Showing 1–12 of 12 results for author: Smith, T A

Skillful Global Ocean Emulation and the Role of Correlation-Aware Loss

Authors: Niraj Agarwal, Timothy A. Smith, Sergey Frolov, Laura C. Slivinski

Abstract: Machine learning emulators have shown extraordinary skill in forecasting atmospheric states, and their application to global ocean dynamics offers similar promise. Here, we adapt the GraphCast architecture into a dedicated ocean-only emulator, driven by prescribed atmospheric conditions, for medium-range predictions. The emulator is trained on NOAA's UFS-Replay dataset. Using a 24 hour time step,… ▽ More Machine learning emulators have shown extraordinary skill in forecasting atmospheric states, and their application to global ocean dynamics offers similar promise. Here, we adapt the GraphCast architecture into a dedicated ocean-only emulator, driven by prescribed atmospheric conditions, for medium-range predictions. The emulator is trained on NOAA's UFS-Replay dataset. Using a 24 hour time step, single initial condition, and without using autoregressive training, we produce an emulator that provides skillful forecasts for 10-15 day lead times. We further demonstrate the use of Mahalanobis distance as loss that improves the forecast skill compared to the Mean Squared Error loss by explicitly accounting for the correlations between tendencies of the target variables. Using spatial correlation analysis of the forecasted fields, we also show that the proposed correlation-aware loss acts as a statistical-dynamical regularizer for the slow, correlated dynamics of the global oceans, offering a better background forecast for downstream tasks like data assimilation. △ Less

Submitted 20 April, 2026; originally announced April 2026.

Comments: 13 pages, 4 figures

HRRRCast: a data-driven emulator for regional weather forecasting at convection allowing scales

Authors: Daniel Abdi, Isidora Jankov, Paul Madden, Vanderlei Vargas, Timothy A. Smith, Sergey Frolov, Montgomery Flora, Corey Potvin

Abstract: The High-Resolution Rapid Refresh (HRRR) model is a convection-allowing model used in operational weather forecasting across the contiguous United States (CONUS). To provide a computationally efficient alternative, we introduce HRRRCast, a data-driven emulator built with advanced machine learning techniques. HRRRCast includes two architectures: a ResNet-based model (ResHRRR) and a Graph Neural Net… ▽ More The High-Resolution Rapid Refresh (HRRR) model is a convection-allowing model used in operational weather forecasting across the contiguous United States (CONUS). To provide a computationally efficient alternative, we introduce HRRRCast, a data-driven emulator built with advanced machine learning techniques. HRRRCast includes two architectures: a ResNet-based model (ResHRRR) and a Graph Neural Network-based model (GraphHRRR). ResHRRR uses convolutional neural networks enhanced with squeeze-and-excitation blocks and Feature-wise Linear Modulation, and supports probabilistic forecasting via the Denoising Diffusion Implicit Model (DDIM). To better handle longer lead times, we train a single model to predict multiple lead times (1h, 3h, and 6h), then use a greedy rollout strategy during inference. When evaluated on composite reflectivity over the full CONUS domain using ensembles of 3 to 10 members, ResHRRR outperforms HRRR forecast at light rainfall threshold (20 dBZ) and achieves competitive performance at moderate thresholds (30 dBZ). Our work advances the StormCast model of Pathak et al. [21] by: a) training on the full CONUS domain, b) using multiple lead times to improve long-range skill, c) training on analysis data instead of the +1h post-analysis data inadvertently used in StormCast, and d) incorporating future GFS states as inputs, enabling downscaling that improves long-lead accuracy. Grid-, neighborhood-, and object-based metrics confirm better storm placement, lower frequency bias, and higher success ratios than HRRR. HRRRCast ensemble forecasts also maintain sharper spatial detail, with power spectra more closely matching HRRR analysis. While GraphHRRR underperforms in its current form, it lays groundwork for future graph-based forecasting. HRRRCast represents a step toward efficient, data-driven regional weather prediction with competitive accuracy and ensemble capability. △ Less

Submitted 8 July, 2025; originally announced July 2025.

