Judith Gerighausen, Joshua Dorrington, Marisol Osman, Christian M. Grams
Weather regimes describe the large-scale atmospheric circulation in the mid-latitudes in terms of a few circulation states that modulate regional surface weather. Subseasonal forecasts of prevailing weather regimes have proven skillful and valuable to energy applications. Previous studies have mainly focused on the mean surface weather associated with a regime. However, we show in this paper that variability of surface weather within a regime cannot be ignored. These intra-regime variations, caused by different `subflavors' of the same regime, can be captured by continuous regime indices and allow a refined application of weather regimes. Here we discuss wintertime temperature and wind speed regime anomalies for four selected countries, and provide guidance on the operational use and interpretation of regime forecasts. In an accompanying supplementary dataset we provide similar analysis for all European countries, seasons and key energy variables, useful as an applied reference.
{"title":"Quantifying intra-regime weather variability for energy applications","authors":"Judith Gerighausen, Joshua Dorrington, Marisol Osman, Christian M. Grams","doi":"arxiv-2408.04302","DOIUrl":"https://doi.org/arxiv-2408.04302","url":null,"abstract":"Weather regimes describe the large-scale atmospheric circulation in the\u0000mid-latitudes in terms of a few circulation states that modulate regional\u0000surface weather. Subseasonal forecasts of prevailing weather regimes have\u0000proven skillful and valuable to energy applications. Previous studies have\u0000mainly focused on the mean surface weather associated with a regime. However,\u0000we show in this paper that variability of surface weather within a regime\u0000cannot be ignored. These intra-regime variations, caused by different\u0000`subflavors' of the same regime, can be captured by continuous regime indices\u0000and allow a refined application of weather regimes. Here we discuss wintertime\u0000temperature and wind speed regime anomalies for four selected countries, and\u0000provide guidance on the operational use and interpretation of regime forecasts.\u0000In an accompanying supplementary dataset we provide similar analysis for all\u0000European countries, seasons and key energy variables, useful as an applied\u0000reference.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anna Vaughan, Gonzalo Mateo-Garcia, Itziar Irakulis-Loitxate, Marc Watine, Pablo Fernandez-Poblaciones, Richard E. Turner, James Requeima, Javier Gorroño, Cynthia Randles, Manfredi Caltagirone, Claudio Cifarelli
Mitigating methane emissions is the fastest way to stop global warming in the short-term and buy humanity time to decarbonise. Despite the demonstrated ability of remote sensing instruments to detect methane plumes, no system has been available to routinely monitor and act on these events. We present MARS-S2L, an automated AI-driven methane emitter monitoring system for Sentinel-2 and Landsat satellite imagery deployed operationally at the United Nations Environment Programme's International Methane Emissions Observatory. We compile a global dataset of thousands of super-emission events for training and evaluation, demonstrating that MARS-S2L can skillfully monitor emissions in a diverse range of regions globally, providing a 216% improvement in mean average precision over a current state-of-the-art detection method. Running this system operationally for six months has yielded 457 near-real-time detections in 22 different countries of which 62 have already been used to provide formal notifications to governments and stakeholders.
{"title":"AI for operational methane emitter monitoring from space","authors":"Anna Vaughan, Gonzalo Mateo-Garcia, Itziar Irakulis-Loitxate, Marc Watine, Pablo Fernandez-Poblaciones, Richard E. Turner, James Requeima, Javier Gorroño, Cynthia Randles, Manfredi Caltagirone, Claudio Cifarelli","doi":"arxiv-2408.04745","DOIUrl":"https://doi.org/arxiv-2408.04745","url":null,"abstract":"Mitigating methane emissions is the fastest way to stop global warming in the\u0000short-term and buy humanity time to decarbonise. Despite the demonstrated\u0000ability of remote sensing instruments to detect methane plumes, no system has\u0000been available to routinely monitor and act on these events. We present\u0000MARS-S2L, an automated AI-driven methane emitter monitoring system for\u0000Sentinel-2 and Landsat satellite imagery deployed operationally at the United\u0000Nations Environment Programme's International Methane Emissions Observatory. We\u0000compile a global dataset of thousands of super-emission events for training and\u0000evaluation, demonstrating that MARS-S2L can skillfully monitor emissions in a\u0000diverse range of regions globally, providing a 216% improvement in mean average\u0000precision over a current state-of-the-art detection method. Running this system\u0000operationally for six months has yielded 457 near-real-time detections in 22\u0000different countries of which 62 have already been used to provide formal\u0000notifications to governments and stakeholders.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrew Steyer, Luca Bertagna, Graham Harper, Jerry Watkins, Irina Tezaur, Diana Bull
We propose an approach for characterizing source-impact pathways, the interactions of a set of variables in space-time due to an external forcing, in climate models using in-situ analyses that circumvent computationally expensive read/write operations. This approach makes use of a lightweight open-source software library we developed known as CLDERA-Tools. We describe how CLDERA-Tools is linked with the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) in a minimally invasive way for in-situ extraction of quantities of interested and associated statistics. Subsequently, these quantities are used to represent source-impact pathways with time-dependent directed acyclic graphs (DAGs). The utility of CLDERA-Tools is demonstrated by using the data it extracts in-situ to compute a spatially resolved DAG from an idealized configuration of the atmosphere with a parameterized representation of a volcanic eruption known as HSW-V.
