Articles | Volume 10, issue 5
Atmos. Meas. Tech., 10, 1859–1874, 2017
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.
Atmos. Meas. Tech., 10, 1859–1874, 2017
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.
Research article 24 May 2017
Research article | 24 May 2017
Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data
Sanggyun Lee et al.
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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
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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
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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.
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
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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
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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
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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
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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
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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
Improving cloud type classification of ground-based images using region covariance descriptors
Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers
Applying deep learning to NASA MODIS data to create a community record of marine low-cloud mesoscale morphology
Microwave single-scattering properties of non-spheroidal raindrops
Determining cloud thermodynamic phase from the polarized Micro Pulse Lidar
Improved cloud detection over sea ice and snow during Arctic summer using MERIS data
A study of polarimetric noise induced by satellite motion: Application to the 3MI and similar sensors
A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection
Cloud-top pressure retrieval with DSCOVR EPIC oxygen A- and B-band observations
Analysis of 3D Cloud Effects in OCO-2 XCO2 Retrievals
Two-dimensional and multi-channel feature detection algorithm for the CALIPSO lidar measurements
Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars
A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar
A new Orbiting Carbon Observatory 2 cloud flagging method and rapid retrieval of marine boundary layer cloud properties
CALIOP V4 cloud thermodynamic phase assignment and the impact of near-nadir viewing angles
Detection of the cloud liquid water path horizontal inhomogeneity in a coastline area by means of ground-based microwave observations: feasibility study
Synergistic radar and radiometer retrievals of ice hydrometeors
Improvement in cloud retrievals from VIIRS through the use of infrared absorption channels constructed from VIIRS+CrIS data fusion
Radiative transfer simulations and observations of infrared spectra in the presence of polar stratospheric clouds: Detection and discrimination of cloud types
Using two-stream theory to capture fluctuations of satellite-perceived TOA SW radiances reflected from clouds over ocean
Exploration of machine learning methods for the classification of infrared limb spectra of polar stratospheric clouds
Three-dimensional wind profiles using a stabilized shipborne cloud radar in wind profiler mode
Low-level liquid cloud properties during ORACLES retrieved using airborne polarimetric measurements and a neural network algorithm
MICRU background map and effective cloud fraction algorithms designed for UV/vis satellite instruments with large viewing angles
A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations
SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation
Spatial distribution of cloud droplet size properties from Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) measurements
Towards objective identification and tracking of convective outflow boundaries in next-generation geostationary satellite imagery
Cloud detection over snow and ice with oxygen A- and B-band observations from the Earth Polychromatic Imaging Camera (EPIC)
Ground-based observations of cloud and drizzle liquid water path in stratocumulus clouds
Increasing the spatial resolution of cloud property retrievals from Meteosat SEVIRI by use of its high-resolution visible channel: evaluation of candidate approaches with MODIS observations
Estimation of cloud optical thickness, single scattering albedo and effective droplet radius using a shortwave radiative closure study in Payerne
Towards an operational Ice Cloud Imager (ICI) retrieval product
Ice crystal number concentration from lidar, cloud radar and radar wind profiler measurements
Retrieval of cloud properties from spectral zenith radiances observed by sky radiometers
A new approach to estimate supersaturation fluctuations in stratocumulus cloud using ground-based remote-sensing measurements
ELIFAN, an algorithm for the estimation of cloud cover from sky imagers
Estimating solar irradiance using sky imagers
Toward autonomous surface-based infrared remote sensing of polar clouds: retrievals of cloud optical and microphysical properties
Use of spectral cloud emissivities and their related uncertainties to infer ice cloud boundaries: methodology and assessment using CALIPSO cloud products
The importance of particle size distribution and internal structure for triple-frequency radar retrievals of the morphology of snow
Calibration of the 2007–2017 record of Atmospheric Radiation Measurements cloud radar observations using CloudSat
All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud
peakTree: a framework for structure-preserving radar Doppler spectra analysis
Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm
Footprint-scale cloud type mixtures and their impacts on Atmospheric Infrared Sounder cloud property retrievals
Estimation of liquid water path below the melting layer in stratiform precipitation systems using radar measurements during MC3E
Correlated observation error models for assimilating all-sky infrared radiances
Cloud identification and classification from high spectral resolution data in the far infrared and mid-infrared
Investigating the liquid water path over the tropical Atlantic with synergistic airborne measurements
Yuzhu Tang, Pinglv Yang, Zeming Zhou, Delu Pan, Jianyu Chen, and Xiaofeng Zhao
Atmos. Meas. Tech., 14, 737–747, https://doi.org/10.5194/amt-14-737-2021, https://doi.org/10.5194/amt-14-737-2021, 2021
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An automatic cloud classification method on whole-sky images is presented. We first extract multiple pixel-level features to form region covariance descriptors (RCovDs) and then encode RCovDs by the Riemannian bag-of-feature (BoF) method to output the histogram representation. Reults show that a very high prediction accuracy can be obtained with a small number of training samples, which validate the proposed method and exhibit the competitive performance against state-of-the-art methods.
