Articles | Volume 2, issue 1
https://doi.org/10.5194/ascmo-2-49-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/ascmo-2-49-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
10 Jun 2016
A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors
Laura D. Riihimaki
CORRESPONDING AUTHOR
Pacific Northwest National Laboratory, Richland, Washington, USA
Jennifer M. Comstock
Pacific Northwest National Laboratory, Richland, Washington, USA
Kevin K. Anderson
Pacific Northwest National Laboratory, Richland, Washington, USA
Aimee Holmes
Pacific Northwest National Laboratory, Richland, Washington, USA
Edward Luke
Brookhaven National Laboratory, Upton, New York, USA
Related authors
Kelly A. Balmes, Laura D. Riihimaki, John Wood, Connor Flynn, Adam Theisen, Michael Ritsche, Lynn Ma, Gary B. Hodges, and Christian Herrera
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-115, https://doi.org/10.5194/amt-2023-115, 2023
Revised manuscript under review for AMT
Short summary
Short summary
A new hyperspectral radiometer (HSR1) was deployed and evaluated in the central United States in northern Oklahoma. The HSR1 total spectral irradiance agreed well to nearby existing instruments but the diffuse spectral irradiance was slightly smaller. The HSR1 retrieved aerosol optical depth (AOD) also agreed well with other retrieved AODs. The HSR1 performance is encouraging that new hyperspectral knowledge is possible that could inform atmospheric process understanding and weather forecasting.
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.
Erin A. Riley, Jessica M. Kleiss, Laura D. Riihimaki, Charles N. Long, Larry K. Berg, and Evgueni Kassianov
Atmos. Meas. Tech., 13, 2099–2117, https://doi.org/10.5194/amt-13-2099-2020, https://doi.org/10.5194/amt-13-2099-2020, 2020
Short summary
Short summary
Discrepancies in hourly shallow cumuli cover estimates can be substantial. Instrument detection differences contribute to long-term bias in shallow cumuli cover estimates, whereas narrow field-of-view configurations impact measurement uncertainty as averaging time decreases. A new tool is introduced to visually assess both impacts on sub-hourly cloud cover estimates. Accurate shallow cumuli cover estimation is needed for model–observation comparisons and studying cloud-surface interactions.
Qi Tang, Stephen A. Klein, Shaocheng Xie, Wuyin Lin, Jean-Christophe Golaz, Erika L. Roesler, Mark A. Taylor, Philip J. Rasch, David C. Bader, Larry K. Berg, Peter Caldwell, Scott E. Giangrande, Richard B. Neale, Yun Qian, Laura D. Riihimaki, Charles S. Zender, Yuying Zhang, and Xue Zheng
Geosci. Model Dev., 12, 2679–2706, https://doi.org/10.5194/gmd-12-2679-2019, https://doi.org/10.5194/gmd-12-2679-2019, 2019
Evgueni Kassianov, Connor Flynn, James Barnard, Brian Ermold, and Jennifer Comstock
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-13, https://doi.org/10.5194/amt-2024-13, 2024
Preprint under review for AMT
Short summary
Short summary
Conventional ground-based radiometers commonly measure solar radiation at a few wavelengths within a narrow spectral range. These limitations prevent improved retrievals of aerosol, cloud, and surface characteristics. To address these limitations, an advanced ground-based radiometer with expanded spectral coverage and hyperspectral capability is introduced. Its good performance is demonstrated using reference data collected over three coastal regions with diverse types of aerosols and clouds.
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
Short summary
Short summary
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.
Kelly A. Balmes, Laura D. Riihimaki, John Wood, Connor Flynn, Adam Theisen, Michael Ritsche, Lynn Ma, Gary B. Hodges, and Christian Herrera
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-115, https://doi.org/10.5194/amt-2023-115, 2023
Revised manuscript under review for AMT
Short summary
Short summary
A new hyperspectral radiometer (HSR1) was deployed and evaluated in the central United States in northern Oklahoma. The HSR1 total spectral irradiance agreed well to nearby existing instruments but the diffuse spectral irradiance was slightly smaller. The HSR1 retrieved aerosol optical depth (AOD) also agreed well with other retrieved AODs. The HSR1 performance is encouraging that new hyperspectral knowledge is possible that could inform atmospheric process understanding and weather forecasting.
Zackary Mages, Pavlos Kollias, Zeen Zhu, and Edward P. Luke
Atmos. Chem. Phys., 23, 3561–3574, https://doi.org/10.5194/acp-23-3561-2023, https://doi.org/10.5194/acp-23-3561-2023, 2023
Short summary
Short summary
Cold-air outbreaks (when cold air is advected over warm water and creates low-level convection) are a dominant cloud regime in the Arctic, and we capitalized on ground-based observations, which did not previously exist, from the COMBLE field campaign to study them. We characterized the extent and strength of the convection and turbulence and found evidence of secondary ice production. This information is useful for model intercomparison studies that will represent cold-air outbreak processes.
