Articles | Volume 18, issue 15
https://doi.org/10.5194/gmd-18-4935-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-4935-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
13 Aug 2025
Model description paper |
| 13 Aug 2025
| 13 Aug 2025
COSP-RTTOV-1.0: flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA
Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, Colorado, USA
Dustin J. Swales
NOAA Global Systems Laboratory, Boulder, Colorado, USA
Sergio DeSouza-Machado
Joint Center for Earth Systems Technology/Physics Department, University of Maryland, Baltimore County, Baltimore, Maryland, USA
David D. Turner
NOAA Global Systems Laboratory, Boulder, Colorado, USA
Jennifer E. Kay
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, USA
Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, Colorado, USA
David P. Schneider
Cooperative Institute for Research in Environmental Science, University of Colorado Boulder, Boulder, Colorado, USA
Related authors
No articles found.
Serena Lipari, Raghavendra Krishnamurthy, Bianca Adler, Laura Bianco, Nicola Bodini, Matthis Chabert d'Hières, Harindra J. S. Fernando, Robert Jackson, Michael Kieburtz, Anthony Kirincich, Stefano Letizia, Julie K. Lundquist, Paytsar Muradyan, Rob K. Newsom, Joseph O'Brien, Sujan Pal, Mikhail Pekour, David D. Turner, Sonia Wharton, and Seth Zippel
EGUsphere, https://doi.org/10.5194/egusphere-2026-5984, https://doi.org/10.5194/egusphere-2026-5984, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Wind energy and weather forecasts depend on knowing how winds change with height. We combined laser, radar, and tower measurements to map winds and their fluctuations every ten minutes, using 19 months of observations at four coastal sites in the northeastern United States. In fog, which limits laser measurements, median data availability at 100–300 meters was 94.5% at a site with radar, compared with 39.3% at a site using only lasers. The method and dataset are freely available.
Nicola Bodini, Emina Maric, Ulrike Egerer, Grant Buster, Luke Lavin, Pavlo Pinchuk, Brandon Benton, and David D. Turner
Wind Energ. Sci., 11, 3919–3948, https://doi.org/10.5194/wes-11-3919-2026, https://doi.org/10.5194/wes-11-3919-2026, 2026
Short summary
Short summary
To plan a reliable United States power grid, engineers need continuous weather data. Because the previous standard record ended in 2013, we created a new, easy-to-use weather database by repackaging complex weather forecasting models onto the same historical grid. We compared our data against real-world observations nationwide, finding it is highly accurate. This provides a seamless, reliable new standard to help plan the future of our national energy system.
Andrea Burgos-Cuevas, David D. Turner, Jan H. Schween, Tobias Marke, Tobias Böck, and Ulrich Löhnert
EGUsphere, https://doi.org/10.5194/egusphere-2026-4994, https://doi.org/10.5194/egusphere-2026-4994, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
We developed a new method to estimate heat and moisture transport in the lower atmosphere using measurements from a single site. The method combines temperature, humidity, and wind observations and quantifies their uncertainty. Results show that strong transport events can be reliably detected, reducing the need for complex networks of instruments and providing a practical way to monitor important atmospheric processes.
Paulo Ceppi, Alejandro Bodas-Salcedo, Mark D. Zelinka, Timothy Andrews, Florent Brient, Robin Chadwick, An-Zhuo Dai, Jonathan M. Gregory, Yen-Ting Hwang, Sarah M. Kang, Jennifer E. Kay, Thorsten Mauritsen, Tomoo Ogura, George Tselioudis, Masahiro Watanabe, Mark J. Webb, and Allison A. Wing
Geosci. Model Dev., 19, 8693–8708, https://doi.org/10.5194/gmd-19-8693-2026, https://doi.org/10.5194/gmd-19-8693-2026, 2026
Short summary
Short summary
Clouds constitute a key uncertainty for climate change projections. The Cloud Feedback Model Intercomparison Project (CFMIP) aims to address this challenge by evaluating and understanding clouds and their impacts on atmospheric circulation, precipitation, and climate sensitivity. The present paper describes the CFMIP experiment protocol for the Coupled Model Intercomparison Project phase 7 (CMIP7), and discusses the accompanying science questions and opportunities for progress.
Li Zhang, Haiqin Li, Georg A. Grell, Partha S. Bhattacharjee, Gonzalo A. Ferrada, Benjamin W. Green, Shan Sun, Ligia Bernardet, Anders Jensen, Barry Baker, Li Pan, Jian He, Jordan Schnell, Ravan Ahmadov, Samuel Trahan, Dustin Swales, Anning Cheng, Fanglin Yang, Rebecca H. Schwantes, Brian C. McDonald, Dominikus Heinzeller, and Shobha Kondragunta
Geosci. Model Dev., 19, 8597–8626, https://doi.org/10.5194/gmd-19-8597-2026, https://doi.org/10.5194/gmd-19-8597-2026, 2026
Short summary
Short summary
Based on the operational Global Ensemble Forecast System-Aerosols at the National Centers for Environmental Prediction, we developed an upgraded system using the Common Community Physics Package framework that allows aerosol particles to directly influence radiation and cloud formation, including how precipitation removes particles from the atmosphere. Evaluation against observations and reanalysis data demonstrates improved forecast skill for weather and subseasonal prediction.
