Articles | Volume 29, issue 1
https://doi.org/10.5194/npg-29-37-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-29-37-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Direct Bayesian model reduction of smaller scale convective activity conditioned on large-scale dynamics
Robert Polzin
CORRESPONDING AUTHOR
Institute of Mathematics, Free University Berlin, Berlin, Germany
Annette Müller
Institute of Meteorology, Free University Berlin, Berlin, Germany
Henning Rust
Institute of Meteorology, Free University Berlin, Berlin, Germany
Peter Névir
Institute of Meteorology, Free University Berlin, Berlin, Germany
Péter Koltai
Institute of Mathematics, Free University Berlin, Berlin, Germany
Related authors
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Andreas Trojand, Henning Rust, and Uwe Ulbrich
EGUsphere, https://doi.org/10.5194/egusphere-2024-1506, https://doi.org/10.5194/egusphere-2024-1506, 2024
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The study investigates how the intensity of previous windstorm events and the time between two events affect the vulnerability of residential buildings in Germany. By analyzing 23 years of data, it was found that higher intensity of previous events generally reduces vulnerability in subsequent storms, while shorter intervals between events increase vulnerability. The results emphasize the approach of considering vulnerability in risk assessments as temporal dynamic.
Yan Li, Bo Huang, Chunping Tan, Xia Zhang, Francesco Cherubini, and Henning W. Rust
EGUsphere, https://doi.org/10.5194/egusphere-2024-1270, https://doi.org/10.5194/egusphere-2024-1270, 2024
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Forest cover changes primarily affect the global climate system by altering the energy and water balance on the surface. This study explores how large-scale deforestation impacts drought across diverse climate zones and time scales. Results reveal drier conditions in tropics but wetter climates in arid regions post-deforestation. Minimal impact observed in temperate zones. Long-term drought is more affected than short-term. These insights enhance understanding of vegetation-climate dynamics.
Madlen Peter, Henning W. Rust, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 24, 1261–1285, https://doi.org/10.5194/nhess-24-1261-2024, https://doi.org/10.5194/nhess-24-1261-2024, 2024
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The paper introduces a statistical modeling approach describing daily extreme precipitation in Germany more accurately by including changes within the year and between the years simultaneously. The changing seasonality over years is regionally divergent and mainly weak. However, some regions stand out with a more pronounced linear rise of summer intensities, indicating a possible climate change signal. Improved modeling of extreme precipitation is beneficial for risk assessment and adaptation.
Andy Richling, Jens Grieger, and Henning W. Rust
EGUsphere, https://doi.org/10.5194/egusphere-2023-2582, https://doi.org/10.5194/egusphere-2023-2582, 2024
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score –a measure of forecast performance– as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Yan Li, Bo Huang, and Henning W. Rust
Hydrol. Earth Syst. Sci., 28, 321–339, https://doi.org/10.5194/hess-28-321-2024, https://doi.org/10.5194/hess-28-321-2024, 2024
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The inconsistent changes in temperature and precipitation induced by forest cover change are very likely to affect drought condition. We use a set of statistical models to explore the relationship between forest cover change and drought change in different timescales and climate zones. We find that the influence of forest cover on droughts varies under different precipitation and temperature quantiles. Forest cover also could modulate the impacts of precipitation and temperature on drought.
Johannes Riebold, Andy Richling, Uwe Ulbrich, Henning Rust, Tido Semmler, and Dörthe Handorf
Weather Clim. Dynam., 4, 663–682, https://doi.org/10.5194/wcd-4-663-2023, https://doi.org/10.5194/wcd-4-663-2023, 2023
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Arctic sea ice loss might impact the atmospheric circulation outside the Arctic and therefore extremes over mid-latitudes. Here, we analyze model experiments to initially assess the influence of sea ice loss on occurrence frequencies of large-scale circulation patterns. Some of these detected circulation changes can be linked to changes in occurrences of European temperature extremes. Compared to future global temperature increases, the sea-ice-related impacts are however of secondary relevance.
