Pub Date : 2024-03-18DOI: 10.5194/egusphere-2024-552
Karlie N. Rees, Timothy J. Garrett, Thomas D. DeWitt, Corey Bois, Steven K. Krueger, Jérôme C. Riedi
Abstract. The deterministic motions of clouds and turbulence, despite their chaotic nature, nonetheless follow simple statistical power-law scalings: a fractal dimension D relates individual cloud perimeters p to measurement resolution, and turbulent fluctuations scale with separation distance through the Hurst exponent ℌ. It remains uncertain whether atmospheric turbulence is best characterized by split isotropy that is three-dimensional with ℌ = 1/3 at small scales and two-dimensional with ℌ = 1 at large scales, or by wide-range anisotropic scaling with an intermediate value of ℌ. Here, we introduce an “ensemble fractal dimension” De – analogous to D – that relates the total cloud perimeter per domain area 𝒫 as seen from space to measurement resolution, and show theoretically how turbulent dimensionality and cloud edge geometry are linked through ℌ =De − 1. Observationally, by progressively coarsening the resolution of cloud mask arrays from various global satellite platforms and a numerical simulation of a tropical domain we find the scaling De ~ 5/3, or ℌ ~ 2/3, a value nearly consistent with a previously proposed “23/9D” anisotropic turbulent scaling. Remarkably, the same scaling links two “limiting case” estimates of 𝒫 evaluated at the planetary scale and the Kolmogorov microscale, as separated by 11 orders of magnitude, suggesting that cloud and turbulent behaviors are constrained by basic planetary parameters.
{"title":"A global analysis of the fractal properties of clouds revealing anisotropy of turbulence across scales","authors":"Karlie N. Rees, Timothy J. Garrett, Thomas D. DeWitt, Corey Bois, Steven K. Krueger, Jérôme C. Riedi","doi":"10.5194/egusphere-2024-552","DOIUrl":"https://doi.org/10.5194/egusphere-2024-552","url":null,"abstract":"<strong>Abstract.</strong> The deterministic motions of clouds and turbulence, despite their chaotic nature, nonetheless follow simple statistical power-law scalings: a fractal dimension <em>D</em> relates individual cloud perimeters <em>p</em> to measurement resolution, and turbulent fluctuations scale with separation distance through the Hurst exponent ℌ. It remains uncertain whether atmospheric turbulence is best characterized by split isotropy that is three-dimensional with ℌ = 1/3 at small scales and two-dimensional with ℌ = 1 at large scales, or by wide-range anisotropic scaling with an intermediate value of ℌ. Here, we introduce an “ensemble fractal dimension” <em>D<sub>e</sub></em> – analogous to <em>D</em> – that relates the total cloud perimeter per domain area 𝒫 as seen from space to measurement resolution, and show theoretically how turbulent dimensionality and cloud edge geometry are linked through ℌ =<em>D<sub>e </sub></em> − 1. Observationally, by progressively coarsening the resolution of cloud mask arrays from various global satellite platforms and a numerical simulation of a tropical domain we find the scaling <em>D<sub>e </sub></em> ~ 5/3, or ℌ ~ 2/3, a value nearly consistent with a previously proposed “23/9D” anisotropic turbulent scaling. Remarkably, the same scaling links two “limiting case” estimates of 𝒫 evaluated at the planetary scale and the Kolmogorov microscale, as separated by 11 orders of magnitude, suggesting that cloud and turbulent behaviors are constrained by basic planetary parameters.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"26 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140151570","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. Due to their role as primary producers, phytoplankton are essential to the productivity of estuarine ecosystems. However, it is important to understand how these nearly passive organisms are able to persist within estuaries when river inflow results in a net outflow to the ocean. Estuaries also represent challenging habitats due to a strong salinity gradient. Little is known about how phytoplankton are able to be retained within estuaries. We present a new individual-based Lagrangian model of the Elbe estuary which examines possible retention mechanisms for phytoplankton. Specifically, we investigated how reproduction, sinking and rising, and diel vertical migration may allow populations to persist within the estuary. We find that vertical migration, especially rising, favors retention, while fast sinking does not. We further provide first estimates of outwashing losses. Our simulations illustrate that riverbanks and tidal flats are essential for the long-term survival of phytoplankton populations, as they provide refuges from strong downstream currents. These results contribute to the understanding needed to advance the ecosystem-based management of estuaries.
