Articles | Volume 9, issue 5
https://doi.org/10.5194/amt-9-2357-2016
© Author(s) 2016. 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-9-2357-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
30 May 2016
Research article |
| 30 May 2016
OCRA radiometric cloud fractions for GOME-2 on MetOp-A/B
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), 82234 Weßling, Germany
Diego Loyola
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), 82234 Weßling, Germany
Sebastián Gimeno García
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), 82234 Weßling, Germany
Fabian Romahn
German Aerospace Center (DLR), Remote Sensing Technology Institute (IMF), 82234 Weßling, Germany
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Maria-Elissavet Koukouli, Konstantinos Michailidis, Pascal Hedelt, Isabelle A. Taylor, Antje Inness, Lieven Clarisse, Dimitris Balis, Dmitry Efremenko, Diego Loyola, Roy G. Grainger, and Christian Retscher
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Antje Inness, Melanie Ades, Dimitris Balis, Dmitry Efremenko, Johannes Flemming, Pascal Hedelt, Maria-Elissavet Koukouli, Diego Loyola, and Roberto Ribas
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Christophe Lerot, François Hendrick, Michel Van Roozendael, Leonardo M. A. Alvarado, Andreas Richter, Isabelle De Smedt, Nicolas Theys, Jonas Vlietinck, Huan Yu, Jeroen Van Gent, Trissevgeni Stavrakou, Jean-François Müller, Pieter Valks, Diego Loyola, Hitoshi Irie, Vinod Kumar, Thomas Wagner, Stefan F. Schreier, Vinayak Sinha, Ting Wang, Pucai Wang, and Christian Retscher
Atmos. Meas. Tech., 14, 7775–7807, https://doi.org/10.5194/amt-14-7775-2021, https://doi.org/10.5194/amt-14-7775-2021, 2021
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Daan Hubert, Klaus-Peter Heue, Jean-Christopher Lambert, Tijl Verhoelst, Marc Allaart, Steven Compernolle, Patrick D. Cullis, Angelika Dehn, Christian Félix, Bryan J. Johnson, Arno Keppens, Debra E. Kollonige, Christophe Lerot, Diego Loyola, Matakite Maata, Sukarni Mitro, Maznorizan Mohamad, Ankie Piters, Fabian Romahn, Henry B. Selkirk, Francisco R. da Silva, Ryan M. Stauffer, Anne M. Thompson, J. Pepijn Veefkind, Holger Vömel, Jacquelyn C. Witte, and Claus Zehner
Atmos. Meas. Tech., 14, 7405–7433, https://doi.org/10.5194/amt-14-7405-2021, https://doi.org/10.5194/amt-14-7405-2021, 2021
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We assess the first 2 years of TROPOMI tropical tropospheric ozone column data. Comparisons to reference measurements by ozonesonde and satellite sensors show that TROPOMI bias (−0.1 to +2.3 DU) and precision (1.5 to 2.5 DU) meet mission requirements. Potential causes of bias and its spatio-temporal structure are discussed, as well as ways to identify sampling errors. Our analysis of known geophysical patterns demonstrates the improved performance of TROPOMI with respect to its predecessors.
Song Liu, Pieter Valks, Gaia Pinardi, Jian Xu, Ka Lok Chan, Athina Argyrouli, Ronny Lutz, Steffen Beirle, Ehsan Khorsandi, Frank Baier, Vincent Huijnen, Alkiviadis Bais, Sebastian Donner, Steffen Dörner, Myrto Gratsea, François Hendrick, Dimitris Karagkiozidis, Kezia Lange, Ankie J. M. Piters, Julia Remmers, Andreas Richter, Michel Van Roozendael, Thomas Wagner, Mark Wenig, and Diego G. Loyola
Atmos. Meas. Tech., 14, 7297–7327, https://doi.org/10.5194/amt-14-7297-2021, https://doi.org/10.5194/amt-14-7297-2021, 2021
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Nicolas Theys, Vitali Fioletov, Can Li, Isabelle De Smedt, Christophe Lerot, Chris McLinden, Nickolay Krotkov, Debora Griffin, Lieven Clarisse, Pascal Hedelt, Diego Loyola, Thomas Wagner, Vinod Kumar, Antje Innes, Roberto Ribas, François Hendrick, Jonas Vlietinck, Hugues Brenot, and Michel Van Roozendael
Atmos. Chem. Phys., 21, 16727–16744, https://doi.org/10.5194/acp-21-16727-2021, https://doi.org/10.5194/acp-21-16727-2021, 2021
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We present a new algorithm to retrieve sulfur dioxide from space UV measurements. We apply the technique to high-resolution TROPOMI measurements and demonstrate the high sensitivity of the approach to weak SO2 emissions worldwide with an unprecedented limit of detection of 8 kt yr−1. This result has broad implications for atmospheric science studies dealing with improving emission inventories and identifying and quantifying missing sources, in the context of air quality and climate.
