Abstract. Stationarity is a critical assumption in the eddy-covariance method that is widely used to calculate turbulent fluxes. Many methods have been proposed to diagnose non-stationarity attributed to external non-turbulent flows. In this paper, we focus on intrinsic non-stationarity (IN) attributed to turbulence randomness. The detrended fluctuation analysis is used to quantify IN of CO2 turbulent fluxes in the downtown of Beijing. Results show that the IN is common in CO2 turbulent fluxes and is a small-scale phenomenon related to the inertial sub-range turbulence. The small-scale IN of CO2 turbulent fluxes can be simulated by the Ornstein–Uhlenbeck (OU) process as a first approximation. Based on the simulation results, we find that the flux-averaging time should be greater than 27 s to avoid the effects of IN. Besides, the non-stationarity diagnosis methods that do not take into account IN would possibly make a wrong diagnosis with some parameters.
{"title":"Characteristics of intrinsic non-stationarity and its effect on eddy-covariance measurements of CO<sub>2</sub> fluxes","authors":"Lei Liu, Yu Shi, F. Hu","doi":"10.5194/npg-29-123-2022","DOIUrl":"https://doi.org/10.5194/npg-29-123-2022","url":null,"abstract":"Abstract. Stationarity is a critical assumption in the eddy-covariance method that is widely used to calculate turbulent fluxes. Many methods have been proposed to diagnose non-stationarity attributed to external non-turbulent flows. In this paper, we focus on intrinsic non-stationarity (IN) attributed to turbulence randomness. The detrended fluctuation analysis is used to quantify IN of CO2 turbulent fluxes in the downtown of Beijing. Results show that the IN is common in CO2 turbulent fluxes and is a small-scale phenomenon related to the inertial sub-range turbulence. The small-scale IN of CO2 turbulent fluxes can be simulated by the Ornstein–Uhlenbeck (OU) process as a first approximation. Based on the simulation results, we find that the flux-averaging time should be greater than 27 s to avoid the effects of IN. Besides, the non-stationarity diagnosis methods that do not take into account IN would possibly make a wrong diagnosis with some parameters.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45092357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Tamang, A. Ebtehaj, P. V. van Leeuwen, Gilad Lerman, E. Foufoula‐Georgiou
Abstract. This paper presents the results of the ensemble Riemannian data assimilation for relatively high-dimensional nonlinear dynamical systems, focusing on the chaotic Lorenz-96 model and a two-layer quasi-geostrophic (QG) model of atmospheric circulation. The analysis state in this approach is inferred from a joint distribution that optimally couples the background probability distribution and the likelihood function, enabling formal treatment of systematic biases without any Gaussian assumptions. Despite the risk of the curse of dimensionality in the computation of the coupling distribution, comparisons with the classic implementation of the particle filter and the stochastic ensemble Kalman filter demonstrate that, with the same ensemble size, the presented methodology could improve the predictability of dynamical systems. In particular, under systematic errors, the root mean squared error of the analysis state can be reduced by 20 % (30 %) in the Lorenz-96 (QG) model.