{"title":"Phytoplankton retention mechanisms in estuaries: a case study of the Elbe estuary","authors":"Laurin Steidle, Ross Vennell","doi":"10.5194/npg-31-151-2024","DOIUrl":"https://doi.org/10.5194/npg-31-151-2024","url":null,"abstract":"Abstract. Due to their role as primary producers, phytoplankton are essential to the productivity of estuarine ecosystems. However, it is important to understand how these nearly passive organisms are able to persist within estuaries when river inflow results in a net outflow to the ocean. Estuaries also represent challenging habitats due to a strong salinity gradient. Little is known about how phytoplankton are able to be retained within estuaries. We present a new individual-based Lagrangian model of the Elbe estuary which examines possible retention mechanisms for phytoplankton. Specifically, we investigated how reproduction, sinking and rising, and diel vertical migration may allow populations to persist within the estuary. We find that vertical migration, especially rising, favors retention, while fast sinking does not. We further provide first estimates of outwashing losses. Our simulations illustrate that riverbanks and tidal flats are essential for the long-term survival of phytoplankton populations, as they provide refuges from strong downstream currents. These results contribute to the understanding needed to advance the ecosystem-based management of estuaries.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"39 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140115129","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}
Gianmarco Del Sarto, Jochen Bröcker, Franco Flandoli, Tobias Kuna
Abstract. A one-dimensional climate energy balance model (1D EBM) is a simplified climate model for the zonally averaged global temperature profile, based on the Earth's energy budget. We examine a class of 1D EBMs which emerges as the parabolic equation corresponding to the Euler–Lagrange equations of an associated variational problem, covering spatially inhomogeneous models such as with latitude-dependent albedo. Sufficient conditions are provided for the existence of at least three steady-state solutions in the form of two local minima and one saddle, that is, of coexisting “cold”, “warm” and unstable “intermediate” climates. We also give an interpretation of minimizers as “typical” or “likely” solutions of time-dependent and stochastic 1D EBMs. We then examine connections between the value function, which represents the minimum value (across all temperature profiles) of the objective functional, regarded as a function of greenhouse gas concentration, and the global mean temperature (also as a function of greenhouse gas concentration, i.e. the bifurcation diagram). Specifically, the global mean temperature varies continuously as long as there is a unique minimizing temperature profile, but coexisting minimizers must have different global mean temperatures. Furthermore, global mean temperature is non-decreasing with respect to greenhouse gas concentration, and its jumps must necessarily be upward. Applicability of our findings to more general spatially heterogeneous reaction–diffusion models is also discussed, as are physical interpretations of our results.
