Articles | Volume 16, issue 6
https://doi.org/10.5194/amt-16-1723-2023
© Author(s) 2023. 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-16-1723-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2023
Research article |
| 31 Mar 2023
| 31 Mar 2023
Simulation and sensitivity analysis for cloud and precipitation measurements via spaceborne millimeter-wave radar
Leilei Kou
CORRESPONDING AUTHOR
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of
China Meteorological Administration, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Zhengjian Lin
School of Atmospheric Physics, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Haiyang Gao
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of
China Meteorological Administration, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Shujun Liao
School of Atmospheric Physics, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Piman Ding
The First Research Department, Shanghai Institute of Satellite Engineering, Shanghai 201109, China
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations
Near-global distributions of overshooting tops derived from Terra and Aqua MODIS observations
Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy
Optimizing cloud motion estimation on the edge with phase correlation and optical flow
A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations
The CHROMA cloud-top pressure retrieval algorithm for the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission
High-spatial-resolution retrieval of cloud droplet size distribution from polarized observations of the cloudbow
Evaluation of the spectral misalignment on the Earth Clouds, Aerosols and Radiation Explorer/multi-spectral imager cloud product
Retrieval of terahertz ice cloud properties from airborne measurements based on the irregularly shaped Voronoi ice scattering models
Evaluation of polarimetric ice microphysical retrievals with OLYMPEX campaign data
Insights into 3D cloud radiative transfer for OCO-2
Latent heating profiles from GOES-16 and its impacts on precipitation forecasts
A CO2-independent cloud mask from Infrared Atmospheric Sounding Interferometer (IASI) radiances for climate applications
Across-track Extension of Retrieved Cloud and Aerosol Properties for the EarthCARE Mission: The ACM-3D Product
Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network
Intercomparison of Sentinel-5P TROPOMI cloud products for tropospheric trace gas retrievals
Improved spectral processing for a multi-mode pulse compression Ka–Ku-band cloud radar system
Uncertainty-bounded estimates of ash cloud properties using the ORAC algorithm: application to the 2019 Raikoke eruption
Ice water path retrievals from Meteosat-9 using quantile regression neural networks
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation
An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties
Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks
Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network
Top-of-the-atmosphere reflected shortwave radiative fluxes from GOES-R
Optimizing radar scan strategies for tracking isolated deep convection using observing system simulation experiments
A kriging-based analysis of cloud liquid water content using CloudSat data
High-resolution satellite-based cloud detection for the analysis of land surface effects on boundary layer clouds
Retrievals of ice microphysical properties using dual-wavelength polarimetric radar observations during stratiform precipitation events
The surface longwave cloud radiative effect derived from space lidar observations
Cloud phase and macrophysical properties over the Southern Ocean during the MARCUS field campaign
Detection of supercooled liquid water containing clouds with ceilometers: development and evaluation of deterministic and data-driven retrievals
An all-sky camera image classification method using cloud cover features
Determination of atmospheric column condensate using active and passive remote sensing technology
Improving discrimination between clouds and optically thick aerosol plumes in geostationary satellite data
Towards the use of conservative thermodynamic variables in data assimilation: a case study using ground-based microwave radiometer measurements
Empirical model of multiple-scattering effect on single-wavelength lidar data of aerosols and clouds
Analytic characterization of random errors in spectral dual-polarized cloud radar observations
Assessing synergistic radar and radiometer capability in retrieving ice cloud microphysics based on hybrid Bayesian algorithms
Applying self-supervised learning for semantic cloud segmentation of all-sky images
Coincident in situ and triple-frequency radar airborne observations in the Arctic
Analysis of improvements in MOPITT observational coverage over Canada
Using artificial neural networks to predict riming from Doppler cloud radar observations
Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network
Cloud optical properties retrieval and associated uncertainties using multi-angular and multi-spectral measurements of the airborne radiometer OSIRIS
PARAFOG v2.0: a near-real-time decision tool to support nowcasting fog formation events at local scales
Inpainting radar missing data regions with deep learning
Improved cloud detection for the Aura Microwave Limb Sounder (MLS): training an artificial neural network on colocated MLS and Aqua MODIS data
Triple-frequency radar retrieval of microphysical properties of snow
Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations
Comparison of mid-latitude single- and mixed-phase cloud optical depth from co-located infrared spectrometer and backscatter lidar measurements
Heike Kalesse-Los, Anton Kötsche, Andreas Foth, Johannes Röttenbacher, Teresa Vogl, and Jonas Witthuhn
Atmos. Meas. Tech., 16, 1683–1704, https://doi.org/10.5194/amt-16-1683-2023, https://doi.org/10.5194/amt-16-1683-2023, 2023
Short summary
Short summary
The Virga-Sniffer, a new modular open-source Python package tool to characterize full precipitation evaporation (so-called virga) from ceilometer cloud base height and vertically pointing cloud radar reflectivity time–height fields, is described. Results of its first application to RV Meteor observations during the EUREC4A field experiment in January–February 2020 are shown. About half of all detected clouds with bases below the trade inversion height were found to produce virga.
Yulan Hong, Stephen W. Nesbitt, Robert J. Trapp, and Larry Di Girolamo
Atmos. Meas. Tech., 16, 1391–1406, https://doi.org/10.5194/amt-16-1391-2023, https://doi.org/10.5194/amt-16-1391-2023, 2023
Short summary
Short summary
Deep convective updrafts form overshooting tops (OTs) when they extend into the upper troposphere and lower stratosphere. An OT often indicates hazardous weather conditions. The global distribution of OTs is useful for understanding global severe weather conditions. The Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Terra satellites provides 2 decades of records on the Earth–atmosphere system with stable orbits, which are used in this study to derive 20-year OT climatology.
Pragya Vishwakarma, Julien Delanoë, Susana Jorquera, Pauline Martinet, Frederic Burnet, Alistair Bell, and Jean-Charles Dupont
Atmos. Meas. Tech., 16, 1211–1237, https://doi.org/10.5194/amt-16-1211-2023, https://doi.org/10.5194/amt-16-1211-2023, 2023
Short summary
Short summary
Cloud observations are necessary to characterize the cloud properties at local and global scales. The observations must be translated to cloud geophysical parameters. This paper presents the estimation of liquid water content (LWC) using radar and microwave radiometer (MWR) measurements. Liquid water path from MWR scales LWC and retrieves the scaling factor (ln a). The retrievals are compared with in situ observations. A climatology of ln a is built to estimate LWC using only radar information.
Bhupendra A. Raut, Paytsar Muradyan, Rajesh Sankaran, Robert C. Jackson, Seongha Park, Sean A. Shahkarami, Dario Dematties, Yongho Kim, Joseph Swantek, Neal Conrad, Wolfgang Gerlach, Sergey Shemyakin, Pete Beckman, Nicola J. Ferrier, and Scott M. Collis
Atmos. Meas. Tech., 16, 1195–1209, https://doi.org/10.5194/amt-16-1195-2023, https://doi.org/10.5194/amt-16-1195-2023, 2023
Short summary
Short summary
We studied the stability of a blockwise phase correlation (PC) method to estimate cloud motion using a total sky imager (TSI). Shorter frame intervals and larger block sizes improve stability, while image resolution and color channels have minor effects. Raindrop contamination can be identified by the rotational motion of the TSI mirror. The correlations of cloud motion vectors (CMVs) from the PC method with wind data vary from 0.38 to 0.59. Optical flow vectors are more stable than PC vectors.