Bianca Adler, Laura Bianco, David D. Turner, Joseph Olson, Xia Sun, Joshua Gebauer, Nicola Bodini, Stefano Letizia, and James M. Wilczak
Geosci. Model Dev., 19, 8627–8649, https://doi.org/10.5194/gmd-19-8627-2026, https://doi.org/10.5194/gmd-19-8627-2026, 2026
Short summary
Short summary
Accurate operational forecasts of temperature and wind in the coastal marine boundary layer are important for a wide range of applications. Leveraging data that were collected along the United States northeast coast during a multi-year period for the Third Wind Forecast Improvement project, we investigated the performance of the operational forecast model and identified systematic errors in wind and temperature forecasts that are now being addressed by the model developers.
Nicola Bodini, Grant Buster, and David D. Turner
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-680, https://doi.org/10.5194/essd-2026-680, 2026
Preprint under review for ESSD
Short summary
Short summary
Alaska's power systems depend on weather, yet weather stations there are few and far between. We turned seven years of hourly output from a national weather forecast model into an easy-to-use record of wind and temperature across Alaska, and checked it against measurements from more than 150 sites. The data generally match observations well, and are freely available to support energy planning, resilient power grids, and artificial intelligence research in this remote region.
Anders A. Jensen, David D. Turner, Jorge Guerra, Stanley G. Benjamin, Clark Evans, David J. Gagne, Justin R. Minder, and Joseph B. Olson
EGUsphere, https://doi.org/10.5194/egusphere-2026-4026, https://doi.org/10.5194/egusphere-2026-4026, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Clouds and precipitation are predicted by complex algorithms in weather models. In this study, parts of these algorithms were replaced by machine learning. This approach successfully added cloud details vital for cloud-radiation interactions and temperature forecasts, and replicated a model component while maintaining accurate total precipitation forecasts. Ultimately, weather models benefit from faster algorithms and the addition of physical details from machine learning.
Leah J. Bertrand, Jennifer E. Kay, and Gijs de Boer
EGUsphere, https://doi.org/10.5194/egusphere-2026-4740, https://doi.org/10.5194/egusphere-2026-4740, 2026
Short summary
Short summary
Using 23 years of US DOE and NOAA observations on the North Slope of Alaska, we show how long-term changes in cloud properties strongly modulate surface warming: during the dark season (October–March), cloud changes double the infrared surface warming effect of atmospheric warming and moistening. During the sunlit season (April–September), changing clouds and rising mid-summer surface albedo reverse that infrared surface warming effect entirely.
Nicola Bodini, Joseph Olson, Brian Gaudet, Giacomo Valerio Iungo, Mojtaba Shams Solari, Sayahnya Roy, Julie K. Lundquist, Nathan Agarwal, Timothy A. Myers, Bianca Adler, Jeffrey D. Mirocha, Eric James, Laura Bianco, James M. Wilczak, and David D. Turner
Wind Energ. Sci., 11, 1949–1961, https://doi.org/10.5194/wes-11-1949-2026, https://doi.org/10.5194/wes-11-1949-2026, 2026
Short summary
Short summary
To improve offshore wind forecasts, the Third Wind Forecast Improvement Project monitored the United States east coast for 18 months. We compiled a daily log of weather events using advanced scanners and expert notes. This public dataset identifies important wind patterns, helping scientists test computer models and choose specific cases to study.
Stefano Letizia, David D. Turner, Aliza Abraham, Luc Rochette, and Patrick J. Moriarty
Wind Energ. Sci., 11, 1653–1677, https://doi.org/10.5194/wes-11-1653-2026, https://doi.org/10.5194/wes-11-1653-2026, 2026
Short summary
Short summary
Characterizing the wind resource is much more than just measuring wind speeds. In fact, the physics of the atmosphere is governed by a complex interplay of different quantities, temperature being one of the most important. We used a new technology to remotely sense temperature profiles around wind farms at AWAKEN (American WAKE ExperimeNt). Here, we discuss the methodology and guide readers through a comprehensive, step-by-step validation effort to quantify the accuracy of temperature profiling for wind energy.
David D. Turner, Bianca Adler, Laura Bianco, James M. Wilczak, Vincent Michaud-Belleau, and Luc Rochette
Atmos. Meas. Tech., 19, 1573–1586, https://doi.org/10.5194/amt-19-1573-2026, https://doi.org/10.5194/amt-19-1573-2026, 2026
Short summary
Short summary
It is critical that a network of ground-based instruments that measure temperature and humidity profiles be well calibrated, so that differences between any two profiles can be attributed to atmospheric differences and not instrument calibration issues. This study evaluated the relative accuracy of 7 ground-based infrared spectrometers and their ability to measure these profiles, and found that the profile bias was much smaller than the uncertainty in the retrieved profiles themselves.
Linus von Klitzing, David D. Turner, Diego Lange, and Volker Wulfmeyer
Atmos. Meas. Tech., 19, 359–370, https://doi.org/10.5194/amt-19-359-2026, https://doi.org/10.5194/amt-19-359-2026, 2026
Short summary
Short summary
Many atmospheric science endeavors require temporally resolved profiles of temperature, humidity, and winds. Radiosondes are considered the gold standard for measuring these profiles, but the temporal resolution is frequently too coarse for many applications within the atmospheric boundary layer. This study proposes a new method using a normalized height grid in the temporal interpolation process that yields more accurate profiles in the convective boundary layer.
