Articles | Volume 26, issue 4
https://doi.org/10.5194/npg-26-381-2019
© Author(s) 2019. 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-26-381-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Nov 2019
Research article |
| 05 Nov 2019
| 05 Nov 2019
Generalization properties of feed-forward neural networks trained on Lorenz systems
Sebastian Scher
CORRESPONDING AUTHOR
Department of Meteorology and Bolin
Centre for Climate Research, Stockholm
University, Stockholm, Sweden
Gabriele Messori
Department of Meteorology and Bolin
Centre for Climate Research, Stockholm
University, Stockholm, Sweden
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Related authors
Sebastian Scher and Stefanie Peßenteiner
Hydrol. Earth Syst. Sci., 25, 3207–3225, https://doi.org/10.5194/hess-25-3207-2021, https://doi.org/10.5194/hess-25-3207-2021, 2021
Short summary
Short summary
In hydrology, it is often necessary to infer from a daily sum of precipitation a possible distribution over the day – for example how much it rained in each hour. In principle, for a given daily sum, there are endless possibilities. However, some are more likely than others. We show that a method from artificial intelligence called generative adversarial networks (GANs) can
learnwhat a typical distribution over the day looks like.
Assaf Hochman, Sebastian Scher, Julian Quinting, Joaquim G. Pinto, and Gabriele Messori
Earth Syst. Dynam., 12, 133–149, https://doi.org/10.5194/esd-12-133-2021, https://doi.org/10.5194/esd-12-133-2021, 2021
Short summary
Short summary
Skillful forecasts of extreme weather events have a major socioeconomic relevance. Here, we compare two approaches to diagnose the predictability of eastern Mediterranean heat waves: one based on recent developments in dynamical systems theory and one leveraging numerical ensemble weather forecasts. We conclude that the former can be a useful and cost-efficient complement to conventional numerical forecasts for understanding the dynamics of eastern Mediterranean heat waves.
Sebastian Scher and Gabriele Messori
Geosci. Model Dev., 12, 2797–2809, https://doi.org/10.5194/gmd-12-2797-2019, https://doi.org/10.5194/gmd-12-2797-2019, 2019
Short summary
Short summary
Currently, weather forecasts are mainly produced by using computer models based on physical equations. It is an appealing idea to use neural networks and “deep learning” for weather forecasting instead. We successfully test the possibility of using deep learning for weather forecasting by considering climate models as simplified versions of reality. Our work therefore is a step towards potentially using deep learning to replace or accompany current weather forecasting models.
Leonardo Olivetti and Gabriele Messori
EGUsphere, https://doi.org/10.5194/egusphere-2024-1042, https://doi.org/10.5194/egusphere-2024-1042, 2024
Short summary
Short summary
Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models at forecasting weather extremes. Here, we evaluate their performance in forecasting near-surface cold, hot and windy extremes globally. We find that data-driven models can compete with physics-based models, and that the choice of best model mainly depends on region and type of extreme.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
Short summary
In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Marco Gaetani, Gabriele Messori, Francesco S. R. Pausata, Shivangi Tiwari, M. Carmen Alvarez Castro, and Qiong Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2024-272, https://doi.org/10.5194/egusphere-2024-272, 2024
Short summary
Short summary
Around 6,000 years ago, paleoclimate reconstructions suggest a greening of the Sahara, accompanied by climate changes in the Northern Hemisphere at mid to high latitudes. In this study, a climate model is used to investigate how this drastic environmental change in the Sahara impacted remote regions. In particular, climate simulations show significant modifications in the atmospheric circulation in the North Atlantic, affecting climate in North America and Europe.
Gabriele Messori, Antonio Segalini, and Alexandre M. Ramos
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2023-45, https://doi.org/10.5194/esd-2023-45, 2024
Preprint under review for ESD
Short summary
Short summary
Simultaneous heatwaves or cold spells in remote geographical regions have potentially far-reaching impacts on society and the environment. Notwithstanding this, we have little knowledge of when and where these extreme events have occurred in the past decades. Here, we present a summary of past simultaneous heatwaves or cold spells, and provide a computer program to enable other researchers to study them.
