Pub Date : 2023-01-30DOI: 10.1175/aies-d-22-0051.1
M. Molina, J. Richter, A. Glanville, K. Dagon, J. Berner, A. Hu, G. Meehl
This study focuses on assessing the representation and predictability of North American weather regimes, which are persistent large-scale atmospheric patterns, in a set of initialized subseasonal reforecasts created using the Community Earth System Model version 2 (CESM2). K-means clustering was used to extract four key North American (10-70°N, 150-40°W) weather regimes within ERA5 reanalysis, which were used to interpret CESM2 subseasonal forecast performance. Results show that CESM2 can recreate the climatology of the four main North American weather regimes with skill, but exhibits biases during later lead times with over occurrence of the West Coast High regime and under occurrence of the Greenland High and Alaskan Ridge regimes. Overall, the West Coast High and Pacific Trough regimes exhibited higher predictability within CESM2, partly related to El Niño. Despite biases, several reforecasts were skillful and exhibited high predictability during later lead times, which could be partly attributed to skillful representation of the atmosphere from the tropics to extratropics upstream of North America. The high predictability at the subseasonal time scale of these case study examples was manifested as an “ensemble realignment,” in which most ensemble members agreed on a prediction despite ensemble trajectory dispersion during earlier lead times. Weather regimes were also shown to project distinct temperature and precipitation anomalies across North America that largely agree with observational products. This study further demonstrates that unsupervised learning methods can be used to uncover sources and limits of subseasonal predictability, along with systematic biases present in numerical prediction systems.
{"title":"Subseasonal Representation and Predictability of North American Weather Regimes using Cluster Analysis","authors":"M. Molina, J. Richter, A. Glanville, K. Dagon, J. Berner, A. Hu, G. Meehl","doi":"10.1175/aies-d-22-0051.1","DOIUrl":"https://doi.org/10.1175/aies-d-22-0051.1","url":null,"abstract":"\u0000This study focuses on assessing the representation and predictability of North American weather regimes, which are persistent large-scale atmospheric patterns, in a set of initialized subseasonal reforecasts created using the Community Earth System Model version 2 (CESM2). K-means clustering was used to extract four key North American (10-70°N, 150-40°W) weather regimes within ERA5 reanalysis, which were used to interpret CESM2 subseasonal forecast performance. Results show that CESM2 can recreate the climatology of the four main North American weather regimes with skill, but exhibits biases during later lead times with over occurrence of the West Coast High regime and under occurrence of the Greenland High and Alaskan Ridge regimes. Overall, the West Coast High and Pacific Trough regimes exhibited higher predictability within CESM2, partly related to El Niño. Despite biases, several reforecasts were skillful and exhibited high predictability during later lead times, which could be partly attributed to skillful representation of the atmosphere from the tropics to extratropics upstream of North America. The high predictability at the subseasonal time scale of these case study examples was manifested as an “ensemble realignment,” in which most ensemble members agreed on a prediction despite ensemble trajectory dispersion during earlier lead times. Weather regimes were also shown to project distinct temperature and precipitation anomalies across North America that largely agree with observational products. This study further demonstrates that unsupervised learning methods can be used to uncover sources and limits of subseasonal predictability, along with systematic biases present in numerical prediction systems.","PeriodicalId":94369,"journal":{"name":"Artificial intelligence for the earth systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90264225","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}
Pub Date : 2023-01-19DOI: 10.1175/aies-d-22-0025.1
Maria Reinhardt, S. Schoger, Frederik Kurzrock, R. Potthast
This paper presents an innovational way of assimilating observations of clouds into the ICOsahedral Nonhydrostatic weather forecasting model for regional scale, ICON-D2, which is operated by Deutscher Wetterdienst (DWD). A convolutional neural network (CNN) is trained to detect clouds in camera photographs. The network’s output is a greyscale picture, in which each pixel has a value between 0 and 1, describing the probability of the pixel belonging to a cloud (1) or not (0). By averaging over a certain box of the picture a value for the cloud cover of that region is obtained. A forward operator is built to map an ICON model state into the observation space. A three dimensional grid in the space of the camera’s perspective is constructed and the ICON model variable cloud cover (CLC) is interpolated onto that grid. The maximum CLC along the rays that fabricate the camera grid, is taken as a model equivalent for each pixel. After superobbing, monitoring experiments have been conducted to compare the observations and model equivalents over a longer time period, yielding promising results. Further we show the performance of a single assimilation step as well as a longer assimilation experiment over a time period of six days which also yields good results. These findings are a proof of concept and further research has to be invested before these new innovational observations can be assimilated operationally in any numerical weather prediction (NWP) model.
