Spatial Statistical Downscaling for Constructing High-Resolution Nature Runs in Global Observing System Simulation Experiments

TL;DR

This paper introduces a statistical downscaling method to generate high-resolution nature runs from coarse global atmospheric model outputs, improving the fidelity of synthetic observations for climate and atmospheric research.

Contribution

The authors develop a nonstationary spatial covariance model with basis functions and a data-driven basis selection algorithm for efficient high-resolution downscaling of nature runs.

Findings

Successfully downscaled coarse-resolution CO2 data to high-resolution hexagons.

Demonstrated computational efficiency for large datasets.

Enhanced the realism of synthetic high-resolution atmospheric data.

Abstract

Observing system simulation experiments (OSSEs) have been widely used as a rigorous and cost-effective way to guide development of new observing systems, and to evaluate the performance of new data assimilation algorithms. Nature runs (NRs), which are outputs from deterministic models, play an essential role in building OSSE systems for global atmospheric processes because they are used both to create synthetic observations at high spatial resolution, and to represent the "true" atmosphere against which the forecasts are verified. However, most NRs are generated at resolutions coarser than actual observations. Here, we propose a principled statistical downscaling framework to construct high-resolution NRs via conditional simulation from coarse-resolution numerical model output. We use nonstationary spatial covariance function models that have basis function representations. This approach not only explicitly addresses the change-of-support problem, but also allows fast computation with large volumes of numerical model output. We also propose a data-driven algorithm to select the required basis functions adaptively, in order to increase the flexibility of our nonstationary covariance function models. In this article we demonstrate these techniques by downscaling a coarse-resolution physical NR at a native resolution of $1^{\circ} \text{ latitude} \times 1.25^{\circ} \text{ longitude}$ of global surface $\text{CO}_2$ concentrations to 655,362 equal-area hexagons.