Artificial Intelligence for the Earth Systems最新文献

{"title":"Hypoxia forecasting for Chesapeake Bay using artificial intelligence","authors":"Guangming Zheng, Stephanie Schollaert Uz, P. St‐Laurent, M. Friedrichs, Amita Mehta, P. DiGiacomo","doi":"10.1175/aies-d-23-0054.1","DOIUrl":"https://doi.org/10.1175/aies-d-23-0054.1","url":null,"abstract":"\u0000Seasonal hypoxia is a recurring threat to ecosystems and fisheries in the Chesapeake Bay. Hypoxia forecasting based on coupled hydrodynamic and biogeochemical models has proven useful for many stakeholders, as these models excel in accounting for the effects of physical forcing on oxygen supply, but may fall short in replicating the more complex biogeochemical processes that govern oxygen consumption. Satellite-derived reflectances could be used to indicate the presence of surface organic matter over the Bay. However, teasing apart the contribution of atmospheric and aquatic constituents from the signal received by the satellite is not straightforward. As a result, it is difficult to derive surface concentrations of organic matter from satellite data in a robust fashion. A potential solution to this complexity is to use deep learning to build end-to-end applications that do not require precise accounting of the satellite signal from atmosphere or water, phytoplankton blooms, or sediment plumes. By training a deep neural network with data from a vast suite of variables that could potentially affect oxygen in the water column, improvement of short-term (daily) hypoxia forecast may be possible. Here we predict oxygen concentrations using inputs that account for both physical and biogeochemical factors. The physical inputs include wind velocity reanalysis information, together with 3D outputs from an estuarine hydrodynamic model, including current velocity, water temperature, and salinity. Satellite-derived spectral reflectance data are used as a surrogate for the biogeochemical factors. These input fields are time series of weekly statistics calculated from daily information, starting 8 weeks before each oxygen observation was collected. To accommodate this input data structure, we adopted a model architecture of long short-term memory networks with 8 time steps. At each time step, a set of convolutional neural networks are used to extract information from the inputs. Ablation and cross validation tests suggest that among all input features, the strongest predictor is the 3D temperature field, with which the new model can outperform the state-of-the-art by ∼20% in terms of median absolute error. Our approach represents a novel application of deep learning to address a complex water management challenge.","PeriodicalId":513581,"journal":{"name":"Artificial Intelligence for the Earth Systems","volume":"7 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140711954","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}

{"title":"UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models","authors":"Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, Daniel Galea","doi":"10.1175/aies-d-23-0076.1","DOIUrl":"https://doi.org/10.1175/aies-d-23-0076.1","url":null,"abstract":"\u0000Precipitation values produced by climate models are biased due to the parameterization of physical processes and limited spatial resolution. Current bias correction approaches usually focus on correcting lower-order statistics (mean, standard deviation), which make it difficult to capture precipitation extremes. However, accurate modeling of extremes is critical for policymaking to mitigate and adapt to the effects of climate change. We develop a deep learning framework, leveraging information from key dynamical variables impacting precipitation to also match higher-order statistics (skewness and kurtosis) for the entire precipitation distribution, including extremes. The deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Using climate model simulation data and observations that are climatologically similar but not strictly paired, the UFNet identifies and corrects the climate model biases, significantly improving the estimation of daily precipitation as measured by a broad range of spatiotemporal statistics. In particular, UFNet significantly improves the underestimation of extreme precipitation values seen with current bias-correction methods. Our approach constitutes a generalized framework for correcting other climate model variables which improves the accuracy of the climate model predictions, while utilizing a simpler and more stable training process.","PeriodicalId":513581,"journal":{"name":"Artificial Intelligence for the Earth Systems","volume":"26 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140728326","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}

