MC1

Climate monitoring, data rescue, management, quality and homogenization
Convener: S. Rösner  | Co-Convener: M. Brunet-India 
Oral Programme
 / Thu, 13 Sep, 14:00–18:30  / Room Aula
Poster Programme
 / Attendance Thu, 13 Sep, 10:30–11:30  / Display Mon, 10 Sep, 09:00–Fri, 14 Sep, 13:00  / Ground Floor

Robust and reliable climatic studies, particularly those assessments dealing with climate variability and change, greatly depend on availability and accessibility to high-quality/high-resolution and long-term instrumental climate data. At present, a restricted availability and accessibility to long-term and high-quality climate records and datasets is still limiting our ability to better understand, detect, predict and respond to climate variability and change at lower spatial scales than global. Despite of the wealthy heritage of climate data produced by monitoring agencies across the world since their inception, which is remarkable rich over Europe, still there are less and of not enough quality data availability than the required for confidently basing on any climate assessment.

To enhance availability and accessibility to high-quality and high-resolution climate data requires further research and innovative applications about data rescue techniques and procedures, data management systems, networks interoperability, climate data quality controls and homogenisation methods, all of them of paramount importance in order to develop climate records and datasets of high-standard and increase climate data availability and accessibility, as well as uncertainty estimation of the reconstructed time-series and improving their traceability.

In this session, we welcome contributions (oral or poster) in the following major topics leading to improve techniques and procedures for undertaking integrated climate data and metadata rescue activities:

Topics related to improve the quality of the observational meteorological networks include:
• GCOS climate monitoring principles; application and problems
• GCOS (National) Implementation Plans
• The role of observations for climate services
• Support for improved observations in developing countries
• Assimilation of data from new instruments and technologies

- More efficient transfer of the data rescued into the digital format by means of:
• Improving the current state-of-the-art on image enhancement, image segmentation and post-correction techniques
• Innovating on adaptive Optical Character Recognition and Speech Recognition technologies and their application to transfer data
• Defining best practices about the operational context for digitisation
• Improving techniques for inventorying, organising, identifying and validating the data rescued
• Enhancing history of observing techniques in meteorology and climatology and in data archaeology techniques
• Conserving, imaging, inventorying and archiving historical documents containing weather records

- Processing of climate data and metadata, including topics such as:
• Climate data flow management systems, from improved database models to better data extraction
• Development of relational metadata bases
• Data exchange platforms and networks interoperability

- Innovative, improved and extended climate data quality controls (QC), including:
• From gross-error and tolerance checks to spatial coherence tests
• Tailored QC application to monthly, daily and sub-daily data and to all essential climate variables

- Improvements to the current state-of-the-art of climate data homogeneity and homogenisation methods, including methods intercomparison and evaluation and topics such as:
• Climate timeseries inhomogeneities detection and correction techniques/algorithms, either absolute or relative approaches, and their software
• Extending approaches to detect/adjust monthly and, especially, daily and sub-daily timeseries and to homogenise all essential climate variables
• Practical application of homogenisation methods to develop high-quality and homogeneous climate records and datasets
- Fostering evaluation of the uncertainty in reconstructed time-series