Clime is a software, developed in a GIS environment, with the main goal to manage climate data, link climate and impact studies and assist a wide users range. It is a user-friendly interactive platform allowing users to evaluate multiple features of simulated and observed data over worldwide geographical domains having local, national and continental extension. Since many years, CLIME is used in several national and international projects and scientific publications for the realization of post processing analysis including high-level quality maps. Clime is supported by an Import Data Module for the storage in a Geodatabase of simulated and observed climate data, available in the several format adopted by end users. The CLIME engine can quickly manage massive quantity of data performing the required analyses by own tools (C#) or by integrated pre-existing tools developed by users in different languages (e.g. Matlab exe, R packages…). Multiple user interfaces (optionally): ArcMAP Plug-IN, Standalone APP or WEB services. Currently CLIME includes different databases, with worldwide-simulated climate data (performed by CMCC or by other international programs) and gridded and in situ observation data.  

The main functionality are:

  • Storage of massive quantity of climate data (observed and simulated);
  • Flexible system for temporal and spatial filtering;
  • Observation harmonization ( e.g. quality check, homogenization, breakpoint analysis, outliers);
  • Elaboration of different statistical analysis (e.g. time series, trend, ETCCDI extreme indices, significance test, seasonal cycle, ensemble mean and spread, climate anomalies, pdf, Exceedance probability of changes);
  • Visualization of results through temporal and statistic plots or spatially referenced maps with possibility to include different background (e.g. google maps);
  • Output of analysis available in different format (e.g. .xls, .csv, .txt, jpeg, png) easily adoptable by other software for further analysis;
  • Bias correction: more than 15 different methods of bias correction (e.g. delta change, quantile mapping approaches, linear scaling) are included for the reduction of the systematic climate model bias;
  • Stochastic disaggregation model for precipitation: for the evaluation of the sub daily precipitation starting from daily observation and simulated data;
  • Statistical downscaling technique with analogue method (under development).

References:

  • Bucchignani E., Montesarchio M., Zollo A.L., Mercogliano P. (2015). High-resolution climate simulations with COSMO-CLM over Italy: performance evaluation and climate projections for the XXI century. International Journal of Climatology DOI: 10.1002/joc.4379
  • Bucchignani E., Mercogliano P., Rianna G., Panitz H.J. (2015) – Analysis of ERA-Interim-driven COSMO-CLM simulations over Middle East – North Africa domain at different spatial resolutions – International Journal Of Climatology DOI: 10.1002/joc.4559
  • Bucchignani E., Cattaneo L., Panitz H.-J., Mercogliano P. (2015) Sensitivity analysis with the regional climate model COSMO-CLM over the CORDEX-MENA domain. Meteorology and Atmospheric Physics DOI: 10.1007/s00703-015-0403-3
  • Bucchignani E., Mercogliano P., Rianna G., Panitz H.J. (2015) – Analysis of ERA-Interim-driven COSMO-CLM simulations over Middle East – North Africa domain at different spatial resolutions – International Journal Of Climatology DOI: 10.1002/joc.4559
  • Bucchignani E., Zollo A.L., Cattaneo L., Montesarchio M., Mercogliano P. (2016) – Extreme weather events over China: assessment of COSMO CLM simulations and future scenarios – International Journal of Climatology (ISI/SCOPUS)   DOI: 10.1002/joc.4798
  • Cattaneo L., Rillo V., Manzi M.P., Villani V. and Mercogliano P. (2015) – RP0257 – Clime: climate data processing in GIS environment – CMCC Research Paper
  • Garcia A., Bucchignani E.,  Manzi M.P. (2015)  – Patterns in climate-related parameters as proxy for rainfall  deficiency and aridity: application to Burkina Faso – Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering DOI: 10.1061/AJRUA6.0000860
  • Montesarchio M., Zollo A. L. , Bucchignani E., Mercogliano P., Castellari S. (2014) “Performance evaluation of high-resolution regional climate simulations in the Alpine space and analysis of extreme events”, Journal of Geophysical Research: Atmospheres, 119, DOI: 10.1002/2013JD021105.
  • Villani V., Cattaneo L., Zollo A.L., Mercogliano P. (2015) – RP0262 – Climate data processing with GIS support: description of Bias Correction and Temporal Downscaling tools implemented in Clime software – CMCC Research Paper

Contacts: clime@cmcc.it

Contacts