William K. Jones, Matthew W. Christensen, and Philip Stier
Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, https://doi.org/10.5194/amt-16-1043-2023, 2023
Short summary
Short summary
Geostationary weather satellites have been used to detect storm clouds since their earliest applications. However, this task remains difficult as imaging satellites cannot observe the strong vertical winds that are characteristic of storm clouds. Here we introduce a new method that allows us to detect the early development of storms and continue to track them throughout their lifetime, allowing us to study how their early behaviour affects subsequent weather.
Andrew M. Sayer, Luca Lelli, Brian Cairns, Bastiaan van Diedenhoven, Amir Ibrahim, Kirk D. Knobelspiesse, Sergey Korkin, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 969–996, https://doi.org/10.5194/amt-16-969-2023, https://doi.org/10.5194/amt-16-969-2023, 2023
Short summary
Short summary
This paper presents a method to estimate the height of the top of clouds above Earth's surface using satellite measurements. It is based on light absorption by oxygen in Earth's atmosphere, which darkens the signal that a satellite will see at certain wavelengths of light. Clouds "shield" the satellite from some of this darkening, dependent on cloud height (and other factors), because clouds scatter light at these wavelengths. The method will be applied to the future NASA PACE mission.
Veronika Pörtge, Tobias Kölling, Anna Weber, Lea Volkmer, Claudia Emde, Tobias Zinner, Linda Forster, and Bernhard Mayer
Atmos. Meas. Tech., 16, 645–667, https://doi.org/10.5194/amt-16-645-2023, https://doi.org/10.5194/amt-16-645-2023, 2023
Short summary
Short summary
In this work, we analyze polarized cloudbow observations by the airborne camera system specMACS to retrieve the cloud droplet size distribution defined by the effective radius (reff) and the effective variance (veff). Two case studies of trade-wind cumulus clouds observed during the EUREC4A field campaign are presented. The results are combined into maps of reff and veff with a very high spatial resolution (100 m × 100 m) that allow new insights into cloud microphysics.
Minrui Wang, Takashi Y. Nakajima, Woosub Roh, Masaki Satoh, Kentaroh Suzuki, Takuji Kubota, and Mayumi Yoshida
Atmos. Meas. Tech., 16, 603–623, https://doi.org/10.5194/amt-16-603-2023, https://doi.org/10.5194/amt-16-603-2023, 2023
Short summary
Short summary
SMILE (a spectral misalignment in which a shift in the center wavelength appears as a distortion in the spectral image) was detected during our recent work. To evaluate how it affects the cloud retrieval products, we did a simulation of EarthCARE-MSI forward radiation, evaluating the error in simulated scenes from a global cloud system-resolving model and a satellite simulator. Our results indicated that the error from SMILE was generally small and negligible for oceanic scenes.
Ming Li, Husi Letu, Hiroshi Ishimoto, Shulei Li, Lei Liu, Takashi Y. Nakajima, Dabin Ji, Huazhe Shang, and Chong Shi
Atmos. Meas. Tech., 16, 331–353, https://doi.org/10.5194/amt-16-331-2023, https://doi.org/10.5194/amt-16-331-2023, 2023
Short summary
Short summary
Influenced by the representativeness of ice crystal scattering models, the existing terahertz ice cloud remote sensing inversion algorithms still have significant uncertainties. We developed an ice cloud remote sensing retrieval algorithm of the ice water path and particle size from aircraft-based terahertz radiation measurements based on the Voronoi model. Validation revealed that the Voronoi model performs better than the sphere and hexagonal column models.
Armin Blanke, Andrew J. Heymsfield, Manuel Moser, and Silke Trömel
EGUsphere, https://doi.org/10.5194/egusphere-2022-1488, https://doi.org/10.5194/egusphere-2022-1488, 2023
Short summary
Short summary
We present an evaluation of current retrieval techniques in the ice phase applied to polarimetric radar measurements with collocated in situ observations of aircraft conducted over the Olympic Mountains, Washington State during winter 2015. Radar estimates of ice properties most agreed with aircraft observations in regions with pronounced radar signatures, but uncertainties were identified that indicate issues of some retrievals, particularly in warmer temperature regimes.
Steven Massie, Heather Cronk, Aronne Merrelli, Sebastian Schmidt, and Steffen Mauceri
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-323, https://doi.org/10.5194/amt-2022-323, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
Clouds near observations can increase observed radiances. This paper discusses cloud 3D radiative transfer effects upon Orbiting Carbon Observatory (OCO-2) retrievals of CO2. Ratios of 1D to 3D radiance intensity ratios are calculated using the SHDOM 3D radiative transfer program for 36 scenes of ocean glint, land nadir, and land glint observations in order to gain insight as to how 3D cloud effects impact the OCO-2 retrievals of CO2 and other variables.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 15, 7119–7136, https://doi.org/10.5194/amt-15-7119-2022, https://doi.org/10.5194/amt-15-7119-2022, 2022
Short summary
Short summary
Vertical profiles of latent heating are derived from GOES-16 to be used in convective initialization. They are compared with other latent heating products derived from NEXRAD and GPM satellites, and the results show that their values are very similar to the radar-derived products. Finally, using latent heating derived from GOES-16 for convective initialization shows improvements in precipitation forecasts, which are comparable to the results using latent heating derived from NEXRAD.
Simon Whitburn, Lieven Clarisse, Marc Crapeau, Thomas August, Tim Hultberg, Pierre François Coheur, and Cathy Clerbaux
Atmos. Meas. Tech., 15, 6653–6668, https://doi.org/10.5194/amt-15-6653-2022, https://doi.org/10.5194/amt-15-6653-2022, 2022
Short summary
Short summary
With more than 15 years of measurements, the IASI radiance dataset is becoming a reference climate data record. Its exploitation for satellite applications requires an accurate and unbiased detection of cloud scenes. Here, we present a new cloud detection algorithm for IASI that is both sensitive and consistent over time. It is based on the use of a neural network, relying on IASI radiance information only and taking as a reference the last version of the operational IASI L2 cloud product.
Zhipeng Qu, Howard W. Barker, Jason N. S. Cole, and Mark W. Shephard
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-301, https://doi.org/10.5194/amt-2022-301, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
This paper describes EarthCARE’s L2 product ACM-3D. It includes the Scene Construction Algorithm (SCA) used to produce the indexes for reconstructing 3D atmospheric scene based on satellite nadir retrievals. It also provide the information about the buffer zone sizes of 3D assessment domains and the ranking scores for selecting the best 3D assessment domains. These output variables are needed to run 3D radiative transfer models for the radiative closure assessment of EarthCARE’s L2 retrievals.
Wenyu Wang, Zhenzhan Wang, Qiurui He, and Lanjie Zhang
Atmos. Meas. Tech., 15, 6489–6506, https://doi.org/10.5194/amt-15-6489-2022, https://doi.org/10.5194/amt-15-6489-2022, 2022
Short summary
Short summary
This paper uses a neural network approach to retrieve the ice water path from FY-3B/MWHS polarimetric measurements, focusing on its unique 150 GHz quasi-polarized channels. The Level 2 product of CloudSat is used as the reference value for the neural network. The results show that the polarization information is helpful for the retrieval in scenes with thicker cloud ice, and the 150 GHz channels give a significant improvement compared to using only 183 GHz channels.
Miriam Latsch, Andreas Richter, Henk Eskes, Maarten Sneep, Ping Wang, Pepijn Veefkind, Ronny Lutz, Diego Loyola, Athina Argyrouli, Pieter Valks, Thomas Wagner, Holger Sihler, Michel van Roozendael, Nicolas Theys, Huan Yu, Richard Siddans, and John P. Burrows
Atmos. Meas. Tech., 15, 6257–6283, https://doi.org/10.5194/amt-15-6257-2022, https://doi.org/10.5194/amt-15-6257-2022, 2022
Short summary
Short summary
The article investigates different S5P TROPOMI cloud retrieval algorithms for tropospheric trace gas retrievals. The cloud products show differences primarily over snow and ice and for scenes under sun glint. Some issues regarding across-track dependence are found for the cloud fractions as well as for the cloud heights.
