Articles | Volume 8, issue 4
https://doi.org/10.5194/amt-8-1757-2015
© Author(s) 2015. 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-8-1757-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
15 Apr 2015
Research article |
| 15 Apr 2015
Bayesian cloud detection for MERIS, AATSR, and their combination
A. Hollstein
CORRESPONDING AUTHOR
GeoForschungsZentrum Potsdam (GFZ), Telegrafenberg A17, 14473 Potsdam Germany
J. Fischer
Institute for Space Sciences, Department of Earth Sciences, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany
C. Carbajal Henken
Institute for Space Sciences, Department of Earth Sciences, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany
R. Preusker
Institute for Space Sciences, Department of Earth Sciences, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany
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A. Hollstein and J. Fischer
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Atmos. Chem. Phys., 19, 13535–13546, https://doi.org/10.5194/acp-19-13535-2019, https://doi.org/10.5194/acp-19-13535-2019, 2019
Lena Kritten, Rene Preusker, Carsten Brockmann, Tonio Fincke, Sampsa Koponen, and Jürgen Fischer
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2018-5, https://doi.org/10.5194/essd-2018-5, 2018
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Martin Stengel, Stefan Stapelberg, Oliver Sus, Cornelia Schlundt, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Cintia Carbajal Henken, Rene Preusker, Jürgen Fischer, Abhay Devasthale, Ulrika Willén, Karl-Göran Karlsson, Gregory R. McGarragh, Simon Proud, Adam C. Povey, Roy G. Grainger, Jan Fokke Meirink, Artem Feofilov, Ralf Bennartz, Jedrzej S. Bojanowski, and Rainer Hollmann
Earth Syst. Sci. Data, 9, 881–904, https://doi.org/10.5194/essd-9-881-2017, https://doi.org/10.5194/essd-9-881-2017, 2017
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We present new cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS. Retrieval systems were developed that include cloud detection and cloud typing followed by optimal estimation retrievals of cloud properties (e.g. cloud-top pressure, effective radius, optical thickness, water path). Special features of all datasets are spectral consistency and rigorous uncertainty propagation from pixel-level data to monthly properties.
Hannes Diedrich, Falco Wittchen, René Preusker, and Jürgen Fischer
Atmos. Chem. Phys., 16, 8331–8339, https://doi.org/10.5194/acp-16-8331-2016, https://doi.org/10.5194/acp-16-8331-2016, 2016
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As water vapour is the most important greenhouse gas, the remote sensing of total column water vapour (TCWV) is an important part of climate research. The remote sensing from polar orbiting, sun-synchronous satellites has some limitations. This study investigates the representativeness of observations from space regarding these limitations. The mean daily variability of the diurnal cycle of TCWV was quantified using a water vapour data set from ground-based observations.
C. K. Carbajal Henken, L. Doppler, R. Lindstrot, R. Preusker, and J. Fischer
Atmos. Meas. Tech., 8, 3419–3431, https://doi.org/10.5194/amt-8-3419-2015, https://doi.org/10.5194/amt-8-3419-2015, 2015
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This work presents a study on the sensitivity of two independent satellite cloud height retrievals to cloud vertical distribution. The difference in sensitivity of an oxygen-A absorption band and a thermal infrared based cloud height retrieval, the former being more sensitive to cloud vertical distribution, is exploited by relating the cloud height differences to cloud vertical extent. This could potentially provide additional information on cloud vertical distribution on a global scale.
H. Diedrich, R. Preusker, R. Lindstrot, and J. Fischer
Atmos. Meas. Tech., 8, 823–836, https://doi.org/10.5194/amt-8-823-2015, https://doi.org/10.5194/amt-8-823-2015, 2015
J. Slobodda, A. Hünerbein, R. Lindstrot, R. Preusker, K. Ebell, and J. Fischer
Atmos. Meas. Tech., 8, 567–578, https://doi.org/10.5194/amt-8-567-2015, https://doi.org/10.5194/amt-8-567-2015, 2015
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In this paper the representativeness of ground-based cloud observatories and their comparability to satellite data and weather prediction models is examined. It is performed by analysing correlation of time series of SEVIRI pixels. The representativeness strongly depends on the used channels and ranges between 1km and over 20km.
