Articles | Volume 10, issue 5
https://doi.org/10.5194/amt-10-1859-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-10-1859-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data
Sanggyun Lee
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology (UNIST), Ulsan, 44949, South Korea
Hyangsun Han
Unit of Arctic Sea-Ice prediction, Korea Polar Research Institute,
Incheon, 21990, South Korea
Jungho Im
CORRESPONDING AUTHOR
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology (UNIST), Ulsan, 44949, South Korea
Eunna Jang
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology (UNIST), Ulsan, 44949, South Korea
Myong-In Lee
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology (UNIST), Ulsan, 44949, South Korea
Related authors
Sanggyun Lee, Hyun-cheol Kim, and Jungho Im
The Cryosphere, 12, 1665–1679, https://doi.org/10.5194/tc-12-1665-2018, https://doi.org/10.5194/tc-12-1665-2018, 2018
Short summary
Short summary
Arctic sea ice leads play a major role in exchanging heat and momentum between the Arctic atmosphere and ocean. In this study, we propose a novel lead
detection approach based on waveform mixture analysis. The performance of the proposed approach in detecting leads was promising when compared to the
existing methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters, as it directly uses L1B waveform data.
Dongmin Kim, Myong-In Lee, Su-Jong Jeong, Jungho Im, Dong Hyun Cha, and Sanggyun Lee
Biogeosciences Discuss., https://doi.org/10.5194/bg-2016-536, https://doi.org/10.5194/bg-2016-536, 2016
Manuscript not accepted for further review
Short summary
Short summary
This study compares historical simulations of the terrestrial carbon cycle produced by 10 ESMs that participated in the CMIP5. The models show noticeable deficiencies compared to the MODIS data and large differences among the simulations, although the MME mean provides a realistic global mean value and spatial distributions. MME is reflected by the systematic biases of simulated biogeochemical processes which depends on temperature conditions strongly in every plant functional types.
Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-221, https://doi.org/10.5194/gmd-2023-221, 2023
Preprint under review for GMD
Short summary
Short summary
We developed a snow assimilation system using satellite data based on a land surface model. The snow states produced by the assimilation system demonstrate high performance in all regions, including transition regions, compared to the satellite data and land model. As snow significantly influences energy and water balance at the atmosphere-land boundary, this approach allows for a more accurate prediction of atmospheric conditions by realistically representing atmosphere-land interactions.
Daehyeon Han, Jungho Im, Yeji Shin, and Juhyun Lee
Geosci. Model Dev., 16, 5895–5914, https://doi.org/10.5194/gmd-16-5895-2023, https://doi.org/10.5194/gmd-16-5895-2023, 2023
Short summary
Short summary
To identify the key factors affecting quantitative precipitation nowcasting (QPN) using deep learning (DL), we carried out a comprehensive evaluation and analysis. We compared four key factors: DL model, length of the input sequence, loss function, and ensemble approach. Generally, U-Net outperformed ConvLSTM. Loss function and ensemble showed potential for improving performance when they synergized well. The length of the input sequence did not significantly affect the results.
Young Jun Kim, Hyun-Cheol Kim, Daehyeon Han, Sanggyun Lee, and Jungho Im
The Cryosphere, 14, 1083–1104, https://doi.org/10.5194/tc-14-1083-2020, https://doi.org/10.5194/tc-14-1083-2020, 2020
Short summary
Short summary
In this study, we proposed a novel 1-month sea ice concentration (SIC) prediction model with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). The proposed CNN model was evaluated and compared with the two baseline approaches, random-forest and simple-regression models, resulting in better performance. This study also examined SIC predictions for two extreme cases in 2007 and 2012 in detail and the influencing factors through a sensitivity analysis.
Nakbin Choi, Kyu-Myong Kim, Young-Kwon Lim, and Myong-In Lee
The Cryosphere, 13, 3007–3021, https://doi.org/10.5194/tc-13-3007-2019, https://doi.org/10.5194/tc-13-3007-2019, 2019
Short summary
Short summary
This study compares the decadal changes of the leading patterns of sea level pressure between the early (1982–1997) and the recent (1998–2017) periods as well as their influences on the Arctic sea ice extent (SIE) variability. The correlation between the Arctic Dipole (AD) mode and SIE becomes significant in the recent period, not in the past, due to its spatial pattern change. This tends to enhance meridional wind over the Fram Strait and sea ice discharge to the Atlantic.
