- Articles & preprints
- Submission
- Policies
- Peer review
- Editorial board
- About
- EGU publications
- Manuscript tracking
Journal cover
Journal topic
Atmospheric Measurement Techniques
An interactive open-access journal of the European Geosciences Union
Journal topic
- Articles & preprints
- Submission
- Policies
- Peer review
- Editorial board
- About
- EGU publications
- Manuscript tracking
Journal metrics
IF3.668
IF 5-year
3.707
3.707
CiteScore
6.3
6.3
SNIP1.383
IPP3.75
SJR1.525
Scimago H
index77
index77
h5-index49
Abstracted/indexed
Abstracted/indexed
AMT | Articles | Volume 13, issue 9
Atmos. Meas. Tech., 13, 5065–5085, 2020
https://doi.org/10.5194/amt-13-5065-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-5065-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 13, 5065–5085, 2020
https://doi.org/10.5194/amt-13-5065-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-5065-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article 28 Sep 2020
Research article | 28 Sep 2020
Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars
Frédéric Tridon et al.
Related authors
No articles found.
Davide Ori, Leonie von Terzi, Markus Karrer, and Stefan Kneifel
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-359, https://doi.org/10.5194/gmd-2020-359, 2020
Preprint under review for GMD
Short summary
Short summary
Snowflakes have very complex shapes and modeling their properties requires vast computing power. We produced a large number of realistic snowflakes and modeled their average properties by leveraging on their fractal structure. Our approach allows modeling the properties of big ensembles of snowflakes, taking into account their natural variability, at a much lower cost. This enables the usage of remote sensing instruments, such as radars, to monitor the evolution of clouds and precipitation.
Alexander Myagkov, Stefan Kneifel, and Thomas Rose
Atmos. Meas. Tech., 13, 5799–5825, https://doi.org/10.5194/amt-13-5799-2020, https://doi.org/10.5194/amt-13-5799-2020, 2020
Short summary
Short summary
This study shows two methods for evaluating the reflectivity calibration of W-band cloud radars. Both methods use natural rain as a reference target. The first method is based on spectral polarimetric observations and requires a polarimetric cloud radar with a scanner. The second method utilizes disdrometer observations and can be applied to scanning and vertically pointed radars. Both methods show consistent results and can be applied for operational monitoring of measurement quality.
Jie Gong, Xiping Zeng, Dong L. Wu, S. Joseph Munchak, Xiaowen Li, Stefan Kneifel, Davide Ori, Liang Liao, and Donifan Barahona
Atmos. Chem. Phys., 20, 12633–12653, https://doi.org/10.5194/acp-20-12633-2020, https://doi.org/10.5194/acp-20-12633-2020, 2020
Short summary
Short summary
This work provides a novel way of using polarized passive microwave measurements to study the interlinked cloud–convection–precipitation processes. The magnitude of differences between polarized radiances is found linked to ice microphysics (shape, size, orientation and density), mesoscale dynamic and thermodynamic structures, and surface precipitation. We conclude that passive sensors with multiple polarized channel pairs may serve as cheaper and useful substitutes for spaceborne radar sensors.
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.
Kamil Mróz, Alessandro Battaglia, Stefan Kneifel, Leonie von Terzi, Markus Karrer, and Davide Ori
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-272, https://doi.org/10.5194/amt-2020-272, 2020
Revised manuscript under review for AMT
Short summary
Short summary
The article examines the relationship between the characteristics of rain and the properties of the ice cloud from which the rain originated. Our results confirm widely accepted expectation that the mass flux through the melting zone is well preserved with an exception of extreme aggregation and riming conditions. Moreover, it is shown that the mean (mass weighted) size of particles above and below the melting zone are strongly linked with the former being on average 21 % larger.
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.
Darielle Dexheimer, Martin Airey, Erika Roesler, Casey Longbottom, Keri Nicoll, Stefan Kneifel, Fan Mei, R. Giles Harrison, Graeme Marlton, and Paul D. Williams
Atmos. Meas. Tech., 12, 6845–6864, https://doi.org/10.5194/amt-12-6845-2019, https://doi.org/10.5194/amt-12-6845-2019, 2019
Short summary
Short summary
A tethered-balloon system deployed supercooled liquid water content sondes and fiber optic distributed temperature sensing to collect in situ atmospheric measurements within mixed-phase Arctic clouds. These data were validated against collocated surface-based and remote sensing datasets. From these measurements and sensor evaluations, tethered-balloon flights are shown to offer an effective method of collecting data to inform numerical models and calibrate remote sensing instrumentation.
Shannon L. Mason, Robin J. Hogan, Christopher D. Westbrook, Stefan Kneifel, Dmitri Moisseev, and Leonie von Terzi
Atmos. Meas. Tech., 12, 4993–5018, https://doi.org/10.5194/amt-12-4993-2019, https://doi.org/10.5194/amt-12-4993-2019, 2019
Short summary
Short summary
The mass contents of snowflakes are critical to remotely sensed estimates of snowfall. The signatures of snow measured at three radar frequencies can distinguish fluffy, fractal snowflakes from dense and more homogeneous rimed snow. However, we show that the shape of the particle size spectrum also has a significant impact on triple-frequency radar signatures and must be accounted for when making triple-frequency radar estimates of snow that include variations in particle structure and density.
José Dias Neto, Stefan Kneifel, Davide Ori, Silke Trömel, Jan Handwerker, Birger Bohn, Normen Hermes, Kai Mühlbauer, Martin Lenefer, and Clemens Simmer
Earth Syst. Sci. Data, 11, 845–863, https://doi.org/10.5194/essd-11-845-2019, https://doi.org/10.5194/essd-11-845-2019, 2019
Short summary
Short summary
This study describes a 2-month dataset of ground-based, vertically pointing triple-frequency cloud radar observations recorded during the winter season 2015/2016 in Jülich, Germany. Intensive quality control has been applied to the unique long-term dataset, which allows the multifrequency signatures of ice and snow particles to be statistically analyzed for the first time. The analysis includes, for example, aggregation and its dependence on cloud temperature, riming, and onset of melting.
Mengistu Wolde, Alessandro Battaglia, Cuong Nguyen, Andrew L. Pazmany, and Anthony Illingworth
Atmos. Meas. Tech., 12, 253–269, https://doi.org/10.5194/amt-12-253-2019, https://doi.org/10.5194/amt-12-253-2019, 2019
Short summary
Short summary
This paper presents an implementation of polarization diversity pulse-pair processing (PDPP) on the National Research Council of Canada airborne W-band radar (NAW) system. A description of the NAW PDPP pulsing schemes and an analysis of comprehensive airborne data collected in diverse weather conditions in Canada is presented. The analysis shows a successful airborne measurement of Doppler velocity exceeding 100 m s−1 using PDPP approach, the first such measurement from a moving platform.
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.
Francesco De Angelis, Domenico Cimini, James Hocking, Pauline Martinet, and Stefan Kneifel
Geosci. Model Dev., 9, 2721–2739, https://doi.org/10.5194/gmd-9-2721-2016, https://doi.org/10.5194/gmd-9-2721-2016, 2016
Short summary
Short summary
Ground-based microwave radiometers (MWRs) offer to bridge the observational gap in the atmospheric boundary layer. Currently MWRs are operational at many sites worldwide. However, their potential is largely under-exploited, partly due to the lack of a fast radiative transfer model (RTM) suited for data assimilation into numerical weather prediction models. Here we propose and test an RTM, building on satellite heritage and adapting for ground-based MWRs, which addresses this shortage.
Heike Kalesse, Wanda Szyrmer, Stefan Kneifel, Pavlos Kollias, and Edward Luke
Atmos. Chem. Phys., 16, 2997–3012, https://doi.org/10.5194/acp-16-2997-2016, https://doi.org/10.5194/acp-16-2997-2016, 2016
Short summary
Short summary
Mixed-phase clouds are ubiquitous. Process-level understanding is needed to address the complexity of mixed-phase clouds and to improve their representation in models. This study illustrates steps to identify the impact of a microphysical process (riming) on cloud Doppler radar observations. It suggests that in situ observations of key ice properties are needed to complement radar observations before process-oriented studies can effectively evaluate ice microphysical parameterizations in models.
I. V. Gorodetskaya, S. Kneifel, M. Maahn, K. Van Tricht, W. Thiery, J. H. Schween, A. Mangold, S. Crewell, and N. P. M. Van Lipzig
The Cryosphere, 9, 285–304, https://doi.org/10.5194/tc-9-285-2015, https://doi.org/10.5194/tc-9-285-2015, 2015
Short summary
Short summary
Our paper presents a new cloud-precipitation-meteorological observatory established in the escarpment zone of Dronning Maud Land, East Antarctica. The site is characterised by bimodal cloud occurrence (clear sky or overcast) with liquid-containing clouds occurring 20% of the cloudy periods. Local surface mass balance strongly depends on rare intense snowfall events. A substantial part of the accumulated snow is removed by surface and drifting snow sublimation and wind-driven snow erosion.
