Articles | Volume 11, issue 10
https://doi.org/10.5194/amt-11-5471-2018
© Author(s) 2018. 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-11-5471-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Oct 2018
Research article |
| 05 Oct 2018
| 05 Oct 2018
Retrieval of snowflake microphysical properties from multifrequency radar observations
Jussi Leinonen
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Joint Institute for Earth System Science and Engineering, University of California, Los Angeles, California, USA
Matthew D. Lebsock
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Simone Tanelli
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Ousmane O. Sy
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Brenda Dolan
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
Randy J. Chase
Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
Joseph A. Finlon
Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
Annakaisa von Lerber
Radar Science, Finnish Meteorological Institute, Helsinki, Finland
Dmitri Moisseev
Radar Science, Finnish Meteorological Institute, Helsinki, Finland
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
Related authors
Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann
Nat. Hazards Earth Syst. Sci., 24, 133–144, https://doi.org/10.5194/nhess-24-133-2024, https://doi.org/10.5194/nhess-24-133-2024, 2024
Short summary
Short summary
Severe weather such as hail, lightning, and heavy rainfall can be hazardous to humans and property. Dual-polarization weather radars provide crucial information to forecast these events by detecting precipitation types. This study analyses the importance of dual-polarization data for predicting severe weather for 60 min using an existing deep learning model. The results indicate that including these variables improves the accuracy of predicting heavy rainfall and lightning.
Jussi Leinonen, Ulrich Hamann, Urs Germann, and John R. Mecikalski
Nat. Hazards Earth Syst. Sci., 22, 577–597, https://doi.org/10.5194/nhess-22-577-2022, https://doi.org/10.5194/nhess-22-577-2022, 2022
Short summary
Short summary
We evaluate the usefulness of different data sources and variables to the short-term prediction (
nowcasting) of severe thunderstorms using machine learning. Machine-learning models are trained with data from weather radars, satellite images, lightning detection and weather forecasts and with terrain elevation data. We analyze the benefits provided by each of the data sources to predicting hazards (heavy precipitation, lightning and hail) caused by the thunderstorms.
Jussi Leinonen, Jacopo Grazioli, and Alexis Berne
Atmos. Meas. Tech., 14, 6851–6866, https://doi.org/10.5194/amt-14-6851-2021, https://doi.org/10.5194/amt-14-6851-2021, 2021
Short summary
Short summary
Measuring the shape, size and mass of a large number of snowflakes is a challenging task; it is hard to achieve in an automatic and instrumented manner. We present a method to retrieve these properties of individual snowflakes using as input a triplet of images/pictures automatically collected by a multi-angle snowflake camera (MASC) instrument. Our method, based on machine learning, is trained on artificially generated snowflakes and evaluated on 3D-printed snowflake replicas.
Jie Gong, Yuli Liu, Joseph A. Finlon, Ian S. Adams, Rachael A. Kroodsma, Dong L. Wu, and Ralf Bennartz
Atmos. Chem. Phys., 26, 12457–12477, https://doi.org/10.5194/acp-26-12457-2026, https://doi.org/10.5194/acp-26-12457-2026, 2026
Short summary
Short summary
This is a closure and polarimetric study using a variety of airborne observations to disentangle hydrometeor microphysical properties and their vertical distribution. This work paves a concrete step in assuring timely delivery of high-quality science products for several future sub-mm radiometer missions as well as possibilities of new science products beyond the mission requirements.
Ari Leskinen, Antonia Radlwimmer, Uula Isopahkala, Silvia Calderón, David Brus, Konstantinos Doulgeris, Ville Kaikkonen, Eero Molkoselkä, Dmitri Moiseev, Marie Lou Hirschy, Anssi Mäkynen, Sami Romakkaniemi, and Mika Komppula
EGUsphere, https://doi.org/10.5194/egusphere-2026-2088, https://doi.org/10.5194/egusphere-2026-2088, 2026
Short summary
Short summary
We present a lightweight holographic imaging instrument, designed for high-resolution in-cloud particle measurements at a rate of 10 holograms per second, which leads to sub-meter spatial resolution on-board UAV. The approach offers a cost-effective and versatile alternative, capable of accessing confined or remote areas, enabling improved validation of remote sensing products and numerical cloud models. We present the potential of such instrumentation for high-resolution cloud observations.
John E. Yorks, Edward P. Nowottnick, Steven Platnick, Kerry G. Meyer, Matthew Walker McLinden, Meloe S. F. Kacenelenbogen, Kenneth E. Christian, Joseph A. Finlon, Natalie A. Midzak, Natalia Roldán-Henao, Patrick A. Selmer, Matthew J. McGill, Erica K. Dolinar, Charles N. Helms, Robert Koopman, Jonas von Bismark, and Montserrat Pińol Solé
Earth Syst. Sci. Data, 18, 3833–3852, https://doi.org/10.5194/essd-18-3833-2026, https://doi.org/10.5194/essd-18-3833-2026, 2026
Short summary
Short summary
The Goddard Space Flight Center's Lidar Observation and Validation Experiment (GLOVE) was a NASA field campaign from January–February 2025 that used a special airplane with scientific instruments to check if satellites measuring Earth's atmosphere were working correctly. The plane flew under two key satellites to compare measurements of clouds, dust, and other particles in the air. Data from 8 flights help scientists better understand how well these space-based instruments perform, especially for detecting different types of clouds and atmospheric conditions.
Jenna Ritvanen, Martin Aregger, Dmitri Moisseev, Urs Germann, Alessandro Hering, and Seppo Pulkkinen
Atmos. Meas. Tech., 19, 1853–1874, https://doi.org/10.5194/amt-19-1853-2026, https://doi.org/10.5194/amt-19-1853-2026, 2026
Short summary
Short summary
Convective storms pose several hazards, like heavy rainfall, but operational short-term forecasting (nowcasting) suffers from limited models of storm development. Cell tracking, commonly used for nowcasting of convective storms and analyzing storm evolution, is complicated by splits and merges. We show how splits and merges can be integrated into cell track analysis, using case studies and analysis of split and merge events with operational data from the Swiss weather radar network.
