The UGOS 2024 Annual Meeting took place at Texas A&M University in College Station, TX, from September 30 to October 2, 2024. On behalf of MSEAS, Pierre presented MSEAS’ contributions on MASTR Modeling Guidance and Prediction. Afterward, he joined the full UGOS team for a picture (top), and then joined the MASTR team with the glider Stommel that was deployed in the Yucatan Channel during the experiment (bottom):

Abstract: Realistic sonars radiate spherically spreading waves and have directivity. Therefore, they insonify a target over a finite number of Fresnel zones and span a continuum of oblique incident angles, even when the center of the beam is at normal incidence. These effects strongly influence both the overall scattered pressure levels and resonances. For example, because of the spreading of the beam and associated oblique insonification within the beam, normal modes associated with axially propagating guided waves are excited that would not have otherwise existed for an idealized incident plane wave. This thesis analyzes acoustic scattering by solid elastic cylinders insonified by realistic sonars both theoretically and experimentally. A theoretical model to predict scattering by arbitrary-length cylinders is derived based on the apparent volume flow accounting for the above-mentioned practical sonar properties, namely, spherical spreading and directionality. The formulation is first bench-marked against the formally exact T-matrix solution and tested against previously published laboratory data for finite cylinders. It is found that the formulation outperforms the T-matrix solution in predicting laboratory observations at near-normal incidence. Laboratory experiments are then conducted on arbitrary length smooth cylinders insonified by a directional sonar, with a small number of Fresnel zone excited, to evaluate the theory for monostatic as well as bistatic geometries. The formulation is found to outperform the classical scattering models in predicting the new measurements. For example, resonances associated with axially propagating guided waves excited at broadside incidence observed in the experiments are predicted by the proposed formulation but not by the classical models. The measurements are found to agree well with predictions in terms of overall scattering levels and resonance locations. However, the resonance shapes exhibit inconsistent agreement with data. In addition to testing the predictions, the bistatic laboratory observations presented herein substantiate the significant effects on scattering due to the properties of the incident field from practical sonars. The comparison between theoretical and experimental results is then extended for the more complex case involving statistically rough elastic cylinders with one-dimensional Gaussian roughness. The roughness is found to have a considerable impact on all aspects of scattering—overall levels as well as locations and shapes of resonances. General agreement is found between the theoretically predicted and measured ensemble averaged scattered pressure. Both the theory and data reveal two main observations in the ensemble-averaged scattered field: overall scattered pressure levels are seen to decrease, and resonance effects are diminished compared to the corresponding case of smooth cylinders. Effect of various statistical properties of the rough cylinder, namely, different root mean square (RMS) roughness for fixed correlation length and different correlation lengths for fixed RMS roughness on the scattered field are investigated. Finally, the fluctuations of the scattered field are analyzed using the derived formulation.

Abstract: AI-driven weather forecast models are now more accurate than the best physics-based models. Using similar technology, the open-source Ai2 Climate Emulator (ACE) has been trained to accurately emulate both the daily weather variability (including rainfall extremes) and climate of two leading global atmospheric models, at 100-1000x smaller computational cost. Unlike reanalysis-trained AI weather forecast models, ACE is designed to be trained and deployed across multiple climates; for this purpose, it can be coupled to a simple ‘slab ocean’ model. In the near future, ACE coupled to a companion emulator of a full-physics ocean will affordably emulate hyper-realistic but hyper-expensive ‘digital twin’ models of the atmosphere and ocean with km-scale grids. Digital twin models avoid key uncertainties in present-day coarser-grid climate models and may help us more reliably predict regional trends in precipitation and other climate extremes. The combination of ACE and digital twins promises to provide more reliable local information for climate-sensitive decision making without the complexity of dynamical downscaling.

Biography: Chris Bretherton directs a climate modeling group at the Allen Institute for AI (Ai2) in Seattle which uses machine learning trained on global storm-resolving model output and observational data to improve climate model simulations. He is an Emeritus Professor of the Atmospheric Science and Applied Mathematics Departments at the University of Washington, where for 35 years he studied cloud formation and turbulence and improved their simulation in atmospheric models. He is an American Meteorological Society Charney Award winner, IPCC author, AMS, and AGU Fellow, and a member of the National Academy of Sciences.

MSEAS had a party to celebrate Pierre’s birthday today (Nov 1, 2024)…