C. K. Carbajal Henken, R. Lindstrot, R. Preusker, and J. Fischer
Atmos. Meas. Tech., 7, 3873–3890, https://doi.org/10.5194/amt-7-3873-2014, https://doi.org/10.5194/amt-7-3873-2014, 2014
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Presented here is the FAME-C (Freie Universität Berlin AATSR and MERIS cloud) algorithm, which uses satellite measurements in the visible, near-infrared and infrared part of the spectrum to retrieve cloud macrophysical properties, such as cloud amount and two independent cloud top heights, and cloud optical and microphysical properties, such as cloud top thermodynamic phase, cloud optical thickness and effective radius, which describes the particle size distribution.
A. Hollstein and J. Fischer
Atmos. Meas. Tech., 7, 1429–1441, https://doi.org/10.5194/amt-7-1429-2014, https://doi.org/10.5194/amt-7-1429-2014, 2014
A. Hollstein and R. Lindstrot
Atmos. Meas. Tech., 7, 599–607, https://doi.org/10.5194/amt-7-599-2014, https://doi.org/10.5194/amt-7-599-2014, 2014
H. Diedrich, R. Preusker, R. Lindstrot, and J. Fischer
Atmos. Meas. Tech., 6, 359–370, https://doi.org/10.5194/amt-6-359-2013, https://doi.org/10.5194/amt-6-359-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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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
Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data
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High-spatial-resolution retrieval of cloud droplet size distribution from polarized observations of the cloudbow
Particle Inertia Effects on Radar Doppler Spectra Simulation
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
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
ATLID Cloud Climate Product
A unified synergistic retrieval of clouds, aerosols and precipitation from EarthCARE: the ACM-CAP product
Global evaluation of Doppler velocity errors of EarthCARE Cloud Profiling Radar using global storm-resolving simulation
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
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Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network
Top-of-the-atmosphere reflected shortwave radiative fluxes from GOES-R
Optimizing radar scan strategies for tracking isolated deep convection using observing system simulation experiments
A kriging-based analysis of cloud liquid water content using CloudSat data
High-resolution satellite-based cloud detection for the analysis of land surface effects on boundary layer clouds
Retrievals of ice microphysical properties using dual-wavelength polarimetric radar observations during stratiform precipitation events
The surface longwave cloud radiative effect derived from space lidar observations
Cloud phase and macrophysical properties over the Southern Ocean during the MARCUS field campaign
Detection of supercooled liquid water containing clouds with ceilometers: development and evaluation of deterministic and data-driven retrievals
An all-sky camera image classification method using cloud cover features
Determination of atmospheric column condensate using active and passive remote sensing technology
Improving discrimination between clouds and optically thick aerosol plumes in geostationary satellite data
Towards the use of conservative thermodynamic variables in data assimilation: a case study using ground-based microwave radiometer measurements
Empirical model of multiple-scattering effect on single-wavelength lidar data of aerosols and clouds
Analytic characterization of random errors in spectral dual-polarized cloud radar observations
Assessing synergistic radar and radiometer capability in retrieving ice cloud microphysics based on hybrid Bayesian algorithms
Applying self-supervised learning for semantic cloud segmentation of all-sky images
Coincident in situ and triple-frequency radar airborne observations in the Arctic
Analysis of improvements in MOPITT observational coverage over Canada
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Philipp Gregor, Tobias Zinner, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-26, https://doi.org/10.5194/amt-2023-26, 2023
Revised manuscript accepted for AMT
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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 irradiances. The model is applied to artificial images of clouds from a weather model. The artificial cloud data allows for a more in-depth evaluation and attribution of errors compared to real data. Good performance of derived cloud information and significant forecast improvements over a baseline forecast were found.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-18, https://doi.org/10.5194/amt-2023-18, 2023
Revised manuscript accepted for AMT
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JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM 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.
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
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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.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-13, https://doi.org/10.5194/amt-2023-13, 2023
Revised manuscript accepted for AMT
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We show that big liquid droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration of this inertia effect leads to an unrealistic broadening of the simulated radar Doppler spectrum. Based on the physics-based simulation, we propose a new simulator that can generate more consistent radar Doppler spectra compared with observation, providing a valuable tool to decode the cloud microphysics and dynamics properties.