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
Short summary
Short summary
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.
Zeen Zhu, Pavlos Kollias, Edward Luke, and Fan Yang
Atmos. Chem. Phys., 22, 7405–7416, https://doi.org/10.5194/acp-22-7405-2022, https://doi.org/10.5194/acp-22-7405-2022, 2022
Short summary
Short summary
Drizzle (small rain droplets) is an important component of warm clouds; however, its existence is poorly understood. In this study, we capitalized on a machine-learning algorithm to develop a drizzle detection method. We applied this algorithm to investigate drizzle occurrence and found out that drizzle is far more ubiquitous than previously thought. This study demonstrates the ubiquitous nature of drizzle in clouds and will improve understanding of the associated microphysical process.
Yun Lin, Jiwen Fan, Pengfei Li, Lai-yung Ruby Leung, Paul J. DeMott, Lexie Goldberger, Jennifer Comstock, Ying Liu, Jong-Hoon Jeong, and Jason Tomlinson
Atmos. Chem. Phys., 22, 6749–6771, https://doi.org/10.5194/acp-22-6749-2022, https://doi.org/10.5194/acp-22-6749-2022, 2022
Short summary
Short summary
How sea spray aerosols may affect cloud and precipitation over the region by acting as ice-nucleating particles (INPs) is unknown. We explored the effects of INPs from marine aerosols on orographic cloud and precipitation for an atmospheric river event observed during the 2015 ACAPEX field campaign. The marine INPs enhance the formation of ice and snow, leading to less shallow warm clouds but more mixed-phase and deep clouds. This work suggests models need to consider the impacts of marine INPs.
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.
Heike Kalesse-Los, Willi Schimmel, Edward Luke, and Patric Seifert
Atmos. Meas. Tech., 15, 279–295, https://doi.org/10.5194/amt-15-279-2022, https://doi.org/10.5194/amt-15-279-2022, 2022
Short summary
Short summary
It is important to detect the vertical distribution of cloud droplets and ice in mixed-phase clouds. Here, an artificial neural network (ANN) previously developed for Arctic clouds is applied to a mid-latitudinal cloud radar data set. The performance of this technique is contrasted to the Cloudnet target classification. For thick/multi-layer clouds, the machine learning technique is better at detecting liquid than Cloudnet, but if lidar data are available Cloudnet is at least as good as the ANN.
Erin A. Riley, Jessica M. Kleiss, Laura D. Riihimaki, Charles N. Long, Larry K. Berg, and Evgueni Kassianov
Atmos. Meas. Tech., 13, 2099–2117, https://doi.org/10.5194/amt-13-2099-2020, https://doi.org/10.5194/amt-13-2099-2020, 2020
Short summary
Short summary
Discrepancies in hourly shallow cumuli cover estimates can be substantial. Instrument detection differences contribute to long-term bias in shallow cumuli cover estimates, whereas narrow field-of-view configurations impact measurement uncertainty as averaging time decreases. A new tool is introduced to visually assess both impacts on sub-hourly cloud cover estimates. Accurate shallow cumuli cover estimation is needed for model–observation comparisons and studying cloud-surface interactions.
Fan Mei, Jian Wang, Jennifer M. Comstock, Ralf Weigel, Martina Krämer, Christoph Mahnke, John E. Shilling, Johannes Schneider, Christiane Schulz, Charles N. Long, Manfred Wendisch, Luiz A. T. Machado, Beat Schmid, Trismono Krisna, Mikhail Pekour, John Hubbe, Andreas Giez, Bernadett Weinzierl, Martin Zoeger, Mira L. Pöhlker, Hans Schlager, Micael A. Cecchini, Meinrat O. Andreae, Scot T. Martin, Suzane S. de Sá, Jiwen Fan, Jason Tomlinson, Stephen Springston, Ulrich Pöschl, Paulo Artaxo, Christopher Pöhlker, Thomas Klimach, Andreas Minikin, Armin Afchine, and Stephan Borrmann
Atmos. Meas. Tech., 13, 661–684, https://doi.org/10.5194/amt-13-661-2020, https://doi.org/10.5194/amt-13-661-2020, 2020
Short summary
Short summary
In 2014, the US DOE G1 aircraft and the German HALO aircraft overflew the Amazon basin to study how aerosols influence cloud cycles under a clean condition and around a tropical megacity. This paper describes how to meaningfully compare similar measurements from two research aircraft and identify the potential measurement issue. We also discuss the uncertainty range for each measurement for further usage in model evaluation and satellite data validation.