Calvin Coulbury, Ivy Tan, David Turner, and Chen Zhou
EGUsphere, https://doi.org/10.5194/egusphere-2025-6553, https://doi.org/10.5194/egusphere-2025-6553, 2026
Short summary
Short summary
Observations of low-level, single-layer clouds above the North Slope of Alaska, within the Arctic circle, are used to assess how cloud properties have changed from 2004 to 2023. Evidence is provided for a cooling influence of cloud property changes on Arctic amplified warming. Local temperature changes result in clouds becoming more opaque, driven by changes in cloud liquid water content. This work represents an avenue for the evaluation and improvement of model representations of Arctic clouds.
Ash Gilbert, Jennifer E. Kay, and Penny Rowe
Geosci. Model Dev., 18, 7185–7197, https://doi.org/10.5194/gmd-18-7185-2025, https://doi.org/10.5194/gmd-18-7185-2025, 2025
Short summary
Short summary
We developed a novel methodology for assessing whether a new physics parameterization should be added to a climate model based on its effect across a hierarchy of model dynamical constraints. Our study used this model hierarchy to evaluate the effect of a new cloud radiation parameterization on longwave radiation and determined that the parameterization should be added to climate radiation models, but its effect is not large enough to be a priority.
Tessa E. Rosenberger, Thijs Heus, Girish N. Raghunathan, David D. Turner, Timothy J. Wagner, and Julia M. Simonson
Atmos. Meas. Tech., 18, 5129–5140, https://doi.org/10.5194/amt-18-5129-2025, https://doi.org/10.5194/amt-18-5129-2025, 2025
Short summary
Short summary
Entrainment is key in understanding temperature and moisture changes within the boundary layer, but it is difficult to observe using ground-based observations. This work used simulations to verify an assumption that simplifies entrainment estimations from ground-based observational data, recognizing that entrainment is the combination of the transfer of heat and moisture from above the boundary layer into it and the change in concentration of heat and moisture as boundary layer depth changes.
Laura Bianco, Reagan Mendeke, Jakob Lindblom, Irina V. Djalalova, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 10, 2117–2136, https://doi.org/10.5194/wes-10-2117-2025, https://doi.org/10.5194/wes-10-2117-2025, 2025
Short summary
Short summary
Adding more renewable energy into the electric grid is a critical part of the strategy to increase energy availability. Reliable numerical weather prediction (NWP) models need to be able to predict the intrinsic nature of weather-dependent resources such as wind ramp events, as wind energy could quickly be available in abundance or temporarily cease to exist. We assess the ability of the operational High Resolution Rapid Refresh NWP model to forecast wind ramp events in the two most recent versions.
Vincent Michaud-Belleau, Michel Gaudreau, Jean Lacoursière, Éric Boisvert, Lalaina Ravelomanantsoa, David D. Turner, and Luc Rochette
Atmos. Meas. Tech., 18, 3585–3609, https://doi.org/10.5194/amt-18-3585-2025, https://doi.org/10.5194/amt-18-3585-2025, 2025
Short summary
Short summary
The Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST) is a commercially available ground-based infrared spectroradiometer. It is designed for automated and passive measurement of the thermal radiation emitted by the atmosphere, providing information about the vertical distribution of temperature and humidity, trace gases, clouds, and aerosols in the boundary layer. In this paper, we outline the key characteristics of the ASSIST hardware and signal processing algorithm that yields downwelling radiance spectra in near real-time.
David D. Turner, Maria P. Cadeddu, Julia M. Simonson, and Timothy J. Wagner
Atmos. Meas. Tech., 18, 3533–3546, https://doi.org/10.5194/amt-18-3533-2025, https://doi.org/10.5194/amt-18-3533-2025, 2025
Short summary
Short summary
When deriving a geophysical variable from remote sensors, the uncertainty and information content are critical. The latter quantify specifies what fraction of a real perturbation would be observed in the derived variable. This paper outlines, for the first time, a methodology for propagating the information content from multiple remote sensors into a derived product using horizontal advection as an example.
Bianca Adler, David D. Turner, Laura Bianco, Irina V. Djalalova, Timothy Myers, and James M. Wilczak
Atmos. Meas. Tech., 17, 6603–6624, https://doi.org/10.5194/amt-17-6603-2024, https://doi.org/10.5194/amt-17-6603-2024, 2024
Short summary
Short summary
Continuous profile observations of temperature and humidity in the lowest part of the atmosphere are essential for the evaluation of numerical weather prediction models and data assimilation for better weather forecasts. Such profiles can be retrieved from passive ground-based remote sensing instruments like infrared spectrometers and microwave radiometers. In this study, we describe three recent modifications to the retrieval framework TROPoe for improved temperature and humidity profiles.
Tessa E. Rosenberger, David D. Turner, Thijs Heus, Girish N. Raghunathan, Timothy J. Wagner, and Julia Simonson
Atmos. Meas. Tech., 17, 6595–6602, https://doi.org/10.5194/amt-17-6595-2024, https://doi.org/10.5194/amt-17-6595-2024, 2024
Short summary
Short summary
This work used model output to show that considering the changes in boundary layer depth over time in the calculations of variables such as fluxes and variance yields more accurate results than cases where calculations were done at a constant height. This work was done to improve future observations of these variables at the top of the boundary layer.