{"title":"Ensemble Riemannian data assimilation: towards large-scale dynamical systems","authors":"S. Tamang, A. Ebtehaj, P. V. van Leeuwen, Gilad Lerman, E. Foufoula‐Georgiou","doi":"10.5194/npg-29-77-2022","DOIUrl":"https://doi.org/10.5194/npg-29-77-2022","url":null,"abstract":"Abstract. This paper presents the results of the ensemble Riemannian data assimilation for relatively high-dimensional nonlinear dynamical systems, focusing on the chaotic Lorenz-96 model and a two-layer quasi-geostrophic (QG) model of atmospheric circulation. The analysis state in this approach is inferred from a joint distribution that optimally couples the background probability distribution and the likelihood function, enabling formal treatment of systematic biases without any Gaussian assumptions. Despite the risk of the curse of dimensionality in the computation of the coupling distribution, comparisons with the classic implementation of the particle filter and the stochastic ensemble Kalman filter demonstrate that, with the same ensemble size, the presented methodology could improve the predictability of dynamical systems. In particular, under systematic errors, the root mean squared error of the analysis state can be reduced by 20 % (30 %) in the Lorenz-96 (QG) model.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46129449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Schachtschneider, J. Saynisch‐Wagner, V. Klemann, M. Bagge, Maik Thomas
Abstract. Glacial isostatic adjustment is largely governed by the rheologicalproperties of the Earth's mantle. Large mass redistributions in theocean–cryosphere system and the subsequent response of theviscoelastic Earth have led to dramatic sea level changes in thepast. This process is ongoing, and in order to understand and predictcurrent and future sea level changes, the knowledge of mantleproperties such as viscosity is essential. In this study, we present amethod to obtain estimates of mantle viscosities by the assimilation ofrelative sea level rates of change into a viscoelastic model of thelithosphere and mantle. We set up a particle filter with probabilisticresampling. In an identical twin experiment, we show that mantleviscosities can be recovered in a glacial isostatic adjustment modelof a simple three-layer Earth structure consisting of an elasticlithosphere and two mantle layers of different viscosity. Weinvestigate the ensemble behaviour on different parameters in the following three set-ups: (1) global observations data set since last glacial maximumwith different ensemble initialisations and observation uncertainties,(2) regional observations from Fennoscandia or Laurentide/Greenlandonly, and (3) limiting the observation period to 10 ka until thepresent. We show that the recovery is successful in all cases if thetarget parameter values are properly sampled by the initial ensembleprobability distribution. This even includes cases in which the targetviscosity values are located far in the tail of the initial ensembleprobability distribution. Experiments show that the method issuccessful if enough near-field observations are available. This makesit work best for a period after substantial deglaciation until the presentwhen the number of sea level indicators is relatively high.
{"title":"An approach for constraining mantle viscosities through assimilation of palaeo sea level data into a glacial isostatic adjustment model","authors":"R. Schachtschneider, J. Saynisch‐Wagner, V. Klemann, M. Bagge, Maik Thomas","doi":"10.5194/npg-29-53-2022","DOIUrl":"https://doi.org/10.5194/npg-29-53-2022","url":null,"abstract":"Abstract. Glacial isostatic adjustment is largely governed by the rheological\u0000properties of the Earth's mantle. Large mass redistributions in the\u0000ocean–cryosphere system and the subsequent response of the\u0000viscoelastic Earth have led to dramatic sea level changes in the\u0000past. This process is ongoing, and in order to understand and predict\u0000current and future sea level changes, the knowledge of mantle\u0000properties such as viscosity is essential. In this study, we present a\u0000method to obtain estimates of mantle viscosities by the assimilation of\u0000relative sea level rates of change into a viscoelastic model of the\u0000lithosphere and mantle. We set up a particle filter with probabilistic\u0000resampling. In an identical twin experiment, we show that mantle\u0000viscosities can be recovered in a glacial isostatic adjustment model\u0000of a simple three-layer Earth structure consisting of an elastic\u0000lithosphere and two mantle layers of different viscosity. We\u0000investigate the ensemble behaviour on different parameters in the following three set-ups: (1) global observations data set since last glacial maximum\u0000with different ensemble initialisations and observation uncertainties,\u0000(2) regional observations from Fennoscandia or Laurentide/Greenland\u0000only, and (3) limiting the observation period to 10 ka until the\u0000present. We show that the recovery is successful in all cases if the\u0000target parameter values are properly sampled by the initial ensemble\u0000probability distribution. This even includes cases in which the target\u0000viscosity values are located far in the tail of the initial ensemble\u0000probability distribution. Experiments show that the method is\u0000successful if enough near-field observations are available. This makes\u0000it work best for a period after substantial deglaciation until the present\u0000when the number of sea level indicators is relatively high.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46254183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Polzin, A. Müller, H. Rust, P. Névir, P. Koltai
Abstract. We pursue a simplified stochastic representation of smaller scale convective activity conditioned on large-scale dynamics in the atmosphere. For identifying a Bayesian model describing the relation of different scales we use a probabilistic approach by Gerber and Horenko (2017) called Direct Bayesian Model Reduction (DBMR). This is a Bayesian relation model between categorical processes (discrete states), formulated via the conditional probabilities. The convective available potential energy (CAPE) is applied as a large-scale flow variable combined with a subgrid smaller scale time series for the vertical velocity. We found a probabilistic relation of CAPE and vertical up- and downdraft for day and night. This strategy is part of a development process for parametrizations in models of atmospheric dynamics representing the effective influence of unresolved vertical motion on the large-scale flows. The direct probabilistic approach provides a basis for further research on smaller scale convective activity conditioned on other possible large-scale drivers.