Seohui Park, Minso Shin, Jungho Im, Chang-Keun Song, Myungje Choi, Jhoon Kim, Seungun Lee, Rokjin Park, Jiyoung Kim, Dong-Won Lee, and Sang-Kyun Kim
Atmos. Chem. Phys., 19, 1097–1113, https://doi.org/10.5194/acp-19-1097-2019, https://doi.org/10.5194/acp-19-1097-2019, 2019
Short summary
Short summary
This study proposed machine-learning-based models to estimate ground-level particulate matter concentrations using satellite observations and numerical model-derived data. Aerosol optical depth was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed and solar radiation. The results show that the proposed models produced better performance than the existing approaches, particularly in improving on the biases of the process-based models.
Sanggyun Lee, Hyun-cheol Kim, and Jungho Im
The Cryosphere, 12, 1665–1679, https://doi.org/10.5194/tc-12-1665-2018, https://doi.org/10.5194/tc-12-1665-2018, 2018
Short summary
Short summary
Arctic sea ice leads play a major role in exchanging heat and momentum between the Arctic atmosphere and ocean. In this study, we propose a novel lead
detection approach based on waveform mixture analysis. The performance of the proposed approach in detecting leads was promising when compared to the
existing methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters, as it directly uses L1B waveform data.
Dongmin Kim, Myong-In Lee, and Eunkyo Seo
Biogeosciences Discuss., https://doi.org/10.5194/bg-2016-549, https://doi.org/10.5194/bg-2016-549, 2017
Preprint retracted
Short summary
Short summary
This study investigates the impacts of temperature sensitivity of soil respiration (Q10) on the terrestrial carbon cycle using CLM4 off-line simulation. This study develops a new parameterization for determining Q10 by considering the soil respiration dependence on soil temperature and moisture obtained by multiple regression. The results show that distribution of heterogenous Q10 induces to overcome the soil respiration and GPP distribution comparing with original Q10 parameterization.
Dongmin Kim, Myong-In Lee, Su-Jong Jeong, Jungho Im, Dong Hyun Cha, and Sanggyun Lee
Biogeosciences Discuss., https://doi.org/10.5194/bg-2016-536, https://doi.org/10.5194/bg-2016-536, 2016
Manuscript not accepted for further review
Short summary
Short summary
This study compares historical simulations of the terrestrial carbon cycle produced by 10 ESMs that participated in the CMIP5. The models show noticeable deficiencies compared to the MODIS data and large differences among the simulations, although the MME mean provides a realistic global mean value and spatial distributions. MME is reflected by the systematic biases of simulated biogeochemical processes which depends on temperature conditions strongly in every plant functional types.
H. M. Park, M. A. Kim, and J. Im
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 325–327, https://doi.org/10.5194/isprs-archives-XLI-B7-325-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-325-2016, 2016
S. Park and J. Im
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 703–704, https://doi.org/10.5194/isprs-archives-XLI-B7-703-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-703-2016, 2016
J.-M. Yoo, M.-J. Jeong, D. Kim, W. R. Stockwell, J.-H. Yang, H.-W. Shin, M.-I. Lee, C.-K. Song, and S.-D. Lee
Atmos. Chem. Phys., 15, 10857–10885, https://doi.org/10.5194/acp-15-10857-2015, https://doi.org/10.5194/acp-15-10857-2015, 2015
Short summary
Short summary
Major air pollutants (O3, NO2, SO2, CO, PM10, and VOCs) with long-term records from a dense observation network over Korea were extensively analyzed with land-use types, classified by Korean government, consistent with satellite-observed land covers. The weekly cycles of the pollutant showed different behaviors with the types. Regardless of land-use types, ozone has an increasing trend, while the other pollutants have decreasing trends. Most areas in Korea were VOCs-limited for ozone chemistry.