Han Ding, Haoran Li, and Liping Liu
Atmos. Meas. Tech., 15, 6181–6200, https://doi.org/10.5194/amt-15-6181-2022, https://doi.org/10.5194/amt-15-6181-2022, 2022
Short summary
Short summary
In this study, a framework for processing the Doppler spectra observations of a multi-mode pulse compression Ka–Ku cloud radar system is presented. We first proposed an approach to identify and remove the clutter signals in the Doppler spectrum. Then, we developed a new algorithm to remove the range sidelobe at the modes implementing the pulse compression technique. The radar observations from different modes were then merged using the shift-then-average method.
Andrew T. Prata, Roy G. Grainger, Isabelle A. Taylor, Adam C. Povey, Simon R. Proud, and Caroline A. Poulsen
Atmos. Meas. Tech., 15, 5985–6010, https://doi.org/10.5194/amt-15-5985-2022, https://doi.org/10.5194/amt-15-5985-2022, 2022
Short summary
Short summary
Satellite observations are often used to track ash clouds and estimate their height, particle sizes and mass; however, satellite-based techniques are always associated with some uncertainty. We describe advances in a satellite-based technique that is used to estimate ash cloud properties for the June 2019 Raikoke (Russia) eruption. Our results are significant because ash warning centres increasingly require uncertainty information to correctly interpret,
aggregate and utilise the data.
Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, https://doi.org/10.5194/amt-15-5701-2022, 2022
Short summary
Short summary
Geostationary satellites continuously image a given location on Earth, a feature that satellites designed to characterize atmospheric ice lack. However, the relationship between geostationary images and atmospheric ice is complex. Machine learning is used here to leverage such images to characterize atmospheric ice throughout the day in a probabilistic manner. Using structural information from the image improves the characterization, and this approach compares favourably to traditional methods.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-251, https://doi.org/10.5194/amt-2022-251, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
We describe a new method for measuring the 3D spatial variations of water within clouds using the reflected light of the sun viewed at multiple different angles by satellites. This is a great improvement over older methods which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Alistair Bell, Pauline Martinet, Olivier Caumont, Frédéric Burnet, Julien Delanoë, Susana Jorquera, Yann Seity, and Vinciane Unger
Atmos. Meas. Tech., 15, 5415–5438, https://doi.org/10.5194/amt-15-5415-2022, https://doi.org/10.5194/amt-15-5415-2022, 2022
Short summary
Short summary
Cloud radars and microwave radiometers offer the potential to improve fog forecasts when assimilated into a high-resolution model. As this process can be complex, a retrieval of model variables is sometimes made as a first step. In this work, results from a 1D-Var algorithm for the retrieval of temperature, humidity and cloud liquid water content are presented. The algorithm is applied first to a synthetic dataset and then to a dataset of real measurements from a recent field campaign.
Willi Schimmel, Heike Kalesse-Los, Maximilian Maahn, Teresa Vogl, Andreas Foth, Pablo Saavedra Garfias, and Patric Seifert
Atmos. Meas. Tech., 15, 5343–5366, https://doi.org/10.5194/amt-15-5343-2022, https://doi.org/10.5194/amt-15-5343-2022, 2022
Short summary
Short summary
This study introduces the novel Doppler radar spectra-based machine learning approach VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn). VOODOO is a powerful probability-based extension to the existing Cloudnet hydrometeor target classification, enabling the detection of liquid-bearing cloud layers beyond complete lidar attenuation via user-defined p* threshold. VOODOO performs best for (multi-layer) stratiform and deep mixed-phase clouds with liquid water path > 100 g m−2.
Vikas Nataraja, Sebastian Schmidt, Hong Chen, Takanobu Yamaguchi, Jan Kazil, Graham Feingold, Kevin Wolf, and Hironobu Iwabuchi
Atmos. Meas. Tech., 15, 5181–5205, https://doi.org/10.5194/amt-15-5181-2022, https://doi.org/10.5194/amt-15-5181-2022, 2022
Short summary
Short summary
A convolutional neural network (CNN) is introduced to retrieve cloud optical thickness (COT) from passive cloud imagery. The CNN, trained on large eddy simulations from the Sulu Sea, learns from spatial information at multiple scales to reduce cloud inhomogeneity effects. By considering the spatial context of a pixel, the CNN outperforms the traditional independent pixel approximation (IPA) across several cloud morphology metrics.
Rachel T. Pinker, Yingtao Ma, Wen Chen, Istvan Laszlo, Hongqing Liu, Hye-Yun Kim, and Jaime Daniels
Atmos. Meas. Tech., 15, 5077–5094, https://doi.org/10.5194/amt-15-5077-2022, https://doi.org/10.5194/amt-15-5077-2022, 2022
Short summary
Short summary
Scene-dependent narrow-to-broadband transformations are developed to facilitate the use of observations from the Advanced Baseline Imager (ABI), the primary instrument on GOES-R, to derive surface shortwave radiative fluxes. This is a first NOAA product at the high resolution of about 5 k over the contiguous United States (CONUS) region. The product is archived and can be downloaded from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).
Mariko Oue, Stephen M. Saleeby, Peter J. Marinescu, Pavlos Kollias, and Susan C. van den Heever
Atmos. Meas. Tech., 15, 4931–4950, https://doi.org/10.5194/amt-15-4931-2022, https://doi.org/10.5194/amt-15-4931-2022, 2022
Short summary
Short summary
This study provides an optimization of radar observation strategies to better capture convective cell evolution in clean and polluted environments as well as a technique for the optimization. The suggested optimized radar observation strategy is to better capture updrafts at middle and upper altitudes and precipitation particle evolution of isolated deep convective clouds. This study sheds light on the challenge of designing remote sensing observation strategies in pre-field campaign periods.
Jean-Marie Lalande, Guillaume Bourmaud, Pierre Minvielle, and Jean-François Giovannelli
Atmos. Meas. Tech., 15, 4411–4429, https://doi.org/10.5194/amt-15-4411-2022, https://doi.org/10.5194/amt-15-4411-2022, 2022
Short summary
Short summary
In this paper we describe the implementation of an interpolation–prediction estimator applied to cloud properties derived from CloudSat observations. The objective is to evaluate the uncertainty associated with the estimated quantity. The model developed in this study can be valuable for satellite applications (GPS, telecommunication) as well as for cloud product comparisons. This paper is didactic and beneficial for anyone interested in kriging estimators.
Julia Fuchs, Hendrik Andersen, Jan Cermak, Eva Pauli, and Rob Roebeling
Atmos. Meas. Tech., 15, 4257–4270, https://doi.org/10.5194/amt-15-4257-2022, https://doi.org/10.5194/amt-15-4257-2022, 2022
Short summary
Short summary
Two cloud-masking approaches, a local and a regional approach, using high-resolution satellite data are developed and validated for the region of Paris to improve applicability for analyses of urban effects on low clouds. We found that cloud masks obtained from the regional approach are more appropriate for the high-resolution analysis of locally induced cloud processes. Its applicability is tested for the analysis of typical fog conditions over different surface types.