Edmund P. Meredith, Uwe Ulbrich, and Henning W. Rust
Geosci. Model Dev., 16, 851–867, https://doi.org/10.5194/gmd-16-851-2023, https://doi.org/10.5194/gmd-16-851-2023, 2023
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Cell-tracking algorithms allow for the study of properties of a convective cell across its lifetime and, in particular, how these respond to climate change. We investigated whether the design of the algorithm can affect the magnitude of the climate-change signal. The algorithm's criteria for identifying a cell were found to have a strong impact on the warming response. The sensitivity of the warming response to different algorithm settings and cell types should thus be fully explored.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
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In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Tarek Beutler, Annette Rudolph, Daniel Goehring, and Nikki Vercauteren
EGUsphere, https://doi.org/10.5194/egusphere-2022-440, https://doi.org/10.5194/egusphere-2022-440, 2022
Preprint withdrawn
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Precipitation nowcasting refers to the prediction of precipitation intensity in a local region and in a short timeframe up to 6 hours. The increasing possibilities to store and evaluate data combined with the advancements in the developments of artificial intelligence algorithms make it natural to use these methods to improve precipitation nowcasting. The positive effectiveness of finetuning and promising skill scores for a prediction time up to 100 minutes are shown.
Noelia Otero, Oscar E. Jurado, Tim Butler, and Henning W. Rust
Atmos. Chem. Phys., 22, 1905–1919, https://doi.org/10.5194/acp-22-1905-2022, https://doi.org/10.5194/acp-22-1905-2022, 2022
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Surface ozone and temperature are strongly dependent and their extremes might be exacerbated by underlying climatological drivers, such as atmospheric blocking. Using an observational data set, we measure the dependence structure between ozone and temperature under the influence of atmospheric blocking. Blocks enhanced the probability of occurrence of compound ozone and temperature extremes over northwestern and central Europe, leading to greater health risks.
Felix S. Fauer, Jana Ulrich, Oscar E. Jurado, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6479–6494, https://doi.org/10.5194/hess-25-6479-2021, https://doi.org/10.5194/hess-25-6479-2021, 2021
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Extreme rainfall events are modeled in this study for different timescales. A new parameterization of the dependence between extreme values and their timescale enables our model to estimate extremes on very short (1 min) and long (5 d) timescales simultaneously. We compare different approaches of modeling this dependence and find that our new model improves performance for timescales between 2 h and 2 d without affecting model performance on other timescales.
Jana Ulrich, Felix S. Fauer, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6133–6149, https://doi.org/10.5194/hess-25-6133-2021, https://doi.org/10.5194/hess-25-6133-2021, 2021
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The characteristics of extreme precipitation on different timescales as well as in different seasons are relevant information, e.g., for designing hydrological structures or managing water supplies. Therefore, our aim is to describe these characteristics simultaneously within one model. We find similar characteristics for short extreme precipitation at all considered stations in Germany but pronounced regional differences with respect to the seasonality of long-lasting extreme events.
Carola Detring, Annette Müller, Lisa Schielicke, Peter Névir, and Henning W. Rust
Weather Clim. Dynam., 2, 927–952, https://doi.org/10.5194/wcd-2-927-2021, https://doi.org/10.5194/wcd-2-927-2021, 2021
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Stationary, long-lasting blocked weather patterns can lead to extreme conditions. Within this study the temporal evolution of the occurrence probability is analyzed, and the onset, decay and transition probabilities of blocking within the past 30 years are modeled. Using Markov models combined with logistic regression, we found large changes in summer, where the probability of transitions to so-called Omega blocks increases strongly, while the unblocked state becomes less probable.
Alexander Pasternack, Jens Grieger, Henning W. Rust, and Uwe Ulbrich
Geosci. Model Dev., 14, 4335–4355, https://doi.org/10.5194/gmd-14-4335-2021, https://doi.org/10.5194/gmd-14-4335-2021, 2021
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Decadal climate ensemble forecasts are increasingly being used to guide adaptation measures. To ensure the applicability of these probabilistic predictions, inherent systematic errors of the prediction system must be adjusted. Since it is not clear which statistical model is optimal for this purpose, we propose a recalibration strategy with a systematic model selection based on non-homogeneous boosting for identifying the most relevant features for both ensemble mean and ensemble spread.