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products
Raman lidar-derived optical and microphysical properties of ice crystals within thin Arctic clouds during PARCS campaign
Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements
Deep convective cloud system size and structure across the global tropics and subtropics
A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images
Numerical model generation of test frames for pre-launch studies of EarthCARE's retrieval algorithms and data management system
Segmentation of polarimetric radar imagery using statistical texture
Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives
The EarthCARE Mission: Science Data Processing Chain Overview
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: Analysis with the two-way transmittance method of 5 years of lidar measurements
AI-derived 3D cloud tomography from geostationary 2D satellite data
Particle inertial effects on radar Doppler spectra simulation
Detection of aerosol and cloud features for the EarthCARE atmospheric lidar (ATLID): the ATLID FeatureMask (A-FM) product
A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: the ACM-CAP product
Incorporating EarthCARE observations into a multi-lidar cloud climate record: the ATLID (Atmospheric Lidar) cloud climate product
Introduction to EarthCARE synthetic data using a global storm-resolving simulation
Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data
Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS
Global evaluation of Doppler velocity errors of EarthCARE cloud-profiling radar using a global storm-resolving simulation
Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product
Cloud mask algorithm from the EarthCARE Multi-Spectral Imager: the M-CM products
Across-track extension of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-3D product
Insights into 3D cloud radiative transfer effects for the Orbiting Carbon Observatory
Evaluation of polarimetric ice microphysical retrievals with OLYMPEX campaign data
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation
Simulation and sensitivity analysis for cloud and precipitation measurements via spaceborne millimeter-wave radar
The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations
Near-global distributions of overshooting tops derived from Terra and Aqua MODIS observations
Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy
Optimizing cloud motion estimation on the edge with phase correlation and optical flow
A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations
The CHROMA cloud-top pressure retrieval algorithm for the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission
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
Determination of the vertical distribution of in-cloud particle shape using SLDR mode 35-GHz scanning cloud radar
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
Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network
Intercomparison of Sentinel-5P TROPOMI cloud products for tropospheric trace gas retrievals
Improved spectral processing for a multi-mode pulse compression Ka–Ku-band cloud radar system
Uncertainty-bounded estimates of ash cloud properties using the ORAC algorithm: application to the 2019 Raikoke eruption
Ice water path retrievals from Meteosat-9 using quantile regression neural networks
An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties
Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks
Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network
Top-of-the-atmosphere reflected shortwave radiative fluxes from GOES-R
Optimizing radar scan strategies for tracking isolated deep convection using observing system simulation experiments
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
Short summary
Short summary
The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
Short summary
Short summary
The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
Short summary
Short summary
The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
Short summary
Short summary
Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
Short summary
Short summary
A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
Short summary
Short summary
Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
Short summary
Short summary
The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
Short summary
Short summary
We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
Short summary
Short summary
Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
EGUsphere, https://doi.org/10.5194/egusphere-2023-1998, https://doi.org/10.5194/egusphere-2023-1998, 2023
Short summary
Short summary
The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA-JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors development, with processors developed by teams in Europe, Japan and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
Short summary
Short summary
We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth Judd Welton, and Simone Lolli
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-134, https://doi.org/10.5194/amt-2023-134, 2023
Revised manuscript accepted for AMT
Short summary
Short summary
In this paper, a statistical study of cirrus geometrical and optical properties based on 5 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The optical properties of cirrus clouds have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems.
Sarah Brüning, Stefan Niebler, and Holger Tost
EGUsphere, https://doi.org/10.5194/egusphere-2023-1834, https://doi.org/10.5194/egusphere-2023-1834, 2023
Short summary
Short summary
This study applies a Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. This data is fed into a neural network to predict cloud reflectivities on the whole satellite domain between 5–24 km height. With an average RMSE of 3.41 dBZ, we contribute to closing existing data gaps in the representation of real-world cloud structures.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
Short summary
Short summary
We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
Short summary
Short summary
The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023, https://doi.org/10.5194/amt-16-3459-2023, 2023
Short summary
Short summary
We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
Short summary
Short summary
The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech., 16, 3331–3344, https://doi.org/10.5194/amt-16-3331-2023, https://doi.org/10.5194/amt-16-3331-2023, 2023
Short summary
Short summary
JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM (Nonhydrostatic ICosahedral
Atmospheric Model) simulation with 3.5 km horizontal resolution and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals.