我们提出了一种表征源-影响途径的方法，即在外部作用力的影响下，一组变量在时空中的相互作用。这种方法利用了我们开发的名为 CLDERA-Tools 的轻量级开源软件库。我们介绍了如何将 CLDERA-Tools 与美国能源部的 Energy ExascaleEarth System Model (E3SM) 相结合，以最小的侵入方式就地提取感兴趣的数量和相关统计数据。随后，这些数量被用于用随时间变化的定向无循环图（DAG）来表示源-影响路径。CLDERA 工具的实用性体现在利用其现场提取的数据，从理想化的大气配置中计算出空间解析的 DAG，并以参数化的方式表示称为 HSW-V 的火山喷发。
{"title":"In-situ data extraction for pathway analysis in an idealized atmosphere configuration of E3SM","authors":"Andrew Steyer, Luca Bertagna, Graham Harper, Jerry Watkins, Irina Tezaur, Diana Bull","doi":"arxiv-2408.04099","DOIUrl":"https://doi.org/arxiv-2408.04099","url":null,"abstract":"We propose an approach for characterizing source-impact pathways, the\u0000interactions of a set of variables in space-time due to an external forcing, in\u0000climate models using in-situ analyses that circumvent computationally expensive\u0000read/write operations. This approach makes use of a lightweight open-source\u0000software library we developed known as CLDERA-Tools. We describe how\u0000CLDERA-Tools is linked with the U.S. Department of Energy's Energy Exascale\u0000Earth System Model (E3SM) in a minimally invasive way for in-situ extraction of\u0000quantities of interested and associated statistics. Subsequently, these\u0000quantities are used to represent source-impact pathways with time-dependent\u0000directed acyclic graphs (DAGs). The utility of CLDERA-Tools is demonstrated by\u0000using the data it extracts in-situ to compute a spatially resolved DAG from an\u0000idealized configuration of the atmosphere with a parameterized representation\u0000of a volcanic eruption known as HSW-V.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ankur Mahesh, William Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Joshua Elms, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis OBrien, Michael Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard
Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1,000-10,000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part I, we construct an ensemble weather forecasting system based on Spherical Fourier Neural Operators (SFNO), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. Using large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states, and the ML ensemble thus passes a crucial spectral test in the literature. The IFS and ML ensembles have similar Extreme Forecast Indices, and we show that the ML extreme weather forecasts are reliable and discriminating.
