Articles | Volume 2, issue 2
https://doi.org/10.5194/ascmo-2-115-2016
© 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-2-115-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
12 Oct 2016
Mixture model-based atmospheric air mass classification: a probabilistic view of thermodynamic profiles
Jérôme Pernin
CORRESPONDING AUTHOR
LMD, IPSL, CNRS, Ecole polytechnique, Université Paris-Saclay,
91128 Palaiseau, France
Mathieu Vrac
LSCE, IPSL, CEA, CNRS, UVSQ, Centre d'Études de Saclay, Orme des
Merisiers, Bât. 701, 91191 Gif-sur-Yvette, France
Cyril Crevoisier
LMD, IPSL, CNRS, Ecole polytechnique, Université Paris-Saclay,
91128 Palaiseau, France
Alain Chédin
LMD, IPSL, CNRS, Ecole polytechnique, Université Paris-Saclay,
91128 Palaiseau, France
Related authors
Matthias Schneider, Benjamin Ertl, Qiansi Tu, Christopher J. Diekmann, Farahnaz Khosrawi, Amelie N. Röhling, Frank Hase, Darko Dubravica, Omaira E. García, Eliezer Sepúlveda, Tobias Borsdorff, Jochen Landgraf, Alba Lorente, André Butz, Huilin Chen, Rigel Kivi, Thomas Laemmel, Michel Ramonet, Cyril Crevoisier, Jérome Pernin, Martin Steinbacher, Frank Meinhardt, Kimberly Strong, Debra Wunch, Thorsten Warneke, Coleen Roehl, Paul O. Wennberg, Isamu Morino, Laura T. Iraci, Kei Shiomi, Nicholas M. Deutscher, David W. T. Griffith, Voltaire A. Velazco, and David F. Pollard
Atmos. Meas. Tech., 15, 4339–4371, https://doi.org/10.5194/amt-15-4339-2022, https://doi.org/10.5194/amt-15-4339-2022, 2022
Short summary
Short summary
We present a computationally very efficient method for the synergetic use of level 2 remote-sensing data products. We apply the method to IASI vertical profile and TROPOMI total column space-borne methane observations and thus gain sensitivity for the tropospheric methane partial columns, which is not achievable by the individual use of TROPOMI and IASI. These synergetic effects are evaluated theoretically and empirically by inter-comparisons to independent references of TCCON, AirCore, and GAW.
C. Crevoisier, D. Nobileau, R. Armante, L. Crépeau, T. Machida, Y. Sawa, H. Matsueda, T. Schuck, T. Thonat, J. Pernin, N. A. Scott, and A. Chédin
Atmos. Chem. Phys., 13, 4279–4289, https://doi.org/10.5194/acp-13-4279-2013, https://doi.org/10.5194/acp-13-4279-2013, 2013
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Proc. IAHS, 385, 121–127, https://doi.org/10.5194/piahs-385-121-2024, https://doi.org/10.5194/piahs-385-121-2024, 2024
Short summary
Short summary
This study assesses the impact of climate change on the timing, seasonality and magnitude of mean annual minimum (MAM) flows and annual maximum flows (AMF) in the Volta River basin (VRB). Several climate change projection data are use to simulate river flow under multiple greenhouse gas emission scenarios. Future projections show that AMF could increase with various magnitude but negligible shift in time across the VRB, while MAM could decrease with up to 14 days of delay in occurrence.
Mathieu Vrac, Denis Allard, Grégoire Mariéthoz, Soulivanh Thao, and Lucas Schmutz
EGUsphere, https://doi.org/10.5194/egusphere-2023-3004, https://doi.org/10.5194/egusphere-2023-3004, 2024
Short summary
Short summary
We aim to combine multiple Global Climate Models (GCMs) to enhance the robustness of future projections. We introduce a novel approach, called "α-pooling", aggregating the Cumulative Distribution Functions (CDFs) of the models into a CDF more aligned with historical data. The new CDFs allow us to perform bias adjustment of all the raw climate simulations at once. Experiments on European temperature and precipitation demonstrate the superiority of this approach over conventional techniques.
Davide Faranda, Gabriele Messori, Erika Coppola, Tommaso Alberti, Mathieu Vrac, Flavio Pons, Pascal Yiou, Marion Saint Lu, Andreia N. S. Hisi, Patrick Brockmann, Stavros Dafis, and Robert Vautard
EGUsphere, https://doi.org/10.5194/egusphere-2023-2643, https://doi.org/10.5194/egusphere-2023-2643, 2023
Short summary
Short summary
We introduce ClimaMeter, a tool offering real-time insights into weather extreme events. Our tool unveils how climate change and natural variability affect these events, affecting communities worldwide. Our research equips policymakers and the public with essential knowledge, fostering informed decisions and enhancing climate resilience. We analyzed four distinct events, showcasing ClimaMeter's global relevance.
Cedric Gacial Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, and Cyrille Flamant
Nat. Hazards Earth Syst. Sci., 23, 1313–1333, https://doi.org/10.5194/nhess-23-1313-2023, https://doi.org/10.5194/nhess-23-1313-2023, 2023
Short summary
Short summary
Heat waves (HWs) are climatic hazards that affect the planet. We assess here uncertainties encountered in the process of HW detection and analyse their recent trends in West Africa using reanalysis data. Three types of uncertainty have been investigated. We identified 6 years with higher frequency of HWs, possibly due to higher sea surface temperatures in the equatorial Atlantic. We noticed an increase in HW characteristics during the last decade, which could be a consequence of climate change.
Bastien François and Mathieu Vrac
Nat. Hazards Earth Syst. Sci., 23, 21–44, https://doi.org/10.5194/nhess-23-21-2023, https://doi.org/10.5194/nhess-23-21-2023, 2023
Short summary
Short summary
Compound events (CEs) result from a combination of several climate phenomena. In this study, we propose a new methodology to assess the time of emergence of CE probabilities and to quantify the contribution of marginal and dependence properties of climate phenomena to the overall CE probability changes. By applying our methodology to two case studies, we show the importance of considering changes in both marginal and dependence properties for future risk assessments related to CEs.