本文提出了一种将云观测资料同化到区域尺度icosterdral non - hydro静力天气预报模式ICON-D2中的创新方法，该模式由Deutscher weterdienst (DWD)运行。训练卷积神经网络(CNN)来检测相机照片中的云。网络的输出是一幅灰度图像，其中每个像素的值在0到1之间，描述了像素属于云(1)或不属于云(0)的概率。通过对图像的某一框进行平均，得到该区域的云覆盖值。建立了一个前向算子，将ICON模型状态映射到观测空间。在摄像机视角空间中构造一个三维网格，并将ICON模型变量云量(CLC)插值到该网格中。沿构成相机网格的射线的最大CLC被作为每个像素的模型等效。在超级取样之后，进行了监测实验，以比较较长时间内的观测结果和模型当量，得出了有希望的结果。此外，我们还展示了单一同化步骤的性能以及为期六天的较长同化实验，这也产生了良好的结果。这些发现是概念的证明，在这些新的创新观测能够在任何数值天气预报(NWP)模式中进行业务吸收之前，必须进行进一步的研究。
{"title":"Convective-scale Assimilation of Cloud Cover from Photographs using a Machine Learning Forward Operator","authors":"Maria Reinhardt, S. Schoger, Frederik Kurzrock, R. Potthast","doi":"10.1175/aies-d-22-0025.1","DOIUrl":"https://doi.org/10.1175/aies-d-22-0025.1","url":null,"abstract":"\u0000This paper presents an innovational way of assimilating observations of clouds into the ICOsahedral Nonhydrostatic weather forecasting model for regional scale, ICON-D2, which is operated by Deutscher Wetterdienst (DWD). A convolutional neural network (CNN) is trained to detect clouds in camera photographs. The network’s output is a greyscale picture, in which each pixel has a value between 0 and 1, describing the probability of the pixel belonging to a cloud (1) or not (0). By averaging over a certain box of the picture a value for the cloud cover of that region is obtained. A forward operator is built to map an ICON model state into the observation space. A three dimensional grid in the space of the camera’s perspective is constructed and the ICON model variable cloud cover (CLC) is interpolated onto that grid. The maximum CLC along the rays that fabricate the camera grid, is taken as a model equivalent for each pixel. After superobbing, monitoring experiments have been conducted to compare the observations and model equivalents over a longer time period, yielding promising results. Further we show the performance of a single assimilation step as well as a longer assimilation experiment over a time period of six days which also yields good results. These findings are a proof of concept and further research has to be invested before these new innovational observations can be assimilated operationally in any numerical weather prediction (NWP) model.","PeriodicalId":94369,"journal":{"name":"Artificial intelligence for the earth systems","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83939235","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}
Pub Date : 2023-01-10DOI: 10.1175/aies-d-22-0037.1
A. Milstein, J. Santanello, W. Blackwell
In recent decades, spaceborne microwave and hyperspectral infrared sounding instruments have significantly benefited weather forecasting and climate science. However, existing retrievals of lower troposphere temperature and humidity profiles have limitations in vertical resolution, and often cannot accurately represent key features such as the mixed layer thermodynamic structure and the inversion at the planetary boundary layer (PBL) top. Because of the existing limitations in PBL remote sensing from space, there is a compelling need to improve routine, global observations of the PBL and enable advances in scientific understanding and weather and climate prediction. To address this, we have developed a new 3D deep neural network (DNN) which enhances detail and reduces noise in Level 2 granules of temperature and humidity profiles from the Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit (AMSU) sounder instruments aboard NASA’s Aqua spacecraft. We show that the enhancement improves accuracy and detail including key features such as capping inversions at the top of the PBL over land, resulting in improved accuracy in estimations of PBL height.