Eleni Tetoni, Florian Ewald, Martin Hagen, Gregor Köcher, Tobias Zinner, and Silke Groß
Atmos. Meas. Tech., 15, 3969–3999, https://doi.org/10.5194/amt-15-3969-2022, https://doi.org/10.5194/amt-15-3969-2022, 2022
Short summary
Short summary
We use the C-band POLDIRAD and the Ka-band MIRA-35 to perform snowfall dual-wavelength polarimetric radar measurements. We develop an ice microphysics retrieval for mass, apparent shape, and median size of the particle size distribution by comparing observations to T-matrix ice spheroid simulations while varying the mass–size relationship. We furthermore show how the polarimetric measurements from POLDIRAD help to narrow down ambiguities between ice particle shape and size.
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, https://doi.org/10.5194/amt-15-3893-2022, 2022
Short summary
Short summary
We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from observations collected by a space-based lidar on board the CALIPSO satellite and radiative transfer computations. Our estimate appropriately captures the surface LW CRE annual variability over bright polar surfaces, and it provides a dataset more than 13 years long.
Baike Xi, Xiquan Dong, Xiaojian Zheng, and Peng Wu
Atmos. Meas. Tech., 15, 3761–3777, https://doi.org/10.5194/amt-15-3761-2022, https://doi.org/10.5194/amt-15-3761-2022, 2022
Short summary
Short summary
This study develops an innovative method to determine the cloud phases over the Southern Ocean (SO) using the combination of radar and lidar measurements during the ship-based field campaign of MARCUS. Results from our study show that the low-level, deep, and shallow cumuli are dominant, and the mixed-phase clouds occur more than single phases over the SO. The mixed-phase cloud properties are similar to liquid-phase (ice-phase) clouds in the midlatitudes (polar) region of the SO.
Adrien Guyot, Alain Protat, Simon P. Alexander, Andrew R. Klekociuk, Peter Kuma, and Adrian McDonald
Atmos. Meas. Tech., 15, 3663–3681, https://doi.org/10.5194/amt-15-3663-2022, https://doi.org/10.5194/amt-15-3663-2022, 2022
Short summary
Short summary
Ceilometers are instruments that are widely deployed as part of operational networks. They are usually not able to detect cloud phase. Here, we propose an evaluation of various methods to detect supercooled liquid water with ceilometer observations, using an extensive dataset from Davis, Antarctica. Our results highlight the possibility for ceilometers to detect supercooled liquid water in clouds.
Xiaotong Li, Baozhu Wang, Bo Qiu, and Chao Wu
Atmos. Meas. Tech., 15, 3629–3639, https://doi.org/10.5194/amt-15-3629-2022, https://doi.org/10.5194/amt-15-3629-2022, 2022
Short summary
Short summary
The all-sky camera images can reflect the local cloud cover, which is considerable for astronomical observatory site selection. Therefore, the realization of automatic classification of the images is very important. In this paper, three cloud cover features are proposed to classify the images. The proposed method is evaluated on a large dataset, and the method achieves an accuracy of 96.58 % and F1_score of 96.24 %, which greatly improves the efficiency of automatic processing of the images.
Huige Di, Yun Yuan, Qing Yan, Wenhui Xin, Shichun Li, Jun Wang, Yufeng Wang, Lei Zhang, and Dengxin Hua
Atmos. Meas. Tech., 15, 3555–3567, https://doi.org/10.5194/amt-15-3555-2022, https://doi.org/10.5194/amt-15-3555-2022, 2022
Short summary
Short summary
It is necessary to correctly evaluate the amount of cloud water resources in an area. Currently, there is a lack of effective observation methods for atmospheric column condensate evaluation. We propose a method for atmospheric column condensate by combining millimetre cloud radar, lidar and microwave radiometers. The method can realise determination of atmospheric column condensate. The variation of cloud before precipitation is considered, and the atmospheric column is deduced and obtained.
Daniel Robbins, Caroline Poulsen, Steven Siems, and Simon Proud
Atmos. Meas. Tech., 15, 3031–3051, https://doi.org/10.5194/amt-15-3031-2022, https://doi.org/10.5194/amt-15-3031-2022, 2022
Short summary
Short summary
A neural network (NN)-based cloud mask for a geostationary satellite instrument, AHI, is developed using collocated data and is better at not classifying thick aerosols as clouds versus the Japanese Meteorological Association and the Bureau of Meteorology masks, identifying 1.13 and 1.29 times as many non-cloud pixels than each mask, respectively. The improvement during the day likely comes from including the shortest wavelength bands from AHI in the NN mask, which the other masks do not use.
Pascal Marquet, Pauline Martinet, Jean-François Mahfouf, Alina Lavinia Barbu, and Benjamin Ménétrier
Atmos. Meas. Tech., 15, 2021–2035, https://doi.org/10.5194/amt-15-2021-2022, https://doi.org/10.5194/amt-15-2021-2022, 2022
Short summary
Short summary
Two conservative thermodynamic variables (moist-air entropy potential temperature and total water content) are introduced into a one-dimensional EnVar data assimilation system to demonstrate their benefit for future operational assimilation schemes, with the use of microwave brightness temperatures from a ground-based radiometer installed during the field campaign SOFGO3D. Results show that the brightness temperatures analysed with the new variables are improved, including the liquid water.
Valery Shcherbakov, Frédéric Szczap, Alaa Alkasem, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 15, 1729–1754, https://doi.org/10.5194/amt-15-1729-2022, https://doi.org/10.5194/amt-15-1729-2022, 2022
Short summary
Short summary
We performed extensive Monte Carlo (MC) simulations of lidar signals and developed an empirical model to account for the multiple scattering in the lidar signals. The simulations have taken into consideration four types of lidar configurations (the ground based, the airborne, the CALIOP, and the ATLID) and four types of particles (coarse aerosol, water cloud, jet-stream cirrus, and cirrus).
The empirical model has very good quality of MC data fitting for all considered cases.
Alexander Myagkov and Davide Ori
Atmos. Meas. Tech., 15, 1333–1354, https://doi.org/10.5194/amt-15-1333-2022, https://doi.org/10.5194/amt-15-1333-2022, 2022
Short summary
Short summary
This study provides equations to characterize random errors of spectral polarimetric observations from cloud radars. The results can be used for a broad spectrum of applications. For instance, accurate error characterization is essential for advanced retrievals of microphysical properties of clouds and precipitation. Moreover, error characterization allows for the use of measurements from polarimetric cloud radars to potentially improve weather forecasts.
Yuli Liu and Gerald G. Mace
Atmos. Meas. Tech., 15, 927–944, https://doi.org/10.5194/amt-15-927-2022, https://doi.org/10.5194/amt-15-927-2022, 2022
Short summary
Short summary
We propose a suite of Bayesian algorithms for synergistic radar and radiometer retrievals to evaluate the next-generation NASA Cloud, Convection and Precipitation (CCP) observing system. The algorithms address pixel-level retrievals using active-only, passive-only, and synergistic active–passive observations. Novel techniques in developing synergistic algorithms are presented. Quantitative assessments of the CCP observing system's capability in retrieving ice cloud microphysics are provided.
Yann Fabel, Bijan Nouri, Stefan Wilbert, Niklas Blum, Rudolph Triebel, Marcel Hasenbalg, Pascal Kuhn, Luis F. Zarzalejo, and Robert Pitz-Paal
Atmos. Meas. Tech., 15, 797–809, https://doi.org/10.5194/amt-15-797-2022, https://doi.org/10.5194/amt-15-797-2022, 2022
Short summary
Short summary
This work presents a new approach to exploit unlabeled image data from ground-based sky observations to train neural networks. We show that our model can detect cloud classes within images more accurately than models trained with conventional methods using small, labeled datasets only. Novel machine learning techniques as applied in this work enable training with much larger datasets, leading to improved accuracy in cloud detection and less need for manual image labeling.