Aleksa Stanković, Gabriele Messori, Joaquim G. Pinto, and Rodrigo Caballero
EGUsphere, https://doi.org/10.5194/egusphere-2024-38, https://doi.org/10.5194/egusphere-2024-38, 2024
Short summary
Short summary
The article studies extreme winds near the surface over the North Atlantic ocean. These winds are caused by storms that pass through this region. The strongest storms that have occurred in the winters from 1950–2020 are studied in detail and compared to weaker, but still strong storms. The analysis shows that the storms associated with the strongest winds are preceded by another, older storm that traveled through the same region and made the conditions suitable for development of extreme winds.
Sina Loriani, Yevgeny Aksenov, David Armstrong McKay, Govindasamy Bala, Andreas Born, Cristiano M. Chiessi, Henk Dijkstra, Jonathan F. Donges, Sybren Drijfhout, Matthew H. England, Alexey V. Fedorov, Laura Jackson, Kai Kornhuber, Gabriele Messori, Francesco Pausata, Stefanie Rynders, Jean-Baptiste Salée, Bablu Sinha, Steven Sherwood, Didier Swingedouw, and Thejna Tharammal
EGUsphere, https://doi.org/10.5194/egusphere-2023-2589, https://doi.org/10.5194/egusphere-2023-2589, 2023
Short summary
Short summary
In this work, we draw on paleoreords, observations and modelling studies to review tipping points in the ocean overturning circulations, monsoon systems and global atmospheric circulations. We find indications for tipping in the ocean overturning circulations and the West African monsoon, with potentially severe impacts on the Earth system and humans. Tipping in the other considered systems is considered conceivable but currently not sufficiently supported by evidence.
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.
Emma Holmberg, Gabriele Messori, Rodrigo Caballero, and Davide Faranda
Earth Syst. Dynam., 14, 737–765, https://doi.org/10.5194/esd-14-737-2023, https://doi.org/10.5194/esd-14-737-2023, 2023
Short summary
Short summary
We analyse the duration of large-scale patterns of air movement in the atmosphere, referred to as persistence, and whether unusually persistent patterns favour warm-temperature extremes in Europe. We see no clear relationship between summertime heatwaves and unusually persistent patterns. This suggests that heatwaves do not necessarily require the continued flow of warm air over a region and that local effects could be important for their occurrence.
Derrick Muheki, Axel Antonius Johannes Deijns, Emanuele Bevacqua, Gabriele Messori, Jakob Zscheischler, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2023-1712, https://doi.org/10.5194/egusphere-2023-1712, 2023
Short summary
Short summary
Climate change affects the interaction, dependence, and joint occurrence of climate extremes. Here we investigate the joint occurrence of pairs of river floods, droughts, heatwaves, crop failures, wildfires, and tropical cyclones over East Africa, under past and future climate conditions. Our results show that, across all warming scenarios, the frequency and spatial extent of these co-occurring extremes will increase in this region, particularly in areas close to the Nile and Congo rivers.
Antonio Segalini, Jacopo Riboldi, Volkmar Wirth, and Gabriele Messori
EGUsphere, https://doi.org/10.5194/egusphere-2023-316, https://doi.org/10.5194/egusphere-2023-316, 2023
Short summary
Short summary
Planetary Rossby waves are created by topography and evolve in time. In this work, an analytical solution of this classical problem is proposed under the approximation of linear wave dynamics. The theory is able to describe reasonably well the evolution of the perturbation and compares well with full nonlinear simulations. Several relevant cases with single and double zonal jets are assessed with the theoretical framework
Axel Kleidon, Gabriele Messori, Somnath Baidya Roy, Ira Didenkulova, and Ning Zeng
Earth Syst. Dynam., 14, 241–242, https://doi.org/10.5194/esd-14-241-2023, https://doi.org/10.5194/esd-14-241-2023, 2023
Patrick Johannes Stoll, Rune Grand Graversen, and Gabriele Messori
Weather Clim. Dynam., 4, 1–17, https://doi.org/10.5194/wcd-4-1-2023, https://doi.org/10.5194/wcd-4-1-2023, 2023
Short summary
Short summary
The atmosphere is in motion and hereby transporting warm, cold, moist, and dry air to different climate zones. In this study, we investigate how this transport of energy organises in different manners. Outside the tropics, atmospheric waves of sizes between 2000 and 8000 km, which we perceive as cyclones from the surface, transport most of the energy and moisture poleward. In the winter, large-scale weather situations become very important for transporting energy into the polar regions.