{"title":"Detail Enhancement of AIRS/AMSU Temperature and Moisture Profiles Using a 3D Deep Neural Network","authors":"A. Milstein, J. Santanello, W. Blackwell","doi":"10.1175/aies-d-22-0037.1","DOIUrl":"https://doi.org/10.1175/aies-d-22-0037.1","url":null,"abstract":"\u0000In recent decades, spaceborne microwave and hyperspectral infrared sounding instruments have significantly benefited weather forecasting and climate science. However, existing retrievals of lower troposphere temperature and humidity profiles have limitations in vertical resolution, and often cannot accurately represent key features such as the mixed layer thermodynamic structure and the inversion at the planetary boundary layer (PBL) top. Because of the existing limitations in PBL remote sensing from space, there is a compelling need to improve routine, global observations of the PBL and enable advances in scientific understanding and weather and climate prediction. To address this, we have developed a new 3D deep neural network (DNN) which enhances detail and reduces noise in Level 2 granules of temperature and humidity profiles from the Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit (AMSU) sounder instruments aboard NASA’s Aqua spacecraft. We show that the enhancement improves accuracy and detail including key features such as capping inversions at the top of the PBL over land, resulting in improved accuracy in estimations of PBL height.","PeriodicalId":94369,"journal":{"name":"Artificial intelligence for the earth systems","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82176067","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}
Pub Date : 2023-01-10DOI: 10.1175/aies-d-22-0036.1
M. Momoi, S. Kotsuki, Ryota Kikuchi, Satoshi Watanabe, Masafumi Yamada, Shiori Abe
Predicting the spatial distribution of maximum inundation depth (depth-MAP) is important for the mitigation of hydrological disasters induced by extreme precipitation. However, physics-based rainfall-runoff-inundation (RRI) models, which are used operationally to predict hydrological disasters in Japan, require massive computational resources for numerical simulations. Here, we aimed at developing a computationally inexpensive deep learning model (Rain2Depth) that emulates an RRI model. Our study focused on the Omono River (Akita Prefecture, Japan) and predicted the depth-MAP from spatial and temporal rainfall data for individual events. Rain2Depth was developed based on a convolutional neural network (CNN), and predicts depth-MAP from 7-day successive hourly rainfall at 13 rain gauge stations in the basin. For training the Rain2Depth, we simulated the depth-MAP by the RRI model forced by 50-ensembles of 30-year data from large-ensemble weather/climate predictions. Instead of using the input and output data directly, we extracted important features from input and output data with two dimensionality reduction techniques (principal component analysis (PCA) and the CNN approach) prior to training the network. This dimensionality reduction aimed to avoid overfitting caused by insufficient training data. The nonlinear CNN approach was superior to the linear PCA for extracting features. Finally, Rain2Depth was architected by connecting the extracted features between input and output data through a neural network. Rain2Depth-based predictions were more accurate than predictions from our previous model (K20), which used ensemble learning of multiple regularized regressions for a specific station. Whereas the K20 can predict maximum inundation depth only at stations, our study achieved depth-MAP prediction by training only the single model Rain2Depth.