{"title":"Storm gust prediction with the integration of machine learning algorithms and WRF model variables for the Northeast United States","authors":"Israt Jahan, Diego Cerrai, Marina Astitha","doi":"10.1175/aies-d-23-0047.1","DOIUrl":"https://doi.org/10.1175/aies-d-23-0047.1","url":null,"abstract":"\u0000Wind gusts are often associated with severe hazards and can cause structural and environmental damages, making gust prediction a crucial element of weather forecasting services. In this study, we explored the utilization of machine learning (ML) algorithms integrated with numerical weather prediction outputs from the Weather Research and Forecasting model (WRF), to align the estimation of wind gust potential with observed gusts. We have used two ML algorithms, namely Random Forest (RF), and Extreme Gradient Boosting (XGB), along with two statistical techniques: Generalized Linear Models with identity link function (GLM-Identity) and log link function (GLM-Log), to predict storm wind gusts for the NE USA. We used 61 simulated extratropical and tropical storms that occurred between 2005 and 2020, to develop and validate the ML and statistical models. To assess the ML model performance, we compared our results with post-processed gust potential from WRF. Our findings showed that ML models, especially XGB, performed significantly better than statistical models and WRF-UPP, and were able to better align predicted with observed gusts across all storms. The ML models faced challenges capturing the upper tail of the gust distribution, and the learning curves suggested that XGB was more effective than RF in generating better predictions with fewer storms.","PeriodicalId":513581,"journal":{"name":"Artificial Intelligence for the Earth Systems","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140738750","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}

{"title":"Development of a full-scale connected U-Net for reflectivity inpainting in spaceborne radar blind zones","authors":"Fraser King, Claire Pettersen, Christopher G. Fletcher, Andrew Geiss","doi":"10.1175/aies-d-23-0063.1","DOIUrl":"https://doi.org/10.1175/aies-d-23-0063.1","url":null,"abstract":"\u0000CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground-clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen—Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near surface cloud with a critical success index (CSI) of 72%, and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. A mechanistic interpretiability analysis highlights the fact that a combination of both the reflectivity information throughout the scene (most notably across cloud edges and at the 1.2 km blind zone threshold), along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state.","PeriodicalId":513581,"journal":{"name":"Artificial Intelligence for the Earth Systems","volume":"108 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140772120","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}

{"title":"A Convolutional Neural Network for Tropical Cyclone Wind Structure Identification in kilometre-scale forecasts","authors":"L. Raynaud, G. Faure, M. Puig, C. Dauvilliers, J-N Trosino, P. Béjean","doi":"10.1175/aies-d-23-0059.1","DOIUrl":"https://doi.org/10.1175/aies-d-23-0059.1","url":null,"abstract":"\u0000Detection and tracking of Tropical Cyclones (TCs) in numerical weather prediction model outputs is essential for many applications, such as forecast guidance and real-time monitoring of events. While this task has been automated in the 90s with heuristic models, relying on a set of empirical rules and thresholds, the recent success of machine learning methods to detect objects in images opens new perspectives. This paper introduces and evaluates the capacity of a convolutional neural network based on the U-Net architecture to detect the TC wind structure, including maximum wind speed area and hurricane-force wind speed area, in the outputs of the convective-scale Arome model. A dataset of 400 Arome forecasts over the West Indies domain has been entirely hand labeled by experts, following a rigorous process to reduce heterogeneities. The U-Net performs well on a wide variety of TC intensities and shapes, with an average intersection-over-union metric around 0.8. Its performances however strongly depend on the TC strength, the detection of weak cyclones being more challenging since their structure is less well defined. The U-Net also significantly outperforms an operational heuristic detection model, with a significant gain for weak TCs, while running much faster. In a last part, the capacity of the U-Net to generalize on slightly different data is demonstrated in the context of a domain change and a resolution increase. In both cases, the pre-trained U-Net achieves similar performances as on the original dataset.","PeriodicalId":513581,"journal":{"name":"Artificial Intelligence for the Earth Systems","volume":"57 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140784335","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}

