Articles | Volume 13, issue 9
https://doi.org/10.5194/amt-13-4669-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-4669-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Sep 2020
Research article |
| 02 Sep 2020
| 02 Sep 2020
Gradient boosting machine learning to improve satellite-derived column water vapor measurement error
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
Meytar Sorek-Hamer
Universities Space Research Association (USRA), Mountain View, California, USA
NASA Ames Research Center, Mountain View, California, USA
Johnathan Rush
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
Michael Dorman
Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beersheba, Israel
Robert Chatfield
NASA Ames Research Center, Mountain View, California, USA
Yujie Wang
Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland, USA
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Alexei Lyapustin
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Itai Kloog
Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beersheba, Israel
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Haihui Zhu, Randall Martin, Aaron van Donkelaar, Melanie Hammer, Chi Li, Jun Meng, Christopher Oxford, Xuan Liu, Yanshun Li, Dandan Zhang, Inderjeet Singh, and Alexei Lyapustin
EGUsphere, https://doi.org/10.5194/egusphere-2024-950, https://doi.org/10.5194/egusphere-2024-950, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Ambient fine particulate matter (PM2.5) contributes to 4 million deaths every year globally. Satellite remote sensing of aerosol optical depth (AOD) coupled with a simulated PM2.5 to AOD relationship (η) can provide global PM2.5 estimation. This study aims to understand the spatial pattern and driving factors of η to guide future measurement and model efforts. We quantified η globally and regionally and found its spatial variation is strongly influenced by the aerosol composition.
Matthew S. Johnson, Sajeev Philip, Scott Meech, Rajesh Kumar, Meytar Sorek-Hamer, and Yoichi P. Shiga
EGUsphere, https://doi.org/10.5194/egusphere-2024-583, https://doi.org/10.5194/egusphere-2024-583, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Satellites, such as the Ozone Monitoring Instrument (OMI), retrieve proxy species of ozone (O3) formation (formaldehyde and nitrogen dioxide) and the ratios (FNRs) which can define O3 production sensitivity regimes. Here we investigate trends of OMI FNRs from 2005 to 2021 and they have increased in major cities suggesting a transition from radical- to nitrogen oxide-limited regimes. OMI also observed the impact of reduced emissions during the 2020 COVID-lockdown resulting in increased FNRs.
Xavier Ceamanos, Bruno Six, Suman Moparthy, Dominique Carrer, Adèle Georgeot, Josef Gasteiger, Jérôme Riedi, Jean-Luc Attié, Alexei Lyapustin, and Iosif Katsev
Atmos. Meas. Tech., 16, 2575–2599, https://doi.org/10.5194/amt-16-2575-2023, https://doi.org/10.5194/amt-16-2575-2023, 2023
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A new algorithm to retrieve the diurnal evolution of aerosol optical depth over land and ocean from geostationary meteorological satellites is proposed and successfully evaluated with reference ground-based and satellite data. The high-temporal-resolution aerosol observations that are obtained from the EUMETSAT Meteosat Second Generation mission are unprecedented and open the door to studies that cannot be conducted with the once-a-day observations available from low-Earth-orbit satellites.
Ricardo Dalagnol, Lênio Soares Galvão, Fabien Hubert Wagner, Yhasmin Mendes de Moura, Nathan Gonçalves, Yujie Wang, Alexei Lyapustin, Yan Yang, Sassan Saatchi, and Luiz Eduardo Oliveira Cruz Aragão
Earth Syst. Sci. Data, 15, 345–358, https://doi.org/10.5194/essd-15-345-2023, https://doi.org/10.5194/essd-15-345-2023, 2023
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The AnisoVeg dataset brings 22 years of monthly satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for South America at 1 km resolution aimed at vegetation applications. It has nadir-normalized data, which is the most traditional approach to correct satellite data but also unique anisotropy data with strong biophysical meaning, explaining 55 % of Amazon forest height. We expect this dataset to help large-scale estimates of vegetation biomass and carbon.
Gonzalo A. Ferrada, Meng Zhou, Jun Wang, Alexei Lyapustin, Yujie Wang, Saulo R. Freitas, and Gregory R. Carmichael
Geosci. Model Dev., 15, 8085–8109, https://doi.org/10.5194/gmd-15-8085-2022, https://doi.org/10.5194/gmd-15-8085-2022, 2022
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The smoke from fires is composed of different compounds that interact with the atmosphere and can create poor air-quality episodes. Here, we present a new fire inventory based on satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). We named this inventory the VIIRS-based Fire Emission Inventory (VFEI). Advantages of VFEI are its high resolution (~500 m) and that it provides information for many species. VFEI is publicly available and has provided data since 2012.
Sujung Go, Alexei Lyapustin, Gregory L. Schuster, Myungje Choi, Paul Ginoux, Mian Chin, Olga Kalashnikova, Oleg Dubovik, Jhoon Kim, Arlindo da Silva, Brent Holben, and Jeffrey S. Reid
Atmos. Chem. Phys., 22, 1395–1423, https://doi.org/10.5194/acp-22-1395-2022, https://doi.org/10.5194/acp-22-1395-2022, 2022
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This paper presents a retrieval algorithm of iron-oxide species (hematite, goethite) content in the atmosphere from DSCOVR EPIC observations. Our results display variations within the published range of hematite and goethite over the main dust-source regions but show significant seasonal and spatial variability. This implies a single-viewing satellite instrument with UV–visible channels may provide essential information on shortwave dust direct radiative effects for climate modeling.
Xinxin Ye, Pargoal Arab, Ravan Ahmadov, Eric James, Georg A. Grell, Bradley Pierce, Aditya Kumar, Paul Makar, Jack Chen, Didier Davignon, Greg R. Carmichael, Gonzalo Ferrada, Jeff McQueen, Jianping Huang, Rajesh Kumar, Louisa Emmons, Farren L. Herron-Thorpe, Mark Parrington, Richard Engelen, Vincent-Henri Peuch, Arlindo da Silva, Amber Soja, Emily Gargulinski, Elizabeth Wiggins, Johnathan W. Hair, Marta Fenn, Taylor Shingler, Shobha Kondragunta, Alexei Lyapustin, Yujie Wang, Brent Holben, David M. Giles, and Pablo E. Saide
Atmos. Chem. Phys., 21, 14427–14469, https://doi.org/10.5194/acp-21-14427-2021, https://doi.org/10.5194/acp-21-14427-2021, 2021
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Wildfire smoke has crucial impacts on air quality, while uncertainties in the numerical forecasts remain significant. We present an evaluation of 12 real-time forecasting systems. Comparison of predicted smoke emissions suggests a large spread in magnitudes, with temporal patterns deviating from satellite detections. The performance for AOD and surface PM2.5 and their discrepancies highlighted the role of accurately represented spatiotemporal emission profiles in improving smoke forecasts.