{"title":"Emulating Rainfall-Runoff-Inundation Model using Deep Neural Network with Dimensionality Reduction","authors":"M. Momoi, S. Kotsuki, Ryota Kikuchi, Satoshi Watanabe, Masafumi Yamada, Shiori Abe","doi":"10.1175/aies-d-22-0036.1","DOIUrl":"https://doi.org/10.1175/aies-d-22-0036.1","url":null,"abstract":"\u0000Predicting the spatial distribution of maximum inundation depth (depth-MAP) is important for the mitigation of hydrological disasters induced by extreme precipitation. However, physics-based rainfall-runoff-inundation (RRI) models, which are used operationally to predict hydrological disasters in Japan, require massive computational resources for numerical simulations. Here, we aimed at developing a computationally inexpensive deep learning model (Rain2Depth) that emulates an RRI model. Our study focused on the Omono River (Akita Prefecture, Japan) and predicted the depth-MAP from spatial and temporal rainfall data for individual events.\u0000Rain2Depth was developed based on a convolutional neural network (CNN), and predicts depth-MAP from 7-day successive hourly rainfall at 13 rain gauge stations in the basin. For training the Rain2Depth, we simulated the depth-MAP by the RRI model forced by 50-ensembles of 30-year data from large-ensemble weather/climate predictions. Instead of using the input and output data directly, we extracted important features from input and output data with two dimensionality reduction techniques (principal component analysis (PCA) and the CNN approach) prior to training the network. This dimensionality reduction aimed to avoid overfitting caused by insufficient training data. The nonlinear CNN approach was superior to the linear PCA for extracting features. Finally, Rain2Depth was architected by connecting the extracted features between input and output data through a neural network.\u0000Rain2Depth-based predictions were more accurate than predictions from our previous model (K20), which used ensemble learning of multiple regularized regressions for a specific station. Whereas the K20 can predict maximum inundation depth only at stations, our study achieved depth-MAP prediction by training only the single model Rain2Depth.","PeriodicalId":94369,"journal":{"name":"Artificial intelligence for the earth systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89724844","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}
Pub Date : 2023-01-09DOI: 10.1175/aies-d-22-0061.1
Katherine Haynes, Ryan Lagerquist, M. McGraw, K. Musgrave, I. Ebert‐Uphoff
Neural networks (NN) have become an important tool for prediction tasks – both regression and classification – in environmental science. Since many environmental-science problems involve life-or-death decisions and policy-making, it is crucial to provide not only predictions but also an estimate of the uncertainty in the predictions. Until recently, very few tools were available to provide uncertainty quantification (UQ) for NN predictions. However, in recent years the computer-science field has developed numerous UQ approaches, and several research groups are exploring how to apply these approaches in environmental science. We provide an accessible introduction to six of these UQ approaches, then focus on tools for the next step, namely to answer the question: Once we obtain an uncertainty estimate (using any approach), how do we know whether it is good or bad? To answer this question, we highlight four evaluation graphics and eight evaluation scores that are well suited for evaluating and comparing uncertainty estimates (NN-based or otherwise) for environmental-science applications. We demonstrate the UQ approaches and UQ-evaluation methods for two real-world problems: (1) estimating vertical profiles of atmospheric dewpoint (a regression task) and (2) predicting convection over Taiwan based on Himawari-8 satellite imagery (a classification task). We also provide Jupyter notebooks with Python code for implementing the UQ approaches and UQ-evaluation methods discussed herein. This article provides the environmental-science community with the knowledge and tools to start incorporating the large number of emerging UQ methods into their research.
{"title":"Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications","authors":"Katherine Haynes, Ryan Lagerquist, M. McGraw, K. Musgrave, I. Ebert‐Uphoff","doi":"10.1175/aies-d-22-0061.1","DOIUrl":"https://doi.org/10.1175/aies-d-22-0061.1","url":null,"abstract":"\u0000Neural networks (NN) have become an important tool for prediction tasks – both regression and classification – in environmental science. Since many environmental-science problems involve life-or-death decisions and policy-making, it is crucial to provide not only predictions but also an estimate of the uncertainty in the predictions. Until recently, very few tools were available to provide uncertainty quantification (UQ) for NN predictions. However, in recent years the computer-science field has developed numerous UQ approaches, and several research groups are exploring how to apply these approaches in environmental science. We provide an accessible introduction to six of these UQ approaches, then focus on tools for the next step, namely to answer the question: Once we obtain an uncertainty estimate (using any approach), how do we know whether it is good or bad? To answer this question, we highlight four evaluation graphics and eight evaluation scores that are well suited for evaluating and comparing uncertainty estimates (NN-based or otherwise) for environmental-science applications. We demonstrate the UQ approaches and UQ-evaluation methods for two real-world problems: (1) estimating vertical profiles of atmospheric dewpoint (a regression task) and (2) predicting convection over Taiwan based on Himawari-8 satellite imagery (a classification task). We also provide Jupyter notebooks with Python code for implementing the UQ approaches and UQ-evaluation methods discussed herein. This article provides the environmental-science community with the knowledge and tools to start incorporating the large number of emerging UQ methods into their research.","PeriodicalId":94369,"journal":{"name":"Artificial intelligence for the earth systems","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87885477","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}
Pub Date : 2023-01-09DOI: 10.1175/aies-d-22-0038.1
Elizabeth Weirich Benet, M. Pyrina, B. Jiménez-Esteve, E. Fraenkel, J. Cohen, D. Domeisen
Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. EarlyWarning Systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on timescales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecasts’ hindcast system. We thus show that machine learning can help improve sub-seasonal forecasts of summer temperature anomalies and heatwaves.
