A continuous vertically resolved ozone dataset from the fusion of chemistry climate models with observations using a Bayesian neural network

Continuous historic datasets of vertically resolved stratospheric ozone, support the case for ozone recovery, are necessary for the running of offline models and increase understanding of the impacts of ozone on the wider atmospheric system. Vertically resolved ozone datasets are typically constructed from multiple satellite, sonde and ground-based measurements that do not provide continuous coverage. As a result, several methods have been used to infill these gaps, most commonly relying on regression against observed time series. However, these existing methods either provide low accuracy infilling especially over polar regions, unphysical extrapolation, or an incomplete estimation of uncertainty. To address these methodological shortcomings we used and further developed an infilling framework that fuses observations with output from an ensemble of chemistry-climate models within a Bayesian neural network. We used this deep learning framework to produce a continuous record of vertically resolved ozone with uncertainty estimates. Under rigorous testing the infilling framework extrapolated and interpolated skillfully and maintained realistic interannual variability due to the inclusion of physically and chemically realistic models. This framework and the ozone dataset it produced, enables a more thorough investigation of vertically resolved trends throughout the atmosphere.

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Fusing model ensembles and observations together with Bayesian neural networks

10.5194/egusphere-egu21-11905 ◽

2021 ◽

Author(s):

Matt Amos ◽

Ushnish Sengupta ◽

Scott Hosking ◽

Paul Young

Keyword(s):

Neural Network ◽

Total Ozone ◽

Climate Models ◽

Model Performance ◽

Physical Models ◽

Bayesian Neural Network ◽

Total Ozone Column ◽

Uncertainty Estimates ◽

Model Ensembles ◽

Ozone Column

To fuse together output from ensembles of climate models with observations, we have developed a custom Bayesian neural network that produces more accurate and uncertainty aware projections.Ensembles of physical models are typically used to increase the accuracy of projections and quantify projective uncertainties. However, few methods for combining ensemble output consider differing model performance or similarity between models. Current weighting strategies that do, typically assume model weights are invariant in time and space though this is rarely the case in models.Our Bayesian neural network infers spatiotemporally varying model weights, bias and uncertainty to capture that some regions or seasons are better simulated in certain models. The Bayesian neural network learns how to optimally combine multiple models in order to replicate observations and can also be used to infill gaps in historic observations. In regions of sparse observations, it infers from both the surrounding data and similar physical conditions. Although we are using a typically black box technique, the attribution of model weights and bias maintains interpretability.We demonstrate the utility of the Bayesian neural network by using it to combine multiple chemistry climate models to produce continuous historic predictions of the total ozone column (1980-2010) and projections of total ozone column for the 21st century, both with principled uncertainty estimates. Rigorous validation shows that our Bayesian neural network predictions outperform standard methods of assimilating models.

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Assessing Hydrologic Impact of Climate Change with Uncertainty Estimates: Bayesian Neural Network Approach

Journal of Hydrometeorology ◽

10.1175/2009jhm1160.1 ◽

2010 ◽

Vol 11 (2) ◽

pp. 482-495 ◽

Cited By ~ 27

Author(s):

Mohammad Sajjad Khan ◽

Paulin Coulibaly

Keyword(s):

Neural Network ◽

Climate Change ◽

Rainfall Runoff ◽

Bayesian Neural Network ◽

Uncertainty Estimates ◽

Change Impact ◽

Climate Change Impact Study ◽

Rainfall Runoff Model ◽

Runoff Model ◽

Different Sources

Abstract A major challenge in assessing the hydrologic effect of climate change remains the estimation of uncertainties associated with different sources, such as the global climate models, emission scenarios, downscaling methods, and hydrologic models. There is a demand for an efficient and easy-to-use rainfall–runoff modeling tool that can capture the different sources of uncertainties to generate future flow simulations that can be used for decision making. To manage the large range of uncertainties in the climate change impact study on water resources, a neural network–based rainfall–runoff model—namely, Bayesian neural network (BNN)—is proposed. The BNN model is used with Canadian Centre for Climate Modelling and Analysis Coupled GCM, versions 1 and 2 (CGCM1 and CGCM2, respectively) with two emission scenarios, Intergovernmental Panel on Climate Change (IPCC) IS92a and Special Report on Emissions Scenarios (SRES) B2. One widely used statistical downscaling model (SDSM) is used in the analysis. The study is undertaken to simulate daily river flow and daily reservoir inflow in the Serpent and the Chute-du-Diable watersheds, respectively, in northeastern Canada. It is found that the uncertainty bands of the mean ensemble flow (i.e., flow simulated using the mean of the ensemble members of downscaled meteorological variables) is able to mostly encompass all other flows simulated with various individual downscaled meteorological ensemble members whichever CGCM or emission scenario is used. In addition, the uncertainty bands are also able to typically encompass most of the flows simulated with another rainfall–runoff model, namely, Hydrologiska Byråns Vattenbalansavdelning (HBV). The study results suggest that the BNN model could be used as an effective hydrological modeling tool in assessing the hydrologic effect of climate change with uncertainty estimates in the form of confidence intervals. It could be a good alternative method where resources are not available to implement the general multimodel ensembles approach. The BNN approach makes the climate change impact study on water resources with uncertainty estimate relatively simple, cost effective, and time efficient.