Antoine Grisart, Mathieu Casado, Vasileios Gkinis, Bo Vinther, Philippe Naveau, Mathieu Vrac, Thomas Laepple, Bénédicte Minster, Frederic Prié, Barbara Stenni, Elise Fourré, Hans Christian Steen-Larsen, Jean Jouzel, Martin Werner, Katy Pol, Valérie Masson-Delmotte, Maria Hoerhold, Trevor Popp, and Amaelle Landais
Clim. Past, 18, 2289–2301, https://doi.org/10.5194/cp-18-2289-2022, https://doi.org/10.5194/cp-18-2289-2022, 2022
Short summary
Short summary
This paper presents a compilation of high-resolution (11 cm) water isotopic records, including published and new measurements, for the last 800 000 years from the EPICA Dome C ice core, Antarctica. Using this new combined water isotopes (δ18O and δD) dataset, we study the variability and possible influence of diffusion at the multi-decadal to multi-centennial scale. We observe a stronger variability at the onset of the interglacial interval corresponding to a warm period.
Matthias Schneider, Benjamin Ertl, Qiansi Tu, Christopher J. Diekmann, Farahnaz Khosrawi, Amelie N. Röhling, Frank Hase, Darko Dubravica, Omaira E. García, Eliezer Sepúlveda, Tobias Borsdorff, Jochen Landgraf, Alba Lorente, André Butz, Huilin Chen, Rigel Kivi, Thomas Laemmel, Michel Ramonet, Cyril Crevoisier, Jérome Pernin, Martin Steinbacher, Frank Meinhardt, Kimberly Strong, Debra Wunch, Thorsten Warneke, Coleen Roehl, Paul O. Wennberg, Isamu Morino, Laura T. Iraci, Kei Shiomi, Nicholas M. Deutscher, David W. T. Griffith, Voltaire A. Velazco, and David F. Pollard
Atmos. Meas. Tech., 15, 4339–4371, https://doi.org/10.5194/amt-15-4339-2022, https://doi.org/10.5194/amt-15-4339-2022, 2022
Short summary
Short summary
We present a computationally very efficient method for the synergetic use of level 2 remote-sensing data products. We apply the method to IASI vertical profile and TROPOMI total column space-borne methane observations and thus gain sensitivity for the tropospheric methane partial columns, which is not achievable by the individual use of TROPOMI and IASI. These synergetic effects are evaluated theoretically and empirically by inter-comparisons to independent references of TCCON, AirCore, and GAW.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
Short summary
Short summary
Climate change impacts on water resources in the Volta River basin are investigated under various global warming scenarios. Results reveal contrasting changes in future hydrological processes and water availability, depending on greenhouse gas emission scenarios, with implications for floods and drought occurrence over the 21st century. These findings provide insights for the elaboration of regional adaptation and mitigation strategies for climate change.
Yoann Robin and Mathieu Vrac
Earth Syst. Dynam., 12, 1253–1273, https://doi.org/10.5194/esd-12-1253-2021, https://doi.org/10.5194/esd-12-1253-2021, 2021
Short summary
Short summary
We propose a new multivariate downscaling and bias correction approach called
time-shifted multivariate bias correction, which aims to correct temporal dependencies in addition to inter-variable and spatial ones. Our method is evaluated in a
perfect model experimentcontext where simulations are used as pseudo-observations. The results show a large reduction of the biases in the temporal properties, while inter-variable and spatial dependence structures are still correctly adjusted.
Cedric G. Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, Philippe Peyrillé, and Cyrille Flamant
Weather Clim. Dynam., 2, 893–912, https://doi.org/10.5194/wcd-2-893-2021, https://doi.org/10.5194/wcd-2-893-2021, 2021
Short summary
Short summary
This work assesses the forecast of the temperature over the Sahara, a key driver of the West African Monsoon, at a seasonal timescale. The seasonal models are able to reproduce the climatological state and some characteristics of the temperature during the rainy season in the Sahel. But, because of errors in the timing, the forecast skill scores are significant only for the first 4 weeks.
Anna Denvil-Sommer, Marion Gehlen, and Mathieu Vrac
Ocean Sci., 17, 1011–1030, https://doi.org/10.5194/os-17-1011-2021, https://doi.org/10.5194/os-17-1011-2021, 2021
Short summary
Short summary
In this work we explored design options for a future Atlantic-scale observational network enabling the release of carbon system estimates by combining data streams from various platforms. We used outputs of a physical–biogeochemical global ocean model at sites of real-world observations to reconstruct surface ocean pCO2 by applying a non-linear feed-forward neural network. The results provide important information for future BGC-Argo deployment, i.e. important regions and the number of floats.
Mathieu Vrac and Soulivanh Thao
Geosci. Model Dev., 13, 5367–5387, https://doi.org/10.5194/gmd-13-5367-2020, https://doi.org/10.5194/gmd-13-5367-2020, 2020
Short summary
Short summary
We propose a multivariate bias correction (MBC) method to adjust the spatial and/or inter-variable properties of climate simulations, while also accounting for their temporal dependences (e.g., autocorrelations).
It consists on a method reordering the ranks of the time series according to their multivariate distance to a reference time series.
Results show that temporal correlations are improved while spatial and inter-variable correlations are still satisfactorily corrected.