Fan Yang, Robert McGraw, Edward P. Luke, Damao Zhang, Pavlos Kollias, and Andrew M. Vogelmann
Atmos. Meas. Tech., 12, 5817–5828, https://doi.org/10.5194/amt-12-5817-2019, https://doi.org/10.5194/amt-12-5817-2019, 2019
Short summary
Short summary
In-cloud supersaturation is crucial for droplet activation, growth, and drizzle initiation but is poorly known and hardly measured. Here we provide a novel method to estimate supersaturation fluctuation in stratocumulus clouds using remote-sensing measurements, and results show that our estimated supersaturation agrees reasonably well with in situ measurements. Our method provides a unique way to estimate supersaturation in stratocumulus clouds from long-term ground-based observations.
Heike Kalesse, Teresa Vogl, Cosmin Paduraru, and Edward Luke
Atmos. Meas. Tech., 12, 4591–4617, https://doi.org/10.5194/amt-12-4591-2019, https://doi.org/10.5194/amt-12-4591-2019, 2019
Short summary
Short summary
In a cloud, different particles like liquid water droplets and ice particles can exist simultaneously. To study the evolution of cloud particles from cloud top to bottom one has to find out how many different types of particles with different fall velocities are present. This can be done by analyzing the number of peaks in upward-looking cloud radar Doppler spectra. A new machine-learning algorithm (named PEAKO) that determines the number of peaks is introduced and compared to existing methods.
Qi Tang, Stephen A. Klein, Shaocheng Xie, Wuyin Lin, Jean-Christophe Golaz, Erika L. Roesler, Mark A. Taylor, Philip J. Rasch, David C. Bader, Larry K. Berg, Peter Caldwell, Scott E. Giangrande, Richard B. Neale, Yun Qian, Laura D. Riihimaki, Charles S. Zender, Yuying Zhang, and Xue Zheng
Geosci. Model Dev., 12, 2679–2706, https://doi.org/10.5194/gmd-12-2679-2019, https://doi.org/10.5194/gmd-12-2679-2019, 2019
Maximilian Maahn, Fabian Hoffmann, Matthew D. Shupe, Gijs de Boer, Sergey Y. Matrosov, and Edward P. Luke
Atmos. Meas. Tech., 12, 3151–3171, https://doi.org/10.5194/amt-12-3151-2019, https://doi.org/10.5194/amt-12-3151-2019, 2019
Short summary
Short summary
Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Here, we present three novel methods for calibrating vertically pointing cloud radars. These calibration methods are based on microphysical processes of liquid clouds, such as the transition of cloud droplets to drizzle drops. We successfully apply the methods to cloud radar data from the North Slope of Alaska (NSA) and Oliktok Point (OLI) ARM sites.
Guangjie Zheng, Yang Wang, Allison C. Aiken, Francesca Gallo, Michael P. Jensen, Pavlos Kollias, Chongai Kuang, Edward Luke, Stephen Springston, Janek Uin, Robert Wood, and Jian Wang
Atmos. Chem. Phys., 18, 17615–17635, https://doi.org/10.5194/acp-18-17615-2018, https://doi.org/10.5194/acp-18-17615-2018, 2018
Short summary
Short summary
Here, we elucidate the key processes that drive marine boundary layer (MBL) aerosol size distribution in the eastern North Atlantic (ENA) using long-term measurements. The governing equations of particle concentration are established for different modes. Particles entrained from the free troposphere represent the major source of MBL cloud condensation nuclei (CCN), contributing both directly to CCN population and indirectly by supplying Aitken-mode particles that grow to CCN in the MBL.
Scott E. Giangrande, Zhe Feng, Michael P. Jensen, Jennifer M. Comstock, Karen L. Johnson, Tami Toto, Meng Wang, Casey Burleyson, Nitin Bharadwaj, Fan Mei, Luiz A. T. Machado, Antonio O. Manzi, Shaocheng Xie, Shuaiqi Tang, Maria Assuncao F. Silva Dias, Rodrigo A. F de Souza, Courtney Schumacher, and Scot T. Martin
Atmos. Chem. Phys., 17, 14519–14541, https://doi.org/10.5194/acp-17-14519-2017, https://doi.org/10.5194/acp-17-14519-2017, 2017
Short summary
Short summary
The Amazon forest is the largest tropical rain forest on the planet, featuring
prolific and diverse cloud conditions. The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment was motivated by demands to gain a better understanding of aerosol and cloud interactions on climate and the global circulation. The routine DOE ARM observations from this 2-year campaign are summarized to help quantify controls on clouds and precipitation over this undersampled region.
Micael A. Cecchini, Luiz A. T. Machado, Jennifer M. Comstock, Fan Mei, Jian Wang, Jiwen Fan, Jason M. Tomlinson, Beat Schmid, Rachel Albrecht, Scot T. Martin, and Paulo Artaxo
Atmos. Chem. Phys., 16, 7029–7041, https://doi.org/10.5194/acp-16-7029-2016, https://doi.org/10.5194/acp-16-7029-2016, 2016
Short summary
Short summary
This work focuses on the analysis of anthropogenic impacts on Amazonian clouds. The experiment was conducted around Manaus (Brazil), which is a city with 2 million inhabitants and is surrounded by the Amazon forest in every direction. The clouds that form over the pristine atmosphere of the forest are understood as the background clouds and the ones that form over the city pollution are the anthropogenically impacted ones. The paper analyses microphysical characteristics of both types of clouds.