Robert B. Chatfield, Meinrat O. Andreae, ARCTAS Science Team, and SEAC4RS Science Team
Atmos. Meas. Tech., 13, 7069–7096, https://doi.org/10.5194/amt-13-7069-2020, https://doi.org/10.5194/amt-13-7069-2020, 2020
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Forest burning affects air pollution and global climate. A NASA aircraft studied fire emissions including the Rim Fire near Yosemite. We found frequent confusions between the actual fire emission factors and other effects on the air samples. Effects on CO2 and CO can originate far upwind; the gases can mix variably into a smoke plume. We devised a theory of constant features in plumes. A statistical mixed-effects analysis of a co-emitted tracers model disentangles such mixing from fire effects.
Cheng Chen, Oleg Dubovik, David Fuertes, Pavel Litvinov, Tatyana Lapyonok, Anton Lopatin, Fabrice Ducos, Yevgeny Derimian, Maurice Herman, Didier Tanré, Lorraine A. Remer, Alexei Lyapustin, Andrew M. Sayer, Robert C. Levy, N. Christina Hsu, Jacques Descloitres, Lei Li, Benjamin Torres, Yana Karol, Milagros Herrera, Marcos Herreras, Michael Aspetsberger, Moritz Wanzenboeck, Lukas Bindreiter, Daniel Marth, Andreas Hangler, and Christian Federspiel
Earth Syst. Sci. Data, 12, 3573–3620, https://doi.org/10.5194/essd-12-3573-2020, https://doi.org/10.5194/essd-12-3573-2020, 2020
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Aerosol products obtained from POLDER/PARASOL processed by the GRASP algorithm have been released. The entire archive of PARASOL/GRASP aerosol products is evaluated against AERONET and compared with MODIS (DT, DB and MAIAC), as well as PARASOL/Operational products. PARASOL/GRASP aerosol products provide spectral 443–1020 nm AOD correlating well with AERONET with a maximum bias of 0.02. Finally, GRASP shows capability to derive detailed spectral properties, including aerosol absorption.
Nick Schutgens, Andrew M. Sayer, Andreas Heckel, Christina Hsu, Hiren Jethva, Gerrit de Leeuw, Peter J. T. Leonard, Robert C. Levy, Antti Lipponen, Alexei Lyapustin, Peter North, Thomas Popp, Caroline Poulsen, Virginia Sawyer, Larisa Sogacheva, Gareth Thomas, Omar Torres, Yujie Wang, Stefan Kinne, Michael Schulz, and Philip Stier
Atmos. Chem. Phys., 20, 12431–12457, https://doi.org/10.5194/acp-20-12431-2020, https://doi.org/10.5194/acp-20-12431-2020, 2020
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We intercompare 14 different datasets of satellite observations of aerosol. Such measurements are challenging but also provide the best opportunity to globally observe an atmospheric component strongly related to air pollution and climate change. Our study shows that most datasets perform similarly well on a global scale but that locally errors can be quite different. We develop a technique to estimate satellite errors everywhere, even in the absence of surface reference data.
Alexander Sinyuk, Brent N. Holben, Thomas F. Eck, David M. Giles, Ilya Slutsker, Sergey Korkin, Joel S. Schafer, Alexander Smirnov, Mikhail Sorokin, and Alexei Lyapustin
Atmos. Meas. Tech., 13, 3375–3411, https://doi.org/10.5194/amt-13-3375-2020, https://doi.org/10.5194/amt-13-3375-2020, 2020
Robert B. Chatfield, Meytar Sorek-Hamer, Robert F. Esswein, and Alexei Lyapustin
Atmos. Chem. Phys., 20, 4379–4397, https://doi.org/10.5194/acp-20-4379-2020, https://doi.org/10.5194/acp-20-4379-2020, 2020
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There is a great need to define health-affecting pollution by small particles as “respirable aerosol”. The wintertime San Joaquin Valley experiences severe episodes that need full maps. A few air pollution monitors are set out by agencies in such regions. Satellite data on haziness and daily calibration using the monitors map out improved pollution estimates for the winter of 2012–2013. These show patterns of valuable empirical information about sources, transport, and cleanout of pollution.
Jing Wei, Zhanqing Li, Maureen Cribb, Wei Huang, Wenhao Xue, Lin Sun, Jianping Guo, Yiran Peng, Jing Li, Alexei Lyapustin, Lei Liu, Hao Wu, and Yimeng Song
Atmos. Chem. Phys., 20, 3273–3289, https://doi.org/10.5194/acp-20-3273-2020, https://doi.org/10.5194/acp-20-3273-2020, 2020
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This study introduced an enhanced space–time extremely randomized trees (STET) approach to improve the 1 km resolution ground-level PM2.5 estimates across China using the remote sensing technology. The STET model shows high accuracy and strong predictive power and appears to outperform most models reported by previous studies. Thus, it is of great importance for future air pollution studies at medium- or small-scale areas and will be applied to generate the historical PM2.5 dataset across China.
Larisa Sogacheva, Thomas Popp, Andrew M. Sayer, Oleg Dubovik, Michael J. Garay, Andreas Heckel, N. Christina Hsu, Hiren Jethva, Ralph A. Kahn, Pekka Kolmonen, Miriam Kosmale, Gerrit de Leeuw, Robert C. Levy, Pavel Litvinov, Alexei Lyapustin, Peter North, Omar Torres, and Antti Arola
Atmos. Chem. Phys., 20, 2031–2056, https://doi.org/10.5194/acp-20-2031-2020, https://doi.org/10.5194/acp-20-2031-2020, 2020
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The typical lifetime of a single satellite platform is on the order of 5–15 years; thus, for climate studies the usage of multiple satellite sensors should be considered.
Here we introduce and evaluate a monthly AOD merged product and AOD global and regional time series for the period 1995–2017 created from 12 individual satellite AOD products, which provide a long-term perspective on AOD changes over different regions of the globe.
Ekaterina Y. Zhdanova, Natalia Y. Chubarova, and Alexei I. Lyapustin
Atmos. Meas. Tech., 13, 877–891, https://doi.org/10.5194/amt-13-877-2020, https://doi.org/10.5194/amt-13-877-2020, 2020
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We estimated the distribution of aerosol optical thickness (AOT) with a spatial resolution of 1 km over the Moscow megacity using the MAIAC satellite aerosol product from May to September over the years 2000–2017. We revealed that the MAIAC product is a reliable instrument for assessing the spatial features of urban aerosol pollution and its temporal dynamics. The local aerosol effect is about 0.02–0.04 in AOT in the visible spectral range over the Moscow megacity.
David M. Giles, Alexander Sinyuk, Mikhail G. Sorokin, Joel S. Schafer, Alexander Smirnov, Ilya Slutsker, Thomas F. Eck, Brent N. Holben, Jasper R. Lewis, James R. Campbell, Ellsworth J. Welton, Sergey V. Korkin, and Alexei I. Lyapustin
Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, https://doi.org/10.5194/amt-12-169-2019, 2019
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Clouds or instrumental anomalies may perturb ground-based solar measurements used to calculate aerosol optical depth (AOD). This study presents a new algorithm of automated near-real-time (NRT) quality controls with improved cloud screening for AERONET AOD measurements. Results from the new and old algorithms have excellent agreement for the highest-quality AOD level, while the new algorithm provides higher-quality NRT AOD for applications such as data assimilation and satellite evaluation.