Isabelle De Smedt, Gaia Pinardi, Corinne Vigouroux, Steven Compernolle, Alkis Bais, Nuria Benavent, Folkert Boersma, Ka-Lok Chan, Sebastian Donner, Kai-Uwe Eichmann, Pascal Hedelt, François Hendrick, Hitoshi Irie, Vinod Kumar, Jean-Christopher Lambert, Bavo Langerock, Christophe Lerot, Cheng Liu, Diego Loyola, Ankie Piters, Andreas Richter, Claudia Rivera Cárdenas, Fabian Romahn, Robert George Ryan, Vinayak Sinha, Nicolas Theys, Jonas Vlietinck, Thomas Wagner, Ting Wang, Huan Yu, and Michel Van Roozendael
Atmos. Chem. Phys., 21, 12561–12593, https://doi.org/10.5194/acp-21-12561-2021, https://doi.org/10.5194/acp-21-12561-2021, 2021
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This paper assess the performances of the TROPOMI formaldehyde observations compared to its predecessor OMI at different spatial and temporal scales. We also use a global network of MAX-DOAS instruments to validate both satellite datasets for a large range of HCHO columns. The precision obtained with daily TROPOMI observations is comparable to monthly OMI observations. We present clear detection of weak HCHO column enhancements related to shipping emissions in the Indian Ocean.
Nikita M. Fedkin, Can Li, Nickolay A. Krotkov, Pascal Hedelt, Diego G. Loyola, Russell R. Dickerson, and Robert Spurr
Atmos. Meas. Tech., 14, 3673–3691, https://doi.org/10.5194/amt-14-3673-2021, https://doi.org/10.5194/amt-14-3673-2021, 2021
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This study presents a new volcanic sulfur dioxide (SO2) layer height retrieval algorithm for the Ozone Monitoring Instrument (OMI). We generated a large spectral dataset with a radiative transfer model and used it to train neural networks to predict SO2 height from OMI radiance data. The algorithm is fast and takes less than 10 min for a single orbit. Retrievals were tested on four eruption cases, and results had reasonable agreement (within 2 km) with other retrievals and previous studies.
Viktoria F. Sofieva, Hei Shing Lee, Johanna Tamminen, Christophe Lerot, Fabian Romahn, and Diego G. Loyola
Atmos. Meas. Tech., 14, 2993–3002, https://doi.org/10.5194/amt-14-2993-2021, https://doi.org/10.5194/amt-14-2993-2021, 2021
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Our paper discusses the structure function method, which allows validation of random uncertainties in the data and, at the same time, probing of the small-scale natural variability. We applied this method to the clear-sky total ozone measurements by TROPOMI Sentinel-5P satellite instrument and found that the TROPOMI random error estimation is adequate. The discussed method is a powerful tool, which can be used in various applications.
Steven Compernolle, Athina Argyrouli, Ronny Lutz, Maarten Sneep, Jean-Christopher Lambert, Ann Mari Fjæraa, Daan Hubert, Arno Keppens, Diego Loyola, Ewan O'Connor, Fabian Romahn, Piet Stammes, Tijl Verhoelst, and Ping Wang
Atmos. Meas. Tech., 14, 2451–2476, https://doi.org/10.5194/amt-14-2451-2021, https://doi.org/10.5194/amt-14-2451-2021, 2021
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The high-resolution satellite Sentinel-5p TROPOMI observes several atmospheric gases. To account for cloud interference with the observations, S5P cloud data products (CLOUD OCRA/ROCINN_CAL, OCRA/ROCINN_CRB, and FRESCO) provide vital input: cloud fraction, cloud height, and cloud optical thickness. Here, S5P cloud parameters are validated by comparing with other satellite sensors (VIIRS, MODIS, and OMI) and with ground-based CloudNet data. The agreement depends on product type and cloud height.
Martin Dameris, Diego G. Loyola, Matthias Nützel, Melanie Coldewey-Egbers, Christophe Lerot, Fabian Romahn, and Michel van Roozendael
Atmos. Chem. Phys., 21, 617–633, https://doi.org/10.5194/acp-21-617-2021, https://doi.org/10.5194/acp-21-617-2021, 2021
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Record low ozone values were observed in March 2020. Dynamical and chemical circumstances leading to low ozone values in spring 2020 are discussed and are compared to similar dynamical conditions in the Northern Hemisphere in 1996/1997 and 2010/2011. 2019/2020 showed an unusual persistent polar vortex with low stratospheric temperatures, which were permanently below 195 K at 50 hPa. This enabled enhanced formation of polar stratospheric clouds and a subsequent clear reduction of total ozone.