A. Battaglia, C. D. Westbrook, S. Kneifel, P. Kollias, N. Humpage, U. Löhnert, J. Tyynelä, and G. W. Petty
Atmos. Meas. Tech., 7, 1527–1546, https://doi.org/10.5194/amt-7-1527-2014, https://doi.org/10.5194/amt-7-1527-2014, 2014
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection
Cloud-top pressure retrieval with DSCOVR EPIC oxygen A- and B-band observations
A new Orbiting Carbon Observatory 2 cloud flagging method and rapid retrieval of marine boundary layer cloud properties
CALIOP V4 cloud thermodynamic phase assignment and the impact of near-nadir viewing angles
Detection of the cloud liquid water path horizontal inhomogeneity in a coastline area by means of ground-based microwave observations: feasibility study
Determining Cloud Thermodynamic Phase from the Polarized Micro Pulse Lidar
Synergistic radar and radiometer retrievals of ice hydrometeors
Improvement in cloud retrievals from VIIRS through the use of infrared absorption channels constructed from VIIRS+CrIS data fusion
Using two-stream theory to capture fluctuations of satellite-perceived TOA SW radiances reflected from clouds over ocean
Exploration of machine learning methods for the classification of infrared limb spectra of polar stratospheric clouds
Three-dimensional wind profiles using a stabilized shipborne cloud radar in wind profiler mode
Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm
A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations
Global Cloud Property Models for Real Time Triage Onboard Visible-Shortwave Infrared Spectrometers
Microwave single scattering properties of non-spheroidal rain drops
SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation
Spatial distribution of cloud droplet size properties from Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) measurements
Towards objective identification and tracking of convective outflow boundaries in next-generation geostationary satellite imagery
Cloud detection over snow and ice with oxygen A- and B-band observations from the Earth Polychromatic Imaging Camera (EPIC)
Ground-based observations of cloud and drizzle liquid water path in stratocumulus clouds
Applying Deep Learning to NASA MODIS Data to Create a Community Record of Marine Low Cloud Mesoscale Morphology
Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: evaluation of candidate approaches with MODIS observations
Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne
Improved cloud detection over sea ice and snow during Arctic summer using MERIS data
Towards an operational Ice Cloud Imager (ICI) retrieval product
Ice crystal number concentration from lidar, cloud radar and radar wind profiler measurements
Retrieval of cloud properties from spectral zenith radiances observed by sky radiometers
A new approach to estimate supersaturation fluctuations in stratocumulus cloud using ground-based remote-sensing measurements
ELIFAN, an algorithm for the estimation of cloud cover from sky imagers
Estimating solar irradiance using sky imagers
Toward autonomous surface-based infrared remote sensing of polar clouds: retrievals of cloud optical and microphysical properties
Use of spectral cloud emissivities and their related uncertainties to infer ice cloud boundaries: methodology and assessment using CALIPSO cloud products
The importance of particle size distribution and internal structure for triple-frequency radar retrievals of the morphology of snow
Calibration of the 2007–2017 record of Atmospheric Radiation Measurements cloud radar observations using CloudSat
All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud
peakTree: a framework for structure-preserving radar Doppler spectra analysis
Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm
Footprint-scale cloud type mixtures and their impacts on Atmospheric Infrared Sounder cloud property retrievals
Estimation of liquid water path below the melting layer in stratiform precipitation systems using radar measurements during MC3E
Correlated observation error models for assimilating all-sky infrared radiances
Cloud identification and classification from high spectral resolution data in the far infrared and mid-infrared
Investigating the liquid water path over the tropical Atlantic with synergistic airborne measurements
Homogeneity criteria from AVHRR information within IASI pixels in a numerical weather prediction context
Sensitivity of liquid cloud optical thickness and effective radius retrievals to cloud bow and glory conditions using two SEVIRI imagers
Evolution of DARDAR-CLOUD ice cloud retrievals: new parameters and impacts on the retrieved microphysical properties
FRESCO-B: a fast cloud retrieval algorithm using oxygen B-band measurements from GOME-2
Application of high-dimensional fuzzy k-means cluster analysis to CALIOP/CALIPSO version 4.1 cloud–aerosol discrimination
Cloud products from the Earth Polychromatic Imaging Camera (EPIC): algorithms and initial evaluation
Cloud base height retrieval from multi-angle satellite data
Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach
Benjamin R. Scarino, Kristopher Bedka, Rajendra Bhatt, Konstantin Khlopenkov, David R. Doelling, and William L. Smith Jr.
Atmos. Meas. Tech., 13, 5491–5511, https://doi.org/10.5194/amt-13-5491-2020, https://doi.org/10.5194/amt-13-5491-2020, 2020
Short summary
Short summary
This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles. A 1-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bidirectional reflectance distribution function model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angles.
Bangsheng Yin, Qilong Min, Emily Morgan, Yuekui Yang, Alexander Marshak, and Anthony B. Davis
Atmos. Meas. Tech., 13, 5259–5275, https://doi.org/10.5194/amt-13-5259-2020, https://doi.org/10.5194/amt-13-5259-2020, 2020
Short summary
Short summary
Cloud-top pressure (CTP) is an important cloud property for climate and weather studies. Based on differential oxygen absorption, both oxygen A-band and B-band pairs can be used to retrieve CTP. However, it is currently very challenging to perform a CTP retrieval accurately due to the complicated in-cloud penetration effect. To address this issue, we propose an analytic transfer inverse model for DSCOVR EPIC observations to retrieve CTP considering in-cloud photon penetration.
Mark Richardson, Matthew D. Lebsock, James McDuffie, and Graeme L. Stephens
Atmos. Meas. Tech., 13, 4947–4961, https://doi.org/10.5194/amt-13-4947-2020, https://doi.org/10.5194/amt-13-4947-2020, 2020
Short summary
Short summary
We previously combined CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) lidar data and reflected-sunlight measurements from OCO-2 (Orbiting Carbon Observatory 2) for information about low clouds over oceans. The satellites are no longer formation-flying, so this work is a step towards getting new information about these clouds using only OCO-2. We can rapidly and accurately identify liquid oceanic clouds and obtain their height better than a widely used passive sensor.
Melody A. Avery, Robert A. Ryan, Brian J. Getzewich, Mark A. Vaughan, David M. Winker, Yongxiang Hu, Anne Garnier, Jacques Pelon, and Carolus A. Verhappen
Atmos. Meas. Tech., 13, 4539–4563, https://doi.org/10.5194/amt-13-4539-2020, https://doi.org/10.5194/amt-13-4539-2020, 2020
Short summary
Short summary
CALIOP data users will find more cloud layers detected in V4, with edges that extend further than in V3, for an increase in total atmospheric cloud volume of 6 %–9 % for high-confidence cloud phases and 1 %–2 % for all cloudy bins, including cloud fringes and unknown cloud phases. In V4 there are many fewer cloud layers identified as horizontally oriented ice, particularly in the 3° off-nadir view. Depolarization at 532 nm is the predominant parameter determining cloud thermodynamic phase.
Vladimir S. Kostsov, Dmitry V. Ionov, and Anke Kniffka
Atmos. Meas. Tech., 13, 4565–4587, https://doi.org/10.5194/amt-13-4565-2020, https://doi.org/10.5194/amt-13-4565-2020, 2020
Short summary
Short summary
Previously, observations from satellites provided evidence for systematic differences between the values of the cloud liquid water path over land and water areas in northern Europe. An attempt is made to detect such differences by means of ground-based microwave measurements performed near the coastline of the Gulf of Finland. The results demonstrate the existence of the cloud liquid water path gradient, which is positive as in the case of the satellite measurements (larger values over land).
Jasper R. Lewis, James R. Campbell, Simone Lolli, Sebastian A. Stewart, Ivy Tan, and Ellsworth J. Welton
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-298, https://doi.org/10.5194/amt-2020-298, 2020
Revised manuscript accepted for AMT
Short summary
Short summary
In this work, the authors describe a process to determine the thermodynamic cloud phase using Micro Pulse Lidar Network volume depolarization ratio measurements and temperature profiles from the Global Modeling and Assimilation Office GEOS-5 model. A multi-year analysis and comparisons to supercooled liquid-water fractions derived from CALIPSO satellite measurements are used to demonstrate the efficacy of the method.
Simon Pfreundschuh, Patrick Eriksson, Stefan A. Buehler, Manfred Brath, David Duncan, Richard Larsson, and Robin Ekelund
Atmos. Meas. Tech., 13, 4219–4245, https://doi.org/10.5194/amt-13-4219-2020, https://doi.org/10.5194/amt-13-4219-2020, 2020
Short summary
Short summary
The next generation of European operational weather satellites will carry a novel microwave sensor, the Ice Cloud Imager (ICI), which will provide observations of clouds at microwave frequencies that were not available before. We investigate the potential benefits of combining observations from ICI with that of a radar. We find that such combined observations provide additional information on the properties of the cloud and help to reduce uncertainties in retrieved mass and number densities.
Yue Li, Bryan A. Baum, Andrew K. Heidinger, W. Paul Menzel, and Elisabeth Weisz
Atmos. Meas. Tech., 13, 4035–4049, https://doi.org/10.5194/amt-13-4035-2020, https://doi.org/10.5194/amt-13-4035-2020, 2020
Short summary
Short summary
Use of VIIRS+CrIS fusion products, which provide VIIRS with MODIS-like IR sounding channels, improves cloud mask, cloud phase, and cloud top height retrievals when compared to those using VIIRS data only. NOAA CLAVR-x cloud retrievals for both S-NPP and NOAA-20 data are evaluated through comparisons to the CALIPSO v4 and MODIS Collection 6.1 cloud products. Cloud height retrievals show significant improvement for semitransparent ice clouds, with a reduction in retrieval uncertainties.