Xiaomeng Jin, Arlene M. Fiore, Gabriele Curci, Alexei Lyapustin, Kevin Civerolo, Michael Ku, Aaron van Donkelaar, and Randall V. Martin
Atmos. Chem. Phys., 19, 295–313, https://doi.org/10.5194/acp-19-295-2019, https://doi.org/10.5194/acp-19-295-2019, 2019
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We use a forward geophysical approach to derive surface PM2.5 distribution from satellite AOD over the northeastern US by applying relationships between surface PM2.5 and column AOD from a regional air quality model (CMAQ). We use multi-platform surface, aircraft, and radiosonde measurements to quantify different sources of uncertainties. We highlight model representation of aerosol vertical distribution and speciation as major sources of uncertainties for satellite-derived PM2.5.
Alexei Lyapustin, Yujie Wang, Sergey Korkin, and Dong Huang
Atmos. Meas. Tech., 11, 5741–5765, https://doi.org/10.5194/amt-11-5741-2018, https://doi.org/10.5194/amt-11-5741-2018, 2018
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MAIAC algorithm used for the MODIS C6 processing is described. MAIAC combines time series analysis and pixel/image-based processing to improve the accuracy of cloud detection, aerosol retrievals and atmospheric correction. MAIAC offers an interdisciplinary suite of atmospheric, land surface and snow products. Due to generally high quality, high resolution and high coverage, MAIAC AOD and surface reflectance/BRDF have been widely used for air quality and land research and applications.
Nandita Singh, Tirthankar Banerjee, Made P. Raju, Karine Deboudt, Meytar Sorek-Hamer, Ram S. Singh, and Rajesh K. Mall
Atmos. Chem. Phys., 18, 14197–14215, https://doi.org/10.5194/acp-18-14197-2018, https://doi.org/10.5194/acp-18-14197-2018, 2018
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Airborne particulate emissions from burning of agricultural residue over the Indo-Gangetic Plain have often been associated with formation of haze and adverse health impacts. Short-term variations in aerosol climatology during extreme biomass burning emissions were investigated using both ground and spaceborne sensors. Results highlight three exclusive but interrelated mechanisms, i.e., aerosol chemistry, regional transport, and radiative forcing, which may be useful in regional climate models.
Robert C. Levy, Shana Mattoo, Virginia Sawyer, Yingxi Shi, Peter R. Colarco, Alexei I. Lyapustin, Yujie Wang, and Lorraine A. Remer
Atmos. Meas. Tech., 11, 4073–4092, https://doi.org/10.5194/amt-11-4073-2018, https://doi.org/10.5194/amt-11-4073-2018, 2018
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Global aerosol data sets are essential for assessing climate-related questions. When comparing data sets derived from twin satellite sensors, we find consistent global offsets between morning and afternoon observations. Applying satellite-like sampling to a global model derives much weaker morning/afternoon offsets, suggesting that the observational differences are due to calibration. However, applying additional calibration corrections appears to reduce (but not remove) the global offsets.
Matthew J. Cooper, Randall V. Martin, Alexei I. Lyapustin, and Chris A. McLinden
Atmos. Meas. Tech., 11, 2983–2994, https://doi.org/10.5194/amt-11-2983-2018, https://doi.org/10.5194/amt-11-2983-2018, 2018
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To accurately infer air pollutant concentrations from satellite observations, we must first know the reflectivity of the Earth’s surface. Using a model, we show that satellite observations are better able to observe NO2 near the surface if snow is present. However, knowing when snow is present is difficult due to its variability. We test seven existing snow cover data sets to assess their ability to inform future satellite observations and find that the IMS data set is best suited for this task.
I. Veselovskii, D. N Whiteman, M. Korenskiy, A. Suvorina, A. Kolgotin, A. Lyapustin, Y. Wang, M. Chin, H. Bian, T. L. Kucsera, D. Pérez-Ramírez, and B. Holben
Atmos. Chem. Phys., 15, 1647–1660, https://doi.org/10.5194/acp-15-1647-2015, https://doi.org/10.5194/acp-15-1647-2015, 2015
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The multi-wavelength lidar technique was applied to the study of a smoke event near Washington, DC on 26-28 August 2013. Satellite observations combined with transport model predictions imply that the smoke plume originated mainly from Wyoming/Idaho forest fires. The NASA GSFC multi-wavelength Mie-Raman lidar was used to profile the smoke particle parameters such as volume density, effective radius and the real part of the refractive index.
B. Arvani, R. B. Pierce, A. I. Lyapustin, Y. Wang, G. Ghermandi, and S. Teggi
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-15-123-2015, https://doi.org/10.5194/acpd-15-123-2015, 2015
Revised manuscript not accepted
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The manuscript compares 10km Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5.1 and new 1 km Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Optical Depth (AOD) retrievals to small (<10 micron) particulate matter (PM10) surface measurements from monitoring stations within the Po Valley in Northern Italy during 2012. When the depth of the planetary boundary layer (PBL) is used to normalize the AOD, we find PM – AOD correlations of 0.98 for both retrievals.
A. Lyapustin, Y. Wang, X. Xiong, G. Meister, S. Platnick, R. Levy, B. Franz, S. Korkin, T. Hilker, J. Tucker, F. Hall, P. Sellers, A. Wu, and A. Angal
Atmos. Meas. Tech., 7, 4353–4365, https://doi.org/10.5194/amt-7-4353-2014, https://doi.org/10.5194/amt-7-4353-2014, 2014
T. F. Eck, B. N. Holben, J. S. Reid, A. Arola, R. A. Ferrare, C. A. Hostetler, S. N. Crumeyrolle, T. A. Berkoff, E. J. Welton, S. Lolli, A. Lyapustin, Y. Wang, J. S. Schafer, D. M. Giles, B. E. Anderson, K. L. Thornhill, P. Minnis, K. E. Pickering, C. P. Loughner, A. Smirnov, and A. Sinyuk
Atmos. Chem. Phys., 14, 11633–11656, https://doi.org/10.5194/acp-14-11633-2014, https://doi.org/10.5194/acp-14-11633-2014, 2014
J. Strandgren, L. Mei, M. Vountas, J. P. Burrows, A. Lyapustin, and Y. Wang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-25869-2014, https://doi.org/10.5194/acpd-14-25869-2014, 2014
Revised manuscript not accepted
X. Hu, L. A. Waller, A. Lyapustin, Y. Wang, and Y. Liu
Atmos. Chem. Phys., 14, 6301–6314, https://doi.org/10.5194/acp-14-6301-2014, https://doi.org/10.5194/acp-14-6301-2014, 2014
A. Chudnovsky, C. Tang, A. Lyapustin, Y. Wang, J. Schwartz, and P. Koutrakis
Atmos. Chem. Phys., 13, 10907–10917, https://doi.org/10.5194/acp-13-10907-2013, https://doi.org/10.5194/acp-13-10907-2013, 2013
H. Zhang, R. M. Hoff, S. Kondragunta, I. Laszlo, and A. Lyapustin
Atmos. Meas. Tech., 6, 471–486, https://doi.org/10.5194/amt-6-471-2013, https://doi.org/10.5194/amt-6-471-2013, 2013
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Lei Liu, Natalia Bliankinshtein, Yi Huang, John R. Gyakum, Philip M. Gabriel, Shiqi Xu, and Mengistu Wolde
Atmos. Meas. Tech., 17, 2219–2233, https://doi.org/10.5194/amt-17-2219-2024, https://doi.org/10.5194/amt-17-2219-2024, 2024
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We conducted a radiance closure experiment using a unique combination of two hyperspectral radiometers, one operating in the microwave and the other in the infrared. By comparing the measurements of the two hyperspectrometers to synthetic radiance simulated from collocated atmospheric profiles, we affirmed the proper performance of the two instruments and quantified their radiometric uncertainty for atmospheric sounding applications.