Davide Faranda, Stella Bourdin, Mireia Ginesta, Meriem Krouma, Robin Noyelle, Flavio Pons, Pascal Yiou, and Gabriele Messori
Weather Clim. Dynam., 3, 1311–1340, https://doi.org/10.5194/wcd-3-1311-2022, https://doi.org/10.5194/wcd-3-1311-2022, 2022
Short summary
Short summary
We analyze the atmospheric circulation leading to impactful extreme events for the calendar year 2021 such as the Storm Filomena, Westphalia floods, Hurricane Ida and Medicane Apollo. For some of the events, we find that climate change has contributed to their occurrence or enhanced their intensity; for other events, we find that they are unprecedented. Our approach underscores the importance of considering changes in the atmospheric circulation when performing attribution studies.
Gabriele Messori, Marlene Kretschmer, Simon H. Lee, and Vivien Wendt
Weather Clim. Dynam., 3, 1215–1236, https://doi.org/10.5194/wcd-3-1215-2022, https://doi.org/10.5194/wcd-3-1215-2022, 2022
Short summary
Short summary
Over 10 km above the ground, there is a region of the atmosphere called the stratosphere. While there is very little air in the stratosphere itself, its interactions with the lower parts of the atmosphere – where we live – can affect the weather. Here we study a specific example of such an interaction, whereby processes occurring at the boundary of the stratosphere can lead to a continent-wide drop in temperatures in North America during winter.
Valerio Lembo, Federico Fabiano, Vera Melinda Galfi, Rune Grand Graversen, Valerio Lucarini, and Gabriele Messori
Weather Clim. Dynam., 3, 1037–1062, https://doi.org/10.5194/wcd-3-1037-2022, https://doi.org/10.5194/wcd-3-1037-2022, 2022
Short summary
Short summary
Eddies in mid-latitudes characterize the exchange of heat between the tropics and the poles. This exchange is largely uneven, with a few extreme events bearing most of the heat transported across latitudes in a season. It is thus important to understand what the dynamical mechanisms are behind these events. Here, we identify recurrent weather regime patterns associated with extreme transports, and we identify scales of mid-latitudinal eddies that are mostly responsible for the transport.
Assaf Hochman, Francesco Marra, Gabriele Messori, Joaquim G. Pinto, Shira Raveh-Rubin, Yizhak Yosef, and Georgios Zittis
Earth Syst. Dynam., 13, 749–777, https://doi.org/10.5194/esd-13-749-2022, https://doi.org/10.5194/esd-13-749-2022, 2022
Short summary
Short summary
Gaining a complete understanding of extreme weather, from its physical drivers to its impacts on society, is important in supporting future risk reduction and adaptation measures. Here, we provide a review of the available scientific literature, knowledge gaps and key open questions in the study of extreme weather events over the vulnerable eastern Mediterranean region.
Francesco S. R. Pausata, Gabriele Messori, Jayoung Yun, Chetankumar A. Jalihal, Massimo A. Bollasina, and Thomas M. Marchitto
Clim. Past, 17, 1243–1271, https://doi.org/10.5194/cp-17-1243-2021, https://doi.org/10.5194/cp-17-1243-2021, 2021
Short summary
Short summary
Far-afield changes in vegetation such as those that occurred over the Sahara during the middle Holocene and the consequent changes in dust emissions can affect the intensity of the South Asian Monsoon (SAM) rainfall and the lengthening of the monsoon season. This remote influence is mediated by anomalies in Indian Ocean sea surface temperatures and may have shaped the evolution of the SAM during the termination of the African Humid Period.