Omar Torres, Hiren Jethva, Changwoo Ahn, Glen Jaross, and Diego G. Loyola
Atmos. Meas. Tech., 13, 6789–6806, https://doi.org/10.5194/amt-13-6789-2020, https://doi.org/10.5194/amt-13-6789-2020, 2020
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TROPOMI measures the quantity of small suspended particles (aerosols). We describe initial results of aerosol measurements using a NASA algorithm that retrieves the UV aerosol index, aerosol optical depth, and single-scattering albedo. An evaluation of derived products using sun-photometer observations shows close agreement. We also use these results to discuss important biomass burning and wildfire events around the world that got the attention of scientists and news media alike.
Ka Lok Chan, Pieter Valks, Sander Slijkhuis, Claas Köhler, and Diego Loyola
Atmos. Meas. Tech., 13, 4169–4193, https://doi.org/10.5194/amt-13-4169-2020, https://doi.org/10.5194/amt-13-4169-2020, 2020
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The paper presents a new water vapor retrieval algorithm in the blue spectral band for the Global Ozone Monitoring Experience-2 (GOME-2) satellite instruments. The new retrieval features a dynamic a priori optimization module, which makes it less dependent on input from chemistry transport models and better suited for climate studies. As the blue band wavelength is available to various satellites, retrieving water vapor in the blue band potentially extends the water vapor climate record.
Corinne Vigouroux, Bavo Langerock, Carlos Augusto Bauer Aquino, Thomas Blumenstock, Zhibin Cheng, Martine De Mazière, Isabelle De Smedt, Michel Grutter, James W. Hannigan, Nicholas Jones, Rigel Kivi, Diego Loyola, Erik Lutsch, Emmanuel Mahieu, Maria Makarova, Jean-Marc Metzger, Isamu Morino, Isao Murata, Tomoo Nagahama, Justus Notholt, Ivan Ortega, Mathias Palm, Gaia Pinardi, Amelie Röhling, Dan Smale, Wolfgang Stremme, Kim Strong, Ralf Sussmann, Yao Té, Michel van Roozendael, Pucai Wang, and Holger Winkler
Atmos. Meas. Tech., 13, 3751–3767, https://doi.org/10.5194/amt-13-3751-2020, https://doi.org/10.5194/amt-13-3751-2020, 2020
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We validate the TROPOMI HCHO product with ground-based FTIR (Fourier-transform infrared) measurements from 25 stations. We find that TROPOMI overestimates HCHO under clean conditions, while it underestimates it at high HCHO levels. Both TROPOMI precision and accuracy reach the pre-launch requirements, and its precision can even be 2 times better. The observed TROPOMI seasonal variability is in agreement with the FTIR data. The TROPOMI random uncertainty and data filtering should be refined.
Vitali Fioletov, Chris A. McLinden, Debora Griffin, Nicolas Theys, Diego G. Loyola, Pascal Hedelt, Nickolay A. Krotkov, and Can Li
Atmos. Chem. Phys., 20, 5591–5607, https://doi.org/10.5194/acp-20-5591-2020, https://doi.org/10.5194/acp-20-5591-2020, 2020
Melanie Coldewey-Egbers, Diego G. Loyola, Gordon Labow, and Stacey M. Frith
Atmos. Meas. Tech., 13, 1633–1654, https://doi.org/10.5194/amt-13-1633-2020, https://doi.org/10.5194/amt-13-1633-2020, 2020
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We compare total ozone columns from the satellite-based GOME-type Total Ozone Essential Climate Variable record and the adjusted Modern Era Retrospective Analysis for Research and Applications version 2 reanalysis during their overlap period from 1995 to 2018. Ozone columns and anomalies show a very good agreement in terms of spatial and temporal patterns. In the tropics the interannual variability is assessed by means of an EOF analysis and both data records show a remarkable consistency.
Diego G. Loyola, Jian Xu, Klaus-Peter Heue, and Walter Zimmer
Atmos. Meas. Tech., 13, 985–999, https://doi.org/10.5194/amt-13-985-2020, https://doi.org/10.5194/amt-13-985-2020, 2020
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In this paper we present a novel algorithm for the retrieval of geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) from UVN sensors based on the full-physics inverse learning machine (FP_ILM) retrieval.
The GE_LER retrieval is optimized for the trace gas retrievals using the DOAS technique and the large amount of data of TROPOMI on board the EU/ESA Sentinel-5 Precursor mission.
Song Liu, Pieter Valks, Gaia Pinardi, Jian Xu, Athina Argyrouli, Ronny Lutz, L. Gijsbert Tilstra, Vincent Huijnen, François Hendrick, and Michel Van Roozendael
Atmos. Meas. Tech., 13, 755–787, https://doi.org/10.5194/amt-13-755-2020, https://doi.org/10.5194/amt-13-755-2020, 2020
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This paper presents an improved tropospheric nitrogen dioxide (NO2) retrieval algorithm from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument based on air mass factor (AMF) calculations that are
performed with a more accurate knowledge of surface albedo, the a priori NO2 profile, and cloud and aerosol corrections.