Marielle Saunois, Ann R. Stavert, Ben Poulter, Philippe Bousquet, Josep G. Canadell, Robert B. Jackson, Peter A. Raymond, Edward J. Dlugokencky, Sander Houweling, Prabir K. Patra, Philippe Ciais, Vivek K. Arora, David Bastviken, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Kimberly M. Carlson, Mark Carrol, Simona Castaldi, Naveen Chandra, Cyril Crevoisier, Patrick M. Crill, Kristofer Covey, Charles L. Curry, Giuseppe Etiope, Christian Frankenberg, Nicola Gedney, Michaela I. Hegglin, Lena Höglund-Isaksson, Gustaf Hugelius, Misa Ishizawa, Akihiko Ito, Greet Janssens-Maenhout, Katherine M. Jensen, Fortunat Joos, Thomas Kleinen, Paul B. Krummel, Ray L. Langenfelds, Goulven G. Laruelle, Licheng Liu, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Joe McNorton, Paul A. Miller, Joe R. Melton, Isamu Morino, Jurek Müller, Fabiola Murguia-Flores, Vaishali Naik, Yosuke Niwa, Sergio Noce, Simon O'Doherty, Robert J. Parker, Changhui Peng, Shushi Peng, Glen P. Peters, Catherine Prigent, Ronald Prinn, Michel Ramonet, Pierre Regnier, William J. Riley, Judith A. Rosentreter, Arjo Segers, Isobel J. Simpson, Hao Shi, Steven J. Smith, L. Paul Steele, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Francesco N. Tubiello, Aki Tsuruta, Nicolas Viovy, Apostolos Voulgarakis, Thomas S. Weber, Michiel van Weele, Guido R. van der Werf, Ray F. Weiss, Doug Worthy, Debra Wunch, Yi Yin, Yukio Yoshida, Wenxin Zhang, Zhen Zhang, Yuanhong Zhao, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, https://doi.org/10.5194/essd-12-1561-2020, 2020
Short summary
Short summary
Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. We have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. This is the second version of the review dedicated to the decadal methane budget, integrating results of top-down and bottom-up estimates.
Emanuele Bevacqua, Michalis I. Vousdoukas, Theodore G. Shepherd, and Mathieu Vrac
Nat. Hazards Earth Syst. Sci., 20, 1765–1782, https://doi.org/10.5194/nhess-20-1765-2020, https://doi.org/10.5194/nhess-20-1765-2020, 2020
Short summary
Short summary
Coastal compound flooding (CF), caused by interacting storm surges and high water runoff, is typically studied based on concurring storm surge extremes with either precipitation or river discharge extremes. Globally, these two approaches show similar CF spatial patterns, especially where the CF potential is the highest. Deviations between the two approaches increase with the catchment size. The precipitation-based analysis allows for considering
local-rainfall-driven CF and CF in small rivers.
Bastien François, Mathieu Vrac, Alex J. Cannon, Yoann Robin, and Denis Allard
Earth Syst. Dynam., 11, 537–562, https://doi.org/10.5194/esd-11-537-2020, https://doi.org/10.5194/esd-11-537-2020, 2020
Short summary
Short summary
Recently, multivariate bias correction (MBC) methods designed to adjust climate simulations have been proposed. However, they use different approaches, leading potentially to different results. Therefore, this study intends to intercompare four existing MBC methods to provide end users with aid in choosing such methods for their applications. To do so, a wide range of evaluation criteria have been used to assess the ability of MBC methods to correct statistical properties of climate models.
Eric Pohl, Christophe Grenier, Mathieu Vrac, and Masa Kageyama
Hydrol. Earth Syst. Sci., 24, 2817–2839, https://doi.org/10.5194/hess-24-2817-2020, https://doi.org/10.5194/hess-24-2817-2020, 2020
Short summary
Short summary
Existing approaches to quantify the emergence of climate change require several user choices that make these approaches less objective. We present an approach that uses a minimum number of choices and showcase its application in the extremely sensitive, permafrost-dominated region of eastern Siberia. Designed as a Python toolbox, it allows for incorporating climate model, reanalysis, and in situ data to make use of numerous existing data sources and reduce uncertainties in obtained estimates.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 24, 2841–2854, https://doi.org/10.5194/hess-24-2841-2020, https://doi.org/10.5194/hess-24-2841-2020, 2020
Short summary
Short summary
At subdaily resolution, rain intensity exhibits a strong variability in space and time due to the diversity of processes that produce rain (e.g., frontal storms, mesoscale convective systems and local convection). In this paper we explore a new method to simulate rain type time series conditional to meteorological covariates. Afterwards, we apply stochastic rain type simulation to the downscaling of precipitation of a regional climate model.
Florentin Breton, Mathieu Vrac, Pascal Yiou, Pradeebane Vaittinada Ayar, and Aglaé Jézéquel
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2020-26, https://doi.org/10.5194/esd-2020-26, 2020
Revised manuscript not accepted
Short summary
Short summary
We investigate North Atlantic weather seasonality over 1979–2100 by classifying year-round fields of 500 hPa geopotential height from one reanalysis dataset and 12 climate models. Generally, models have seasonal structures similar to the reanalyses. Historical winter (summer) conditions decrease (increase), due to uniform Z500 increase (i.e. uniform warming). However, relative to the increasing Z500 seasonal cycle, future seasonality (spatial patterns, seasonal cycle) appears almost stationary.
Giulia Carella, Mathieu Vrac, Hélène Brogniez, Pascal Yiou, and Hélène Chepfer
Earth Syst. Sci. Data, 12, 1–20, https://doi.org/10.5194/essd-12-1-2020, https://doi.org/10.5194/essd-12-1-2020, 2020
Short summary
Short summary
Observations of relative humidity for ice clouds over the tropical oceans from a passive microwave sounder are downscaled by incorporating the high-resolution variability derived from simultaneous co-located cloud profiles from a lidar. By providing a method to generate pseudo-observations of relative humidity at high spatial resolution, this work will help revisit some of the current key barriers in atmospheric science.
Joël Thanwerdas, Marielle Saunois, Antoine Berchet, Isabelle Pison, Didier Hauglustaine, Michel Ramonet, Cyril Crevoisier, Bianca Baier, Colm Sweeney, and Philippe Bousquet
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-925, https://doi.org/10.5194/acp-2019-925, 2019
Revised manuscript not accepted
Short summary
Short summary
Oxidation by the hydroxyl radical (OH) is the dominant atmospheric sink for methane, contributing to approximately 90 % of the total methane loss. Chemical losses by reaction with atomic oxygen (O1D) and chlorine radicals (Cl) in the stratosphere are other sinks, contributing about 3 % to the total methane destruction. We assess here the impact of atomic Cl on atmospheric methane mixing ratios, methane atmospheric loss and atmospheric isotopic δ13C-CH4 values.