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
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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
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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.
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
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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
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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.
Artem Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
EGUsphere, https://doi.org/10.5194/egusphere-2022-1187, https://doi.org/10.5194/egusphere-2022-1187, 2022
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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 the ATLID's advanced technology should allow detecting thinner clouds during daytime than before.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
EGUsphere, https://doi.org/10.5194/egusphere-2022-1195, https://doi.org/10.5194/egusphere-2022-1195, 2022
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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. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
EGUsphere, https://doi.org/10.5194/egusphere-2022-1255, https://doi.org/10.5194/egusphere-2022-1255, 2022
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We evaluated effectiveness of horizontal integration and unfolding method for the reduction of Doppler velocity error in the Level 2 algorithm of CPR. We used radar reflectivity and Doppler data from a global storm-resolving simulation and a satellite simulator. The Doppler error was higher in the tropics than in the other latitudes because of frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the Doppler errors become small enough.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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This study provides an optimization of radar observation strategies to better capture convective cell evolution in clean and polluted environments as well as a technique for the optimization. The suggested optimized radar observation strategy is to better capture updrafts at middle and upper altitudes and precipitation particle evolution of isolated deep convective clouds. This study sheds light on the challenge of designing remote sensing observation strategies in pre-field campaign periods.
Jean-Marie Lalande, Guillaume Bourmaud, Pierre Minvielle, and Jean-François Giovannelli
Atmos. Meas. Tech., 15, 4411–4429, https://doi.org/10.5194/amt-15-4411-2022, https://doi.org/10.5194/amt-15-4411-2022, 2022
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In this paper we describe the implementation of an interpolation–prediction estimator applied to cloud properties derived from CloudSat observations. The objective is to evaluate the uncertainty associated with the estimated quantity. The model developed in this study can be valuable for satellite applications (GPS, telecommunication) as well as for cloud product comparisons. This paper is didactic and beneficial for anyone interested in kriging estimators.
Julia Fuchs, Hendrik Andersen, Jan Cermak, Eva Pauli, and Rob Roebeling
Atmos. Meas. Tech., 15, 4257–4270, https://doi.org/10.5194/amt-15-4257-2022, https://doi.org/10.5194/amt-15-4257-2022, 2022
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Two cloud-masking approaches, a local and a regional approach, using high-resolution satellite data are developed and validated for the region of Paris to improve applicability for analyses of urban effects on low clouds. We found that cloud masks obtained from the regional approach are more appropriate for the high-resolution analysis of locally induced cloud processes. Its applicability is tested for the analysis of typical fog conditions over different surface types.
Eleni Tetoni, Florian Ewald, Martin Hagen, Gregor Köcher, Tobias Zinner, and Silke Groß
Atmos. Meas. Tech., 15, 3969–3999, https://doi.org/10.5194/amt-15-3969-2022, https://doi.org/10.5194/amt-15-3969-2022, 2022
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We use the C-band POLDIRAD and the Ka-band MIRA-35 to perform snowfall dual-wavelength polarimetric radar measurements. We develop an ice microphysics retrieval for mass, apparent shape, and median size of the particle size distribution by comparing observations to T-matrix ice spheroid simulations while varying the mass–size relationship. We furthermore show how the polarimetric measurements from POLDIRAD help to narrow down ambiguities between ice particle shape and size.
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, https://doi.org/10.5194/amt-15-3893-2022, 2022
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We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from observations collected by a space-based lidar on board the CALIPSO satellite and radiative transfer computations. Our estimate appropriately captures the surface LW CRE annual variability over bright polar surfaces, and it provides a dataset more than 13 years long.
Baike Xi, Xiquan Dong, Xiaojian Zheng, and Peng Wu
Atmos. Meas. Tech., 15, 3761–3777, https://doi.org/10.5194/amt-15-3761-2022, https://doi.org/10.5194/amt-15-3761-2022, 2022
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This study develops an innovative method to determine the cloud phases over the Southern Ocean (SO) using the combination of radar and lidar measurements during the ship-based field campaign of MARCUS. Results from our study show that the low-level, deep, and shallow cumuli are dominant, and the mixed-phase clouds occur more than single phases over the SO. The mixed-phase cloud properties are similar to liquid-phase (ice-phase) clouds in the midlatitudes (polar) region of the SO.