Philipp Gregor, Tobias Zinner, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 16, 3257–3271, https://doi.org/10.5194/amt-16-3257-2023, https://doi.org/10.5194/amt-16-3257-2023, 2023
Short summary
Short summary
This work introduces MACIN, a model for short-term forecasting of direct irradiance for solar energy applications. MACIN exploits cloud images of multiple cameras to predict irradiance. The model is applied to artificial images of clouds from a weather model. The artificial cloud data allow for a more in-depth evaluation and attribution of errors compared with real data. Good performance of derived cloud information and significant forecast improvements over a baseline forecast were found.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Marc Nicolas
Atmos. Meas. Tech., 16, 3221–3243, https://doi.org/10.5194/amt-16-3221-2023, https://doi.org/10.5194/amt-16-3221-2023, 2023
Short summary
Short summary
The optimal estimation formalism is applied to OSIRIS airborne high-resolution multi-angular measurements to retrieve COT and Reff. The corresponding uncertainties related to measurement errors, which are up to 6 and 12 %, the non-retrieved parameters, which are less than 0.5 %, and the cloud model assumptions show that the heterogeneous vertical profiles and the 3D radiative transfer effects lead to average uncertainties of 5 and 4 % for COT and 13 and 9 % for Reff.
Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
Atmos. Meas. Tech., 16, 3211–3219, https://doi.org/10.5194/amt-16-3211-2023, https://doi.org/10.5194/amt-16-3211-2023, 2023
Short summary
Short summary
The CPR on the EarthCARE satellite is the first satellite-borne Doppler radar. We evaluated the effectiveness of horizontal integration and the unfolding method for the reduction of the Doppler error (the standard deviation of the random error) in the CPR_ECO product. The error was higher in the tropics than in the other latitudes due to frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the errors become small enough.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
Short summary
Short summary
We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
Anja Hünerbein, Sebastian Bley, Stefan Horn, Hartwig Deneke, and Andi Walther
Atmos. Meas. Tech., 16, 2821–2836, https://doi.org/10.5194/amt-16-2821-2023, https://doi.org/10.5194/amt-16-2821-2023, 2023
Short summary
Short summary
The Multi-Spectral Imager (MSI) on board the EarthCARE satellite will provide the information needed for describing the cloud and aerosol properties in the cross-track direction, complementing the measurements from the Cloud Profiling Radar, Atmospheric Lidar and Broad-Band Radiometer. The accurate discrimination between clear and cloudy pixels is an essential first step. Therefore, the cloud mask algorithm provides a cloud flag, cloud phase and cloud type product for the MSI observations.
Zhipeng Qu, Howard W. Barker, Jason N. S. Cole, and Mark W. Shephard
Atmos. Meas. Tech., 16, 2319–2331, https://doi.org/10.5194/amt-16-2319-2023, https://doi.org/10.5194/amt-16-2319-2023, 2023
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 provides the information about the buffer zone sizes of 3D assessment domains and the ranking scores for selecting the best 3D assessment domains. These output variables are needed to run 3D radiative transfer models for the radiative closure assessment of EarthCARE’s L2 retrievals.
Steven T. Massie, Heather Cronk, Aronne Merrelli, Sebastian Schmidt, and Steffen Mauceri
Atmos. Meas. Tech., 16, 2145–2166, https://doi.org/10.5194/amt-16-2145-2023, https://doi.org/10.5194/amt-16-2145-2023, 2023
Short summary
Short summary
This paper provides insights into the effects of clouds on Orbiting Carbon Observatory (OCO-2) measurements of CO2. Calculations are carried out that indicate the extent to which this satellite experiment underestimates CO2, due to these cloud effects, as a function of the distance between the surface observation footprint and the nearest cloud. The paper discusses how to lessen the influence of these cloud effects.
Armin Blanke, Andrew J. Heymsfield, Manuel Moser, and Silke Trömel
Atmos. Meas. Tech., 16, 2089–2106, https://doi.org/10.5194/amt-16-2089-2023, https://doi.org/10.5194/amt-16-2089-2023, 2023
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 agreed most with aircraft observations in regions with pronounced radar signatures, but uncertainties were identified that indicate issues of some retrievals, particularly in warmer temperature regimes.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, https://doi.org/10.5194/amt-16-1803-2023, 2023
Short summary
Short summary
We describe a new method for measuring the 3D spatial variations in water within clouds using the reflected light of the Sun viewed at multiple different angles by satellites. This is a great improvement over older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Leilei Kou, Zhengjian Lin, Haiyang Gao, Shujun Liao, and Piman Ding
Atmos. Meas. Tech., 16, 1723–1744, https://doi.org/10.5194/amt-16-1723-2023, https://doi.org/10.5194/amt-16-1723-2023, 2023
Short summary
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.