{"title":"Sub-seasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models","authors":"Elizabeth Weirich Benet, M. Pyrina, B. Jiménez-Esteve, E. Fraenkel, J. Cohen, D. Domeisen","doi":"10.1175/aies-d-22-0038.1","DOIUrl":"https://doi.org/10.1175/aies-d-22-0038.1","url":null,"abstract":"\u0000Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. EarlyWarning Systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on timescales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecasts’ hindcast system. We thus show that machine learning can help improve sub-seasonal forecasts of summer temperature anomalies and heatwaves.","PeriodicalId":94369,"journal":{"name":"Artificial intelligence for the earth systems","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87197752","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}
Pub Date : 2023-01-02DOI: 10.1175/aies-d-22-0094.1
Charlotte Connolly, E. Barnes, P. Hassanzadeh, M. Pritchard
Two distinct features of anthropogenic climate change, warming in the tropical upper troposphere and warming at the Arctic surface, have competing effects on the mid-latitude jet stream’s latitudinal position, often referred to as a “tug-of-war”. Studies that investigate the jet’s response to these thermal forcings show that it is sensitive to model type, season, initial atmospheric conditions, and the shape and magnitude of the forcing. Much of this past work focuses on studying a simulation’s response to external manipulation. In contrast, we explore the potential to train a convolutional neural network (CNN) on internal variability alone and then use it to examine possible nonlinear responses of the jet to tropospheric thermal forcing that more closely resemble anthropogenic climate change. Our approach leverages the idea behind the fluctuationdissipation theorem, which relates the internal variability of a system to its forced response but so far has been only used to quantify linear responses. We train a CNN on data from a long control run of the CESM dry dynamical core and show that it is able to skillfully predict the nonlinear response of the jet to sustained external forcing. The trained CNN provides a quick method for exploring the jet stream sensitivity to a wide range of tropospheric temperature tendencies and, considering that this method can likely be applied to any model with a long control run, could lend itself useful for early stage experiment design.
