315 research outputs found
Introduction to the Publishing Industry
This individual study was designed to help me come to a better understanding of the publishing industry and how a writer bridges the gap between writing and publication, between a hobby and a profession. Creatively, my goal was to revise, develop, and polish a novella rooted in ENGL 470B, fiction seminar. Professionally, I learned and implemented the process of producing, preparing, and sending out original creative work to literary magazines for publication. The outcomes of the project included a significantly revised fantasy novella, a series of cover letters, a strong and flexible understanding of the literary marketplace, and completed submissions for several literary magazines. During the project I submitted to the Magazine of Fantasy & Science Fiction, the Clay Reynolds Novella Prize, and Driftwood Press. This project built on what I have learned throughout my time at UMW, particularly my creative writing classes; but more than that, this project has been the beginning of the rest of my career as a writer
Data-Driven Equation Discovery of a Cloud Cover Parameterization
A promising method for improving the representation of clouds in climate
models, and hence climate projections, is to develop machine learning-based
parameterizations using output from global storm-resolving models. While neural
networks can achieve state-of-the-art performance within their training
distribution, they can make unreliable predictions outside of it. Additionally,
they often require post-hoc tools for interpretation. To avoid these
limitations, we combine symbolic regression, sequential feature selection, and
physical constraints in a hierarchical modeling framework. This framework
allows us to discover new equations diagnosing cloud cover from coarse-grained
variables of global storm-resolving model simulations. These analytical
equations are interpretable by construction and easily transferable to other
grids or climate models. Our best equation balances performance and complexity,
achieving a performance comparable to that of neural networks ()
while remaining simple (with only 11 trainable parameters). It reproduces cloud
cover distributions more accurately than the Xu-Randall scheme across all cloud
regimes (Hellinger distances ), and matches neural networks in
condensate-rich regimes. When applied and fine-tuned to the ERA5 reanalysis,
the equation exhibits superior transferability to new data compared to all
other optimal cloud cover schemes. Our findings demonstrate the effectiveness
of symbolic regression in discovering interpretable, physically-consistent, and
nonlinear equations to parameterize cloud cover.Comment: 35 pages, 10 figures, Submitted to 'Journal of Advances in Modeling
Earth Systems' (JAMES
An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes
Despite the importance of quantifying how the spatial patterns of extreme
precipitation will change with warming, we lack tools to objectively analyze
the storm-scale outputs of modern climate models. To address this gap, we
develop an unsupervised machine learning framework to quantify how storm
dynamics affect precipitation extremes and their changes without sacrificing
spatial information. Over a wide range of precipitation quantiles, we find that
the spatial patterns of extreme precipitation changes are dominated by spatial
shifts in storm regimes rather than intrinsic changes in how these storm
regimes produce precipitation.Comment: 14 Pages, 9 Figures, Accepted to "Tackling Climate Change with
Machine Learning: workshop at NeurIPS 2022". arXiv admin note: text overlap
with arXiv:2208.1184
Convective dynamics and the response of precipitation extremes to warming in radiative-convective equilibrium
Tropical precipitation extremes are expected to strengthen with warming, but
quantitative estimates remain uncertain because of a poor understanding of
changes in convective dynamics. This uncertainty is addressed here by analyzing
idealized convection-permitting simulations of radiative-convective equilibrium
in long-channel geometry. Across a wide range of climates, the thermodynamic
contribution to changes in instantaneous precipitation extremes follows
near-surface moisture, and the dynamic contribution is positive and small, but
sensitive to domain size. The shapes of mass flux profiles associated with
precipitation extremes are determined by conditional sampling that favors
strong vertical motion at levels where the vertical saturation specific
humidity gradient is large, and mass flux profiles collapse to a common shape
across climates when plotted in a moisture-based vertical coordinate. The
collapse, robust to changes in microphysics and turbulence schemes, implies a
thermodynamic contribution that scales with near-surface moisture despite
substantial convergence aloft and allows the dynamic contribution to be defined
by the pressure velocity at a single level. Linking the simplified dynamic mode
to vertical velocities from entraining plume models reveals that the small
dynamic mode in channel simulations (<~2 %/K) is caused by opposing
height-dependences of vertical velocity and density, together with the
buffering influence of cloud-base buoyancies that vary little with surface
temperature. These results reinforce an emerging picture of the response of
extreme tropical precipitation rates to warming: a thermodynamic mode of about
7 %/K dominates, with a minor contribution from changes in dynamics.Comment: 28 pages, 15 figures, 1 table. This work has been accepted to Journal
of the Atmospheric Sciences. The AMS does not guarantee that the copy
provided here is an accurate copy of the final published wor
Next-Generation Earth System Models: Towards Reliable Hybrid Models for Weather and Climate Applications
We review how machine learning has transformed our ability to model the Earth
system, and how we expect recent breakthroughs to benefit end-users in
Switzerland in the near future. Drawing from our review, we identify three
recommendations.
Recommendation 1: Develop Hybrid AI-Physical Models: Emphasize the
integration of AI and physical modeling for improved reliability, especially
for longer prediction horizons, acknowledging the delicate balance between
knowledge-based and data-driven components required for optimal performance.
Recommendation 2: Emphasize Robustness in AI Downscaling Approaches, favoring
techniques that respect physical laws, preserve inter-variable dependencies and
spatial structures, and accurately represent extremes at the local scale.
Recommendation 3: Promote Inclusive Model Development: Ensure Earth System
Model development is open and accessible to diverse stakeholders, enabling
forecasters, the public, and AI/statistics experts to use, develop, and engage
with the model and its predictions/projections.Comment: 12 pages, 1 figure, submitted as part of the Swiss Academy of
Engineering Sciences' 2024 whitepaper on "Artificial Intelligence for Climate
Change Mitigation
Convective environments in AI-models - What have AI-models learned about atmospheric profiles?
The recently released suite of AI-based medium-range forecast models can produce multi-day forecasts within seconds, with a skill on par with the IFS model of ECMWF. Traditional model evaluation predominantly targets global scores on single levels. Specific prediction tasks, such as severe convective environments, require much more precision on a local scale and with the correct vertical gradients in between levels. With a focus on the North American and European convective season of 2020, we assess the performance of Panguweather, Graphcast and Fourcastnet for convective available potential energy (CAPE) and storm relative helicity (SRH) at lead times of up to 7 days. Looking at the example of a US tornado outbreak on April 12 and 13, 2020, all models predict elevated CAPE and SRH values multiple days in advance. The spatial structures in the AI-models are smoothed in comparison to IFS and the reanalysis ERA5. The models show differing biases in the prediction of CAPE values, with Graphcast capturing the value distribution the most accurately and Fourcastnet showing a consistent underestimation. By advancing the assessment of large AI-models towards process-based evaluations we lay the foundation for hazard-driven applications of AI-weather-forecasts
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