Articles | Volume 15, issue 1
https://doi.org/10.5194/amt-15-117-2022
© Author(s) 2022. 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-15-117-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jan 2022
Research article |
| 05 Jan 2022
| 05 Jan 2022
New sampling strategy mitigates a solar-geometry-induced bias in sub-kilometre vapour scaling statistics derived from imaging spectroscopy
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 90095, USA
David R. Thompson
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Marcin J. Kurowski
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Matthew D. Lebsock
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
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Mark T. Richardson, Brian H. Kahn, and Peter Kalmus
Atmos. Chem. Phys., 23, 7699–7717, https://doi.org/10.5194/acp-23-7699-2023, https://doi.org/10.5194/acp-23-7699-2023, 2023
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Convection over land often triggers hours after a satellite last passed overhead and measured the state of the atmosphere, and during those hours the atmosphere can change greatly. Here we show that it is possible to reconstruct most of those changes by using weather forecast winds to predict where warm and moist air parcels will travel. The results can be used to better predict where precipitation is likely to happen in the hours after satellite measurements.
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Modern and upcoming hyperspectral imagers will take images with spatial resolutions as fine as 20 m. They can retrieve column water vapour, and we show evidence that from these column measurements you can get statistics of planetary boundary layer (PBL) water vapour. This is important information for climate models that need to account for sub-grid mixing of water vapour near the surface in their PBL schemes.
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Concentrations of water vapor in the atmosphere vary dramatically over space and time. Mapping this variability can provide insights into atmospheric processes that help us understand atmospheric processes in the Earth system. Here we use a new measurement strategy based on imaging spectroscopy to map atmospheric water vapor concentrations at very small spatial scales. Experiments demonstrate the accuracy of this technique and some initial results from an airborne remote sensing experiment.
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Riley Duren, Daniel Cusworth, Alana Ayasse, Kate Howell, Alex Diamond, Tia Scarpelli, Jinsol Kim, Kelly O'neill, Judy Lai-Norling, Andrew Thorpe, Sander R. Zandbergen, Lucas Shaw, Mark Keremedjiev, Jeff Guido, Paul Giuliano, Malkam Goldstein, Ravi Nallapu, Geert Barentsen, David R. Thompson, Keely Roth, Daniel Jensen, Michael Eastwood, Frances Reuland, Taylor Adams, Adam Brandt, Eric A. Kort, James Mason, and Robert O. Green
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Richard M. Schulte, Matthew D. Lebsock, and John M. Haynes
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Mark T. Richardson, Brian H. Kahn, and Peter Kalmus
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Convection over land often triggers hours after a satellite last passed overhead and measured the state of the atmosphere, and during those hours the atmosphere can change greatly. Here we show that it is possible to reconstruct most of those changes by using weather forecast winds to predict where warm and moist air parcels will travel. The results can be used to better predict where precipitation is likely to happen in the hours after satellite measurements.
Maria J. Chinita, Mikael Witte, Marcin J. Kurowski, Joao Teixeira, Kay Suselj, Georgios Matheou, and Peter Bogenschutz
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Kevin M. Smalley, Matthew D. Lebsock, Ryan Eastman, Mark Smalley, and Mikael K. Witte
Atmos. Chem. Phys., 22, 8197–8219, https://doi.org/10.5194/acp-22-8197-2022, https://doi.org/10.5194/acp-22-8197-2022, 2022
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We use geostationary satellite observations to track pockets of open-cell (POC) stratocumulus and analyze how precipitation, cloud microphysics, and the environment change. Precipitation becomes more intense, corresponding to increasing effective radius and decreasing number concentrations, while the environment remains relatively unchanged. This implies that changes in cloud microphysics are more important than the environment to POC development.
Richard J. Roy, Matthew Lebsock, and Marcin J. Kurowski
Atmos. Meas. Tech., 14, 6443–6468, https://doi.org/10.5194/amt-14-6443-2021, https://doi.org/10.5194/amt-14-6443-2021, 2021
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Short summary
This study describes the potential capabilities of a hypothetical spaceborne radar to observe water vapor within clouds.
Mark T. Richardson, David R. Thompson, Marcin J. Kurowski, and Matthew D. Lebsock
Atmos. Meas. Tech., 14, 5555–5576, https://doi.org/10.5194/amt-14-5555-2021, https://doi.org/10.5194/amt-14-5555-2021, 2021
Short summary
Short summary
Modern and upcoming hyperspectral imagers will take images with spatial resolutions as fine as 20 m. They can retrieve column water vapour, and we show evidence that from these column measurements you can get statistics of planetary boundary layer (PBL) water vapour. This is important information for climate models that need to account for sub-grid mixing of water vapour near the surface in their PBL schemes.