{"title":"Direct Bayesian model reduction of smaller scale convective activity conditioned on large-scale dynamics","authors":"R. Polzin, A. Müller, H. Rust, P. Névir, P. Koltai","doi":"10.5194/npg-29-37-2022","DOIUrl":"https://doi.org/10.5194/npg-29-37-2022","url":null,"abstract":"Abstract. We pursue a simplified stochastic representation of smaller scale convective activity conditioned on large-scale dynamics in the atmosphere. For identifying a Bayesian model describing the relation of different scales we use a probabilistic approach by Gerber and Horenko (2017) called Direct Bayesian Model Reduction (DBMR). This is a Bayesian relation model between categorical processes (discrete states), formulated via the conditional probabilities. The convective available potential energy (CAPE) is applied as a large-scale flow variable combined with a subgrid smaller scale time series for the vertical velocity. We found a probabilistic relation of CAPE and vertical up- and downdraft for day and night. This strategy is part of a development process for parametrizations in models of atmospheric dynamics representing the effective influence of unresolved vertical motion on the large-scale flows. The direct probabilistic approach provides a basis for further research on smaller scale convective activity conditioned on other possible large-scale drivers.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47549103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Dubrulle, F. Daviaud, D. Faranda, L. Marié, B. Saint-Michel
Abstract. According to everyone's experience, predicting the weather reliably over more than 8 d seems an impossible task for our best weather agencies. At the same time, politicians and citizens are asking scientists for climate projections several decades into the future to guide economic and environmental policies, especially regarding the maximum admissible emissions of CO2. To what extent is this request scientifically admissible? In this review we will investigate this question, focusing on the topic of predictions of transitions between metastable states of the atmospheric or oceanic circulations. Two relevant examples are the switching between zonal and blocked atmospheric circulation at mid-latitudes and the alternation of El Niño and La Niña phases in the Pacific Ocean. The main issue is whether present climate models, which necessarily have a finite resolution and a smaller number of degrees of freedom than the actual terrestrial system, are able to reproduce such spontaneous or forced transitions. To do so, we will draw an analogy between climate observations and results obtained in our group on a laboratory-scale, turbulent, von Kármán flow in which spontaneous transitions between different states of the circulation take place. We will detail the analogy, investigate the nature of the transitions and the number of degrees of freedom that characterize the latter, and discuss the effect of reducing the number of degrees of freedom in such systems. We will also discuss the role of fluctuations and their origin and stress the importance of describing very small scales to capture fluctuations of correct intensity and scale.
