Articles | Volume 26, issue 21
https://doi.org/10.5194/acp-26-16029-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-16029-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluating the representativeness of aircraft sampling strategies for cloud water content in LASSO simulations over the ARM Southern Great Plains (SGP) Site
Jiakun Liang
Department of Atmospheric Sciences, University of Hawai`i at Mānoa, Honolulu, HI 96822, USA
William I. Gustafson Jr.
Atmospheric and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
Dana R. Caulton
Department of Atmospheric Science, University of Wyoming, Laramie, WY 82071, USA
Jennifer D. Small Griswold
CORRESPONDING AUTHOR
Department of Atmospheric Sciences, University of Hawai`i at Mānoa, Honolulu, HI 96822, USA
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Lindsay M. Sheridan, Raghavendra Krishnamurthy, Tien Manh Nguyen, Yi-Leng Chen, William I. Gustafson Jr., Ye Liu, Feng Hsiao, Rob K. Newsom, Preston Spicer, Evgueni Kassianov, Mikhail Pekour, Nicola Bodini, and Mark Severy
Wind Energ. Sci., 11, 2257–2285, https://doi.org/10.5194/wes-11-2257-2026, https://doi.org/10.5194/wes-11-2257-2026, 2026
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Wind simulations can contain significant errors, which can lead to inaccurate estimates of wind energy generation. Using observations from a floating lidar off Hawai'i, we hypothesize and establish that distinct simulation datasets will exhibit diverse ranges of errors in this offshore environment. The most commonly used simulation dataset produces the largest wind speed biases due to underestimation of fast wind speeds and misrepresentation of how wind speed varies throughout the day and night.
Kerry Meyer, Steven Platnick, G. Thomas Arnold, Nandana Amarasinghe, Daniel Miller, Jennifer Small-Griswold, Mikael Witte, Brian Cairns, Siddhant Gupta, Greg McFarquhar, and Joseph O'Brien
Atmos. Meas. Tech., 18, 981–1011, https://doi.org/10.5194/amt-18-981-2025, https://doi.org/10.5194/amt-18-981-2025, 2025
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Satellite remote sensing retrievals of cloud droplet size are used to understand clouds and their interactions with aerosols and radiation but require many simplifying assumptions. Evaluation of these retrievals is typically done by comparing against direct measurements of droplets from airborne cloud probes. This paper details an evaluation of proxy airborne remote sensing droplet size retrievals against several cloud probes and explores the impact of key assumptions on retrieval agreement.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
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In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
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Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
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.
Megan E. McCabe, Ilana B. Pollack, Emily V. Fischer, Kathryn M. Steinmann, and Dana R. Caulton
Atmos. Chem. Phys., 23, 7479–7494, https://doi.org/10.5194/acp-23-7479-2023, https://doi.org/10.5194/acp-23-7479-2023, 2023
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Agriculture emissions, including those from beef and dairy cattle feeding operations, make up a large portion of the United States’ total greenhouse gas emissions, but many of these operations reside in areas where methane from oil and natural gas is prevalent, making it difficult to attribute methane in these areas. This work investigates two approaches to emission attribution for cattle feeding operations and provides guidance for emission attribution in other complicated regions.
Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
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Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Sagar P. Parajuli, Georgiy L. Stenchikov, Alexander Ukhov, Suleiman Mostamandi, Paul A. Kucera, Duncan Axisa, William I. Gustafson Jr., and Yannian Zhu
Atmos. Chem. Phys., 22, 8659–8682, https://doi.org/10.5194/acp-22-8659-2022, https://doi.org/10.5194/acp-22-8659-2022, 2022
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Rainfall affects the distribution of surface- and groundwater resources, which are constantly declining over the Middle East and North Africa (MENA) due to overexploitation. Here, we explored the effects of dust on rainfall using WRF-Chem model simulations. Although dust is considered a nuisance from an air quality perspective, our results highlight the positive fundamental role of dust particles in modulating rainfall formation and distribution, which has implications for cloud seeding.
Edward Gryspeerdt, Daniel T. McCoy, Ewan Crosbie, Richard H. Moore, Graeme J. Nott, David Painemal, Jennifer Small-Griswold, Armin Sorooshian, and Luke Ziemba
Atmos. Meas. Tech., 15, 3875–3892, https://doi.org/10.5194/amt-15-3875-2022, https://doi.org/10.5194/amt-15-3875-2022, 2022
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Droplet number concentration is a key property of clouds, influencing a variety of cloud processes. It is also used for estimating the cloud response to aerosols. The satellite retrieval depends on a number of assumptions – different sampling strategies are used to select cases where these assumptions are most likely to hold. Here we investigate the impact of these strategies on the agreement with in situ data, the droplet number climatology and estimates of the indirect radiative forcing.
Siddhant Gupta, Greg M. McFarquhar, Joseph R. O'Brien, Michael R. Poellot, David J. Delene, Rose M. Miller, and Jennifer D. Small Griswold
Atmos. Chem. Phys., 22, 2769–2793, https://doi.org/10.5194/acp-22-2769-2022, https://doi.org/10.5194/acp-22-2769-2022, 2022
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This study evaluates the impact of biomass burning aerosols on precipitation in marine stratocumulus clouds using observations from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign over the Southeast Atlantic. Instances of contact and separation between aerosol and cloud layers show polluted clouds have a lower precipitation rate and a lower precipitation susceptibility. This information will help improve cloud representation in Earth system models.
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Short summary
Aircraft measurements are essential for studying clouds, but capture only limited parts of a cloud field. This study uses high-resolution atmospheric simulations to assess how flight paths, altitude patterns, and speed affect the representativeness of aircraft measurements. The results show that faster flights generally improve agreement and staircase sampling performs consistently well. Cloud tops are harder to capture than cloud bases. These findings help interpret aircraft observations.
Aircraft measurements are essential for studying clouds, but capture only limited parts of a...
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