Sebastian Scher and Stefanie Peßenteiner
Hydrol. Earth Syst. Sci., 25, 3207–3225, https://doi.org/10.5194/hess-25-3207-2021, https://doi.org/10.5194/hess-25-3207-2021, 2021
Short summary
Short summary
In hydrology, it is often necessary to infer from a daily sum of precipitation a possible distribution over the day – for example how much it rained in each hour. In principle, for a given daily sum, there are endless possibilities. However, some are more likely than others. We show that a method from artificial intelligence called generative adversarial networks (GANs) can
learnwhat a typical distribution over the day looks like.
Gabriele Messori and Davide Faranda
Clim. Past, 17, 545–563, https://doi.org/10.5194/cp-17-545-2021, https://doi.org/10.5194/cp-17-545-2021, 2021
Short summary
Short summary
The palaeoclimate community must both analyse large amounts of model data and compare very different climates. Here, we present a seemingly very abstract analysis approach that may be fruitfully applied to palaeoclimate numerical simulations. This approach characterises the dynamics of a given climate through a small number of metrics and is thus suited to face the above challenges.
Gabriele Messori, Nili Harnik, Erica Madonna, Orli Lachmy, and Davide Faranda
Earth Syst. Dynam., 12, 233–251, https://doi.org/10.5194/esd-12-233-2021, https://doi.org/10.5194/esd-12-233-2021, 2021
Short summary
Short summary
Atmospheric jets are a key component of the climate system and of our everyday lives. Indeed, they affect human activities by influencing the weather in many mid-latitude regions. However, we still lack a complete understanding of their dynamical properties. In this study, we try to relate the understanding gained in idealized computer simulations of the jets to our knowledge from observations of the real atmosphere.
Assaf Hochman, Sebastian Scher, Julian Quinting, Joaquim G. Pinto, and Gabriele Messori
Earth Syst. Dynam., 12, 133–149, https://doi.org/10.5194/esd-12-133-2021, https://doi.org/10.5194/esd-12-133-2021, 2021
Short summary
Short summary
Skillful forecasts of extreme weather events have a major socioeconomic relevance. Here, we compare two approaches to diagnose the predictability of eastern Mediterranean heat waves: one based on recent developments in dynamical systems theory and one leveraging numerical ensemble weather forecasts. We conclude that the former can be a useful and cost-efficient complement to conventional numerical forecasts for understanding the dynamics of eastern Mediterranean heat waves.
Paolo De Luca, Gabriele Messori, Davide Faranda, Philip J. Ward, and Dim Coumou
Earth Syst. Dynam., 11, 793–805, https://doi.org/10.5194/esd-11-793-2020, https://doi.org/10.5194/esd-11-793-2020, 2020
Short summary
Short summary
In this paper we quantify Mediterranean compound temperature and precipitation dynamical extremes (CDEs) over the 1979–2018 period. The strength of the temperature–precipitation coupling during summer increased and is driven by surface warming. We also link the CDEs to compound hot–dry and cold–wet events during summer and winter respectively.
Paolo De Luca, Gabriele Messori, Robert L. Wilby, Maurizio Mazzoleni, and Giuliano Di Baldassarre
Earth Syst. Dynam., 11, 251–266, https://doi.org/10.5194/esd-11-251-2020, https://doi.org/10.5194/esd-11-251-2020, 2020
Short summary
Short summary
We show that floods and droughts can co-occur in time across remote regions on the globe and introduce metrics that can help in quantifying concurrent wet and dry hydrological extremes. We then link wet–dry extremes to major modes of climate variability (i.e. ENSO, PDO, and AMO) and provide their spatial patterns. Such concurrent extreme hydrological events may pose risks to regional hydropower production and agricultural yields.
Davide Faranda, Yuzuru Sato, Gabriele Messori, Nicholas R. Moloney, and Pascal Yiou
Earth Syst. Dynam., 10, 555–567, https://doi.org/10.5194/esd-10-555-2019, https://doi.org/10.5194/esd-10-555-2019, 2019
Short summary
Short summary
We show how the complex dynamics of the jet stream at midlatitude can be described by a simple mathematical model. We match the properties of the model to those obtained by the jet data derived from observations.