Heike Kalesse, Wanda Szyrmer, Stefan Kneifel, Pavlos Kollias, and Edward Luke
Atmos. Chem. Phys., 16, 2997–3012, https://doi.org/10.5194/acp-16-2997-2016, https://doi.org/10.5194/acp-16-2997-2016, 2016
Short summary
Short summary
Mixed-phase clouds are ubiquitous. Process-level understanding is needed to address the complexity of mixed-phase clouds and to improve their representation in models. This study illustrates steps to identify the impact of a microphysical process (riming) on cloud Doppler radar observations. It suggests that in situ observations of key ice properties are needed to complement radar observations before process-oriented studies can effectively evaluate ice microphysical parameterizations in models.
J. Fan, L. R. Leung, P. J. DeMott, J. M. Comstock, B. Singh, D. Rosenfeld, J. M. Tomlinson, A. White, K. A. Prather, P. Minnis, J. K. Ayers, and Q. Min
Atmos. Chem. Phys., 14, 81–101, https://doi.org/10.5194/acp-14-81-2014, https://doi.org/10.5194/acp-14-81-2014, 2014
K. Zhang, X. Liu, M. Wang, J. M. Comstock, D. L. Mitchell, S. Mishra, and G. G. Mace
Atmos. Chem. Phys., 13, 4963–4982, https://doi.org/10.5194/acp-13-4963-2013, https://doi.org/10.5194/acp-13-4963-2013, 2013
Related subject area
Atmospheric science
Forecasting 24 h averaged PM2.5 concentration in the Aburrá Valley using tree-based machine learning models, global forecasts, and satellite information
A generalized Spatio-Temporal Threshold Clustering method for identification of extreme event patterns
Nonlinear time series models for the North Atlantic Oscillation
Comparing forecast systems with multiple correlation decomposition based on partial correlation
Postprocessing ensemble forecasts of vertical temperature profiles
Using wavelets to verify the scale structure of precipitation forecasts
Automated detection of weather fronts using a deep learning neural network
Low-visibility forecasts for different flight planning horizons using tree-based boosting models
Skewed logistic distribution for statistical temperature post-processing in mountainous areas
Hourly probabilistic snow forecasts over complex terrain: a hybrid ensemble postprocessing approach
A statistical framework for conditional extreme event attribution
Mixture model-based atmospheric air mass classification: a probabilistic view of thermodynamic profiles
Characterization of extreme precipitation within atmospheric river events over California
Jhayron S. Pérez-Carrasquilla, Paola A. Montoya, Juan Manuel Sánchez, K. Santiago Hernández, and Mauricio Ramírez
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 121–135, https://doi.org/10.5194/ascmo-9-121-2023, https://doi.org/10.5194/ascmo-9-121-2023, 2023
Short summary
Short summary
This study uses tree-based machine learning (ML) to forecast PM2.5 in a complex terrain region. The models show the potential to predict pollution events with several hours of anticipation, and they integrate multiple sources of information, including in situ stations, satellite data, and deterministic model outputs. The importance analysis helps understand the processes affecting air quality in the region and highlights the relevance of external sources of pollution in PM2.5 predictability.
Vitaly Kholodovsky and Xin-Zhong Liang
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 35–52, https://doi.org/10.5194/ascmo-7-35-2021, https://doi.org/10.5194/ascmo-7-35-2021, 2021
Short summary
Short summary
Consistent definition and verification of extreme events are still lacking. We propose a new generalized spatio-temporal threshold clustering method to identify extreme event episodes. We observe changes in the distribution of extreme precipitation frequency from large-scale well-connected spatial patterns to smaller-scale, more isolated rainfall clusters, possibly leading to more localized droughts and heat waves.
Thomas Önskog, Christian L. E. Franzke, and Abdel Hannachi
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 141–157, https://doi.org/10.5194/ascmo-6-141-2020, https://doi.org/10.5194/ascmo-6-141-2020, 2020
Short summary
Short summary
The North Atlantic Oscillation (NAO) has a significant impact on seasonal climate and surface weather conditions throughout Europe, North America and the North Atlantic. In this paper, we study a number of linear and nonlinear models for a station-based time series of the daily winter NAO. We find that a class of nonlinear models, including both short and long lags, excellently reproduce the characteristic statistical properties of the NAO. These models can hence be used to simulate the NAO.
Rita Glowienka-Hense, Andreas Hense, Sebastian Brune, and Johanna Baehr
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 103–113, https://doi.org/10.5194/ascmo-6-103-2020, https://doi.org/10.5194/ascmo-6-103-2020, 2020
Short summary
Short summary
A new method for weather and climate forecast model evaluation with respect to observations is proposed. Individually added values are estimated for each model, together with shared information both models provide equally on the observations. Finally, shared model information, which is not present in the observations, is calculated. The method is applied to two examples from climate and weather forecasting, showing new perspectives for model evaluation.