Macey W. Sandford, David R. Thompson, Robert O. Green, Brian H. Kahn, Raffaele Vitulli, Steve Chien, Amruta Yelamanchili, and Winston Olson-Duvall
Atmos. Meas. Tech., 13, 7047–7057, https://doi.org/10.5194/amt-13-7047-2020, https://doi.org/10.5194/amt-13-7047-2020, 2020
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We demonstrate an onboard cloud-screening approach to significantly reduce the amount of cloud-contaminated data transmitted from orbit. We have produced location-specific models that improve performance by taking into account the unique cloud statistics in different latitudes. We have shown that screening clouds based on their location or surface type will improve the ability for a cloud-screening tool to improve the volume of usable science data.
Tianle Yuan, Hua Song, Robert Wood, Johannes Mohrmann, Kerry Meyer, Lazaros Oreopoulos, and Steven Platnick
Atmos. Meas. Tech., 13, 6989–6997, https://doi.org/10.5194/amt-13-6989-2020, https://doi.org/10.5194/amt-13-6989-2020, 2020
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We use deep transfer learning techniques to classify satellite cloud images into different morphology types. It achieves the state-of-the-art results and can automatically process a large amount of satellite data. The algorithm will help low-cloud researchers to better understand their mesoscale organizations.
Robin Ekelund, Patrick Eriksson, and Michael Kahnert
Atmos. Meas. Tech., 13, 6933–6944, https://doi.org/10.5194/amt-13-6933-2020, https://doi.org/10.5194/amt-13-6933-2020, 2020
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Raindrops become flattened due to aerodynamic drag as they increase in mass and fall speed. This study calculated the electromagnetic interaction between microwave radiation and non-spheroidal raindrops. The calculations are made publicly available to the scientific community, in order to promote accurate representations of raindrops in measurements. Tests show that the drop shape can have a noticeable effect on microwave observations of heavy rainfall.
Jasper R. Lewis, James R. Campbell, Sebastian A. Stewart, Ivy Tan, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 13, 6901–6913, https://doi.org/10.5194/amt-13-6901-2020, https://doi.org/10.5194/amt-13-6901-2020, 2020
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In this work, the authors describe a process to determine the thermodynamic cloud phase using the Micro Pulse Lidar Network volume depolarization ratio measurements and temperature profiles from the Global Modeling and Assimilation Office GEOS-5 model. A multi-year analysis and comparisons to supercooled liquid water fractions derived from CALIPSO satellite measurements are used to demonstrate the efficacy of the method.
Larysa Istomina, Henrik Marks, Marcus Huntemann, Georg Heygster, and Gunnar Spreen
Atmos. Meas. Tech., 13, 6459–6472, https://doi.org/10.5194/amt-13-6459-2020, https://doi.org/10.5194/amt-13-6459-2020, 2020
Souichiro Hioki, Jérôme Riedi, and Mohamed S. Djellali
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-407, https://doi.org/10.5194/amt-2020-407, 2020
Revised manuscript accepted for AMT
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This research estimates the magnitude of a motion-induced noise in the measurement of polarimetric state of light by a planned instrument on a future satellite. We discovered that the motion-induced noise can not be cancelled out by spatio-temporal averaging, but it can be predicted from the along-track change of the intensity of light. With the estimated statistics and the simulation model, this research paves a way to provide pixel-level quality information in the future satellite products.
Benjamin R. Scarino, Kristopher Bedka, Rajendra Bhatt, Konstantin Khlopenkov, David R. Doelling, and William L. Smith Jr.
Atmos. Meas. Tech., 13, 5491–5511, https://doi.org/10.5194/amt-13-5491-2020, https://doi.org/10.5194/amt-13-5491-2020, 2020
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This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles. A 1-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bidirectional reflectance distribution function model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angles.
Bangsheng Yin, Qilong Min, Emily Morgan, Yuekui Yang, Alexander Marshak, and Anthony B. Davis
Atmos. Meas. Tech., 13, 5259–5275, https://doi.org/10.5194/amt-13-5259-2020, https://doi.org/10.5194/amt-13-5259-2020, 2020
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Cloud-top pressure (CTP) is an important cloud property for climate and weather studies. Based on differential oxygen absorption, both oxygen A-band and B-band pairs can be used to retrieve CTP. However, it is currently very challenging to perform a CTP retrieval accurately due to the complicated in-cloud penetration effect. To address this issue, we propose an analytic transfer inverse model for DSCOVR EPIC observations to retrieve CTP considering in-cloud photon penetration.