Cuong M. Nguyen, Mengistu Wolde, Alessandro Battaglia, Leonid Nichman, Natalia Bliankinshtein, Samuel Haimov, Kenny Bala, and Dirk Schuettemeyer
Atmos. Meas. Tech., 15, 775–795, https://doi.org/10.5194/amt-15-775-2022, https://doi.org/10.5194/amt-15-775-2022, 2022
Short summary
Short summary
An analysis of airborne triple-frequency radar and almost perfectly co-located coincident in situ data from an Arctic storm confirms the main findings of modeling work with radar dual-frequency ratios (DFRs) at different zones of the DFR plane associated with different ice habits. High-resolution CPI images provide accurate identification of rimed particles within the DFR plane. The relationships between the triple-frequency signals and cloud microphysical properties are also presented.
Heba S. Marey, James R. Drummond, Dylan B. A. Jones, Helen Worden, Merritt N. Deeter, John Gille, and Debbie Mao
Atmos. Meas. Tech., 15, 701–719, https://doi.org/10.5194/amt-15-701-2022, https://doi.org/10.5194/amt-15-701-2022, 2022
Short summary
Short summary
In this study, an analysis has been performed to understand the improvements in observational coverage over Canada in the new MOPITT V9 product. Temporal and spatial analysis of V9 indicates a general coverage gain of 15–20 % relative to V8, which varies regionally and seasonally; e.g., the number of successful MOPITT retrievals in V9 was doubled over Canada in winter. Also, comparison with the corresponding IASI instrument indicated generally good agreement, with about a 5–10 % positive bias.
Teresa Vogl, Maximilian Maahn, Stefan Kneifel, Willi Schimmel, Dmitri Moisseev, and Heike Kalesse-Los
Atmos. Meas. Tech., 15, 365–381, https://doi.org/10.5194/amt-15-365-2022, https://doi.org/10.5194/amt-15-365-2022, 2022
Short summary
Short summary
We are using machine learning techniques, a type of artificial intelligence, to detect graupel formation in clouds. The measurements used as input to the machine learning framework were performed by cloud radars. Cloud radars are instruments located at the ground, emitting radiation with wavelenghts of a few millimeters vertically into the cloud and measuring the back-scattered signal. Our novel technique can be applied to different radar systems and different weather conditions.
Heike Kalesse-Los, Willi Schimmel, Edward Luke, and Patric Seifert
Atmos. Meas. Tech., 15, 279–295, https://doi.org/10.5194/amt-15-279-2022, https://doi.org/10.5194/amt-15-279-2022, 2022
Short summary
Short summary
It is important to detect the vertical distribution of cloud droplets and ice in mixed-phase clouds. Here, an artificial neural network (ANN) previously developed for Arctic clouds is applied to a mid-latitudinal cloud radar data set. The performance of this technique is contrasted to the Cloudnet target classification. For thick/multi-layer clouds, the machine learning technique is better at detecting liquid than Cloudnet, but if lidar data are available Cloudnet is at least as good as the ANN.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Jean-Marc Nicolas
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-414, https://doi.org/10.5194/amt-2021-414, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
The uncertainties in cloud remote sensing can propagate to the retrieved cloud properties and they need to be quantified. We present the formalism of error extraction and we apply it on the cloud properties retrieved from the measurements of the airborne radiometer OSIRIS. We show that errors related to measurement uncertainties reach 10 %. Errors related to the simplified model assuming that the clouds are plane-parallel and homogeneous lead to uncertainties exceeding 10 %.
Jean-François Ribaud, Martial Haeffelin, Jean-Charles Dupont, Marc-Antoine Drouin, Felipe Toledo, and Simone Kotthaus
Atmos. Meas. Tech., 14, 7893–7907, https://doi.org/10.5194/amt-14-7893-2021, https://doi.org/10.5194/amt-14-7893-2021, 2021
Short summary
Short summary
PARAFOG is a near-real-time decision tool that aims to retrieve pre-fog alert levels minutes to hours prior to fog onset. The second version of PARAFOG allows us to discriminate between radiation and stratus lowering fog situations. It is based upon the combination of visibility observations and automatic lidar and ceilometer measurements. The overall performance of the second version of PARAFOG over more than 300 fog cases at five different locations presents a good perfomance.
Andrew Geiss and Joseph C. Hardin
Atmos. Meas. Tech., 14, 7729–7747, https://doi.org/10.5194/amt-14-7729-2021, https://doi.org/10.5194/amt-14-7729-2021, 2021
Short summary
Short summary
Radars can suffer from missing or poor-quality data regions for several reasons: beam blockage, instrument failure, and near-ground blind zones, etc. Here, we demonstrate how deep convolutional neural networks can be used for filling in radar-missing data regions and that they can significantly outperform conventional approaches in terms of realism and accuracy.
Frank Werner, Nathaniel J. Livesey, Michael J. Schwartz, William G. Read, Michelle L. Santee, and Galina Wind
Atmos. Meas. Tech., 14, 7749–7773, https://doi.org/10.5194/amt-14-7749-2021, https://doi.org/10.5194/amt-14-7749-2021, 2021
Short summary
Short summary
In this study we present an improved cloud detection scheme for the Microwave Limb Sounder, which is based on a feedforward artificial neural network. This new algorithm is shown not only to reliably detect high and mid-level convection containing even small amounts of cloud water but also to distinguish between high-reaching and mid-level to low convection.
Kamil Mroz, Alessandro Battaglia, Cuong Nguyen, Andrew Heymsfield, Alain Protat, and Mengistu Wolde
Atmos. Meas. Tech., 14, 7243–7254, https://doi.org/10.5194/amt-14-7243-2021, https://doi.org/10.5194/amt-14-7243-2021, 2021
Short summary
Short summary
A method for estimating microphysical properties of ice clouds based on radar measurements is presented. The algorithm exploits the information provided by differences in the radar response at different frequency bands in relation to changes in the snow morphology. The inversion scheme is based on a statistical relation between the radar simulations and the properties of snow calculated from in-cloud sampling.
Nicholas J. Kedzuf, J. Christine Chiu, V. Chandrasekar, Sounak Biswas, Shashank S. Joshil, Yinghui Lu, Peter Jan van Leeuwen, Christopher Westbrook, Yann Blanchard, and Sebastian O'Shea
Atmos. Meas. Tech., 14, 6885–6904, https://doi.org/10.5194/amt-14-6885-2021, https://doi.org/10.5194/amt-14-6885-2021, 2021
Short summary
Short summary
Ice clouds play a key role in our climate system due to their strong controls on precipitation and the radiation budget. However, it is difficult to characterize co-existing ice species using radar observations. We present a new method that separates the radar signals of pristine ice embedded in snow aggregates and retrieves their respective abundances and sizes for the first time. The ability to provide their quantitative microphysical properties will open up many research opportunities.
Gianluca Di Natale, Marco Barucci, Claudio Belotti, Giovanni Bianchini, Francesco D'Amato, Samuele Del Bianco, Marco Gai, Alessio Montori, Ralf Sussmann, Silvia Viciani, Hannes Vogelmann, and Luca Palchetti
Atmos. Meas. Tech., 14, 6749–6758, https://doi.org/10.5194/amt-14-6749-2021, https://doi.org/10.5194/amt-14-6749-2021, 2021
Short summary
Short summary
The importance of cirrus and mixed-phase clouds in the Earth radiation budget has been proven by many studies. In this paper the properties that characterize these clouds are retrieved from lidar and far-infrared spectral measurements performed in winter 2018/19 on the Zugspitze (Germany). The synergy of lidar and spectrometer measurements allowed us to assess the exponent k of the power-law relationship between the backscattering and the extinction coefficients.