David Schoenach, Thorsten Simon, and Georg Johann Mayr
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 45–60, https://doi.org/10.5194/ascmo-6-45-2020, https://doi.org/10.5194/ascmo-6-45-2020, 2020
Short summary
Short summary
State-of-the-art statistical methods are applied to postprocess an ensemble of numerical forecasts for vertical profiles of air temperature. These profiles are important tools in weather forecasting as they show the stratification and the static stability of the atmosphere. Flexible regression models combined with the multi-dimensionality of the data lead to better calibration and representation of uncertainty of the vertical profiles.
Sebastian Buschow and Petra Friederichs
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 13–30, https://doi.org/10.5194/ascmo-6-13-2020, https://doi.org/10.5194/ascmo-6-13-2020, 2020
Short summary
Short summary
Two-dimensional wavelet transformations can be used to analyse the local structure of predicted and observed precipitation fields and allow for a forecast verification which focuses on the spatial correlation structure alone. This paper applies the novel concept to real numerical weather predictions and radar observations. Systematic similarities and differences between nature and simulation can be detected, localized in space and attributed to particular weather situations.
James C. Biard and Kenneth E. Kunkel
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 147–160, https://doi.org/10.5194/ascmo-5-147-2019, https://doi.org/10.5194/ascmo-5-147-2019, 2019
Short summary
Short summary
A deep learning convolutional neural network (DL-FRONT) was around 90 % successful in determining the locations of weather fronts over North America when compared against front locations determined manually by forecasters. DL-FRONT detects fronts using maps of air pressure, temperature, humidity, and wind from historical observations or climate models. DL-FRONT makes it possible to do science that was previously impractical because manual front identification would take too much time.
Sebastian J. Dietz, Philipp Kneringer, Georg J. Mayr, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 101–114, https://doi.org/10.5194/ascmo-5-101-2019, https://doi.org/10.5194/ascmo-5-101-2019, 2019
Short summary
Short summary
Low-visibility conditions reduce the flight capacity of airports and can lead to delays and supplemental costs for airlines and airports. In this study, the forecasting skill and most important model predictors of airport-relevant low visibility are investigated for multiple flight planning horizons with different statistical models.
Manuel Gebetsberger, Reto Stauffer, Georg J. Mayr, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 87–100, https://doi.org/10.5194/ascmo-5-87-2019, https://doi.org/10.5194/ascmo-5-87-2019, 2019
Short summary
Short summary
This article presents a method for improving probabilistic air temperature forecasts, particularly at Alpine sites. Using a nonsymmetric forecast distribution, the probabilistic forecast quality can be improved with respect to the common symmetric Gaussian distribution used. Furthermore, a long-term training approach of 3 years is presented to ensure the stability of the regression coefficients. The research was based on a PhD project on building an automated forecast system for northern Italy.
Reto Stauffer, Georg J. Mayr, Jakob W. Messner, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 65–86, https://doi.org/10.5194/ascmo-4-65-2018, https://doi.org/10.5194/ascmo-4-65-2018, 2018
Short summary
Short summary
Snowfall forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. This work presents a novel statistical approach to provide accurate forecasts for fresh snow amounts and the probability of snowfall combining data from various sources. The results demonstrate that the new approach is able to provide reliable high-resolution hourly snowfall forecasts for the eastern European Alps up to 3 days ahead.
Pascal Yiou, Aglaé Jézéquel, Philippe Naveau, Frederike E. L. Otto, Robert Vautard, and Mathieu Vrac
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 17–31, https://doi.org/10.5194/ascmo-3-17-2017, https://doi.org/10.5194/ascmo-3-17-2017, 2017
Short summary
Short summary
The attribution of classes of extreme events, such as heavy precipitation or heatwaves, relies on the estimate of small probabilities (with and without climate change). Such events are connected to the large-scale atmospheric circulation. This paper links such probabilities with properties of the atmospheric circulation by using a Bayesian decomposition. We test this decomposition on a case of extreme precipitation in the UK, in January 2014.
Jérôme Pernin, Mathieu Vrac, Cyril Crevoisier, and Alain Chédin
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 115–136, https://doi.org/10.5194/ascmo-2-115-2016, https://doi.org/10.5194/ascmo-2-115-2016, 2016
Short summary
Short summary
Here, we propose a classification methodology of various space-time atmospheric datasets into discrete air mass groups homogeneous in temperature and humidity through a probabilistic point of view: both the classification process and the data are probabilistic. Unlike conventional classification algorithms, this methodology provides the probability of belonging to each class as well as the corresponding uncertainty, which can be used in various applications.