Kyriakoula Papachristopoulou, Ilias Fountoulakis, Alkiviadis F. Bais, Basil E. Psiloglou, Nikolaos Papadimitriou, Ioannis-Panagiotis Raptis, Andreas Kazantzidis, Charalampos Kontoes, Maria Hatzaki, and Stelios Kazadzis
Atmos. Meas. Tech., 17, 1851–1877, https://doi.org/10.5194/amt-17-1851-2024, https://doi.org/10.5194/amt-17-1851-2024, 2024
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The upgraded systems SENSE2 and NextSENSE2 focus on improving the quality of solar nowcasting and forecasting. SENSE2 provides real-time estimates of solar irradiance across a wide region every 15 min. NextSENSE2 offers short-term forecasts of irradiance up to 3 h ahead. Evaluation with actual data showed that the instantaneous comparison yields the most discrepancies due to the uncertainties of cloud-related information and satellite versus ground-based spatial representativeness limitations.
Giandomenico Pace, Alcide di Sarra, Filippo Cali Quaglia, Virginia Ciardini, Tatiana Di Iorio, Antonio Iaccarino, Daniela Meloni, Giovanni Muscari, and Claudio Scarchilli
Atmos. Meas. Tech., 17, 1617–1632, https://doi.org/10.5194/amt-17-1617-2024, https://doi.org/10.5194/amt-17-1617-2024, 2024
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This study investigates the performances of 17 formulas to determine the clear sky longwave downward irradiance in the Arctic environment. The formulas need to be tuned to the environmental conditions of the studied region and, to date, few of them have been developed and/or tested in the Arctic. The best formulas provide biases and root mean squared errors respectively smaller than 1 and 5 W m-2. We intend to use these results to estimate the longwave cloud radiative perturbation.
Bruna Barbosa Silveira, Emma Catherine Turner, and Jérôme Vidot
Atmos. Meas. Tech., 17, 1279–1296, https://doi.org/10.5194/amt-17-1279-2024, https://doi.org/10.5194/amt-17-1279-2024, 2024
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A fast radiative transfer model, used to speed up the full spectral simulation of meteorological satellite channels in weather forecast models, is tested using 25 000 modelled atmospheres. The differences between calculations from the fast and the high-resolution reference models are examined for nine historic weather satellite instruments. The study confirms that a reduced set of 83 atmospheric profiles is robust enough to estimate the scale of the differences obtained from the larger sample.
Simon Pfreundschuh, Clément Guilloteau, Paula J. Brown, Christian D. Kummerow, and Patrick Eriksson
Atmos. Meas. Tech., 17, 515–538, https://doi.org/10.5194/amt-17-515-2024, https://doi.org/10.5194/amt-17-515-2024, 2024
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The latest version of the GPROF retrieval algorithm that produces global precipitation estimates using observations from the Global Precipitation Measurement mission is validated against ground-based radars. The validation shows that the algorithm accurately estimates precipitation on scales ranging from continental to regional. In addition, we validate candidates for the next version of the algorithm and identify principal challenges for further improving space-borne rain measurements.
Linda Bogerd, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet
Atmos. Meas. Tech., 17, 247–259, https://doi.org/10.5194/amt-17-247-2024, https://doi.org/10.5194/amt-17-247-2024, 2024
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Algorithms merge satellite radiometer data from various frequency channels, each tied to a different footprint size. We studied the uncertainty associated with sampling (over the Netherlands using 4 years of data) as precipitation is highly variable in space and time by simulating ground-based data as satellite footprints. Though sampling affects precipitation estimates, it doesn’t explain all discrepancies. Overall, uncertainties in the algorithm seem more influential than how data is sampled.
Hua Lu, Min Xie, Wei Zhao, Bojun Liu, Tijian Wang, and Bingliang Zhuang
Atmos. Meas. Tech., 17, 167–179, https://doi.org/10.5194/amt-17-167-2024, https://doi.org/10.5194/amt-17-167-2024, 2024
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Observations of vertical wind in regions with complex terrain are essential, but they are always sparse and have poor representation. Data verification and quality control are conducted on the wind profile radar and Aeolus wind products in this study, trying to compensate for the limitations of wind field observations. The results shed light on the comprehensive applications of multi-source wind profile data in complicated terrain regions with sparse ground-based wind observations.
Timothy J. Wagner, Thomas August, Tim Hultberg, and Ralph A. Petersen
Atmos. Meas. Tech., 17, 1–14, https://doi.org/10.5194/amt-17-1-2024, https://doi.org/10.5194/amt-17-1-2024, 2024
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Commercial passenger and freight aircraft need to know the temperature and pressure of the environments they fly through in order to safely operate. In this paper, we investigate how these observations can be used to evaluate and monitor the performance of satellite observations. Normally weather balloons are used for this, but in places like the United States there are many more airplane flights than weather balloon launches. This makes it much easier to compare them to satellites.
Hai Nguyen, Derek Posselt, Igor Yanovsky, Longtao Wu, and Svetla Hristova-Veleva
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-239, https://doi.org/10.5194/amt-2023-239, 2023
Revised manuscript accepted for AMT
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Accurate global wind estimation is crucial for weather prediction and environmental modeling. Our study investigates a method to refine Atmospheric Motion Vectors (AMVs) by comparing them with high-precision active-sensor winds. Leveraging supervised learning, we discovered that using high-precision active-sensor data can significantly reduce biases in passive-sensor winds in addition to providing estimates of the wind errors, thereby improving their reliability.
Maria Lívia L. M. Gava, Simone M. S. Costa, and Anthony C. S. Porfírio
Atmos. Meas. Tech., 16, 5429–5441, https://doi.org/10.5194/amt-16-5429-2023, https://doi.org/10.5194/amt-16-5429-2023, 2023
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This study assesses the effectiveness of two geostationary satellite-based sunshine duration datasets over Brazil. Statistical parameters were used to evaluate the performance of the products. The results showed generally good agreement between satellite and ground observations, with some regional discrepancies. Overall, both satellite products offer reliable data for various applications, which benefit from their high spatial resolution and low time latency.