Sebastian Scher and Gabriele Messori
Geosci. Model Dev., 12, 2797–2809, https://doi.org/10.5194/gmd-12-2797-2019, https://doi.org/10.5194/gmd-12-2797-2019, 2019
Short summary
Short summary
Currently, weather forecasts are mainly produced by using computer models based on physical equations. It is an appealing idea to use neural networks and “deep learning” for weather forecasting instead. We successfully test the possibility of using deep learning for weather forecasting by considering climate models as simplified versions of reality. Our work therefore is a step towards potentially using deep learning to replace or accompany current weather forecasting models.
Davide Faranda, Gabriele Messori, M. Carmen Alvarez-Castro, and Pascal Yiou
Nonlin. Processes Geophys., 24, 713–725, https://doi.org/10.5194/npg-24-713-2017, https://doi.org/10.5194/npg-24-713-2017, 2017
Short summary
Short summary
We study the dynamical properties of the Northern Hemisphere atmospheric circulation by analysing the sea-level pressure, 2 m temperature, and precipitation frequency field over the period 1948–2013. The metrics are linked to the predictability and the persistence of the atmospheric flows. We study the dependence on the seasonal cycle and the fields corresponding to maxima and minima of the dynamical indicators.
F. S. R. Pausata, M. Gaetani, G. Messori, S. Kloster, and F. J. Dentener
Atmos. Chem. Phys., 15, 1725–1743, https://doi.org/10.5194/acp-15-1725-2015, https://doi.org/10.5194/acp-15-1725-2015, 2015
Short summary
Short summary
our study suggests that future aerosol abatement may be the primary driver of increased blocking events over the western Mediterranean. This modification of the atmospheric circulation over the Euro-Atlantic sector leads to more stagnant weather conditions that favour air pollutant accumulation especially in the western Mediterranean sector. Changes in atmospheric circulation should therefore be included in future air pollution mitigation assessments.
Related subject area
Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Simulation
Comparative study of strongly and weakly coupled data assimilation with a global land–atmosphere coupled model
Reducing manipulations in a control simulation experiment based on instability vectors with the Lorenz-63 model
Control simulation experiments of extreme events with the Lorenz-96 model
A range of outcomes: the combined effects of internal variability and anthropogenic forcing on regional climate trends over Europe
Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis
Control simulation experiment with Lorenz's butterfly attractor
Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction
A study of capturing Atlantic meridional overturning circulation (AMOC) regime transition through observation-constrained model parameters
Fast hybrid tempered ensemble transform filter formulation for Bayesian elliptical problems via Sinkhorn approximation
Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales
Data-driven versus self-similar parameterizations for stochastic advection by Lie transport and location uncertainty
Kenta Kurosawa, Shunji Kotsuki, and Takemasa Miyoshi
Nonlin. Processes Geophys., 30, 457–479, https://doi.org/10.5194/npg-30-457-2023, https://doi.org/10.5194/npg-30-457-2023, 2023
Short summary
Short summary
This study aimed to enhance weather and hydrological forecasts by integrating soil moisture data into a global weather model. By assimilating atmospheric observations and soil moisture data, the accuracy of forecasts was improved, and certain biases were reduced. The method was found to be particularly beneficial in areas like the Sahel and equatorial Africa, where precipitation patterns vary seasonally. This new approach has the potential to improve the precision of weather predictions.
Mao Ouyang, Keita Tokuda, and Shunji Kotsuki
Nonlin. Processes Geophys., 30, 183–193, https://doi.org/10.5194/npg-30-183-2023, https://doi.org/10.5194/npg-30-183-2023, 2023
Short summary
Short summary
This research found that weather control would change the chaotic behavior of an atmospheric model. We proposed to introduce chaos theory in the weather control. Experimental results demonstrated that the proposed approach reduced the manipulations, including the control times and magnitudes, which throw light on the weather control in a real atmospheric model.
