arXiv subjects

Paul Ullrich

Publications and source records attributed to Paul Ullrich.

5 recordsLinked to original sources

arXiv2026

A PMP-inspired Evaluation Framework for Assessing Deep-Learning Earth System Models

In recent years, Deep-Learning Earth System Models (DL-ESMs) have emerged as promising, computationally efficient complements to traditional Earth system models. Here, we present an evaluation framework for testing DL-ESMs from an Earth system model-development perspective using standardized diagnostics from the PCMDI Metrics Package (PMP). This framework allows DL-ESMs, including Ai2's ACE2 and Google's NeuralGCM, to be assessed with metrics that quantify their ability to reproduce climatology, major modes of variability, monsoons, and precipitation variability relative to observational reference datasets and CMIP-class benchmarks. By evaluating DL-ESMs with tools commonly used for traditional models, we extend their assessment beyond short-range forecast skill and toward longer Earth System-relevant applications. The results identify encouraging strengths in several large-scale fields and modes of variability, while also highlighting persistent challenges in precipitation, tropical variability, and long-run stability for some model versions. This evaluation is a critical step toward building trust in DL-ESMs, guiding future model development, and clarifying their fit-for-purpose for Earth system science applications.

physics.ao-ph↗

arXiv2024

Spatiotemporally adaptive compression for scientific dataset with feature preservation -- a case study on simulation data with extreme climate events analysis

Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated over timesteps to accommodate storage and I/O limitations. In this paper, we propose a technique that addresses storage costs while improving post-analysis accuracy through spatiotemporal adaptive, error-controlled lossy compression. We investigate the trade-off between data precision and temporal output rates, revealing that reducing data precision and increasing timestep frequency lead to more accurate analysis outcomes. Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor. To evaluate our approach, we conduct experiments using the well-known E3SM climate simulation code and apply our method to compress variables used for cyclone tracking. Our results show a significant reduction in storage size while enhancing the quality of cyclone tracking analysis, both quantitatively and qualitatively, in comparison to the prevalent timestep decimation approach. Compared to three state-of-the-art lossy compressors lacking feature preservation capabilities, our adaptive compression framework improves perfectly matched cases in TC tracking by 26.4-51.3% at medium compression ratios and by 77.3-571.1% at large compression ratios, with a merely 5-11% computational overhead.

cs.CV↗

arXiv2022

AutoML-based Almond Yield Prediction and Projection in California

Almonds are one of the most lucrative products of California, but are also among the most sensitive to climate change. In order to better understand the relationship between climatic factors and almond yield, an automated machine learning framework is used to build a collection of machine learning models. The prediction skill is assessed using historical records. Future projections are derived using 17 downscaled climate outputs. The ensemble mean projection displays almond yield changes under two different climate scenarios, along with two technology development scenarios, where the role of technology development is highlighted. The mean projections and distributions provide insightful results to stakeholders and can be utilized by policymakers for climate adaptation.

cs.LG↗

arXiv2019

An energy consistent discretization of the nonhydrostatic equations in primitive variables

We derive a formulation of the nonhydrostatic equations in spherical geometry with a Lorenz staggered vertical discretization. The combination conserves a discrete energy in exact time integration when coupled with a mimetic horizontal discretization. The formulation is a version of Dubos and Tort (2014) rewritten in terms of primitive variables. It is valid for terrain following mass or height coordinates and for both Eulerian or vertically Lagrangian discretizations. The discretization relies on an extension to Simmons and Burridge (1981) vertical differencing which we show obeys a discrete derivative product rule. This product rule allows us to simplify the treatment of the vertical transport terms. Energy conservation is obtained via a term-by-term balance in the kinetic, internal and potential energy budgets, ensuring an energy-consistent discretization with no spurious sources of energy. We demonstrate convergence with respect to time truncation error in a spectral element code with a HEVI IMEX timestepping algorithm

math.NA↗

arXiv2007

Exact and Perturbed Friedmann-Lemaitre Cosmologies

In this thesis we first apply the 1+3 covariant description of general relativity to analyze n-fluid Friedmann-Lemaitre (FL) cosmologies; that is, homogeneous and isotropic cosmologies whose matter-energy content consists of n non-interacting fluids. We are motivated to study FL models of this type as observations suggest the physical universe is closely described by a FL model with a matter content consisting of radiation, dust and a cosmological constant. Secondly, we use the 1+3 covariant description to analyse scalar, vector and tensor perturbations of FL cosmologies containing a perfect fluid and a cosmological constant. In particular, we provide a thorough discussion of the behaviour of perturbations in the physically interesting cases of a dust or radiation background.

gr-qc↗