{"title":"Environmental Justice and Lessons Learned from COVID-19 Outcomes – Uncovering Hidden Patterns with Geometric Deep Learning and New NASA Satellite Data","authors":"Zhiwei Zhen, Huikyo Lee, I. Segovia-Dominguez, Meichen Huang, Yuzhou Chen, Michael Garay, Daniel Crichton, Yulia R. Gel","doi":"10.1175/aies-d-23-0040.1","DOIUrl":"https://doi.org/10.1175/aies-d-23-0040.1","url":null,"abstract":"\u0000Virtually all aspects of our societal functioning – from food security to energy supply to healthcare – depend on the dynamics of environmental factors. Nevertheless, the social dimensions of weather and climate are noticeably less explored by the artificial intelligence community. By harnessing the strength of geometric deep learning (GDL), we aim to investigate the pressing societal question the potential disproportional impacts of air quality on COVID-19 clinical severity. To quantify air pollution levels, here we use aerosol optical depth (AOD) records which measure the reduction of the sunlight due to atmospheric haze, dust, and smoke. We also introduce unique and not yet broadly available NASA satellite records (NASAdat) on AOD, temperature and relative humidity and discuss the utility of these new data for biosurveillance and climate justice applications, with a specific focus on COVID-19 in the States of Texas and Pennsylvania in USA. The results indicate that, in general, the poorer air quality tends to be associated with higher rates for clinical severity and, in case of Texas, this phenomenon particularly stands out in Texan counties characterized by higher socioeconomic vulnerability. This, in turn, raises a concern of environmental injustice in these socio-economically disadvantaged communities. Furthermore, given that one of NASA’s recent long-term commitments is to address such inequitable burden of environmental harm by expanding the use of Earth science data such as NASAdat, this project is one of the first steps toward developing a new platform integrating NASA’s satellite observations with DL tools for social good.","PeriodicalId":513581,"journal":{"name":"Artificial Intelligence for the Earth Systems","volume":"60 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139960751","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}

{"title":"Super-Resolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network","authors":"Charles H. White, Imme Ebert-Uphoff, John M. Haynes, Yoo-Jeong Noh","doi":"10.1175/aies-d-23-0065.1","DOIUrl":"https://doi.org/10.1175/aies-d-23-0065.1","url":null,"abstract":"\u0000 Super-resolution is the general task of artificially increasing the spatial resolution of an image. The recent surge in machine learning (ML) research has yielded many promising ML-based approaches for performing single-image super-resolution including applications to satellite remote sensing. We develop a convolutional neural network (CNN) to super-resolve the 1-km and 2-km bands on the GOES-R series Advanced Baseline Imager (ABI) to a common high resolution of 0.5-km. Access to 0.5-km imagery from ABI Band-2 enables the CNN to realistically sharpen lower-resolution bands without significant blurring. We first train the CNN on a proxy task which allows us to only use ABI imagery, namely degrading resolution of ABI bands and training the CNN to restore the original imagery. Comparisons at reduced resolution and at full resolution with Landsat-8/9 observations illustrate that the CNN produces images with realistic high-frequency detail that is not present in a bicubic interpolation baseline. Estimating all ABI bands at 0.5-km resolution allows for more easily combining information across bands without reconciling differences in spatial resolution. However, more analysis is needed to determine impacts on derived products or multispectral imagery that use super-resolved bands. This approach is extensible to other remote sensing instruments that have bands with different spatial resolutions and requires only a small amount of data and knowledge of each channel’s modulation transfer function.","PeriodicalId":513581,"journal":{"name":"Artificial Intelligence for the Earth Systems","volume":"66 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778749","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}

