Abstract
Abstract. A scientific approach is presented to aggregate and harmonize a set of 60
geophysical variables at hourly timescale over a decade, and to allow multiannual
and multi-variable studies combining atmospheric dynamics and
thermodynamics, radiation, clouds and aerosols from ground-based
observations. Many datasets from ground-based observations are currently in
use worldwide. They are very valuable because they contain complete and
precise information due to their spatio-temporal co-localization over more
than a decade. These datasets, in particular the synergy between different
type of observations, are under-used because of their complexity and
diversity due to calibration, quality control, treatment, format, temporal
averaging, metadata, etc. Two main results are presented in this article: (1) a set of methods available for the community to robustly and reliably process
ground-based data at an hourly timescale over a decade is described and (2) a single netCDF file is provided based on the SIRTA supersite observations.
This file contains approximately 60 geophysical variables (atmospheric and
in ground) hourly averaged over a decade for the longest variables. The
netCDF file is available and easy to use for the community. In this article,
observations are “re-analyzed”. The prefix “re” refers to six main steps:
calibration, quality control, treatment, hourly averaging, homogenization of
the formats and associated metadata, as well as expertise on more than a decade of
observations. In contrast, previous studies (i) took only some of these six
steps into account for each variable, (ii) did not aggregate all variables
together in a single file and (iii) did not offer an hourly resolution for
about 60 variables over a decade (for the longest variables). The approach
described in this article can be applied to different supersites and to
additional variables. The main implication of this work is that complex
atmospheric observations are made readily available for scientists who are
non-experts in measurements. The dataset from SIRTA observations can be
downloaded at http://sirta.ipsl.fr/reobs.html (last access: April 2017) (Downloads tab, no
password required) under https://doi.org/10.14768/4F63BAD4-E6AF-4101-AD5A-61D4A34620DE.
Subject
General Earth and Planetary Sciences
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