Hubert Luce, Lakshmi Kantha, and Hiroyuki Hashiguchi
Atmos. Meas. Tech., 16, 5091–5101, https://doi.org/10.5194/amt-16-5091-2023, https://doi.org/10.5194/amt-16-5091-2023, 2023
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The potential ability of clear air radars to measure turbulence kinetic energy (TKE) dissipation rate ε in the atmosphere is a major asset of these instruments because of their continuous measurements. In the present work, we successfully tested the relevance of a model relating ε to the width of the Doppler spectrum peak and wind shear for shear-generated turbulence and we provide a physical interpretation of an empirical model in this context.
Liqin Jin, Mauro Ghirardelli, Jakob Mann, Mikael Sjöholm, Stephan T. Kral, and Joachim Reuder
EGUsphere, https://doi.org/10.5194/egusphere-2023-1546, https://doi.org/10.5194/egusphere-2023-1546, 2023
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Three-dimensional wind fields can be accurately measured by sonic anemometers. However, the traditional mast-mounted sonic anemometers are difficult to be placed for offshore wind energy, which can be potentially overcome by drones. Therefore, we conducted a proof-of-concept study by applying three continuous-wave Doppler lidars to characterize the complex flow around a drone to validate the results obtained by simulations. Both methods show a good agreement with a velocity difference of 0.1m/s.
Sheila Kirkwood, Evgenia Belova, Peter Voelger, Sourav Chatterjee, and Karathazhiyath Satheesan
Atmos. Meas. Tech., 16, 4215–4227, https://doi.org/10.5194/amt-16-4215-2023, https://doi.org/10.5194/amt-16-4215-2023, 2023
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We compared 2 years of wind measurements by the Aeolus satellite with winds from two wind-profiler radars in Arctic Sweden and coastal Antarctica. Biases are similar in magnitude to results from other locations. They are smaller than in earlier studies due to more comparison points and improved criteria for data rejection. On the other hand, the standard deviation is somewhat higher because of degradation of the Aeolus lidar.
Haichen Zuo and Charlotte Bay Hasager
Atmos. Meas. Tech., 16, 3901–3913, https://doi.org/10.5194/amt-16-3901-2023, https://doi.org/10.5194/amt-16-3901-2023, 2023
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Aeolus is a satellite equipped with a Doppler wind lidar to detect global wind profiles. This study evaluates the impact of Aeolus winds on surface wind forecasts over tropical oceans and high-latitude regions based on the ECMWF observing system experiments. We find that Aeolus can slightly improve surface wind forecasts for the region > 60° N, especially from day 5 onwards. For other study regions, the impact of Aeolus is nearly neutral or limited, which requires further investigation.
Ewa Agnieszka Krajny, Leszek Osrodka, and Marek Jan Wojtylak
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-116, https://doi.org/10.5194/amt-2023-116, 2023
Revised manuscript accepted for AMT
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The use of SODAR data to support the air quality forecasting system is encouraging. 1. SODAR model: a. is a supplement to forecasting methods because it is useful due to the simplicity and speed of calculations. b. does not require emission data, for which it is difficult to quickly verify temporal and spatial variability. 2. The use of simple formulas of regression models in forecasting, while maintaining their multi-variant nature, facilitates the optimization of the prediction process.
Holger Baars, Joshua Walchester, Elizaveta Basharova, Henriette Gebauer, Martin Radenz, Johannes Bühl, Boris Barja, Ulla Wandinger, and Patric Seifert
Atmos. Meas. Tech., 16, 3809–3834, https://doi.org/10.5194/amt-16-3809-2023, https://doi.org/10.5194/amt-16-3809-2023, 2023
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In 2018, the Aeolus satellite of the European Space Agency (ESA) was launched to improve weather forecasts through global measurements of wind profiles. Given the novel lidar technique onboard, extensive validation efforts have been needed to verify the observations. For this reason, we performed long-term validation measurements in Germany and Chile. We found significant improvement in the data products due to a new algorithm version and can confirm the general validity of Aeolus observations.
Hubert Luce, Lakshmi Kantha, Hiroyuki Hashiguchi, Dale Lawrence, Abhiram Doddi, Tyler Mixa, and Masanori Yabuki
Atmos. Meas. Tech., 16, 3561–3580, https://doi.org/10.5194/amt-16-3561-2023, https://doi.org/10.5194/amt-16-3561-2023, 2023
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Doppler radars can be used to estimate turbulence kinetic energy dissipation rates in the atmosphere. The performance of various models is evaluated from comparisons between UHF wind profiler and in situ measurements with UAVs. For the first time, we assess a model supposed to be valid for weak stratification or strong shear conditions. This model provides better agreements with in situ measurements than the classical model based on the hypothesis of a stable stratification.
Chengfeng Feng and Zhaoxia Pu
Atmos. Meas. Tech., 16, 2691–2708, https://doi.org/10.5194/amt-16-2691-2023, https://doi.org/10.5194/amt-16-2691-2023, 2023
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This study demonstrates the positive impacts of assimilating Aeolus Mie-cloudy and Rayleigh-clear near-real-time horizontal line-of-sight winds on the analysis and forecasts of Hurricane Ida (2021) and a mesoscale convective system associated with an African easterly wave using the mesoscale community Weather Research and Forecasting model and the NCEP Gridpoint Statistical Interpolation-based three-dimensional ensemble-variational hybrid data assimilation system.
Tim Trent, Richard Siddans, Brian Kerridge, Marc Schröder, Noëlle A. Scott, and John Remedios
Atmos. Meas. Tech., 16, 1503–1526, https://doi.org/10.5194/amt-16-1503-2023, https://doi.org/10.5194/amt-16-1503-2023, 2023
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Modern weather satellites provide essential information on our lower atmosphere's moisture content and temperature structure. This measurement record will span over 40 years, making it a valuable resource for climate studies. This study characterizes atmospheric temperature and humidity profiles from a European Space Agency climate project. Using weather balloon measurements, we demonstrated the performance of this dataset was within the tolerances required for future climate studies.
Benjamin Witschas, Christian Lemmerz, Alexander Geiß, Oliver Lux, Uwe Marksteiner, Stephan Rahm, Oliver Reitebuch, Andreas Schäfler, and Fabian Weiler
Atmos. Meas. Tech., 15, 7049–7070, https://doi.org/10.5194/amt-15-7049-2022, https://doi.org/10.5194/amt-15-7049-2022, 2022
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In August 2018, the first wind lidar Aeolus was launched into space and has since then been providing data of the global wind field. The primary goal of Aeolus was the improvement of numerical weather prediction. To verify the quality of Aeolus wind data, DLR performed four airborne validation campaigns with two wind lidar systems. In this paper, we report on results from the two later campaigns, performed in Iceland and the tropics.
