arXiv subjects

Eric Schmitt

Publications and source records attributed to Eric Schmitt.

7 recordsLinked to original sources

arXiv2026

Partial recovery of meter-scale surface weather

Near-surface atmospheric conditions can differ sharply over tens to hundreds of meters due to land cover and topography, yet this variability is absent from current weather analyses and forecasts. It is unclear whether such meter-scale variability reflects irreducibly chaotic dynamics or contains a component predictable from surface characteristics and large-scale atmospheric forcing. Here we show that a substantial, physically coherent component of meter-scale near-surface weather is statistically recoverable from existing observations. By conditioning coarse atmospheric state on sparse surface station measurements and high-resolution Earth observation data, we infer spatially continuous fields of near-surface wind, temperature, and humidity at 10 m resolution across the contiguous United States. Relative to ERA5, the inferred fields reduce wind error by 29% and temperature and dewpoint error by 6%, while explaining substantially more spatial variance at fixed time steps. They also exhibit physically interpretable structure, including urban heat islands, evapotranspiration-driven humidity contrasts, and wind speed differences across land cover types. Our findings expand the frontier of weather modeling by demonstrating a computationally feasible approach to continental-scale meter-resolution inference. More broadly, they illustrate how conditioning coarse dynamical models on static fine-scale features can reveal previously unresolved components of the Earth system.

cs.LG↗

arXiv2024

Local Off-Grid Weather Forecasting with Multi-Modal Earth Observation Data

Urgent applications like wildfire management and renewable energy generation require precise, localized weather forecasts near the Earth's surface. However, forecasts produced by machine learning models or numerical weather prediction systems are typically generated on large-scale regular grids, where direct downscaling fails to capture fine-grained, near-surface weather patterns. In this work, we propose a multi-modal transformer model trained end-to-end to downscale gridded forecasts to off-grid locations of interest. Our model directly combines local historical weather observations (e.g., wind, temperature, dewpoint) with gridded forecasts to produce locally accurate predictions at various lead times. Multiple data modalities are collected and concatenated at station-level locations, treated as a token at each station. Using self-attention, the token corresponding to the target location aggregates information from its neighboring tokens. Experiments using weather stations across the Northeastern United States show that our model outperforms a range of data-driven and non-data-driven off-grid forecasting methods. They also reveal that direct input of station data provides a phase shift in local weather forecasting accuracy, reducing the prediction error by up to 80% compared to pure gridded data based models. This approach demonstrates how to bridge the gap between large-scale weather models and locally accurate forecasts to support high-stakes, location-sensitive decision-making.

cs.LG↗

arXiv2017

Extending Bayesian structural time-series estimates of causal impact to many-household conservation initiatives

Government agencies offer economic incentives to citizens for conservation actions, such as rebates for installing efficient appliances and compensation for modifications to homes. The intention of these conservation actions is frequently to reduce the consumption of a utility. Measuring the conservation impact of incentives is important for guiding policy, but doing so is technically difficult. However, the methods for estimating the impact of public outreach efforts have seen substantial developments in marketing to consumers in recent years as marketers seek to substantiate the value of their services. One such method uses Bayesian Stuctural Time Series (BSTS) to compare a market exposed to an advertising campaign with control markets identified through a matching procedure. This paper introduces an extension of the matching/BSTS method for impact estimation to make it applicable for general conservation program impact estimation when multi-household data is available. This is accomplished by household matching/BSTS steps to obtain conservation estimates and then aggregating the results using a meta-regression step to aggregate the findings. A case study examining the impact of rebates for household turf removal on water consumption in multiple Californian water districts is conducted to illustrate the work flow of this method.

stat.ME↗

arXiv2016

Transforming how water is managed in the West

California is challenged by its worst drought in 600 years and faces future water uncertainty. Pioneering new data infrastructure to integrate water use data across California's more than a thousand water providers will support water managers in ensuring water reliability. The California Data Collaborative is a coalition of municipal water utilities serving ten percent of California's population who are delivering on that promise by centralizing customer water use data in a recently completed pilot project. This project overview describes tools that have shown promising early results in improving water efficiency programs and optimizing system operations. Longer term, these tools will help navigate future uncertainty and support water managers in ensuring water reliability no matter what the future holds. The uniquely publicly-owned data infrastructure deployed in this project is envisioned to enable the world's first "marketplace of civic analytics" to power the volume of water efficiency measurements water managers require at a radically more cost effective price. More broadly, this data-utility approach is adaptable to domains other than water and shows specific potential for the broader universe of natural resources.

cs.CY↗

arXiv2014

Finite Sample Breakdown of PCS

The Projection Congruent Subset (PCS) is new method for finding multivariate outliers. PCS returns an outlyingness index which can be used to construct affine equivariant estimates of multivariate location and scatter. In this note, we derive the finite sample breakdown point of these estimators.

math.ST↗

arXiv2014

Finding Regression Outliers With FastRCS

The Residual Congruent Subset (RCS) is a new method for finding outliers in the linear regression setting. Like many other outlier detection procedures, RCS searches for a subset which minimizes a criterion. The difference is that the new criterion was designed to be insensitive to the outliers. RCS is supported by FastRCS, a fast regression and affine equivariant algorithm which we also detail. Both an extensive simulation study and two real data applications show that FastRCS performs better than its competitors.

stat.ME↗

arXiv2013

Finding Multivariate Outliers With FastPCS

The Projection Congruent Subset (PCS) Outlyingness is a new index of multivariate outlyingness obtained by considering univariate projections of the data. Like many other outlier detection procedures, PCS searches for a subset which minimizes a criterion. The difference is that the new criterion was designed to be insensitive to the outliers. PCS is supported by FastPCS, a fast and affine equivariant algorithm which we also detail. Both an extensive simulation study and a real data application from the field of engineering show that FastPCS performs better than its competitors.

stat.ME↗