Abstract
Abstract. The new platforms for Earth observation from space are characterized by measurements made at great spatial and temporal resolutions. While this abundance of information makes it possible to detect and study localized phenomena, it may be difficult to manage this large amount of data for the study of global and large-scale phenomena. A particularly significant example is the use by assimilation systems of Level 2 products that represent gas profiles in the atmosphere. The models on which assimilation systems are based are discretized on spatial grids with horizontal dimensions of the order of tens of kilometres in which tens or hundreds of measurements may fall in the future. A simple procedure to overcome this problem is to extract a subset of the original measurements, but this involves a loss of information. Another option is the use of simple averages of the profiles, but this approach also has some limitations that we will discuss in the paper. A more advanced solution is to resort to the so-called fusion algorithms, capable of compressing the size of the dataset while limiting the information loss. A novel data fusion method, the Complete Data Fusion algorithm, was recently developed to merge a set of retrieved products in a single product a posteriori. In the present paper, we apply the Complete Data Fusion method to ozone profile measurements simulated in the thermal infrared and ultraviolet bands in a realistic scenario. Following this, the fused products are compared with the input profiles; comparisons show that the output products of data fusion have smaller total errors and higher information contents in general. The comparisons of the fused products with the fusing products are presented both at single fusion grid box scale and with a statistical analysis of the results obtained on large sets of fusion grid boxes of the same size. We also evaluate the grid box size impact, showing that the Complete Data Fusion method can be used with different grid box sizes even if this possibility is connected to the natural variability of the considered atmospheric molecule.
Highlights
In the context of the Copernicus programme coordinated by the European Commission, the European Space Agency is responsible for the Space Component, consisting of a novel set of Earth Observation (EO) satellite missions for environmental monitoring applications: the Sentinel missions
This paper presents a feasibility study of the Complete Data Fusion (CDF) technique applied to Level 2 (L2) products simulated according to the characteristics of the atmospheric Sentinel missions
Despite the approximations that characterize the simulated L2 products, this analysis allows for the evaluation of the performances of the CDF algorithm in a realistic scenario
Summary
In the context of the Copernicus programme (https://www. copernicus.eu, last access: 29 December 2020) coordinated by the European Commission, the European Space Agency is responsible for the Space Component, consisting of a novel set of Earth Observation (EO) satellite missions for environmental monitoring applications: the Sentinel missions Copernicus.eu, last access: 29 December 2020) coordinated by the European Commission, the European Space Agency is responsible for the Space Component, consisting of a novel set of Earth Observation (EO) satellite missions for environmental monitoring applications: the Sentinel missions The geostationary (GEO) mission Sentinel and the two low-Earth-orbit (LEO) missions (Sentinel-5p and Sentinel-5), referred to as the atmospheric Sentinels, are dedicated to monitoring air quality, stratospheric ozone, ultraviolet surface radiation and climate. The atmospheric Sentinels will provide an enormous amount of data with unprecedented accuracy and spatiotemporal resolution. In this scenario, a central challenge is to enable a generic data user (for example, an assimilation system) to exploit such a large amount of data
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