Adrien Guyot, Alain Protat, Simon P. Alexander, Andrew R. Klekociuk, Peter Kuma, and Adrian McDonald
Atmos. Meas. Tech., 15, 3663–3681, https://doi.org/10.5194/amt-15-3663-2022, https://doi.org/10.5194/amt-15-3663-2022, 2022
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Ceilometers are instruments that are widely deployed as part of operational networks. They are usually not able to detect cloud phase. Here, we propose an evaluation of various methods to detect supercooled liquid water with ceilometer observations, using an extensive dataset from Davis, Antarctica. Our results highlight the possibility for ceilometers to detect supercooled liquid water in clouds.
Xiaotong Li, Baozhu Wang, Bo Qiu, and Chao Wu
Atmos. Meas. Tech., 15, 3629–3639, https://doi.org/10.5194/amt-15-3629-2022, https://doi.org/10.5194/amt-15-3629-2022, 2022
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The all-sky camera images can reflect the local cloud cover, which is considerable for astronomical observatory site selection. Therefore, the realization of automatic classification of the images is very important. In this paper, three cloud cover features are proposed to classify the images. The proposed method is evaluated on a large dataset, and the method achieves an accuracy of 96.58 % and F1_score of 96.24 %, which greatly improves the efficiency of automatic processing of the images.
Huige Di, Yun Yuan, Qing Yan, Wenhui Xin, Shichun Li, Jun Wang, Yufeng Wang, Lei Zhang, and Dengxin Hua
Atmos. Meas. Tech., 15, 3555–3567, https://doi.org/10.5194/amt-15-3555-2022, https://doi.org/10.5194/amt-15-3555-2022, 2022
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It is necessary to correctly evaluate the amount of cloud water resources in an area. Currently, there is a lack of effective observation methods for atmospheric column condensate evaluation. We propose a method for atmospheric column condensate by combining millimetre cloud radar, lidar and microwave radiometers. The method can realise determination of atmospheric column condensate. The variation of cloud before precipitation is considered, and the atmospheric column is deduced and obtained.
Daniel Robbins, Caroline Poulsen, Steven Siems, and Simon Proud
Atmos. Meas. Tech., 15, 3031–3051, https://doi.org/10.5194/amt-15-3031-2022, https://doi.org/10.5194/amt-15-3031-2022, 2022
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A neural network (NN)-based cloud mask for a geostationary satellite instrument, AHI, is developed using collocated data and is better at not classifying thick aerosols as clouds versus the Japanese Meteorological Association and the Bureau of Meteorology masks, identifying 1.13 and 1.29 times as many non-cloud pixels than each mask, respectively. The improvement during the day likely comes from including the shortest wavelength bands from AHI in the NN mask, which the other masks do not use.
Pascal Marquet, Pauline Martinet, Jean-François Mahfouf, Alina Lavinia Barbu, and Benjamin Ménétrier
Atmos. Meas. Tech., 15, 2021–2035, https://doi.org/10.5194/amt-15-2021-2022, https://doi.org/10.5194/amt-15-2021-2022, 2022
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Two conservative thermodynamic variables (moist-air entropy potential temperature and total water content) are introduced into a one-dimensional EnVar data assimilation system to demonstrate their benefit for future operational assimilation schemes, with the use of microwave brightness temperatures from a ground-based radiometer installed during the field campaign SOFGO3D. Results show that the brightness temperatures analysed with the new variables are improved, including the liquid water.
Valery Shcherbakov, Frédéric Szczap, Alaa Alkasem, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 15, 1729–1754, https://doi.org/10.5194/amt-15-1729-2022, https://doi.org/10.5194/amt-15-1729-2022, 2022
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We performed extensive Monte Carlo (MC) simulations of lidar signals and developed an empirical model to account for the multiple scattering in the lidar signals. The simulations have taken into consideration four types of lidar configurations (the ground based, the airborne, the CALIOP, and the ATLID) and four types of particles (coarse aerosol, water cloud, jet-stream cirrus, and cirrus).