Nico Becker, Henning W. Rust, and Uwe Ulbrich
Nat. Hazards Earth Syst. Sci., 20, 2857–2871, https://doi.org/10.5194/nhess-20-2857-2020, https://doi.org/10.5194/nhess-20-2857-2020, 2020
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A set of models is developed to forecast hourly probabilities of weather-related road accidents in Germany at the spatial scale of administrative districts. Model verification shows that using precipitation and temperature data leads to the best accident forecasts. Based on weather forecast data we show that skilful predictions of accident probabilities of up to 21 h ahead are possible. The models can be used to issue impact-based warnings, which are relevant for road users and authorities.
Noelia Otero, Henning W. Rust, and Tim Butler
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-691, https://doi.org/10.5194/acp-2020-691, 2020
Revised manuscript not accepted
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Surface ozone concentrations are strongly correlated with temperature in summertime. Using long-term measurements, we investigate changes in the observed relationship between ozone and temperature over Germany. We propose a new statistical approach based on Generalized Additive Models (GAMs) to describe ozone production rates as a function of nitrogen oxides (NOx) and temperature. Our results suggest that NOx reductions alone can not explain the changes in the temperature dependence of ozone.
Noelia Otero, Jana Sillmann, Kathleen A. Mar, Henning W. Rust, Sverre Solberg, Camilla Andersson, Magnuz Engardt, Robert Bergström, Bertrand Bessagnet, Augustin Colette, Florian Couvidat, Cournelius Cuvelier, Svetlana Tsyro, Hilde Fagerli, Martijn Schaap, Astrid Manders, Mihaela Mircea, Gino Briganti, Andrea Cappelletti, Mario Adani, Massimo D'Isidoro, María-Teresa Pay, Mark Theobald, Marta G. Vivanco, Peter Wind, Narendra Ojha, Valentin Raffort, and Tim Butler
Atmos. Chem. Phys., 18, 12269–12288, https://doi.org/10.5194/acp-18-12269-2018, https://doi.org/10.5194/acp-18-12269-2018, 2018
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This paper evaluates the capability of air-quality models to capture the observed relationship between surface ozone concentrations and meteorology over Europe. The air-quality models tended to overestimate the influence of maximum temperature and surface solar radiation. None of the air-quality models captured the strength of the observed relationship between ozone and relative humidity appropriately, underestimating the effect of relative humidity, a key factor in the ozone removal processes.
Edmund P. Meredith, Henning W. Rust, and Uwe Ulbrich
Hydrol. Earth Syst. Sci., 22, 4183–4200, https://doi.org/10.5194/hess-22-4183-2018, https://doi.org/10.5194/hess-22-4183-2018, 2018
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Kilometre-scale climate-model data are of great benefit to both hydrologists and end users studying extreme precipitation, though often unavailable due to the computational expense associated with such high-resolution simulations. We develop a method which identifies days with enhanced risk of extreme rainfall over a catchment, so that high-resolution simulations can be performed only when such a risk exists, reducing computational expense by over 90 % while still well capturing the extremes.
Stefanie Kremser, Jordis S. Tradowsky, Henning W. Rust, and Greg E. Bodeker
Atmos. Meas. Tech., 11, 3021–3029, https://doi.org/10.5194/amt-11-3021-2018, https://doi.org/10.5194/amt-11-3021-2018, 2018
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We investigate the feasibility of quantifying the difference in biases of two instrument types (i.e. radiosondes) by flying the old and new instruments on alternating days, so-called interlacing, to statistically derive the systematic biases between the instruments. While it is in principle possible to estimate the difference between two instrument biases from interlaced measurements, the number of required interlaced flights is very large for reasonable autocorrelation coefficient values.