Bernd Mom and Dmitri Moisseev
EGUsphere, https://doi.org/10.5194/egusphere-2026-507, https://doi.org/10.5194/egusphere-2026-507, 2026
Short summary
Short summary
A method for estimating wet radome and rain attenuation in cloud radar observations using a disdrometer is developed. Because of the uncertainty in disdrometer measurements, we created a statistical model that provides an estimate for the drop size distribution parameters and radar variables. Analysis of selected case studies indicates that wet radome attenuation can be successfully estimated. The mitigation of rain attenuation, however, is affected by cloud layers embedded in rain.
Marcin J. Kurowski, Matthew D. Lebsock, and Kevin M. Smalley
Atmos. Chem. Phys., 25, 15329–15342, https://doi.org/10.5194/acp-25-15329-2025, https://doi.org/10.5194/acp-25-15329-2025, 2025
Short summary
Short summary
This study explores how clouds respond to pollution throughout the day using high-resolution simulations. Polluted clouds show stronger daily changes: thicker clouds at night and in the morning but faster thinning in the afternoon. Pollution reduces rainfall but enhances drying, deepening the cloud layer. While the pollution initially brightens clouds, the daily cycle of cloudiness slightly reduces this brightening effect.
Bernat Puigdomènech Treserras, Pavlos Kollias, Alessandro Battaglia, Simone Tanelli, and Hirotaka Nakatsuka
Atmos. Meas. Tech., 18, 5607–5618, https://doi.org/10.5194/amt-18-5607-2025, https://doi.org/10.5194/amt-18-5607-2025, 2025
Short summary
Short summary
We investigate how seasonal solar illumination affects the pointing accuracy of EarthCARE’s cloud profile radar (CPR) antenna and introduce a correction based on surface Doppler measurements. The correction improves measurement accuracy by reducing Doppler velocity biases to within 5 and 7 cm s−1. Our results demonstrate the importance of continuous pointing characterization to maintain the scientific accuracy of EarthCARE’s CPR Doppler observations.
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
Atmos. Chem. Phys., 25, 12335–12355, https://doi.org/10.5194/acp-25-12335-2025, https://doi.org/10.5194/acp-25-12335-2025, 2025
Short summary
Short summary
Decoupling aerosol and environmental impacts on convection is challenging. Using airborne data, we correlated convective metrics with aerosol concentrations in several different environments. Results were mixed, but some comparisons suggest that medium-to-high aerosol concentrations were occasionally strongly correlated with convective intensity and prevalence, especially when the atmosphere was relatively unstable. It is important to consider storm environment when evaluating aerosol effects.
Nitika Yadlapalli Yurk, Matt D. Lebsock, Juan M. Socuellamos, Raquel Rodriguez Monje, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 18, 5141–5155, https://doi.org/10.5194/amt-18-5141-2025, https://doi.org/10.5194/amt-18-5141-2025, 2025
Short summary
Short summary
Current knowledge of the link between clouds and climate is limited by a 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 step and elevation.
Shuai Zhang, Haoran Li, Dmitri Moisseev, and Matti Leskinen
Atmos. Meas. Tech., 18, 4839–4855, https://doi.org/10.5194/amt-18-4839-2025, https://doi.org/10.5194/amt-18-4839-2025, 2025
Short summary
Short summary
The data quality of weather radar near coastlines can be affected by echoes from ships, and this interference is exacerbated when pulse compression technology is used. This study developed a hybrid ship clutter identification algorithm based on artificial intelligence and heuristic criteria, effectively mitigating the issue. The successful reproduction of ship tracks in the Gulf of Finland supports this conclusion.
Luis F. Millán, Matthew D. Lebsock, and Marcin J. Kurowski
Atmos. Meas. Tech., 18, 4483–4495, https://doi.org/10.5194/amt-18-4483-2025, https://doi.org/10.5194/amt-18-4483-2025, 2025
Short summary
Short summary
This study explores the potential of a hypothetical spaceborne radar to observe water vapor within clouds.
Juan Socuellamos, Matthew Lebsock, Raquel Rodriguez Monje, Marcin Kurowski, Derek Posselt, and Robert Beauchamp
EGUsphere, https://doi.org/10.5194/egusphere-2025-4248, https://doi.org/10.5194/egusphere-2025-4248, 2025
Preprint archived
Short summary
Short summary
Using simulated airborne and spaceborne W-band (94 GHz) and G-band (239 GHz) radar observations, this article presents an optimal estimation framework that includes dual-frequency reflectivity and differential absorption measurements to obtain more accurate drizzle retrievals. We demonstrate that an improvement of more than one order of magnitude in the retrieval of the drizzle mixing ratio, droplet concentration, and mass-weighted diameter can be achieved compared to W-band only results.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
Short summary
Short summary
Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Atmos. Meas. Tech., 18, 793–816, https://doi.org/10.5194/amt-18-793-2025, https://doi.org/10.5194/amt-18-793-2025, 2025
Short summary
Short summary
Accurate KDP estimates are crucial in radar-based applications. We quantify the uncertainties of several publicly available KDP estimation methods for multiple rainfall intensities. We use C-band weather radar observations and employed a self-consistency KDP, estimated from reflectivity and differential reflectivity, as a framework for the examination. Our study provides guidance for the performance, uncertainties, and optimisation of the methods, focusing mainly on accuracy and robustness.
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.
Zoé Brasseur, Julia Schneider, Janne Lampilahti, Ville Vakkari, Victoria A. Sinclair, Christina J. Williamson, Carlton Xavier, Dmitri Moisseev, Markus Hartmann, Pyry Poutanen, Markus Lampimäki, Markku Kulmala, Tuukka Petäjä, Katrianne Lehtipalo, Erik S. Thomson, Kristina Höhler, Ottmar Möhler, and Jonathan Duplissy
Atmos. Chem. Phys., 24, 11305–11332, https://doi.org/10.5194/acp-24-11305-2024, https://doi.org/10.5194/acp-24-11305-2024, 2024
Short summary
Short summary
Ice-nucleating particles (INPs) strongly influence the formation of clouds by initiating the formation of ice crystals. However, very little is known about the vertical distribution of INPs in the atmosphere. Here, we present aircraft measurements of INP concentrations above the Finnish boreal forest. Results show that near-surface INPs are efficiently transported and mixed within the boundary layer and occasionally reach the free troposphere.