Cited articles
Battaglia, A., Kollias, P., Dhillon, R., Roy, R., Tanelli, S., Lamer, K.,
Grecu, M., Lebsock, M., Watters, D., Mroz, K, Heymsfield, G., Li, L. H., and
Furukawa, K.: Spaceborne cloud and precipitation radars status challenges
and ways forward, Rev. Geophys., 58, e2019RG000686, https://doi.org/10.1029/2019RG000686, 2020.
Behrangi, A., Tian, Y. D., Lambrigtsen, B., and Stephens, G. L.: What does
CloudSat reveal about global land precipitation detection by other
spaceborne sensors?, Water Resour. Res., 50, 4893–4905,
https://doi.org/10.1002/2013WR014566, 2013.
Bohren, C. F. and Huffman, D. R.: Absorption and scattering of light by small
particles, Wiley, New York, https://doi.org/10.1002/9783527618156, 1983.
Brandes, E., Zhang, G. F., and Vivekanandan, J.: Experiments in rainfall
estimation with polarimetric radar in subtropical environment, J. Appl.
Meteorol., 41, 2245–2264, https://doi.org/10.1175/1520-0450(2002)041<0674:EIREWA>2.0.CO;2, 2002.
Brandes, E., Ikeda, K., Zhang, G., Schoenhuber, M., and Rasmussen, R.: A
statistical and physical description of hydrometeor distributions in
Colorado snowstorms using a video disdrometer, J. Appl. Meteorol. Clim.,
46, 634–650, https://doi.org/10.1175/JAM2489.1, 2007.
Bringi, V. N. and Chandrasekar, V.: Polarimetric Doppler weather radar:
principles and applications, Cambridge Univ., New York, NY, https://doi.org/10.1017/CBO9780511541094, 2001.
Chase, R. J., Nesbitt, S. W., and McFarquhar, G. M.: Evaluation of the
microphysical assumptions within GPM-DPR using ground-based observations of
rain and snow, Atmosphere, 11, 169, https://doi.org/10.3390/atmos11060619, 2020.
Chen, B. J., Yang, J., and Pu, J. P.: Statistical characteristics of raindrop
size distribution in the Meiyu season observed in Eastern China, J.
Meteorol. Soc. Jpn., 91, 215–227, https://doi.org/10.2151/jmsj.2013-208, 2013.
Dodson, J. B., Taylor, P. C., and Branson, M.: Microphysical variability of Amazonian deep convective cores observed by CloudSat and simulated by a multi-scale modeling framework, Atmos. Chem. Phys., 18, 6493–6510, https://doi.org/10.5194/acp-18-6493-2018, 2018.
Ellis, T. D., L'Ecuyer, T., Haynes, J. M., and Stephens, G. L.: How often
does it rain over the global oceans? The perspective from CloudSat, Geophys.
Res. Lett., 36, L03815, https://doi.org/10.1029/2008GL036728, 2009.
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.
Fu, Q.: An accurate parameterization of the solar radiative properties of
cirrus clouds for climate models, J. Climate, 9, 2058–2082, https://doi.org/10.1175/1520-0442(1996)009<2058:AAPOTS>2.0.CO;2, 1996.
Garrett, T., Yuter, S., Fallgatter, C., Shkurko, K., Rhodes, S., and
Endries, J.: Orientations and aspect ratios of falling snow, Geophys. Res.
Lett., 42, 4617-4622, https://doi.org/10.1002/2015GL064040, 2015.
Hayden, L. and Liu, C. T.: A multiyear analysis of global precipitation
combining CloudSat and GPM precipitation retrievals, J. Hydrometeorol., 19,
1925–1944, https://doi.org/10.1175/JHM-D-18-0053.1, 2018.
Haynes, J. M., Marchand, R. T., Luo, Z., Bodas-Salcedo, A., and Stephens, G.
L.: A multipurpose radar simulation package: quick beam, B. Am. Meteorol.
Soc., 88, 1723–1727, https://doi.org/10.1175/BAMS-88-11-1723, 2007.
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., 114, D00A22, https://doi.org/10.1029/2008JD009973, 2009.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Moñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G.D., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803,
2020.
Heymsfield, A. J., Schmitt, C., Bansemer, A., and Twohy, C. H.: Improved
representation of ice particle masses based on observations in natural
clouds, J. Atmos. Sci., 67, 3303–3318, https://doi.org/10.1175/2010JAS3507.1, 2010.
Heymsfield, A. J., Schmitt, C., and Bansemer, A.: Ice cloud particle size
distributions and pressure-dependent terminal velocities from in situ
observations at temperatures from 0∘ to −86 ∘C, J. Atmos. Sci., 70, 4123–4154, https://doi.org/10.1175/JAS-D-12-0124.1, 2013.
Heymsfield, A., Szakáll, M., Jost, A., Giammanco, I., and Wright, R.: A
comprehensive observational study of graupel and hail terminal velocity,
mass flux, and kinetic energy, J. Atmos. Sci., 75, 3861–3885, https://doi.org/10.1175/JAS-D-18-0035.s1, 2018.
Hogan, R. J., Tian, L., Brown, P. R. A., Westbrook, C. D., Heymsfield, A. J., and Eastment, J. D.: Radar scattering from ice aggregates using the horizontally aligned oblate spheroid approximation, J. Appl. Meteorol. Clim., 51, 655–671, https://doi.org/10.1175/JAMC-D-11-074.1, 2012.
Horie, H., Takahashi, N., Ohno, Y., Sato, K., Hayasaka, T., Nakamura, K.,
and Im, E.: Simulation for spaceborne cloud profiling Doppler Radar:
EarthCARE/CPR, Proc. SPIE 8523, Remote Sensing of the Atmosphere, Clouds,
and Precipitation IV, 8 November 2012, Kyoto, Japan, https://doi.org/10.1117/12.977253,
2012.
Huang, G.-J., Bringi, V. N., Newman, A. J., Lee, G., Moisseev, D., and Notaroš, B. M.: Dual-wavelength radar technique development for snow rate estimation: a case study from GCPEx, Atmos. Meas. Tech., 12, 1409–1427, https://doi.org/10.5194/amt-12-1409-2019, 2019.
Iguchi, T., Matsui, T., Shi, J. J., Tao, W.-K., Khain, A. P., Hou, A.,
Cifelli, R., Heymsfield, A., and Tokay, A.: Numerical analysis using WRF-SBM
for the cloud microphysical structures in the C3VP field campaign: impacts
of supercooled droplets and resultant riming on snow microphysics, J.
Geophys. Res., 117, D23206, https://doi.org/10.1029/2012JD018101, 2012.
Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H.,
Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D. P.,
Fukuda, S., Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota,
T., Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H., Oki,
R., Sato, K., Satoh, M., Shephard, M. W., Velázquez-Blázquez, A.,
Wandinger, U., Wehr, T., and Van, Z. G.: The EarthCARE Satellite: The Next
Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and
Radiation, B. Am. Meteorol. Soc., 96, 1311–1332, https://doi.org/10.1175/BAMS-D-12-00227.1, 2015.
Jung, Y., Zhang, G., and Xue, M.: Assimilation of simulated polarimetric
radar data for a convective storm using the ensemble Kalman filter. Part I:
observation operators for reflectivity and polarimetric variables, Mon. Weather Rev., 136, 2228–2245, https://doi.org/10.1175/2007mwr2083.1, 2008.