Anna Denvil-Sommer, Marion Gehlen, Mathieu Vrac, and Carlos Mejia
Geosci. Model Dev., 12, 2091–2105, https://doi.org/10.5194/gmd-12-2091-2019, https://doi.org/10.5194/gmd-12-2091-2019, 2019
Short summary
Short summary
This work is dedicated to a new model that reconstructs the surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean on a monthly 1°×1° grid. The model is based on a feed-forward neural network and represents the nonlinear relationships between pCO2 and the ocean drivers. Reconstructed pCO2 has a satisfying accuracy compared to independent observational data and shows a good agreement in seasonal and interannual variability with three existing mapping methods.
Yoann Robin, Mathieu Vrac, Philippe Naveau, and Pascal Yiou
Hydrol. Earth Syst. Sci., 23, 773–786, https://doi.org/10.5194/hess-23-773-2019, https://doi.org/10.5194/hess-23-773-2019, 2019
Short summary
Short summary
Bias correction methods are used to calibrate climate model outputs with respect to observations. In this article, a non-stationary, multivariate and stochastic bias correction method is developed based on optimal transport, accounting for inter-site and inter-variable correlations. Optimal transport allows us to construct a joint distribution that minimizes energy spent in bias correction. Our methodology is tested on precipitation and temperatures over 12 locations in southern France.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 22, 5919–5933, https://doi.org/10.5194/hess-22-5919-2018, https://doi.org/10.5194/hess-22-5919-2018, 2018
Short summary
Short summary
We propose a method for unsupervised classification of the space–time–intensity structure of weather radar images. The resulting classes are interpreted as rain types, i.e. pools of rain fields with homogeneous statistical properties. Rain types can in turn be used to define stationary periods for further stochastic rainfall modelling. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm.
Claire Waelbroeck, Sylvain Pichat, Evelyn Böhm, Bryan C. Lougheed, Davide Faranda, Mathieu Vrac, Lise Missiaen, Natalia Vazquez Riveiros, Pierre Burckel, Jörg Lippold, Helge W. Arz, Trond Dokken, François Thil, and Arnaud Dapoigny
Clim. Past, 14, 1315–1330, https://doi.org/10.5194/cp-14-1315-2018, https://doi.org/10.5194/cp-14-1315-2018, 2018
Short summary
Short summary
Recording the precise timing and sequence of events is essential for understanding rapid climate changes and improving climate model predictive skills. Here, we precisely assess the relative timing between ocean and atmospheric changes, both recorded in the same deep-sea core over the last 45 kyr. We show that decreased mid-depth water mass transport in the western equatorial Atlantic preceded increased rainfall over the adjacent continent by 120 to 980 yr, depending on the type of climate event.
Guillaume Latombe, Ariane Burke, Mathieu Vrac, Guillaume Levavasseur, Christophe Dumas, Masa Kageyama, and Gilles Ramstein
Geosci. Model Dev., 11, 2563–2579, https://doi.org/10.5194/gmd-11-2563-2018, https://doi.org/10.5194/gmd-11-2563-2018, 2018
Short summary
Short summary
It is still unclear how climate conditions, and especially climate variability, influenced the spatial distribution of past human populations. Global climate models (GCMs) cannot simulate climate at sufficiently fine scale for this purpose. We propose a statistical method to obtain fine-scale climate projections for 15 000 years ago from coarse-scale GCM outputs. Our method agrees with local reconstructions from fossil and pollen data, and generates sensible climate variability maps over Europe.
Mathieu Vrac
Hydrol. Earth Syst. Sci., 22, 3175–3196, https://doi.org/10.5194/hess-22-3175-2018, https://doi.org/10.5194/hess-22-3175-2018, 2018
Short summary
Short summary
This study presents a multivariate bias correction method named R2D2 to adjust both the 1d-distributions and inter-variable/site dependence structures of climate simulations in a high-dimensional context, while providing some stochasticity. R2D2 is tested on temperature and precipitation reanalyses and illustrated on future simulations. In both cases, R2D2 is able to correct the spatial and physical dependence, opening proper use of climate simulations for impact (e.g. hydrological) models.
Adjoua Moise Famien, Serge Janicot, Abe Delfin Ochou, Mathieu Vrac, Dimitri Defrance, Benjamin Sultan, and Thomas Noël
Earth Syst. Dynam., 9, 313–338, https://doi.org/10.5194/esd-9-313-2018, https://doi.org/10.5194/esd-9-313-2018, 2018
Short summary
Short summary
This study uses the cumulative distribution function transform (CDF-t) method to provide bias-corrected data over Africa using WFDEI as a reference dataset. It is shown that CDF-t is very effective in removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets, particularly for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields.
Javier Andrey-Andrés, Nadia Fourrié, Vincent Guidard, Raymond Armante, Pascal Brunel, Cyril Crevoisier, and Bernard Tournier
Atmos. Meas. Tech., 11, 803–818, https://doi.org/10.5194/amt-11-803-2018, https://doi.org/10.5194/amt-11-803-2018, 2018
Short summary
Short summary
A new generation of the Infrared Atmospheric Sounding Interferometer (IASI) sounders, whose highly accurate measurements are commonly used in environment applications, has already been designed: IASI New Generation (IASI-NG). A database of IASI and IASI-NG simulated observations was built to set a common framework for future impact studies. This first study showed the IASI-NG benefit with an improvement of the temperature retrievals throughout the atmosphere and a lower benefit for the humidity.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Ray Weiss, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Atmos. Chem. Phys., 17, 11135–11161, https://doi.org/10.5194/acp-17-11135-2017, https://doi.org/10.5194/acp-17-11135-2017, 2017
Short summary
Short summary
Following the Global Methane Budget 2000–2012 published in Saunois et al. (2016), we use the same dataset of bottom-up and top-down approaches to discuss the variations in methane emissions over the period 2000–2012. The changes in emissions are discussed both in terms of trends and quasi-decadal changes. The ensemble gathered here allows us to synthesise the robust changes in terms of regional and sectorial contributions to the increasing methane emissions.