S. Jeon, Prabhat, S. Byna, J. Gu, W. D. Collins, and M. F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 45–57, https://doi.org/10.5194/ascmo-1-45-2015, https://doi.org/10.5194/ascmo-1-45-2015, 2015
Short summary
Short summary
This paper investigates the influence of atmospheric rivers on spatial coherence of extreme precipitation under a changing climate. We use our TECA software developed for detecting atmospheric river events and apply statistical techniques based on extreme value theory to characterize the spatial dependence structure between precipitation extremes within the events. The results show that extreme rainfall caused by atmospheric river events is less spatially correlated under the warming scenario.
Cited articles
Anderson, T. W.: An Introduction to Multivariate Statistical Analysis, John Wiley and Sons, Inc., New York, 1958.
Atlas, D., Matrosov, S. Y., Heymsfield, A. J., Chou, M.-D., and Wolff, D. B.: Radar and Radiation Properties of Ice Clouds, J. Appl. Meteorol., 34, 2329–2345, https://doi.org/10.1175/1520-0450(1995)034<2329:RARPOI>2.0.CO;2, 1995.
Bühl, J., Ansmann, A., Seifert, P., Baars, H., and Engelmann, R.: Toward a quantitative characterization of heterogeneous ice formation with lidar/radar: Comparison of CALIPSO/CloudSat with ground-based observations, Geophys. Res. Lett., 40, 4404–4408, https://doi.org/10.1002/grl.50792, 2013.
Cesana, G. and Chepfer, H.: Evaluation of the cloud thermodynamic phase in a climate model using CALIPSO-GOCCP, J. Geophys. Res.-Atmos., 118, 7922–7937, https://doi.org/10.1002/jgrd.50376, 2013.
Choi, Y. S., Lindzen, R. S., Ho, C. H., and Kim, J.: Space observations of cold-cloud phase change, P. Natl. Acad. Sci. USA, 107, 11211–11216, https://doi.org/10.1073/pnas.1006241107, 2010.
Clothiaux, E., Ackerman, T., Mace, G., Moran, K., Marchand, R., Miller, M., Martner, B., Ackerman, T., Mace, G. G., Marchand, R., Widener, K. B., Rodriguez, D. J., Uttal, T., Mather, J. H., Flynn, C. J., Gaustad, K. L., and Ermold, B.: The ARM millimeter wave cloud radars (MMCRs) and the active remote sensing of clouds (ARSCL) value added product (VAP), US Department of Energy Tech. Memo. ARM VAP-002.1, 2001.
Croux, C., Filzmoser, P., and Oliveira, M. R.: Algorithms for Projection–Pursuit robust principal component analysis, Chemometr. Intell. Lab., 87, 218–225, https://doi.org/10.1016/j.chemolab.2007.01.004, 2007.
Domingos, P. and Pazzani, M.: On the optimality of the Simple Bayesian Classifier under zero-one loss, Mach. Learn., 29, 103–130, 1997.
Eloranta, E. W.: High spectral resolution lidar, in: Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, edited by: Weitcamp, C., Springer-Verlag, New York, 143–163, 2005a.
Eloranta, E. W.: M-PACE HSR Lidar, Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Archive: Oak Ridge, Tennessee, USA, https://doi.org/10.5439/1254822, 2005b.
Forbes, R. M. and Ahlgrimm, M.: On the Representation of High-Latitude Boundary Layer Mixed-Phase Cloud in the ECMWF Global Model, Mon. Weather Rev., 142, 3425–3445, https://doi.org/10.1175/MWR-D-13-00325.1, 2014.
Hogan, R. J. and O'Connor, E. J.: Facilitating cloud radar and lidar algorithms: The Cloudnet instrument synergy/target categorization product, Cloudnet documentation, Univ. of Reading, Reading, UK, available at: http://www.met.rdg.ac.uk/~swrhgnrj/publications/categorization.pdf (last access: 6 June 2016), 2006.
Hogan, R. J., Francis, P. N., Flentje, H., Illingworth, A. J., Quante, M., and Pelon, J.: Characteristics of mixed-phase clouds. I: Lidar, radar and aircraft observations from CLARE'98, Q. J. Roy. Meteor. Soc., 129, 2089–2116, https://doi.org/10.1256/rj.01.208, 2003.
Hogan, R. J., Behera, M. D., O'Connor, E. J., and Illingworth, A. J.: Estimate of the global distribution of stratiform supercooled liquid water clouds using the LITE lidar, Geophys. Res. Lett., 31, L05106, https://doi.org/10.1029/2003gl018977, 2004.
Illingworth, A. J., Hogan, R. J., O'Connor, E. J., Bouniol, D., Brooks, M. E., Delanoe, J., Donovan, D. P., Eastment, J. D., Gaussiat, N., Goddard, J. W. F., Haeffelin, M., Baltink, H. K., Krasnov, O. A., Pelon, J., Piriou, J. M., Protat, A., Russchenberg, H. W. J., Seifert, A., Tompkins, A. M., van Zadelhoff, G. J., Vinit, F., Willen, U., Wilson, D. R., and Wrench, C. L.: Cloudnet – Continuous evaluation of cloud profiles in seven operational models using ground-based observations, B. Am. Meteorol. Soc., 88, 883–891, 893–898, https://doi.org/10.1175/Bams-88-6-883, 2007.