Journal ref: Artificial Intelligence for the Earth Systems, Vol. 5, No. 2, 2026, Article 250061

Highly squeezed nanophotonic quantum microcombs with broadband frequency tunability

Authors: Yichen Shen, Ping-Yen Hsieh, Dhruv Srinivasan, Antoine Henry, Gregory Moille, Sashank Kaushik Sridhar, Alessandro Restelli, You-Chia Chang, Kartik Srinivasan, Thomas A. Smith, Avik Dutt

Abstract: Squeezed light offers genuine quantum advantage in enhanced sensing and quantum computation; yet the level of squeezing or quantum noise reduction generated from nanophotonic chips has been limited. In addition to strong quantum noise reduction, key desiderata for such a nanophotonic squeezer include frequency agility or tunability over a broad frequency range, and simultaneous operation in many d… ▽ More Squeezed light offers genuine quantum advantage in enhanced sensing and quantum computation; yet the level of squeezing or quantum noise reduction generated from nanophotonic chips has been limited. In addition to strong quantum noise reduction, key desiderata for such a nanophotonic squeezer include frequency agility or tunability over a broad frequency range, and simultaneous operation in many distinct, well-defined quantum modes (qumodes). Here we present a strongly overcoupled silicon nitride squeezer based on a below-threshold optical parametric amplifier (OPA) that produces directly detected squeezing of 5.6 dB $\pm$ 0.2 dB, surpassing previous demonstrations in both continuous-wave and pulsed regimes. We introduce a seed-assisted detection technique into such nanophotonic squeezers that reveals a quantum frequency comb (QFC) of 16 qumodes, with a separation of 11~THz between the furthest qumode pair, while maintaining a strong squeezing. Additionally, we report spectral tuning of a qumode comb pair over one free-spectral range of the OPA, thus bridging the spacing between the discrete modes of the QFC. Our results significantly advance both the generation and detection of nanophotonic squeezed light in a broadband and multimode platform, establishing a scalable, chip-integrated path for compact quantum sensors and continuous-variable quantum information processing systems. △ Less

Submitted 6 May, 2025; originally announced May 2025.

Assimilating Observed Surface Pressure into ML Weather Prediction Models

Authors: Laura C. Slivinski, Jeffrey S. Whitaker, Sergey Frolov, Timothy A. Smith, Niraj Agarwal

Abstract: There has been a recent surge in development of accurate machine learning (ML) weather prediction models, but evaluation of these models has mainly been focused on medium-range forecasts, not their performance in cycling data assimilation (DA) systems. Cycling DA provides a statistically optimal estimate of model initial conditions, given observations and previous model forecasts. Here, real surfa… ▽ More There has been a recent surge in development of accurate machine learning (ML) weather prediction models, but evaluation of these models has mainly been focused on medium-range forecasts, not their performance in cycling data assimilation (DA) systems. Cycling DA provides a statistically optimal estimate of model initial conditions, given observations and previous model forecasts. Here, real surface pressure observations are assimilated into several popular ML models using an ensemble Kalman filter, where accurate ensemble covariance estimation is essential to constrain unobserved state variables from sparse observations. In this cycling DA system, deterministic ML models accumulate small-scale noise until they diverge. Mitigating this noise with a spectral filter can stabilize the system, but with larger errors than traditional models. Perturbation experiments illustrate that these models do not accurately represent short-term error growth, leading to poor estimation of cross-variable covariances. △ Less

Submitted 23 December, 2024; originally announced December 2024.