Olivier Bock, Pierre Bosser, and Carl Mears
Atmos. Meas. Tech., 15, 5643–5665, https://doi.org/10.5194/amt-15-5643-2022, https://doi.org/10.5194/amt-15-5643-2022, 2022
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Integrated water vapour measurements are often compared for the calibration and validation of instruments or techniques. Measurements made at different altitudes must be corrected to account for the vertical variation of water vapour. This paper shows that the widely used empirical correction model has severe limitations that are overcome using the proposed model. The method is applied to the inter-comparison of GPS and satellite microwave radiometer data in a tropical mountainous area.
Anthony J. Mannucci, Chi O. Ao, Byron A. Iijima, Thomas K. Meehan, Panagiotis Vergados, E. Robert Kursinski, and William S. Schreiner
Atmos. Meas. Tech., 15, 4971–4987, https://doi.org/10.5194/amt-15-4971-2022, https://doi.org/10.5194/amt-15-4971-2022, 2022
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The Global Positioning System (GPS) radio occultation (RO) technique is a satellite-based method for producing highly accurate vertical profiles of atmospheric temperature and pressure. RO profiles are used to monitor global climate trends, particularly in that region of the atmosphere that includes the lower stratosphere. Two data sets spanning 1995–1997 that were produced from the first RO satellite are highly accurate and can be used to assess global atmospheric models.
Ze Chen, Yufang Tian, Yinan Wang, Yongheng Bi, Xue Wu, Juan Huo, Linjun Pan, Yong Wang, and Daren Lü
Atmos. Meas. Tech., 15, 4785–4800, https://doi.org/10.5194/amt-15-4785-2022, https://doi.org/10.5194/amt-15-4785-2022, 2022
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Small-scale turbulence plays a vital role in the vertical exchange of heat, momentum and mass in the atmosphere. There are currently three models that can use spectrum width data of MST radar to calculate turbulence parameters. However, few studies have explored the applicability of the three calculation models. We compared and analysed the turbulence parameters calculated by three models. These results can provide a reference for the selection of models for calculating turbulence parameters.
Damao Zhang, Jennifer Comstock, and Victor Morris
Atmos. Meas. Tech., 15, 4735–4749, https://doi.org/10.5194/amt-15-4735-2022, https://doi.org/10.5194/amt-15-4735-2022, 2022
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The planetary boundary layer is the lowest part of the atmosphere. Its structure and depth (PBLHT) significantly impact air quality, global climate, land–atmosphere interactions, and a wide range of atmospheric processes. To test the robustness of the ceilometer-estimated PBLHT under different atmospheric conditions, we compared ceilometer- and radiosonde-estimated PBLHTs using multiple years of U.S. DOE ARM measurements at various ARM observatories located around the world.
Rachel Robey and Julie K. Lundquist
Atmos. Meas. Tech., 15, 4585–4622, https://doi.org/10.5194/amt-15-4585-2022, https://doi.org/10.5194/amt-15-4585-2022, 2022
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Our work investigates the behavior of errors in remote-sensing wind lidar measurements due to turbulence. Using a virtual instrument, we measured winds in simulated atmospheric flows and decomposed the resulting error. Dominant error mechanisms, particularly vertical velocity variations and interactions with shear, were identified in ensemble data over three test cases. By analyzing the underlying mechanisms, the response of the error behavior to further varying flow conditions may be projected.
Donato Summa, Fabio Madonna, Noemi Franco, Benedetto De Rosa, and Paolo Di Girolamo
Atmos. Meas. Tech., 15, 4153–4170, https://doi.org/10.5194/amt-15-4153-2022, https://doi.org/10.5194/amt-15-4153-2022, 2022
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The evolution of the atmospheric boundary layer height (ABLH) has an important impact on meteorology. However, the complexity of the phenomena occurring within the ABL and the influence of advection and local accumulation processes often prevent an unambiguous determination of the ABLH. The paper reports results from an inter-comparison effort involving different sensors and techniques to measure the ABLH. Correlations between the ABLH and other atmospheric variables are also assessed.
Haichen Zuo, Charlotte Bay Hasager, Ioanna Karagali, Ad Stoffelen, Gert-Jan Marseille, and Jos de Kloe
Atmos. Meas. Tech., 15, 4107–4124, https://doi.org/10.5194/amt-15-4107-2022, https://doi.org/10.5194/amt-15-4107-2022, 2022
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The Aeolus satellite was launched in 2018 for global wind profile measurement. After successful operation, the error characteristics of Aeolus wind products have not yet been studied over Australia. To complement earlier validation studies, we evaluated the Aeolus Level-2B11 wind product over Australia with ground-based wind profiling radar measurements and numerical weather prediction model equivalents. The results show that the Aeolus can detect winds with sufficient accuracy over Australia.
Carmen González, José M. Vilaplana, José A. Bogeat, and Antonio Serrano
Atmos. Meas. Tech., 15, 4125–4133, https://doi.org/10.5194/amt-15-4125-2022, https://doi.org/10.5194/amt-15-4125-2022, 2022
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Monitoring ultraviolet (UV) radiation is important since it can have harmful effects on the biosphere. Array spectroradiometers are increasingly used to measure UV as they are more versatile than scanning spectroradiometers. In this study, the long-term performance of the BTS-2048-UV-S-WP array spectroradiometer was assessed. The results show that the BTS can reliably measure both the UV index and UV radiation in the 300–360 nm range. Moreover, the BTS was stable and showed no seasonal behavior.
Charlotte Rahlves, Frank Beyrich, and Siegfried Raasch
Atmos. Meas. Tech., 15, 2839–2856, https://doi.org/10.5194/amt-15-2839-2022, https://doi.org/10.5194/amt-15-2839-2022, 2022
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Lidars can measure the wind profile in the lower part of the atmosphere, provided that the wind field is horizontally uniform and does not change during the time of the measurement. These requirements are mostly not fulfilled in reality, and the lidar wind measurement will thus hold a certain error. We investigate different strategies for lidar wind profiling using a lidar simulator implemented in a numerical simulation of the wind field. Our findings can help to improve wind measurements.
Katherine E. Lukens, Kayo Ide, Kevin Garrett, Hui Liu, David Santek, Brett Hoover, and Ross N. Hoffman
Atmos. Meas. Tech., 15, 2719–2743, https://doi.org/10.5194/amt-15-2719-2022, https://doi.org/10.5194/amt-15-2719-2022, 2022
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Winds that are crucial to weather forecasting derived from two different techniques – tracking satellite images (AMVs) and direct measurement of molecular and aerosol motions by Doppler lidar (Aeolus satellite winds) – are compared. We find that AMVs and Aeolus winds are highly correlated. Aeolus Mie-cloudy winds have great potential value as a comparison standard for AMVs. Larger differences are found in the Southern Hemisphere related to higher wind speed and higher vertical variation in wind.