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.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
EGUsphere, https://doi.org/10.5194/egusphere-2022-1431, https://doi.org/10.5194/egusphere-2022-1431, 2023
Short summary
Short summary
The Vertical-Distribution-of-Particle-Shape (VDPS) method, introduced in this study, aids one to characterize the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR and cross-correlation coefficient.
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.
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.
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.
Cited articles
Amorati, R., Alberoni, P. P., Levizzani, V., and Nanni, S.: IR-based satellite and radar rainfall estimates of convective storms over northern Italy, Meteorol. Appl., 7, 1–18, https://doi.org/10.1017/S1350482700001328, 2000.
Banacos, P. C. and Schultz, D. M.: The Use of Moisture Flux Convergence in Forecasting Convective Initiation: Historical and Operational Perspectives, Weather Forecast., 20, 351–366, https://doi.org/10.1175/WAF858.1, 2005.
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y., Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama, H., Uesawa, D., Yokota, H., and Yoshida, R.: An Introduction to Himawari-8/9; Japan's New-Generation Geostationary Meteorological Satellites, J. Meteorol. Soc. Jpn., 94, 151–183, https://doi.org/10.2151/jmsj.2016-009, 2016.
Breiman, L.: Random Forests, Machine Learning, 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001.
Craven, J. P., Jewell, R. E., and Brooks, H. E.: Comparison between Observed Convective Cloud-Base Heights and Lifting Condensation Level for Two Different Lifted Parcels, Weather Forecast., 17, 885–890, https://doi.org/10.1175/1520-0434(2002)017<0885:CBOCCB>2.0.CO;2, 2002.
Guo, Z. and Du, S.: Mining parameter information for building extraction and change detection with very high resolution imagery and GIS data, GIS. Remote Sens., 54, 38–63, 2017.
Haile, A. T., Rientjes, T., Gieske, A., and Gebremichael, M.: Multispectral remote sensing for rainfall detection and estimation at the source of the Blue Nile River, Int. J. Appl. Earth Obs. Geoinf., S76–S82, 2010.
Han, H., Lee, S., Im, J., Kim, M., Lee, M. I., Ahn, M. H., and Chung, S. R.: Detection of convective initiation using Meteorological Imager onboard Communication, Ocean, and Meteorological satellite based on machine learning approaches, Remote Sens., 7, 9184–9204, 2015.
Hane, C. E., Rabin, R. M., Crawford, T. M., Bluestein, H. B., and Baldwin, M. E.: A Case Study of Severe Storm Development along a Dryline within a Synoptically Active Environment, Part II: Multiple Boundaries and Convective Initiation, Mon. Weather Rev., 130, 900–920, https://doi.org/10.1175/1520-0493(2002)130<0900:ACSOSS>2.0.CO;2, 2002.
Hosmer, D. W. and Lemeshow, S.: Applied Logistic Regressio, John Wiley and Sons, Inc., New York, 528 pp., 2000.
Houze, R. A.: Mesoscale convective systems, Rev. Geophys., 42, RG4003, 10.1029/2004RG000150, 2004.
Im, J., Jensen, J. R., and Tullis, J. A.: Object-based change detection using correlation image analysis and image segmentation, Int. J. Remote Sens., 29, 399–423, https://doi.org/10.1080/01431160601075582, 2008.
Im, J., Jensen, J., Jensen, R., Gladden, J., Waugh, J., and Serrato, M.: Vegetation cover analysis of hazardous waste sites in utah and arizona using hyperspectral remote sensing, Remote Sens., 4, 327–353, 2012.
Jensen, J. R. and Im, J.: Remote Sensing Change Detection in Urban Environments, in: Geo-Spatial Technologies in Urban Environments: Policy, Practice, and Pixels, edited by: Jensen, R. R., Gatrell, J. D., and McLean, D., Springer Berlin Heidelberg, Berlin, Heidelberg, 7–31, 2007.
Jewett, C. P. and Mecikalski, J. R.: Adjusting thresholds of satellite-based convective initiation interest fields based on the cloud environment, J. Geophys. Res-Atmos., 118, 12649–612660, https://doi.org/10.1002/2013JD019700, 2013.