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, United Kingdom and New York, NY, USA, 1535 pp., 2013.
Jensen, M. P., Luke, E., and Kollias, P.: Micro-ARSCL, Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Archive: Oak Ridge, Tennessee, USA, https://doi.org/10.5439/1225811, 2016.
Johnson, K. and Jensen, M. P.: Active Remotely-Sensed Cloud Locations (ARSCL1CLOTH), Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Archive: Oak Ridge, Tennessee, USA, https://doi.org/10.5439/1027282, 2009.
Klein, S. A., McCoy, R. B., Morrison, H., Ackerman, A. S., Avramov, A., Boer, G. d., Chen, M., Cole, J. N. S., Del Genio, A. D., Falk, M., Foster, M. J., Fridlind, A., Golaz, J.-C., Hashino, T., Harrington, J. Y., Hoose, C., Khairoutdinov, M. F., Larson, V. E., Liu, X., Luo, Y., McFarquhar, G. M., Menon, S., Neggers, R. A. J., Park, S., Poellot, M. R., Schmidt, J. M., Sednev, I., Shipway, B. J., Shupe, M. D., Spangenberg, D. A., Sud, Y. C., Turner, D. D., Veron, D. E., Salzen, K. v., Walker, G. K., Wang, Z., Wolf, A. B., Xie, S., Xu, K.-M., Yang, F., and Zhang, G.: Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. I: single-layer cloud, Q. J. Roy. Meteor. Soc., 135, 979–1002, https://doi.org/10.1002/qj.416, 2009.
Kollias, P., Miller, M. A., Luke, E. P., Johnson, K. L., Clothiaux, E. E., Moran, K. P., Widener, K. B., and Albrecht, B. A.: The Atmospheric Radiation Measurement Program Cloud Profiling Radars: Second-Generation Sampling Strategies, Processing, and Cloud Data Products, J. Atmos. Ocean. Tech., 24, 1199–1214, https://doi.org/10.1175/JTECH2033.1, 2007.
Komurcu, M., Storelvmo, T., Tan, I., Lohmann, U., Yun, Y., Penner, J. E., Wang, Y., Liu, X., and Takemura, T.: Intercomparison of the cloud water phase among global climate models, J. Geophys. Res.-Atmos., 119, 3372–3400, https://doi.org/10.1002/2013JD021119, 2014.
Luke, E. P., Kollias, P., and Shupe, M. D.: Detection of supercooled liquid ivn mixed-phase clouds using radar Doppler spectra, J. Geophys. Res.-Atmos., 115, D19201, https://doi.org/10.1029/2009jd012884, 2010.
McFarquhar, G. and Zhang, G.: In-Situ Microphysics from the MPACE IOP, Atmospheric Radiation Measurement (ARM) Climate Research Facility Data Archive ARM Data Archive: Oak Ridge, Tennessee, USA, https://doi.org/10.5439/1171943, 2007.
McFarquhar, G. M., Zhang, G., Poellot, M. R., Kok, G. L., Mccoy, R., Tooman, T., Fridlind, A., and Heymsfield, A. J.: Ice properties of single-layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment: 1. Observations, J. Geophys. Res.-Atmos., 112, D24201, https://doi.org/10.1029/2007jd008633, 2007.
Morrison, H., McCoy, R. B., Klein, S. A., Xie, S. C., Luo, Y. L., Avramov, A., Chen, M. X., Cole, J. N. S., Falk, M., Foster, M. J., Del Genio, A. D., Harrington, J. Y., Hoose, C., Khairoutdinov, M. F., Larson, V. E., Liu, X. H., McFarquhar, G. M., Poellot, M. R., von Salzen, K., Shipway, B. J., Shupe, M. D., Sud, Y. C., Turner, D. D., Veron, D. E., Walker, G. K., Wang, Z. E., Wolf, A. B., Xu, K. M., Yang, F. L., and Zhang, G.: Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. II: Multilayer cloud, Q. J. Roy. Meteor. Soc., 135, 1003–1019, https://doi.org/10.1002/Qj.415, 2009.
Rangno, A. L. and Hobbs, P. V.: Ice particles in stratiform clouds in the Arctic and possible mechanisms for the production of high ice concentrations, J. Geophys. Res.-Atmos., 106, 15065–15075, https://doi.org/10.1029/2000JD900286, 2001.
Sassen, K.: The Polarization Lidar Technique for Cloud Research: A Review and Current Assessment, B. Am. Meteorol. Soc., 72, 1848–1866, https://doi.org/10.1175/1520-0477(1991)072<1848:TPLTFC>2.0.CO;2, 1991.
