Automatic procedures for submitting essential climate variables (ECVs) recorded at Italian Atmospheric Observatories to WMO/GAW data centers

Abstract. In the framework of the National Project of Interest Nextdata, we developed procedures for the automatic flagging and formatting of trace gas, atmospheric aerosol and meteorological data to be submitted to Global Atmosphere Watch programme by the World Meteorological Organization (WMO/GAW). In this work, we describe a first prototype of a centralized system to support Italian atmospheric observatories towards a more efficient and objective data production and subsequent submission to WMO/GAW World Data Centers (WDCs). In particular, the atmospheric variables covered by this work were focused on near-surface trace gases, aerosol properties and (ancillary) meteorological parameters which are under the umbrella of the World Data Center for Greenhouse Gases (WDCGG, see https://ds.data.jma.go.jp/gmd/wdcgg/), World Data Center for Reactive Gases and World Data Center for Aerosol (WDCRG and WDCA, see http://ebas.nilu.no). For different Essential Climate Variables (ECVs), we developed specific routines for data filtering, flagging, format harmonization and creation of data products (i.e. plot of raw and valid-corrected-averaged ECV data and internal instrument parameters) useful for detecting instrumental problems or atmospheric events. A special suite of products based on the temporal aggregation of valid ECV data (like the “calendar” or “timevariation” products) were implemented for quick data dissemination towards stakeholders or citizens Currently, the automatic processing of data is active for a subset of ECVs and 4 measurement sites in Italy. The Nextdata system does not generate “consolidated” data to be directly submitted to WDCs, but it represents a valuable tool to facilitate data providers towards a more efficient data production for those data streams. Our effort is expected to accelerate the process of data submission to GAW/WMO or to other reference data centers or repositories as well as to make the data flagging more “objective”, which means that it is based on a set of well-defined selection criteria and not strictly related to the subjective judgment of station operators. Moreover, the adoption of automatic procedures for data flagging and data correction allows to keep track of the process that led to the final validated data, and makes data evaluation and revisions more efficient.