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.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, Robert M. Beauchamp, and Arturo Umeyama
Earth Syst. Sci. Data, 16, 2701–2715, https://doi.org/10.5194/essd-16-2701-2024, https://doi.org/10.5194/essd-16-2701-2024, 2024
Short summary
Short summary
This paper describes multifrequency radar observations of clouds and precipitation during the EPCAPE campaign. The data sets were obtained from CloudCube, a Ka-, W-, and G-band atmospheric profiling radar, to demonstrate synergies between multifrequency retrievals. This data collection provides a unique opportunity to study hydrometeors with diameters in the millimeter and submillimeter size range that can be used to better understand the drop size distribution within clouds and precipitation.
Maximilian Maahn, Dmitri Moisseev, Isabelle Steinke, Nina Maherndl, and Matthew D. Shupe
Atmos. Meas. Tech., 17, 899–919, https://doi.org/10.5194/amt-17-899-2024, https://doi.org/10.5194/amt-17-899-2024, 2024
Short summary
Short summary
The open-source Video In Situ Snowfall Sensor (VISSS) is a novel instrument for characterizing particle shape, size, and sedimentation velocity in snowfall. It combines a large observation volume with relatively high resolution and a design that limits wind perturbations. The open-source nature of the VISSS hardware and software invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.
Luis F. Millán, Matthew D. Lebsock, Ken B. Cooper, Jose V. Siles, Robert Dengler, Raquel Rodriguez Monje, Amin Nehrir, Rory A. Barton-Grimley, James E. Collins, Claire E. Robinson, Kenneth L. Thornhill, and Holger Vömel
Atmos. Meas. Tech., 17, 539–559, https://doi.org/10.5194/amt-17-539-2024, https://doi.org/10.5194/amt-17-539-2024, 2024
Short summary
Short summary
In this study, we describe and validate a new technique in which three radar tones are used to estimate the water vapor inside clouds and precipitation. This instrument flew on board NASA's P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign and the Synergies Of Active optical and Active microwave Remote Sensing Experiment (SOA2RSE) campaign.
Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann
Nat. Hazards Earth Syst. Sci., 24, 133–144, https://doi.org/10.5194/nhess-24-133-2024, https://doi.org/10.5194/nhess-24-133-2024, 2024
Short summary
Short summary
Severe weather such as hail, lightning, and heavy rainfall can be hazardous to humans and property. Dual-polarization weather radars provide crucial information to forecast these events by detecting precipitation types. This study analyses the importance of dual-polarization data for predicting severe weather for 60 min using an existing deep learning model. The results indicate that including these variables improves the accuracy of predicting heavy rainfall and lightning.
Matthew D. Lebsock and Mikael Witte
Atmos. Chem. Phys., 23, 14293–14305, https://doi.org/10.5194/acp-23-14293-2023, https://doi.org/10.5194/acp-23-14293-2023, 2023
Short summary
Short summary
This paper evaluates measurements of cloud drop size distributions made from airplanes. We find that as the number of cloud drops increases the distribution of the cloud drop sizes narrows. The data are used to develop a simple equation that relates the drop number to the width of the drop sizes. We then use this equation to demonstrate that existing approaches to observe the drop number from satellites contain errors that can be corrected by including the new relationship.
Richard M. Schulte, Matthew D. Lebsock, and John M. Haynes
Atmos. Meas. Tech., 16, 3531–3546, https://doi.org/10.5194/amt-16-3531-2023, https://doi.org/10.5194/amt-16-3531-2023, 2023
Short summary
Short summary
In order to constrain climate models and better understand how clouds might change in future climates, accurate satellite estimates of cloud liquid water content are important. The satellite currently best suited to this purpose, CloudSat, is not sensitive enough to detect some non-raining low clouds. In this study we show that information from two other satellite instruments, MODIS and CALIOP, can be combined to provide cloud water estimates for many of the clouds that are missed by CloudSat.
Roberto Cremonini, Tanel Voormansik, Piia Post, and Dmitri Moisseev
Atmos. Meas. Tech., 16, 2943–2956, https://doi.org/10.5194/amt-16-2943-2023, https://doi.org/10.5194/amt-16-2943-2023, 2023
Short summary
Short summary
Extreme rainfall for a specific location is commonly evaluated when designing stormwater management systems. This study investigates the use of quantitative precipitation estimations (QPEs) based on polarimetric weather radar data, without rain gauge corrections, to estimate 1 h rainfall total maxima in Italy and Estonia. We show that dual-polarization weather radar provides reliable QPEs and effective estimations of return periods for extreme rainfall in climatologically homogeneous regions.
Haoran Li, Dmitri Moisseev, Yali Luo, Liping Liu, Zheng Ruan, Liman Cui, and Xinghua Bao
Hydrol. Earth Syst. Sci., 27, 1033–1046, https://doi.org/10.5194/hess-27-1033-2023, https://doi.org/10.5194/hess-27-1033-2023, 2023
Short summary
Short summary
A rainfall event that occurred at Zhengzhou on 20 July 2021 caused tremendous loss of life and property. This study compares different KDP estimation methods as well as the resulting QPE outcomes. The results show that the selection of the KDP estimation method has minimal impact on QPE, whereas the inadequate assumption of rain microphysics and unquantified vertical air motion may explain the underestimated 201.9 mm h−1 record.
Jenna Ritvanen, Ewan O'Connor, Dmitri Moisseev, Raisa Lehtinen, Jani Tyynelä, and Ludovic Thobois
Atmos. Meas. Tech., 15, 6507–6519, https://doi.org/10.5194/amt-15-6507-2022, https://doi.org/10.5194/amt-15-6507-2022, 2022
Short summary
Short summary
Doppler lidars and weather radars provide accurate wind measurements, with Doppler lidar usually performing better in dry weather conditions and weather radar performing better when there is precipitation. Operating both instruments together should therefore improve the overall performance. We investigate how well a co-located Doppler lidar and X-band radar perform with respect to various weather conditions, including changes in horizontal visibility, cloud altitude, and precipitation.
