorm, Mon. Weather Rev., 140, 3487–3494,
https://doi.org/10.1175/MWR-D-12-00063.1, 2012a.
Potvin, C. K., Wicker, L. J., and Shapiro A.: Assessing errors in
variational dual-Doppler wind syntheses of supercell thunderstorms observed
by storm-scale mobile radars, J. Atmos. Ocean. Tech., 29, 1009–1025,
https://doi.org/10.1175/JTECH-D-11-00177.1, 2012b.
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J.,
Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Peckham, S. E., Grell,
G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R.,
Dimego, G. J., Wang, W., Schwartz, C. S., Romine, G. S., Liu, Z., Snyder,
C., Chen, F., Barlage, M. J., Yu, W., and Duda, M. G.: The Weather Research
and Forecasting Model: Overview, System Efforts, and Future Directions,
B. Am. Meteorol. Soc., 98, 1717–1737, 2017.
Ryzhkov, A., Pinsky, M., Pokrovsky, A., and Khain, A.: Polarimetric Radar
Observation Operator for a Cloud Model with Spectral Microphysics, J. Appl.
Meteor. Climatol., 50, 873–894, 2011.
Saleeby, S. M. and van den Heever, S. C.: Developments in the CSU-RAMS Aerosol Model: Emissions, Nucleation, Regeneration, Deposition, and Radiation, J. Appl. Meteorol. Climatol., 52, 2601–2622, https://doi.org/10.1175/JAMC-D-12-0312.1, 2013.
Shusse, Y., Nakagawa, K., Takahashi, N., Satoh, S., and Iguchi, T.: Characteristics of polarimetric radar variables in three types of rainfalls in a Baiu front event over the East China Sea, J. Meteorol. Soc. Jpn., 87, 865–875, 2009.
Snyder, J. C., Bluestein, H., Venkatesh, V., and Frasier, S. J.: Observations of polarimetric signatures in supercells by an X-band mobile
Doppler radar, Mon. Weather Rev., 141, 3–29, https://doi.org/10.1175/MWR-D-12-00068.1, 2013.
Stein, T. H. M., Hogan, R. J., Clark, P. A., Halliwell, C. E., Hanley, K.
E., Lean, H. W., Nicol, J. C., and Plant, R. S.: The DYMECS Project: A
Statistical Approach for the Evaluation of Convective Storms in
High-Resolution NWP Models, B. Am. Meteorol. Soc., 96, 939–951, 2015.
Steiner, M., Houze Jr., R. A., and Yuter, S. E.: Climatological
Characterization of Three-Dimensional Storm Structure from Operational Radar
and Rain Gauge Data, J. Appl. Meteor. Climatol., 34, 1978–2007, 1995.
van den Heever, S. C., Fridlind, A. M., Marinescu, P. J., Heikenfeld, M., White, B., and Stier, P.: Aerosol-Cloud-Precipitation-Climate (ACPC) initiative: Deep Convective Cloud Group roadmap, ACPC Rep., 13 pp., http://acpcinitiative.org/Docs/ACPC_DCC_Roadmap_171019.pdf (last access: 23 August 2022), 2018.
van den Heever, S. C., Grant, L. D., Freeman, S. W., Marinescu, P. J.,
Barnum, J., Bukowski, J., Casas, E., Drager, A. J., Fuchs, B., Herman, G.
R., Hitchcock, S. M., Kennedy, P. C., Nielsen, E. R., Park, J. M.,
Rasmussen, K., Razin, M. N., Riesenberg, R., Dellaripa, E. R., Slocum, C.
J., Toms, B. A., and van den Heever, A.: The Colorado State University
Convective Cloud Outflows and UpDrafts Experiment (C3LOUD-Ex), B. Am. Meteorol. Soc., 102, E1283–E1305, 2021.
Wurman, J.: The DOW mobile multiple-Doppler network. Preprints, 30th
Int. Conf. on Radar Meteorology, 19–24 July 2001, Munich, Germany, Am. Meteorol. Soc., 95–97, https://ams.confex.com/ams/30radar/techprogram/paper_21572.htm (last access: 18 August 2022), 2001.
Wurman, J., Dowell, D., Richardson, Y., Markowski, P., Rasmussen, E.,
Burgess, D., Wicker, L., and Bluestein, H. B.: The Second Verification of the Origins of Rotation in Tornadoes Experiment: VORTEX2, B. Am. Meteorol. Soc., 93, 1147–1170, https://doi.org/10.1175/BAMS-D-11-00010.1, 2012.
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON
(ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M:
Description of the non-hydrostatic dynamical core, Q. J. Roy. Meteor. Soc.,
141, 563–579, https://doi.org/10.1002/qj.2378, 2015.
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.
This study provides an optimization of radar observation strategies to better capture convective...