{"title":"Variational techniques for a one-dimensional energy balance model","authors":"Gianmarco Del Sarto, Jochen Bröcker, Franco Flandoli, Tobias Kuna","doi":"10.5194/npg-31-137-2024","DOIUrl":"https://doi.org/10.5194/npg-31-137-2024","url":null,"abstract":"Abstract. A one-dimensional climate energy balance model (1D EBM) is a simplified climate model for the zonally averaged global temperature profile, based on the Earth's energy budget. We examine a class of 1D EBMs which emerges as the parabolic equation corresponding to the Euler–Lagrange equations of an associated variational problem, covering spatially inhomogeneous models such as with latitude-dependent albedo. Sufficient conditions are provided for the existence of at least three steady-state solutions in the form of two local minima and one saddle, that is, of coexisting “cold”, “warm” and unstable “intermediate” climates. We also give an interpretation of minimizers as “typical” or “likely” solutions of time-dependent and stochastic 1D EBMs. We then examine connections between the value function, which represents the minimum value (across all temperature profiles) of the objective functional, regarded as a function of greenhouse gas concentration, and the global mean temperature (also as a function of greenhouse gas concentration, i.e. the bifurcation diagram). Specifically, the global mean temperature varies continuously as long as there is a unique minimizing temperature profile, but coexisting minimizers must have different global mean temperatures. Furthermore, global mean temperature is non-decreasing with respect to greenhouse gas concentration, and its jumps must necessarily be upward. Applicability of our findings to more general spatially heterogeneous reaction–diffusion models is also discussed, as are physical interpretations of our results.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"35 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140072403","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}
Kévin Robache, François G. Schmitt, Yongxiang Huang
Abstract. In this study the multi-scale dynamics of 38 oceanic and atmospheric pCO2 time series from fixed Eulerian buoys recorded with three-hour resolution are considered. The difference between these time series, the sea surface temperature and the sea surface salinity data were also studied. These series possess multi-scale turbulent-like fluctuations and display scaling properties from three hours to the annual scale. Scaling exponents are estimated through Fourier analysis and their average quantities considered globally for all parameters, as well as for different ecosystems (e.g. coastal shelf, coral reefs, open ocean). Sea surface temperature is the only parameter for which a spectral slope close to 5/3 is found, corresponding to a passive scalar in homogeneous and isotropic turbulence. The other parameters had smaller spectral slopes, from 1.18 to 1.35. By using empirical mode decomposition of the time series, together with generalized Hilbert spectral analysis, the intermittency of the time series was considered in the multifractal framework. Concave moment functions were estimated and Hurst index and intermittency parameters estimated in the framework of a lognormal multifractal fit. It is the first time that atmospheric and oceanic pCO2 and their difference ∆pCO2 are studied using such intermittent turbulence framework. The ∆pCO2 time series was shown to possess power-law scaling with an exponent of β = 1.32 ± 0.2.
{"title":"Scaling and intermittent properties of oceanic and atmospheric pCO2 time series and their difference","authors":"Kévin Robache, François G. Schmitt, Yongxiang Huang","doi":"10.5194/npg-2024-7","DOIUrl":"https://doi.org/10.5194/npg-2024-7","url":null,"abstract":"<strong>Abstract.</strong> In this study the multi-scale dynamics of 38 oceanic and atmospheric <em>p</em>CO<sub>2</sub> time series from fixed Eulerian buoys recorded with three-hour resolution are considered. The difference between these time series, the sea surface temperature and the sea surface salinity data were also studied. These series possess multi-scale turbulent-like fluctuations and display scaling properties from three hours to the annual scale. Scaling exponents are estimated through Fourier analysis and their average quantities considered globally for all parameters, as well as for different ecosystems (e.g. coastal shelf, coral reefs, open ocean). Sea surface temperature is the only parameter for which a spectral slope close to 5/3 is found, corresponding to a passive scalar in homogeneous and isotropic turbulence. The other parameters had smaller spectral slopes, from 1.18 to 1.35. By using empirical mode decomposition of the time series, together with generalized Hilbert spectral analysis, the intermittency of the time series was considered in the multifractal framework. Concave moment functions were estimated and Hurst index and intermittency parameters estimated in the framework of a lognormal multifractal fit. It is the first time that atmospheric and oceanic <em>p</em>CO<sub>2</sub> and their difference ∆<em>p</em>CO<sub>2</sub> are studied using such intermittent turbulence framework. The ∆<em>p</em>CO<sub>2</sub> time series was shown to possess power-law scaling with an exponent of <em>β</em> = 1.32 ± 0.2.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"10 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140045103","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. Correlation does not necessarily imply causation, and this is why causal methods have been developed to try to disentangle true causal links from spurious relationships. In our study, we use two causal methods, namely, the Liang–Kleeman information flow (LKIF) and the Peter and Clark momentary conditional independence (PCMCI) algorithm, and we apply them to four different artificial models of increasing complexity and one real-world case study based on climate indices in the Atlantic and Pacific regions. We show that both methods are superior to the classical correlation analysis, especially in removing spurious links. LKIF and PCMCI display some strengths and weaknesses for the three simplest models, with LKIF performing better with a smaller number of variables and with PCMCI being best with a larger number of variables. Detecting causal links from the fourth model is more challenging as the system is nonlinear and chaotic. For the real-world case study with climate indices, both methods present some similarities and differences at monthly timescale. One of the key differences is that LKIF identifies the Arctic Oscillation (AO) as the largest driver, while the El Niño–Southern Oscillation (ENSO) is the main influencing variable for PCMCI. More research is needed to confirm these links, in particular including nonlinear causal methods.