Qiwen Sun, Takemasa Miyoshi, and Serge Richard
Nonlin. Processes Geophys., 30, 117–128, https://doi.org/10.5194/npg-30-117-2023, https://doi.org/10.5194/npg-30-117-2023, 2023
Short summary
Short summary
This paper is a follow-up of a work by Miyoshi and Sun which was published in NPG Letters in 2022. The control simulation experiment is applied to the Lorenz-96 model for avoiding extreme events. The results show that extreme events of this partially and imperfectly observed chaotic system can be avoided by applying pre-designed small perturbations. These investigations may be extended to more realistic numerical weather prediction models.
Clara Deser and Adam S. Phillips
Nonlin. Processes Geophys., 30, 63–84, https://doi.org/10.5194/npg-30-63-2023, https://doi.org/10.5194/npg-30-63-2023, 2023
Short summary
Short summary
Past and future climate change at regional scales is a result of both human influences and natural (internal) variability. Here, we provide an overview of recent advances in climate modeling and physical understanding that has led to new insights into their respective roles, illustrated with original results for the European climate. Our findings highlight the confounding role of internal variability in attribution, climate model evaluation, and accuracy of future projections.
Dikraa Khedhaouiria, Stéphane Bélair, Vincent Fortin, Guy Roy, and Franck Lespinas
Nonlin. Processes Geophys., 29, 329–344, https://doi.org/10.5194/npg-29-329-2022, https://doi.org/10.5194/npg-29-329-2022, 2022
Short summary
Short summary
This study introduces a well-known use of hybrid methods in data assimilation (DA) algorithms that has not yet been explored for precipitation analyses. Our approach combined an ensemble-based DA approach with an existing deterministically based DA. Both DA scheme families have desirable aspects that can be leveraged if combined. The DA hybrid method showed better precipitation analyses in regions with a low rate of assimilated surface observations, which is typically the case in winter.
Takemasa Miyoshi and Qiwen Sun
Nonlin. Processes Geophys., 29, 133–139, https://doi.org/10.5194/npg-29-133-2022, https://doi.org/10.5194/npg-29-133-2022, 2022
Short summary
Short summary
The weather is chaotic and hard to predict, but the chaos implies an effective control where a small control signal grows rapidly to make a big difference. This study proposes a control simulation experiment where we apply a small signal to control
naturein a computational simulation. Idealized experiments with a low-order chaotic system show successful results by small control signals of only 3 % of the observation error. This is the first step toward realistic weather simulations.
Juan Ruiz, Guo-Yuan Lien, Keiichi Kondo, Shigenori Otsuka, and Takemasa Miyoshi
Nonlin. Processes Geophys., 28, 615–626, https://doi.org/10.5194/npg-28-615-2021, https://doi.org/10.5194/npg-28-615-2021, 2021
Short summary
Short summary
Effective use of observations with numerical weather prediction models, also known as data assimilation, is a key part of weather forecasting systems. For precise prediction at the scales of thunderstorms, fast nonlinear processes pose a grand challenge because most data assimilation systems are based on linear processes and normal distribution errors. We investigate how, every 30 s, weather radar observations can help reduce the effect of nonlinear processes and nonnormal distributions.
Zhao Liu, Shaoqing Zhang, Yang Shen, Yuping Guan, and Xiong Deng
Nonlin. Processes Geophys., 28, 481–500, https://doi.org/10.5194/npg-28-481-2021, https://doi.org/10.5194/npg-28-481-2021, 2021
Short summary
Short summary
A general methodology is introduced to capture regime transitions of the Atlantic meridional overturning circulation (AMOC). The assimilation models with different parameters simulate different paths for the AMOC to switch between equilibrium states. Constraining model parameters with observations can significantly mitigate the model deviations, thus capturing AMOC regime transitions. This simple model study serves as a guideline for improving coupled general circulation models.
Sangeetika Ruchi, Svetlana Dubinkina, and Jana de Wiljes
Nonlin. Processes Geophys., 28, 23–41, https://doi.org/10.5194/npg-28-23-2021, https://doi.org/10.5194/npg-28-23-2021, 2021
Short summary
Short summary
To infer information of an unknown quantity that helps to understand an associated system better and to predict future outcomes, observations and a physical model that connects the data points to the unknown parameter are typically used as information sources. Yet this problem is often very challenging due to the fact that the unknown is generally high dimensional, the data are sparse and the model can be non-linear. We propose a novel approach to address these challenges.