Steven T. Massie, Heather Cronk, Aronne Merrelli, K. Sebastian Schmidt, Hong Chen, and David Baker
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-366, https://doi.org/10.5194/amt-2020-366, 2020
Revised manuscript accepted for AMT
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The OCO-2 science team is working to retrieve CO2 measurements that can be used by the
carbon cycle community to calculate regional sources and sinks of CO2. The retrieved data, however, is in need of improvements in accuracy. This paper discusses several ways in which 3D cloud metrics (such as the distance of a measurement to the nearest cloud) can be used to account for cloud effects in the OCO-2 CO2 data files.
Thibault Vaillant de Guélis, Mark A. Vaughan, David M. Winker, and Zhaoyan Liu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-369, https://doi.org/10.5194/amt-2020-369, 2020
Revised manuscript accepted for AMT
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We introduce a new lidar feature detection algorithm that dramatically improves the fine details of layers identified in the CALIOP data. By applying our two-dimensional scanning technique to the measurements in all three channels, we minimize false positives while accurately identifying previously undetected features such as subvisible cirrus and the full vertical extent of dense smoke plumes. Multiple comparisons to version 4.2 CALIOP retrievals illustrate the scope of the improvements made.
Frédéric Tridon, Alessandro Battaglia, and Stefan Kneifel
Atmos. Meas. Tech., 13, 5065–5085, https://doi.org/10.5194/amt-13-5065-2020, https://doi.org/10.5194/amt-13-5065-2020, 2020
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The droplets and ice crystals composing clouds and precipitation interact with microwaves and can therefore be observed by radars, but they can also attenuate the signal they emit. By combining the observations made by two ground-based radars, this study describes an original approach for estimating such attenuation. As a result, the latter can be not only corrected in the radar observations but also exploited for providing an accurate characterization of droplet and ice crystal properties.
Xiaoyu Hu, Jinming Ge, Jiajing Du, Qinghao Li, Jianping Huang, and Qiang Fu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-230, https://doi.org/10.5194/amt-2020-230, 2020
Revised manuscript accepted for AMT
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Cloud radars are powerful instruments that can probe detailed cloud structures. However, radar echoes in the lower atmosphere are always contaminated by clutters. We proposed a multi-dimensional probability distribution function that can effectively discriminate low-level clouds from clutters by considering their different features in several variables. We applied this method to the radar observations at SACOL site and found the results have a good agreement with lidar detections.
Mark Richardson, Matthew D. Lebsock, James McDuffie, and Graeme L. Stephens
Atmos. Meas. Tech., 13, 4947–4961, https://doi.org/10.5194/amt-13-4947-2020, https://doi.org/10.5194/amt-13-4947-2020, 2020
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We previously combined CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) lidar data and reflected-sunlight measurements from OCO-2 (Orbiting Carbon Observatory 2) for information about low clouds over oceans. The satellites are no longer formation-flying, so this work is a step towards getting new information about these clouds using only OCO-2. We can rapidly and accurately identify liquid oceanic clouds and obtain their height better than a widely used passive sensor.
Melody A. Avery, Robert A. Ryan, Brian J. Getzewich, Mark A. Vaughan, David M. Winker, Yongxiang Hu, Anne Garnier, Jacques Pelon, and Carolus A. Verhappen
Atmos. Meas. Tech., 13, 4539–4563, https://doi.org/10.5194/amt-13-4539-2020, https://doi.org/10.5194/amt-13-4539-2020, 2020
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CALIOP data users will find more cloud layers detected in V4, with edges that extend further than in V3, for an increase in total atmospheric cloud volume of 6 %–9 % for high-confidence cloud phases and 1 %–2 % for all cloudy bins, including cloud fringes and unknown cloud phases. In V4 there are many fewer cloud layers identified as horizontally oriented ice, particularly in the 3° off-nadir view. Depolarization at 532 nm is the predominant parameter determining cloud thermodynamic phase.
Vladimir S. Kostsov, Dmitry V. Ionov, and Anke Kniffka
Atmos. Meas. Tech., 13, 4565–4587, https://doi.org/10.5194/amt-13-4565-2020, https://doi.org/10.5194/amt-13-4565-2020, 2020
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Previously, observations from satellites provided evidence for systematic differences between the values of the cloud liquid water path over land and water areas in northern Europe. An attempt is made to detect such differences by means of ground-based microwave measurements performed near the coastline of the Gulf of Finland. The results demonstrate the existence of the cloud liquid water path gradient, which is positive as in the case of the satellite measurements (larger values over land).