{"title":"A comparison of two causal methods in the context of climate analyses","authors":"David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, Stéphane Vannitsem","doi":"10.5194/npg-31-115-2024","DOIUrl":"https://doi.org/10.5194/npg-31-115-2024","url":null,"abstract":"Abstract. Correlation does not necessarily imply causation, and this is why causal methods have been developed to try to disentangle true causal links from spurious relationships. In our study, we use two causal methods, namely, the Liang–Kleeman information flow (LKIF) and the Peter and Clark momentary conditional independence (PCMCI) algorithm, and we apply them to four different artificial models of increasing complexity and one real-world case study based on climate indices in the Atlantic and Pacific regions. We show that both methods are superior to the classical correlation analysis, especially in removing spurious links. LKIF and PCMCI display some strengths and weaknesses for the three simplest models, with LKIF performing better with a smaller number of variables and with PCMCI being best with a larger number of variables. Detecting causal links from the fourth model is more challenging as the system is nonlinear and chaotic. For the real-world case study with climate indices, both methods present some similarities and differences at monthly timescale. One of the key differences is that LKIF identifies the Arctic Oscillation (AO) as the largest driver, while the El Niño–Southern Oscillation (ENSO) is the main influencing variable for PCMCI. More research is needed to confirm these links, in particular including nonlinear causal methods.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"51 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139978001","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. Empirical modal decomposition (EMD) is an efficient tool for extracting a signal from stationary or non-stationary time series and is enhanced in stability and robustness by ensemble empirical mode decomposition (EEMD). Adaptive EEMD further improves computational efficiency through adaptability in the white noise amplitude and set average number. However, its effectiveness in the periodic signal extraction in Global Navigation Satellite System (GNSS) coordinate time series regarding the inevitable missing data and offset issues has not been comprehensively validated. In order to thoroughly investigate their impacts, we simulated 5 years of daily time series data with different missing data percentages or a different number of offsets and conducted them 300 times for each simulation. The results show that high accuracy could reach the overall random missing rate below 15 % and avoid consecutive misses exceeding 30 d. Meanwhile, offsets should be corrected in advance regardless of their magnitudes. The analysis of the vertical components of 13 stations within the Australian Global Sea Level Observing System (GLOSS) monitoring network demonstrates the advantage of adaptive EEMD in revealing the time-varying characteristics of periodic signals. From the perspectives of correlation coefficients (CCs), root mean square error (RMSE), power spectral density indices (κ) and signal-to-noise ratio (SNR), the means for adaptive EEMD are 0.36, 0.81, −0.18 and 0.48, respectively, while for least squares (LS), they are 0.27, 0.86, −0.50 and 0.23. Meanwhile, a significance test of the residuals further substantiates the effectiveness in periodic signal extraction, which shows that there is no annual signal remaining. Also, the longer the series, the higher the accuracy of the reasonable extracted periodic signal concluded via the significance test. Moreover, driving factors are more effectively facilitated by the time-varying periodic characteristics compared with the constant periodic signal derived by LS. Overall, the application of adaptive EEMD could achieve high accuracy in analyzing GNSS time series, but it should be based on properly dealing with missing data and offsets.