Khain, A. and Pinsky, M.: Physical processes in clouds and cloud modeling, Cambridge University Press, UK, https://doi.org/10.1017/9781139049481, 2018.
Kneifel, S., Maahn, M., Peters, G., and Simmer, C.: Observations of snowfall
with a low-power FM-CW K-band radar (Micro Rain Radar), Meteorol. Atmos.
Phys., 113, 75–87, https://doi.org/10.1007/s00703-011-0142-z, 2011.
Kollias, P. and Albrecht, B. A.: Why the melting layer reflectivity is not
bright at 94-GHz, Geophys. Res. Lett., 32, L24818, https://doi.org/10.1029/2005GL024074, 2005.
Kollias, P., Clothiaux, E. E., and Miller, M. A.: 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.
Lamer, K., Oue, M., Battaglia, A., Roy, R. J., Cooper, K. B., Dhillon, R., and Kollias, P.: Multifrequency radar observations of clouds and precipitation including the G-band, Atmos. Meas. Tech., 14, 3615–3629, https://doi.org/10.5194/amt-14-3615-2021, 2021.
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.
Li, R., Min, Q. L., Wu, X. Q., and Fu, Y. F.: Retrieving latent heating
vertical structure from cloud and precipitation profiles – part II: deep
convective and stratiform rain processes, J. Quant. Spectrosc. Ra., 122, 47–63, https://doi.org/10.1016/j.jqsrt.2012.11.029, 2013.
Liebe, H. J.: Modeling attenuation and phase of radio waves in air at
frequencies below 1000 GHz, Radio Sci., 16, 1183–1199, https://doi.org/10.1029/RS016i006p01183, 1981.
Lin, Y. L. and Colle, B. A.: A new bulk microphysical scheme that includes
riming intensity and temperature-dependent ice characteristics, Mon. Weather
Rev., 139, 1013–1035, https://doi.org/10.1175/2010MWR3293.1, 2011.
Liu, G. S.: A database of microwave single-scattering properties for
nonspherical ice particles, B. Am. Meteorol. Soc., 89, 1563–1570, https://doi.org/10.1175/2008BAMS2486.1, 2008.
Liu, L. P. and Zhou, M.: Characteristics of bright band and automatic detection algorithm with vertical pointed Ka band cloud radar, Plateau Meteor., 35, 734–744, http://www.gyqx.ac.cn/EN/10.7522/j.issn.1000-0534.2014.00002 (last access: 26 March 2023), 2016.
Luo, Z. Z., Liu, G. Y., and Stephens, G. L.: CloudSat adding new insight into
tropical penetrating convection, Geophys. Res. Lett., 35, L19819, https://doi.org/10.1029/2008GL035330, 2008.
Mace, G. and Benson, S.: Diagnosing cloud microphysical process information
from remote sensing measurements-A feasibility study using aircraft data.
Part I: Tropical anvils measured during TC4, J. Appl. Meteorol. Clim.,
56, 633–649, https://doi.org/10.1175/JAMC-D-16-0083.1, 2017.
Marchand, R. and Mace, G.: Level 2 GEOPROF product process description and interface control document version P1_R05, CloudSat mission and the Data Processing Center (CloudSat DPC) [data set], https://www.cloudsat.cira.colostate.edu/data-products/2b-geoprof (last access: 25 March 2023), 2018.
Marra, A. C., Marra, G. P., and Prodi, F.: Numerical scattering simulations
for interpreting simultaneous observations of clouds by a W-band spaceborne
and a C-band ground radar, Eur. J. Remote Sens., 46, 909–927, https://doi.org/10.5721/EuJRS20134654, 2013.
Mason, B. J.: The physics of clouds, Oxford University Press, London, https://doi.org/10.1007/978-3-642-45881-1_9, 1971.
Mason, S. L., Chiu, C. J., Hogan, R. J., Moisseev, D., and Kneifel, S.:
Retrievals of riming and snow density from vertically pointing Doppler
radars, J. Geophys. Res., 123, 807–834, https://doi.org/10.1029/2018JD028603, 2018.
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.
Masunaga, H., Matsui, T., Tao, W.-K., Hou, A. Y., Kummerow, C. D., Nakajima,
T., Bauer, P., Olson, W. S., Sekiguchi, M., and Nakajima, T. Y.: Satellite
data simulator unit: a multisensor, multispectral satellite simulator
package, B. Am. Meteorol. Soc., 91, 1625–1632, https://doi.org/10.1175/2010BAMS2809.1, 2010.
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.
Matsui, T., Iguchi, T., Li, X. W., Han, M., Tao, W. K., Petersen, W.,
L'Ecuyer, T., Meneghini, R., Olson, W., Kummerow, C. D., Hou, A. Y.,
Schwaller, M. R., Stocker, E. F., and Kwiatkowski, J.: GPM satellite
simulator over ground validation sites, B. Am. Meteorol. Soc., 94,
1653–1660, https://doi.org/10.1175/BAMS-D-12-00160.1, 2013.
Melnikov, V. and Straka, J.: Axis ratios and flutter angles of cloud ice
particles: Retrievals from radar data, J. Atmos. Ocean. Tech., 30,
1691–1703, https://doi.org/10.1175/JTECH-D-12-00212.1, 2013.
Menzel, W. P., Frey, R. A., Zhang, H., Wylie, D. P., Moeller, C. C., Holz, R. E., Maddux, B., Baum, B. A., Strabala, K. I., and Gumley, L. E.: MODIS global
cloud-top pressure and amount estimation: algorithm description and results,
J. Appl. Meteorol. Clim., 47, 1175–1198, https://doi.org/10.1175/2007JAMC1705.1, 2008.
Miles, N. L., Verlinde, J., and Clothiaux, E. E.: Cloud droplet size
distributions in low-level stratiform clouds, J. Atmos. Sci., 57, 295–311,
https://doi.org/10.1175/1520-0469(2000)057<0295:CDSDIL>2.0.CO;2, 2000.
Mishchenko, M. I. and Travis, L. D.: Capabilities and limitations of a
current FORTRAN implementation of the T-matrix method for randomly oriented,
rotationally symmetric scatterers, J. Quant. Spectrosc. Ra.,
60, 309–324, https://doi.org/10.1016/S0022-4073(98)00008-9, 1998.
Moisseev, D., von Lerber, A., and Tiira, J.: Quantifying the effect of
riming on snowfall using ground-based observations, J. Geophys. Res., 122,
4019–4037, https://doi.org/10.1002/2016JD026272, 2017.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, updated daily, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6M043C6, 2000.
Niu, S. J. and He, Z. Z.: Study on the water drop size distribution (WDSD)
in precipitous stratiform clouds, Plateau Meteor., 14, 114–120, http://www.gyqx.ac.cn/EN/Y1995/V14/I1/114 (last access: 26 March 2023), 1995.
Nowell, H., Liu, G. S., and Honeyage, R.: Modeling the microwave
single-scattering properties of aggregate snowflakes, J. Geophys. Res., 118,
7873–7885, https://doi.org/10.1002/jgrd.50620, 2013.
Ray, P.: Broadband complex refractive indices of ice and water, Appl. Opt.,
11, 1836–1844, https://doi.org/10.1364/AO.11.001836, 1972.
Ryzhkov, A., Pinsky, M., Pokrovsky, A., and Khain, A.: Polarimetric radar
observation operator for a cloud model with spectral microphysics, J. Appl.
Meteorol. Clim., 50, 873–894, https://doi.org/10.1175/2010JAMC2363.1, 2011.
