Browse

You are looking at 1 - 10 of 134 items for :

  • Artificial Intelligence for the Earth Systems x
  • Refine by Access: All Content x
Clear All
Guangming Zheng
,
Stephanie Schollaert Uz
,
Pierre St-Laurent
,
Marjorie A. M. Friedrichs
,
Amita Mehta
, and
Paul M. DiGiacomo

Abstract

Seasonal 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.

Open access
Daniel Galea
,
Kevin Hodges
, and
Bryan N. Lawrence

Abstract

Tropical cyclones (TCs) are important phenomena, and understanding their behavior requires being able to detect their presence in simulations. Detection algorithms vary; here we compare a novel deep learning–based detection algorithm (TCDetect) with a state-of-the-art tracking system (TRACK) and an observational dataset (IBTrACS) to provide context for potential use in climate simulations. Previous work has shown that TCDetect has good recall, particularly for hurricane-strength events. The primary question addressed here is to what extent the structure of the systems plays a part in detection. To compare with observations of TCs, it is necessary to apply detection techniques to reanalysis. For this purpose, we use ERA-Interim, and a key part of the comparison is the recognition that ERA-Interim itself does not fully reflect the observations. Despite that limitation, both TCDetect and TRACK applied to ERA-Interim mostly agree with each other. Also, when considering only hurricane-strength TCs, TCDetect and TRACK correspond well to the TC observations from IBTrACS. Like TRACK, TCDetect has good recall for strong systems; however, it finds a significant number of false positives associated with weaker TCs (i.e., events detected as having hurricane strength but are weaker in reality) and extratropical storms. Because TCDetect was not trained to locate TCs, a post hoc method to perform comparisons was used. Although this method was not always successful, some success in matching tracks and events in physical space was also achieved. The analysis of matches suggested that the best results were found in the Northern Hemisphere and that in most regions the detections followed the same patterns in time no matter which detection method was used.

Open access
Charles H. White
,
Imme Ebert-Uphoff
,
John M. Haynes
, and
Yoo-Jeong Noh

Abstract

Superresolution 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 superresolution including applications to satellite remote sensing. We develop a convolutional neural network (CNN) to superresolve the 1- 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 the resolution of ABI bands and training the CNN to restore the original imagery. Comparisons at reduced resolution and at full resolution with Landsat-8/Landsat-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 superresolved 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.

Significance Statement

Satellite remote sensing instruments often have bands with different spatial resolutions. This work shows that we can artificially increase the resolution of some lower-resolution bands by taking advantage of the texture of higher-resolution bands on the GOES-16 ABI instrument using a convolutional neural network. This may help reconcile differences in spatial resolution when combining information across bands, but future analysis is needed to precisely determine impacts on derived products that might use superresolved bands.

Open access
Shuang Yu
,
Indrasis Chakraborty
,
Gemma J. Anderson
,
Donald D. Lucas
,
Yannic Lops
, and
Daniel Galea

Abstract

Precipitation 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.

Open access
Israt Jahan
,
Diego Cerrai
, and
Marina Astitha

Abstract

Wind 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.

Open access
Neelesh Rampal
,
Sanaa Hobeichi
,
Peter B. Gibson
,
Jorge Baño-Medina
,
Gab Abramowitz
,
Tom Beucler
,
Jose González-Abad
,
William Chapman
,
Paula Harder
, and
José Manuel Gutiérrez

Abstract

Despite the sophistication of global climate models (GCMs), their coarse spatial resolution limits their ability to resolve important aspects of climate variability and change at the local scale. Both dynamical and empirical methods are used for enhancing the resolution of climate projections through downscaling, each with distinct advantages and challenges. Dynamical downscaling is physics based but comes with a large computational cost, posing a barrier for downscaling an ensemble of GCMs large enough for reliable uncertainty quantification of climate risks. In contrast, empirical downscaling, which encompasses statistical and machine learning techniques, provides a computationally efficient alternative to downscaling GCMs. Empirical downscaling algorithms can be developed to emulate the behavior of dynamical models directly, or through frameworks such as perfect prognosis in which relationships are established between large-scale atmospheric conditions and local weather variables using observational data. However, the ability of empirical downscaling algorithms to apply their learned relationships out of distribution into future climates remains uncertain, as is their ability to represent certain types of extreme events. This review covers the growing potential of machine learning methods to address these challenges, offering a thorough exploration of the current applications and training strategies that can circumvent certain issues. Additionally, we propose an evaluation framework for machine learning algorithms specific to the problem of climate downscaling as needed to improve transparency and foster trust in climate projections.

