arXiv subjects

Nick Krall

Publications and source records attributed to Nick Krall.

3 recordsLinked to original sources

arXiv2026

Deep-Learned Observation Operators for Artificial Intelligence Weather Forecasting Models

Satellite observation operators play an essential role in atmospheric data assimilation by translating model state variables into observation space. Previous work has shown that deep-learned emulators can effectively predict the outputs of classic observation operators, like the Community Radiative Transfer Model (CRTM), with reduced inference time. This study expands previous work to show the potential for integrating observation operators into artificial intelligence (AI) weather forecasting models. Specifically, this study shows that (1) deep-learned models can effectively predict the innovations (or differences between the simulated and observed radiances) used by data assimilation models and (2) deep-learned observation models suffer only minor degradations in performance when the model state is represented with fewer vertical levels, as is commonly used by AI forecasting models. Experiments were performed using the Unified Forecast System (UFS) replay dataset, including Gridpoint Statistical Interpolation (GSI) observational data for the Advanced Technology Microwave Sounder (ATMS) sensor from 2022 and 2023. Code is available at https://github.com/mitre/deep-obs.

physics.ao-ph↗

arXiv2025

Scaling Remote Sensing Foundation Models: Data Domain Tradeoffs at the Peta-Scale

We explore the scaling behaviors of artificial intelligence to establish practical techniques for training foundation models on high-resolution electro-optical (EO) datasets that exceed the current state-of-the-art scale by orders of magnitude. Modern multimodal machine learning (ML) applications, such as generative artificial intelligence (GenAI) systems for image captioning, search, and reasoning, depend on robust, domain-specialized encoders for non-text modalities. In natural image domains where internet-scale data is plentiful, well-established scaling laws help optimize the joint scaling of model capacity, training compute, and dataset size. Unfortunately, these relationships are much less well understood in high-value domains like remote sensing (RS). Using over a quadrillion pixels of commercial satellite EO data and MITRE's Federal AI Sandbox, we train progressively larger vision transformer (ViT) backbones, report successes and failure modes observed at peta-scale, and analyze implications for bridging domain gaps across additional RS modalities. We observe that even at this scale, performance is consistent with a data-limited regime rather than a model parameter-limited one. These practical insights are intended to inform data collection strategies, compute budgets, and optimization schedules that advance the future development of frontier scale RS foundation models.

cs.CV↗

arXiv2024

Anticipatory Understanding of Resilient Agriculture to Climate

With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. While we feel that the methods are adaptable to other regions of the world, we focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population. We present a quantitative analysis of deep learning domain adaptation methods for wheat farm identification based on curated remote sensing data from France. We model climate change impacts on crop yields using the existing crop yield modeling tool WOFOST and we identify key drivers of crop simulation error using a longitudinal penalized functional regression. A description of a system dynamics model of the food distribution system in India is also presented, along with results of food insecurity identification based on seeding this model with the predicted crop yields.

cs.CV↗