Ryzhkov, A. V. and Zrnic, D. S.: Radar polarimetry for weather
observations, Springer Press, Cham, Switzerland, https://doi.org/10.1007/978-3-030-05093-1, 2019.
Sassen, K., Matrosov, S., and Campbell, J.: CloudSat spaceborne 94 GHz radar
bright bands in the melting layer: an attenuation-driven upside-down lidar
analog, Geophys. Res. Lett., 34, L16818, https://doi.org/10.1029/2007GL030291, 2007.
Seto, S., Iguchi, T., Meneghini, R., Awaka, J., Kubota, T., Masaki, T., and
Takahashi, N.: The precipitation rate retrieval algorithms for the GPM
dual-frequency precipitation radar, J. Meteorol. Soc. Jpn., 99, 205–237, https://doi.org/10.2151/jmsj.2020-010, 2021.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z. Q., Berner,
J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X. Y.: A
description of the advanced research WRF version 4, Tech. Note Rep.,
NCAR/TN-556+STR, NCAR, Boulder, CO, USA, https://doi.org/10.5065/1dfh-6p97, 2019.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang,
Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., and Durden, S. L.: The CloudSat mission and the A-train: a new dimension of space-based
observations of clouds and precipitation, B. Am. Meteorol. Soc., 83,
1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002.
Stephens, G. L., Vane, D. G., Tanelli, S., Im, E., and Suzuki, K.: CloudSat
mission: Performance and early science after the first year of operation, J.
Geophys. Res., 113, D00A18, https://doi.org/10.1029/2008JD009982, 2008.
Stephens, G. L., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C.,
L'Ecuyer, T., and Lebsock, M.: CloudSat and CALIPSO within the A-Train: Ten
years of actively observing the Earth system, B. Am. Meteorol. Soc., 99,
569–581, https://doi.org/10.1175/BAMS-D-16-0324.1, 2018.
Sy, O. O., Tanelli, S., Durden, S. L., Heymsfield, A., Bansemer, A., Kuo,
K.-S., Niamsuwan, N., Beauchamp, R. M., Chandrasekar, V., Vega, M., and
Johnson, M. P.: Impact of mass-size parameterizations of frozen hydrometeors
on microphysical retrievals: evaluation by matching radar to in situ
observations from GCPEX and OLYMPEx, J. Atmos. Ocean. Tech., 37, 93–112,
https://doi.org/10.1175/JTECH-D-19-0104.1, 2020.
Szyrmer, W. and Zawadzki, I.: Snow studies. Part II: Average relationship
between mass of snowflakes and their terminal fall velocity, J. Atmos. Sci.,
67, 3319–3335, https://doi.org/10.1175/2010JAS3390.1, 2010.
Tanelli, S., Durden, S. L., and Im, E.: CloudSat's cloud profiling radar after two years in orbit: performance, calibration, and processing, IEEE T.
Geosci. Remote, 46, 3560–3573, https://doi.org/10.1109/TGRS.2008.2002030, 2008.
Tanelli, S., Haddad, Z. S., Im, E., and Durden, S. L.: Radar concepts for
the next generation of spaceborne observations of cloud and precipitation
processes, IEEE Radar Conference, 23–27 April 2018, Oklahoma City, OK, USA,
https://doi.org/10.1109/RADAR.2018.8378741, 2018.
Tiira, J., Moisseev, D. N., von Lerber, A., Ori, D., Tokay, A., Bliven, L. F., and Petersen, W.: Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland, Atmos. Meas. Tech., 9, 4825–4841, https://doi.org/10.5194/amt-9-4825-2016, 2016.
Tinel, C., Testud, J., Pelon, J., Hogan, R. J., Protat, A., Delano, J., and
Bouniol, D.: The retrieval of ice-cloud properties from cloud radar and
lidar synergy, J. Appl. Meteorol., 44, 860–875, https://doi.org/10.1175/JAM2229.1,
2005.
Tomita, H.: New microphysical schemes with five and six categories by
diagnostic generation of cloud ice, J. Meteorol. Soc. Jpn., 86A, 121–142,
https://doi.org/10.2151/jmsj.86A.121, 2008.
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.
Wang, D. W., Liu, L. P., Zhong, L. Z., Wei, Y. Q., and Wang, X. B.: Analysis of the characters of melting layer of cloud radar data and its identification, Meteorol. Mon., 38, 712–721, https://doi.org/10.7519/j.issn.1000-0526.2012.06.009, 2012.
Wang, Y., Han, T., Guo, J. C., Jiang, K., Li, R., Shao, W. C., and Liu, G. S.: Simulation of the capability of Ku, Ka and W tri-frequency satellite-borne radar measuring the three-dimensional structure of cloud and precipitation, Chin. Sci. Bull., 64, 430–443, https://doi.org/10.1360/N972018-00089, 2019.
Wen, L., Zhao, K., Yang, Z. L., Chen, H. N., Huang, H., Chen, G., and Yang,
Z. W.: Microphysics of stratiform and convective precipitation during Meiyu
season in eastern China, J. Geophys. Res., 125, e2020JD032677, https://doi.org/10.1029/2020JD032677, 2020.
Wood, N. B. and L'Ecuyer, T. S.: What millimeter-wavelength radar reflectivity reveals about snowfall: an information-centric analysis, Atmos. Meas. Tech., 14, 869–888, https://doi.org/10.5194/amt-14-869-2021, 2021.
Wood, N. B., L'Ecuyer, T. S., Bliven, F. L., and Stephens, G. L.: Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity, Atmos. Meas. Tech., 6, 3635–3648, https://doi.org/10.5194/amt-6-3635-2013, 2013.
Wood, N. B., L'Ecuyer, T. S., Heymsfield, A. J., and Stephens, G. L.:
Microphysical constraints on millimeter-wavelength scattering properties of
snow particles, J. Appl. Meteorol. Clim., 54, 909–931, https://doi.org/10.1175/JAMC-D-14-0137.1, 2015.
Woods, C. P., Stoelinga, M. T., and Locatelli, J. D.: Size spectra of snow
particles measured in wintertime precipitation in the Pacific Northwest, J.
Atmos. Sci., 65, 189–205, https://doi.org/10.1175/2007JAS2243.1, 2008.
Wu, Q., Hu, Y., and Lu, N. M.: A simulation research on the wave bands selection of a satellite-borne rain measuring radar, Acta Meteorol. Sin., 69, 344–351, https://doi.org/10.11676/qxxb2011.029, 2011.
Wu, Q., Yang, M. L., Dou, F. L., Guo, Y., and An, D. W.: A study of cloud
parameters retrieval algorithm for spaceborne millimeter wavelength cloud
radar, Acta Meteorol. Sin., 76, 160–168, https://doi.org/10.11676/qxxb2017.069, 2018.
Yin, J. F., Wang, D. H., and Zhai, G. Q.: Long-term in situ measurements of
the cloud-precipitation microphysical properties over East Asia, Atmos.
Res., 102, 206–217, https://doi.org/10.1016/j.atmosres.2011.07.002, 2011.
Yin, M. T., Liu, G. S., 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.
Zhang, G. F.: Weather radar polarimetry, CRC Press, New York, NY, https://doi.org/10.1201/9781315374666, 2017.
Short summary
Forward modeling of spaceborne millimeter-wave radar composed of eight submodules is presented. We quantify the uncertainties in radar reflectivity that may be caused by the physical model parameters via a sensitivity analysis. The simulations with improved and conventional settings are compared with CloudSat data, and the simulation results are evaluated and analyzed. The results are instructive to the optimization of forward modeling and microphysical parameter retrieval.
Forward modeling of spaceborne millimeter-wave radar composed of eight submodules is presented....