Florian Tornow, Carlos Domenech, Howard W. Barker, René Preusker, and Jürgen Fischer
Atmos. Meas. Tech., 13, 3909–3922, https://doi.org/10.5194/amt-13-3909-2020, https://doi.org/10.5194/amt-13-3909-2020, 2020
Short summary
Short summary
Clouds reflect sunlight unevenly, which makes it difficult to quantify the portion reflected back to space via satellite observation. To improve quantification, we propose a new statistical model that incorporates more satellite-inferred cloud and atmospheric properties than state-of-the-art models. We use concepts from radiative transfer theory that we statistically optimize to fit observations. The new model often explains past satellite observations better and predicts reflection plausibly.
Rocco Sedona, Lars Hoffmann, Reinhold Spang, Gabriele Cavallaro, Sabine Griessbach, Michael Höpfner, Matthias Book, and Morris Riedel
Atmos. Meas. Tech., 13, 3661–3682, https://doi.org/10.5194/amt-13-3661-2020, https://doi.org/10.5194/amt-13-3661-2020, 2020
Short summary
Short summary
Polar stratospheric clouds (PSCs) play a key role in polar ozone depletion in the stratosphere. In this paper, we explore the potential of applying machine learning (ML) methods to classify PSC observations of infrared spectra to classify PSC types. ML methods have proved to reach results in line with those obtained using well-established approaches. Among the considered ML methods, random forest (RF) seems to be the most promising one, being able to produce explainable classification results.
Alain Protat and Ian McRobert
Atmos. Meas. Tech., 13, 3609–3620, https://doi.org/10.5194/amt-13-3609-2020, https://doi.org/10.5194/amt-13-3609-2020, 2020
Short summary
Short summary
Three-dimensional (3D) wind motions play a major role in driving the life cycle of clouds. In this pilot study we have developed a technique to measure the 3D winds in clouds, using a shipborne Doppler cloud radar on a stabilized platform. The stabilized platform is driven to point in a series of predefined directions to collect the required measurements. Comparisons with radiosondes demonstrate that accurate 1 min resolution 3D wind motions can be obtained from this instrumental setup.
Daniel J. Miller, Michal Segal-Rozenhaimer, Kirk Knobelspiesse, Jens Redemann, Brian Cairns, Mikhail Alexandrov, Bastiaan van Diedenhoven, and Andrzej Wasilewski
Atmos. Meas. Tech., 13, 3447–3470, https://doi.org/10.5194/amt-13-3447-2020, https://doi.org/10.5194/amt-13-3447-2020, 2020
Short summary
Short summary
A neural network (NN) is developed and used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. The NN is applied to research scanning polarimeter (RSP) observations obtained during the ORACLES field campaign and compared to other co-located remote sensing retrievals of cloud effective radius and optical thickness. A NN approach can advance more complex iterative search retrieval algorithms by providing a quick initial guess.
Chenxi Wang, Steven Platnick, Kerry Meyer, Zhibo Zhang, and Yaping Zhou
Atmos. Meas. Tech., 13, 2257–2277, https://doi.org/10.5194/amt-13-2257-2020, https://doi.org/10.5194/amt-13-2257-2020, 2020
Short summary
Short summary
A machine-learning (ML)-based approach that can be used for cloud mask and phase detection is developed. An all-day model that uses infrared (IR) observations and a daytime model that uses shortwave and IR observations from a passive instrument are trained separately for different surface types. The training datasets are selected by using reference pixel types from collocated space lidar. The ML approach is validated carefully and the overall performance is better than traditional methods.
Macey W. Sandford, David R. Thompson, Robert O. Green, Brian H. Kahn, Raffaele Vitulli, Steve Chien, Amruta Yelamanchili, and Winston Olson-Duvall
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-139, https://doi.org/10.5194/amt-2020-139, 2020
Revised manuscript accepted for AMT
Short summary
Short summary
We demonstrate an onboard cloud screening approach to significantly reduce the amount of cloud-contaminated data transmitted from orbit. We have produced location-specific models that improve performance by taking into account the unique cloud statistics in different latitudes. We have shown that screening clouds based on their location or surface type will improve the ability for a cloud-screening tool to improve the volume of usable science data.
Robin Ekelund, Patrick Eriksson, and Michael Kahnert
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-85, https://doi.org/10.5194/amt-2020-85, 2020
Revised manuscript accepted for AMT
Short summary
Short summary
Rain drops become flattened due to aerodynamic drag, as they increase in mass and fall-speed. The shape of such rain drops was calculated, and the electromagnetic interaction between microwave radiation and the rain drops was calculated. The calculations are made publicly available to the scientific community, in order to promote accurate representations of rain drops in measurements. Tests show that the drop shape can indeed have a noticeable effect on microwave observations of heavy rainfall.
Wanyi Xie, Dong Liu, Ming Yang, Shaoqing Chen, Benge Wang, Zhenzhu Wang, Yingwei Xia, Yong Liu, Yiren Wang, and Chaofang Zhang
Atmos. Meas. Tech., 13, 1953–1961, https://doi.org/10.5194/amt-13-1953-2020, https://doi.org/10.5194/amt-13-1953-2020, 2020
Brent A. McBride, J. Vanderlei Martins, Henrique M. J. Barbosa, William Birmingham, and Lorraine A. Remer
Atmos. Meas. Tech., 13, 1777–1796, https://doi.org/10.5194/amt-13-1777-2020, https://doi.org/10.5194/amt-13-1777-2020, 2020
Short summary
Short summary
Clouds play a large role in the way our Earth system distributes energy. The measurement of cloud droplet size distribution (DSD) is one way to connect small-scale cloud processes to scattered radiation. Our small satellite instrument, the Airborne Hyper-Angular Rainbow Polarimeter, is the first to infer DSDs over a wide spatial cloud field using polarized light. This study improves the way we interpret cloud properties and shows that high-quality science does not require a large taxpayer cost.
Jason M. Apke, Kyle A. Hilburn, Steven D. Miller, and David A. Peterson
Atmos. Meas. Tech., 13, 1593–1608, https://doi.org/10.5194/amt-13-1593-2020, https://doi.org/10.5194/amt-13-1593-2020, 2020
Short summary
Short summary
Objective identification of deep convection outflow boundaries (OFBs) in next-generation geostationary satellite imagery is explored here using motion derived from a tuned advanced optical flow algorithm. Motion discontinuity preservation within the derivation is found crucial for successful OFB tracking between images, which yields new meteorological data for objective systems to use. These results provide the first step towards a fully automated satellite-based OFB identification algorithm.
Yaping Zhou, Yuekui Yang, Meng Gao, and Peng-Wang Zhai
Atmos. Meas. Tech., 13, 1575–1591, https://doi.org/10.5194/amt-13-1575-2020, https://doi.org/10.5194/amt-13-1575-2020, 2020
Short summary
Short summary
Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and the bright and cold surfaces; the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) has very limited channels. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios for cloud detection over snow and ice surfaces.
Maria P. Cadeddu, Virendra P. Ghate, and Mario Mech
Atmos. Meas. Tech., 13, 1485–1499, https://doi.org/10.5194/amt-13-1485-2020, https://doi.org/10.5194/amt-13-1485-2020, 2020
Short summary
Short summary
A combination of ground-based active and passive observations is used to partition cloud and precipitation liquid water path in precipitating stratocumulous clouds. Results show that neglecting scattering effects from drizzle drops leads to 8–15 % overestimation of the liquid amount in the cloud. In closed-cell systems only ~20 % of the available drizzle in the cloud falls below the cloud base, compared to ~40 % in open-cell systems.
Tianle Yuan, Hua Song, Robert Wood, Johannes Mohrmann, Kerry Meyer, Lazaros Oreopoulos, and Steven Platnick
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-61, https://doi.org/10.5194/amt-2020-61, 2020
Revised manuscript accepted for AMT
Short summary
Short summary
We use deep transfer learning techniques to classify satellite cloud images into different morphology types. It achieves the state-of-the-art results and can automatically process large amount of satellite data. The algorithm will help low cloud researchers to better understand their mesoscale organiizations.
Frank Werner and Hartwig Deneke
Atmos. Meas. Tech., 13, 1089–1111, https://doi.org/10.5194/amt-13-1089-2020, https://doi.org/10.5194/amt-13-1089-2020, 2020
Short summary
Short summary
The reliability of remotely sensed cloud variables from space depends on the horizontal resolution of the instrument. This study presents and evaluates several candidate approaches for increasing the spatial resolution of observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) from the native 3 km scale to a horizontal resolution of 1 km. It is shown that uncertainties in the derived cloud products can be significantly mitigated by applying an appropriate downscaling scheme.
Christine Aebi, Julian Gröbner, Stelios Kazadzis, Laurent Vuilleumier, Antonis Gkikas, and Niklaus Kämpfer
Atmos. Meas. Tech., 13, 907–923, https://doi.org/10.5194/amt-13-907-2020, https://doi.org/10.5194/amt-13-907-2020, 2020
Short summary
Short summary
Clouds are one of the largest sources of uncertainties in climate models. The current study estimates the cloud optical thickness (COT), the effective droplet radius and the single scattering albedo of stratus–altostratus and cirrus–cirrostratus clouds in Payerne, Switzerland, by combining ground- and satellite-based measurements and radiative transfer models. The estimated values are thereafter compared with data retrieved from other methods. The mean COT is distinct for different seasons.