Silvia M. Calderón, Juha Tonttila, Angela Buchholz, Jorma Joutsensaari, Mika Komppula, Ari Leskinen, Liqing Hao, Dmitri Moisseev, Iida Pullinen, Petri Tiitta, Jian Xu, Annele Virtanen, Harri Kokkola, and Sami Romakkaniemi
Atmos. Chem. Phys., 22, 12417–12441, https://doi.org/10.5194/acp-22-12417-2022, https://doi.org/10.5194/acp-22-12417-2022, 2022
Short summary
Short summary
The spatial and temporal restrictions of observations and oversimplified aerosol representation in large eddy simulations (LES) limit our understanding of aerosol–stratocumulus interactions. In this closure study of in situ and remote sensing observations and outputs from UCLALES–SALSA, we have assessed the role of convective overturning and aerosol effects in two cloud events observed at the Puijo SMEAR IV station, Finland, a diurnal-high aerosol case and a nocturnal-low aerosol case.
Dongwei Fu, Larry Di Girolamo, Robert M. Rauber, Greg M. McFarquhar, Stephen W. Nesbitt, Jesse Loveridge, Yulan Hong, Bastiaan van Diedenhoven, Brian Cairns, Mikhail D. Alexandrov, Paul Lawson, Sarah Woods, Simone Tanelli, Sebastian Schmidt, Chris Hostetler, and Amy Jo Scarino
Atmos. Chem. Phys., 22, 8259–8285, https://doi.org/10.5194/acp-22-8259-2022, https://doi.org/10.5194/acp-22-8259-2022, 2022
Short summary
Short summary
Satellite-retrieved cloud microphysics are widely used in climate research because of their central role in water and energy cycles. Here, we provide the first detailed investigation of retrieved cloud drop sizes from in situ and various satellite and airborne remote sensing techniques applied to real cumulus cloud fields. We conclude that the most widely used passive remote sensing method employed in climate research produces high biases of 6–8 µm (60 %–80 %) caused by 3-D radiative effects.
Kevin M. Smalley, Matthew D. Lebsock, Ryan Eastman, Mark Smalley, and Mikael K. Witte
Atmos. Chem. Phys., 22, 8197–8219, https://doi.org/10.5194/acp-22-8197-2022, https://doi.org/10.5194/acp-22-8197-2022, 2022
Short summary
Short summary
We use geostationary satellite observations to track pockets of open-cell (POC) stratocumulus and analyze how precipitation, cloud microphysics, and the environment change. Precipitation becomes more intense, corresponding to increasing effective radius and decreasing number concentrations, while the environment remains relatively unchanged. This implies that changes in cloud microphysics are more important than the environment to POC development.
Victoria Anne Sinclair, Jenna Ritvanen, Gabin Urbancic, Irene Erner, Yurii Batrak, Dmitri Moisseev, and Mona Kurppa
Atmos. Meas. Tech., 15, 3075–3103, https://doi.org/10.5194/amt-15-3075-2022, https://doi.org/10.5194/amt-15-3075-2022, 2022
Short summary
Short summary
We investigate the boundary-layer (BL) height and surface stability in southern Finland using radiosondes, a microwave radiometer and ERA5 reanalysis. Accurately quantifying the BL height is challenging, and the diagnosed BL height can depend strongly on the method used. Microwave radiometers provide reliable estimates of the BL height but only in unstable conditions. ERA5 captures the BL height well except under very stable conditions, which occur most commonly at night during the warm season.
Zoé Brasseur, Dimitri Castarède, Erik S. Thomson, Michael P. Adams, Saskia Drossaart van Dusseldorp, Paavo Heikkilä, Kimmo Korhonen, Janne Lampilahti, Mikhail Paramonov, Julia Schneider, Franziska Vogel, Yusheng Wu, Jonathan P. D. Abbatt, Nina S. Atanasova, Dennis H. Bamford, Barbara Bertozzi, Matthew Boyer, David Brus, Martin I. Daily, Romy Fösig, Ellen Gute, Alexander D. Harrison, Paula Hietala, Kristina Höhler, Zamin A. Kanji, Jorma Keskinen, Larissa Lacher, Markus Lampimäki, Janne Levula, Antti Manninen, Jens Nadolny, Maija Peltola, Grace C. E. Porter, Pyry Poutanen, Ulrike Proske, Tobias Schorr, Nsikanabasi Silas Umo, János Stenszky, Annele Virtanen, Dmitri Moisseev, Markku Kulmala, Benjamin J. Murray, Tuukka Petäjä, Ottmar Möhler, and Jonathan Duplissy
Atmos. Chem. Phys., 22, 5117–5145, https://doi.org/10.5194/acp-22-5117-2022, https://doi.org/10.5194/acp-22-5117-2022, 2022
Short summary
Short summary
The present measurement report introduces the ice nucleation campaign organized in Hyytiälä, Finland, in 2018 (HyICE-2018). We provide an overview of the campaign settings, and we describe the measurement infrastructure and operating procedures used. In addition, we use results from ice nucleation instrument inter-comparison to show that the suite of these instruments deployed during the campaign reports consistent results.
Jussi Leinonen, Ulrich Hamann, Urs Germann, and John R. Mecikalski
Nat. Hazards Earth Syst. Sci., 22, 577–597, https://doi.org/10.5194/nhess-22-577-2022, https://doi.org/10.5194/nhess-22-577-2022, 2022
Short summary
Short summary
We evaluate the usefulness of different data sources and variables to the short-term prediction (
nowcasting) of severe thunderstorms using machine learning. Machine-learning models are trained with data from weather radars, satellite images, lightning detection and weather forecasts and with terrain elevation data. We analyze the benefits provided by each of the data sources to predicting hazards (heavy precipitation, lightning and hail) caused by the thunderstorms.