Pascal Hedelt, Dmitry S. Efremenko, Diego G. Loyola, Robert Spurr, and Lieven Clarisse
Atmos. Meas. Tech., 12, 5503–5517, https://doi.org/10.5194/amt-12-5503-2019, https://doi.org/10.5194/amt-12-5503-2019, 2019
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Sulfur dioxide (SO2) emitted during volcanic eruptions poses not only a major threat to local populations, air quality, and aviation but also has an impact on the climate. The satellite-based detection of the SO2 plume is easy; however, it requires exact knowledge of the SO2 layer height. This paper presents a new method for the extremely fast and accurate determination of the layer height, which is essential in volcanic plume forecasts and the exact determination of the SO2 density.
Katerina Garane, Maria-Elissavet Koukouli, Tijl Verhoelst, Christophe Lerot, Klaus-Peter Heue, Vitali Fioletov, Dimitrios Balis, Alkiviadis Bais, Ariane Bazureau, Angelika Dehn, Florence Goutail, Jose Granville, Debora Griffin, Daan Hubert, Arno Keppens, Jean-Christopher Lambert, Diego Loyola, Chris McLinden, Andrea Pazmino, Jean-Pierre Pommereau, Alberto Redondas, Fabian Romahn, Pieter Valks, Michel Van Roozendael, Jian Xu, Claus Zehner, Christos Zerefos, and Walter Zimmer
Atmos. Meas. Tech., 12, 5263–5287, https://doi.org/10.5194/amt-12-5263-2019, https://doi.org/10.5194/amt-12-5263-2019, 2019
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The Sentinel-5 Precursor TROPOMI near real time (NRTI) and offline (OFFL) total ozone column (TOC) products are validated against direct-sun and twilight zenith-sky ground-based TOC measurements and other already known spaceborne sensors. The results show that the TROPOMI TOC measurements are in very good agreement with the ground-based measurements and satellite sensor measurements and that they are well within the product requirements.
Antje Inness, Johannes Flemming, Klaus-Peter Heue, Christophe Lerot, Diego Loyola, Roberto Ribas, Pieter Valks, Michel van Roozendael, Jian Xu, and Walter Zimmer
Atmos. Chem. Phys., 19, 3939–3962, https://doi.org/10.5194/acp-19-3939-2019, https://doi.org/10.5194/acp-19-3939-2019, 2019
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This paper documents the use of total column ozone data from the TROPOMI satellite in the global forecasting system of the Copernicus Atmosphere Monitoring Service (CAMS). The data are of good quality over large parts of the globe but have some issues at high latitudes, at low solar elevations and over snow/ice. Assimilating the data in the CAMS system has a small positive impact, especially in the tropical troposphere.
Kostas Eleftheratos, Christos S. Zerefos, Dimitris S. Balis, Maria-Elissavet Koukouli, John Kapsomenakis, Diego G. Loyola, Pieter Valks, Melanie Coldewey-Egbers, Christophe Lerot, Stacey M. Frith, Amund S. Haslerud, Ivar S. A. Isaksen, and Seppo Hassinen
Atmos. Meas. Tech., 12, 987–1011, https://doi.org/10.5194/amt-12-987-2019, https://doi.org/10.5194/amt-12-987-2019, 2019
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We examine the ability of GOME-2A total ozone data to capture variability related to known natural oscillations, such as the QBO, ENSO and NAO, with respect to other satellite datasets, ground-based data, and chemical transport model simulations. The analysis is based on the GOME-2 satellite total ozone columns for the period 2007–2016 which form part of the operational EUMETSAT AC SAF GOME-2 MetOp A GDP4.8 latest data product.
Melanie Coldewey-Egbers, Sander Slijkhuis, Bernd Aberle, Diego Loyola, and Angelika Dehn
Atmos. Meas. Tech., 11, 5237–5259, https://doi.org/10.5194/amt-11-5237-2018, https://doi.org/10.5194/amt-11-5237-2018, 2018
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We present a detailed analysis of the long-term performance of the Global Ozone Monitoring Experiment (GOME) on-board ERS-2, which provided measurements of atmospheric constituents for the 16-year period from 1995 to 2011. By means of various in-flight calibration parameters, we monitor the behavior and stability during the entire mission. Furthermore, we introduce the new homogenized level 1 product generated using the recently developed GOME Data Processor Version 5.1.