Larysa Istomina, Henrik Marks, Marcus Huntemann, Georg Heygster, and Gunnar Spreen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-413, https://doi.org/10.5194/amt-2019-413, 2020
Revised manuscript accepted for AMT
Patrick Eriksson, Bengt Rydberg, Vinia Mattioli, Anke Thoss, Christophe Accadia, Ulf Klein, and Stefan A. Buehler
Atmos. Meas. Tech., 13, 53–71, https://doi.org/10.5194/amt-13-53-2020, https://doi.org/10.5194/amt-13-53-2020, 2020
Short summary
Short summary
The Ice Cloud Imager (ICI) will be the first operational satellite sensor operating at sub-millimetre wavelengths and this novel mission will thus provide important new data to weather forecasting and climate studies. The series of ICI instruments will together cover about 20 years. This article presents the basic technical characteristics of the sensor and outlines the day-one operational retrievals. An updated estimation of the expected retrieval performance is also presented.
Johannes Bühl, Patric Seifert, Martin Radenz, Holger Baars, and Albert Ansmann
Atmos. Meas. Tech., 12, 6601–6617, https://doi.org/10.5194/amt-12-6601-2019, https://doi.org/10.5194/amt-12-6601-2019, 2019
Short summary
Short summary
In the present paper, we present a novel remote-sensing technique for the measurement of ice crystal number concentrations in clouds. The fall velocity of ice crystals measured with values from cloud radar and a radar wind profiler is used in order to derive information about ice crystal size and number concentration. In contrast to existing methods based on the combination of lidar and cloud radar, the present method can also be used in optically thick clouds.
Pradeep Khatri, Hironobu Iwabuchi, Tadahiro Hayasaka, Hitoshi Irie, Tamio Takamura, Akihiro Yamazaki, Alessandro Damiani, Husi Letu, and Qin Kai
Atmos. Meas. Tech., 12, 6037–6047, https://doi.org/10.5194/amt-12-6037-2019, https://doi.org/10.5194/amt-12-6037-2019, 2019
Short summary
Short summary
In an attempt to make cloud retrievals from the surface more common and convenient, we developed a cloud retrieval algorithm applicable for sky radiometers. It is based on an optimum method by fitting measured transmittances with modeled values. Further, a cost-effective and easy-to-use calibration procedure is proposed and validated using data obtained from the standard method. A detailed error analysis and quality assessment are also performed.
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.
Marie Lothon, Paul Barnéoud, Omar Gabella, Fabienne Lohou, Solène Derrien, Sylvain Rondi, Marjolaine Chiriaco, Sophie Bastin, Jean-Charles Dupont, Martial Haeffelin, Jordi Badosa, Nicolas Pascal, and Nadège Montoux
Atmos. Meas. Tech., 12, 5519–5534, https://doi.org/10.5194/amt-12-5519-2019, https://doi.org/10.5194/amt-12-5519-2019, 2019
Short summary
Short summary
In the context of an atmospheric network of instrumented sites equipped with sky cameras for cloud monitoring, we present an algorithm named ELIFAN, which aims to estimate the cloud cover amount from full-sky visible daytime images. ELIFAN is based on red-to-blue ratio thresholding applied on the image pixels and on the use of a blue-sky library. We present its principle and its performance and highlight the interest of combining several complementary instruments.
Soumyabrata Dev, Florian M. Savoy, Yee Hui Lee, and Stefan Winkler
Atmos. Meas. Tech., 12, 5417–5429, https://doi.org/10.5194/amt-12-5417-2019, https://doi.org/10.5194/amt-12-5417-2019, 2019
Short summary
Short summary
Ground-based whole-sky cameras are now extensively used for the localized monitoring of clouds. In this paper, we derive a model for estimating solar irradiance using the pictures taken by those imagers. Unlike pyranometers, these sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards short-term solar energy generation forecasting.
Penny M. Rowe, Christopher J. Cox, Steven Neshyba, and Von P. Walden
Atmos. Meas. Tech., 12, 5071–5086, https://doi.org/10.5194/amt-12-5071-2019, https://doi.org/10.5194/amt-12-5071-2019, 2019
Short summary
Short summary
A better understanding of polar clouds is needed for predicting climate change, including cloud thickness and the sizes and amounts of liquid droplets and ice crystals. These properties can be estimated from an instrument (an infrared spectrometer) that sits on the surface and measures how much infrared radiation is emitted by the cloud. In this work we use model data to investigate how well such an instrument could retrieve cloud properties for different instrument and error characteristics.
Hye-Sil Kim, Bryan A. Baum, and Yong-Sang Choi
Atmos. Meas. Tech., 12, 5039–5054, https://doi.org/10.5194/amt-12-5039-2019, https://doi.org/10.5194/amt-12-5039-2019, 2019
Short summary
Short summary
This study demonstrates that ice cloud emissivity uncertainties at 11, 12, and 13.3 µm can be used to provide a reasonable range of ice cloud layer boundaries. We test this methodology using MODIS Collection 6 cloud properties over the western North Pacific Ocean during August 2015. The cloud boundaries for single-layer optically thin ice clouds show good agreement with those from CALIOP version 4 products, with biases increasing for optically thick and multilayered clouds.
Shannon L. Mason, Robin J. Hogan, Christopher D. Westbrook, Stefan Kneifel, Dmitri Moisseev, and Leonie von Terzi
Atmos. Meas. Tech., 12, 4993–5018, https://doi.org/10.5194/amt-12-4993-2019, https://doi.org/10.5194/amt-12-4993-2019, 2019
Short summary
Short summary
The mass contents of snowflakes are critical to remotely sensed estimates of snowfall. The signatures of snow measured at three radar frequencies can distinguish fluffy, fractal snowflakes from dense and more homogeneous rimed snow. However, we show that the shape of the particle size spectrum also has a significant impact on triple-frequency radar signatures and must be accounted for when making triple-frequency radar estimates of snow that include variations in particle structure and density.
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.
Alan J. Geer, Stefano Migliorini, and Marco Matricardi
Atmos. Meas. Tech., 12, 4903–4929, https://doi.org/10.5194/amt-12-4903-2019, https://doi.org/10.5194/amt-12-4903-2019, 2019
Short summary
Short summary
Satellite radiance observations have only recently become usable in conditions of cloud and precipitation for the initialization of weather forecasts. The move to
all-skyassimilation started with data from the microwave part of the spectrum, with substantial benefit to the quality of operational forecasts. The current work shows a framework in which cloudy infrared data, with its stronger and more non-linear sensitivity, can also benefit operational-quality forecasts.
Martin Radenz, Johannes Bühl, Patric Seifert, Hannes Griesche, and Ronny Engelmann
Atmos. Meas. Tech., 12, 4813–4828, https://doi.org/10.5194/amt-12-4813-2019, https://doi.org/10.5194/amt-12-4813-2019, 2019
Short summary
Short summary
Clouds may be composed of more than one particle population even at the smallest scales. Cloud radar observations can contain information on multiple particle species, showing up as distinct peaks and subpeaks in the Doppler spectrum. We propose the use of binary tree structures to recursively structure these peaks. Two case studies from different locations and instruments illustrate how this approach can be used to disentangle particle populations in multilayered mixed-phase clouds.
Heike Kalesse, Teresa Vogl, Cosmin Paduraru, and Edward Luke
Atmos. Meas. Tech., 12, 4591–4617, https://doi.org/10.5194/amt-12-4591-2019, https://doi.org/10.5194/amt-12-4591-2019, 2019
Short summary
Short summary
In a cloud, different particles like liquid water droplets and ice particles can exist simultaneously. To study the evolution of cloud particles from cloud top to bottom one has to find out how many different types of particles with different fall velocities are present. This can be done by analyzing the number of peaks in upward-looking cloud radar Doppler spectra. A new machine-learning algorithm (named PEAKO) that determines the number of peaks is introduced and compared to existing methods.
Alexandre Guillaume, Brian H. Kahn, Eric J. Fetzer, Qing Yue, Gerald J. Manipon, Brian D. Wilson, and Hook Hua
Atmos. Meas. Tech., 12, 4361–4377, https://doi.org/10.5194/amt-12-4361-2019, https://doi.org/10.5194/amt-12-4361-2019, 2019
Short summary
Short summary
A method is described to classify cloud mixtures of cloud top types, termed cloud scenes, using cloud type classification derived from the CloudSat radar. The scale dependence of the cloud scenes is quantified. The cloud scenes are used to assess the characteristics of spatially collocated Atmospheric Infrared Sounder (AIRS) thermodynamic-phase and ice cloud property retrievals within scenes of varying cloud type complexity.
Jingjing Tian, Xiquan Dong, Baike Xi, Christopher R. Williams, and Peng Wu
Atmos. Meas. Tech., 12, 3743–3759, https://doi.org/10.5194/amt-12-3743-2019, https://doi.org/10.5194/amt-12-3743-2019, 2019
Short summary
Short summary
Liquid water path (LWP) is a combination of rain liquid water path (RLWP) and cloud liquid water path (CLWP) in stratiform precipitation systems. LWP partitioning is important but poorly understood. Here we estimate the RLWP and CLWP below the melting base simultaneously and separately using ceilometer and radar measurements. Results show that the occurrence of cloud particles below the melting base is low; however, when cloud particles exist, the CLWP value is much larger than the RLWP.
Alan J. Geer
Atmos. Meas. Tech., 12, 3629–3657, https://doi.org/10.5194/amt-12-3629-2019, https://doi.org/10.5194/amt-12-3629-2019, 2019
Short summary
Short summary
Using more satellite data in cloudy areas helps improve weather forecasts, but all-sky assimilation is still tricky, particularly for infrared data. To allow the use of hyperspectral infrared sounder radiances in all-sky conditions, an error model is developed that, in the presence of cloud, broadens the correlations between channels and increases error variances. After fixing problems of gravity wave and bias amplification, the results of all-sky assimilation trials were promising.
