Spatial mapping of ground-based observations of total ozone

Abstract

Abstract. Total column ozone variations estimated using ground-based stations provide important independent source of information in addition to satellite-based estimates. This estimation has been vigorously challenged by data inhomogeneity in time and by the irregularity of the spatial distribution of stations, as well as by interruptions in observation records. Furthermore, some stations have calibration issues and thus observations may drift. In this paper we compare the spatial interpolation of ozone levels using the novel stochastic partial differential equation (SPDE) approach with the covariance-based kriging. We show how these new spatial predictions are more accurate, less uncertain and more robust. We construct long-term zonal means to investigate the robustness against the absence of measurements at some stations as well as instruments drifts. We conclude that time series analyzes can benefit from the SPDE approach compared to the covariance-based kriging when stations are missing, but the positive impact of the technique is less pronounced in the case of drifts.

Computer Model Calibration with Large Non-Stationary Spatial Outputs: Application to the Calibration of a Climate Model

“…The link between the Matérn field and the SPDE is given by α = ν + d /2, which makes explicit the relationship between dimension and regularity for fixed α . On more general manifolds than , such as the sphere (Chang, Guillas and Fioletov, ), the direct Matérn representation is not easy to implement (for example, Matérn covariance with great circle distance is valid only at ν ∈ (0,0.5] (Gneiting, )), but the SPDE formulation provides a natural generalization, and the ν ‐parameter will keep its meaning as the quantitative measure of regularity. Instead of defining Matérn fields by the covariance function, Lindgren et al .…”

Uncertainty in regional temperatures inferred from sparse global observations: Application to a probabilistic classification of El Niño

“…Moreover, it is challenging for covariance functions with light tails to capture variation among observations widely separated over the spatial domain (e.g., Matérn) [ Kleiber and Porcu , ] as it is the case for temperatures over the globe. Newly developed statistical techniques outperform covariance‐based kriging in terms of accuracy and uncertainty quantification over the globe [ Chang et al , ].…”

Spatially continuous mapping of daily global ozone distribution (2004–2014) with the Aura OMI sensor

“…Concerning this issue, Chang et al . [] presented a possible solution. In our study, we assume that the prediction residuals satisfy the intrinsic stationarity assumption, and then the coefficients λ i in equation can be solved by variogram γ .…”