{"title":"Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability","authors":"Charlotte Connolly, E. Barnes, P. Hassanzadeh, M. Pritchard","doi":"10.1175/aies-d-22-0094.1","DOIUrl":"https://doi.org/10.1175/aies-d-22-0094.1","url":null,"abstract":"\u0000Two distinct features of anthropogenic climate change, warming in the tropical upper troposphere and warming at the Arctic surface, have competing effects on the mid-latitude jet stream’s latitudinal position, often referred to as a “tug-of-war”. Studies that investigate the jet’s response to these thermal forcings show that it is sensitive to model type, season, initial atmospheric conditions, and the shape and magnitude of the forcing. Much of this past work focuses on studying a simulation’s response to external manipulation. In contrast, we explore the potential to train a convolutional neural network (CNN) on internal variability alone and then use it to examine possible nonlinear responses of the jet to tropospheric thermal forcing that more closely resemble anthropogenic climate change. Our approach leverages the idea behind the fluctuationdissipation theorem, which relates the internal variability of a system to its forced response but so far has been only used to quantify linear responses. We train a CNN on data from a long control run of the CESM dry dynamical core and show that it is able to skillfully predict the nonlinear response of the jet to sustained external forcing. The trained CNN provides a quick method for exploring the jet stream sensitivity to a wide range of tropospheric temperature tendencies and, considering that this method can likely be applied to any model with a long control run, could lend itself useful for early stage experiment design.","PeriodicalId":94369,"journal":{"name":"Artificial intelligence for the earth systems","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82488604","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}
Pub Date : 2023-01-01DOI: 10.1175/aies-d-22-0024.1
Fatemeh Farokhmanesh, Kevin Höhlein, R. Westermann
Numerical simulations in Earth-system sciences consider a multitude of physical parameters in space and time, leading to severe input/output (I/O) bandwidth requirements and challenges in subsequent data analysis tasks. Deep learning–based identification of redundant parameters and prediction of those from other parameters, that is, variable-to-variable (V2V) transfer, has been proposed as an approach to lessening the bandwidth requirements and streamlining subsequent data analysis. In this paper, we examine the applicability of V2V to meteorological reanalysis data. We find that redundancies within pairs of parameter fields are limited, which hinders application of the original V2V algorithm. Therefore, we assess the predictive strength of reanalysis parameters by analyzing the learning behavior of V2V reconstruction networks in an ablation study. We demonstrate that efficient V2V transfer becomes possible when considering groups of parameter fields for transfer and propose an algorithm to implement this. We investigate further whether the neural networks trained in the V2V process can yield insightful representations of recurring patterns in the data. The interpretability of these representations is assessed via layerwise relevance propagation that highlights field areas and parameters of high importance for the reconstruction model. Applied to reanalysis data, this allows for uncovering mutual relationships between landscape orography and different regional weather situations. We see our approach as an effective means to reduce bandwidth requirements in numerical weather simulations, which can be used on top of conventional data compression schemes. The proposed identification of multiparameter features can spawn further research on the importance of regional weather situations for parameter prediction and also in other kinds of simulation data.
{"title":"Deep Learning–Based Parameter Transfer in Meteorological Data","authors":"Fatemeh Farokhmanesh, Kevin Höhlein, R. Westermann","doi":"10.1175/aies-d-22-0024.1","DOIUrl":"https://doi.org/10.1175/aies-d-22-0024.1","url":null,"abstract":"\u0000Numerical simulations in Earth-system sciences consider a multitude of physical parameters in space and time, leading to severe input/output (I/O) bandwidth requirements and challenges in subsequent data analysis tasks. Deep learning–based identification of redundant parameters and prediction of those from other parameters, that is, variable-to-variable (V2V) transfer, has been proposed as an approach to lessening the bandwidth requirements and streamlining subsequent data analysis. In this paper, we examine the applicability of V2V to meteorological reanalysis data. We find that redundancies within pairs of parameter fields are limited, which hinders application of the original V2V algorithm. Therefore, we assess the predictive strength of reanalysis parameters by analyzing the learning behavior of V2V reconstruction networks in an ablation study. We demonstrate that efficient V2V transfer becomes possible when considering groups of parameter fields for transfer and propose an algorithm to implement this. We investigate further whether the neural networks trained in the V2V process can yield insightful representations of recurring patterns in the data. The interpretability of these representations is assessed via layerwise relevance propagation that highlights field areas and parameters of high importance for the reconstruction model. Applied to reanalysis data, this allows for uncovering mutual relationships between landscape orography and different regional weather situations. We see our approach as an effective means to reduce bandwidth requirements in numerical weather simulations, which can be used on top of conventional data compression schemes. The proposed identification of multiparameter features can spawn further research on the importance of regional weather situations for parameter prediction and also in other kinds of simulation data.","PeriodicalId":94369,"journal":{"name":"Artificial intelligence for the earth systems","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81152718","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}
