Abstract

Earth observations have many missing values. Their abundance and often complex patterns can be a barrier for combining different observational datasets and may cause biased estimates. To overcome this, missing values in geoscientific data are regularly infilled with estimates through univariate gap-filling techniques such as spatio-temporal interpolation. However, these mostly ignore valuable information that may be present in other dependent observed variables. Here we propose CLIMFILL, a multivariate gap-filling procedure that builds up upon simple interpolation by additionally applying a statistical imputation method that is designed to account for dependence across variables. In contrast to popular up-scaling approaches, CLIMFILL does not need a gap-free gridded "donor" variable for gap-filling. CLIMFILL is tested using gap-free ERA5 re-analysis data of ground temperature, surface layer soil moisture, precipitation, and terrestrial water storage to represent central interactions between soil moisture and climate. These observations were matched with corresponding remote sensing observations and masked where the observations have missing values. CLIMFILL successfully recovers the dependence structure among the variables across all land cover types and altitudes, thereby enabling subsequent mechanistic interpretations. Soil moisture-temperature feedback, which is underestimated in high latitude regions due to sparse satellite coverage, is adequately represented in the multivariate gap-filling. Univariate performance metrics such as correlation and bias are improved compared to spatiotemporal interpolation gap-fill for a wide range of missing values and missingness patterns. Especially estimates for surface layer soil moisture profit taking into account the multivariate dependence structure of the data. The framework al- lows tailoring the gap-filling process to different environmental conditions, domains, or specific use cases and hence can be used as a flexible tool for gap-filling a large range of remote sensing and in situ observations commonly used in climate and environmental research.

Highlights

  • We present an approach to consolidate fragmented Earth 95 observations into a coherent, multivariate, gap-free dataset by tackling the problem of missing values in the multivariate Earth observation record with the gap-filling framework CLIMFILL

  • We found the biggest improvement compared to interpolation for surface layer soil moisture despite its large fraction of missing values

  • CLIMFILL is a framework for gap-filling multivariate gridded Earth observations that estimates missing values by taking into account the spatial, temporal 440 and the multivariate context of a missing value

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Summary

Introduction

How ever, combining observations from several physical variables into a coherent "view" of the state of the Earth system is crucial for many applications These include, but are not limited to, analysis of local and regional land surface dynamics, tracing of compound extreme events or observational water and energy budget closures. In state-of-the-art atmospheric reanalysis product ERA5 the already difficult observational record of soil moisture is used only 85 sparsely (Hersbach et al, 2020), the added value for example remote sensing soil moisture assimilation been shown for weather forecast models (Zhan et al, 2016) and flood forecasting (Brocca et al, 2014; Sahoo et al, 2013). The newly developed methodology is exemplarily tested for variables relevant in the study of land-atmosphere dynamics

Gap-filling in the methodological literature
Gap-filling in Earth system sciences
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Data-constrained upper perfomance limits
Recovery of regional and local land-climate dynamics
Findings
Discussion and conclusions
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