Stefan Liersch, Julia Tecklenburg, Henning Rust, Andreas Dobler, Madlen Fischer, Tim Kruschke, Hagen Koch, and Fred Fokko Hattermann
Hydrol. Earth Syst. Sci., 22, 2163–2185, https://doi.org/10.5194/hess-22-2163-2018, https://doi.org/10.5194/hess-22-2163-2018, 2018
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Application-oriented regional impact studies require accurate simulations of future climate variables and water availability. We analyse the quality of global and regional climate projections and discuss potentials of correction methods that partly overcome this quality issue. The model ensemble used in this study projects increasing average annual discharges and a shift in seasonal patterns, with decreasing discharges in June and July and increasing discharges from August to November.
Alexander Pasternack, Jonas Bhend, Mark A. Liniger, Henning W. Rust, Wolfgang A. Müller, and Uwe Ulbrich
Geosci. Model Dev., 11, 351–368, https://doi.org/10.5194/gmd-11-351-2018, https://doi.org/10.5194/gmd-11-351-2018, 2018
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We propose a decadal forecast recalibration strategy (DeFoReSt) which simultaneously adjusts unconditional and conditional bias, as well as the ensemble spread while considering the typical setting of decadal predictions, i.e., model drift and a climate trend. We apply DeFoReSt to decadal toy model data and surface temperature forecasts from the MiKlip system and find consistent improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.
Christoph Ritschel, Uwe Ulbrich, Peter Névir, and Henning W. Rust
Hydrol. Earth Syst. Sci., 21, 6501–6517, https://doi.org/10.5194/hess-21-6501-2017, https://doi.org/10.5194/hess-21-6501-2017, 2017
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A stochastic model for precipitation is used to simulate an observed precipitation series; it is compared to the original series in terms of intensity–duration frequency curves. Basis for the latter curves is a parametric model for the duration dependence of the underlying extreme value model allowing a consistent estimation of one single duration-dependent distribution using all duration series simultaneously. The stochastic model reproduces the curves except for very rare extreme events.
L. Schielicke and P. Névir
Nonlin. Processes Geophys., 20, 47–57, https://doi.org/10.5194/npg-20-47-2013, https://doi.org/10.5194/npg-20-47-2013, 2013
Related subject area
Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Simulation
Empirical adaptive wavelet decomposition (EAWD): an adaptive decomposition for the variability analysis of observation time series in atmospheric science
Lévy noise versus Gaussian-noise-induced transitions in the Ghil–Sellers energy balance model
Improvements to the use of the Trajectory-Adaptive Multilevel Sampling algorithm for the study of rare events
Simulation-based comparison of multivariate ensemble post-processing methods
A prototype stochastic parameterization of regime behaviour in the stably stratified atmospheric boundary layer
Olivier Delage, Thierry Portafaix, Hassan Bencherif, Alain Bourdier, and Emma Lagracie
Nonlin. Processes Geophys., 29, 265–277, https://doi.org/10.5194/npg-29-265-2022, https://doi.org/10.5194/npg-29-265-2022, 2022
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The complexity of geophysics systems results in time series with fluctuations at all timescales. The analysis of their variability then consists in decomposing them into a set of basis signals. We developed here a new adaptive filtering method called empirical adaptive wavelet decomposition that optimizes the empirical-mode decomposition existing technique, overcoming its drawbacks using the rigour of wavelets as defined in the recently published empirical wavelet transform method.
Valerio Lucarini, Larissa Serdukova, and Georgios Margazoglou
Nonlin. Processes Geophys., 29, 183–205, https://doi.org/10.5194/npg-29-183-2022, https://doi.org/10.5194/npg-29-183-2022, 2022
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In most of the investigations on metastable systems, the stochastic forcing is modulated by Gaussian noise. Lévy noise laws, which describe jump processes, have recently received a lot of attention, but much less is known. We study stochastic versions of the Ghil–Sellers energy balance model, and we highlight the fundamental difference between how transitions are performed between the competing warm and snowball states, depending on whether Gaussian or Lévy noise acts as forcing.
Pascal Wang, Daniele Castellana, and Henk A. Dijkstra
Nonlin. Processes Geophys., 28, 135–151, https://doi.org/10.5194/npg-28-135-2021, https://doi.org/10.5194/npg-28-135-2021, 2021
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This paper proposes two improvements to the use of Trajectory-Adaptive Multilevel Sampling, a rare-event algorithm which computes noise-induced transition probabilities. The first improvement uses locally linearised dynamics in order to reduce the arbitrariness associated with defining what constitutes a transition. The second improvement uses empirical transition paths accumulated at high noise in order to formulate the score function which determines the performance of the algorithm.