Michiel Van Ginderachter, Daan Degrauwe, Stéphane Vannitsem, and Piet Termonia
Nonlin. Processes Geophys., 27, 187–207, https://doi.org/10.5194/npg-27-187-2020, https://doi.org/10.5194/npg-27-187-2020, 2020
Short summary
Short summary
A generic methodology is developed to estimate the model error and simulate the model uncertainty related to a specific physical process. The method estimates the model error by comparing two different representations of the physical process in otherwise identical models. The found model error can then be used to perturb the model and simulate the model uncertainty. When applying this methodology to deep convection an improvement in the probabilistic skill of the ensemble forecast is found.
Valentin Resseguier, Wei Pan, and Baylor Fox-Kemper
Nonlin. Processes Geophys., 27, 209–234, https://doi.org/10.5194/npg-27-209-2020, https://doi.org/10.5194/npg-27-209-2020, 2020
Short summary
Short summary
Geophysical flows span a broader range of temporal and spatial scales than can be resolved numerically. One way to alleviate the ensuing numerical errors is to combine simulations with measurements, taking account of the accuracies of these two sources of information. Here we quantify the distribution of numerical simulation errors without relying on high-resolution numerical simulations. Specifically, small-scale random vortices are added to simulations while conserving energy or circulation.
Cited articles
Bakker, R., Schouten, J. C., Giles, C. L., Takens, F., and Bleek, C. M. v. d.:
Learning Chaotic Attractors by Neural Networks, Neural Comput.,
12, 2355–2383, https://doi.org/10.1162/089976600300014971, 2000. a, b
Bau, D., Zhu, J.-Y., Strobelt, H., Bolei, Z., Tenenbaum, J. B., Freeman, W. T.,
and Torralba, A.: GAN Dissection: Visualizing and Understanding Generative
Adversarial Networks, in: Proceedings of the International Conference on
Learning Representations (ICLR), 2019. a
Dueben, P. D. and Bauer, P.: Challenges and design choices for global weather and climate models based on machine learning, Geosci. Model Dev., 11, 3999–4009, https://doi.org/10.5194/gmd-11-3999-2018, 2018. a, b, c
Faranda, D., Messori, G., and Yiou, P.: Dynamical proxies of North Atlantic
predictability and extremes, Sci. Rep., 7, 41278,
https://doi.org/10.1038/srep41278, 2017. a
Hardt, M., Recht, B., and Singer, Y.: Train faster, generalize better:
Stability of stochastic gradient descent, arXiv preprint arXiv:1509.01240,
2015. a
Hindmarsh, A. C.: ODEPACK, a systematized collection of ODE solvers, Scientific computing, 55–64, 1983. a
Hochreiter, S. and Schmidhuber, J.: Simplifying neural nets by discovering flat
minima, in: Advances in neural information processing systems, edited by: Tesauro, G., Touretzky, D. S., and Leen, T. K.,
MIT Press, Cambridge, 529–536,
1995. a
Hornik, K.: Approximation capabilities of multilayer feedforward networks,
Neural Networks, 4, 251–257, https://doi.org/10.1016/0893-6080(91)90009-T,
1991. a
Jones, E., Oliphant, T., Peterson, P., et al.: SciPy: Open source scientific
tools for Python, available at: http://www.scipy.org/ (last access:
12 September 2019), 2001. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, CoRR,
abs/1412.6980, arXiv:1412.6980 , 2015. a
Krasnopolsky, V. M. and Fox-Rabinovitz, M. S.: Complex hybrid models combining
deterministic and machine learning components for numerical climate modeling
and weather prediction, Neural Networks, 19, 122–134,
https://doi.org/10.1016/j.neunet.2006.01.002,
2006. a
Krasnopolsky, V. M., Fox-Rabinovitz, M. S., and Belochitski, A. A.: Using
ensemble of neural networks to learn stochastic convection parameterizations