{"title":"How many modes are needed to predict climate bifurcations? Lessons from an experiment","authors":"B. Dubrulle, F. Daviaud, D. Faranda, L. Marié, B. Saint-Michel","doi":"10.5194/npg-29-17-2022","DOIUrl":"https://doi.org/10.5194/npg-29-17-2022","url":null,"abstract":"Abstract. According to everyone's experience, predicting the weather reliably over more than 8 d seems an impossible task for our best weather agencies. At the same time, politicians and citizens are asking scientists for climate projections several decades into the future to guide economic and environmental policies, especially regarding the maximum admissible emissions of CO2. To what extent is this request scientifically admissible? In this review we will investigate this question, focusing on the topic of predictions of transitions between metastable states of the atmospheric or oceanic circulations. Two relevant examples are the switching between zonal and blocked atmospheric circulation at mid-latitudes and the alternation of El Niño and La Niña phases in the Pacific Ocean. The main issue is whether present climate models, which necessarily have a finite resolution and a smaller number of degrees of freedom than the actual terrestrial system, are able to reproduce such spontaneous or forced transitions. To do so, we will draw an analogy between climate observations and results obtained in our group on a laboratory-scale, turbulent, von Kármán flow in which spontaneous transitions between different states of the circulation take place. We will detail the analogy, investigate the nature of the transitions and the number of degrees of freedom that characterize the latter, and discuss the effect of reducing the number of degrees of freedom in such systems. We will also discuss the role of fluctuations and their origin and stress the importance of describing very small scales to capture fluctuations of correct intensity and scale.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47706510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Sea surface temperature (SST) is a key factor in understanding the greater climate of the Earth and is an important variable when making weather predictions. Methods of machine learning have become ever more present and important in data-driven science and engineering including in important areas for Earth Science. We propose here an efficient framework that allows us to make global SST forecasts by use of a coupled reservoir computer method that we have specialized to this domain allowing for template regions that accommodate irregular coastlines. Reservoir computing is an especially good method for forecasting spatiotemporally complex dynamical systems, as it is a machine learning method that despite many randomly selected weights, it is nonetheless highly accurate and easy to train. Our approach provides the benefit of a simple and computationally efficient model that is able to predict sea surface temperatures across the entire Earth’s oceans. The results are demonstrated to replicate the actual dynamics of the system over a forecasting period of several weeks.
{"title":"Predicting Sea Surface Temperatures with Coupled Reservoir Computers","authors":"Benjamin Walleshauser, E. Bollt","doi":"10.5194/npg-2022-4","DOIUrl":"https://doi.org/10.5194/npg-2022-4","url":null,"abstract":"Abstract. Sea surface temperature (SST) is a key factor in understanding the greater climate of the Earth and is an important variable when making weather predictions. Methods of machine learning have become ever more present and important in data-driven science and engineering including in important areas for Earth Science. We propose here an efficient framework that allows us to make global SST forecasts by use of a coupled reservoir computer method that we have specialized to this domain allowing for template regions that accommodate irregular coastlines. Reservoir computing is an especially good method for forecasting spatiotemporally complex dynamical systems, as it is a machine learning method that despite many randomly selected weights, it is nonetheless highly accurate and easy to train. Our approach provides the benefit of a simple and computationally efficient model that is able to predict sea surface temperatures across the entire Earth’s oceans. The results are demonstrated to replicate the actual dynamics of the system over a forecasting period of several weeks.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47451402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kolja L. Kypke, W. Langford, G. Lewis, Allan R. Willms
Abstract. A column model of the Arctic atmosphere-ocean system is developed including the nonlinear responses of surface albedo and water vapor to temperature. The atmosphere is treated as a gray gas and the flux of longwave radiation is governed by the two-stream Schwarzschild equations. Representative carbon pathways (RCPs) are used to model carbon dioxide concentrations into the future. The resulting nine-dimensional two-point boundary value problem is solved under various RCPs and the solutions analyzed. The model predicts that under the highest carbon pathway, the Arctic climate will undergo an irreversible bifurcation to a warm steady state, which would correspond to an annually ice-free situation. Under the lowest carbon pathway, corresponding to very aggressive carbon emission reductions, the model exhibits only a mild increase in Arctic temperatures. Under the two moderate carbon pathways, temperatures increase more substantially, and the system enters a region of bistability where external perturbations could possibly cause an irreversible switch to a warm, ice-free state.