Olivier Membrive, Cyril Crevoisier, Colm Sweeney, François Danis, Albert Hertzog, Andreas Engel, Harald Bönisch, and Laurence Picon
Atmos. Meas. Tech., 10, 2163–2181, https://doi.org/10.5194/amt-10-2163-2017, https://doi.org/10.5194/amt-10-2163-2017, 2017
Short summary
Short summary
A new high-resolution AirCore system is presented. This system flown with stratospheric balloons allows us to sample atmospheric air during the descent. The analysis of trace gases (CO2 and CH4 in this case) in the collected air sample provides information on the vertical distribution along the atmospheric column. The continuous vertical profiles retrieved may contribute to several research topics concerning the observation of greenhouse gases and, more generally, carbon cycle studies.
Andreas Engel, Harald Bönisch, Markus Ullrich, Robert Sitals, Olivier Membrive, Francois Danis, and Cyril Crevoisier
Atmos. Chem. Phys., 17, 6825–6838, https://doi.org/10.5194/acp-17-6825-2017, https://doi.org/10.5194/acp-17-6825-2017, 2017
Short summary
Short summary
AirCore is new technique for sampling stratospheric air. We present new observations of CO2 and CH4 using AirCore and derive stratospheric transport time, called the mean age of air. The purpose of using AirCore is to provide a cost-effective tool for deriving mean age. Mean age may serve as a proxy to investigate changes in stratospheric circulation. We show that there is no statistically significant trend in our 40-year time series of mean age, which is now extended using AirCore.
Emanuele Bevacqua, Douglas Maraun, Ingrid Hobæk Haff, Martin Widmann, and Mathieu Vrac
Hydrol. Earth Syst. Sci., 21, 2701–2723, https://doi.org/10.5194/hess-21-2701-2017, https://doi.org/10.5194/hess-21-2701-2017, 2017
Short summary
Short summary
We develop a conceptual model to quantify the risk of compound events (CEs), i.e. extreme impacts to society which are driven by statistically dependent climatic variables. Based on this model we study compound floods, i.e. joint storm surge and high river level, in Ravenna (Italy). The model includes meteorological predictors which (1) provide insight into the physical processes underlying CEs, as well as into the temporal variability, and (2) allow us to statistically downscale CEs.
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.
Claudia Volosciuk, Douglas Maraun, Mathieu Vrac, and Martin Widmann
Hydrol. Earth Syst. Sci., 21, 1693–1719, https://doi.org/10.5194/hess-21-1693-2017, https://doi.org/10.5194/hess-21-1693-2017, 2017
Short summary
Short summary
For impact modeling, infrastructure design, or adaptation strategy planning, high-quality climate data on the point scale are often demanded. Due to the scale gap between gridbox and point scale and biases in climate models, we combine a statistical bias correction and a stochastic downscaling model and apply it to climate model-simulated precipitation. The method performs better in summer than in winter and in winter best for mild winter climate (Mediterranean) and worst for continental winter.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Victor Brovkin, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Charles Curry, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Julia Marshall, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Catherine Prigent, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Paul Steele, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Michiel van Weele, Guido R. van der Werf, Ray Weiss, Christine Wiedinmyer, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Earth Syst. Sci. Data, 8, 697–751, https://doi.org/10.5194/essd-8-697-2016, https://doi.org/10.5194/essd-8-697-2016, 2016
Short summary
Short summary
An accurate assessment of the methane budget is important to understand the atmospheric methane concentrations and trends and to provide realistic pathways for climate change mitigation. The various and diffuse sources of methane as well and its oxidation by a very short lifetime radical challenge this assessment. We quantify the methane sources and sinks as well as their uncertainties based on both bottom-up and top-down approaches provided by a broad international scientific community.
Benjamin Grouillet, Denis Ruelland, Pradeebane Vaittinada Ayar, and Mathieu Vrac
Hydrol. Earth Syst. Sci., 20, 1031–1047, https://doi.org/10.5194/hess-20-1031-2016, https://doi.org/10.5194/hess-20-1031-2016, 2016
Short summary
Short summary
This original paper provides a guideline to select statistical downscaling methods (SDMs) in climate change impact studies (CCIS) to minimize uncertainty from downscaling. Three SDMs were applied to NCEP reanalysis and 2 GCM data values. We then analyzed the sensitivity of the hydrological model to the various downscaled data via 5 hydrological indicators representing the main features of the hydrograph. Our results enable selection of the appropriate SDMs to be used to build climate scenarios.
T. Thonat, C. Crevoisier, N. A. Scott, A. Chédin, R. Armante, and L. Crépeau
Atmos. Chem. Phys., 15, 13041–13057, https://doi.org/10.5194/acp-15-13041-2015, https://doi.org/10.5194/acp-15-13041-2015, 2015
Short summary
Short summary
Based on 5 years of CO columns derived from the IASI sounder, we show that tropospheric CO displays a diurnal signal with a seasonality and intensity that agrees well with the evolution of fires. We suggest that at night, after the passing of IASI at 21:30, a large amount of CO emissions from the smoldering phase is trapped in the boundary layer before being uplifted the next morning by natural and pyroconvection up to the free troposphere.