Sebastian Lerch, Sándor Baran, Annette Möller, Jürgen Groß, Roman Schefzik, Stephan Hemri, and Maximiliane Graeter
Nonlin. Processes Geophys., 27, 349–371, https://doi.org/10.5194/npg-27-349-2020, https://doi.org/10.5194/npg-27-349-2020, 2020
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Accurate models of spatial, temporal, and inter-variable dependencies are of crucial importance for many practical applications. We review and compare several methods for multivariate ensemble post-processing, where such dependencies are imposed via copula functions. Our investigations utilize simulation studies that mimic challenges occurring in practical applications and allow ready interpretation of the effects of different misspecifications of the numerical weather prediction ensemble.
Carsten Abraham, Amber M. Holdsworth, and Adam H. Monahan
Nonlin. Processes Geophys., 26, 401–427, https://doi.org/10.5194/npg-26-401-2019, https://doi.org/10.5194/npg-26-401-2019, 2019
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Atmospheric stably stratified boundary layers display transitions between regimes of sustained and intermittent turbulence. These transitions are not well represented in numerical weather prediction and climate models. A prototype explicitly stochastic turbulence parameterization simulating regime dynamics is presented and tested in an idealized model. Results demonstrate that the approach can improve the regime representation in models.
Cited articles
Berner, J., Achatz, U., Batté, L., Bengtsson, L., Cámara, A. D. L.,
Christensen, H. M., Colangeli, M., Coleman, D. R. B., Crommelin, D., Dolaptchiev, S. I., Franzke, C. L. E., Friederichs, P., Peter Imkeller, P., Järvinen, H., Juricke, S., Kitsios, V., Lott, F., Lucarini, V., Mahajan, S., Palmer, T. N., Penland, C., Sakradzija, M., von Storch, J.-S., Weisheimer, A., Weniger, M., Williams, P. D., and Yano, J.-I.: Stochastic parameterization: Toward a new view of weather and climate models, B. Am. Meteorol. Soc., 98, 565–588, 2017. a
Blanchard, D. O.: Assessing the vertical distribution of convective available
potential energy, Weather Forecast., 13, 870–877, 1998. a
Bott, A.: Synoptische Meteorologie: Methoden der Wetteranalyse und-prognose,
Springer-Verlag, ISBN 9-78366-248-1943, 2016. a
Coifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F.,
and Zucker, S. W.: Geometric diffusions as a tool for harmonic analysis and
structure definition of data: Diffusion maps, P. Natl. Acad. Sci. USA, 102, 7426–7431, 2005. a
Donoho, D. L. and Grimes, C.: Hessian eigenmaps: Locally linear embedding
techniques for high-dimensional data, P. Natl. Acad. Sci. USA, 100, 5591–5596, 2003. a
Dorrestijn, J., Crommelin, D., Biello, J., and Böing, S.: A data-driven
multi-cloud model for stochastic parametrization of deep convection,
Philos. T. Roy. Soc. A, 371, 20120374, https://doi.org/10.1098/rsta.2012.0374, 2013a. a
Dorrestijn, J., Crommelin, D. T., Siebesma, A. P., and Jonker, H. J.:
Stochastic parameterization of shallow cumulus convection estimated from
high-resolution model data, Theor. Comp. Fluid Dyn., 27,
133–148, 2013b. a
Dutton, J.: Dynamics of Atmospheric Motion, Dover Publications Inc., 617 pp., ISBN 9780486684864, 1976. a
Franzke, C. L., O'Kane, T. J., Berner, J., Williams, P. D., and Lucarini, V.:
Stochastic climate theory and modeling, WIRES Climate Change, 6, 63–78, 2015. a
Fritsch, J. and Chappell, C.: Numerical prediction of convectively driven
mesoscale pressure systems. Part I: Convective parameterization, J.
