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Data Assimilation with Sparse Observations
Abstract
Data assimilation by nudging (also called CDA) yields exponentially decaying errors and an infinite predictability horizon if the method parameter is large enough and the observations are frequent enough in time and dense enough in space. We consider the complementary case of moderate parameters and sparse and infrequent observations. We prove that assimilation with any data and any(positive) parameter strictly decreases errors and strictly increases the (now finite) predictability horizon.
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BibTeX
@misc{jiang2026data,
title = {{Data Assimilation with Sparse Observations}},
author = {Nan Jiang and William Layton and Nanda Nechingal Raghunathan and Troy Yang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.06901},
eprint = {2609.06901},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.06901},
}
RIS
TY - UNPB
TI - Data Assimilation with Sparse Observations
AU - Nan Jiang
AU - William Layton
AU - Nanda Nechingal Raghunathan
AU - Troy Yang
PY - 2026
DA - 2026/09/07
PB - arXiv
DO - 10.48550/arxiv.2609.06901
UR - https://arxiv.org/abs/2609.06901
AB - Data assimilation by nudging (also called CDA) yields exponentially decaying errors and an infinite predictability horizon if the method parameter is large enough and the observations are frequent enough in time and dense enough in space. We consider the complementary case of moderate parameters and sparse and infrequent observations. We prove that assimilation with any data and any(positive) parameter strictly decreases errors and strictly increases the (now finite) predictability horizon.
ER -