Simon Pfreundschuh, Patrick Eriksson, Stefan A. Buehler, Manfred Brath, David Duncan, Richard Larsson, and Robin Ekelund
Atmos. Meas. Tech., 13, 4219–4245, https://doi.org/10.5194/amt-13-4219-2020, https://doi.org/10.5194/amt-13-4219-2020, 2020
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The next generation of European operational weather satellites will carry a novel microwave sensor, the Ice Cloud Imager (ICI), which will provide observations of clouds at microwave frequencies that were not available before. We investigate the potential benefits of combining observations from ICI with that of a radar. We find that such combined observations provide additional information on the properties of the cloud and help to reduce uncertainties in retrieved mass and number densities.
Yue Li, Bryan A. Baum, Andrew K. Heidinger, W. Paul Menzel, and Elisabeth Weisz
Atmos. Meas. Tech., 13, 4035–4049, https://doi.org/10.5194/amt-13-4035-2020, https://doi.org/10.5194/amt-13-4035-2020, 2020
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Use of VIIRS+CrIS fusion products, which provide VIIRS with MODIS-like IR sounding channels, improves cloud mask, cloud phase, and cloud top height retrievals when compared to those using VIIRS data only. NOAA CLAVR-x cloud retrievals for both S-NPP and NOAA-20 data are evaluated through comparisons to the CALIPSO v4 and MODIS Collection 6.1 cloud products. Cloud height retrievals show significant improvement for semitransparent ice clouds, with a reduction in retrieval uncertainties.
Christoph Kalicinsky, Sabine Griessbach, and Reinhold Spang
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-144, https://doi.org/10.5194/amt-2020-144, 2020
Revised manuscript accepted for AMT
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For an airborne viewing geometry radiative transfer simulations of infrared limb emission spectra in the presence of PSCs (NAT, STS, ice, and mixtures) were used to develop a size sensitive NAT detection algorithm. Characteristic size dependent spectral features in the region 810–820 cm−1 were exploited to subgroup the NAT into three size regimes: small NAT (≤ 1.0 μm), medium NAT (1.5–4.0 μm), and large NAT(≥ 3.5 μm).
Florian Tornow, Carlos Domenech, Howard W. Barker, René Preusker, and Jürgen Fischer
Atmos. Meas. Tech., 13, 3909–3922, https://doi.org/10.5194/amt-13-3909-2020, https://doi.org/10.5194/amt-13-3909-2020, 2020
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Clouds reflect sunlight unevenly, which makes it difficult to quantify the portion reflected back to space via satellite observation. To improve quantification, we propose a new statistical model that incorporates more satellite-inferred cloud and atmospheric properties than state-of-the-art models. We use concepts from radiative transfer theory that we statistically optimize to fit observations. The new model often explains past satellite observations better and predicts reflection plausibly.
Rocco Sedona, Lars Hoffmann, Reinhold Spang, Gabriele Cavallaro, Sabine Griessbach, Michael Höpfner, Matthias Book, and Morris Riedel
Atmos. Meas. Tech., 13, 3661–3682, https://doi.org/10.5194/amt-13-3661-2020, https://doi.org/10.5194/amt-13-3661-2020, 2020
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Polar stratospheric clouds (PSCs) play a key role in polar ozone depletion in the stratosphere. In this paper, we explore the potential of applying machine learning (ML) methods to classify PSC observations of infrared spectra to classify PSC types. ML methods have proved to reach results in line with those obtained using well-established approaches. Among the considered ML methods, random forest (RF) seems to be the most promising one, being able to produce explainable classification results.
Alain Protat and Ian McRobert
Atmos. Meas. Tech., 13, 3609–3620, https://doi.org/10.5194/amt-13-3609-2020, https://doi.org/10.5194/amt-13-3609-2020, 2020
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Three-dimensional (3D) wind motions play a major role in driving the life cycle of clouds. In this pilot study we have developed a technique to measure the 3D winds in clouds, using a shipborne Doppler cloud radar on a stabilized platform. The stabilized platform is driven to point in a series of predefined directions to collect the required measurements. Comparisons with radiosondes demonstrate that accurate 1 min resolution 3D wind motions can be obtained from this instrumental setup.