Megan Thompson-Munson, Jennifer E. Kay, and Bradley R. Markle
The Cryosphere, 18, 3333–3350, https://doi.org/10.5194/tc-18-3333-2024, https://doi.org/10.5194/tc-18-3333-2024, 2024
Short summary
Short summary
The upper layers of the Greenland Ice Sheet are absorbent and can store meltwater that would otherwise flow into the ocean and raise sea level. The amount of meltwater that the ice sheet can store changes when the air temperature changes. We use a model to show that warming and cooling have opposite but unequal effects. Warming has a stronger effect than cooling, which highlights the vulnerability of the Greenland Ice Sheet to modern climate change.
Laura Bianco, Bianca Adler, Ludovic Bariteau, Irina V. Djalalova, Timothy Myers, Sergio Pezoa, David D. Turner, and James M. Wilczak
Atmos. Meas. Tech., 17, 3933–3948, https://doi.org/10.5194/amt-17-3933-2024, https://doi.org/10.5194/amt-17-3933-2024, 2024
Short summary
Short summary
The Tropospheric Remotely Observed Profiling via Optimal Estimation physical retrieval is used to retrieve temperature and humidity profiles from various combinations of passive and active remote sensing instruments, surface platforms, and numerical weather prediction models. The retrieved profiles are assessed against collocated radiosonde in non-cloudy conditions to assess the sensitivity of the retrievals to different input combinations. Case studies with cloudy conditions are also inspected.
Leah Bertrand, Jennifer E. Kay, John Haynes, and Gijs de Boer
Earth Syst. Sci. Data, 16, 1301–1316, https://doi.org/10.5194/essd-16-1301-2024, https://doi.org/10.5194/essd-16-1301-2024, 2024
Short summary
Short summary
The vertical structure of clouds has a major impact on global energy flows, air circulation, and the hydrologic cycle. Two satellite instruments, CloudSat radar and CALIPSO lidar, have taken complementary measurements of cloud vertical structure for over a decade. Here, we present the 3S-GEOPROF-COMB product, a globally gridded satellite data product combining CloudSat and CALIPSO observations of cloud vertical structure.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
Short summary
Short summary
Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Volker Wulfmeyer, Christoph Senff, Florian Späth, Andreas Behrendt, Diego Lange, Robert M. Banta, W. Alan Brewer, Andreas Wieser, and David D. Turner
Atmos. Meas. Tech., 17, 1175–1196, https://doi.org/10.5194/amt-17-1175-2024, https://doi.org/10.5194/amt-17-1175-2024, 2024
Short summary
Short summary
A simultaneous deployment of Doppler, temperature, and water-vapor lidar systems is used to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of weather forecast and climate models.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
Short summary
Short summary
Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Sunil Baidar, Timothy J. Wagner, David D. Turner, and W. Alan Brewer
Atmos. Meas. Tech., 16, 3715–3726, https://doi.org/10.5194/amt-16-3715-2023, https://doi.org/10.5194/amt-16-3715-2023, 2023
Short summary
Short summary
This paper provides a new method to retrieve wind profiles from coherent Doppler lidar (CDL) measurements. It takes advantage of layer-to-layer correlation in wind profiles to provide continuous profiles of up to 3 km by filling in the gaps where the CDL signal is too small to retrieve reliable results by itself. Comparison with the current method and collocated radiosonde wind measurements showed excellent agreement with no degradation in results where the current method gives valid results.
Maria P. Cadeddu, Virendra P. Ghate, David D. Turner, and Thomas E. Surleta
Atmos. Chem. Phys., 23, 3453–3470, https://doi.org/10.5194/acp-23-3453-2023, https://doi.org/10.5194/acp-23-3453-2023, 2023
Short summary
Short summary
We analyze the variability in marine boundary layer moisture at the Eastern North Atlantic site on a monthly and daily temporal scale and examine its fundamental role in the control of boundary layer cloudiness and precipitation. The study also highlights the complex interaction between large-scale and local processes controlling the boundary layer moisture and the importance of the mesoscale spatial distribution of vapor to support convection and precipitation.
Bianca Adler, James M. Wilczak, Jaymes Kenyon, Laura Bianco, Irina V. Djalalova, Joseph B. Olson, and David D. Turner
Geosci. Model Dev., 16, 597–619, https://doi.org/10.5194/gmd-16-597-2023, https://doi.org/10.5194/gmd-16-597-2023, 2023
Short summary
Short summary
Rapid changes in wind speed make the integration of wind energy produced during persistent orographic cold-air pools difficult to integrate into the electrical grid. By evaluating three versions of NOAA’s High-Resolution Rapid Refresh model, we demonstrate how model developments targeted during the second Wind Forecast Improvement Project improve the forecast of a persistent cold-air pool event.