Teresa Vogl, Maximilian Maahn, Stefan Kneifel, Willi Schimmel, Dmitri Moisseev, and Heike Kalesse-Los
Atmos. Meas. Tech., 15, 365–381, https://doi.org/10.5194/amt-15-365-2022, https://doi.org/10.5194/amt-15-365-2022, 2022
Short summary
Short summary
We are using machine learning techniques, a type of artificial intelligence, to detect graupel formation in clouds. The measurements used as input to the machine learning framework were performed by cloud radars. Cloud radars are instruments located at the ground, emitting radiation with wavelenghts of a few millimeters vertically into the cloud and measuring the back-scattered signal. Our novel technique can be applied to different radar systems and different weather conditions.
Mark T. Richardson, David R. Thompson, Marcin J. Kurowski, and Matthew D. Lebsock
Atmos. Meas. Tech., 15, 117–129, https://doi.org/10.5194/amt-15-117-2022, https://doi.org/10.5194/amt-15-117-2022, 2022
Short summary
Short summary
Sunlight can pass diagonally through the atmosphere, cutting through the 3-D water vapour field in a way that
smears2-D maps of imaging spectroscopy vapour retrievals. In simulations we show how this smearing is
towardsor
away fromthe Sun, so calculating
across the solar direction allows sub-kilometre information about water vapour's spatial scaling to be calculated. This could be tested by airborne campaigns and used to obtain new information from upcoming spaceborne data products.
Anna Franck, Dmitri Moisseev, Ville Vakkari, Matti Leskinen, Janne Lampilahti, Veli-Matti Kerminen, and Ewan O'Connor
Atmos. Meas. Tech., 14, 7341–7353, https://doi.org/10.5194/amt-14-7341-2021, https://doi.org/10.5194/amt-14-7341-2021, 2021
Short summary
Short summary
We proposed a method to derive a convective boundary layer height, using insects in radar observations, and we investigated the consistency of these retrievals among different radar frequencies (5, 35 and 94 GHz). This method can be applied to radars at other measurement stations and serve as additional way to estimate the boundary layer height during summer. The entrainment zone was also observed by the 5 GHz radar above the boundary layer in the form of a Bragg scatter layer.
Rachel Atlas, Johannes Mohrmann, Joseph Finlon, Jeremy Lu, Ian Hsiao, Robert Wood, and Minghui Diao
Atmos. Meas. Tech., 14, 7079–7101, https://doi.org/10.5194/amt-14-7079-2021, https://doi.org/10.5194/amt-14-7079-2021, 2021
Short summary
Short summary
Many clouds with temperatures between 0 °C and −40 °C contain both liquid and ice particles, and the ratio of liquid to ice particles influences how the clouds interact with radiation and moderate Earth's climate. We use a machine learning method called random forest to classify images of individual cloud particles as either liquid or ice. We apply our algorithm to images captured by aircraft within clouds overlying the Southern Ocean, and we find that it outperforms two existing algorithms.
Jussi Leinonen, Jacopo Grazioli, and Alexis Berne
Atmos. Meas. Tech., 14, 6851–6866, https://doi.org/10.5194/amt-14-6851-2021, https://doi.org/10.5194/amt-14-6851-2021, 2021
Short summary
Short summary
Measuring the shape, size and mass of a large number of snowflakes is a challenging task; it is hard to achieve in an automatic and instrumented manner. We present a method to retrieve these properties of individual snowflakes using as input a triplet of images/pictures automatically collected by a multi-angle snowflake camera (MASC) instrument. Our method, based on machine learning, is trained on artificially generated snowflakes and evaluated on 3D-printed snowflake replicas.
Richard J. Roy, Matthew Lebsock, and Marcin J. Kurowski
Atmos. Meas. Tech., 14, 6443–6468, https://doi.org/10.5194/amt-14-6443-2021, https://doi.org/10.5194/amt-14-6443-2021, 2021
Short summary
Short summary
This study describes the potential capabilities of a hypothetical spaceborne radar to observe water vapor within clouds.
Haoran Li, Ottmar Möhler, Tuukka Petäjä, and Dmitri Moisseev
Atmos. Chem. Phys., 21, 14671–14686, https://doi.org/10.5194/acp-21-14671-2021, https://doi.org/10.5194/acp-21-14671-2021, 2021
Short summary
Short summary
In natural clouds, ice-nucleating particles are expected to be rare above –10 °C. In the current paper, we found that the formation of ice columns is frequent in stratiform clouds and is associated with increased precipitation intensity and liquid water path. In single-layer shallow clouds, the production of ice columns was attributed to secondary ice production, despite the rime-splintering process not being expected to take place in such clouds.
Cited articles
Bailey, M. P. and Hallett, J.: A Comprehensive Habit Diagram for Atmospheric
Ice Crystals: Confirmation from the Laboratory, AIRS II, and Other Field
Studies, J. Atmos. Sci., 66, 2888–2899, https://doi.org/10.1175/2009JAS2883.1, 2009. a
Beyer, W. H.: CRC Handbook of Mathematical Sciences, CRC Press, Boca Raton, Florida, USA, 1987. a
Bohren, C. F. and Huffman, D. R.: Absorption and Scattering of Light by Small
Particles, John Wiley & Sons, Inc., New York, USA, 1983. a
Botta, G., Aydin, K., Verlinde, J., Avramov, A. E., Ackerman, A. S., Fridlind,
A. M., McFarquhar, G. M., and Wolde, M.: Millimeter wave scattering from ice
crystals and their aggregates: Comparing cloud model simulations with X-and
Ka-band radar measurements, J. Geophys. Res., 116, D00T04,
https://doi.org/10.1029/2011JD015909, 2011. a
Delanoë, J. M. E., Heymsfield, A. J., Protat, A., Bansemer, A., and Hogan,
R. J.: Normalized particle size distribution for remote sensing application,
J. Geophys. Res.-Atmos., 119, 4204–4227, https://doi.org/10.1002/2013JD020700,
2014. a
Dolan, B. and Rutledge, S. A.: A theory-based hydrometeor identification
algorithm for X-band polarimetric radars, J. Atmos. Ocean. Tech., 46,
1196–1213, https://doi.org/10.1175/2009JTECHA1208.1, 2009. a
Durden, S. L. and Tanelli, S.: GPM Ground Validation Airborne Precipitation Radar 3rd Generation
(APR-3) OLYMPEX V2, Dataset available online from the NASA EOSDIS Global Hydrology Resource Center Distributed
Active Archive Center, Huntsville, Alabama, USA,
https://doi.org/10.5067/GPMGV/OLYMPEX/APR3/DATA201, 2018.