Tiziano Maestri, William Cossich, and Iacopo Sbrolli
Atmos. Meas. Tech., 12, 3521–3540, https://doi.org/10.5194/amt-12-3521-2019, https://doi.org/10.5194/amt-12-3521-2019, 2019
Short summary
Short summary
An innovative and flexible methodology for cloud identification and classification, CIC, is tested on a synthetic dataset of high spectral resolution radiances in the far- and mid-infrared part of the spectrum, simulating measurements from the FORUM (Far Infrared Outgoing Radiation Understanding and Monitoring) mission. Results show that classification scores are greatly increased when far-infrared channels are accounted for and the identification of thin cirrus clouds is improved.
Marek Jacob, Felix Ament, Manuel Gutleben, Heike Konow, Mario Mech, Martin Wirth, and Susanne Crewell
Atmos. Meas. Tech., 12, 3237–3254, https://doi.org/10.5194/amt-12-3237-2019, https://doi.org/10.5194/amt-12-3237-2019, 2019
Short summary
Short summary
Tropical clouds are a key climate component but are still not fully understood. Therefore, we analyze airborne remote sensing measurements that were taken in the dry and wet seasons over the Atlantic east of Barbados. From these we derive sub-kilometer resolution data of vertically integrated atmospheric water vapor and liquid water. Results show that although the humidity is lower in the dry season, clouds are more frequent, contain more water, and produce more rain than in the wet season.
Imane Farouk, Nadia Fourrié, and Vincent Guidard
Atmos. Meas. Tech., 12, 3001–3017, https://doi.org/10.5194/amt-12-3001-2019, https://doi.org/10.5194/amt-12-3001-2019, 2019
Short summary
Short summary
A selection for homogeneous scenes for the assimilation of IASI radiances is proposed by using information on the collocated imager pixels inside each infrared observation. A revised method for the selection, which represents a compromise between two methods to select homogeneous scenes using homogeneity criteria already proposed in the literature, has a positive impact on the observation minus the simulation statistics. It has been tested in a numerical weather prediction model for clear sky.
Nikos Benas, Jan Fokke Meirink, Martin Stengel, and Piet Stammes
Atmos. Meas. Tech., 12, 2863–2879, https://doi.org/10.5194/amt-12-2863-2019, https://doi.org/10.5194/amt-12-2863-2019, 2019
Short summary
Short summary
Cloud glory and bow phenomena cause irregularities in satellite-based retrievals of cloud optical and microphysical properties. Here we combine two geostationary satellites over the same areas to analyze retrievals under those conditions. Results show a high sensitivity of retrievals to the assumed width of the cloud droplet size distribution and provide insights into possible improvements in satellite retrievals by appropriately adjusting this assumed parameter.
Quitterie Cazenave, Marie Ceccaldi, Julien Delanoë, Jacques Pelon, Silke Groß, and Andrew Heymsfield
Atmos. Meas. Tech., 12, 2819–2835, https://doi.org/10.5194/amt-12-2819-2019, https://doi.org/10.5194/amt-12-2819-2019, 2019
Short summary
Short summary
The impact of ice clouds on the water cycle and radiative budget is still uncertain due to the complexity of cloud processes that makes it difficult to acquire adequate observations of ice cloud properties and parameterize them into climate and weather prediction models. In this paper we present the latest refinements brought to the DARDAR-CLOUD product, which contains ice cloud microphysical properties retrieved from the cloud radar and lidar measurements from the A-Train space mission.
Marine Desmons, Ping Wang, Piet Stammes, and L. Gijsbert Tilstra
Atmos. Meas. Tech., 12, 2485–2498, https://doi.org/10.5194/amt-12-2485-2019, https://doi.org/10.5194/amt-12-2485-2019, 2019
Short summary
Short summary
The FRESCO algorithm is a simple, fast and robust algorithm used to retrieve cloud information during operational satellite data processing. FRESCO retrieves effective cloud fraction and cloud pressure from measurements in the oxygen A band around 761 nm. In this paper, we propose a new version of the algorithm, called FRESCO-B, which is based on measurements in the oxygen B band around 687 nm. Such a method leads to more accurate retrievals for vegetated surfaces.
Shan Zeng, Mark Vaughan, Zhaoyan Liu, Charles Trepte, Jayanta Kar, Ali Omar, David Winker, Patricia Lucker, Yongxiang Hu, Brian Getzewich, and Melody Avery
Atmos. Meas. Tech., 12, 2261–2285, https://doi.org/10.5194/amt-12-2261-2019, https://doi.org/10.5194/amt-12-2261-2019, 2019
Short summary
Short summary
We use a fuzzy k-means (FKM) classifier to assess the ability of the CALIPSO cloud–aerosol discrimination (CAD) algorithm to correctly distinguish between clouds and aerosols detected in the CALIPSO lidar backscatter signals. FKM is an unsupervised learning algorithm, so the classifications it derives are wholly independent from those reported by the CAD scheme. For a full month of measurements, the two techniques agree in ~ 95 % of all cases, providing strong evidence for CAD correctness.
Yuekui Yang, Kerry Meyer, Galina Wind, Yaping Zhou, Alexander Marshak, Steven Platnick, Qilong Min, Anthony B. Davis, Joanna Joiner, Alexander Vasilkov, David Duda, and Wenying Su
Atmos. Meas. Tech., 12, 2019–2031, https://doi.org/10.5194/amt-12-2019-2019, https://doi.org/10.5194/amt-12-2019-2019, 2019
Short summary
Short summary
The physical basis of the EPIC cloud product algorithms and an initial evaluation of their performance are presented. EPIC cloud products include cloud mask, effective height, and optical depth. Comparison with co-located retrievals from geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellites shows that the algorithms are performing well and are consistent with theoretical expectations. These products are publicly available at the NASA Langley Atmospheric Sciences Data Center.
Christoph Böhm, Odran Sourdeval, Johannes Mülmenstädt, Johannes Quaas, and Susanne Crewell
Atmos. Meas. Tech., 12, 1841–1860, https://doi.org/10.5194/amt-12-1841-2019, https://doi.org/10.5194/amt-12-1841-2019, 2019
Short summary
Short summary
The cloud base height (CBH) is important for air traffic, for describing the energy budget of the Earth and for other applications. Ground-based CBH measurements are only available for individual sites and mostly limited to land. Satellites are a powerful tool for global coverage. While the cloud top height is derived operationally, the derivation of CBH from space is more difficult as the clouds hide their base. Here, we present a method to retrieve the CBH from multi-angle satellite data.
Antonio Di Noia, Otto P. Hasekamp, Bastiaan van Diedenhoven, and Zhibo Zhang
Atmos. Meas. Tech., 12, 1697–1716, https://doi.org/10.5194/amt-12-1697-2019, https://doi.org/10.5194/amt-12-1697-2019, 2019
Short summary
Short summary
We present a neural network algorithm for the retrieval of cloud physical properties from multi-angle polarimetric measurements. We have trained the algorithm on a large dataset of synthetic measurements and applied it to a year of POLDER-3 data. A comparison against MODIS cloud products reveals that our algorithm is capable of performing cloud property retrievals on a global scale and possibly improves the estimates of cloud effective radius over land with respect to existing POLDER-3 products.
Cited articles
Barrett, A. I., Westbrook, C. D., Nicol, J. C., and Stein, T. H. M.: Rapid ice aggregation process revealed through triple-wavelength Doppler spectrum radar analysis, Atmos. Chem. Phys., 19, 5753–5769, https://doi.org/10.5194/acp-19-5753-2019, 2019. a
Battaglia, A., Kummerow, C., Shin, D.-B., and Williams, C.: Toward
characterizing the effect of radar bright bands on microwave brightness
temperatures, J. Atmos. Ocean. Technol., 20, 856–871,
https://doi.org/10.1175/1520-0426(2003)020<0856:CMBTBR>2.0.CO;2, 2003. a
Battaglia, A., Westbrook, C. D., Kneifel, S., Kollias, P., Humpage, N., Löhnert, U., Tyynelä, J., and Petty, G. W.: G band atmospheric radars: new frontiers in cloud physics, Atmos. Meas. Tech., 7, 1527–1546, https://doi.org/10.5194/amt-7-1527-2014, 2014. a, b, c
Battaglia, A., Mroz, K., Lang, T., Tridon, F., Tanelli, S., Tian, L., and
Heymsfield, G. M.: Using a multiwavelength suite of microwave instruments to
investigate the microphysical structure of deep convective cores, J. Geophys. Res.-Atmos., 121,
9356–9381, https://doi.org/10.1002/2016JD025269, 2016. a, b, c
Battaglia, A., Kollias, P., Dhillon, R., Roy, R., Tanelli, S., Lamer, K.,
Grecu, M., Lebsock, M., Watters, D., Mroz, K., Heymsfield, G., Li, L., and
Furukawa, K.: Spaceborne Cloud and Precipitation Radars: Status, Challenges,
and Ways Forward, Rev. Geophys., 58, e2019RG000686,
https://doi.org/10.1029/2019RG000686, 2020a. a, b, c, d, e, f, g
Cadeddu, M. and Ghate, V.: Microwave Radiometer, 3 Channel (MWR3C),
2014-02-21 to 2014-03-22, ARM Mobile Facility (TMP) University of Helsinki Research
Station (SMEAR II), Hyytiala, Finland; AMF2 (M1), Atmospheric Radiation
Measurement (ARM) user facility, https://doi.org/10.5439/1025248, 2014a. a, b
Cadeddu, M. and Ghate, V.: Microwave Radiometer (MWRLOS), 2014-02-21 to 2014-03-22, ARM Mobile Facility (TMP) U. of Helsinki Research Station (SMEAR II), Hyytiala, Finland; AMF2 (M1), Atmospheric Radiation Measurement (ARM) user facility, https://doi.org/10.5439/1046211,
2014b. a, b
Chase, R. J., Finlon, J. A., Borque, P., McFarquhar, G. M., Nesbitt, S. W.,
Tanelli, S., Sy, O. O., Durden, S. L., and Poellot, M. R.: Evaluation of
Triple-Frequency Radar Retrieval of Snowfall Properties Using Coincident
Airborne In Situ Observations During OLYMPEX, Geophys. Res. Lett., 45,
5752–5760, https://doi.org/10.1029/2018GL077997, 2018. a
Cooper, K. B., Rodriguez Monje, R., Millán, L., Lebsock, M.,
Tanelli, S., Siles, J. V., Lee, C., and Brown, A.: Atmospheric
Humidity Sounding Using Differential Absorption Radar Near 183 GHz, IEEE
Geosci. Remote Sens. Lett., 15, 163–167,
https://doi.org/10.1109/LGRS.2017.2776078, 2018. a
Delrieu, G., Caoudal, S., and Creutin, J. D.: Feasibility of Using Mountain
Return for the Correction of Ground-Based X-Band Weather Radar Data, J.