Significance Statement

This review offers a significant contribution to our understanding of how machine learning can offer a transformative change in climate downscaling. It serves as a guide to navigate recent advances in machine learning and how these advances can be better aligned toward inherent challenges in climate downscaling. In this review, we provide an overview of these recent advances with a critical discussion of their advantages and limitations. We also discuss opportunities to refine existing machine learning methods alongside new approaches for the generation of large ensembles of high-resolution climate projections.

Open access
L. Raynaud
,
G. Faure
,
M. Puig
,
C. Dauvilliers
,
J-N Trosino
, and
P. Béjean

Abstract

Detection 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.

Open access
Fraser King
,
Claire Pettersen
,
Christopher G. Fletcher
, and
Andrew Geiss

Abstract

CloudSat’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.

Open access
Philine Lou Bommer
,
Marlene Kretschmer
,
Anna Hedström
,
Dilyara Bareeva
, and
Marina M.-C. Höhne

Abstract

Explainable artificial intelligence (XAI) methods shed light on the predictions of machine learning algorithms. Several different approaches exist and have already been applied in climate science. However, usually missing ground truth explanations complicate their evaluation and comparison, subsequently impeding the choice of the XAI method. Therefore, in this work, we introduce XAI evaluation in the climate context and discuss different desired explanation properties, namely robustness, faithfulness, randomization, complexity, and localization. To this end, we chose previous work as a case study where the decade of annual-mean temperature maps is predicted. After training both a multi-layer perceptron (MLP) and a convolutional neural network (CNN), multiple XAI methods are applied and their skill scores in reference to a random uniform explanation are calculated for each property. Independent of the network, we find that XAI methods Integrated Gradients, layer-wise relevance propagation, and input times gradients exhibit considerable robustness, faithfulness, and complexity while sacrificing randomization performance. Sensitivity methods – gradient, SmoothGrad, NoiseGrad, and FusionGrad, match the robustness skill but sacrifice faithfulness and complexity for randomization skill. We find architecture-dependent performance differences regarding robustness, complexity and localization skills of different XAI methods, highlighting the necessity for research task-specific evaluation. Overall, our work offers an overview of different evaluation properties in the climate science context and shows how to compare and benchmark different explanation methods, assessing their suitability based on strengths and weaknesses, for the specific research problem at hand. By that, we aim to support climate researchers in the selection of a suitable XAI method.

Open access
Marius Appel

Abstract

The abundance of gaps in satellite image time series often complicates the application of deep learning models such as convolutional neural networks for spatiotemporal modeling. Based on previous work in computer vision on image inpainting, this paper shows how three-dimensional spatiotemporal partial convolutions can be used as layers in neural networks to fill gaps in satellite image time series. To evaluate the approach, we apply a U-Net-like model on incomplete image time series of quasi-global carbon monoxide observations from the Sentinel-5 Precursor (Sentinel-5P) satellite. Prediction errors were comparable to two considered statistical approaches while computation times for predictions were up to three orders of magnitude faster, making the approach applicable to process large amounts of satellite data. Partial convolutions can be added as layers to other types of neural networks, making it relatively easy to integrate with existing deep learning models. However, the approach does not provide prediction uncertainties and further research is needed to understand and improve model transferability. The implementation of spatiotemporal partial convolutions and the U-Net-like model is available as open-source software.

Significance Statement

Gaps in satellite-based measurements of atmospheric variables can make the application of complex analysis methods such as deep learning approaches difficult. The purpose of this study is to present and evaluate a purely data-driven method to fill incomplete satellite image time series. The application on atmospheric carbon monoxide data suggests that the method can achieve prediction errors comparable to other approaches with much lower computation times. Results highlight that the method is promising for larger datasets but also that care must be taken to avoid extrapolation. Future studies may integrate the approach into more complex deep learning models for understanding spatiotemporal dynamics from incomplete data.

Open access