Erfani, E. and Mitchell, D. L.: Growth of ice particle mass and projected area during riming, Atmos. Chem. Phys.,
17, 1241–1257, https://doi.org/10.5194/acp-17-1241-2017, 2017. a
Field, P. R. and Heymsfield, A. J.: Importance of snow to global precipitation,
Geophys. Res. Lett., 42, 9512–9520, https://doi.org/10.1002/2015GL065497, 2015. a
Gergely, M., Cooper, S. J., and Garrett, T. J.: Using snowflake surface-area-to-volume ratio to model and interpret snowfall
triple-frequency radar signatures, Atmos. Chem. Phys., 17, 12011–12030, https://doi.org/10.5194/acp-17-12011-2017, 2017. a
Harrington, J. Y., Sulia, K., and Morrison, H.: A Method for Adaptive Habit
Prediction in Bulk Microphysical Models. Part I: Theoretical Development,
J. Atmos. Sci., 70, 349–364, https://doi.org/10.1175/JAS-D-12-040.1, 2013. a
Helmus, J. J. and Collis, S. M.: The Python ARM Radar Toolkit (Py-ART), a
Library for Working with Weather Radar Data in the Python Programming
Language, J. Open Res. Software, 4, e25, https://doi.org/10.5334/jors.119, 2016. a
Heymsfield, A. J. and Kajikawa, M.: An Improved Approach to Calculating
Terminal Velocities of Plate-like Crystals and Graupel, J. Atmos. Sci., 44,
1088–1099, https://doi.org/10.1175/1520-0469(1987)044<1088:AIATCT>2.0.CO;2, 1987. a
Heymsfield, A. J., Field, P., and Bansemer, A.: Exponential size distributions
for snow, J. Atmos. Sci., 65, 4017–4031, https://doi.org/10.1175/2008JAS2583.1,
2008. a
Hitschfeld, W. and Bordan, J.: Errors Inherent in the Radar Measurement of
Rainfall at Attenuating Wavelenghts, J. Meteorol., 11, 58–67,
https://doi.org/10.1175/1520-0469(1954)011<0058:EIITRM>2.0.CO;2, 1954. a
Hogan, R. J., Illingworth, A. J., and Sauvageot, H.: Measuring crystal size in
cirrus using 35- and 94-GHz radars, J. Atmos. Ocean. Tech., 17,
27–37, https://doi.org/10.1175/1520-0426(2000)017<0027:MCSICU>2.0.CO;2, 2000. a
Houze Jr., R. A., McMurdie, L., Tanelli, S., Mace, J., and Nesbitt, S.: OLYMPEX
Science Summary for 3 December 2015,
available at: http://olympex.atmos.washington.edu/archive/reports/20151203/20151203Science_summary.html
(last access: 2 February 2018), 2015a. a
Houze Jr., R. A., McMurdie, L., Zagrodnik, J., Duffy, G., Durden, S., and Funk,
A.: OLYMPEX Science Summary for 4 December 2015,
available at: http://olympex.atmos.washington.edu/archive/reports/20151204/20151204Science_summary.html
(last access: 2 February 2018), 2015b. a
Houze Jr., R. A., McMurdie, L. A., Petersen, W. A., Schwaller, M. R., Baccus,
W., Lundquist, J. D., Mass, C. F., Nijssen, B., Rutledge, S. A., Hudak,
D. R., Tanelli, S., Mace, G. G., Poellot, M. R., Lettenmaier, D. P.,
Zagrodnik, J. P., Rowe, A. K., DeHart, J. C., Madaus, L. E., and Barnes,
H. C.: The Olympic Mountains Experiment (OLYMPEX), B. Am. Meteorol. Soc., 98, 2167–2188, https://doi.org/10.1175/BAMS-D-16-0182.1, 2017. a, b
ITU: Recommendation ITU-R P.676-11: Attenuation by atmospheric gases,
International Telecommunications Union, 2016. a
Jackson, R. C., McFarquhar, G. M., Stith, J., Beals, M., Shaw, R. A., Jensen,
J., Fugal, J., and Korolev, A.: An Assessment of the Impact of Antishattering
Tips and Artifact Removal Techniques on Cloud Ice Size Distributions Measured
by the 2D Cloud Probe, J. Atmos. Ocean. Tech., 31, 2567–2590,
https://doi.org/10.1175/JTECH-D-13-00239.1, 2014. a
Jaynes, E. T.: Probability Theory: The Logic of Science, Cambridge University
Press, Cambridge, UK, 2003. a
Kedem, B. and Chiu, L.: On the lognormality of rain rate, P. Natl. Acad. Sci. USA, 84, 901–905, 1987. a
Kneifel, S., Kulie, M. S., and Bennartz, R.: A triple frequency approach to
retrieve microphysical snowfall parameters, J. Geophys. Res., 116, D11203,
https://doi.org/10.1029/2010JD015430, 2011. a, b
Kneifel, S., von Lerber, A., Tiira, J., Moisseev, D., Kollias, P., and
Leinonen, J.: Observed relations between snowfall microphysics and
triple-frequency radar measurements, J. Geophys. Res.-Atmos., 120,
6034–6055, https://doi.org/10.1002/2015JD023156, 2015. a
Korolev, A., Strapp, J. W., Isaac, G. A., and Emery, E.: Improved Airborne
Hot-Wire Measurements of Ice Water Content in Clouds, J. Atmos. Ocean. Tech., 30, 2121–2131, https://doi.org/10.1175/JTECH-D-13-00007.1, 2013. a
Korolev, A. V., Strapp, J. W., Isaac, G. A., and Nevzorov, A. N.: The
Nevzorov Airborne Hot-Wire LWC-TWC Probe: Principle of Operation and
Performance Characteristics, J. Atmos. Ocean. Tech., 15, 1495–1510,
https://doi.org/10.1175/1520-0426(1998)015<1495:TNAHWL>2.0.CO;2, 1998. a
Kulie, M. S., Hiley, M. J., Bennartz, R., Kneifel, S., and Tanelli, S.: Triple