David R. Thompson, Brian H. Kahn, Philip G. Brodrick, Matthew D. Lebsock, Mark Richardson, and Robert O. Green
Atmos. Meas. Tech., 14, 2827–2840, https://doi.org/10.5194/amt-14-2827-2021, https://doi.org/10.5194/amt-14-2827-2021, 2021
Short summary
Short summary
Concentrations of water vapor in the atmosphere vary dramatically over space and time. Mapping this variability can provide insights into atmospheric processes that help us understand atmospheric processes in the Earth system. Here we use a new measurement strategy based on imaging spectroscopy to map atmospheric water vapor concentrations at very small spatial scales. Experiments demonstrate the accuracy of this technique and some initial results from an airborne remote sensing experiment.
Longlei Li, Natalie M. Mahowald, Ron L. Miller, Carlos Pérez García-Pando, Martina Klose, Douglas S. Hamilton, Maria Gonçalves Ageitos, Paul Ginoux, Yves Balkanski, Robert O. Green, Olga Kalashnikova, Jasper F. Kok, Vincenzo Obiso, David Paynter, and David R. Thompson
Atmos. Chem. Phys., 21, 3973–4005, https://doi.org/10.5194/acp-21-3973-2021, https://doi.org/10.5194/acp-21-3973-2021, 2021
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For the first time, this study quantifies the range of the dust direct radiative effect due to uncertainty in the soil mineral abundance using all currently available information. We show that the majority of the estimated direct radiative effect range is due to uncertainty in the simulated mass fractions of iron oxides and thus their soil abundance, which is independent of the model employed. We therefore prove the necessity of considering mineralogy for understanding dust–climate interactions.
Jakob Borchardt, Konstantin Gerilowski, Sven Krautwurst, Heinrich Bovensmann, Andrew K. Thorpe, David R. Thompson, Christian Frankenberg, Charles E. Miller, Riley M. Duren, and John Philip Burrows
Atmos. Meas. Tech., 14, 1267–1291, https://doi.org/10.5194/amt-14-1267-2021, https://doi.org/10.5194/amt-14-1267-2021, 2021
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The AVIRIS-NG hyperspectral imager has been used successfully to identify and quantify anthropogenic methane sources utilizing different retrieval and inversion methods. Here, we examine the adaption and application of the WFM-DOAS algorithm to AVIRIS-NG measurements to retrieve local methane column enhancements, compare the results with other retrievals, and quantify the uncertainties resulting from the retrieval method. Additionally, we estimate emissions from five detected methane plumes.
Macey W. Sandford, David R. Thompson, Robert O. Green, Brian H. Kahn, Raffaele Vitulli, Steve Chien, Amruta Yelamanchili, and Winston Olson-Duvall
Atmos. Meas. Tech., 13, 7047–7057, https://doi.org/10.5194/amt-13-7047-2020, https://doi.org/10.5194/amt-13-7047-2020, 2020
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We demonstrate an onboard cloud-screening approach to significantly reduce the amount of cloud-contaminated data transmitted from orbit. We have produced location-specific models that improve performance by taking into account the unique cloud statistics in different latitudes. We have shown that screening clouds based on their location or surface type will improve the ability for a cloud-screening tool to improve the volume of usable science data.
Luis Millán, Richard Roy, and Matthew Lebsock
Atmos. Meas. Tech., 13, 5193–5205, https://doi.org/10.5194/amt-13-5193-2020, https://doi.org/10.5194/amt-13-5193-2020, 2020
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This paper describes the feasibility of using a differential absorption radar technique for the remote sensing of total column water vapor from a spaceborne platform.
Mark Richardson, Matthew D. Lebsock, James McDuffie, and Graeme L. Stephens
Atmos. Meas. Tech., 13, 4947–4961, https://doi.org/10.5194/amt-13-4947-2020, https://doi.org/10.5194/amt-13-4947-2020, 2020
Short summary
Short summary
We previously combined CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) lidar data and reflected-sunlight measurements from OCO-2 (Orbiting Carbon Observatory 2) for information about low clouds over oceans. The satellites are no longer formation-flying, so this work is a step towards getting new information about these clouds using only OCO-2. We can rapidly and accurately identify liquid oceanic clouds and obtain their height better than a widely used passive sensor.
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Short summary
Sunlight can pass diagonally through the atmosphere, cutting through the 3-D water vapour field in a way that
smears2-D maps of imaging spectroscopy vapour retrievals. In simulations we show how this smearing is
towardsor
away fromthe Sun, so calculating
across the solar direction allows sub-kilometre information about water vapour's spatial scaling to be calculated. This could be tested by airborne campaigns and used to obtain new information from upcoming spaceborne data products.
Sunlight can pass diagonally through the atmosphere, cutting through the 3-D water vapour field...