{"title":"Extraction of periodic signals in Global Navigation Satellite System (GNSS) vertical coordinate time series using the adaptive ensemble empirical modal decomposition method","authors":"Weiwei Li, Jing Guo","doi":"10.5194/npg-31-99-2024","DOIUrl":"https://doi.org/10.5194/npg-31-99-2024","url":null,"abstract":"Abstract. Empirical modal decomposition (EMD) is an efficient tool for extracting a signal from stationary or non-stationary time series and is enhanced in stability and robustness by ensemble empirical mode decomposition (EEMD). Adaptive EEMD further improves computational efficiency through adaptability in the white noise amplitude and set average number. However, its effectiveness in the periodic signal extraction in Global Navigation Satellite System (GNSS) coordinate time series regarding the inevitable missing data and offset issues has not been comprehensively validated. In order to thoroughly investigate their impacts, we simulated 5 years of daily time series data with different missing data percentages or a different number of offsets and conducted them 300 times for each simulation. The results show that high accuracy could reach the overall random missing rate below 15 % and avoid consecutive misses exceeding 30 d. Meanwhile, offsets should be corrected in advance regardless of their magnitudes. The analysis of the vertical components of 13 stations within the Australian Global Sea Level Observing System (GLOSS) monitoring network demonstrates the advantage of adaptive EEMD in revealing the time-varying characteristics of periodic signals. From the perspectives of correlation coefficients (CCs), root mean square error (RMSE), power spectral density indices (κ) and signal-to-noise ratio (SNR), the means for adaptive EEMD are 0.36, 0.81, −0.18 and 0.48, respectively, while for least squares (LS), they are 0.27, 0.86, −0.50 and 0.23. Meanwhile, a significance test of the residuals further substantiates the effectiveness in periodic signal extraction, which shows that there is no annual signal remaining. Also, the longer the series, the higher the accuracy of the reasonable extracted periodic signal concluded via the significance test. Moreover, driving factors are more effectively facilitated by the time-varying periodic characteristics compared with the constant periodic signal derived by LS. Overall, the application of adaptive EEMD could achieve high accuracy in analyzing GNSS time series, but it should be based on properly dealing with missing data and offsets.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"10 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139920631","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. Assessing wind fields at a local scale in mountainous terrain has long been a scientific challenge, partly because of the complex interaction between large-scale flows and local topography. Traditionally, the operational applications that require high-resolution wind forcings rely on downscaled outputs of numerical weather prediction systems. Downscaling models either proceed from a function that links large-scale wind fields to local observations (hence including a corrective step) or use operations that account for local-scale processes, through statistics or dynamical simulations and without prior knowledge of large-scale modeling errors. This work presents a strategy to first correct and then downscale the wind fields of the numerical weather prediction model AROME (Application of Research to Operations at Mesoscale) operating at 1300 m grid spacing by using a modular architecture composed of two artificial neural networks and the DEVINE downscaling model. We show that our method is able to first correct the wind direction and speed from the large-scale model (1300 m) and then accurately downscale it to a local scale (30 m) by using the DEVINE downscaling model. The innovative aspect of our method lies in its optimization scheme that accounts for the downscaling step in the computations of the corrections of the coarse-scale wind fields. This modular architecture yields competitive results without suppressing the versatility of the DEVINE downscaling model, which remains unbounded to any wind observations.