frequency radar reflectivity signatures of snow: Observations and comparisons
to theoretical ice particle scattering models, J. Appl. Meteorol. Clim.,
53, 1080–1098, https://doi.org/10.1175/JAMC-D-13-066.1, 2014. a, b
Kuo, K.-S., Olson, W. S., Johnson, B. T., Grecu, M., Tian, L., Clune, T. L.,
van Aartsen, B. H., Heymsfield, A. J., Liao, L., and Meneghini, R.: The
Microwave Radiative Properties of Falling Snow Derived from Nonspherical Ice
Particle Models. Part I: An Extensive Database of Simulated Pristine
Crystals and Aggregate Particles, and Their Scattering Properties, J. Appl. Meteorol. Clim., 55, 691–708, https://doi.org/10.1175/JAMC-D-15-0130.1, 2016. a, b, c
Lawson, R. P., O'Connor, D., Zmarzly, P., Weaver, K., Baker, B., Mo, Q., and
Jonsson, H.: The 2D-S (Stereo) Probe: Design and Preliminary Tests of a
New Airborne, High-Speed, High-Resolution Particle Imaging Probe, J. Atmos. Ocean. Tech., 23, 1462–1477, https://doi.org/10.1175/JTECH1927.1, 2006. a
Leinonen, J. and Moisseev, D.: What do triple-frequency radar signatures reveal
about aggregate snowflakes?, J. Geophys. Res., 120, 229–239,
https://doi.org/10.1002/2014JD022072, 2015. a, b
Leinonen, J., Kneifel, S., Moisseev, D., Tyynelä, J., Tanelli, S., and
Nousiainen, T.: Evidence of Nonspheroidal Behavior in Millimeter-Wavelength
Radar Observations of Snowfall, J. Geophys. Res., 117, D18205,
https://doi.org/10.1029/2012JD017680, 2012a. a
Leinonen, J., Moisseev, D., Leskinen, M., and Petersen, W.: A Climatology of
Disdrometer Measurements of Rainfall in Finland over Five Years with
Implications for Global Radar Observations, J. Appl. Meteorol. Clim.,
51, 392–404, https://doi.org/10.1175/JAMC-D-11-056.1, 2012b. a
Leinonen, J., Lebsock, M. D., Tanelli, S., Suzuki, K., Yashiro, H., and Miyamoto, Y.: Performance assessment of a triple-frequency
spaceborne cloud-precipitation radar concept using a global cloud-resolving model, Atmos. Meas. Tech., 8, 3493–3517,
https://doi.org/10.5194/amt-8-3493-2015, 2015. a
Leinonen, J., Lebsock, M. D., Stephens, G. L., and Suzuki, K.: Improved
Retrieval of Cloud Liquid Water from CloudSat and MODIS, J. Appl. Meteorol. Clim., 55, 1831–1844, https://doi.org/10.1175/JAMC-D-16-0077.1, 2016. a
Liao, L., Meneghini, R., Iguchi, T., and Detwiler, A.: Use of dual-wavelength
radar for snow parameter estimates, J. Atmos. Ocean. Tech., 22,
1494–1506, https://doi.org/10.1175/JTECH1808.1, 2005. a
Locatelli, J. D. and Hobbs, P. V.: Fall speeds and masses of solid
precipitation particles, J. Geophys. Res., 79, 2185–2197,
https://doi.org/10.1029/JC079i015p02185,
1974. a
Lu, Y., Jiang, Z., Aydin, K., Verlinde, J., Clothiaux, E. E., and Botta, G.: A polarimetric scattering database for non-spherical
ice particles at microwave wavelengths, Atmos. Meas. Tech., 9, 5119–5134, https://doi.org/10.5194/amt-9-5119-2016, 2016. a
Mascio, J. and Mace, G. G.: Quantifying uncertainties in radar forward models
through a comparison between CloudSat and SPartICus reflectivity factors,
J. Geophys. Res.-Atmos., 122, 1665–1684, https://doi.org/10.1002/2016JD025183,
2017. a
Mascio, J., Xu, Z., and Mace, G. G.: The Mass-Dimensional Properties of Cirrus
Clouds During TC4, J. Geophys. Res.-Atmos., 122, 10402–10417,
https://doi.org/10.1002/2017JD026787, 2017. a
Matrosov, S. Y.: Possibilities of cirrus particle sizing from dual-frequency
radar measurements, J. Geophys. Res., 98, 20675–20683,
https://doi.org/10.1029/93JD02335, 1993. a
Matrosov, S. Y.: A dual-wavelength radar method to measure snowfall rate, J. Appl. Meteorol., 37, 1510–1521,
https://doi.org/10.1175/1520-0450(1998)037<1510:ADWRMT>2.0.CO;2, 1998. a
Mitchell, D. L. and Heymsfield, A. J.: Refinements in the Treatment of Ice
Particle Terminal Velocities, Highlighting Aggregates, J. Atmos. Sci., 62,
1637–1644, https://doi.org/10.1175/JAS3413.1, 2005. a
Mitchell, D. L., Zhang, R., and Pitter, R. L.: Mass-Dimensional Relationships
for Ice Particles and the Influence of Riming on Snowfall Rates, J. Appl. Meteorol., 29, 153–163, https://doi.org/10.1175/1520-0450(1990)029<0153:MDRFIP>2.0.CO;2,
1990. a, b
Moisseev, D., von Lerber, A., and Tiira, J.: Quantifying the effect of riming
on snowfall using ground-based observations, J. Geophys. Res.-Atmos., 122,
4019–4037, https://doi.org/10.1002/2016JD026272, 2017. a, b
Morrison, H. and Milbrandt, J. A.: Parameterization of Cloud Microphysics Based
on the Prediction of Bulk Ice Particle Properties. Part I: Scheme
Description and Idealized Tests, J. Atmos. Sci., 72, 287–311,
https://doi.org/10.1175/JAS-D-14-0065.1, 2015. a