{"title":"Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators","authors":"Ankur Mahesh, William Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Joshua Elms, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis OBrien, Michael Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard","doi":"arxiv-2408.03100","DOIUrl":"https://doi.org/arxiv-2408.03100","url":null,"abstract":"Studying low-likelihood high-impact extreme weather events in a warming world\u0000is a significant and challenging task for current ensemble forecasting systems.\u0000While these systems presently use up to 100 members, larger ensembles could\u0000enrich the sampling of internal variability. They may capture the long tails\u0000associated with climate hazards better than traditional ensemble sizes. Due to\u0000computational constraints, it is infeasible to generate huge ensembles\u0000(comprised of 1,000-10,000 members) with traditional, physics-based numerical\u0000models. In this two-part paper, we replace traditional numerical simulations\u0000with machine learning (ML) to generate hindcasts of huge ensembles. In Part I,\u0000we construct an ensemble weather forecasting system based on Spherical Fourier\u0000Neural Operators (SFNO), and we discuss important design decisions for\u0000constructing such an ensemble. The ensemble represents model uncertainty\u0000through perturbed-parameter techniques, and it represents initial condition\u0000uncertainty through bred vectors, which sample the fastest growing modes of the\u0000forecast. Using the European Centre for Medium-Range Weather Forecasts\u0000Integrated Forecasting System (IFS) as a baseline, we develop an evaluation\u0000pipeline composed of mean, spectral, and extreme diagnostics. Using\u0000large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve\u0000calibrated probabilistic forecasts. As the trajectories of the individual\u0000members diverge, the ML ensemble mean spectra degrade with lead time,\u0000consistent with physical expectations. However, the individual ensemble\u0000members' spectra stay constant with lead time. Therefore, these members\u0000simulate realistic weather states, and the ML ensemble thus passes a crucial\u0000spectral test in the literature. The IFS and ML ensembles have similar Extreme\u0000Forecast Indices, and we show that the ML extreme weather forecasts are\u0000reliable and discriminating.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study has applied information thermodynamics to a bivariate linear stochastic differential equation (SDE) that describes a synchronization phenomenon of sea surface temperatures (SSTs) between the Gulf Stream and the Kuroshio Current, which is referred to as the boundary current synchronization (BCS). Information thermodynamics divides the entire system fluctuating with stochastic noise into subsystems and describes the interactions between these subsystems from the perspective of information transfer. The SDE coefficients have been estimated through regression analysis using observational and numerical simulation data. In the absence of stochastic noise, the solution of the estimated SDE shows that the SSTs relax toward zero without oscillating. The estimated SDE can be interpreted as a Maxwell's demon system, with the Gulf Stream playing the role of the "Particle" and the Kuroshio Current playing the role of the "Demon." This interpretation gives the asymmetric roles of both ocean currents. The Gulf Stream forces the SST of the Kuroshio Current to be in phase. By contrast, the Kuroshio Current maintains the phase by interfering with the relaxation of the Gulf Stream SST. In the framework of Maxwell's demon, the Gulf Stream is interpreted as being measured by the Kuroshio Current, whereas the Kuroshio Current is interpreted as performing feedback control on the Gulf Stream. When the Gulf Stream and the Kuroshio Current are coupled in an appropriate parameter regime, synchronization is realized with atmospheric and oceanic noise as the driving source.
{"title":"Interpretation of the Boundary Current Synchronization as a Maxwell's Demon","authors":"Yuki Yasuda, Tsubasa Kohyama","doi":"arxiv-2408.01133","DOIUrl":"https://doi.org/arxiv-2408.01133","url":null,"abstract":"This study has applied information thermodynamics to a bivariate linear\u0000stochastic differential equation (SDE) that describes a synchronization\u0000phenomenon of sea surface temperatures (SSTs) between the Gulf Stream and the\u0000Kuroshio Current, which is referred to as the boundary current synchronization\u0000(BCS). Information thermodynamics divides the entire system fluctuating with\u0000stochastic noise into subsystems and describes the interactions between these\u0000subsystems from the perspective of information transfer. The SDE coefficients\u0000have been estimated through regression analysis using observational and\u0000numerical simulation data. In the absence of stochastic noise, the solution of\u0000the estimated SDE shows that the SSTs relax toward zero without oscillating.\u0000The estimated SDE can be interpreted as a Maxwell's demon system, with the Gulf\u0000Stream playing the role of the \"Particle\" and the Kuroshio Current playing the\u0000role of the \"Demon.\" This interpretation gives the asymmetric roles of both\u0000ocean currents. The Gulf Stream forces the SST of the Kuroshio Current to be in\u0000phase. By contrast, the Kuroshio Current maintains the phase by interfering\u0000with the relaxation of the Gulf Stream SST. In the framework of Maxwell's\u0000demon, the Gulf Stream is interpreted as being measured by the Kuroshio\u0000Current, whereas the Kuroshio Current is interpreted as performing feedback\u0000control on the Gulf Stream. When the Gulf Stream and the Kuroshio Current are\u0000coupled in an appropriate parameter regime, synchronization is realized with\u0000atmospheric and oceanic noise as the driving source.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ankur Mahesh, William Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis OBrien, Michael Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard
In Part I, we created an ensemble based on Spherical Fourier Neural Operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multiple checkpoints trained independently from scratch. Based on diagnostics that assess the ensemble's physical fidelity, our ensemble has comparable performance to operational weather forecasting systems. However, it requires several orders of magnitude fewer computational resources. Here in Part II, we generate a huge ensemble (HENS), with 7,424 members initialized each day of summer 2023. We enumerate the technical requirements for running huge ensembles at this scale. HENS precisely samples the tails of the forecast distribution and presents a detailed sampling of internal variability. For extreme climate statistics, HENS samples events 4$sigma$ away from the ensemble mean. At each grid cell, HENS improves the skill of the most accurate ensemble member and enhances coverage of possible future trajectories. As a weather forecasting model, HENS issues extreme weather forecasts with better uncertainty quantification. It also reduces the probability of outlier events, in which the verification value lies outside the ensemble forecast distribution.