{"title":"Super-Resolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network","authors":"Charles H. White, Imme Ebert-Uphoff, John M. Haynes, Yoo-Jeong Noh","doi":"10.1175/aies-d-23-0065.1","DOIUrl":"https://doi.org/10.1175/aies-d-23-0065.1","url":null,"abstract":"\u0000 Super-resolution is the general task of artificially increasing the spatial resolution of an image. The recent surge in machine learning (ML) research has yielded many promising ML-based approaches for performing single-image super-resolution including applications to satellite remote sensing. We develop a convolutional neural network (CNN) to super-resolve the 1-km and 2-km bands on the GOES-R series Advanced Baseline Imager (ABI) to a common high resolution of 0.5-km. Access to 0.5-km imagery from ABI Band-2 enables the CNN to realistically sharpen lower-resolution bands without significant blurring. We first train the CNN on a proxy task which allows us to only use ABI imagery, namely degrading resolution of ABI bands and training the CNN to restore the original imagery. Comparisons at reduced resolution and at full resolution with Landsat-8/9 observations illustrate that the CNN produces images with realistic high-frequency detail that is not present in a bicubic interpolation baseline. Estimating all ABI bands at 0.5-km resolution allows for more easily combining information across bands without reconciling differences in spatial resolution. However, more analysis is needed to determine impacts on derived products or multispectral imagery that use super-resolved bands. This approach is extensible to other remote sensing instruments that have bands with different spatial resolutions and requires only a small amount of data and knowledge of each channel’s modulation transfer function.","PeriodicalId":513581,"journal":{"name":"Artificial Intelligence for the Earth Systems","volume":"96 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139838439","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}

{"title":"Machine learning based lightning parameterizations for CONUS","authors":"Wei-Yi Cheng, Daehyun Kim, Scott Henderson, Y. Ham, Jeong-Hwan Kim, Rober H. Holzworth","doi":"10.1175/aies-d-23-0024.1","DOIUrl":"https://doi.org/10.1175/aies-d-23-0024.1","url":null,"abstract":"\u0000The diversity in the lightning parameterizations for numerical weather and climate models causes a considerable uncertainty in lightning prediction. In this study, we take a data-driven approach to address the lightning parameterization problem, by combining machine learning (ML) techniques with the rich lightning observations from World-Wide Lightning Location Network. Three ML algorithms are trained over the Contiguous United States (CONUS) to predict lightning stroke density in a 1° box based on the information about the atmospheric variables in the same grid (local), or over the entire CONUS (non-local). The performance of the ML-based lightning schemes is examined and compared with that of a simple, conventional lightning parameterization scheme of Romps et al. (2014).\u0000We find that all ML-based lightning schemes exhibit a performance that are superior to that of the conventional scheme in the regions and in the seasons with climatologically higher lightning stroke density. To the west of Rocky Mountains, the non-local ML lightning scheme achieves the best overall performance, with lightning stroke density predictions being 70% more accurate than the conventional scheme. Our results suggest that the ML-based approaches have the potential to improve the representation of lightning and other types of extreme weather events in the weather and climate models.","PeriodicalId":513581,"journal":{"name":"Artificial Intelligence for the Earth Systems","volume":"108 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139847524","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}

{"title":"Machine learning based lightning parameterizations for CONUS","authors":"Wei-Yi Cheng, Daehyun Kim, Scott Henderson, Y. Ham, Jeong-Hwan Kim, Rober H. Holzworth","doi":"10.1175/aies-d-23-0024.1","DOIUrl":"https://doi.org/10.1175/aies-d-23-0024.1","url":null,"abstract":"\u0000The diversity in the lightning parameterizations for numerical weather and climate models causes a considerable uncertainty in lightning prediction. In this study, we take a data-driven approach to address the lightning parameterization problem, by combining machine learning (ML) techniques with the rich lightning observations from World-Wide Lightning Location Network. Three ML algorithms are trained over the Contiguous United States (CONUS) to predict lightning stroke density in a 1° box based on the information about the atmospheric variables in the same grid (local), or over the entire CONUS (non-local). The performance of the ML-based lightning schemes is examined and compared with that of a simple, conventional lightning parameterization scheme of Romps et al. (2014).\u0000We find that all ML-based lightning schemes exhibit a performance that are superior to that of the conventional scheme in the regions and in the seasons with climatologically higher lightning stroke density. To the west of Rocky Mountains, the non-local ML lightning scheme achieves the best overall performance, with lightning stroke density predictions being 70% more accurate than the conventional scheme. Our results suggest that the ML-based approaches have the potential to improve the representation of lightning and other types of extreme weather events in the weather and climate models.","PeriodicalId":513581,"journal":{"name":"Artificial Intelligence for the Earth Systems","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139787602","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}