Spatially enabling the Global Framework for Climate Services: Reviewing geospatial solutions to efficiently share and integrate climate data &amp; information

Giuliani, Gaston; Nativi, Stefano; Obregón, André; Beniston, Martin; Lehmann, Anthony

doi:10.1016/j.cliser.2017.08.003

Cited by 49 publications

(45 citation statements)

References 90 publications

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“…This approach seems to converge on the most recent advancements in geospatial technologies to support CS [73], adopted at higher geographical levels (national and global). Indeed, the different information tools developed guarantee a continuous and on-demand service to several local users, from the Tuscany region or the Tuscan Water Authority (AIT), who can ask at any time updated information related to a specific index or periods more useful for their assessments, to researchers that need data for their further investigations, and even in other geographical areas covered by the available datasets.…”

Section: Future Perspectivesmentioning

confidence: 99%

“…Also suggested by Giuliani [73], the availability of open-source tools and standardized interoperable SDI web services ensures the sustainability in the development of web applications with geo-referenced data and customized territorial analysis that could be connected to other climate services. This approach facilitates the creation of value-added products and services by researchers involved in climate investigations.…”

Section: Climate Servicesmentioning

confidence: 99%

“…Moreover, Giuliani et al [73] assert that GFCS should address future efforts towards the adoption of interoperable geospatial solutions and web-based services.…”

Section: Future Perspectivesmentioning

confidence: 99%

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Semi-Automatic Operational Service for Drought Monitoring and Forecasting in the Tuscany Region

et al. 2018

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Abstract:A drought-monitoring and forecasting system developed for the Tuscany region was improved in order to provide a semi-automatic, more detailed, timely and comprehensive operational service for decision making, water authorities, researchers and general stakeholders. Ground-based and satellite data from different sources (regional meteorological stations network, MODIS Terra satellite and CHIRPS/CRU precipitation datasets) are integrated through an open-source, interoperable SDI (spatial data infrastructure) based on PostgreSQL/PostGIS to produce vegetation and precipitation indices that allow following of the occurrence and evolution of a drought event. The SDI allows the dissemination of comprehensive, up-to-date and customizable information suitable for different end-users through different channels, from a web page and monthly bulletins, to interoperable web services, and a comprehensive climate service. The web services allow geospatial elaborations on the fly, and the geo-database can be increased with new input/output data to respond to specific requests or to increase the spatial resolution.

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Section: Future Perspectivesmentioning

confidence: 99%

Section: Climate Servicesmentioning

confidence: 99%

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Semi-Automatic Operational Service for Drought Monitoring and Forecasting in the Tuscany Region

et al. 2018

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“…Furthermore, the spatial aspect of data is being largely 78 underutilized in processing, although the importance of spatial decision-making is now widely 79 accepted [2][3]. It has been proven that spatial analysis of data gives more meaning to the information 80 extraction and hence enables easier assimilation of large volumes of data [4].…”

mentioning

confidence: 99%

“…Although research scientists have been the main users of these data, an 103 increasing number of resource managers need and are seeking access to spatial data to inform their 104 decisions, just as a growing range of policy-makers rely on spatial data to develop spatial change 105 strategies. With this gravity comes the responsibility to curate spatial data and share it more freely, 106 usefully, and readily than ever before [2,15].…”

mentioning

confidence: 99%

Augmented Smart Cities Integrating Sensor Web and Spatial Data Infrastructure (SmaCiSENS)

Bhattacharya¹,

Paìnho²

2017

Preprint

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A new homogenized daily data set for temperature variability assessment in Italy

Fioravanti

Piervitali

Desiato

2019

Intl Journal of Climatology

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We present a new data set of quality‐controlled and homogenized daily maximum (Tmax) and minimum (Tmin) temperature for Italy. The data set includes 144 Tmax and 139 Tmin long‐term series, covering the period 1961–2017. First, the paper provides a description of data sources and quality controls implemented for the detection of erroneous daily observations. Next, the temperature records used for this work are introduced. Following strict data continuity and completeness requirements, we identified more than 500 time series with at least 20 years of valid data (raw data set), which were spatially partitioned using a hierarchical clustering approach. For each cluster, the time series homogeneity was assessed using two different statistical automatic approaches: ACMANT and Climatol. The results of the homogenization process are illustrated only for the long‐term series subset. Both homogenization methods revealed the presence of non‐climatic discontinuities in most of the temperature series. Although Climatol detected a slightly lower number of breakpoints than ACMANT, the two methods are in good agreement with respect to the statistics which describe the number and timing of the breakpoints. Since no metadata are available, the plausibility of the homogenized time series was evaluated using different statistical measures: RMSE, Spearman correlation coefficient and trends estimation. Our results show that the homogenized data sets are more spatially coherent than the raw time series. In particular, the analysis of the annual temperature trends shows more realistic and reliable climatic patterns when the homogenized data sets are considered. For our data, the homogenization process only marginally changes the annual and seasonal warming trend values found for the area‐averaged anomaly raw series. The Tmax and Tmin homogenized data set will be regularly updated and is intended to be used for a variety of climate studies that require data at daily resolution, as the analysis of climate extremes.

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Spatially enabling the Global Framework for Climate Services: Reviewing geospatial solutions to efficiently share and integrate climate data & information

Cited by 49 publications

References 90 publications

Semi-Automatic Operational Service for Drought Monitoring and Forecasting in the Tuscany Region

Semi-Automatic Operational Service for Drought Monitoring and Forecasting in the Tuscany Region

Augmented Smart Cities Integrating Sensor Web and Spatial Data Infrastructure (SmaCiSENS)

A new homogenized daily data set for temperature variability assessment in Italy

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