{"title":"Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators","authors":"Ankur Mahesh, William Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis OBrien, Michael Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard","doi":"arxiv-2408.01581","DOIUrl":"https://doi.org/arxiv-2408.01581","url":null,"abstract":"In Part I, we created an ensemble based on Spherical Fourier Neural\u0000Operators. As initial condition perturbations, we used bred vectors, and as\u0000model perturbations, we used multiple checkpoints trained independently from\u0000scratch. Based on diagnostics that assess the ensemble's physical fidelity, our\u0000ensemble has comparable performance to operational weather forecasting systems.\u0000However, it requires several orders of magnitude fewer computational resources.\u0000Here in Part II, we generate a huge ensemble (HENS), with 7,424 members\u0000initialized each day of summer 2023. We enumerate the technical requirements\u0000for running huge ensembles at this scale. HENS precisely samples the tails of\u0000the forecast distribution and presents a detailed sampling of internal\u0000variability. For extreme climate statistics, HENS samples events 4$sigma$ away\u0000from the ensemble mean. At each grid cell, HENS improves the skill of the most\u0000accurate ensemble member and enhances coverage of possible future trajectories.\u0000As a weather forecasting model, HENS issues extreme weather forecasts with\u0000better uncertainty quantification. It also reduces the probability of outlier\u0000events, in which the verification value lies outside the ensemble forecast\u0000distribution.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces OTCliM (Optical Turbulence Climatology using Machine learning), a novel approach for deriving comprehensive climatologies of atmospheric optical turbulence strength ($C_n^2$) using gradient boosting machines. OTCliM addresses the challenge of efficiently obtaining reliable site-specific $C_n^2$ climatologies, crucial for ground-based astronomy and free-space optical communication. Using gradient boosting machines and global reanalysis data, OTCliM extrapolates one year of measured $C_n^2$ into a multi-year time series. We assess OTCliM's performance using $C_n^2$ data from 17 diverse stations in New York State, evaluating temporal extrapolation capabilities and geographical generalization. Our results demonstrate accurate predictions of four held-out years of $C_n^2$ across various sites, including complex urban environments, outperforming traditional analytical models. Non-urban models also show good geographical generalization compared to urban models, which captured non-general site-specific dependencies. A feature importance analysis confirms the physical consistency of the trained models. It also indicates the potential to uncover new insights into the physical processes governing $C_n^2$ from data. OTCliM's ability to derive reliable $C_n^2$ climatologies from just one year of observations can potentially reduce resources required for future site surveys or enable studies for additional sites with the same resources.
{"title":"OTCliM: generating a near-surface climatology of optical turbulence strength ($C_n^2$) using gradient boosting","authors":"Maximilian Pierzyna, Sukanta Basu, Rudolf Saathof","doi":"arxiv-2408.00520","DOIUrl":"https://doi.org/arxiv-2408.00520","url":null,"abstract":"This study introduces OTCliM (Optical Turbulence Climatology using Machine\u0000learning), a novel approach for deriving comprehensive climatologies of\u0000atmospheric optical turbulence strength ($C_n^2$) using gradient boosting\u0000machines. OTCliM addresses the challenge of efficiently obtaining reliable\u0000site-specific $C_n^2$ climatologies, crucial for ground-based astronomy and\u0000free-space optical communication. Using gradient boosting machines and global\u0000reanalysis data, OTCliM extrapolates one year of measured $C_n^2$ into a\u0000multi-year time series. We assess OTCliM's performance using $C_n^2$ data from\u000017 diverse stations in New York State, evaluating temporal extrapolation\u0000capabilities and geographical generalization. Our results demonstrate accurate\u0000predictions of four held-out years of $C_n^2$ across various sites, including\u0000complex urban environments, outperforming traditional analytical models.\u0000Non-urban models also show good geographical generalization compared to urban\u0000models, which captured non-general site-specific dependencies. A feature\u0000importance analysis confirms the physical consistency of the trained models. It\u0000also indicates the potential to uncover new insights into the physical\u0000processes governing $C_n^2$ from data. OTCliM's ability to derive reliable\u0000$C_n^2$ climatologies from just one year of observations can potentially reduce\u0000resources required for future site surveys or enable studies for additional\u0000sites with the same resources.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"217 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Satoru Okajima, Hisashi Nakamura, Akira Kuwano-Yoshida, Rhys Parfitt