Daniel J. Miller, Michal Segal-Rozenhaimer, Kirk Knobelspiesse, Jens Redemann, Brian Cairns, Mikhail Alexandrov, Bastiaan van Diedenhoven, and Andrzej Wasilewski
Atmos. Meas. Tech., 13, 3447–3470, https://doi.org/10.5194/amt-13-3447-2020, https://doi.org/10.5194/amt-13-3447-2020, 2020
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A neural network (NN) is developed and used to retrieve cloud microphysical properties from multiangular and multispectral polarimetric remote sensing observations. The NN is applied to research scanning polarimeter (RSP) observations obtained during the ORACLES field campaign and compared to other co-located remote sensing retrievals of cloud effective radius and optical thickness. A NN approach can advance more complex iterative search retrieval algorithms by providing a quick initial guess.
Holger Sihler, Steffen Beirle, Steffen Dörner, Marloes Gutenstein-Penning de Vries, Christoph Hörmann, Christian Borger, Simon Warnach, and Thomas Wagner
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-182, https://doi.org/10.5194/amt-2020-182, 2020
Revised manuscript accepted for AMT
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MICRU is an algorithm for the retrieval of effective cloud fractions (CF) from satellite measurements. CF describe the amount of clouds, which have a significant impact on the vertical sensitivity profile of trace-gases like NO2 and HCHO. MICRU retrieves small CF with an accuracy of 0.04 over the entire satellite swath. It features an empirical surface reflectivity model accounting for physical anisotropy (BRDF, sun glitter) and instrumental effects. MICRU is also applicable to imager data.
Chenxi Wang, Steven Platnick, Kerry Meyer, Zhibo Zhang, and Yaping Zhou
Atmos. Meas. Tech., 13, 2257–2277, https://doi.org/10.5194/amt-13-2257-2020, https://doi.org/10.5194/amt-13-2257-2020, 2020
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A machine-learning (ML)-based approach that can be used for cloud mask and phase detection is developed. An all-day model that uses infrared (IR) observations and a daytime model that uses shortwave and IR observations from a passive instrument are trained separately for different surface types. The training datasets are selected by using reference pixel types from collocated space lidar. The ML approach is validated carefully and the overall performance is better than traditional methods.
Wanyi Xie, Dong Liu, Ming Yang, Shaoqing Chen, Benge Wang, Zhenzhu Wang, Yingwei Xia, Yong Liu, Yiren Wang, and Chaofang Zhang
Atmos. Meas. Tech., 13, 1953–1961, https://doi.org/10.5194/amt-13-1953-2020, https://doi.org/10.5194/amt-13-1953-2020, 2020
Brent A. McBride, J. Vanderlei Martins, Henrique M. J. Barbosa, William Birmingham, and Lorraine A. Remer
Atmos. Meas. Tech., 13, 1777–1796, https://doi.org/10.5194/amt-13-1777-2020, https://doi.org/10.5194/amt-13-1777-2020, 2020
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Clouds play a large role in the way our Earth system distributes energy. The measurement of cloud droplet size distribution (DSD) is one way to connect small-scale cloud processes to scattered radiation. Our small satellite instrument, the Airborne Hyper-Angular Rainbow Polarimeter, is the first to infer DSDs over a wide spatial cloud field using polarized light. This study improves the way we interpret cloud properties and shows that high-quality science does not require a large taxpayer cost.
Jason M. Apke, Kyle A. Hilburn, Steven D. Miller, and David A. Peterson
Atmos. Meas. Tech., 13, 1593–1608, https://doi.org/10.5194/amt-13-1593-2020, https://doi.org/10.5194/amt-13-1593-2020, 2020
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Objective identification of deep convection outflow boundaries (OFBs) in next-generation geostationary satellite imagery is explored here using motion derived from a tuned advanced optical flow algorithm. Motion discontinuity preservation within the derivation is found crucial for successful OFB tracking between images, which yields new meteorological data for objective systems to use. These results provide the first step towards a fully automated satellite-based OFB identification algorithm.
Yaping Zhou, Yuekui Yang, Meng Gao, and Peng-Wang Zhai
Atmos. Meas. Tech., 13, 1575–1591, https://doi.org/10.5194/amt-13-1575-2020, https://doi.org/10.5194/amt-13-1575-2020, 2020
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Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and the bright and cold surfaces; the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) has very limited channels. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios for cloud detection over snow and ice surfaces.
Maria P. Cadeddu, Virendra P. Ghate, and Mario Mech
Atmos. Meas. Tech., 13, 1485–1499, https://doi.org/10.5194/amt-13-1485-2020, https://doi.org/10.5194/amt-13-1485-2020, 2020
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A combination of ground-based active and passive observations is used to partition cloud and precipitation liquid water path in precipitating stratocumulous clouds. Results show that neglecting scattering effects from drizzle drops leads to 8–15 % overestimation of the liquid amount in the cloud. In closed-cell systems only ~20 % of the available drizzle in the cloud falls below the cloud base, compared to ~40 % in open-cell systems.