Gianluca Di Natale, David D. Turner, Giovanni Bianchini, Massimo Del Guasta, Luca Palchetti, Alessandro Bracci, Luca Baldini, Tiziano Maestri, William Cossich, Michele Martinazzo, and Luca Facheris
Atmos. Meas. Tech., 15, 7235–7258, https://doi.org/10.5194/amt-15-7235-2022, https://doi.org/10.5194/amt-15-7235-2022, 2022
Short summary
Short summary
In this paper, we describe a new approach to test the consistency of the precipitating ice cloud optical and microphysical properties in Antarctica, Dome C, retrieved from hyperspectral measurements in the far-infrared, with the reflectivity detected by a co-located micro rain radar operating at 24 GHz. The retrieved ice crystal sizes were found in accordance with the direct measurements of an optical imager, also installed at Dome C, which can collect the falling ice particles.
Xavier Calbet, Cintia Carbajal Henken, Sergio DeSouza-Machado, Bomin Sun, and Tony Reale
Atmos. Meas. Tech., 15, 7105–7118, https://doi.org/10.5194/amt-15-7105-2022, https://doi.org/10.5194/amt-15-7105-2022, 2022
Short summary
Short summary
Water vapor concentration in the atmosphere at small scales (< 6 km) is considered. The measurements show Gaussian random field behavior following Kolmogorov's theory of turbulence two-thirds law. These properties can be useful when estimating the water vapor variability within a given observed satellite scene or when different water vapor measurements have to be merged consistently.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
Short summary
Short summary
This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Heather Guy, David D. Turner, Von P. Walden, Ian M. Brooks, and Ryan R. Neely
Atmos. Meas. Tech., 15, 5095–5115, https://doi.org/10.5194/amt-15-5095-2022, https://doi.org/10.5194/amt-15-5095-2022, 2022
Short summary
Short summary
Fog formation is highly sensitive to near-surface temperatures and humidity profiles. Passive remote sensing instruments can provide continuous measurements of the vertical temperature and humidity profiles and liquid water content, which can improve fog forecasts. Here we compare the performance of collocated infrared and microwave remote sensing instruments and demonstrate that the infrared instrument is especially sensitive to the onset of thin radiation fog.
James B. Duncan Jr., Laura Bianco, Bianca Adler, Tyler Bell, Irina V. Djalalova, Laura Riihimaki, Joseph Sedlar, Elizabeth N. Smith, David D. Turner, Timothy J. Wagner, and James M. Wilczak
Atmos. Meas. Tech., 15, 2479–2502, https://doi.org/10.5194/amt-15-2479-2022, https://doi.org/10.5194/amt-15-2479-2022, 2022
Short summary
Short summary
In this study, several ground-based remote sensing instruments are used to estimate the height of the convective planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the CHEESEHEAD19 field campaign. The impact of clouds (particularly boundary layer clouds) on the estimation of the boundary layer depth is also investigated.
Irina V. Djalalova, David D. Turner, Laura Bianco, James M. Wilczak, James Duncan, Bianca Adler, and Daniel Gottas
Atmos. Meas. Tech., 15, 521–537, https://doi.org/10.5194/amt-15-521-2022, https://doi.org/10.5194/amt-15-521-2022, 2022
Short summary
Short summary
In this paper we investigate the synergy obtained by combining active (radio acoustic sounding system – RASS) and passive (microwave radiometer) remote sensing observations to obtain temperature vertical profiles through a radiative transfer model. Inclusion of the RASS observations leads to more accurate temperature profiles from the surface to 5 km above ground, well above the maximum height of the RASS observations themselves (2000 m), when compared to the microwave radiometer used alone.
Cited articles
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J. L., Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.: COSP: satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011. a
Bodas-Salcedo, A., Williams, K. D., Ringer, M. A., Beau, I., Cole, J. N., Dufresne, J. L., Koshiro, T., Stevens, B., Wang, Z., and Yokohata, T.: Origins of the solar radiation biases over the Southern Ocean in CFMIP2 models, J. Climate, 27, 41–56, https://doi.org/10.1175/JCLI-D-13-00169.1, 2014. a
Computational and Information Systems Laboratory: Derecho: HPE Cray EX System (University Community Computing), NSF National Center for Atmospheric Research, Boulder, CO, https://doi.org/10.5065/qx9a-pg09, 2023a. a
Computational and Information Systems Laboratory: Cheyenne: HPE/SGI ICE XA System (University Community Computing), NSF National Center for Atmospheric Research, Boulder, CO, https://doi.org/10.5065/qx9a-pg09, 2023b. a
