Developing an open data portal for the ESA climate change initiative

We introduce the rationale for, and architecture of, the European Space Agency Climate Change Initiative (CCI) Open Data Portal (http://cci.esa.int/data/). The Open Data Portal hosts a set of richly diverse datasets – 13 “Essential Climate Variables” – from the CCI programme in a consistent and harmonised form and to provides a single point of access for the (>100 TB) data for broad dissemination to an international user community. These data have been produced by a range of different institutions and vary across both scientific and spatio-temporal characteristics. This heterogeneity of the data together with the range of services to be supported presented significant technical challenges. An iterative development methodology was key to tackling these challenges: the system developed exploits a workflow which takes data that conforms to the CCI data specification, ingests it into a managed archive and uses both manual and automatically generated metadata to support data discovery, browse, and delivery services. It utilises both Earth System Grid Federation (ESGF) data nodes and the Open Geospatial Consortium Catalogue Service for the Web (OGC-CSW) interface, serving data into both the ESGF and the Global Earth Observation System of Systems (GEOSS). A key part of the system is a new vocabulary server, populated with CCI specific terms and relationships which integrates OGC-CSW and ESGF search services together, developed as part of a dialogue between domain scientists and linked data specialists. These services have enabled the development of a unified user interface for graphical search and visualisation – the CCI Open Data Portal Web Presence

The ESA climate change initiative: Satellite data records for essential climate variables

The ESA’s Climate Change Initiative is reprocessing and reassessing over 40 years of multi-sensor satellite records to generate consistent, traceable, long-term datasets of “essential climate variables” for the climate modeling and research communities

An Improved and Homogeneous Altimeter Sea Level Record from the ESA Climate Change Initiative

Sea Level is a very sensitive index of climate change since it integrates the impacts of ocean warming and ice mass loss from glaciers and the ice sheets. Sea Level has been listed as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). During the past 25 years, the sea level ECV has been measured from space by different altimetry missions that have provided global and regional observations of sea level variations. As part of the Climate Change Initiative (CCI) program of the European Space Agency (ESA) (established in 2010), the Sea Level project (SL_cci) aimed at providing an accurate and homogeneous long-term satellite-based sea level record. At the end of the first phase of the project (2010-2013), an initial version (v1.1) of the sea level ECV has been made available to users (Ablain et al., 2015). During the second phase (2014-2017), improved altimeter standards have been selected to produce new sea level products (called SL_cci v2.0) based on 9 altimeter missions for the period 1993-2015 (https://doi.org/10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612). Corresponding orbit solutions, geophysical corrections and altimeter standards used in this v2.0 dataset are described in details in Quartly et al. (2017). The present paper focuses on the description of the SL_cci v2.0 ECV and associated uncertainty and discusses how it has been validated. Various approaches have been used for the quality assessment such as internal validation, comparisons with sea level records from other groups and with in-situ measurements, sea level budget closure analyses and comparisons with model outputs. Compared to the previous version of the sea level ECV, we show that use of improved geophysical corrections, careful bias reduction between missions and inclusion of new altimeter missions lead to improved sea level products with reduced uncertainties at different spatial and temporal scales. However, there is still room for improvement since the uncertainties remain larger than the GCOS requirements. Perspectives for subsequent evolutions are also discussed

Validation of the acquisition algorithms for FSSCat’s Soil Moisture product

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2023, Tutors: Yolanda Sola, Adrian Perez-Portero, Adriano Jose CampsThis essay presents a validation of two months (October-November 2020) of soil moisture retrieved by the FSSCat mission by comparing it with the ESA Climate Change Initiative mission (ESA CII) dataset. A data processing pipeline, along with various statistical tests, was designed to detect disparities between the two datasets. The results with RMSE, Bias, and ubRMSE revealed notable discrepancies in some regions, such as Russia, with values of 0.1 m3/m3 for the ubRMSE. In general, FSSCat’s data has underestimated measurements compared to ESA CII’s dataset. These discrepancies can be attributed to instrumental errors, the presence of ice in certain regions, and uncertainties in the re-gridding method

The inter-comparison of AATSR aerosol optical depth retrievals from various algorithms