Frank Werner and Hartwig Deneke
Atmos. Meas. Tech., 13, 1089–1111, https://doi.org/10.5194/amt-13-1089-2020, https://doi.org/10.5194/amt-13-1089-2020, 2020
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The reliability of remotely sensed cloud variables from space depends on the horizontal resolution of the instrument. This study presents and evaluates several candidate approaches for increasing the spatial resolution of observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) from the native 3 km scale to a horizontal resolution of 1 km. It is shown that uncertainties in the derived cloud products can be significantly mitigated by applying an appropriate downscaling scheme.
Christine Aebi, Julian Gröbner, Stelios Kazadzis, Laurent Vuilleumier, Antonis Gkikas, and Niklaus Kämpfer
Atmos. Meas. Tech., 13, 907–923, https://doi.org/10.5194/amt-13-907-2020, https://doi.org/10.5194/amt-13-907-2020, 2020
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Clouds are one of the largest sources of uncertainties in climate models. The current study estimates the cloud optical thickness (COT), the effective droplet radius and the single scattering albedo of stratus–altostratus and cirrus–cirrostratus clouds in Payerne, Switzerland, by combining ground- and satellite-based measurements and radiative transfer models. The estimated values are thereafter compared with data retrieved from other methods. The mean COT is distinct for different seasons.
Patrick Eriksson, Bengt Rydberg, Vinia Mattioli, Anke Thoss, Christophe Accadia, Ulf Klein, and Stefan A. Buehler
Atmos. Meas. Tech., 13, 53–71, https://doi.org/10.5194/amt-13-53-2020, https://doi.org/10.5194/amt-13-53-2020, 2020
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The Ice Cloud Imager (ICI) will be the first operational satellite sensor operating at sub-millimetre wavelengths and this novel mission will thus provide important new data to weather forecasting and climate studies. The series of ICI instruments will together cover about 20 years. This article presents the basic technical characteristics of the sensor and outlines the day-one operational retrievals. An updated estimation of the expected retrieval performance is also presented.
Johannes Bühl, Patric Seifert, Martin Radenz, Holger Baars, and Albert Ansmann
Atmos. Meas. Tech., 12, 6601–6617, https://doi.org/10.5194/amt-12-6601-2019, https://doi.org/10.5194/amt-12-6601-2019, 2019
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In the present paper, we present a novel remote-sensing technique for the measurement of ice crystal number concentrations in clouds. The fall velocity of ice crystals measured with values from cloud radar and a radar wind profiler is used in order to derive information about ice crystal size and number concentration. In contrast to existing methods based on the combination of lidar and cloud radar, the present method can also be used in optically thick clouds.
Pradeep Khatri, Hironobu Iwabuchi, Tadahiro Hayasaka, Hitoshi Irie, Tamio Takamura, Akihiro Yamazaki, Alessandro Damiani, Husi Letu, and Qin Kai
Atmos. Meas. Tech., 12, 6037–6047, https://doi.org/10.5194/amt-12-6037-2019, https://doi.org/10.5194/amt-12-6037-2019, 2019
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In an attempt to make cloud retrievals from the surface more common and convenient, we developed a cloud retrieval algorithm applicable for sky radiometers. It is based on an optimum method by fitting measured transmittances with modeled values. Further, a cost-effective and easy-to-use calibration procedure is proposed and validated using data obtained from the standard method. A detailed error analysis and quality assessment are also performed.
Fan Yang, Robert McGraw, Edward P. Luke, Damao Zhang, Pavlos Kollias, and Andrew M. Vogelmann
Atmos. Meas. Tech., 12, 5817–5828, https://doi.org/10.5194/amt-12-5817-2019, https://doi.org/10.5194/amt-12-5817-2019, 2019
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In-cloud supersaturation is crucial for droplet activation, growth, and drizzle initiation but is poorly known and hardly measured. Here we provide a novel method to estimate supersaturation fluctuation in stratocumulus clouds using remote-sensing measurements, and results show that our estimated supersaturation agrees reasonably well with in situ measurements. Our method provides a unique way to estimate supersaturation in stratocumulus clouds from long-term ground-based observations.