{"title":"Climate Bifurcations in a Schwarzschild Equation Model of the Arctic Atmosphere","authors":"Kolja L. Kypke, W. Langford, G. Lewis, Allan R. Willms","doi":"10.5194/npg-2022-2","DOIUrl":"https://doi.org/10.5194/npg-2022-2","url":null,"abstract":"Abstract. A column model of the Arctic atmosphere-ocean system is developed including the nonlinear responses of surface albedo and water vapor to temperature. The atmosphere is treated as a gray gas and the flux of longwave radiation is governed by the two-stream Schwarzschild equations. Representative carbon pathways (RCPs) are used to model carbon dioxide concentrations into the future. The resulting nine-dimensional two-point boundary value problem is solved under various RCPs and the solutions analyzed. The model predicts that under the highest carbon pathway, the Arctic climate will undergo an irreversible bifurcation to a warm steady state, which would correspond to an annually ice-free situation. Under the lowest carbon pathway, corresponding to very aggressive carbon emission reductions, the model exhibits only a mild increase in Arctic temperatures. Under the two moderate carbon pathways, temperatures increase more substantially, and the system enters a region of bistability where external perturbations could possibly cause an irreversible switch to a warm, ice-free state.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47246579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Delage, T. Portafaix, H. Bencherif, A. Bourdier, Emma Lagracie
Abstract. Most observational data sequences in geophysics can be interpreted as resulting from the interaction of several physical processes at several time and space scales. As a consequence, measurements time series have often characteristics of non-linearity and non-stationarity and thereby exhibit strong fluctuations at different time-scales. The variability analysis of a time series consists in decomposing it into several mode of variability, each mode representing the fluctuations of the original time series at a specific time-scale. Such a decomposition enables to obtain a time-frequency representation of the original time series and turns out to be very useful to estimate the dimensionality of the underlying dynamics. Decomposition techniques very well suited to non-linear and non-stationary time series have recently been developed in the literature. Among the most widely used of these technics are the empirical mode decomposition (EMD) and the empirical wavelet transformation (EWT). The purpose of this paper is to present a new adaptive filtering method that combines the advantages of the EMD and EWT technics, while remaining close to the dynamics of the original signal made of atmospheric observations, which means reconstructing as close as possible to the original time series, while preserving its variability at different time scales.
{"title":"The Empirical Adaptive Wavelet Decomposition (EAWD): An adaptive decomposition for the variability analysis of observation time series in atmospheric science","authors":"O. Delage, T. Portafaix, H. Bencherif, A. Bourdier, Emma Lagracie","doi":"10.5194/npg-2021-37","DOIUrl":"https://doi.org/10.5194/npg-2021-37","url":null,"abstract":"Abstract. Most observational data sequences in geophysics can be interpreted as resulting from the interaction of several physical processes at several time and space scales. As a consequence, measurements time series have often characteristics of non-linearity and non-stationarity and thereby exhibit strong fluctuations at different time-scales. The variability analysis of a time series consists in decomposing it into several mode of variability, each mode representing the fluctuations of the original time series at a specific time-scale. Such a decomposition enables to obtain a time-frequency representation of the original time series and turns out to be very useful to estimate the dimensionality of the underlying dynamics. Decomposition techniques very well suited to non-linear and non-stationary time series have recently been developed in the literature. Among the most widely used of these technics are the empirical mode decomposition (EMD) and the empirical wavelet transformation (EWT). The purpose of this paper is to present a new adaptive filtering method that combines the advantages of the EMD and EWT technics, while remaining close to the dynamics of the original signal made of atmospheric observations, which means reconstructing as close as possible to the original time series, while preserving its variability at different time scales.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44185571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Sampurno, Valentin Vallaeys, Randy Ardianto, E. Hanert
Abstract. Flood forecasting based on water level modeling is an essential non-structural measure against compound flooding over the globe. With its vulnerability increased under climate change, every coastal area became urgently needs a water level model for better flood risk management. Unfortunately, for local water management agencies in developing countries building such a model is challenging due to the limited computational resources and the scarcity of observational data. Here, we attempt to solve the issue by proposing an integrated hydrodynamic and machine learning approach to predict compound flooding in those areas. As a case study, this integrated approach is implemented in Pontianak, the densest coastal urban area over the Kapuas River delta, Indonesia. Firstly, we built a hydrodynamic model to simulate several compound flooding scenarios, and the outputs are then used to train the machine learning model. To obtain a robust machine learning model, we consider three machine learning algorithms, i.e., Random Forest, Multi Linear Regression, and Support Vector Machine. The results show that this integrated scheme is successfully working. The Random Forest performs as the most accurate algorithm to predict flooding hazards in the study area, with RMSE = 0.11 m compared to SVM (RMSE = 0.18 m) and MLR (RMSE = 0.19 m). The machine-learning model with the RF algorithm can predict ten out of seventeen compound flooding events during the testing phase. Therefore, the random forest is proposed as the most appropriate algorithm to build a reliable ML model capable of assessing the compound flood hazards in the area of interest.