C. Crevoisier, C. Clerbaux, V. Guidard, T. Phulpin, R. Armante, B. Barret, C. Camy-Peyret, J.-P. Chaboureau, P.-F. Coheur, L. Crépeau, G. Dufour, L. Labonnote, L. Lavanant, J. Hadji-Lazaro, H. Herbin, N. Jacquinet-Husson, S. Payan, E. Péquignot, C. Pierangelo, P. Sellitto, and C. Stubenrauch
Atmos. Meas. Tech., 7, 4367–4385, https://doi.org/10.5194/amt-7-4367-2014, https://doi.org/10.5194/amt-7-4367-2014, 2014
V. Capelle, A. Chédin, M. Siméon, C. Tsamalis, C. Pierangelo, M. Pondrom, C. Crevoisier, L. Crepeau, and N. A. Scott
Atmos. Chem. Phys., 14, 9343–9362, https://doi.org/10.5194/acp-14-9343-2014, https://doi.org/10.5194/acp-14-9343-2014, 2014
S. Massart, A. Agusti-Panareda, I. Aben, A. Butz, F. Chevallier, C. Crevoisier, R. Engelen, C. Frankenberg, and O. Hasekamp
Atmos. Chem. Phys., 14, 6139–6158, https://doi.org/10.5194/acp-14-6139-2014, https://doi.org/10.5194/acp-14-6139-2014, 2014
P. Yiou, M. Boichu, R. Vautard, M. Vrac, S. Jourdain, E. Garnier, F. Fluteau, and L. Menut
Clim. Past, 10, 797–809, https://doi.org/10.5194/cp-10-797-2014, https://doi.org/10.5194/cp-10-797-2014, 2014
C. Cressot, F. Chevallier, P. Bousquet, C. Crevoisier, E. J. Dlugokencky, A. Fortems-Cheiney, C. Frankenberg, R. Parker, I. Pison, R. A. Scheepmaker, S. A. Montzka, P. B. Krummel, L. P. Steele, and R. L. Langenfelds
Atmos. Chem. Phys., 14, 577–592, https://doi.org/10.5194/acp-14-577-2014, https://doi.org/10.5194/acp-14-577-2014, 2014
C. Crevoisier, D. Nobileau, R. Armante, L. Crépeau, T. Machida, Y. Sawa, H. Matsueda, T. Schuck, T. Thonat, J. Pernin, N. A. Scott, and A. Chédin
Atmos. Chem. Phys., 13, 4279–4289, https://doi.org/10.5194/acp-13-4279-2013, https://doi.org/10.5194/acp-13-4279-2013, 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
A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors
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.
Laura D. Riihimaki, Jennifer M. Comstock, Kevin K. Anderson, Aimee Holmes, and Edward Luke
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 49–62, https://doi.org/10.5194/ascmo-2-49-2016, https://doi.org/10.5194/ascmo-2-49-2016, 2016
Short summary
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.
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
Akaike, A.: Information theory and an extension of the maximum likelihood principle, in: Second International Symposium on Information Theory, edited by: Petrov, B. N. and Csaki, F., Akadémiai Kiado, Budapest, Hungary, 267–281, 1973.
Banfield, J. D. and Raftery, A. E.: Model-based Gaussian and non-Gaussian clustering, Biometrics, 49, 803–821, https://doi.org/10.2307/2532201, 1993.
Barry, R. G. and Perry, A. H.: Synoptic Climatology and Its Applications, in: Synoptic and Dynamic Climatology, edited by: Barry, R. G. and Carleton, A. M., Routledge, London, UK, 547–603, 2001.
Bayes, T. and Price, M.: An Essay towards solving a Problem in the Doctrine of Chances, Philos. T. R. Soc. Lond., 53, 370–418, https://doi.org/10.1098/rstl.1763.0053, 1763.
Bergeron, T.: Richtlinien einer dynamischen klimatologie, Meteorol. Z., 47, 246–262, 1930.
Biernacki, C., Celeux, G., and Govaert, G.: Assessing a mixture model for clustering with the integrated completed likelihood, IEEE T. Pattern Anal., 22, 719–725, https://doi.org/10.1109/34.865189, 2000.
Biernacki, C., Celeux, G., and Govaert, G.: Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models, Comput. Stat. Data An., 41, 561–575, https://doi.org/10.1016/S0167-9473(02)00163-9, 2003.
Billard, L. and Diday, E.: Symbolic Data Analysis: Conceptual Statistics and Data Mining, Wiley series in computational statistics, John Wiley & Sons Ltd, Chichester, UK, 330 pp., https://doi.org/10.1002/9780470090183.ch1, 2012.
Bock, H. H. and Diday, E.: Analysis of Symbolic Data. Exploratory Methods for Extracting Statistical Information from Complex Data, Springer-Verlag, Berlin and Heidelberg, Germany, 443 pp., https://doi.org/10.1007/978-3-642-57155-8, 2000.
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone C. J.: Classification and Regression Trees, The Wadsworth and Brooks-Cole statistics-probability series, The Wadsworth statistics/probability series, Wadsworth and Brooks, Monterey, CA, USA, 368 pp. 1984.
Buck, A. L.: New equations for computing vapor pressure and enhancement factor, J. Appl. Meteorol., 20, 1527–1532, https://doi.org/10.1175/1520-0450(1981)020<1527:NEFCVP>2.0.CO;2, 1981.
Carreau, J. and Vrac, M.: Stochastic downscaling of precipitation with neural network conditional mixture models, Water. Resour. Res., 47, W10502, https://doi.org/10.1029/2010WR010128, 2011.
Celeux, G. and Govaert, G.: A classification EM algorithm for clustering and two stochastic versions. Comput. Stat. Data An., 14, 315–332, https://doi.org/10.1016/0167-9473(92)90042-E, 1992.
Celeux, G. and Govaert, G.: Gaussian parsimonious clustering models, Pattern Recogn., 28, 781–793, https://doi.org/10.1016/0031-3203(94)00125-6, 1995.
Celeux, G., Diday, E., Govaert, G., Lechevallier, Y., and Ralambondrainy, H.: Classification automatique des données, Dunod Informatique, Paris, France, 1989.
Chédin, A. and Scott, N. A.: Initialization of the radiative transfer equation inversion problem from a pattern recognition type approach. Applications to the satellites of the Tiros-N Series, in: Advances in Remote Sensing Retrieval Methods, Deepak A., Academic Press, New York, USA, 495–515, 1985.
Chevallier, F., Chédin, A., Cheruy, F., and Morcrette, J. J.: TIGR-like atmospheric-profile databases for accurate radiative-flux computation, Q. J. Roy. Meteor. Soc., 126, 777–785, https://doi.org/10.1002/qj.49712656319, 2000.
CRAN: EM algorithm, available at: https://cran.r-project.org/web/packages/Rmixmod/index.html, last access: 10 October 2016a.