Danabasoglu, G., Lamarque, J. F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
DeSouza-Machado, S., Strow, L. L., Motteler, H., and Hannon, S.: kCARTA: a fast pseudo line-by-line radiative transfer algorithm with analytic Jacobians, fluxes, nonlocal thermodynamic equilibrium, and scattering for the infrared, Atmos. Meas. Tech., 13, 323–339, https://doi.org/10.5194/amt-13-323-2020, 2020. a
Eyring, V., Gillett, N. P., Achuta Rao, K. M., Barimalala, R., Barreiro Parrillo, M., Bellouin, N., Cassou, C., Durack, P. J., Kosaka, Y., McGregor, S., Min, S., Morgenstern, O., and Sun, Y.: Human Influence on the Climate System, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009157896.005, 423–552, 2021. a
Feldman, D. R., Algieri, C. A., Collins, W. D., Roberts, Y. L., and Pilewskie, P. A.: Simulation studies for the detection of changes in broadband albedo and shortwave nadir reflectance spectra under a climate change scenario, J. Geophys. Res.-Atmos., 116, 24103, https://doi.org/10.1029/2011JD016407, 2011a. a
Feldman, D. R., Algieri, C. A., Ong, J. R., and Collins, W. D.: CLARREO shortwave observing system simulation experiments of the twenty-first century: simulator design and implementation, J. Geophys. Res.-Atmos., 116, 10107, https://doi.org/10.1029/2010JD015350, 2011b. a, b
Feldman, D. R., Collins, W. D., and Paige, J. L.: Pan-spectral observing system simulation experiments of shortwave reflectance and long-wave radiance for climate model evaluation, Geosci. Model Dev., 8, 1943–1954, https://doi.org/10.5194/gmd-8-1943-2015, 2015. a, b
Gettelman, A., Truesdale, J. E., Bacmeister, J. T., Caldwell, P. M., Neale, R. B., Bogenschutz, P. A., and Simpson, I. R.: The Single Column Atmosphere Model Version 6 (SCAM6): not a scam but a tool for model evaluation and development, J. Adv. Model. Earth Sy., 11, 1381–1401, https://doi.org/10.1029/2018MS001578, 2019. a, b
Haynes, J. M., L'Ecuyer, T. S., Stephens, G. L., Miller, S. D., Mitrescu, C., Wood, N. B., and Tanelli, S.: Rainfall retrieval over the ocean with spaceborne W-band radar, J. Geophys. Res.-Atmos., 114, D00A22, https://doi.org/10.1029/2008JD009973, 2009. a, b
Hoffman, R. N. and Atlas, R.: Future observing system simulation experiments, B. Am. Meteorol. Soc., 97, 1601–1616, https://doi.org/10.1175/BAMS-D-15-00200.1, 2016. a
Huang, X., Chen, X., Fan, C., Kato, S., Loeb, N., Bosilovich, M., Ham, S. H., Rose, F. G., and Strow, L. L.: A synopsis of AIRS global-mean clear-sky radiance trends from 2003 to 2020, J. Geophys. Res.-Atmos., 127, e2022JD037598, https://doi.org/10.1029/2022JD037598, 2022. a
Huang, Y., Ramaswamy, V., Huang, X., Fu, Q., and Bardeen, C.: A strict test in climate modeling with spectrally resolved radiances: GCM simulation versus AIRS observations, Geophys. Res. Lett., 34, L24707, https://doi.org/10.1029/2007GL031409, 2007. a
Kay, J. E., Hillman, B. R., Klein, S. A., Zhang, Y., Medeiros, B., Pincus, R., Gettelman, A., Eaton, B., Boyle, J., Marchand, R., and Ackerman, T. P.: Exposing global cloud biases in the Community Atmosphere Model (CAM) using satellite observations and their corresponding instrument simulators, J. Climate, 25, 5190–5207, https://doi.org/10.1175/JCLI-D-11-00469.1, 2012. a
Kay, J. E., Bourdages, L., Miller, N. B., Morrison, A., Yettella, V., Chepfer, H., and Eaton, B.: Evaluating and improving cloud phase in the Community Atmosphere Model version 5 using spaceborne lidar observations, J. Geophys. Res.-Atmos., 121, 4162–4176, https://doi.org/10.1002/2015JD024699, 2016. a
Kay, J. E., L'Ecuyer, T., Pendergrass, A., Chepfer, H., Guzman, R., and Yettella, V.: Scale-aware and definition-aware evaluation of modeled near-surface precipitation frequency using CloudSat observations, J. Geophys. Res.-Atmos., 123, 4294–4309, https://doi.org/10.1002/2017JD028213, 2018. a, b
Klein, S. A. and Jakob, C.: Validation and sensitivities of frontal clouds simulated by the ECMWF model, Mon. Weather Rev., 127, 2514–2531, https://doi.org/10.1175/1520-0493(1999)127<2514:VASOFC>2.0.CO;2, 1999. a
L'Ecuyer, T. S., Drouin, B. J., Anheuser, J., Grames, M., Henderson, D. S., Huang, X., Kahn, B. H., Kay, J. E., Lim, B. H., Mateling, M., Merrelli, A., Miller, N. B., Padmanabhan, S., Peterson, C., Schlegel, N. J., White, M. L., and Xie, Y.: The polar radiant energy in the far infrared experiment: a new perspective on polar longwave energy exchanges, B. Am. Meteorol. Soc., 102, E1431–E1449, https://doi.org/10.1175/BAMS-D-20-0155.1, 2021. a