Schotland, R. M., Sassen, K., and Stone, R.: Observations by Lidar of Linear Depolarization Ratios for Hydrometeors, J. Appl. Meteorol., 10, 1011–1017, https://doi.org/10.1175/1520-0450(1971)010<1011:oblold>2.0.co;2, 1971.
Shupe, M. D.: A ground-based multisensor cloud phase classifier, Geophys. Res. Lett., 34, L22809, https://doi.org/10.1029/2007gl031008, 2007.
Shupe, M. D. and Intrieri, J. M.: Cloud radiative forcing of the Arctic surface: The influence of cloud properties, surface albedo, and solar zenith angle, J. Climate, 17, 616–628, https://doi.org/10.1175/1520-0442(2004)017<0616:Crfota>2.0.Co;2, 2004.
Shupe, M. D., Uttal, T., Matrosov, S. Y., and Frisch, A. S.: Cloud water contents and hydrometeor sizes during the FIRE Arctic Clouds Experiment, J. Geophys. Res.-Atmos., 106, 15015–15028, https://doi.org/10.1029/2000JD900476, 2001.
Shupe, M. D., Daniel, J. S., de Boer, G., Eloranta, E. W., Kollias, P., Long, C. N., Luke, E. P., Turner, D. D., and Verlinde, J.: A Focus on Mixed-Phase Clouds the Status of Ground-Based Observational Methods, B. Am. Meteorol. Soc., 89, 1549–1562, https://doi.org/10.1175/2008bams2378.1, 2008.
Stephens, G. L.: Cloud Feedbacks in the Climate System: A Critical Review, J. Climate, 18, 237–273, https://doi.org/10.1175/JCLI-3243.1, 2005.
Sun, Z. and Shine, K. P.: Studies of the radiative properties of ice and mixed-phase clouds, Q. J. Roy. Meteor. Soc., 120, 111–137, https://doi.org/10.1002/qj.49712051508, 1994.
Turner, D. D.: Arctic mixed-phase cloud properties from AERI lidar observations: Algorithm and results from SHEBA, J. Appl. Meteorol., 44, 427–444, https://doi.org/10.1175/Jam2208.1, 2005.
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, 205–216, 218–221, https://doi.org/10.1175/Bams-88-2-205, 2007.
Verlinde, J., Rambukkange, M. P., Clothiaux, E. E., McFarquhar, G. M., and Eloranta, E. W.: Arctic multilayered, mixed-phase cloud processes revealed in millimeter-wave cloud radar Doppler spectra, J. Geophys. Res.-Atmos., 118, 1–15, https://doi.org/10.1002/2013jd020183, 2013.
Wang, Z., and Sassen, K.: Cloud Type and Macrophysical Property Retrieval Using Multiple Remote Sensors, J. Appl. Meteorol., 40, 1665–1682, https://doi.org/10.1175/1520-0450(2001)040<1665:CTAMPR>2.0.CO;2, 2001.
Young, S. A. and Vaughan, M. A.: The Retrieval of Profiles of Particulate Extinction from Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Data: Algorithm Description, J. Atmos. Ocean. Tech., 26, 1105–1119, https://doi.org/10.1175/2008JTECHA1221.1, 2009.
Yu, G., Verlinde, J., Clothiaux, E. E., and Chen, Y. S.: Mixed-phase cloud phase partitioning using millimeter wavelength cloud radar Doppler velocity spectra, J. Geophys. Res.-Atmos., 119, 7556–7576, https://doi.org/10.1002/2013JD021182, 2014.
Zhang, D. Wang, Z., and Liu, D.: A global view of midlevel liquid-layer topped stratiform cloud distribution and phase partition from CALIPSO and CloudSat measurements, J. Geophys. Res.-Atmos., 115, D00H13, https://doi.org/10.1029/2009JD012143, 2010.
Zhao, C., Xie, S., Klein, S. A., Protat, A., Shupe, M. D., McFarlane, S. A., Comstock, J. M., Delanoë, J., Deng, M., Dunn, M., Hogan, R. J., Huang, D., Jensen, M. P., Mace, G. G., McCoy, R., O'Connor, E. J., Turner, D. D., and Wang, Z.: Toward understanding of differences in current cloud retrievals of ARM ground-based measurements, J. Geophys. Res.-Atmos., 117, D10206, https://doi.org/10.1029/2011JD016792, 2012.
Short summary
Between atmospheric temperatures of 0 and −38 °C, clouds contain ice crystals, super-cooled liquid droplets, or a mixture of both, impacting how they influence the atmospheric energy budget and challenging our ability to simulate climate change. Better cloud-phase measurements are needed to improve simulations. We demonstrate how a Bayesian method to identify cloud phase can improve on currently used methods by including information from multiple measurements and probability estimates.
Between atmospheric temperatures of 0 and −38 °C, clouds contain ice crystals, super-cooled...