Atmos. Ocean. Technol., 14, 368–385,
https://doi.org/10.1175/1520-0426(1997)014<0368:FOUMRF>2.0.CO;2, 1997. a
Dias Neto, J., Kneifel, S., Ori, D., Trömel, S., Handwerker, J., Bohn, B., Hermes, N., Mühlbauer, K., Lenefer, M., and Simmer, C.: The TRIple-frequency and Polarimetric radar Experiment for improving process observations of winter precipitation, Earth Syst. Sci. Data, 11, 845–863, https://doi.org/10.5194/essd-11-845-2019, 2019. a, b, c, d, e, f
Duncan, D. I. and Eriksson, P.: An update on global atmospheric ice estimates from satellite observations and reanalyses, Atmos. Chem. Phys., 18, 11205–11219, https://doi.org/10.5194/acp-18-11205-2018, 2018. a, b
Ellison, W. J.: Permittivity of Pure Water, at Standard Atmospheric Pressure,
over the Frequency Range 0–25THz and the Temperature Range 0–100 ∘C, J. Phys. Chem. Ref. Data, 36, 1–18,
https://doi.org/10.1063/1.2360986, 2007. a, b, c, d
Firda, J. M., Sekelsky, S. M., and McIntosh, R. E.: Application of
Dual-Frequency Millimeter-Wave Doppler Spectra for the Retrieval of Drop Size
Distributions and Vertical Air Motion in Rain, J. Atmos.
Ocean. Technol., 16, 216–236,
https://doi.org/10.1175/1520-0426(1999)016<0216:AODFMW>2.0.CO;2, 1999. a
Grecu, M., Tian, L., Heymsfield, G. M., Tokay, A., Olson, W. S., Heymsfield,
A. J., and Bansemer, A.: Nonparametric Methodology to Estimate Precipitating
Ice from Multiple-Frequency Radar Reflectivity Observations, J.
Appl. Meteorol. Climatol., 57, 2605–2622,
https://doi.org/10.1175/JAMC-D-18-0036.1, 2018. a
Haynes, J. M., L'Ecuyer, T. S., Stephens, G. L., Miller, S. D., Mitrescu, C.,
Wood, N. B., and Tanelli, S.: Rainfall retrieval over the ocean with
spaceborne W-band radar, J. Geophys. Res.-Atmos., 114, D00A22, https://doi.org/10.1029/2008JD009973, 2009. a, b
Hitschfeld, W. and Bordan, J.: Errors Inherent in the Radar Measurement of
Rainfall at Attenuating Wavelengths, J. Meteorology, 11, 58–67,
https://doi.org/10.1175/1520-0469(1954)011<0058:EIITRM>2.0.CO;2, 1954. a
Hogan, R. J. and Illingworth, A. J.: The Potential of Spaceborne
Dual-Wavelength Radar to Make Global Measurements of Cirrus Clouds, J. Atmos. Ocean. Technol., 16, 518–531,
https://doi.org/10.1175/1520-0426(1999)016<0518:TPOSDW>2.0.CO;2, 1999. a
Hogan, R. J., Illingworth, A. J., and Sauvageot, H.: Measuring Crystal Size in Cirrus Using 35- and 94-GHz Radars, J. Atmos. Ocean.
Technol., 17, 27–37,
https://doi.org/10.1175/1520-0426(2000)017<0027:MCSICU>2.0.CO;2, 2000. a
Hogan, R. J., Mittermaier, M. P., and Illingworth, A. J.: The retrieval of ice
water content from radar reflectivity factor and temperature and its use in
the evaluation of a mesoscale model, J. Appl. Meteorol., 45, 301–317,
https://doi.org/10.1175/JAM2340.1, 2006. a
Houze, R. A.: Cloud Dynamics, International Geophysics Series, Academic
Press, 2014. a
Huang, D., Johnson, K., Liu, Y., and Wiscombe, W.: High resolution retrieval of
liquid water vertical distributions using collocated Ka-band and W-band cloud
radars, Geophys. Res. Lett., 36, L24807, https://doi.org/10.1029/2009GL041364, 2009. a, b
Iguchi, T. and Matsui, T.: Remote Sensing of Clouds and Precipitation, chap.
Advances in Clouds and Precipitation Modeling Supported by Remote Sensing
Measurements, Springer Remote Sensing/Photogrammetry, Springer, Cham, 2018. a
Iguchi, T. and Meneghini, R.: Intercomparison of Single-Frequency Methods for
Retrieving a Vertical Rain Profile from Airborne or Spaceborne Radar Data,
J. Atmos. Ocean. Technol., 11, 1507–1516,
https://doi.org/10.1175/1520-0426(1994)011<1507:IOSFMF>2.0.CO;2, 1994. a
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, https://doi.org/10.1017/CBO9781107415324, 2013. a
Isom, B., Lindenmaier, I., and Matthews, A.: Marine W-Band (95 GHz) ARM Cloud Radar (MWACR), 2014-02-21 to 2014-03-22, ARM Mobile Facility (TMP) University of Helsinki Research Station (SMEAR II), Hyytiala, Finland; AMF2 (M1), Atmospheric Radiation Measurement (ARM) user facility, https://doi.org/10.5439/1150242, 2014a. a, b
Isom, B., Lindenmaier, I., Nelson, D., and Matthews, A.: Ka ARM Zenith
Radar (KAZR), 2014-02-21 to 2014-03-22, ARM Mobile Facility (TMP), Univrsity of
Helsinki Research Station (SMEAR II), Hyytiala, Finland; AMF2 (M1),
Atmospheric Radiation Measurement (ARM) user facility, https://doi.org/10.5439/1095601, 2014b. a, b
Kalesse, H., Szyrmer, W., Kneifel, S., Kollias, P., and Luke, E.: Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling, Atmos. Chem. Phys., 16, 2997–3012, https://doi.org/10.5194/acp-16-2997-2016, 2016. a, b, c
Kneifel, S., Löhnert, U., Battaglia, A., Crewell, S., and Siebler, D.: Snow
scattering signals in ground-based passive microwave radiometer measurements,
J. Geophys. Res., 115, D16214, https://doi.org/10.1029/2010JD013856, 2010. a
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., 120, 6034–6055,
https://doi.org/10.1002/2015JD023156, 2015. a, b
Kneifel, S., Kollias, P., Battaglia, A., Leinonen, J., Maahn, M., Kalesse, H.,
and Tridon, F.: First observations of triple-frequency radar Doppler spectra
in snowfall: Interpretation and applications, Geophys. Res. Lett., 43,
2225–2233, https://doi.org/10.1002/2015GL067618, 2016. a
Kneifel, S., Leinonen, J., Tyynelä, J., Ori, D., and Battaglia, A.: Scattering of Hydrometeors, in: Satellite Precipitation Measurement, Advances in Global Change Research, edited by: Levizzani, V., Kidd, C., Kirschbaum, D., Kummerow, C., Nakamura, K., and Turk, F., Vol 67. Springer, Cham., https://doi.org/10.1007/978-3-030-24568-9_15, 2020. a, b
Kollias, P., Clothiaux, E. E., Miller, M. A., Albrecht, B. A., Stephens, G. L.,
and Ackerman, T. P.: Millimeter-Wavelength Radars: New Frontier in
Atmospheric Cloud and Precipitation Research, B. Am. Meteorol. Soc.,
88, 1608–1624, https://doi.org/10.1175/BAMS-88-10-1608, 2007. a
Kollias, P., Bharadwaj, N., Clothiaux, E. E., Lamer, K., Oue, M., Hardin, J.,
Isom, B., Lindenmaier, I., Matthews, A., Luke, E. P., Giangrande, S. E.,
Johnson, K., Collis, S., Comstock, J., and Mather, J. H.: The ARM Radar
Network: At the Leading Edge of Cloud and Precipitation Observations,
B. Am. Meteorol. Soc., 101, E588–E607,
https://doi.org/10.1175/BAMS-D-18-0288.1, 2020. a
Küchler, N., Kneifel, S., Löhnert, U., Kollias, P., Czekala, H., and Rose,
T.: A W-Band Radar-Radiometer System for Accurate and Continuous
Monitoring of Clouds and Precipitation, J. Atmos. Ocean. Technol., 34,
2375–2392, https://doi.org/10.1175/JTECH-D-17-0019.1, 2017. a
Kulie, M. S., Hiley, M. J., Bennartz, R., Kneifel, S., and Tanelli, S.:
Triple-Frequency Radar Reflectivity Signatures of Snow: Observations and