Strong nanophotonic quantum squeezing exceeding 3.5 dB in a foundry-compatible Kerr microresonator

Authors: Yichen Shen, Ping-Yen Hsieh, Sashank Kaushik Sridhar, Samantha Feldman, You-Chia Chang, Thomas A. Smith, Avik Dutt

Abstract: Squeezed light, with its quantum noise reduction capabilities, has emerged as a powerful resource in quantum information processing and precision metrology. To reach noise reduction levels such that a quantum advantage is achieved, off-chip squeezers are typically used. The development of on-chip squeezed light sources, particularly in nanophotonic platforms, has been challenging. We report 3.7… ▽ More Squeezed light, with its quantum noise reduction capabilities, has emerged as a powerful resource in quantum information processing and precision metrology. To reach noise reduction levels such that a quantum advantage is achieved, off-chip squeezers are typically used. The development of on-chip squeezed light sources, particularly in nanophotonic platforms, has been challenging. We report 3.7 $\pm$ 0.2 dB of directly detected nanophotonic quantum squeezing using foundry-fabricated silicon nitride (Si$_3$N$_4$) microrings with an inferred squeezing level of 10.7 dB on-chip. The squeezing level is robust across multiple devices and pump detunings, and is consistent with the overcoupling degree without noticeable degradation from excess classical noise. We also offer insights to mitigate thermally-induced excess noise, that typically degrades squeezing, by using small-radius rings with a larger free spectral range (450 GHz) and consequently lower parametric oscillation thresholds. Our results demonstrate that Si$_3$N$_4$ is a viable platform for strong quantum noise reduction in a CMOS-compatible, scalable architecture. △ Less

Submitted 18 November, 2024; originally announced November 2024.

Journal ref: Optica 12 (3), pp. 302-308 (2025)

Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence

Authors: Timothy A. Smith, Stephen G. Penny, Jason A. Platt, Tse-Chun Chen

Abstract: The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use datasets that are temporally subsampled relative to the time steps required for the numerical integration of differential equations. Here, we investigate how this o… ▽ More The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use datasets that are temporally subsampled relative to the time steps required for the numerical integration of differential equations. Here, we investigate how this often overlooked processing step affects the quality of an emulator's predictions. We implement two ML architectures from a class of methods called reservoir computing: (1) a form of Nonlinear Vector Autoregression (NVAR), and (2) an Echo State Network (ESN). Despite their simplicity, it is well documented that these architectures excel at predicting low dimensional chaotic dynamics. We are therefore motivated to test these architectures in an idealized setting of predicting high dimensional geophysical turbulence as represented by Surface Quasi-Geostrophic dynamics. In all cases, subsampling the training data consistently leads to an increased bias at small spatial scales that resembles numerical diffusion. Interestingly, the NVAR architecture becomes unstable when the temporal resolution is increased, indicating that the polynomial based interactions are insufficient at capturing the detailed nonlinearities of the turbulent flow. The ESN architecture is found to be more robust, suggesting a benefit to the more expensive but more general structure. Spectral errors are reduced by including a penalty on the kinetic energy density spectrum during training, although the subsampling related errors persist. Future work is warranted to understand how the temporal resolution of training data affects other ML architectures. △ Less

Submitted 21 September, 2023; v1 submitted 28 April, 2023; originally announced May 2023.

Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks

Authors: Jason A. Platt, Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, Henry D. I. Abarbanel

Abstract: Drawing on ergodic theory, we introduce a novel training method for machine learning based forecasting methods for chaotic dynamical systems. The training enforces dynamical invariants--such as the Lyapunov exponent spectrum and fractal dimension--in the systems of interest, enabling longer and more stable forecasts when operating with limited data. The technique is demonstrated in detail using th… ▽ More Drawing on ergodic theory, we introduce a novel training method for machine learning based forecasting methods for chaotic dynamical systems. The training enforces dynamical invariants--such as the Lyapunov exponent spectrum and fractal dimension--in the systems of interest, enabling longer and more stable forecasts when operating with limited data. The technique is demonstrated in detail using the recurrent neural network architecture of reservoir computing. Results are given for the Lorenz 1996 chaotic dynamical system and a spectral quasi-geostrophic model, both typical test cases for numerical weather prediction. △ Less

Submitted 23 April, 2023; originally announced April 2023.

