Spatial interpolation of high-frequency monitoring data

TL;DR

This paper presents a statistical method for interpolating high-frequency atmospheric pressure data from irregularly spaced monitoring stations to generate accurate spatial maps and quantify uncertainty, using conditional simulations.

Contribution

It introduces a novel approach for high-frequency meteorological data interpolation that provides both point predictions and uncertainty quantification based on conditional simulations.

Findings

Point predictions are highly accurate.

Uncertainty estimates are well-calibrated for temporal changes.

Uncertainty estimates are overconservative for absolute pressure predictions.

Abstract

Climate modelers generally require meteorological information on regular grids, but monitoring stations are, in practice, sited irregularly. Thus, there is a need to produce public data records that interpolate available data to a high density grid, which can then be used to generate meteorological maps at a broad range of spatial and temporal scales. In addition to point predictions, quantifications of uncertainty are also needed. One way to accomplish this is to provide multiple simulations of the relevant meteorological quantities conditional on the observed data taking into account the various uncertainties in predicting a space-time process at locations with no monitoring data. Using a high-quality dataset of minute-by-minute measurements of atmospheric pressure in north-central Oklahoma, this work describes a statistical approach to carrying out these conditional simulations. Based on observations at 11 stations, conditional simulations were produced at two other sites with monitoring stations. The resulting point predictions are very accurate and the multiple simulations produce well-calibrated prediction uncertainties for temporal changes in atmospheric pressure but are substantially overconservative for the uncertainties in the predictions of (undifferenced) pressure.