CRAN: Decision tree, available at: https://cran.r-project.org/web/packages/rpart/index.html, last access: 10 October 2016b.
Crowe, P. R.: Concepts in Climatology, Longman, London, UK, 612 pp., 1971.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Normann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimverger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Dempster, A., Laird, N., and Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc. B., 39, 1–38, 1977.
Diday, E.: A generalization of the mixture decomposition problem in the symbolic data analysis framework, Research Report, CEREMADE, Paris, France, 2001.
Diday, E. and Vrac, M.: Mixture decomposition of distributions by copulas in the symbolic data analysis framework, Discrete Appl. Math., 147, 27–41, https://doi.org/10.1016/j.dam.2004.06.018, 2005.
Diday, E., Schroeder, A., and Ok, Y.: The dynamic clusters method in pattern recognition, in: Proceedings of International Federation for Information Processing congress 74, Stockholm, Sweden, 5–10 August 1974, 691–697, 1974.
ECMWF: ERA Interim, Daily, available at: http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/, last access: 10 October 2016.
Emanuel, K.: Back to Norway: An essay, Synoptic-Dynamic Meteorology and Weather Analysis and Forecasting, Meteor. Mon., 33, 87–96, https://doi.org/10.1007/978-0-933876-68-2, 2008.
Floriana, E. and Diday, E.: An introduction to symbolic data analysis and the SODAS software, Intell. Data Anal., 7, 583–601, 2003.
Forgy, E. W.: Cluster analysis of multivariate data: efficiency vs interpretability of classifications, Biometrics, 21, 768–769, 1965.
Fraley, C. and Raftery, A. E.: Model-based clustering, discriminant analysis and density estimation, J. Am. Stat. Assoc., 97, 611–631, https://doi.org/10.1198/016214502760047131, 2002.
Gordon, A. D.: Classification (2nd Edition), Chapman and Hall/CRC Press, London, UK, 256 pp., 1999.
Hardy, A.: On the number of clusters, Comput. Stat. Data An., 23, 83–96, https://doi.org/10.1016/S0167-9473(96)00022-9, 1996.
Hardy, A.: NBCLUST, A module for the determination of the number of clusters in the SODAS 2 software, IFCS 2006 Conference, Ljubjiana, Slovenia, 2006.
Hartigan, J. A. and Wong, M. A.: Algorithm AS136, A k-means clustering algorithm, J. Roy. Stat. Soc. C-App., 28, 100–108, https://doi.org/10.2307/2346830, 1979.
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Edition), Springer, New York, USA, 745 pp., https://doi.org/10.1007/978-0-387-84858-7, 2009.
Hewitson, B. C. and Crane, R. G.: Self-organizing maps: applications to synoptic climatology, Clim. Res., 22, 13–26, https://doi.org/10.3354/cr022013, 2002.
Huth, R.: An intercomparison of computer-assisted circulation classification methods, Int. J. Climatol., 16, 893–922, https://doi.org/10.1002/(SICI)1097-0088(199608)16:8<893::AID-JOC51>3.0.CO;2-Q, 1996.
Huth, R.: Disaggregating climatic trends by classification of circulation patterns, Int. J. Climatol., 21, 135–153, https://doi.org/10.1002/joc.605, 2001.
Huth, R., Beck, C., Philipp, A., Demuzere, M., Ustrnul, Z., Cahynová, M., Kyselý, J., and Tveito, O. E.: Classifications of atmospheric circulation patterns. Recent advances and applications, in: Trends and Directions in Climate Research, Ann. NY Acad. Sci., 1146, 105–152, https://doi.org/10.1196/annals.1446.019, 2008.
Kalkstein, L. S., Tan, G., and Skindlov, J. A.: An evaluation of three clustering procedures for use in synoptic climatological classification, J. Clim. Appl. Meteorol., 26, 717–730, https://doi.org/10.1175/1520-0450(1987)026<0717:AEOTCP>2.0.CO;2, 1987.
Kalkstein, L. S., Barthel, C. D., Nichols, M. C., and Greene, J. S.: A New Spatial Synoptic Classification: Application to Air Mass Analysis, Int. J. Climatol., 16, 983–1004, https://doi.org/10.1002/(SICI)1097-0088(199609)16:9<983::AID-JOC61>3.0.CO;2-N, 1996.
Levavasseur, G., Vrac, M., Roche, D. M., and Paillard, D.: Statistical modelling of a new global potential vegetation distribution, Environ. Res. Lett., 7, 044019, https://doi.org/10.1088/1748-9326/7/4/044019, 2012.
Lebret, R., Iovleff, S., Langrognet, F., Biernacki, C., Celeux, G., and Govaert, G.: Rmixmod: The R Package of the Model-Based Unsupervised, Supervised and Semi-Supervised Classification Mixmod Library, J. Stat. Softw., 67, 241–270, https://doi.org/10.18637/jss.v067.i06, 2015.
Lee, C. C. and Sheridan, S. C.: Synoptic Climatology: An Overview, Reference Module in Earth Systems and Environmental Sciences, https://doi.org/10.1016/B978-0-12-409548-9.09421-5, 2015.
Lloyd, S.: Least squares quantization in PCM, Technical Note, Bell Telephone Laboratories Paper, published in journal much later in 1982 in IEEE T. Inform. Theory, 28, 128–137, https://doi.org/10.1109/TIT.1982.1056489, 1957.
MacQueen, J. B.: Some Methods for classification and Analysis of Multivariate Observations, in: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, USA, 1, 281–297, 1967.
McLachlan, G. J. and Krishnan, T.: The EM algorithm and Extensions (2nd edition), Wiley, New York, USA, 400 pp., 2008.
Mixmod Team: Mixmod Statistical Documentation, Technical report, CNRS, Université de Besançon, Université de Franche-Comté, Besançon, France, 2008.
Molliere, J. L.: What is the real number of clusters?, 9th meeting of the German Classification Society, 26–28 June 1985, University of Karlsruhe, Karlsruhe, Germany, 1985.
Nelsen, R. B.: An Introduction to Copulas, Springer-Verlag, New York, USA, 1999.