Jorgensen, D. P. and LeMone, M. A.: Vertical Velocity Characteristics of Oceanic Convection, J. Atmos. Sci., 46, 621–640, https://doi.org/10.1175/1520-0469(1989)046<0621:VVCOOC>2.0.CO;2, 1989.
Kar, S. K. and Ha, K.-J.: Characteristic Differences of Rainfall and Cloud-to-Ground Lightning Activity over South Korea during the Summer Monsoon Season, Mon. Weather Rev., 131, 2312–2323, https://doi.org/10.1175/1520-0493(2003)131<2312:CDORAC>2.0.CO;2, 2003.
Kim, D. H. and Ahn, M. H.: Introduction of the in-orbit test and its performance for the first meteorological imager of the Communication, Ocean, and Meteorological Satellite, Atmos. Meas. Tech., 7, 2471–2485, 10.5194/amt-7-2471-2014, 2014.
Kim, H. W. and Lee, D. K.: An Observational Study of Mesoscale Convective Systems with Heavy Rainfall over the Korean Peninsula, Weather Forecast., 21, 125–148, https://doi.org/10.1175/WAF912.1, 2006.
Kim, M., Im, J., Han, H., Kim, J., Lee, S., Shin, M., and Kim, H.-C.: Landfast sea ice monitoring using multisensor fusion in the Antarctic, GIS. Remote Sens., 52, 239–256, https://doi.org/10.1080/15481603.2015.1026050, 2015.
Kim, Y. H., Im, J., Ha, H. K., Choi, J.-K., and Ha, S.: Machine learning approaches to coastal water quality monitoring using GOCI satellite data, GIS. Remote Sens., 51, 158–174, https://doi.org/10.1080/15481603.2014.900983, 2014.
Lawrence, R. L. and Wright, A.: Rule-based classification systems using classification and regression tree (CART) analysis, Photogramm. Eng. Rem. S., 67, 1137–1142, 2001.
Li, M., Im, J., and Beier, C.: Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest, GIS. Remote Sens., 50, 361–384, https://doi.org/10.1080/15481603.2013.819161, 2013.
Li, M., Im, J., Quackenbush, L. J., and Liu, T.: Forest Biomass and Carbon Stock Quantification Using Airborne LiDAR Data: A Case Study Over Huntington Wildlife Forest in the Adirondack Park, IEEE J. Sel. Top. Appl. Earth Obser. Remote Sens., 7, 3143–3156, https://doi.org/10.1109/JSTARS.2014.2304642, 2014.
Liu, T., Im, J., and Quackenbush, L. J.: A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification, ISPRS J. Photogramme., 110, 34–47, https://doi.org/10.1016/j.isprsjprs.2015.10.002, 2015.
Lu, Z., Im, J., Quackenbush, L. J., and Yoo, S.: Remote Sensing-based House Value Estimation Using an Optimized Regional Regression Model, Photogramm. Eng. Remote Sens., 79, 809–820, https://doi.org/10.14358/PERS.79.9.809, 2013.
Lu, Z., Im, J., Rhee, J., and Hodgson, M.: Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data, Landscape, Urban Plan., 130, 134–148, https://doi.org/10.1016/j.landurbplan.2014.07.005, 2014.
Mecikalski, J. R. and Bedka, K. M.: Forecasting Convective Initiation by Monitoring the Evolution of Moving Cumulus in Daytime GOES Imagery, Mon. Weather Rev., 134, 49–78, https://doi.org/10.1175/MWR3062.1, 2006.
Mecikalski, J. R., Bedka, K. M., Paech, S. J., and Litten, L. A.: A Statistical Evaluation of GOES Cloud-Top Properties for Nowcasting Convective Initiation, Mon. Weather Rev., 136, 4899–4914, https://doi.org/10.1175/2008MWR2352.1, 2008.
Mecikalski, J. R., MacKenzie, W. M., Koenig, M., and Muller, S.: Cloud-Top Properties of Growing Cumulus prior to Convective Initiation as Measured by Meteosat Second Generation. Part I: Infrared Fields, J. Appl. Meteorol. Climatol., 49, 521–534, https://doi.org/10.1175/2009JAMC2344.1, 2009.
