A quick introduction to the project

Presentación realizada para el Taller de trabajo sobre "Uso de predicciones climáticas estacionales para la mejora de la gestión del agua" celebrado en AEMET el día 18 de marzo de 2014. Este taller de trabajo entre científicos del clima y gestores de los recursos hídricos ha sido organizado en el marco del proyecto europeo EUPORIAS y de la implementación en España del Marco Mundial para los Servicios Climáticos

Quality Management Framework for Climate Datasets

Data from a variety of research programmes are increasingly used by policy makers, researchers, and private sectors to make data-driven decisions related to climate change and variability. Climate services are emerging as the link to narrow the gap between climate science and downstream users. The Global Framework for Climate Services (GFCS) of the World Meteorological Organization (WMO) offers an umbrella for the development of climate services and has identified the quality assessment, along with its use in user guidance, as a key aspect of the service provision. This offers an extra stimulus for discussing what type of quality information to focus on and how to present it to downstream users. Quality has become an important keyword for those working on data in both the private and public sectors and significant resources are now devoted to quality management of processes and products. Quality management guarantees reliability and usability of the product served, it is a key element to build trust between consumers and suppliers. Untrustworthy data could lead to a negative economic impact at best and a safety hazard at worst. In a progressive commitment to establish this relation of trust, as well as providing sufficient guidance for users, the Copernicus Climate Change Service (C3S) has made significant investments in the development of an Evaluation and Quality Control (EQC) function. This function offers a homogeneous user-driven service for the quality of the C3S Climate Data Store (CDS). Here we focus on the EQC component targeting the assessment of the CDS datasets, which include satellite and in-situ observations, reanalysis, climate projections, and seasonal forecasts. The EQC function is characterised by a two-tier review system designed to guarantee the quality of the dataset information. While the need of assessing the quality of climate data is well recognised, the methodologies, the metrics, the evaluation framework, and how to present all this information to the users have never been developed before in an operational service, encompassing all the main climate dataset categories. Building the underlying technical solutions poses unprecedented challenges and makes the C3S EQC approach unique. This paper describes the development and the implementation of the operational EQC function providing an overarching quality management service for the whole CDS data.This study is based on work carried out in the C3S_512 contract funded by Copernicus Programme and operated by ECMWF on behalf of the European Commission (Service Contract number: ECMWF/COPERNICUS720187C3S_512_BSC). We would like to acknowledge the work of colleagues from several European institutions, the data providers and C3S, who contributed to the development of the EQC framework as well as to the QAR production. We would also like to acknowledge the focus group users, who took time to review and provide valuable feedback on the QARs, QATs, minimum requirements and the CDS quality assessment tab. The authors are grateful to the anonymous reviewers for their constructive comments that have helped for the improvement of this paper.Peer Reviewed"Article signat per 23 autors/es: Carlo Lacagnina , Francisco Doblas-Reyes, Gilles Larnicol, Carlo Buontempo, André Obregón, Montserrat Costa-Surós, Daniel San-Martín, Pierre-Antoine Bretonnière, Suraj D. Polade, Vanya Romanova, Davide Putero, Federico Serva, Alba Llabrés-Brustenga, Antonio Pérez, Davide Cavaliere, Olivier Membrive, Christian Steger, Núria Pérez-Zanón, Paolo Cristofanelli, Fabio Madonna, Marco Rosoldi, Aku Riihelä, Markel García Díez"Postprint (published version

WFDE5: Bias-adjusted ERA5 reanalysis data for impact studies

The WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5 spatial resolution but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower-resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 result in more plausible global hydrological water balance components when analysed in an uncalibrated hydrological model (WaterGAP) than with the use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S, 2020b), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS, C3S, 2020a) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data - allowing users to regenerate part of the dataset or apply the same approach to other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole of the year 2016, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60 (Cucchi et al., 2020). © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License

Optical measurements of atmospheric particles from airborne platforms: in situ and remote sensing instruments for balloons and aircrafts

Multiwavelength laser backscattersondes (MAS) have been widely used from a variety of airborne platforms for in situ measurements of optical properties of clouds and atmospheric particulate as well as their phase and com- position. Recently, a new miniaturized LIDAR (MULID) has been developed using state-of-art technology for balloon borne profiling of the same quantities. A description of the two instruments, a survey of preliminary re- sults obtained during test flights and indications for future use are given

WFDE5: bias adjusted ERA5 reanalysis data for impact studies

The WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5o spatial resolution, but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 result in more plausible global hydrological water balance components as analyzed in an uncalibrated hydrological model (WaterGAP) than use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S, 2020), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS, C3S, 2020) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data - allowing users to re-generate part of the dataset or apply the same approach on other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole 2016 year, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60