摘要长期以来，评估山区地形局部尺度的风场一直是一项科学挑战，部分原因是大尺度气流与局部地形之间存在复杂的相互作用。传统上，需要高分辨率风馈源的业务应用依赖于数值天气预报系统的降尺度输出。降尺度模型要么从将大尺度风场与本地观测数据联系起来的功能出发（因此包括一个校正步骤），要么通过统计或动态模拟，在事先不了解大尺度建模误差的情况下，使用考虑本地尺度过程的操作。本研究提出了一种策略，通过使用由两个人工神经网络和 DEVINE 降尺度模型组成的模块架构，首先校正在 1300 米网格间距下运行的数值天气预报模式 AROME（中尺度作业研究应用）的风场，然后再对其进行降尺度处理。我们的研究表明，我们的方法能够首先修正大尺度模型（1300 米）的风向和风速，然后利用 DEVINE 降尺度模型将其精确降尺度到局部尺度（30 米）。我们方法的创新之处在于其优化方案，在计算粗尺度风场修正时考虑了降尺度步骤。这种模块化结构在不影响 DEVINE 降尺度模型多功能性的情况下产生了有竞争力的结果，因为 DEVINE 降尺度模型不受任何风观测数据的限制。
{"title":"A two-fold deep-learning strategy to correct and downscale winds over mountains","authors":"Louis Le Toumelin, Isabelle Gouttevin, Clovis Galiez, Nora Helbig","doi":"10.5194/npg-31-75-2024","DOIUrl":"https://doi.org/10.5194/npg-31-75-2024","url":null,"abstract":"Abstract. Assessing wind fields at a local scale in mountainous terrain has long been a scientific challenge, partly because of the complex interaction between large-scale flows and local topography. Traditionally, the operational applications that require high-resolution wind forcings rely on downscaled outputs of numerical weather prediction systems. Downscaling models either proceed from a function that links large-scale wind fields to local observations (hence including a corrective step) or use operations that account for local-scale processes, through statistics or dynamical simulations and without prior knowledge of large-scale modeling errors. This work presents a strategy to first correct and then downscale the wind fields of the numerical weather prediction model AROME (Application of Research to Operations at Mesoscale) operating at 1300 m grid spacing by using a modular architecture composed of two artificial neural networks and the DEVINE downscaling model. We show that our method is able to first correct the wind direction and speed from the large-scale model (1300 m) and then accurately downscale it to a local scale (30 m) by using the DEVINE downscaling model. The innovative aspect of our method lies in its optimization scheme that accounts for the downscaling step in the computations of the corrections of the coarse-scale wind fields. This modular architecture yields competitive results without suppressing the versatility of the DEVINE downscaling model, which remains unbounded to any wind observations.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"49 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139766172","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}
Pub Date : 2024-02-09DOI: 10.5194/egusphere-2024-287
Jun-Ichi Yano
Abstract. A methodology for directly predicting the time evolution of the assumed parameters for the distribution densities based on the Liouville equation, as proposed earlier, is extended to multi–dimensional cases as well as when the systems are constrained by integrals over a part of the variable range. The extended methodology is tested against a convective energy cycle system as well as the Lorenz’s stranger attractor. As a general tendency, the variance tends to collapse to a vanishing value over a finite time regardless of the chosen assumed distribution form. This general tendency is likely due to the common cause as collapse of the variance commonly found in ensemble–based data assimilation.
{"title":"Prognostic Assumed-PDF (DDF) Approach: Further Generalization and Demonstrations","authors":"Jun-Ichi Yano","doi":"10.5194/egusphere-2024-287","DOIUrl":"https://doi.org/10.5194/egusphere-2024-287","url":null,"abstract":"<strong>Abstract.</strong> A methodology for directly predicting the time evolution of the assumed parameters for the distribution densities based on the Liouville equation, as proposed earlier, is extended to multi–dimensional cases as well as when the systems are constrained by integrals over a part of the variable range. The extended methodology is tested against a convective energy cycle system as well as the Lorenz’s stranger attractor. As a general tendency, the variance tends to collapse to a vanishing value over a finite time regardless of the chosen assumed distribution form. This general tendency is likely due to the common cause as collapse of the variance commonly found in ensemble–based data assimilation.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"10 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139766026","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}
Pub Date : 2024-02-06DOI: 10.5194/egusphere-2024-303
Alexander Voronovich
Abstract. A derivation of a dynamical core for the dry atmosphere in the absence of dissipative processes based on the least action (i.e., Hamilton’s) principle is presented. This approach can be considered the finite-element method applied to the calculation and minimization of the action. The algorithm possesses the following characteristic features: (1) For a given set of grid points and a given forward operator the algorithm ensures through the minimization of action maximal closeness (in a broad sense) of the evolution of the discrete system to the motion of the continuous atmosphere (a dynamically-optimal algorithm); (2) The grid points can be irregularly spaced allowing for variable spatial resolution; (3) The spatial resolution can be adjusted locally while executing calculations; (4) By using a set of tetrahedra as finite elements the algorithm ensures a better representation of the topography (piecewise linear rather than staircase); (5) The algorithm automatically calculates the evolution of passive tracers by following the trajectories of the fluid particles, which ensures that all a priori required tracer properties are satisfied. For testing purposes, the algorithm is realized in 2D, and a numerical example representing a convection event is presented.