Integrating Recurrent Neural Networks with Data Assimilation for Scalable Data-Driven State Estimation

Authors: Stephen G. Penny, Timothy A. Smith, Tse-Chun Chen, Jason A. Platt, Hsin-Yi Lin, Michael Goodliff, Henry D. I. Abarbanel

Abstract: Data assimilation (DA) is integrated with machine learning in order to perform entirely data-driven online state estimation. To achieve this, recurrent neural networks (RNNs) are implemented as surrogate models to replace key components of the DA cycle in numerical weather prediction (NWP), including the conventional numerical forecast model, the forecast error covariance matrix, and the tangent l… ▽ More Data assimilation (DA) is integrated with machine learning in order to perform entirely data-driven online state estimation. To achieve this, recurrent neural networks (RNNs) are implemented as surrogate models to replace key components of the DA cycle in numerical weather prediction (NWP), including the conventional numerical forecast model, the forecast error covariance matrix, and the tangent linear and adjoint models. It is shown how these RNNs can be initialized using DA methods to directly update the hidden/reservoir state with observations of the target system. The results indicate that these techniques can be applied to estimate the state of a system for the repeated initialization of short-term forecasts, even in the absence of a traditional numerical forecast model. Further, it is demonstrated how these integrated RNN-DA methods can scale to higher dimensions by applying domain localization and parallelization, providing a path for practical applications in NWP. △ Less

Submitted 24 September, 2021; originally announced September 2021.

Comments: 22 pages, 16 figures

Two-photon X-ray Ghost Microscope

Authors: Thomas A. Smith, Zhehui Wang, Yanhua Shih

Abstract: X-ray imaging allows for a non-invasive image of the internal structure of an object. The most common form of X-ray imaging, projectional radiography, is simply a projection or "shadow" of the object rather than a point-to-point image possible with a lens. This technique fails to take advantage of the resolving capabilities of short-wavelength X rays. Various X-ray microscopes, typically operating… ▽ More X-ray imaging allows for a non-invasive image of the internal structure of an object. The most common form of X-ray imaging, projectional radiography, is simply a projection or "shadow" of the object rather than a point-to-point image possible with a lens. This technique fails to take advantage of the resolving capabilities of short-wavelength X rays. Various X-ray microscopes, typically operating with soft X rays (< 10 keV), use focusing X-ray optics to obtain higher resolution images of the internal structure of an object. Due to the short focal length of focusing X-ray optics, it becomes difficult to focus on the internal structure of larger objects in such a way to provide significant magnification to be resolvable. Here we present an imaging mechanism that utilizes two-photon X-ray ghost imaging to produce a true point-to-point image of the internal structure of an object, with the potential to introduce focusing X-ray optics or a scintillator-lens pairing to produce a magnified secondary ghost image. The focusing X-ray optics would image the primary ghost image (which has no physical structure to it) allowing the imaging of internal structures deeper than a standard X-ray microscope would allow. In principle, once some experimental barriers are overcome, this X-ray "ghost microscope" may achieve nanometer spatial resolution and open up new capabilities that would be of interest to the fields of physics, material science, and medical imaging. △ Less

Submitted 29 June, 2020; v1 submitted 5 February, 2020; originally announced February 2020.

Comments: Updated to correct typos and add a few new statements including one about the resolution of projectional radiology and one discussing experimental requirements for the desired X-ray source. Totaled at 17 pages with four figures. Submitted to Optics Express for consideration

Plasmonic Hot-Carrier Extraction: Mechanisms of Electron Emission

Authors: Charlene Ng, Peng Zeng, Julian A. Lloyd, Debadi Chakraborty, Ann Roberts, Trevor A. Smith, Udo Bach, John E. Sader, Timothy J. Davis, Daniel E. Gómez