Marie Lothon, Paul Barnéoud, Omar Gabella, Fabienne Lohou, Solène Derrien, Sylvain Rondi, Marjolaine Chiriaco, Sophie Bastin, Jean-Charles Dupont, Martial Haeffelin, Jordi Badosa, Nicolas Pascal, and Nadège Montoux
Atmos. Meas. Tech., 12, 5519–5534, https://doi.org/10.5194/amt-12-5519-2019, https://doi.org/10.5194/amt-12-5519-2019, 2019
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In the context of an atmospheric network of instrumented sites equipped with sky cameras for cloud monitoring, we present an algorithm named ELIFAN, which aims to estimate the cloud cover amount from full-sky visible daytime images. ELIFAN is based on red-to-blue ratio thresholding applied on the image pixels and on the use of a blue-sky library. We present its principle and its performance and highlight the interest of combining several complementary instruments.
Soumyabrata Dev, Florian M. Savoy, Yee Hui Lee, and Stefan Winkler
Atmos. Meas. Tech., 12, 5417–5429, https://doi.org/10.5194/amt-12-5417-2019, https://doi.org/10.5194/amt-12-5417-2019, 2019
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Ground-based whole-sky cameras are now extensively used for the localized monitoring of clouds. In this paper, we derive a model for estimating solar irradiance using the pictures taken by those imagers. Unlike pyranometers, these sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards short-term solar energy generation forecasting.
Penny M. Rowe, Christopher J. Cox, Steven Neshyba, and Von P. Walden
Atmos. Meas. Tech., 12, 5071–5086, https://doi.org/10.5194/amt-12-5071-2019, https://doi.org/10.5194/amt-12-5071-2019, 2019
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A better understanding of polar clouds is needed for predicting climate change, including cloud thickness and the sizes and amounts of liquid droplets and ice crystals. These properties can be estimated from an instrument (an infrared spectrometer) that sits on the surface and measures how much infrared radiation is emitted by the cloud. In this work we use model data to investigate how well such an instrument could retrieve cloud properties for different instrument and error characteristics.
Hye-Sil Kim, Bryan A. Baum, and Yong-Sang Choi
Atmos. Meas. Tech., 12, 5039–5054, https://doi.org/10.5194/amt-12-5039-2019, https://doi.org/10.5194/amt-12-5039-2019, 2019
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This study demonstrates that ice cloud emissivity uncertainties at 11, 12, and 13.3 µm can be used to provide a reasonable range of ice cloud layer boundaries. We test this methodology using MODIS Collection 6 cloud properties over the western North Pacific Ocean during August 2015. The cloud boundaries for single-layer optically thin ice clouds show good agreement with those from CALIOP version 4 products, with biases increasing for optically thick and multilayered clouds.
Shannon L. Mason, Robin J. Hogan, Christopher D. Westbrook, Stefan Kneifel, Dmitri Moisseev, and Leonie von Terzi
Atmos. Meas. Tech., 12, 4993–5018, https://doi.org/10.5194/amt-12-4993-2019, https://doi.org/10.5194/amt-12-4993-2019, 2019
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The mass contents of snowflakes are critical to remotely sensed estimates of snowfall. The signatures of snow measured at three radar frequencies can distinguish fluffy, fractal snowflakes from dense and more homogeneous rimed snow. However, we show that the shape of the particle size spectrum also has a significant impact on triple-frequency radar signatures and must be accounted for when making triple-frequency radar estimates of snow that include variations in particle structure and density.
Pavlos Kollias, Bernat Puigdomènech Treserras, and Alain Protat
Atmos. Meas. Tech., 12, 4949–4964, https://doi.org/10.5194/amt-12-4949-2019, https://doi.org/10.5194/amt-12-4949-2019, 2019
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Profiling millimeter-wavelength radars are the cornerstone instrument of surface-based observatories. Calibrating these radars is important for establishing a long record of observations suitable for model evaluation and improvement. Here, the CloudSat CPR is used to assess the calibration of a record over 10 years long of ARM cloud radar observations (a total of 44 years). The results indicate that correction coefficients are needed to improve record reliability and usability.
Alan J. Geer, Stefano Migliorini, and Marco Matricardi
Atmos. Meas. Tech., 12, 4903–4929, https://doi.org/10.5194/amt-12-4903-2019, https://doi.org/10.5194/amt-12-4903-2019, 2019
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Satellite radiance observations have only recently become usable in conditions of cloud and precipitation for the initialization of weather forecasts. The move to
all-skyassimilation started with data from the microwave part of the spectrum, with substantial benefit to the quality of operational forecasts. The current work shows a framework in which cloudy infrared data, with its stronger and more non-linear sensitivity, can also benefit operational-quality forecasts.