Comparisons with Theoretical Ice Particle Scattering Models, J. Appl. Meteor. Climatol., 53, 1080–1098,
https://doi.org/10.1175/JAMC-D-13-066.1, 2014. a
Kumjian, M. R., Rutledge, S. A., Rasmussen, R. M., Kennedy, P. C., and Dixon,
M.: High-Resolution Polarimetric Radar Observations of Snow-Generating Cells, J. Appl. Meteor. Climatol.,
53, 1636–1658, https://doi.org/10.1175/JAMC-D-13-0312.1, 2014. a, b
L'Ecuyer, T. S. and Stephens, G. L.: An Estimation-Based Precipitation
Retrieval Algorithm for Attenuating Radars, J. Appl. Meteorol., 41,
272–285, https://doi.org/10.1175/1520-0450(2002)041<0272:AEBPRA>2.0.CO;2, 2002. a, b
L’Ecuyer, T. S., Beaudoing, H. K., Rodell, M., Olson, W., Lin, B., Kato, S.,
Clayson, C. A., Wood, E., Sheffield, J., Adler, R., Huffman, G., Bosilovich,
M., Gu, G., Robertson, F., Houser, P. R., Chambers, D., Famiglietti, J. S.,
Fetzer, E., Liu, W. T., Gao, X., Schlosser, C. A., Clark, E., Lettenmaier,
D. P., and Hilburn, K.: The Observed State of the Energy Budget in the Early
Twenty-First Century, J. Climate, 28, 8319–8346, https://doi.org/10.1175/JCLI-D-14-00556.1, 2015. a
Leinonen, J., Lebsock, M. D., Tanelli, S., Sy, O. O., Dolan, B., Chase, R. J., Finlon, J. A., von Lerber, A., and Moisseev, D.: Retrieval of snowflake microphysical properties from multifrequency radar observations, Atmos. Meas. Tech., 11, 5471–5488, https://doi.org/10.5194/amt-11-5471-2018, 2018. a, b
Lhermitte, R.: Attenuation and Scattering of Millimeter Wavelength Radiation
by Clouds and Precipitation, J. Atmos. Ocean. Technol., 7, 464–479,
https://doi.org/10.1175/1520-0426(1990)007<0464:AASOMW>2.0.CO;2, 1990. a, b
Li, H. and Moisseev, D.: Melting Layer Attenuation at Ka- and W-Bands as
Derived From Multifrequency Radar Doppler Spectra Observations, J.
Geophys. Res.-Atmos., 124, 9520–9533,
https://doi.org/10.1029/2019JD030316, 2019. a, b, c, d
Liao, L. and Meneghini, R.: A Study on the Feasibility of Dual-Wavelength
Radar for Identification of Hydrometeor Phases, 50, 449–456,
https://doi.org/10.1175/2010JAMC2499.1, 2011. a
Liao, L. and Meneghini, R.: Physical Evaluation of GPM DPR Single- and
Dual-Wavelength Algorithms, J. Atmos. Ocean. Technol., 36, 883–902,
https://doi.org/10.1175/JTECH-D-18-0210.1, 2019. a, b
Löhnert, U. and Crewell, S.: Accuracy of cloud liquid water path from
ground-based microwave radiometry 1. Dependency on cloud model statistics,
Radio Sci., 38, 8041, https://doi.org/10.1029/2002RS002654, 2003. a
Löhnert, U., Schween, J. H., Acquistapace, C., Ebell, K., Maahn, M.,
Barrera-Verdejo, M., Hirsikko, A., Bohn, B., Knaps, A., O'Connor, E., Simmer,
C., Wahner, A., and Crewell, S.: JOYCE: Jülich Observatory for Cloud
Evolution, B. Am. Meteorol. Soc., 96, 1157–1174,
https://doi.org/10.1175/BAMS-D-14-00105.1, 2015. a, b
Lubin, D., Zhang, D., Silber, I., Scott, R. C., Kalogeras, P., Battaglia, A.,
Bromwich, D. H., Cadeddu, M., Eloranta, E., Fridlind, A., Frossard, A.,
Hines, K. M., Kneifel, S., Leaitch, W. R., Lin, W., Nicolas, J., Powers, H.,
Quinn, P. K., Rowe, P., Russell, L. M., Sharma, S., Verlinde, J., and
Vogelmann, A. M.: AWARE: The Atmospheric Radiation Measurement (ARM) West
Antarctic Radiation Experiment, B. Am. Meteorol.
Soc., B. Am. Meteorol. Soc., 101, E1069–E1091, https://doi.org/10.1175/BAMS-D-18-0278.1, 2020. a
Luke, E. P., Kollias, P., and Shupe, M. D.: Detection of supercooled liquid in
mixed-phase clouds using radar Doppler spectra, J. Geophys.
Res.-Atmos., 115, D19201, https://doi.org/10.1029/2009JD012884, 2010. a
Marzoug, M. and Amayenc, P.: A Class of Single- and Dual-Frequency Algorithms
for Rain-Rate Profiling from a Spaceborne Radar. Pad I: Principle and Tests
from Numerical Simulations, J. Atmos. Ocean. Technol., 11, 1480–1506,
https://doi.org/10.1175/1520-0426(1994)011<1480:ACOSAD>2.0.CO;2, 1994. a
Mason, S. L., Chiu, J. C., Hogan, R. J., and Tian, L.: Improved rain rate and drop size retrievals from airborne Doppler radar, Atmos. Chem. Phys., 17, 11567–11589, https://doi.org/10.5194/acp-17-11567-2017, 2017. a
Mason, S. L., Hogan, R. J., Westbrook, C. D., Kneifel, S., Moisseev, D., and von Terzi, L.: The importance of particle size distribution and internal structure for triple-frequency radar retrievals of the morphology of snow, Atmos. Meas. Tech., 12, 4993–5018, https://doi.org/10.5194/amt-12-4993-2019, 2019. a
Matrosov, S.: Attenuation-Based Estimates of Rainfall Rates Aloft with
Vertically Pointing Ka-Band Radars, J. Atmos. Ocean. Technol., 22, 43–54,
https://doi.org/10.1175/JTECH-1677.1, 2005. a, b
Matrosov, S.: Assessment of Radar Signal Attenuation Caused by the Melting
Hydrometeor Layer, IEEE Trans. Geosci. Remote Sens., 46, 1039–1047, https://doi.org/10.1109/TGRS.2008.915757, 2008. a
Matrosov, S., May, P., and Shupe, M.: Rainfall Profiling Using Atmospheric
Radiation Measurement Program Vertically Pointing 8-mm Wavelength Radars, J.
Atmos. Ocean. Technol., 23, 1478–1491, https://doi.org/10.1175/JTECH1957.1, 2006. a, b
Matrosov, S. Y.: Modeling Backscatter Properties of Snowfall at Millimeter
Wavelengths, J. Atmos. Sci., 64, 1727–1736,
https://doi.org/10.1175/JAS3904.1, 2007. a
Matrosov, S. Y.: Feasibility of using radar differential Doppler velocity and
dual-frequency ratio for sizing particles in thick ice clouds, J.
Geophys. Res.-Atmos., 116, D17202, https://doi.org/10.1029/2011JD015857, 2011. a
Matrosov, S. Y.: Characteristic Raindrop Size Retrievals from Measurements of
Differences in Vertical Doppler Velocities at Ka- and W-Band Radar
Frequencies, J. Atmos. Ocean. Technol., 34, 65–71,
https://doi.org/10.1175/JTECH-D-16-0181.1, 2017. a
Matrosov, S. Y. and Turner, D. D.: Retrieving Mean Temperature of Atmospheric
Liquid Water Layers Using Microwave Radiometer Measurements, J. Atmos.
Ocean. Technol., 35, 1091–1102, https://doi.org/10.1175/JTECH-D-17-0179.1, 2018. a
Matrosov, S. Y., Maahn, M., and de Boer, G.: Observational and Modeling Study
of Ice Hydrometeor Radar Dual-Wavelength Ratios, J. Appl. Meteor. Climatol., 58, 2005–2017,
https://doi.org/10.1175/JAMC-D-19-0018.1, 2019. a
Meneghini, R., Iguchi, T., Kozu, T., Liao, L., Okamoto, K., Jones, J. A., and
Kwiatkowski, J.: Use of the Surface Reference Technique for Path Attenuation
Estimates from the TRMM Precipitation Radar, J. Appl. Meteorol., 39,
2053–2070, https://doi.org/10.1175/1520-0450(2001)040<2053:UOTSRT>2.0.CO;2, 2000. a
Meneghini, R., Kim, H., Liao, L., Jones, J. A., and Kwiatkowski, J. M.: An
Initial Assessment of the Surface Reference Technique Applied to Data from
the Dual-Frequency Precipitation Radar (DPR) on the GPM Satellite, J. Atmos.
