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The Earth and environmental sciences (collectively Earth science in what follows) stand to benefit from leveraging rapid advances in artificial intelligence (AI) from diverse applied science fields due to the combination of fast paced increases in data availability and computational capabilities. Leveraging algorithms used in other fields—what might be called meta-transfer learning—is accelerating the use of AI for environmental data and Earth system applications. We summarize here the main
The Earth and environmental sciences (collectively Earth science in what follows) stand to benefit from leveraging rapid advances in artificial intelligence (AI) from diverse applied science fields due to the combination of fast paced increases in data availability and computational capabilities. Leveraging algorithms used in other fields—what might be called meta-transfer learning—is accelerating the use of AI for environmental data and Earth system applications. We summarize here the main
the application of CNNs in the geosciences is their black-box nature, which makes it hard for scientists to interpret predictions and to assess the model from a physical perspective, that is, beyond using prediction performance as the only criterion. The interpretability issue is considered a key issue for deep learning in general, and it has prompted the emergence of a new subfield in computer science, namely, explainable artificial intelligence (XAI; Buhrmester et al. 2019 ; Tjoa and Guan 2019
the application of CNNs in the geosciences is their black-box nature, which makes it hard for scientists to interpret predictions and to assess the model from a physical perspective, that is, beyond using prediction performance as the only criterion. The interpretability issue is considered a key issue for deep learning in general, and it has prompted the emergence of a new subfield in computer science, namely, explainable artificial intelligence (XAI; Buhrmester et al. 2019 ; Tjoa and Guan 2019
information from climate datasets have recently emerged as promising tools to reconstruct spatial fields, while preserving major features of the variability ( Carro-Calvo et al. 2021 ; Kadow et al. 2020 ; Vaccaro et al. 2021 ). Joining statistics and computer science, artificial intelligence (AI) is a multidisciplinary field with different areas of expertise such as machine learning ( LeCun et al. 2015 ; Kadow et al. 2020 ), and optimization ( Swarnkar and Swarnkar 2019 ; Soto et al. 2019 ). Regarding
information from climate datasets have recently emerged as promising tools to reconstruct spatial fields, while preserving major features of the variability ( Carro-Calvo et al. 2021 ; Kadow et al. 2020 ; Vaccaro et al. 2021 ). Joining statistics and computer science, artificial intelligence (AI) is a multidisciplinary field with different areas of expertise such as machine learning ( LeCun et al. 2015 ; Kadow et al. 2020 ), and optimization ( Swarnkar and Swarnkar 2019 ; Soto et al. 2019 ). Regarding
Eq. ( 11 )]. Values were computed for 0000 UTC 1 Jan 2006, Δ t = 60 min, and P = 850 hPa. The structure of the error distribution also raises some questions about overfitting. In this study, we derived an error distribution, under the assumption of an available ground truth V truth . In an operational setting, we would not calculate the error ϵ = V NWP − V truth , since we will use the NWP field itself, e.g., V NWP = V truth + ϵ , rather than constructing an artificial V NWP
Eq. ( 11 )]. Values were computed for 0000 UTC 1 Jan 2006, Δ t = 60 min, and P = 850 hPa. The structure of the error distribution also raises some questions about overfitting. In this study, we derived an error distribution, under the assumption of an available ground truth V truth . In an operational setting, we would not calculate the error ϵ = V NWP − V truth , since we will use the NWP field itself, e.g., V NWP = V truth + ϵ , rather than constructing an artificial V NWP
recognized ( Nohara and Tanaka 2004 ; Zhou and Johnny 2006 ). At present, traditional mathematic modeling methods, such as multivariate analysis and time series analysis are widely used in statistical prediction and dynamical–statistical prediction ( Zhou and Huang 1997 ; Ding et al. 2002 ), in which the future state of a prediction object is forecasted using a statistical prediction equation ( Zhou and Huang 1997 ; Ding et al. 2002 ). With the development of the artificial intelligence technique
recognized ( Nohara and Tanaka 2004 ; Zhou and Johnny 2006 ). At present, traditional mathematic modeling methods, such as multivariate analysis and time series analysis are widely used in statistical prediction and dynamical–statistical prediction ( Zhou and Huang 1997 ; Ding et al. 2002 ), in which the future state of a prediction object is forecasted using a statistical prediction equation ( Zhou and Huang 1997 ; Ding et al. 2002 ). With the development of the artificial intelligence technique