for climate and numerical weather prediction models from data simulated by a
cloud resolving model, Adv. Art. Neural Syst., 2013, 485913, https://doi.org/10.1155/2013/485913, 2013. a
Lu, Z., Hunt, B. R., and Ott, E.: Attractor reconstruction by machine learning,
Chaos: An Interdisciplinary Journal of Nonlinear Science, 28, 061104, https://doi.org/10.1063/1.5039508, 2018. a
Novak, R., Bahri, Y., Abolafia, D. A., Pennington, J., and Sohl-Dickstein, J.:
Sensitivity and generalization in neural networks: an empirical study, arXiv
preprint arXiv:1802.08760, 2018. a
Pasini, A. and Pelino, V.: Can we estimate atmospheric predictability by
performance of neural network forecasting? the toy case studies of unforced
and forced lorenz models, in: CIMSA. 2005 IEEE International Conference on
Computational Intelligence for Measurement Systems and Applications, 2005,
69–74, https://doi.org/10.1109/CIMSA.2005.1522829, 2005. a
Rasp, S. and Lerch, S.: Neural Networks for Postprocessing Ensemble
Weather Forecasts, Mon. Weather Rev., 146, 3885–3900,
https://doi.org/10.1175/MWR-D-18-0187.1, 2018. a, b
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid
processes in climate models, P. Natl. Acad. Sci. USA, 115,
201810286, https://doi.org/10.1073/pnas.1810286115,
2018. a, b
Scher, S.: Toward Data-Driven Weather and Climate Forecasting:
Approximating a Simple General Circulation Model With Deep
Learning, Geophys. Res. Lett., 45, 12616–12622, https://doi.org/10.1029/2018GL080704,
2018. a, b, c
Scher, S. and Messori, G.: Predicting weather forecast uncertainty with machine
learning, Q. J. Roy. Meteorol. Soc., 144,
2830–2841, https://doi.org/10.1002/qj.3410,
2018. a, b
Scher, S.: Code for “Generalization properties of feed-forward neural networks trained on Lorenz systems”, Zenodo, https://doi.org/10.5281/zenodo.3461683, 2019b.
Schevenhoven, F. J. and Selten, F. M.: An efficient training scheme for supermodels, Earth Syst. Dynam., 8, 429–438, https://doi.org/10.5194/esd-8-429-2017, 2017. a
Sugiyama, M. and Kawanabe, M.: Machine learning in non-stationary environments:
Introduction to covariate shift adaptation, MIT press, Cambridge, 2012. a
Vlachas, P. R., Byeon, W., Wan, Z. Y., Sapsis, T. P., and Koumoutsakos, P.:
Data-driven forecasting of high-dimensional chaotic systems with long
short-term memory networks, Proc. R. Soc. A, 474, 20170844,
https://doi.org/10.1098/rspa.2017.0844, 2018. a, b, c
Watson, P. A. G.: Applying Machine Learning to Improve Simulations of a Chaotic
Dynamical System Using Empirical Error Correction, J. Adv.
Model. Earth Sys., 11, 1402–1417, https://doi.org/10.1029/2018MS001597,
2019.
a, b
Weyn, J. A., Durran, D. R., and Caruana, R.: Can Machines Learn to Predict
Weather? Using Deep Learning to Predict Gridded 500-hPa Geopotential Height
From Historical Weather Data, J. Adv. Model. Earth Sys., 11, 2680–2693,
2019. a
Wu, L., Zhu, Z., and Weinan, E.: Towards understanding generalization of deep learning:
Perspective of loss landscapes, arXiv preprint arXiv:1706.10239, 2017. a
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O.: Understanding
deep learning requires rethinking generalization, arXiv preprint
arXiv:1611.03530, 2016. a
Short summary
Neural networks are a technique that is widely used to predict the time evolution of physical systems. For this the past evolution of the system is shown to the neural network – it is
trained– and then can be used to predict the evolution in the future. We show some limitations in this approach for certain systems that are important to consider when using neural networks for climate- and weather-related applications.
Neural networks are a technique that is widely used to predict the time evolution of physical...