Pub Date : 2023-01-01DOI: 10.1175/aies-d-22-0039.1
Lander Ver Hoef, Henry Adams, Emily J. King, Imme Ebert-Uphoff
Abstract Topological data analysis (TDA) is a tool from data science and mathematics that is beginning to make waves in environmental science. In this work, we seek to provide an intuitive and understandable introduction to a tool from TDA that is particularly useful for the analysis of imagery, namely, persistent homology. We briefly discuss the theoretical background but focus primarily on understanding the output of this tool and discussing what information it can glean. To this end, we frame our discussion around a guiding example of classifying satellite images from the sugar, fish, flower, and gravel dataset produced for the study of mesoscale organization of clouds by Rasp et al. We demonstrate how persistent homology and its vectorization, persistence landscapes, can be used in a workflow with a simple machine learning algorithm to obtain good results, and we explore in detail how we can explain this behavior in terms of image-level features. One of the core strengths of persistent homology is how interpretable it can be, so throughout this paper we discuss not just the patterns we find but why those results are to be expected given what we know about the theory of persistent homology. Our goal is that readers of this paper will leave with a better understanding of TDA and persistent homology, will be able to identify problems and datasets of their own for which persistent homology could be helpful, and will gain an understanding of the results they obtain from applying the included GitHub example code. Significance Statement Information such as the geometric structure and texture of image data can greatly support the inference of the physical state of an observed Earth system, for example, in remote sensing to determine whether wildfires are active or to identify local climate zones. Persistent homology is a branch of topological data analysis that allows one to extract such information in an interpretable way—unlike black-box methods like deep neural networks. The purpose of this paper is to explain in an intuitive manner what persistent homology is and how researchers in environmental science can use it to create interpretable models. We demonstrate the approach to identify certain cloud patterns from satellite imagery and find that the resulting model is indeed interpretable.
{"title":"A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science","authors":"Lander Ver Hoef, Henry Adams, Emily J. King, Imme Ebert-Uphoff","doi":"10.1175/aies-d-22-0039.1","DOIUrl":"https://doi.org/10.1175/aies-d-22-0039.1","url":null,"abstract":"Abstract Topological data analysis (TDA) is a tool from data science and mathematics that is beginning to make waves in environmental science. In this work, we seek to provide an intuitive and understandable introduction to a tool from TDA that is particularly useful for the analysis of imagery, namely, persistent homology. We briefly discuss the theoretical background but focus primarily on understanding the output of this tool and discussing what information it can glean. To this end, we frame our discussion around a guiding example of classifying satellite images from the sugar, fish, flower, and gravel dataset produced for the study of mesoscale organization of clouds by Rasp et al. We demonstrate how persistent homology and its vectorization, persistence landscapes, can be used in a workflow with a simple machine learning algorithm to obtain good results, and we explore in detail how we can explain this behavior in terms of image-level features. One of the core strengths of persistent homology is how interpretable it can be, so throughout this paper we discuss not just the patterns we find but why those results are to be expected given what we know about the theory of persistent homology. Our goal is that readers of this paper will leave with a better understanding of TDA and persistent homology, will be able to identify problems and datasets of their own for which persistent homology could be helpful, and will gain an understanding of the results they obtain from applying the included GitHub example code. Significance Statement Information such as the geometric structure and texture of image data can greatly support the inference of the physical state of an observed Earth system, for example, in remote sensing to determine whether wildfires are active or to identify local climate zones. Persistent homology is a branch of topological data analysis that allows one to extract such information in an interpretable way—unlike black-box methods like deep neural networks. The purpose of this paper is to explain in an intuitive manner what persistent homology is and how researchers in environmental science can use it to create interpretable models. We demonstrate the approach to identify certain cloud patterns from satellite imagery and find that the resulting model is indeed interpretable.","PeriodicalId":94369,"journal":{"name":"Artificial intelligence for the earth systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134956073","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}