Parzen, E.: On estimation of a probability density function and mode, Ann. Math. Stat., 33, 1065–1076, https://doi.org/10.1214/aoms/1177704472, 1962.
Peixoto, J. P. and Oort, A. H.: The climatology of relative humidity in the atmosphere, J. Climate, 9, 3443–3463, https://doi.org/10.1175/JCLI3956.1, 1996.
Philipp, A., Bartholy, J., Beck, C., Erpicum, M., Esteban, P., Fettweis, X., Huth, R., James, P., Jourdain, S., Kreienkamp, F., Krennert, T., Lykoudis, S., Michalides, S. C., Pianko-Kluczynska, K., Post, P., Alvarez, D. R., Scheimann, R., Spekat, A., and Tymvios, F. S.: Cost733cat – a database of weather and circulation type classifications, Phys. Chem. Earth, 35, 360–373, 2010.
Pudil, P., Novovičová, J., and Kittler, J.: Floating Search methods in Feature Selection, Pattern Recogn. Lett., 15, 1119–1125, https://doi.org/10.1016/0167-8655(94)90127-9, 1994.
Raftery, A. E.: Bayesian Model Selection in Social Research, Sociol. Methodol., 25, 111–164, https://doi.org/10.2307/271063, 1995.
Raftery, A. E. and Dean, N.: Variable Selection for Model-Based Clustering, J. Am. Stat. Assoc., 101, 168–178, https://doi.org/10.1198/016214506000000113, 2006.
Reusch, D. B., Alley, R. B., and Hewitson, B. C.: North Atlantic climate variability from a self-organizing map perspective, J. Geophys. Res., 112, 2104, https://doi.org/10.1029/2006JD007460, 2007.
Rosenblatt, W.: Remarks on some nonparametric estimates of a density function, Ann. Math. Stat., 27, 832–837, https://doi.org/10.1214/aoms/1177728190, 1956.
Rust, H., Vrac, M., Lengaigne, B., and Sultan, B.: Quantifying differences in circulation patterns based on probabilistic models: IPCC-AR4 multi-model comparison for the North Atlantic, J. Climate, 23, 6573–6589, https://doi.org/10.1175/2010JCLI3432.1, 2010.
Schwarz, G.: Estimating the Dimension of a Model, Ann. Stat., 6, 461–64, https://doi.org/10.1214/aos/1176344136, 1978.
Schweizer, B.: Distributions are the numbers of the future, in: Proceedings of the Mathematics of Fuzzy Systems Meeting, edited by: diNola, A. and Ventre, A., University of Naples, Naples, Italy, pp. 137–149, 1984.
Schweizer, B. and Sklar, A.: Probabilistic Metric Spaces, North-Holland Publishing Company, New York, USA, 1983.
Scott, N. A., Chédin, A., Armante, R., Francis, J., Stubenrauch, C., Chaboureau, J. P., Chevallier, F., Claud, C., and Chéruy, F.: Characteristics of the TOVS Pathfinder Path-B data set, B. Am. Meteorol. Soc., 80, 2679–2701, https://doi.org/10.1175/1520-0477(1999)080<2679:COTTPP>2.0.CO;2, 1999.
Sheridan, S. C. and Lee, C. C.: Synoptic Climatology, in: Oxford Bibliographies in Geography, edited by: Warf, B., Oxford University Press, New York City, USA, 2013.
Symons, M. J.: Clustering criteria and multivariate normal mixtures, Biometrics, 37, 35–43, https://doi.org/10.2307/2530520, 1981.
Therneau, T. M. and Atkinson, E. J.: An Introduction to Recursive Partitioning Using the RPART Routines, Technical Report Series No. 61, MN: Section of Biostatistics, Mayo Clinic, Rochester, USA, 2015.
U.S. Navy Fleet Numerical Oceanography Center: U.S. Navy 10-Minute Global Elevation and Geographic Characteristics, available at: http://rda.ucar.edu/datasets/ds754.0/, last access: 10 October 2016.
Vergados, P., Mannucci, A. J., Ao, C. O., Jiang, J. H., and Su, H.: On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations and MERRA and ECMWF data sets, Atmos. Meas. Tech., 8, 1789–1797, https://doi.org/10.5194/amt-8-1789-2015, 2015.
Vrac, M.: Analyse et Modélisation de Données Probabilistes par Decomposition de Mélange de Copules et Application à une Base de Données Climatologiques, PhD, Dissertation, University of Paris, France, 2002.
Vrac, M., Chédin, A., and Diday, E.: Clustering a Global Field of Atmospheric Profiles by Mixture Decomposition of Copulas, J. Atmos. Ocean. Tech., 22, 1445–1459, https://doi.org/10.1175/JTECH1795.1, 2005.
Vrac, M., Hayhoe, K., and Stein, M.: Identification and intermodal comparison of seasonal circulation patterns over North America, Int. J. Climatol., 27, 603–620, https://doi.org/10.1002/joc.1422, 2007.
Vrac, M., Billard, L., Diday, E., and Chédin, A.: Copula Analysis of Mixture Models, Computational Stat., 27, 427–457, https://doi.org/10.1007/s00180-011-0266-0, 2011.
Willett, H. C.: American air mass properties, Papers Physical Oceanography and Meteorology, Massachusetts Institute of Technology, Cambridge, MA, USA, 2, 1–116, 1933.
Yarnal, B.: Synoptic Climatology in Environmental Analysis: A Primer, Belhaven Press, London, UK, 256 pp., https://doi.org/10.1002/joc.3370140116, 1993.
Yarnal, B., Comrie, A. C., and Frakes, B., and Brown, D. P.: Developments and prospects in synoptic climatology: A Primer, Int. J. Climatol., 21, 1923–1950, https://doi.org/10.1002/joc.675, 2001.
Živkovíć, M.: Hierarchical clustering of atmospheric soundings, Int. J. Climatol., 15, 1099–1124, https://doi.org/10.1002/joc.3370151004, 1995.
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.
Here, we propose a classification methodology of various space-time atmospheric datasets into...