Abstract: When plasmonic nanoparticles are coupled with semiconductors, highly energetic hot carriers can be extracted from the metal-semiconductor interface for various applications in light energy conversion. Hot charge-carrier extraction upon plasmon decay using such an interface has been argued to occur after the formation of an intermediate electron population with a uniform momentum distribution. The… ▽ More When plasmonic nanoparticles are coupled with semiconductors, highly energetic hot carriers can be extracted from the metal-semiconductor interface for various applications in light energy conversion. Hot charge-carrier extraction upon plasmon decay using such an interface has been argued to occur after the formation of an intermediate electron population with a uniform momentum distribution. The efficiency of the charge separation process is thus discussed to be limited by this spatial homogeneity in certain plasmon-induced applications. Here we demonstrate using visible pump, near-infrared probe transient absorption spectroscopy that increases in the contact area between metal and semiconductor leads to an increase in the quantum yield for hot electron injection that is inconsistent with the homogeneous energy-momentum distribution of hot-electrons. Instead, further analysis of the experimental data suggests that the highly energetic electrons are emitted across the interface via a surface charge emission mechanism that occurs via a plasmon excitation △ Less

Submitted 19 November, 2017; originally announced November 2017.

Exact analytical solution of viscous Korteweg-deVries equation for water waves

Authors: S. G. Sajjadi, T. A. Smith

Abstract: The evolution of a solitary wave with very weak nonlinearity which was originally investigated by Miles [4] is revisited. The solution for a one-dimensional gravity wave in a water of uniform depth is considered. This leads to finding the solution to a Korteweg-de Vries (KdV) equation in which the nonlinear term is small. Also considered is the asymptotic solution of the linearized KdV equation bo… ▽ More The evolution of a solitary wave with very weak nonlinearity which was originally investigated by Miles [4] is revisited. The solution for a one-dimensional gravity wave in a water of uniform depth is considered. This leads to finding the solution to a Korteweg-de Vries (KdV) equation in which the nonlinear term is small. Also considered is the asymptotic solution of the linearized KdV equation both analytically and numerically. As in Miles [4], the asymptotic solution of the KdV equation for both linear and weakly nonlinear case is found using the method of inversescattering theory. Additionally investigated is the analytical solution of viscous-KdV equation which reveals the formation of the Peregrine soliton that decays to the initial sech^2(ξ) soliton and eventually growing back to a narrower and higher amplitude bifurcated Peregrine-type soliton. △ Less

Submitted 7 April, 2017; v1 submitted 1 April, 2017; originally announced April 2017.

Comments: 15 pages

Journal ref: Advances and Applications in Fluid Mechanics, Volume 19, Issue 2 (April 2016), Page: 379 - 400

Photo-induced electron transfer in the strong coupling regime: Waveguide-plasmon polaritons

Authors: Peng Zeng, Jasper Cadusch, Debadi Chakraborty, Trevor A. Smith, Ann Roberts, John E. Sader, Timothy J. Davis, Daniel E. Gomez

Abstract: Reversible exchange of photons between a material and an optical cavity can lead to the formation of hybrid light--matter states where material properties such as the work function\cite{Hutchison_AM2013a}, chemical reactivity\cite{Hutchison_ACIE2012a}, ultra--fast energy relaxation \cite{Salomon_ACIE2009a,Gomez_TJOPCB2012a} and electrical conductivity\cite{Orgiu_NM2015a} of matter differ significa… ▽ More Reversible exchange of photons between a material and an optical cavity can lead to the formation of hybrid light--matter states where material properties such as the work function\cite{Hutchison_AM2013a}, chemical reactivity\cite{Hutchison_ACIE2012a}, ultra--fast energy relaxation \cite{Salomon_ACIE2009a,Gomez_TJOPCB2012a} and electrical conductivity\cite{Orgiu_NM2015a} of matter differ significantly to those of the same material in the absence of strong interactions with the electromagnetic fields. Here we show that strong light--matter coupling between confined photons on a semiconductor waveguide and localised plasmon resonances on metal nanowires modifies the efficiency of the photo--induced charge--transfer rate of plasmonic derived (hot) electrons into accepting states in the semiconductor material. Ultra--fast spectroscopy measurements reveal a strong correlation between the amplitude of the transient signals, attributed to electrons residing in the semiconductor, and the hybridization of waveguide and plasmon excitations. △ Less

Submitted 23 December, 2015; originally announced December 2015.

Comments: submitted for publication