The future of Earth system prediction: Advances in model-data fusion

Gettelman, Andrew; Geer, Alan; Forbes, R. M.; Carmichael, G. R.; Feingold, Graham; Posselt, Derek J.; Stephens, Graeme L.; Heever, Susan C. van den; Varble, Adam; Zuidema, Paquita

doi:10.1126/sciadv.abn3488

Cited by 69 publications

(40 citation statements)

References 85 publications

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“…Thus, information from multiple datasets might have to be assimilated. Such multiple‐data or data‐model fusion can be of great benefit for the characterization and prediction of nonlinear dynamics in hydrologic or other geophysical systems (Gettelman et al., 2022; Mazzoleni et al., 2017). Although many of these data sources require rating curves (e.g., water level‐discharge relationship) to estimate discharge, obtaining discharge without field measurements has become possible with the development of many computational inverse methods to build rating curves from remotely sensed data (Gleason & Durand, 2020; Mahdade et al., 2021; Pan et al., 2016).…”

Section: Discussionmentioning

confidence: 99%

Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing

Zhang

Luhar

Brunner

et al. 2023

Water Resources Research

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Many rivers and stream reaches are ungauged or poorly gauged because streamflow measurements are costly and resource intensive. The prediction of streamflow in these watersheds is necessary and beneficial to enhance our understanding of hydrological processes and to improve water resources management. Although the prediction in ungauged basins (PUB) program was initiated by the International Association of Hydrological Sciences (IAHS) in 2003 and many efforts have been made to improve predictions in ungauged watersheds (Hrachowitz Abstract Many rivers and streams are ungauged or poorly gauged and predicting streamflow in such watersheds is challenging. Although streamflow signals result from processes with different frequencies, they can be "sparse" or have a "lower-dimensional" representation in a transformed feature space. In such cases, if this appropriate feature space can be identified from streamflow data in gauged watersheds by dimensionality reduction, streamflow in poorly gauged watersheds can be predicted with a few measurements taken. This study utilized this framework, named data-driven sparse sensing (DSS), to predict daily-scale streamflow in 543 watersheds across the contiguous United States. A tailored library of features was extracted from streamflow training data in watersheds within the same climatic region, and this feature space was used to reconstruct streamflow in poorly gauged watersheds and identify the optimal timings for measurement. Among different regions, streamflow in snowmelt-dominated and baseflow-dominated watersheds (e.g., Rocky Mountains) was more effectively predicted with fewer streamflow measurements taken. The prediction efficiency in some rainfall-dominated regions, for example, New England and the Pacific coast, increased significantly with an increasing number of measurements. The spatial variability of prediction efficiency can be attributed to the process-driven mechanisms and the dimensionality of watershed dynamics. Storage-dominated systems are lower-dimensional and more predictable than rainfall-dominated systems. Measurements taken during periods with large streamflow magnitudes and/or variances are more informative and lead to better predictions. This study demonstrates that DSS can be an especially useful technique to integrate ground-based measurements with remotely sensed data for streamflow prediction, sensor placement, and watershed classification.Plain Language Summary Many rivers and stream reaches are ungauged or poorly gauged because streamflow measurement is costly and resource intensive. Predicting the streamflow time-series in these ungauged or poorly gauged watersheds is still challenging. Here, we use a signal processing technique called data-driven sparse sensing on a national-scale streamflow data set across the contiguous United States. We predict streamflow time-series in each watershed based on existing streamflow data in watersheds nearby, and explore the best times during the year for measuring streamflow. Our analysis shows that data-driven ...

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Section: Discussionmentioning

confidence: 99%

Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing

Zhang

Luhar

Brunner

et al. 2023

Water Resources Research

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“…Weather forecast is a pre-estimation and prediction of weather changes in the future, which has significant social value (Gettelman et al, 2022). Numerical weather prediction (NWP) is a crucial method.…”

Section: Introductionmentioning

confidence: 99%

A hybrid data assimilation system based on machine learning

Dong

Leng²,

Zhao³

et al. 2023

Front. Earth Sci.

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In the earth sciences, numerical weather prediction (NWP) is the primary method of predicting future weather conditions, and its accuracy is affected by the initial conditions. Data assimilation (DA) can provide high-precision initial conditions for NWP. The hybrid 4DVar-EnKF is currently an advanced DA method used by many operational NWP centres. However, it has two major shortcomings: The complex development and maintenance of the tangent linear and adjoint models and the empirical combination of the results of 4DVar and EnKF. In this paper, a new hybrid DA method based on machine learning (HDA-ML) is presented to overcome these drawbacks. In the new method, the tangent linear and adjoint models in the 4DVar part of the hybrid algorithm can be easily obtained by using a bilinear neural network to replace the forecast model, and a CNN model is adopted to fuse the analysis of 4DVar and EnKF to adaptively obtain the optimal coefficient of combination rather than the empirical coefficient as in the traditional hybrid DA method. The hybrid DA methods are compared with the Lorenz-96 model using the true values as labels. The experimental results show that HDA-ML improves the assimilation performance and significantly reduces the time cost. Furthermore, using observations instead of the true values as labels in the training system is more realistic. The results show comparable assimilation performance to that in the experiments with the true values used as the labels. The experimental results show that the new method has great potential for application to operational NWP systems.

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“…Oceanic numerical simulation refers to the process of discretizing and solving seven ocean dynamic equations with the help of boundary conditions and part of ocean observation data to obtain the three-dimensional distribution of hydrological elements. The method can cover the global oceans with a spatial resolution reaching up to 3 km × 3 km [13], and the reliability of the simulated data can be significantly improved by the numerical assimilation technology [14][15][16][17]. Up to now, the simulation data from numerical models have been widely used in the analysis of various physical phenomena in oceanography.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of the Sound Speed Profile in Typical Sea Areas Based on the Single Empirical Orthogonal Function Regression Method

Chen

Ren

Zhang

et al. 2023

JMSE

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The sound speed profile (SSP) is a necessary prerequisite for acoustic field computation and underwater target localization and monitoring. Due to the dynamic nature of the ocean, the reconstruction of SSPs with surface characteristics is a big challenge. In this study, the Single Empirical Orthogonal Function Regression (sEOF-R) method is employed to establish the regression relationship between the surface parameters and the sound speed anomaly profile (SSAP) in three typical sea areas, namely the equator, Kuroshio Extension (KE), and Northeast Pacific. Based on the established regression relationship and the surface parameters, the underwater SSP is reconstructed. Results show that the reconstruction effects in the three areas show the best performance in the Northeast Pacific, followed by the equator and finally the KE. The quantitative analysis suggests that the local sea level anomaly (SLA) plays the dominant role in influencing the reconstruction effect, followed by the sea surface temperature anomaly (SSTA). Further analysis demonstrates that the sEOF-R method is limited in time-varying and space-varying areas. The SSP reconstructed from the sea surface information in this study is useful for the inversion of the underwater structures.

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The future of Earth system prediction: Advances in model-data fusion

Cited by 69 publications

References 85 publications

Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing

Streamflow Prediction in Poorly Gauged Watersheds in the United States Through Data‐Driven Sparse Sensing

A hybrid data assimilation system based on machine learning

Reconstruction of the Sound Speed Profile in Typical Sea Areas Based on the Single Empirical Orthogonal Function Regression Method

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