skill and understanding on the S2S time scale, the World Weather Research Programme (WWRP) and World Climate Research Programme (WCRP) launched the Subseasonal to Seasonal Prediction Project (S2S) ( Vitart et al. 2015 ) in November 2013. Artificial intelligence (AI) can potentially improve S2S predictions because of its potential to explore very large multimodel forecast and observed datasets more agnostically, to discover emergent patterns in the data (e.g., Weyn et al. 2021 ), instead of first
skill and understanding on the S2S time scale, the World Weather Research Programme (WWRP) and World Climate Research Programme (WCRP) launched the Subseasonal to Seasonal Prediction Project (S2S) ( Vitart et al. 2015 ) in November 2013. Artificial intelligence (AI) can potentially improve S2S predictions because of its potential to explore very large multimodel forecast and observed datasets more agnostically, to discover emergent patterns in the data (e.g., Weyn et al. 2021 ), instead of first
VOL. 4, NO. I JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY MARCH 1987Recognizing Low-Altitude Wind Shear Hazards from Doppler Weather Radar: An Artificial Intelligence Approach* STEVEN D. CAMPBELL AND STEPHEN H. OLSONLincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02173(Manuscript received 23 April 1986, in final form 12 December 1986)ABSTRACT This paper describes an artificial
VOL. 4, NO. I JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY MARCH 1987Recognizing Low-Altitude Wind Shear Hazards from Doppler Weather Radar: An Artificial Intelligence Approach* STEVEN D. CAMPBELL AND STEPHEN H. OLSONLincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02173(Manuscript received 23 April 1986, in final form 12 December 1986)ABSTRACT This paper describes an artificial
Modern artificial intelligence (AI) techniques can aid forecasters on a wide variety of high-impact weather phenomena. Weather significantly impacts society for better and for worse. For example, severe weather hazards caused over $7.9 billion of property damage in 2015 ( National Oceanic and Atmospheric Administration/National Centers for Environmental Information 2016 ; CoreLogic 2016 ). The National Academies of Sciences, Engineering, and Medicine (2016) cites improving forecasting of
Modern artificial intelligence (AI) techniques can aid forecasters on a wide variety of high-impact weather phenomena. Weather significantly impacts society for better and for worse. For example, severe weather hazards caused over $7.9 billion of property damage in 2015 ( National Oceanic and Atmospheric Administration/National Centers for Environmental Information 2016 ; CoreLogic 2016 ). The National Academies of Sciences, Engineering, and Medicine (2016) cites improving forecasting of
1. Introduction Explainable artificial intelligence (XAI) aims to provide insights about the decision-making process of AI models and has been increasingly applied to the geosciences (e.g., Toms et al. 2021 ; Ebert-Uphoff and Hilburn 2020 ; Hilburn et al. 2021 ; Barnes et al. 2019 , 2020 ; Mayer and Barnes 2021 ; Keys et al. 2021 ; Sonnewald and Lguensat 2021 ). XAI methods show promising results in calibrating model trust and assisting in learning new science (see for example
1. Introduction Explainable artificial intelligence (XAI) aims to provide insights about the decision-making process of AI models and has been increasingly applied to the geosciences (e.g., Toms et al. 2021 ; Ebert-Uphoff and Hilburn 2020 ; Hilburn et al. 2021 ; Barnes et al. 2019 , 2020 ; Mayer and Barnes 2021 ; Keys et al. 2021 ; Sonnewald and Lguensat 2021 ). XAI methods show promising results in calibrating model trust and assisting in learning new science (see for example
EDITORIALArtificial IntelligencemThe Challenge and the Opportunity The interdisciplinary studies commonly referred to as "Artificial Intelligence" haveresulted in new ways to use computers that are potentially as far-reaching as anyprevious advance in automatic data processing. The challenge to the meteorologicalcommunity is to maintain the proper balance between unrealistic expectations andthe negativism that has been the usual reaction to exaggerated claims. The research efforts
EDITORIALArtificial IntelligencemThe Challenge and the Opportunity The interdisciplinary studies commonly referred to as "Artificial Intelligence" haveresulted in new ways to use computers that are potentially as far-reaching as anyprevious advance in automatic data processing. The challenge to the meteorologicalcommunity is to maintain the proper balance between unrealistic expectations andthe negativism that has been the usual reaction to exaggerated claims. The research efforts