Lee, J.-Y., Marotzke, J., Bala, G., Cao, L., Corti, S., Dunne, J. P., Engelbrecht, F., Fischer, E., Fyfe, J. C., Jones, C., Maycock, A., Mutemi, J., Ndiaye, O., Panickal, S., and Zhou, T.: Future Global Climate: Scenario-Based Projections and Near-Term Information, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., chap. 4, Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009157896.006, 2021. a
Mace, G. G., Houser, S., Benson, S., Klein, S. A., and Min, Q.: Critical evaluation of the ISCCP Simulator Using Ground-Based Remote Sensing Data, J. Climate, 24, 1598–1612, https://doi.org/10.1175/2010JCLI3517.1, 2011. a
Mace, J., Jensen, E., McFarquhar, G., Comstock, J., Ackerman, T., Mitchell, D., Liu, X., and Garrett, T.: SPARTICUS: Small Particles in Cirrus Science and Operations Plan, US Department of Energy Atmospheric Radiation Measurement (ARM) Research Facility, https://doi.org/10.2172/971206, 2009. a
May, P. T., Mather, J. H., Vaughan, G., Jakob, C., McFarquhar, G. M., Bower, K. N., and Mace, G. G.: The tropical warm pool international cloud experiment, B. Am. Meteorol. Soc., 89, 629–646, https://doi.org/10.1175/BAMS-89-5-629, 2008. a
Medeiros, B., Shaw, J., Kay, J. E., and Davis, I.: Assessing clouds using satellite observations through three generations of global atmosphere models, Earth and Space Science, 10, e2023EA002918, https://doi.org/10.1029/2023EA002918, 2023. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997. a
Nam, C., Bony, S., Dufresne, J. L., and Chepfer, H.: The `too few, too bright' tropical low-cloud problem in CMIP5 models, Geophys. Res. Lett., 39, 21801, https://doi.org/10.1029/2012GL053421, 2012. a
Pincus, R., Barker Environment Canada, H. W., Jean-Jacques Morcrette, C., Barker, W., and Morcrette, J.-J.: A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields, J. Geophys. Res.-Atmos., 108, 4376, https://doi.org/10.1029/2002JD003322, 2003. a
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Patrick Hofmann, R. J.: Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators, J. Climate, 25, 4699–4720, https://doi.org/10.1175/JCLI-D-11-00267.1, 2012. a
Raghuraman, S. P., Paynter, D., and Ramaswamy, V.: Anthropogenic forcing and response yield observed positive trend in Earth's energy imbalance, Nat. Commun. 2021 12:1, 12, 1–10, https://doi.org/10.1038/s41467-021-24544-4, 2021. a
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018. a, b, c, d
Shaw, J.: Data for COSP-RTTOV documentation, Zenodo [data set], https://doi.org/10.5281/zenodo.14647157, 2025a. a
Shaw, J.: jshaw35/COSP-RTTOV_figures: Initial submission version, Zenodo [code], https://doi.org/10.5281/zenodo.14646878, 2025b. a
Shaw, J.: jshaw35/COSPv2.0: COSP-RTTOV-1.0, Zenodo [code], https://doi.org/10.5281/zenodo.14750169, 2025c. a
Shaw, J. K. and Kay, J. E.: Processes controlling the seasonally varying emergence of forced Arctic longwave radiation changes, J. Climate, 36, 7337–7354, https://doi.org/10.1175/JCLI-D-23-0020.1, 2023. a, b
Shaw, J. K. and Lenssen, N.: Early and widespread emergence of regional warming is robust to observational and model uncertainty Publication, Environ. Res. Lett., 20, 074066, https://doi.org/10.1088/1748-9326/ade45, 2024. a
Shaw, J., McGraw, Z., Bruno, O., Storelvmo, T., and Hofer, S.: Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds, Geophys. Res. Lett., 49, e2021GL096191, https://doi.org/10.1029/2021GL096191, 2022. a
Simpson, I. R., Shaw, T. A., Ceppi, P., Clement, A. C., Fischer, E., Grise, K. M., Pendergrass, A. G., Screen, J. A., Wills, R. C., Woollings, T., Blackport, R., Kang, J. M., and Po-Chedley, S.: Confronting Earth System Model trends with observations, Science Advances, 11, 8035, https://doi.org/10.1126/sciadv.adt8035, 2025. a
Smalley, M., L'Ecuyer, T., Lebsock, M., and Haynes, J.: A comparison of precipitation occurrence from the NCEP Stage IV QPE product and the CloudSat cloud profiling radar, J. Hydrol., 15, 444–458, https://doi.org/10.1175/JHM-D-13-048.1, 2014. a, b
Stamnes, K., Thomas, G. E., and Stamnes, J. J.: Radiative Transfer in the Atmosphere and Ocean, Cambridge University Press, https://doi.org/10.1017/9781316148549, 2017. a, b
Stephens, G. L., Vane, D. G., Tanelli, S., Im, E., Durden, S., Rokey, M., Reinke, D., Partain, P., Mace, G. G., Austin, R., L'Ecuyer, T., Haynes, J., Lebsock, M., Suzuki, K., Waliser, D., Wu, D., Kay, J., Gettelman, A., Wang, Z., and Marchand, R.: CloudSat mission: performance and early science after the first year of operation, J. Geophys. Res.-Atmos., 113, D00A18, https://doi.org/10.1029/2008JD009982, 2008. a
Strow, L. L. and DeSouza-Machado, S.: Establishment of AIRS climate-level radiometric stability using radiance anomaly retrievals of minor gases and sea surface temperature, Atmos. Meas. Tech., 13, 4619–4644, https://doi.org/10.5194/amt-13-4619-2020, 2020. a