Mülmenstädt, J., Sourdeval, O., Delanoë, J., and Quaas, J.: Frequency of
occurrence of rain from liquid-, mixed-, and ice-phase clouds derived from
A-Train satellite retrievals, Geophys. Res. Lett., 42, 6502–6509,
https://doi.org/10.1002/2015GL064604, 2015. a
Newman, A. J., Kucera, P. A., and Bliven, L. F.: Presenting the Snowflake Video
Imager (SVI), J. Atmos. Ocean. Tech., 26, 167–179,
https://doi.org/10.1175/2008JTECHA1148.1, 2009. a
Oliphant, T. E.: Python for Scientific Computing, Comput. Sci. Eng., 9, 10–20,
https://doi.org/10.1109/MCSE.2007.58, 2007. a
Petäjä, T., O'Connor, E. J., Moisseev, D., Sinclair, V. A., Manninen,
A. J., Väänänen, R., von Lerber, A., Thornton, J. A., Nicoll, K.,
Petersen, W., Chandrasekar, V., Smith, J. N., Winkler, P. M., Krüger, O.,
Hakola, H., Timonen, H., Brus, D., Laurila, T., Asmi, E., Riekkola, M.-L.,
Mona, L., Massoli, P., Engelmann, R., Komppula, M., Wang, J., Kuang, C.,
Bäck, J., Virtanen, A., Levula, J., Ritsche, M., and Hickmon, N.: BAECC: A
Field Campaign to Elucidate the Impact of Biogenic Aerosols on Clouds and
Climate, B. Am. Meteorol. Soc., 97, 1909–1928,
https://doi.org/10.1175/BAMS-D-14-00199.1, 2016. a
Petty, G. W. and Huang, W.: Microwave Backscatter and Extinction by Soft Ice
Spheres and Complex Snow Aggregates, J. Atmos. Sci., 67, 769–787,
https://doi.org/10.1175/2009JAS3146.1, 2010. a
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding – Theory and
Practice, World Scientific Publishing, https://doi.org/10.1142/9789812813718, 2000.
a
Sadowy, G. A., Berkun, A. C., Chun, W., Im, E., and Durden, S. L.: Development
of an advanced airborne precipitation radar, Microwave J., 46, 84,
available at: http://www.microwavejournal.com/articles/3577-development-of-an-advanced-airborne-precipitation-radar (last access: 1 October 2018),
2003. a
Sekhon, R. S. and Srivastava, R. C.: Snow size spectra and radar reflectivity,
J. Atmos. Sci., 27, 299–307,
https://doi.org/10.1175/1520-0469(1970)027<0299:SSSARR>2.0.CO;2, 1970. a
Stein, T. H. M., Westbrook, C. D., and Nicol, J. C.: Fractal geometry of
aggregate snowflakes revealed by triple-wavelength radar measurements,
Geophys. Res. Lett., 43, 176–183, https://doi.org/10.1002/2014GL062170, 2015. a
Tyynelä, J., Leinonen, J., Moisseev, D., and Nousiainen, T.: Radar
backscattering from snowflakes: comparison of fractal, aggregate and
soft-spheroid models, J. Atmos. Ocean. Tech., 28, 1365–1372,
https://doi.org/10.1175/JTECH-D-11-00004.1, 2011. a
von Lerber, A., Moisseev, D., Bliven, L. F., Petersen, W., Harri, A.-M., and
Chandrasekar, V.: Microphysical Properties of Snow and Their Link to Ze'S
Relations during BAECC 2014, J. Appl. Meteorol. Clim., 56, 1561–1582,
https://doi.org/10.1175/JAMC-D-16-0379.1, 2017. a, b
Waliser, D. E., Li, J.-L. F., Woods, C. P., Austin, R. T., Bacmeister, J.,
Chern, J., Del Genio, A., Jiang, J. H., Kuang, Z., Meng, H., Minnis, P.,
Platnick, S., Rossow, W. B., Stephens, G. L., Sun-Mack, S., Tao, W.-K.,
Tompkins, A. M., Vane, D. G., Walker, C., and Wu, D.: Cloud ice: A climate
model challenge with signs and expectations of progress, J. Geophys. Res.-Atmos., 114, D00A21, https://doi.org/10.1029/2008JD010015, 2009. a
Westbrook, C. D., Ball, R. C., Field, P. R., and Heymsfield, A. J.:
Universality in snowflake aggregation, Geophys. Res. Lett., 31, L15104,
https://doi.org/10.1029/2004GL020363, 2004. a
Wolff, D., Marks, D., Petersen, W. A., and Pippitt, J.: GPM Ground Validation NASA S-Band Dual Polarimetric (NPOL)
Doppler Radar OLYMPEX V2. Dataset available online from the NASA EOSDIS Global Hydrology Resource Center Distributed Active
Archive Center, Huntsville, Alabama, USA,
https://doi.org/10.5067/GPMGV/OLYMPEX/NPOL/DATA301, 2017.
Yin, M., Liu, G., Honeyager, R., and Turk, F. J.: Observed differences of
triple-frequency radar signatures between snowflakes in stratiform and
convective clouds, J. Quant. Spectrosc. Ra., 193, 13–20,
https://doi.org/10.1016/j.jqsrt.2017.02.017, 2017. a
Short summary
We developed a technique for inferring the physical properties (amount, size and density) of falling snow from radar observations made using multiple different frequencies. We tested this method using measurements from airborne radar and compared the results to direct measurements from another aircraft, as well as ground-based radar. The results demonstrate that multifrequency radars have significant advantages over those with a single frequency in determining the snow size and density.
We developed a technique for inferring the physical properties (amount, size and density) of...