Ocean. Technol., 32, 2281–2296, https://doi.org/10.1175/JTECH-D-15-0044.1, 2015. a
Mróz, K., Battaglia, A., Kneifel, S., D'Adderio, L. P., and Dias Neto, J.:
Triple-Frequency Doppler Retrieval of Characteristic Raindrop Size, Earth
Space Sci., 7, e2019EA000789, https://doi.org/10.1029/2019EA000789, 2020. a
Nemarich, J., Wellman, R. J., and Lacombe, J.: Backscatter and
attenuation by falling snow and rain at 96, 140, and 225 GHz, IEEE Trans. Geosci. Remote Sens., 26, 319–329,
https://doi.org/10.1109/36.3034, 1988. a, b
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
Protat, A., Delanoë, J., Bouniol, D., Heymsfield, A. J.,
Bansemer, A., and Brown, P.: Evaluation of Ice Water Content Retrievals
from Cloud Radar Reflectivity and Temperature Using a Large Airborne In Situ
Microphysical Database, J. Appl. Meteorol. Climatol., 46,
557–572, https://doi.org/10.1175/JAM2488.1, 2007. a, b
Protat, A., Rauniyar, S., Delanoë, J., Fontaine, E., and Schwarzenboeck, A.:
W-Band (95 GHz) Radar Attenuation in Tropical Stratiform Ice Anvils, J.
Atmos. Ocean. Technol., 36, 1463–1476, https://doi.org/10.1175/JTECH-D-18-0154.1,
2019. a, b
Rose, T., Crewell, S., Löhnert, U., and Simmer, C.: A network
suitable microwave radiometer for operational monitoring of the cloudy
atmosphere, Atmos. Res., 75, 183–200,
https://doi.org/10.1016/j.atmosres.2004.12.005, 2005. a
Rosenkranz, P. W.: A Model for the Complex Dielectric Constant of
Supercooled Liquid Water at Microwave Frequencies, IEEE Trans.
Geosci. Remote Sens., 53, 1387–1393,
https://doi.org/10.1109/TGRS.2014.2339015, 2015. a, b, c
Roy, R. J., Lebsock, M., Millán, L., Dengler, R., Rodriguez Monje, R., Siles, J. V., and Cooper, K. B.: Boundary-layer water vapor profiling using differential absorption radar, Atmos. Meas. Tech., 11, 6511–6523, https://doi.org/10.5194/amt-11-6511-2018, 2018. a
Serrar, S., Delrieu, G., Creutin, J.-D., and Uijlenhoet, R.: Mountain
reference technique: Use of mountain returns to calibrate weather radars
operating at attenuating wavelengths, J. Geophys. Res.-Atmos., 105, 2281–2290,
https://doi.org/10.1029/1999JD901025, 2000. a
Shupe, M. D., Kollias, P., Matrosov, S. Y., and Schneider, T. L.: Deriving
Mixed-Phase Cloud Properties from Doppler Radar Spectra, J.
Atmos. Ocean. Technol., 21, 660–670,
https://doi.org/10.1175/1520-0426(2004)021<0660:DMCPFD>2.0.CO;2, 2004. a
Shupe, M. D., Daniel, J. S., de Boer, G., Eloranta, E. W., Kollias, P., Long,
C. N., Luke, E. P., Turner, D. D., and Verlinde, J.: A Focus On Mixed-Phase
Clouds, B. Am. Meteorol. Soc., 89, 1549–1562,
https://doi.org/10.1175/2008BAMS2378.1, 2008. a
Simmer, C., Thiele-Eich, I., Masbou, M., Amelung, W., Bogena, H., Crewell, S.,
Diekkrüger, B., Ewert, F., Hendricks Franssen, H.-J., Huisman, J. A., Kemna,
A., Klitzsch, N., Kollet, S., Langensiepen, M., Löhnert, U., Rahman, A. S.
M. M., Rascher, U., Schneider, K., Schween, J., Shao, Y., Shrestha, P.,
Stiebler, M., Sulis, M., Vanderborght, J., Vereecken, H., van der Kruk, J.,
Waldhoff, G., and Zerenner, T.: Monitoring and Modeling the Terrestrial
System from Pores to Catchments: The Transregional Collaborative Research
Center on Patterns in the Soil–Vegetation–Atmosphere System, B.
Am. Meteorol. Soc., 96, 1765–1787,
https://doi.org/10.1175/BAMS-D-13-00134.1, 2015. a
Stephens, G. L., Li, J., Wild, M., Clayson, C., Loeb, N., Kato, S., L'Ecuyer,
T., Stackhouse, P., Lebsock, M., and Andrews, T.: An update on Earth's
energy balance in light of the latest global observations, Nat.
Geosci., 5, 691–696, https://doi.org/10.1038/ngeo1580, 2012. a
Tridon, F. and Battaglia, A.: Dual-frequency radar Doppler spectral retrieval
of rain drop size distributions and entangled dynamics variables, J. Geophys. Res.-Atmos., 120,
5585–5601, https://doi.org/10.1002/2014JD023023, 2015. a, b
Tridon, F., Battaglia, A., and Kollias, P.: Disentangling Mie and attenuation
effects in rain using a Ka-W dual-wavelength Doppler spectral ratio
technique, Geophys. Res. Lett., 40, 5548–5552, https://doi.org/10.1002/2013GL057454,
2013a. a, b
Tridon, F., Battaglia, A., Kollias, P., Luke, E., and Williams, C. R.: Signal
Postprocessing and Reflectivity Calibration of the Atmospheric Radiation
Measurement Program 915-MHz Wind Profilers, J. Atmos. Ocean. Technol., 30,
1038–1054, https://doi.org/10.1175/JTECH-D-12-00146.1, 2013b. a
Tridon, F., Battaglia, A., Luke, E., and Kollias, P.: Rain retrieval from
dual-frequency radar Doppler spectra: validation and potential for a
midlatitude precipitating case-study, Q. J. Roy. Meteorol. Soc., 143,
1364–1380, https://doi.org/10.1002/qj.3010, 2017a. a, b, c, d
Tridon, F., Battaglia, A., and Watters, D.: Evaporation in action sensed by
multiwavelength Doppler radars, J. Geophys. Res.-Atmos., 122, 9379–9390, https://doi.org/10.1002/2016JD025998,
2017b. a
Tridon, F., Battaglia, A., Chase, R. J., Turk, F. J., Leinonen, J., Kneifel,
S., Mroz, K., Finlon, J., Bansemer, A., Tanelli, S., Heymsfield, A. J., and
Nesbitt, S. W.: The Microphysics of Stratiform Precipitation During OLYMPEX:
Compatibility Between Triple-Frequency Radar and Airborne In Situ
Observations, J. Geophys. Res.-Atmos., 124, 8764–8792, https://doi.org/10.1029/2018JD029858,
2019a. a
Tridon, F., Planche, C., Mroz, K., Banson, S., Battaglia, A., Van Baelen, J.,
and Wobrock, W.: On the Realism of the Rain Microphysics Representation of a
Squall Line in the WRF Model. Part I: Evaluation with Multifrequency Cloud
Radar Doppler Spectra Observations, Mon. Weather Rev., 147, 2787–2810,
https://doi.org/10.1175/MWR-D-18-0018.1, 2019b.
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. Climatol.,
56, 1561–1582, https://doi.org/10.1175/JAMC-D-16-0379.1, 2017. a, b, c
Wallace, H. B.: Millimeter-wave propagation measurements at the Ballistic
Research Laboratory, IEEE Trans. Geosci. Remote Sens., 26, 253–258, https://doi.org/10.1109/36.3028, 1988. a
Wild, M., Folini, D., Schär, C., Loeb, N., Dutton, E. G., and
König-Langlo, G.: The global energy balance from a surface perspective,
Clim. Dynam., 40, 3107–3134, https://doi.org/10.1007/s00382-012-1569-8, 2013. a
Williams, C. R., Beauchamp, R. M., and Chandrasekar, V.: Vertical Air
Motions and Raindrop Size Distributions Estimated Using Mean Doppler Velocity
Difference From 3- and 35-GHz Vertically Pointing Radars, IEEE Trans. Geosci. Remote Sens., 54, 6048–6060,
https://doi.org/10.1109/TGRS.2016.2580526, 2016. a
Zelinka, M. D., Randall, D. A., Webb, M. J., and Klein, S. A.: Clearing clouds
of uncertainty, Nat. Clim. Change, 7, 674–678,
https://doi.org/10.1038/nclimate3402, 2017. a
Zhu, Z., Lamer, K., Kollias, P., and Clothiaux, E. E.: The Vertical Structure
of Liquid Water Content in Shallow Clouds as Retrieved From Dual-Wavelength
Radar Observations, J. Geophys. Res.-Atmos., 124, 14184–14197, https://doi.org/10.1029/2019JD031188, 2019. a, b
Search articles
Download
Short summary
The droplets and ice crystals composing clouds and precipitation interact with microwaves and can therefore be observed by radars, but they can also attenuate the signal they emit. By combining the observations made by two ground-based radars, this study describes an original approach for estimating such attenuation. As a result, the latter can be not only corrected in the radar observations but also exploited for providing an accurate characterization of droplet and ice crystal properties.
The droplets and ice crystals composing clouds and precipitation interact with microwaves and...
Atmospheric Measurement Techniques
An interactive open-access journal of the European Geosciences Union
All site content, except where otherwise noted, is licensed under the Creative Commons Attribution 4.0 License.