Strow, L. L., Hannon, S. E., De Souza-Machado, S., Motteler, H. E., and Tobin, D.: An overview of the AIRS radiative transfer model, IEEE T. Geosci. Remote, 41, 303–313, https://doi.org/10.1109/TGRS.2002.808244, 2003. a
Strow, L. L., Hannon, S. E., De-Souza Machado, S., Motteler, H. E., and Tobin, D. C.: Validation of the Atmospheric Infrared Sounder radiative transfer algorithm, J. Geophys. Res.-Atmos., 111, 9–15, https://doi.org/10.1029/2005JD006146, 2006. a
Swales, D. J., Pincus, R., and Bodas-Salcedo, A.: The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2, Geosci. Model Dev., 11, 77–81, https://doi.org/10.5194/gmd-11-77-2018, 2018. a, b
Verlinde, J., Harrington, J. Y., McFarquhar, G. M., Yannuzzi, V. T., Avramov, A., Greenberg, S., Johnson, N., Zhang, G., Poellot, M. R., Mather, J. H., Turner, D. D., Eloranta, E. W., Zak, B. D., Prenni, A. J., Daniel, J. S., Kok, G. L., Tobin, D. C., Holz, R., Sassen, K., Spangenberg, D., Minnis, P., Tooman, T. P., Ivey, M. D., Richardson, S. J., Bahrmann, C. P., Shupe, M., DeMott, P. J., Heymsfield, A. J., and Schofield, R.: The mixed-phase Arctic cloud experiment, B. Am. Meteorol. Soc., 88, 205–222, https://doi.org/10.1175/BAMS-88-2-205, 2007. a
Webb, M., Senior, C., Bony, S., and Morcrette, J. J.: Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models, Clim. Dynam., 17, 905–922, https://doi.org/10.1007/s003820100157, 2001. a
Wielicki, B. A., Young, D. F., Mlynczak, M. G., Thome, K. J., Leroy, S., Corliss, J., Anderson, J. G., Ao, C. O., Bantges, R., Best, F., Bowman, K., Brindley, H., Butler, J. J., Collins, W., Dykema, J. A., Doelling, D. R., Feldman, D. R., Fox, N., Huang, X., Holz, R., Huang, Y., Jin, Z., Jennings, D., Johnson, D. G., Jucks, K., Kato, S., Kirk-Davidoff, D. B., Knuteson, R., Kopp, G., Kratz, D. P., Liu, X., Lukashin, C., Mannucci, A. J., Phojanamongkolkij, N., Pilewskie, P., Ramaswamy, V., Revercomb, H., Rice, J., Roberts, Y., Roithmayr, C. M., Rose, F., Sandford, S., Shirley, E. L., Smith, W. L., Soden, B., Speth, P. W., Sun, W., Taylor, P. C., Tobin, D., and Xiong, X.: Achieving climate change absolute accuracy in orbit, B. Am. Meteorol. Soc., 94, 1519–1539, https://doi.org/10.1175/BAMS-D-12-00149.1, 2013. a
Williams, K. D. and Tselioudis, G.: GCM intercomparison of global cloud regimes: present-day evaluation and climate change response, Clim. Dynam., 29, 231–250, https://doi.org/10.1007/s00382-007-0232-2, 2007. a
Zeng, X., Atlas, R., Birk, R. J., Carr, F. H., Carrier, M. J., Cucurull, L., Hooke, W. H., Kalnay, E., Murtugudde, R., Posselt, D. J., Russell, J. L., Tyndall, D. P., Weller, R. A., and Zhang, F.: Use of observing system simulation experiments in the United States, B. Am. Meteorol. Soc., 101, E1427–E1438, https://doi.org/10.1175/BAMS-D-19-0155.1, 2020. a
Zhang, M. H., Lin, W. Y., Klein, S. A., Bacmeister, J. T., Bony, S., Cederwall, R. T., Del Genio, A. D., Hack, J. J., Loeb, N. G., Lohmann, U., Minnis, P., Musat, I., Pincus, R., Stier, P., Suarez, M. J., Webb, M. J., Wu, J. B., Xie, S. C., Yao, M. S., and Zhang, J. H.: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements, J. Geophys. Res.-Atmos., 110, 1–18, https://doi.org/10.1029/2004JD005021, 2005. a
Zhang, M., Bretherton, C. S., Blossey, P. N., Austin, P. H., Bacmeister, J. T., Bony, S., Brient, F., Cheedela, S. K., Cheng, A., Del Genio, A. D., De Roode, S. R., Endo, S., Franklin, C. N., Golaz, J. C., Hannay, C., Heus, T., Isotta, F. A., Dufresne, J. L., Kang, I. S., Kawai, H., Köhler, M., Larson, V. E., Liu, Y., Lock, A. P., Lohmann, U., Khairoutdinov, M. F., Molod, A. M., Neggers, R. A., Rasch, P., Sandu, I., Senkbeil, R., Siebesma, A. P., Siegenthaler-Le Drian, C., Stevens, B., Suarez, M. J., Xu, K. M., von Salzen, K., Webb, M. J., Wolf, A., and Zhao, M.: CGILS: results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models, J. Adv. Model. Earth Sy., 5, 826–842, https://doi.org/10.1002/2013MS000246, 2013. a, b, c
Zhang, M., Somerville, R. C. J., and Xie, S.: The SCM concept and creation of ARM forcing datasets, Meteor. Mon., 57, 1–24, https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0040.1, 2016. a
Short summary
Satellites have observed Earth's emissions of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about Earth and the atmosphere. We present a tool that runs within Earth system models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Satellites have observed Earth's emissions of infrared radiation since the 1970s. Because...
Altmetrics
Final-revised paper
Preprint