Marijn Floris van Dooren, Anantha Padmanabhan Kidambi Sekar, Lars Neuhaus, Torben Mikkelsen, Michael Hölling, and Martin Kühn
Atmos. Meas. Tech., 15, 1355–1372, https://doi.org/10.5194/amt-15-1355-2022, https://doi.org/10.5194/amt-15-1355-2022, 2022
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The remote sensing technique lidar is widely used for wind speed measurements for both industrial and academic applications. Lidars can measure wind statistics accurately but cannot fully capture turbulent fluctuations in the high-frequency range, since they are partly filtered out. This paper therefore investigates the turbulence spectrum measured by a continuous-wave lidar and analytically models the lidar's measured spectrum with a Lorentzian filter function and a white noise term.
Alain Protat, Valentin Louf, Joshua Soderholm, Jordan Brook, and William Ponsonby
Atmos. Meas. Tech., 15, 915–926, https://doi.org/10.5194/amt-15-915-2022, https://doi.org/10.5194/amt-15-915-2022, 2022
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This study uses collocated ship-based, ground-based, and spaceborne radar observations to validate the concept of using the GPM spaceborne radar observations to calibrate national weather radar networks to the accuracy required for operational severe weather applications such as rainfall and hail nowcasting.
Wagner Wolff, Aart Overeem, Hidde Leijnse, and Remko Uijlenhoet
Atmos. Meas. Tech., 15, 485–502, https://doi.org/10.5194/amt-15-485-2022, https://doi.org/10.5194/amt-15-485-2022, 2022
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The existing infrastructure for cellular communication is promising for ground-based rainfall remote sensing. Rain-induced signal attenuation is used in dedicated algorithms for retrieving rainfall depth along commercial microwave links (CMLs) between cell phone towers. This processing is a source of many uncertainties about input data, algorithm structures, parameters, CML network, and local climate. Application of a stochastic optimization method leads to improved CML rainfall estimates.
Songhua Wu, Kangwen Sun, Guangyao Dai, Xiaoye Wang, Xiaoying Liu, Bingyi Liu, Xiaoquan Song, Oliver Reitebuch, Rongzhong Li, Jiaping Yin, and Xitao Wang
Atmos. Meas. Tech., 15, 131–148, https://doi.org/10.5194/amt-15-131-2022, https://doi.org/10.5194/amt-15-131-2022, 2022
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During the VAL-OUC campaign, we established a coherent Doppler lidar (CDL) network over China to verify the Level 2B (L2B) products from Aeolus. By the simultaneous wind measurements with CDLs at 17 stations, the L2B products from Aeolus are compared with those from CDLs. To our knowledge, the VAL-OUC campaign is the most extensive so far between CDLs and Aeolus in the lower troposphere for different atmospheric scenes. The vertical velocity impact on the HLOS retrieval from Aeolus is evaluated.
Karina Wilgan, Galina Dick, Florian Zus, and Jens Wickert
Atmos. Meas. Tech., 15, 21–39, https://doi.org/10.5194/amt-15-21-2022, https://doi.org/10.5194/amt-15-21-2022, 2022
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The assimilation of GNSS data in weather models has a positive impact on the forecasts. The impact is still limited due to using only the GPS zenith direction parameters. We calculate and validate more advanced tropospheric products from three satellite systems: the US American GPS, Russian GLONASS and European Galileo. The quality of all the solutions is comparable; however, combining more GNSS systems enhances the observations' geometry and improves the quality of the weather forecasts.
Hironori Iwai, Makoto Aoki, Mitsuru Oshiro, and Shoken Ishii
Atmos. Meas. Tech., 14, 7255–7275, https://doi.org/10.5194/amt-14-7255-2021, https://doi.org/10.5194/amt-14-7255-2021, 2021
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The first space-based Doppler wind lidar on board the Aeolus satellite was launched on 22 August 2018 to obtain global horizontal wind profiles. In this study, wind profilers, ground-based coherent Doppler wind lidars, and GPS radiosondes were used to validate the quality of Aeolus Level 2B wind products over Japan during three different periods. The results show that Aeolus can measure the horizontal winds over Japan accurately.
Tim A. van Kempen, Filippo Oggionni, and Richard M. van Hees
Atmos. Meas. Tech., 14, 6711–6722, https://doi.org/10.5194/amt-14-6711-2021, https://doi.org/10.5194/amt-14-6711-2021, 2021
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Validation of the instrument stability of the TROPOMI-SWIR module is done by monitoring a group of very stable and remote locations in the Saharan and Arabian deserts. These results confirm the excellent stability and lack of degradation of the TROPOMI-SWIR module derived from the internal calibration sources. The method was done for the first time on a spectrometer in the short-wave infrared and ensures TROPOMI-SWIR can be used for atmospheric research for years to come.
Susanna Hagelin, Roohollah Azad, Magnus Lindskog, Harald Schyberg, and Heiner Körnich
Atmos. Meas. Tech., 14, 5925–5938, https://doi.org/10.5194/amt-14-5925-2021, https://doi.org/10.5194/amt-14-5925-2021, 2021
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In this paper we study the impact of using wind observations from the Aeolus satellite, which provides wind speed profiles globally, in our numerical weather prediction system using a regional model covering the Nordic countries. The wind speed profiles from Aeolus are assimilated by the model, and we see that they have an impact on both the model analysis and forecast, though given the relatively few observations available the impact is often small.
Yuefei Zeng, Tijana Janjic, Yuxuan Feng, Ulrich Blahak, Alberto de Lozar, Elisabeth Bauernschubert, Klaus Stephan, and Jinzhong Min
Atmos. Meas. Tech., 14, 5735–5756, https://doi.org/10.5194/amt-14-5735-2021, https://doi.org/10.5194/amt-14-5735-2021, 2021
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Observation errors (OEs) of radar measurements are correlated. The Desroziers method has been often used to estimate statistics of OE in data assimilation. However, the resulting statistics consist of contributions from different sources and are difficult to interpret. Here, we use an approach based on samples for truncation error to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with OE statistics estimated by the Desroziers method.
Evgenia Belova, Sheila Kirkwood, Peter Voelger, Sourav Chatterjee, Karathazhiyath Satheesan, Susanna Hagelin, Magnus Lindskog, and Heiner Körnich
Atmos. Meas. Tech., 14, 5415–5428, https://doi.org/10.5194/amt-14-5415-2021, https://doi.org/10.5194/amt-14-5415-2021, 2021
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Wind measurements from two radars (ESRAD in Arctic Sweden and MARA at the Indian Antarctic station Maitri) are compared with lidar winds from the ESA satellite Aeolus, for July–December 2019. The aim is to check if Aeolus data processing is adequate for the sunlit conditions of polar summer. Agreement is generally good with bias in Aeolus winds < 1 m/s in most circumstances. The exception is a large bias (7 m/s) when the satellite has crossed a sunlit Antarctic ice cap before passing MARA.
Ramashray Yadav, Ram Kumar Giri, and Virendra Singh
Atmos. Meas. Tech., 14, 4857–4877, https://doi.org/10.5194/amt-14-4857-2021, https://doi.org/10.5194/amt-14-4857-2021, 2021
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We performed an intercomparison of seasonal and annual studies of retrievals of integrated precipitable water vapor (IPWV) carried out by INSAT-3DR satellite-borne infrared radiometer sounding and CAMS reanalysis data with ground-based Indian GNSS data. The magnitude and sign of the bias of INSAT-3DR and CAMS with respect to GNSS IPWV differs from station to station and season to season. A statistical evaluation of the collocated data sets was done to improve day-to-day weather forecasting.