The frequency of extratropical cyclones in East Asia, including those traveling along the Kuroshio off the south coast of Japan, maximizes climatologically in spring in harmony with local enhancement of precipitation. The springtime cyclone activity is of great socioeconomic importance for East Asian countries. However, mechanisms for the spring peak in the East Asian cyclone activity have been poorly understood. This study aims to unravel the mechanisms, focusing particularly on favorable conditions for relevant cyclogenesis. Through a composite analysis based on atmospheric reanalysis data, we show that cyclogenesis enhanced around the East China Sea under anomalously strengthened cyclonic wind shear and temperature gradient, in addition to enhanced moisture flux from the south, is important for the spring peak in the cyclone activity in East Asia. In spring, climatologically strengthened cyclonic shear north of the low-level jet axis and associated frequent atmospheric frontogenesis in southern China and the East China Sea serve as favorable background conditions for low-level cyclogenesis. We also demonstrate that climatologically enhanced diabatic heating around East Asia is pivotal in strengthening of the low-level jet through a set of linear baroclinic model experiments. Our findings suggest the importance of the seasonal evolution of diabatic heating in East Asia for that of the climate system around East Asia from winter to spring, encompassing the spring peak in the cyclone activity and climatological precipitation.
{"title":"Mechanisms for a Spring Peak in East Asian Cyclone Activity","authors":"Satoru Okajima, Hisashi Nakamura, Akira Kuwano-Yoshida, Rhys Parfitt","doi":"arxiv-2407.20864","DOIUrl":"https://doi.org/arxiv-2407.20864","url":null,"abstract":"The frequency of extratropical cyclones in East Asia, including those\u0000traveling along the Kuroshio off the south coast of Japan, maximizes\u0000climatologically in spring in harmony with local enhancement of precipitation.\u0000The springtime cyclone activity is of great socioeconomic importance for East\u0000Asian countries. However, mechanisms for the spring peak in the East Asian\u0000cyclone activity have been poorly understood. This study aims to unravel the\u0000mechanisms, focusing particularly on favorable conditions for relevant\u0000cyclogenesis. Through a composite analysis based on atmospheric reanalysis\u0000data, we show that cyclogenesis enhanced around the East China Sea under\u0000anomalously strengthened cyclonic wind shear and temperature gradient, in\u0000addition to enhanced moisture flux from the south, is important for the spring\u0000peak in the cyclone activity in East Asia. In spring, climatologically\u0000strengthened cyclonic shear north of the low-level jet axis and associated\u0000frequent atmospheric frontogenesis in southern China and the East China Sea\u0000serve as favorable background conditions for low-level cyclogenesis. We also\u0000demonstrate that climatologically enhanced diabatic heating around East Asia is\u0000pivotal in strengthening of the low-level jet through a set of linear\u0000baroclinic model experiments. Our findings suggest the importance of the\u0000seasonal evolution of diabatic heating in East Asia for that of the climate\u0000system around East Asia from winter to spring, encompassing the spring peak in\u0000the cyclone activity and climatological precipitation.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"204 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spatial verification of global high-resolution weather forecasts remains a considerable challenge. Most existing spatial verification techniques either do not properly account for the non-planar geometry of a global domain or their computation complexity becomes too large. We present an adaptation of the recently developed Precipitation Attribution Distance (PAD) metric, designed for verifying precipitation, enabling its use on the Earth's spherical geometry. PAD estimates the magnitude of location errors in the forecasts and is related to the mathematical theory of Optimal Transport as it provides a close upper bound for the Wasserstein distance. The method is fast and flexible with time complexity $O(n log(n))$. Its behavior is analyzed using a set of idealized cases and 7 years of operational global high-resolution deterministic 6-hourly precipitation forecasts from the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts. The summary results for the whole period show how location errors in the IFS model grow steadily with increasing lead time for all analyzed regions. Moreover, by examining the time evolution of the results, we can determine the trends in the score's value and identify the regions where there is a statistically significant improvement (or worsening) of the forecast performance. The results can also be analyzed separately for different intensities of precipitation. Overall, the PAD provides meaningful results for estimating location errors in global high-resolution precipitation forecasts at an affordable computational cost.