Anne Boynard, Daniel Hurtmans, Katerina Garane, Florence Goutail, Juliette Hadji-Lazaro, Maria Elissavet Koukouli, Catherine Wespes, Corinne Vigouroux, Arno Keppens, Jean-Pierre Pommereau, Andrea Pazmino, Dimitris Balis, Diego Loyola, Pieter Valks, Ralf Sussmann, Dan Smale, Pierre-François Coheur, and Cathy Clerbaux
Atmos. Meas. Tech., 11, 5125–5152, https://doi.org/10.5194/amt-11-5125-2018, https://doi.org/10.5194/amt-11-5125-2018, 2018
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In this paper, we perform a comprehensive validation of the IASI/Metop ozone data using independent observations (satellite, ground-based and ozonesonde). The quality of the IASI total and tropospheric ozone columns in terms of bias and long-term stability is generally good. Compared with ozonesonde data, IASI overestimates (underestimates) the ozone abundance in the stratosphere (troposphere). A negative drift in tropospheric ozone is observed, which is not well understood at this point.
Marc Schröder, Maarit Lockhoff, Frank Fell, John Forsythe, Tim Trent, Ralf Bennartz, Eva Borbas, Michael G. Bosilovich, Elisa Castelli, Hans Hersbach, Misako Kachi, Shinya Kobayashi, E. Robert Kursinski, Diego Loyola, Carl Mears, Rene Preusker, William B. Rossow, and Suranjana Saha
Earth Syst. Sci. Data, 10, 1093–1117, https://doi.org/10.5194/essd-10-1093-2018, https://doi.org/10.5194/essd-10-1093-2018, 2018
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This publication presents results achieved within the GEWEX Water Vapor Assessment (G-VAP). An overview of available water vapour data records based on satellite observations and reanalysis is given. If a minimum temporal coverage of 10 years is applied, 22 data records remain. These form the G-VAP data archive, which contains total column water vapour, specific humidity profiles and temperature profiles. The G-VAP data archive is designed to ease intercomparison and climate model evaluation.
J. Xu, K.-P. Heue, M. Coldewey-Egbers, F. Romahn, A. Doicu, and D. Loyola
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1995–1998, https://doi.org/10.5194/isprs-archives-XLII-3-1995-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1995-2018, 2018
Isabelle De Smedt, Nicolas Theys, Huan Yu, Thomas Danckaert, Christophe Lerot, Steven Compernolle, Michel Van Roozendael, Andreas Richter, Andreas Hilboll, Enno Peters, Mattia Pedergnana, Diego Loyola, Steffen Beirle, Thomas Wagner, Henk Eskes, Jos van Geffen, Klaas Folkert Boersma, and Pepijn Veefkind
Atmos. Meas. Tech., 11, 2395–2426, https://doi.org/10.5194/amt-11-2395-2018, https://doi.org/10.5194/amt-11-2395-2018, 2018
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This paper introduces the formaldehyde (HCHO) tropospheric vertical column retrieval algorithm implemented in the TROPOMI/Sentinel-5 Precursor operational processor, and comprehensively describes its various retrieval steps. Furthermore, algorithmic improvements developed in the framework of the EU FP7-project QA4ECV are described for future updates of the processor. Detailed error estimates are discussed in the light of Copernicus user requirements and needs for validation are highlighted.
Steffen Beirle, Johannes Lampel, Yang Wang, Kornelia Mies, Steffen Dörner, Margherita Grossi, Diego Loyola, Angelika Dehn, Anja Danielczok, Marc Schröder, and Thomas Wagner
Earth Syst. Sci. Data, 10, 449–468, https://doi.org/10.5194/essd-10-449-2018, https://doi.org/10.5194/essd-10-449-2018, 2018
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We present time series of the global distribution of water vapor over more than 2 decades based on satellite measurements from different sensors. A particular focus is the consistency amongst the different sensors to avoid jumps from one instrument to another. This is reached by applying robust and simple retrieval settings consistently. The resulting
Climateproduct allows the study of the temporal evolution of water vapor over the last 20 years on a global scale.
Katerina Garane, Christophe Lerot, Melanie Coldewey-Egbers, Tijl Verhoelst, Maria Elissavet Koukouli, Irene Zyrichidou, Dimitris S. Balis, Thomas Danckaert, Florence Goutail, Jose Granville, Daan Hubert, Arno Keppens, Jean-Christopher Lambert, Diego Loyola, Jean-Pierre Pommereau, Michel Van Roozendael, and Claus Zehner
Atmos. Meas. Tech., 11, 1385–1402, https://doi.org/10.5194/amt-11-1385-2018, https://doi.org/10.5194/amt-11-1385-2018, 2018
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The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) is a level-3 data record, which combines individual sensor products into one single cohesive record covering the 22-year period from 1995 to 2017, generated in the frame of the European Space Agency's Climate Change Initiative Phase II. The exceptional quality of the level-3 GTO-ECV v3 TOC record temporal stability satisfies well the requirements for the total ozone measurement decadal stability of between 1 and 3 %.