Martin Radenz, Johannes Bühl, Patric Seifert, Hannes Griesche, and Ronny Engelmann
Atmos. Meas. Tech., 12, 4813–4828, https://doi.org/10.5194/amt-12-4813-2019, https://doi.org/10.5194/amt-12-4813-2019, 2019
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Clouds may be composed of more than one particle population even at the smallest scales. Cloud radar observations can contain information on multiple particle species, showing up as distinct peaks and subpeaks in the Doppler spectrum. We propose the use of binary tree structures to recursively structure these peaks. Two case studies from different locations and instruments illustrate how this approach can be used to disentangle particle populations in multilayered mixed-phase clouds.
Heike Kalesse, Teresa Vogl, Cosmin Paduraru, and Edward Luke
Atmos. Meas. Tech., 12, 4591–4617, https://doi.org/10.5194/amt-12-4591-2019, https://doi.org/10.5194/amt-12-4591-2019, 2019
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In a cloud, different particles like liquid water droplets and ice particles can exist simultaneously. To study the evolution of cloud particles from cloud top to bottom one has to find out how many different types of particles with different fall velocities are present. This can be done by analyzing the number of peaks in upward-looking cloud radar Doppler spectra. A new machine-learning algorithm (named PEAKO) that determines the number of peaks is introduced and compared to existing methods.
Alexandre Guillaume, Brian H. Kahn, Eric J. Fetzer, Qing Yue, Gerald J. Manipon, Brian D. Wilson, and Hook Hua
Atmos. Meas. Tech., 12, 4361–4377, https://doi.org/10.5194/amt-12-4361-2019, https://doi.org/10.5194/amt-12-4361-2019, 2019
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A method is described to classify cloud mixtures of cloud top types, termed cloud scenes, using cloud type classification derived from the CloudSat radar. The scale dependence of the cloud scenes is quantified. The cloud scenes are used to assess the characteristics of spatially collocated Atmospheric Infrared Sounder (AIRS) thermodynamic-phase and ice cloud property retrievals within scenes of varying cloud type complexity.
Jingjing Tian, Xiquan Dong, Baike Xi, Christopher R. Williams, and Peng Wu
Atmos. Meas. Tech., 12, 3743–3759, https://doi.org/10.5194/amt-12-3743-2019, https://doi.org/10.5194/amt-12-3743-2019, 2019
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Liquid water path (LWP) is a combination of rain liquid water path (RLWP) and cloud liquid water path (CLWP) in stratiform precipitation systems. LWP partitioning is important but poorly understood. Here we estimate the RLWP and CLWP below the melting base simultaneously and separately using ceilometer and radar measurements. Results show that the occurrence of cloud particles below the melting base is low; however, when cloud particles exist, the CLWP value is much larger than the RLWP.
Alan J. Geer
Atmos. Meas. Tech., 12, 3629–3657, https://doi.org/10.5194/amt-12-3629-2019, https://doi.org/10.5194/amt-12-3629-2019, 2019
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Using more satellite data in cloudy areas helps improve weather forecasts, but all-sky assimilation is still tricky, particularly for infrared data. To allow the use of hyperspectral infrared sounder radiances in all-sky conditions, an error model is developed that, in the presence of cloud, broadens the correlations between channels and increases error variances. After fixing problems of gravity wave and bias amplification, the results of all-sky assimilation trials were promising.
Tiziano Maestri, William Cossich, and Iacopo Sbrolli
Atmos. Meas. Tech., 12, 3521–3540, https://doi.org/10.5194/amt-12-3521-2019, https://doi.org/10.5194/amt-12-3521-2019, 2019
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An innovative and flexible methodology for cloud identification and classification, CIC, is tested on a synthetic dataset of high spectral resolution radiances in the far- and mid-infrared part of the spectrum, simulating measurements from the FORUM (Far Infrared Outgoing Radiation Understanding and Monitoring) mission. Results show that classification scores are greatly increased when far-infrared channels are accounted for and the identification of thin cirrus clouds is improved.
Marek Jacob, Felix Ament, Manuel Gutleben, Heike Konow, Mario Mech, Martin Wirth, and Susanne Crewell
Atmos. Meas. Tech., 12, 3237–3254, https://doi.org/10.5194/amt-12-3237-2019, https://doi.org/10.5194/amt-12-3237-2019, 2019
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Tropical clouds are a key climate component but are still not fully understood. Therefore, we analyze airborne remote sensing measurements that were taken in the dry and wet seasons over the Atlantic east of Barbados. From these we derive sub-kilometer resolution data of vertically integrated atmospheric water vapor and liquid water. Results show that although the humidity is lower in the dry season, clouds are more frequent, contain more water, and produce more rain than in the wet season.
Cited articles
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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.
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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.
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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.
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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.
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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.
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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.
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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...