Mecikalski, J. R., MacKenzie, W. M., König, M., and Muller, S.: Cloud-Top Properties of Growing Cumulus prior to Convective Initiation as Measured by Meteosat Second Generation. Part II: Use of Visible Reflectance, J. Appl. Meteorol. Climatol., 49, 2544–2558, https://doi.org/10.1175/2010JAMC2480.1, 2010.
Mecikalski, J. R., Williams, J. K., Jewett, C. P., Ahijevych, D., LeRoy, A., and Walker, J. R.: Probabilistic 0–1-h Convective Initiation Nowcasts that Combine Geostationary Satellite Observations and Numerical Weather Prediction Model Data, J. Appl. Meteorol. Climatol., 54, 1039–1059, https://doi.org/10.1175/JAMC-D-14-0129.1, 2015.
Merk, D. and Zinner, T.: Detection of convective initiation using Meteosat SEVIRI: implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM, Atmos. Meas. Tech., 6, 1903–1918, 10.5194/amt-6-1903-2013, 2013.
Miyamoto, Y., Kajikawa, Y., Yoshida, R., Yamaura, T., Yashiro, H., and Tomita, H.: Deep moist atmospheric convection in a subkilometer global simulation, Geophys. Res. Lett., 40, 4922–4926, https://doi.org/10.1002/grl.50944, 2013.
Morel, C. and Senesi, S.: A climatology of mesoscale convective systems over Europe using satellite infrared imagery. I: Methodology, Q. J. Roy. Meteorol. Soc., 128, 1953–1971, https://doi.org/10.1256/003590002320603485, 2002.
Mueller, C., Saxen, T., Roberts, R., Wilson, J., Betancourt, T., Dettling, S., Oien, N., and Yee, J.: NCAR Auto-Nowcast System, Weather Forecast., 18, 545–561, https://doi.org/10.1175/1520-0434(2003)018<0545:NAS>2.0.CO;2, 2003.
Nyarko, B., Diekkruger, B., van de Giesen, N., and Vlek, P.: Floodplain wetland mapping in the White Volta River Basin of Ghana, GIS. Remote Sens. 52, 374–395, 2015.
Park, S., Im, J., Jang, E., and Rhee, J.: Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions, Agr. Forest Meteorol., 216, 157–169, https://doi.org/10.1016/j.agrformet.2015.10.011, 2016.
Quinlan, J. R.: Data mining tools See5 and C4.5, version 2.10, available at: https://www.rulequest.com/see5-info.html (last access: 10 February 2016), 2015.
Rhee, J., Im, J., Carbone, G. J., and Jensen, J. R.: Delineation of climate regions using in-situ and remotely-sensed data for the Carolinas, Remote Sens. Environ., 112, 3099–3111, https://doi.org/10.1016/j.rse.2008.03.001, 2008.
Rhee, J., Park, S., and Lu, Z.: Relationship between land cover patterns and surface temperature in urban areas, GIS. Remote Sens., 51, 521–536, https://doi.org/10.1080/15481603.2014.964455, 2014.
Roberts, N. M. and Lean, H. W.: Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events, Mon. Weather Rev., 136, 78–97, https://doi.org/10.1175/2007MWR2123.1, 2008.
Roberts, R. D. and Rutledge, S.: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data, Weather Forecast., 18, 562–584, 2003.
Rosenfeld, D., Woodley, W. L., Lerner, A., Kelman, G., and Lindsey, D. T.: Satellite detection of severe convective storms by their retrieved vertical profiles of cloud particle effective radius and thermodynamic phase, J. Geophys. Res-Atmos., 113, D04208, https://doi.org/10.1029/2007JD008600, 2008.
Schmit, T. J., Gunshor, M. M., Menzel, W. P., and Gurka, J. J.: Introducing the next-generation Advanced Baseline Imager on GOES-R, B. Am. Meteorol. Soc. 86, 1079–1096, 2005.
Sieglaff, J. M., Cronce, L. M., Feltz, W. F., Bedka, K. M., Pavolonis, M. J., and Heidinger, A. K.: Nowcasting Convective Storm Initiation Using Satellite-Based Box-Averaged Cloud-Top Cooling and Cloud-Type Trends, J. Appl. Meteorol. Climatol., 50, 110–126, https://doi.org/10.1175/2010JAMC2496.1, 2011.