Mark Weber, Melanie Coldewey-Egbers, Vitali E. Fioletov, Stacey M. Frith, Jeannette D. Wild, John P. Burrows, Craig S. Long, and Diego Loyola
Atmos. Chem. Phys., 18, 2097–2117, https://doi.org/10.5194/acp-18-2097-2018, https://doi.org/10.5194/acp-18-2097-2018, 2018
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This paper commemorates the 30-year anniversary of the initial signing of the Montreal Protocol (MP) on substances that deplete the ozone layer. The MP is so far successful in reducing ozone-depleting substances, and total ozone decline was successfully stopped by the late 1990s. Total ozone levels have been mostly stable since then. In some regions, barely significant upward trends are observed that suggest an emergence into the expected ozone recovery phase.
Diego G. Loyola, Sebastián Gimeno García, Ronny Lutz, Athina Argyrouli, Fabian Romahn, Robert J. D. Spurr, Mattia Pedergnana, Adrian Doicu, Víctor Molina García, and Olena Schüssler
Atmos. Meas. Tech., 11, 409–427, https://doi.org/10.5194/amt-11-409-2018, https://doi.org/10.5194/amt-11-409-2018, 2018
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In this paper we present the operational cloud retrieval algorithms for the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor (S5P) mission: OCRA (Optical Cloud Recognition Algorithm) retrieves the cloud fraction using measurements in the UV–VIS spectral regions, and ROCINN (Retrieval of Cloud Information using Neural Networks) retrieves the cloud top height and optical thickness using measurements in and around the oxygen A-band in the NIR.
Nicolas Theys, Isabelle De Smedt, Huan Yu, Thomas Danckaert, Jeroen van Gent, Christoph Hörmann, Thomas Wagner, Pascal Hedelt, Heiko Bauer, Fabian Romahn, Mattia Pedergnana, Diego Loyola, and Michel Van Roozendael
Atmos. Meas. Tech., 10, 119–153, https://doi.org/10.5194/amt-10-119-2017, https://doi.org/10.5194/amt-10-119-2017, 2017
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This paper provides a thorough description of the algorithm to retrieve SO2 columns from TROPOMI/Sentinel-5 Precursor measurements. The different algorithmic steps including error analysis are detailed. Scientific verification of the algorithm and validation needs are also discussed.
Klaus-Peter Heue, Melanie Coldewey-Egbers, Andy Delcloo, Christophe Lerot, Diego Loyola, Pieter Valks, and Michel van Roozendael
Atmos. Meas. Tech., 9, 5037–5051, https://doi.org/10.5194/amt-9-5037-2016, https://doi.org/10.5194/amt-9-5037-2016, 2016
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The tropical tropospheric column ozone (TCO) from 5 GOME-type satellite instruments were harmonised to get a consistent time series of tropospheric ozone for 20 years. The time series showed a global ozone trend below 10 km of 0.7 DU per decade. Also the regional trends were analysed and trends up to 1.8 DU per decade or decreases as low as 0.8 DU per decade were observed. The TCO will be part of the operation product for Tropomi/S5P and thereby extended for at least 7 years.
Anne Boynard, Daniel Hurtmans, Mariliza E. Koukouli, Florence Goutail, Jérôme Bureau, Sarah Safieddine, Christophe Lerot, Juliette Hadji-Lazaro, Catherine Wespes, Jean-Pierre Pommereau, Andrea Pazmino, Irene Zyrichidou, Dimitris Balis, Alain Barbe, Semen N. Mikhailenko, Diego Loyola, Pieter Valks, Michel Van Roozendael, Pierre-François Coheur, and Cathy Clerbaux
Atmos. Meas. Tech., 9, 4327–4353, https://doi.org/10.5194/amt-9-4327-2016, https://doi.org/10.5194/amt-9-4327-2016, 2016
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Seven years of O3 observations retrieved from IASI/MetOp satellite instruments are validated with independent data (UV satellite and ground-based data along with ozonesonde profiles). Overall IASI overestimates the total ozone columns (TOC) by 2–7 % depending on the latitude. The assessment of an updated version of the IASI O3 retrieval sofware shows a correction of ~ 4 % in the IASI TOC product, bringing the overall global bias with UV ground-based and satellite data to ~ 1–2 % on average.