{"title":"Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta","authors":"J. Sampurno, Valentin Vallaeys, Randy Ardianto, E. Hanert","doi":"10.5194/npg-2021-36","DOIUrl":"https://doi.org/10.5194/npg-2021-36","url":null,"abstract":"Abstract. Flood forecasting based on water level modeling is an essential non-structural measure against compound flooding over the globe. With its vulnerability increased under climate change, every coastal area became urgently needs a water level model for better flood risk management. Unfortunately, for local water management agencies in developing countries building such a model is challenging due to the limited computational resources and the scarcity of observational data. Here, we attempt to solve the issue by proposing an integrated hydrodynamic and machine learning approach to predict compound flooding in those areas. As a case study, this integrated approach is implemented in Pontianak, the densest coastal urban area over the Kapuas River delta, Indonesia. Firstly, we built a hydrodynamic model to simulate several compound flooding scenarios, and the outputs are then used to train the machine learning model. To obtain a robust machine learning model, we consider three machine learning algorithms, i.e., Random Forest, Multi Linear Regression, and Support Vector Machine. The results show that this integrated scheme is successfully working. The Random Forest performs as the most accurate algorithm to predict flooding hazards in the study area, with RMSE = 0.11 m compared to SVM (RMSE = 0.18 m) and MLR (RMSE = 0.19 m). The machine-learning model with the RF algorithm can predict ten out of seventeen compound flooding events during the testing phase. Therefore, the random forest is proposed as the most appropriate algorithm to build a reliable ML model capable of assessing the compound flood hazards in the area of interest.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41617389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The residence time of energy in a planetary atmosphere, τ, which was recently introduced and computed for the Earth's atmosphere (Osácar et al., 2020), is here extended to the atmospheres of Venus, Mars and Titan. τ is the timescale for the energy transport across the atmosphere. In the cases ofVenus, Mars and Titan, these computations are lower bounds due to a lack of some energy data. If the analogy between τ and the solar Kelvin–Helmholtz scale is assumed, then τ would also be the time the atmosphere needs to return to equilibrium after a global thermal perturbation.
摘要能量在行星大气中的停留时间τ最近被引入并计算用于地球大气(Osácar et al.， 2020)，这里扩展到金星、火星和土卫六的大气。τ是能量在大气中传输的时间标度。在金星、火星和土卫六的情况下，由于缺乏一些能量数据，这些计算是下限。如果假设τ与太阳开尔文-亥姆霍兹尺度之间的类比，那么τ也将是大气在全球热扰动后恢复平衡所需的时间。
{"title":"Brief communication: Lower-bound estimates for residence time of energy in the atmospheres of Venus, Mars and Titan","authors":"J. Pelegrina, C. Osácar, A. Fernández-Pacheco","doi":"10.5194/npg-28-627-2021","DOIUrl":"https://doi.org/10.5194/npg-28-627-2021","url":null,"abstract":"Abstract. The residence time of energy in a planetary atmosphere, τ, which was recently introduced and computed for the Earth's atmosphere (Osácar et al., 2020), is here extended to the atmospheres of Venus, Mars and Titan. τ is the timescale for the energy transport across the atmosphere. In the cases of\u0000Venus, Mars and Titan, these computations are lower bounds due to a lack of some energy data. If the analogy between τ and the solar Kelvin–Helmholtz scale is assumed, then τ would also be the time the atmosphere needs to return to equilibrium after a global thermal perturbation.\u0000","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46718596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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