Siewert, C. W., Koenig, M., and Mecikalski, J. R.: Application of Meteosat second generation data towards improving the nowcasting of convective initiation, Meteorol. Appl., 17, 442–451, https://doi.org/10.1002/met.176, 2010.
Sobajima, A.: Rapidly Development Cumulus Areas Derivation Algorithm. Japan Meteorological Agency Algorithm Theoretical Basis Document, Meteorological Satellite Center, Tokyo, Japan, 2012.
Sohn, B. J., Ryu, G. H., Song, H. J., and Ou, M. L.: Characteristic features of warm-type rain producing heavy rainfall over the Korean Peninsula inferred from TRMM measurements, Mon. Weather Rev., 141, 3873–3888l, 2013.
Song, H. J. and Sohn, B. J.: Two heavy rainfall types over the Korean peninsula in the humid East Asian summer environment: A satellite observation study, Mon. Weather Rev., 143, 363–382, 2015.
Torbick, N. and Corbiere, M.: Mapping urban sprawl and impervious surfaces in the northeast United States for the past four decades, GIS. Remote Sens., 52, 746–764, https://doi.org/10.1080/15481603.2015.1076561, 2015.
Trier, S. B., Chen, F., and Manning, K. W.: A Study of Convection Initiation in a Mesoscale Model Using High-Resolution Land Surface Initial Conditions, Mon. Weather Rev., 132, 2954–2976, https://doi.org/10.1175/MWR2839.1, 2004.
Tu, J. V.: Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes, J. Clin. Epidemiol., 49, 1225–1231, https://doi.org/10.1016/S0895-4356(96)00002-9, 1996.
Walker, J. R., MacKenzie, W. M., Mecikalski, J. R., and Jewett, C. P.: An Enhanced Geostationary Satellite–Based Convective Initiation Algorithm for 0–2-h Nowcasting with Object Tracking, J. Appl. Meteorol. Climatol., 51, 1931–1949, https://doi.org/10.1175/JAMC-D-11-0246.1, 2012.
Walker, J. R. and Mecikalski, J. R.: Algorithm theoretical basis document (ATBD) for convective initiation. NOAA NESDIS Center for Satellite Applications and Research, available at: http://www.nsstc.uah.edu/SATCAST/docs/GOES-R AWG ATBD Aviation ConvectiveInitiationv2.0.pdf (last access: 10 February 2016), 2011.
Wang, C.-C., Chen, G. T.-J., and Carbone, R. E.: A Climatology of Warm-Season Cloud Patterns over East Asia Based on GMS Infrared Brightness Temperature Observations, Mon. Weather Rev., 132, 1606–1629, https://doi.org/10.1175/1520-0493(2004)132<1606:ACOWCP>2.0.CO;2, 2004.
Weckwerth, T. M. and Parsons, D. B.: A Review of Convection Initiation and Motivation for IHOP_2002, Mon. Weather Rev., 134, 5–22, https://doi.org/10.1175/MWR3067.1, 2006.
Vondou, D. A., Nzeukou, A., and Kamga, F. M.: Diurnal cycle of convective activity over the West of Central Africa based on meteosat images, Int. J. Appl. Earth Obs. Geoinf., S58–S62, 2010.
Yoo, S., Im, J., and Wagner, J. E.: Variable selection for hedonic model using machine learning approaches: A case study in Onondaga County, NY, Landscape. Urban Plan., 107, 293–306, https://doi.org/10.1016/j.landurbplan.2012.06.009, 2012.
Zinner, T., Mannstein, H., and Tafferner, A.: Cb-TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data, Meteorol. Atmos. Phys. 101, 191–210, https://doi.org/10.1007/s00703-008-0290-y, 2008.
Zuidema, P.: Convective Clouds over the Bay of Bengal, Mon. Weather Rev., 131, 780–798, https://doi.org/10.1175/1520-0493(2003)131<0780:CCOTBO>2.0.CO;2, 2003.
Short summary
Deterministic and probabilistic CI detection models based on decision trees (DT), random forest (RF), and logistic regression (LR) were developed using Himawari-8 AHI data obtained over the Korean Peninsula. We used a total of 12 interest fields including time trends to develop the models. We identified contributing variables for CI detection. DT showed a higher hit rate, while RF produced a higher critical success index. The mean lead times by the four models were in the range of 20–40 min.
Deterministic and probabilistic CI detection models based on decision trees (DT), random forest...