The empirical model has very good quality of MC data fitting for all considered cases.
Alexander Myagkov and Davide Ori
Atmos. Meas. Tech., 15, 1333–1354, https://doi.org/10.5194/amt-15-1333-2022, https://doi.org/10.5194/amt-15-1333-2022, 2022
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This study provides equations to characterize random errors of spectral polarimetric observations from cloud radars. The results can be used for a broad spectrum of applications. For instance, accurate error characterization is essential for advanced retrievals of microphysical properties of clouds and precipitation. Moreover, error characterization allows for the use of measurements from polarimetric cloud radars to potentially improve weather forecasts.
Yuli Liu and Gerald G. Mace
Atmos. Meas. Tech., 15, 927–944, https://doi.org/10.5194/amt-15-927-2022, https://doi.org/10.5194/amt-15-927-2022, 2022
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We propose a suite of Bayesian algorithms for synergistic radar and radiometer retrievals to evaluate the next-generation NASA Cloud, Convection and Precipitation (CCP) observing system. The algorithms address pixel-level retrievals using active-only, passive-only, and synergistic active–passive observations. Novel techniques in developing synergistic algorithms are presented. Quantitative assessments of the CCP observing system's capability in retrieving ice cloud microphysics are provided.
Yann Fabel, Bijan Nouri, Stefan Wilbert, Niklas Blum, Rudolph Triebel, Marcel Hasenbalg, Pascal Kuhn, Luis F. Zarzalejo, and Robert Pitz-Paal
Atmos. Meas. Tech., 15, 797–809, https://doi.org/10.5194/amt-15-797-2022, https://doi.org/10.5194/amt-15-797-2022, 2022
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This work presents a new approach to exploit unlabeled image data from ground-based sky observations to train neural networks. We show that our model can detect cloud classes within images more accurately than models trained with conventional methods using small, labeled datasets only. Novel machine learning techniques as applied in this work enable training with much larger datasets, leading to improved accuracy in cloud detection and less need for manual image labeling.
Cuong M. Nguyen, Mengistu Wolde, Alessandro Battaglia, Leonid Nichman, Natalia Bliankinshtein, Samuel Haimov, Kenny Bala, and Dirk Schuettemeyer
Atmos. Meas. Tech., 15, 775–795, https://doi.org/10.5194/amt-15-775-2022, https://doi.org/10.5194/amt-15-775-2022, 2022
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An analysis of airborne triple-frequency radar and almost perfectly co-located coincident in situ data from an Arctic storm confirms the main findings of modeling work with radar dual-frequency ratios (DFRs) at different zones of the DFR plane associated with different ice habits. High-resolution CPI images provide accurate identification of rimed particles within the DFR plane. The relationships between the triple-frequency signals and cloud microphysical properties are also presented.
Heba S. Marey, James R. Drummond, Dylan B. A. Jones, Helen Worden, Merritt N. Deeter, John Gille, and Debbie Mao
Atmos. Meas. Tech., 15, 701–719, https://doi.org/10.5194/amt-15-701-2022, https://doi.org/10.5194/amt-15-701-2022, 2022
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In this study, an analysis has been performed to understand the improvements in observational coverage over Canada in the new MOPITT V9 product. Temporal and spatial analysis of V9 indicates a general coverage gain of 15–20 % relative to V8, which varies regionally and seasonally; e.g., the number of successful MOPITT retrievals in V9 was doubled over Canada in winter. Also, comparison with the corresponding IASI instrument indicated generally good agreement, with about a 5–10 % positive bias.
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Short summary
Cloud detection is one of the key components for the exploitation of Earth observation images. We discuss the use of probabilistic algorithms for MERIS and AATSR on-board the ENVISAT satellite. As a new approach, we used an automated search to find the best combination of channels for the algorithm, which led to a number of unusual combinations that have not been used in the past. We show how very small samples of manually classified cloud truth images can be used to set up efficient algorithms.
Cloud detection is one of the key components for the exploitation of Earth observation images....