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Tropospheric temperature response to stratospheric ozone recovery in the 21st century

Atmospheric Chemistry and Physics ◽

10.5194/acp-11-7687-2011 ◽

2011 ◽

Vol 11 (15) ◽

pp. 7687-7699 ◽

Cited By ~ 20

Author(s):

Y. Hu ◽

Y. Xia ◽

Q. Fu

Keyword(s):

21St Century ◽

Climate Models ◽

Stratospheric Ozone ◽

Temperature Response ◽

Ozone Layer ◽

Montreal Protocol ◽

Polar Regions ◽

Tropospheric Temperature ◽

Intergovernmental Panel ◽

Maximum Enhancement

Abstract. Recent simulations predicted that the stratospheric ozone layer will likely return to pre-1980 levels in the middle of the 21st century, as a result of the decline of ozone depleting substances under the Montreal Protocol. Since the ozone layer is an important component in determining stratospheric and tropospheric-surface energy balance, the recovery of stratospheric ozone may have significant impact on tropospheric-surface climate. Here, using multi-model results from both the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC-AR4) models and coupled chemistry-climate models, we show that as ozone recovery is considered, the troposphere is warmed more than that without considering ozone recovery, suggesting an enhancement of tropospheric warming due to ozone recovery. It is found that the enhanced tropospheric warming is mostly significant in the upper troposphere, with a global and annual mean magnitude of ~0.41 K for 2001–2050. We also find that relatively large enhanced warming occurs in the extratropics and polar regions in summer and autumn in both hemispheres, while the enhanced warming is stronger in the Northern Hemisphere than in the Southern Hemisphere. Enhanced warming is also found at the surface. The global and annual mean enhancement of surface warming is about 0.16 K for 2001–2050, with maximum enhancement in the winter Arctic.

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Uncertainty Quantification of Deep Neural Network-Based Turbulence Model for Reactor Transient Analysis

ASME 2021 Verification and Validation Symposium ◽

10.1115/vvs2021-65045 ◽

2021 ◽

Author(s):

Yang Liu ◽

Rui Hu ◽

Prasanna Balaprakash

Keyword(s):

Neural Network ◽

Computational Cost ◽

Computationally Efficient ◽

Bayesian Neural Network ◽

Convolutional Network ◽

Closure Relation ◽

Sophisticated Method ◽

Linear Relationships ◽

Uncertainty Estimates ◽

Low Computational Cost

Abstract Deep neural networks (DNNs) have demonstrated good performance in learning highly non-linear relationships in large datasets, thus have been considered as a promising surrogate modeling tool for parametric partial differential equations (PDEs). On the other hand, quantifying the predictive uncertainty in DNNs is still a challenging problem. The Bayesian neural network (BNN), a sophisticated method assuming the weights of the DNNs follow certain uncertainty distributions, is considered as a state-of-the-art method for the UQ of DNNs. However, the method is too computationally expensive to be used in complicated DNN architectures. In this work, we utilized two more methods for the UQ of complicated DNNs, i.e. Monte Carlo dropout and deep ensemble. Both methods are computationally efficient and scalable compared to BNN. We applied these two methods to a densely connected convolutional network, which is developed and trained as a coarse-mesh turbulence closure relation for reactor safety analysis. In comparison, the corresponding BNN with the same architecture is also developed and trained. The computational cost and uncertainty evaluation performance of these three UQ methods are comprehensively investigated. It is found that the deep ensemble method is able to produce reasonable uncertainty estimates with good scalability and relatively low computational cost compared to BNN.

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Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

Applied Sciences ◽

10.3390/app11062838 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2838

Author(s):

Nikitha Johnsirani Venkatesan ◽

Dong Ryeol Shin ◽

Choon Sung Nam

Keyword(s):

Neural Network ◽

Radiation Dose ◽

Convolutional Neural Network ◽

Model Performance ◽

Performance Comparison ◽

Apache Spark ◽

Training Time ◽

Learning Framework ◽

Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

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