Pub Date : 2023-01-01DOI: 10.1175/aies-d-22-0058.1
Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert-Uphoff
Abstract Methods of explainable artificial intelligence (XAI) are used in geoscientific applications to gain insights into the decision-making strategy of neural networks (NNs), highlighting which features in the input contribute the most to a NN prediction. Here, we discuss our “lesson learned” that the task of attributing a prediction to the input does not have a single solution. Instead, the attribution results depend greatly on the considered baseline that the XAI method utilizes—a fact that has been overlooked in the geoscientific literature. The baseline is a reference point to which the prediction is compared so that the prediction can be understood. This baseline can be chosen by the user or is set by construction in the method’s algorithm—often without the user being aware of that choice. We highlight that different baselines can lead to different insights for different science questions and, thus, should be chosen accordingly. To illustrate the impact of the baseline, we use a large ensemble of historical and future climate simulations forced with the shared socioeconomic pathway 3-7.0 (SSP3-7.0) scenario and train a fully connected NN to predict the ensemble- and global-mean temperature (i.e., the forced global warming signal) given an annual temperature map from an individual ensemble member. We then use various XAI methods and different baselines to attribute the network predictions to the input. We show that attributions differ substantially when considering different baselines, because they correspond to answering different science questions. We conclude by discussing important implications and considerations about the use of baselines in XAI research. Significance Statement In recent years, methods of explainable artificial intelligence (XAI) have found great application in geoscientific applications, because they can be used to attribute the predictions of neural networks (NNs) to the input and interpret them physically. Here, we highlight that the attributions—and the physical interpretation—depend greatly on the choice of the baseline—a fact that has been overlooked in the geoscientific literature. We illustrate this dependence for a specific climate task, in which a NN is trained to predict the ensemble- and global-mean temperature (i.e., the forced global warming signal) given an annual temperature map from an individual ensemble member. We show that attributions differ substantially when considering different baselines, because they correspond to answering different science questions.
{"title":"Carefully Choose the Baseline: Lessons Learned from Applying XAI Attribution Methods for Regression Tasks in Geoscience","authors":"Antonios Mamalakis, Elizabeth A. Barnes, Imme Ebert-Uphoff","doi":"10.1175/aies-d-22-0058.1","DOIUrl":"https://doi.org/10.1175/aies-d-22-0058.1","url":null,"abstract":"Abstract Methods of explainable artificial intelligence (XAI) are used in geoscientific applications to gain insights into the decision-making strategy of neural networks (NNs), highlighting which features in the input contribute the most to a NN prediction. Here, we discuss our “lesson learned” that the task of attributing a prediction to the input does not have a single solution. Instead, the attribution results depend greatly on the considered baseline that the XAI method utilizes—a fact that has been overlooked in the geoscientific literature. The baseline is a reference point to which the prediction is compared so that the prediction can be understood. This baseline can be chosen by the user or is set by construction in the method’s algorithm—often without the user being aware of that choice. We highlight that different baselines can lead to different insights for different science questions and, thus, should be chosen accordingly. To illustrate the impact of the baseline, we use a large ensemble of historical and future climate simulations forced with the shared socioeconomic pathway 3-7.0 (SSP3-7.0) scenario and train a fully connected NN to predict the ensemble- and global-mean temperature (i.e., the forced global warming signal) given an annual temperature map from an individual ensemble member. We then use various XAI methods and different baselines to attribute the network predictions to the input. We show that attributions differ substantially when considering different baselines, because they correspond to answering different science questions. We conclude by discussing important implications and considerations about the use of baselines in XAI research. Significance Statement In recent years, methods of explainable artificial intelligence (XAI) have found great application in geoscientific applications, because they can be used to attribute the predictions of neural networks (NNs) to the input and interpret them physically. Here, we highlight that the attributions—and the physical interpretation—depend greatly on the choice of the baseline—a fact that has been overlooked in the geoscientific literature. We illustrate this dependence for a specific climate task, in which a NN is trained to predict the ensemble- and global-mean temperature (i.e., the forced global warming signal) given an annual temperature map from an individual ensemble member. We show that attributions differ substantially when considering different baselines, because they correspond to answering different science questions.","PeriodicalId":94369,"journal":{"name":"Artificial intelligence for the earth systems","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136052395","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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