S. Hassinen, D. Balis, H. Bauer, M. Begoin, A. Delcloo, K. Eleftheratos, S. Gimeno Garcia, J. Granville, M. Grossi, N. Hao, P. Hedelt, F. Hendrick, M. Hess, K.-P. Heue, J. Hovila, H. Jønch-Sørensen, N. Kalakoski, A. Kauppi, S. Kiemle, L. Kins, M. E. Koukouli, J. Kujanpää, J.-C. Lambert, R. Lang, C. Lerot, D. Loyola, M. Pedergnana, G. Pinardi, F. Romahn, M. van Roozendael, R. Lutz, I. De Smedt, P. Stammes, W. Steinbrecht, J. Tamminen, N. Theys, L. G. Tilstra, O. N. E. Tuinder, P. Valks, C. Zerefos, W. Zimmer, and I. Zyrichidou
Atmos. Meas. Tech., 9, 383–407, https://doi.org/10.5194/amt-9-383-2016, https://doi.org/10.5194/amt-9-383-2016, 2016
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The three GOME-2 instruments will provide unique and long data sets for atmospheric research and applications. The complete time period will be 2007–2022, including the period of ozone depletion as well as the beginning of ozone layer recovery. The GOME-2 products (ozone, trace gases, aerosols and UV radiation) are important for ozone chemistry, air quality studies, climate modeling, policy monitoring and hazard warnings. The processing and dissemination is done by EUMETSAT O3M SAF project.
M. Coldewey-Egbers, D. G. Loyola, M. Koukouli, D. Balis, J.-C. Lambert, T. Verhoelst, J. Granville, M. van Roozendael, C. Lerot, R. Spurr, S. M. Frith, and C. Zehner
Atmos. Meas. Tech., 8, 3923–3940, https://doi.org/10.5194/amt-8-3923-2015, https://doi.org/10.5194/amt-8-3923-2015, 2015
M. Antón, D. Loyola, R. Román, and H. Vömel
Atmos. Meas. Tech., 8, 1135–1145, https://doi.org/10.5194/amt-8-1135-2015, https://doi.org/10.5194/amt-8-1135-2015, 2015
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The main goal of this article was to validate the total water vapour column (TWVC) measured by the Global Ozone Monitoring Experiment-2 (GOME-2) satellite sensor highly accurate sounding measurements. The smallest relative differences found in this satellite-sounding comparison (below 10%) were achieved for those cloud-free cases with satellite SZA below 50º which can be considered as a good result for satellite retrievals.
M. Grossi, P. Valks, D. Loyola, B. Aberle, S. Slijkhuis, T. Wagner, S. Beirle, and R. Lang
Atmos. Meas. Tech., 8, 1111–1133, https://doi.org/10.5194/amt-8-1111-2015, https://doi.org/10.5194/amt-8-1111-2015, 2015
N. Hao, M. E. Koukouli, A. Inness, P. Valks, D. G. Loyola, W. Zimmer, D. S. Balis, I. Zyrichidou, M. Van Roozendael, C. Lerot, and R. J. D. Spurr
Atmos. Meas. Tech., 7, 2937–2951, https://doi.org/10.5194/amt-7-2937-2014, https://doi.org/10.5194/amt-7-2937-2014, 2014
P. Valks, N. Hao, S. Gimeno Garcia, D. Loyola, M. Dameris, P. Jöckel, and A. Delcloo
Atmos. Meas. Tech., 7, 2513–2530, https://doi.org/10.5194/amt-7-2513-2014, https://doi.org/10.5194/amt-7-2513-2014, 2014
E. W. Chiou, P. K. Bhartia, R. D. McPeters, D. G. Loyola, M. Coldewey-Egbers, V. E. Fioletov, M. Van Roozendael, R. Spurr, C. Lerot, and S. M. Frith
Atmos. Meas. Tech., 7, 1681–1692, https://doi.org/10.5194/amt-7-1681-2014, https://doi.org/10.5194/amt-7-1681-2014, 2014
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
The EarthCARE mission: science data processing chain overview
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products
Raman lidar-derived optical and microphysical properties of ice crystals within thin Arctic clouds during PARCS campaign
Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements
Deep convective cloud system size and structure across the global tropics and subtropics
A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images
Numerical model generation of test frames for pre-launch studies of EarthCARE's retrieval algorithms and data management system
Segmentation of polarimetric radar imagery using statistical texture
Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization
Particle inertial effects on radar Doppler spectra simulation
Detection of aerosol and cloud features for the EarthCARE atmospheric lidar (ATLID): the ATLID FeatureMask (A-FM) product
A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: the ACM-CAP product
Incorporating EarthCARE observations into a multi-lidar cloud climate record: the ATLID (Atmospheric Lidar) cloud climate product
Introduction to EarthCARE synthetic data using a global storm-resolving simulation
Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data
Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS
Global evaluation of Doppler velocity errors of EarthCARE cloud-profiling radar using a global storm-resolving simulation
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product
Cloud mask algorithm from the EarthCARE Multi-Spectral Imager: the M-CM products
Across-track extension of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-3D product
Insights into 3D cloud radiative transfer effects for the Orbiting Carbon Observatory
Evaluation of polarimetric ice microphysical retrievals with OLYMPEX campaign data
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation
Simulation and sensitivity analysis for cloud and precipitation measurements via spaceborne millimeter-wave radar
The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations
Near-global distributions of overshooting tops derived from Terra and Aqua MODIS observations
Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy
Optimizing cloud motion estimation on the edge with phase correlation and optical flow
A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations
The CHROMA cloud-top pressure retrieval algorithm for the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission
High-spatial-resolution retrieval of cloud droplet size distribution from polarized observations of the cloudbow
Evaluation of the spectral misalignment on the Earth Clouds, Aerosols and Radiation Explorer/multi-spectral imager cloud product
Retrieval of terahertz ice cloud properties from airborne measurements based on the irregularly shaped Voronoi ice scattering models
Latent heating profiles from GOES-16 and its impacts on precipitation forecasts
A CO2-independent cloud mask from Infrared Atmospheric Sounding Interferometer (IASI) radiances for climate applications
Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network
Intercomparison of Sentinel-5P TROPOMI cloud products for tropospheric trace gas retrievals
Improved spectral processing for a multi-mode pulse compression Ka–Ku-band cloud radar system
Uncertainty-bounded estimates of ash cloud properties using the ORAC algorithm: application to the 2019 Raikoke eruption
Ice water path retrievals from Meteosat-9 using quantile regression neural networks
An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties
Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks
Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
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In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
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We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
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The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
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A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
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Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
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The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
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Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
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We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023, https://doi.org/10.5194/amt-16-3459-2023, 2023
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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, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
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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 ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech., 16, 3331–3344, https://doi.org/10.5194/amt-16-3331-2023, https://doi.org/10.5194/amt-16-3331-2023, 2023
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JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM (Nonhydrostatic ICosahedral
Atmospheric Model) simulation with 3.5 km horizontal resolution and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals.
Philipp Gregor, Tobias Zinner, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 16, 3257–3271, https://doi.org/10.5194/amt-16-3257-2023, https://doi.org/10.5194/amt-16-3257-2023, 2023
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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 irradiance. The model is applied to artificial images of clouds from a weather model. The artificial cloud data allow for a more in-depth evaluation and attribution of errors compared with real data. Good performance of derived cloud information and significant forecast improvements over a baseline forecast were found.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Marc Nicolas
Atmos. Meas. Tech., 16, 3221–3243, https://doi.org/10.5194/amt-16-3221-2023, https://doi.org/10.5194/amt-16-3221-2023, 2023
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The optimal estimation formalism is applied to OSIRIS airborne high-resolution multi-angular measurements to retrieve COT and Reff. The corresponding uncertainties related to measurement errors, which are up to 6 and 12 %, the non-retrieved parameters, which are less than 0.5 %, and the cloud model assumptions show that the heterogeneous vertical profiles and the 3D radiative transfer effects lead to average uncertainties of 5 and 4 % for COT and 13 and 9 % for Reff.
Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
Atmos. Meas. Tech., 16, 3211–3219, https://doi.org/10.5194/amt-16-3211-2023, https://doi.org/10.5194/amt-16-3211-2023, 2023
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The CPR on the EarthCARE satellite is the first satellite-borne Doppler radar. We evaluated the effectiveness of horizontal integration and the unfolding method for the reduction of the Doppler error (the standard deviation of the random error) in the CPR_ECO product. The error was higher in the tropics than in the other latitudes due to frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the errors become small enough.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-109, https://doi.org/10.5194/amt-2023-109, 2023
Revised manuscript accepted for AMT
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals is always decreasing with the increasing distance from the cloud far edge. The decreasing is the direct consequence of the fact that the forward peak of particles phase functions is much larger than the receiver field of view.
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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This paper provides insights into the effects of clouds on Orbiting Carbon Observatory (OCO-2) measurements of CO2. Calculations are carried out that indicate the extent to which this satellite experiment underestimates CO2, due to these cloud effects, as a function of the distance between the surface observation footprint and the nearest cloud. The paper discusses how to lessen the influence of these cloud effects.
Armin Blanke, Andrew J. Heymsfield, Manuel Moser, and Silke Trömel
Atmos. Meas. Tech., 16, 2089–2106, https://doi.org/10.5194/amt-16-2089-2023, https://doi.org/10.5194/amt-16-2089-2023, 2023
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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.
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.
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.
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.
Cited articles
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Short summary
This paper presents a method for determining global cloud cover by analyzing satellite data. Knowledge of cloud coverage is not only important for climate studies but also provides valuable information in the monitoring of atmospheric trace gases. The research presented here is embedded in an operational chain, which allows us to derive the cloud-cover information in near real time, i.e., only hours after sensing by the satellite.
This paper presents a method for determining global cloud cover by analyzing satellite data....