Atmos. Sci., 37, 1722–1733, 1980. a
Gerber, S., Olsson, S., Noé, F., and Horenko, I.: A scalable approach to
the computation of invariant measures for high-dimensional Markovian systems,
Sci. Rep., 8, 1796, https://doi.org/10.1038/s41598-018-19863-4, 2018. a, b
Gottwald, G. A., Peters, K., and Davies, L.: A data-driven method for the
stochastic parametrisation of subgrid-scale tropical convective area
fraction, Q. J. Roy. Meteor. Soc., 142, 349–359, 2016. a
Holland, P. W.: Statistics and causal inference, J. Am. Stat. Assoc., 81, 945–960, 1986. a
Horenko, I.: On simultaneous data-based dimension reduction and hidden phase
identification, J. Atmos. Sci., 65, 1941–1954, 2008. a
Horenko, I., Dolaptchiev, S. I., Eliseev, A. V., Mokhov, I. I., and Klein, R.: Metastable decomposition of high-dimensional meteorological data with gaps, J. Atmos. Sci., 65, 3479–3496, 2008. a
Jolliffe, I.: Principal component analysis, Technometrics, 45, 276, https://doi.org/10.1198/tech.2003.s783, 2003. a
Khouider, B., Biello, J., and Majda, A. J.: A stochastic multicloud model for tropical convection, Commun. Math. Sci., 8, 187–216, 2010. a
Kirkpatrick, C., McCaul Jr., E. W., and Cohen, C.: Variability of updraft and
downdraft characteristics in a large parameter space study of convective
storms, Mon. Weather Rev., 137, 1550–1561, 2009. a
Klein, R.: Scale-dependent models for atmospheric flows, Annu. Rev. Fluid Mech., 42, 249–274, 2010. a
Lorenz, E. N.: Empirical Orthogonal Functions and Statistical
Weather Prediction Science, Rep. 1, Statistical Forecasting Project, Department of Meteorology, MIT, Cambridge, https://www.worldcat.org/title/empirical-orthogonal-functions-and-statistical-weatherprediction/oclc/2293210 (last access: 16 February 2022), 1956. a
Moncrieff, M. W. and Miller, M. J.: The dynamics and simulation of tropical
cumulonimbus and squall lines, Q. J. Roy. Meteor. Soc., 102, 373–394, 1976. a
Müller, A. and Névir, P.: Using the concept of the Dynamic State Index for a scale-dependent analysis of atmospheric blocking, Meteorol. Z., 28, 487–498, https://doi.org/10.1127/metz/2019/0963, 2019. a, b
Polzin, R. M.: Data repository for Polzin et al. (2022) “Direct Bayesian model reduction of smaller scale convective activity conditioned on large-scale dynamics”, Refubium – Freie Universität Berlin [data set],
https://doi.org/10.17169/refubium-33435, 2022. a, b, c
Schmid, P. J.: Dynamic mode decomposition of numerical and experimental data,
J. Fluid Mech., 656, 5–28, 2010. a
Schölkopf, B., Smola, A., and Müller, K.-R.: Kernel principal component analysis, in: 7th International Conference on Artificial Neural Networks, ICANN 1997, Lausanne, Switzerland, 8–10 October 1997, Springer, 583–588, ISBN 9783540636311, 1997. a
Von Luxburg, U.: A tutorial on spectral clustering, Stat. Comput., 17, 395–416, 2007. a
Weisman, M. L. and Klemp, J. B.: The dependence of numerically simulated
convective storms on vertical wind shear and buoyancy, Mon. Weather Rev., 110, 504–520, 1982. a
Zhao, Y., Levina, E., and Zhu, J.: Consistency of community detection in
networks under degree-corrected stochastic block models, Ann. Stat., 40, 2266–2292, 2012. a
Short summary
In this study, a recent algorithmic framework called Direct Bayesian Model Reduction (DBMR) is applied which provides a scalable probability-preserving identification of reduced models directly from data. The stochastic method is tested in a meteorological application towards a model reduction to latent states of smaller scale convective activity conditioned on large-scale atmospheric flow.
In this study, a recent algorithmic framework called Direct Bayesian Model Reduction (DBMR) is...