{"title":"Dynamically-optimal models of atmospheric motion","authors":"Alexander Voronovich","doi":"10.5194/egusphere-2024-303","DOIUrl":"https://doi.org/10.5194/egusphere-2024-303","url":null,"abstract":"<strong>Abstract.</strong> A derivation of a dynamical core for the dry atmosphere in the absence of dissipative processes based on the least action (i.e., Hamilton’s) principle is presented. This approach can be considered the finite-element method applied to the calculation and minimization of the action. The algorithm possesses the following characteristic features: (1) For a given set of grid points and a given forward operator the algorithm ensures through the minimization of action maximal closeness (in a broad sense) of the evolution of the discrete system to the motion of the continuous atmosphere (a dynamically-optimal algorithm); (2) The grid points can be irregularly spaced allowing for variable spatial resolution; (3) The spatial resolution can be adjusted locally while executing calculations; (4) By using a set of tetrahedra as finite elements the algorithm ensures a better representation of the topography (piecewise linear rather than staircase); (5) The algorithm automatically calculates the evolution of passive tracers by following the trajectories of the fluid particles, which ensures that all <em>a priori</em> required tracer properties are satisfied. For testing purposes, the algorithm is realized in 2D, and a numerical example representing a convection event is presented.","PeriodicalId":54714,"journal":{"name":"Nonlinear Processes in Geophysics","volume":"72 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139766020","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}
Jerry Jose, Auguste Gires, Ernani Schnorenberger, Yelva Roustan, Daniel Schertzer, Ioulia Tchiguirinskaia
Abstract. Wind power production plays an important role in achieving UN’s (United nations) Sustainable development goal (SDG) 7 – affordable and clean energy for all; and in the increasing global transition towards renewable and carbon neutral energy, understanding the uncertainties associated with wind and turbulence is extremely important. Characterization of wind is not straightforward due to its intrinsic intermittency: activity of the field becomes increasingly concentrated at smaller and smaller supports as the scale decreases. When it comes to power production by wind turbines, another complexity arises from the influence of rainfall, which only a limited number of studies have addressed so far suggesting short term as well as long-term effects. To understand this, the project RWTurb (https://hmco.enpc.fr/portfolio-archive/rw-turb/; supported by the French National Research Agency, ANR-19-CE05-0022) employs multiple 3D sonic anemometers (manufactured by Thies), mini meteorological stations (manufactured by Thies), and disdrometers (Parsivel2, manufactured by OTT) on a meteorological mast in the wind farm of Pays d’Othe (110 km south-east of Paris, France; operated by Boralex). With this simultaneously measured data, it is possible to study wind power and associated atmospheric fields under various rain conditions. Variations of wind velocity, power available at the wind farm, power produced by wind turbines and air density are examined here during rain and dry conditions using the framework of Universal Multifractals (UM). UM is a widely used, physically based, scale invariant framework for characterizing and simulating geophysical fields over wide range of scales which accounts for the intermittency in the field. Since rated power acts like an upper threshold in statistical analysis of empirical wind power, efforts were made to use the theoretical available power as a proxy to see the difference. From an event based analysis, differences in UM parameters were observed between rain and dry conditions for the fields illustrating the influence of rain. This is further explored using joint multifractal analysis and an increase in correlation exponent was observed between various fields with an increase in rain rate. Here we also examine the possibility of differences in power production according to type of rain (convective or stratiform) as well as various regimes of wind velocity. While examining time steps according to wind velocity, power curves showed different regions of departure from state curve according to the rain rate.
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