Matic Šavli, Vivien Pourret, Christophe Payan, and Jean-François Mahfouf
Atmos. Meas. Tech., 14, 4721–4736, https://doi.org/10.5194/amt-14-4721-2021, https://doi.org/10.5194/amt-14-4721-2021, 2021
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The ESA's Aeolus satellite wind retrieval is provided through a series of processors. It depends on the temperature and pressure specification, which, however, are not measured by the satellite. The numerical weather predicted values are used instead, but these are erroneous. This article studies the sensitivity of the wind retrieval by introducing errors in temperature and pressure. This has been found to be small for Aeolus but is expected to be more crucial for future missions.
Kristopher M. Bedka, Amin R. Nehrir, Michael Kavaya, Rory Barton-Grimley, Mark Beaubien, Brian Carroll, James Collins, John Cooney, G. David Emmitt, Steven Greco, Susan Kooi, Tsengdar Lee, Zhaoyan Liu, Sharon Rodier, and Gail Skofronick-Jackson
Atmos. Meas. Tech., 14, 4305–4334, https://doi.org/10.5194/amt-14-4305-2021, https://doi.org/10.5194/amt-14-4305-2021, 2021
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This paper demonstrates the Doppler Aerosol WiNd (DAWN) lidar and High Altitude Lidar Observatory (HALO) measurement capabilities across a range of atmospheric conditions, compares DAWN and HALO measurements with Aeolus satellite Doppler wind lidar to gain an initial perspective of Aeolus performance, and discusses how atmospheric dynamic processes can be resolved and better understood through simultaneous observations of wind, water vapour, and aerosol profile observations.
Emranul Sarkar, Alexander Kozlovsky, Thomas Ulich, Ilkka Virtanen, Mark Lester, and Bernd Kaifler
Atmos. Meas. Tech., 14, 4157–4169, https://doi.org/10.5194/amt-14-4157-2021, https://doi.org/10.5194/amt-14-4157-2021, 2021
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The biasing effect in meteor radar temperature has been a pressing issue for the last 2 decades. This paper has addressed the underlying reasons for such a biasing effect on both theoretical and experimental grounds. An improved statistical method has been developed which allows atmospheric temperatures at around 90 km to be measured with meteor radar in an independent way such that any subsequent bias correction or calibration is no longer required.
Wei Zhong, Xianghui Xue, Wen Yi, Iain M. Reid, Tingdi Chen, and Xiankang Dou
Atmos. Meas. Tech., 14, 3973–3988, https://doi.org/10.5194/amt-14-3973-2021, https://doi.org/10.5194/amt-14-3973-2021, 2021
Evgenia Belova, Peter Voelger, Sheila Kirkwood, Susanna Hagelin, Magnus Lindskog, Heiner Körnich, Sourav Chatterjee, and Karathazhiyath Satheesan
Atmos. Meas. Tech., 14, 2813–2825, https://doi.org/10.5194/amt-14-2813-2021, https://doi.org/10.5194/amt-14-2813-2021, 2021
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We validate horizontal wind measurements at altitudes of 0.5–14 km made with atmospheric radars: ESRAD located near Kiruna in the Swedish Arctic and MARA at the Indian research station Maitri in Antarctica, by comparison with radiosondes, the regional model HARMONIE-AROME and the ECMWF ERA5 reanalysis. Good agreement was found in general, and radar bias and uncertainty were estimated. These radars are planned to be used for validation of winds measured by lidar by the ESA satellite Aeolus.
Gizachew Kabite Wedajo, Misgana Kebede Muleta, and Berhan Gessesse Awoke
Atmos. Meas. Tech., 14, 2299–2316, https://doi.org/10.5194/amt-14-2299-2021, https://doi.org/10.5194/amt-14-2299-2021, 2021
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Satellite rainfall estimates (SREs) are alternative data sources for data-scarce basins. However, the accuracy of the products is plagued by multiple sources of errors. Therefore, SREs should be evaluated for particular basins before being used for other applications. The results of the study showed that CHIRPS2 and IMERG6 estimated rainfall and predicted hydrologic simulations well for Dhidhessa River Basin, which shows remote sensing technology could improve hydrologic studies.
Steven Knoop, Fred C. Bosveld, Marijn J. de Haij, and Arnoud Apituley
Atmos. Meas. Tech., 14, 2219–2235, https://doi.org/10.5194/amt-14-2219-2021, https://doi.org/10.5194/amt-14-2219-2021, 2021
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Doppler wind lidars are laser-based remote sensing instruments that measure the wind up to a few hundred metres or even a few kilometres. Their data can improve weather models and help forecasters. To investigate their accuracy and required meteorological conditions, we have carried out a 2-year measurement campaign of a wind lidar at our Cabauw test site and made a comparison with cup anemometers and wind vanes at several levels in a 213 m tall meteorological mast.
Joaquim V. Teixeira, Hai Nguyen, Derek J. Posselt, Hui Su, and Longtao Wu
Atmos. Meas. Tech., 14, 1941–1957, https://doi.org/10.5194/amt-14-1941-2021, https://doi.org/10.5194/amt-14-1941-2021, 2021
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Wind-tracking algorithms produce atmospheric motion vectors (AMVs) by tracking satellite observations. Accurately characterizing the uncertainties in AMVs is essential in assimilating them into data assimilation models. We develop a machine-learning-based approach for error characterization which involves Gaussian mixture model clustering and random forest using a simulation dataset of water vapor, AMVs, and true winds. We show that our method improves on existing AMV error characterizations.
Giovanni Martucci, Francisco Navas-Guzmán, Ludovic Renaud, Gonzague Romanens, S. Mahagammulla Gamage, Maxime Hervo, Pierre Jeannet, and Alexander Haefele
Atmos. Meas. Tech., 14, 1333–1353, https://doi.org/10.5194/amt-14-1333-2021, https://doi.org/10.5194/amt-14-1333-2021, 2021
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This article presents a validation of 1.5 years of pure rotational temperature data measured by the Raman lidar RALMO installed at the MeteoSwiss station of Payerne. The statistical results are in terms of bias and standard deviation with respect to two well-established radiosounding systems. The statistics are divided into daytime (bias = 0.28 K, SD = 0.62±0.03 K) and nighttime (bias = 0.29 K, SD = 0.66±0.06 K). The lidar temperature profiles are applied to cloud supersaturation studies.
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Short summary
A flexible machine-learning model was fit to explain the differences between estimates of water vapor from satellites versus ground stations in Northeastern USA. We use nine variables derived from the satellite acquisition and ground characteristics to explain this measurement error. Our results showed overall good agreement, but data from the Terra satellite were drifting too high in recent summers. Our model reduces measurement error and works well in new locations in the northeast.
A flexible machine-learning model was fit to explain the differences between estimates of water...