The project aerosol-CCI as part of European Space Agency (ESA) Climate Change Initiative (CCI) has provided three aerosol retrieval algorithms for the Advanced Along-Track Scanning Radiometer (AATSR) aboard on ENVISAT. For the purpose of estimating different performance of these three algorithms in Asia, in this paper we compared the Aerosol Optical Depth (AOD) of L2 data (10km×10km) including FMI AATSR Dual-view ADV algorithm, the Oxford RAL Aerosol and Cloud retrieval (ORAC) algorithm and the Swansea University AATSR retrieval (SU) algorithm with the AErosol RObotic NETwork (AERONET) and the China Aerosol Remote Sensing Network (CARSNET) data separately. The result shows that the algorithms of ADV and SU have good performance on the retrieval of AOD, and the ORAC algorithm has relative lower precision than other two algorithms

Impact of AVHRR channel 3b noise on climate data records: filtering method applied to the CM SAF CLARA-A2 data record

A method for reducing the impact of noise in the 3.7 micron spectral channel in climate data records derived from coarse resolution (4 km) global measurements from the Advanced Very High Resolution Radiometer (AVHRR) data is presented. A dynamic size-varying median filter is applied to measurements guided by measured noise levels and scene temperatures for individual AVHRR sensors on historic National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites in the period 1982–2001. The method was used in the preparation of the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data—Second Edition (CLARA-A2), a cloud climate data record produced by the EUMETSAT Satellite Application Facility for Climate Monitoring (CM SAF), as well as in the preparation of the corresponding AVHRR-based datasets produced by the European Space Agency (ESA) Climate Change Initiative (CCI) project ESA-CLOUD-CCI. The impact of the noise filter was equivalent to removing an artificial decreasing trend in global cloud cover of 1–2% per decade in the studied period, mainly explained by the very high noise levels experienced in data from the first satellites in the series (NOAA-7 and NOAA-9). View Full-Tex

Relative Drifts and Biases Between Six Ozone Limb Satellite Measurements From the Last Decade

As part of European Space Agency\u27s (ESA) climate change initiative, high vertical resolution ozone profiles from three instruments all aboard ESA\u27s Envisat (GOMOS, MIPAS, SCIAMACHY) and ESA\u27s third party missions (OSIRIS, SMR, ACE-FTS) are to be combined in order to create an essential climate variable data record for the last decade. A prerequisite before combining data is the examination of differences and drifts between the data sets. In this paper, we present a detailed analysis of ozone profile differences based on pairwise collocated measurements, including the evolution of the differences with time. Such a diagnosis is helpful to identify strengths and weaknesses of each data set that may vary in time and introduce uncertainties in long-term trend estimates. The analysis reveals that the relative drift between the sensors is not statistically significant for most pairs of instruments. The relative drift values can be used to estimate the added uncertainty in physical trends. The added drift uncertainty is estimated at about 3% decade-1 (1σ). Larger differences and variability in the differences are found in the lowermost stratosphere (below 20 km) and in the mesosphere

Easy come, easy go: Short-term land-use dynamics vis à vis regional economic downturns

The present study postulates distinctive land-use dynamics along the economic cycle, and tests against diverging trends over time of urban and non-urban land-uses with characteristic economic potential. Short-term land-use changes over seven time windows encompassing the last three decades (1992–2020) were investigated in metropolitan Athens (Greece), a mono-centric region experiencing complex economic downturns. Based on diachronic land-use maps with homogeneous spatial resolution and nomenclature derived from ESA Climate Change Initiative (ESA-CCI), a change detection analysis was run considering mean patch size, distance from downtown, and specific entropy-based metrics of landscape diversification (Shannon-Wiener H’ diversity index and Pielou J evenness index). Results of a canonical correlation analysis document differential intensity and spatial direction of change during expansions and recessions associated with distinctive socio-demographic profiles. Metropolitan growth followed a radio-centric (land-saving) model during economic expansions with intense urbanization of fringe land. A more dispersed settlement model – reflecting urban sprawl – was associated with economic stagnations, involving land at progressively distant locations from downtown. Landscape diversification was higher under stagnations and lower during expansions