{"title":"Spatial verification of global precipitation forecasts","authors":"Gregor Skok, Llorenç Lledó","doi":"arxiv-2407.20624","DOIUrl":"https://doi.org/arxiv-2407.20624","url":null,"abstract":"Spatial verification of global high-resolution weather forecasts remains a\u0000considerable challenge. Most existing spatial verification techniques either do\u0000not properly account for the non-planar geometry of a global domain or their\u0000computation complexity becomes too large. We present an adaptation of the\u0000recently developed Precipitation Attribution Distance (PAD) metric, designed\u0000for verifying precipitation, enabling its use on the Earth's spherical\u0000geometry. PAD estimates the magnitude of location errors in the forecasts and\u0000is related to the mathematical theory of Optimal Transport as it provides a\u0000close upper bound for the Wasserstein distance. The method is fast and flexible\u0000with time complexity $O(n log(n))$. Its behavior is analyzed using a set of\u0000idealized cases and 7 years of operational global high-resolution deterministic\u00006-hourly precipitation forecasts from the Integrated Forecasting System (IFS)\u0000of the European Centre for Medium-Range Weather Forecasts. The summary results\u0000for the whole period show how location errors in the IFS model grow steadily\u0000with increasing lead time for all analyzed regions. Moreover, by examining the\u0000time evolution of the results, we can determine the trends in the score's value\u0000and identify the regions where there is a statistically significant improvement\u0000(or worsening) of the forecast performance. The results can also be analyzed\u0000separately for different intensities of precipitation. Overall, the PAD\u0000provides meaningful results for estimating location errors in global\u0000high-resolution precipitation forecasts at an affordable computational cost.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhe Li, Ronghui Xu, Jilin Hu, Zhong Peng, Xi Lu, Chenjuan Guo, Bin Yang
Significant wave height (SWH) is a vital metric in marine science, and accurate SWH estimation is crucial for various applications, e.g., marine energy development, fishery, early warning systems for potential risks, etc. Traditional SWH estimation methods that are based on numerical models and physical theories are hindered by computational inefficiencies. Recently, machine learning has emerged as an appealing alternative to improve accuracy and reduce computational time. However, due to limited observational technology and high costs, the scarcity of real-world data restricts the potential of machine learning models. To overcome these limitations, we propose an ocean SWH estimation framework, namely Orca. Specifically, Orca enhances the limited spatio-temporal reasoning abilities of classic LLMs with a novel spatiotemporal aware encoding module. By segmenting the limited buoy observational data temporally, encoding the buoys' locations spatially, and designing prompt templates, Orca capitalizes on the robust generalization ability of LLMs to estimate significant wave height effectively with limited data. Experimental results on the Gulf of Mexico demonstrate that Orca achieves state-of-the-art performance in SWH estimation.
{"title":"Orca: Ocean Significant Wave Height Estimation with Spatio-temporally Aware Large Language Models","authors":"Zhe Li, Ronghui Xu, Jilin Hu, Zhong Peng, Xi Lu, Chenjuan Guo, Bin Yang","doi":"arxiv-2407.20053","DOIUrl":"https://doi.org/arxiv-2407.20053","url":null,"abstract":"Significant wave height (SWH) is a vital metric in marine science, and\u0000accurate SWH estimation is crucial for various applications, e.g., marine\u0000energy development, fishery, early warning systems for potential risks, etc.\u0000Traditional SWH estimation methods that are based on numerical models and\u0000physical theories are hindered by computational inefficiencies. Recently,\u0000machine learning has emerged as an appealing alternative to improve accuracy\u0000and reduce computational time. However, due to limited observational technology\u0000and high costs, the scarcity of real-world data restricts the potential of\u0000machine learning models. To overcome these limitations, we propose an ocean SWH\u0000estimation framework, namely Orca. Specifically, Orca enhances the limited\u0000spatio-temporal reasoning abilities of classic LLMs with a novel spatiotemporal\u0000aware encoding module. By segmenting the limited buoy observational data\u0000temporally, encoding the buoys' locations spatially, and designing prompt\u0000templates, Orca capitalizes on the robust generalization ability of LLMs to\u0000estimate significant wave height effectively with limited data. Experimental\u0000results on the Gulf of Mexico demonstrate that Orca achieves state-of-the-art\u0000performance in SWH estimation.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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