Follow Us


Welcome to SessionD3: How can statistics help making sense of data from multiple sources?


Date: Thursday 2016-05-19
Time: 14.00 - 17.00
Room: to be confirmed later
Session Coordinator: Dr. R. Benestad


This session welcomes contributions related to statistical analysis techniques that make use of such large datasets, innovative methods to combining different sources of regional downscaled climate information/data and approaches dealing with the cascade of uncertainty inherent in these datasets.

Oral: Invited speaker (1 hr, incl discussion) - Richard Chandeler

Group work: how to distill information from lots of data?

  • Priming: Information before data, sources of information, and methods
  • Background: traditional approaches
  • Aim: explain how one can answer a certain question
  • Task: lay out a strategy to answer a question


Submitted talks (1hr)

  • Improving multimodel medium range forecasts over the Greater Horn of Africa using the FSU superensemble - O. Kipkogei, A. Bhardwaj, V. Kumar, L. A. Ogallo, F.J. Opijah, J.N. Mutemi and T.N. Krishnamurti
  • Africa regional climate multi-data analysis and management - N. Jibo
  • A statistical downscaling model with uncertainty quantification for engineering infrastructure design adaptation - E. Linder, M. Zhao, Y. Liu, J. Jacobs and A. Stoner
  • Assessment of the performance of CORDEX-South Asia experiments for monsoonal precipitation over the Himalayan region during present climate: Part I  - S. Ghimire, A. Choudhary and A. P. Dimri


Poster introduction (10 min)

Questions/discussion/break-out-group (30-1.5 hr min)


  1. How can we make use of statistics and the vast volume of data to provide answers to our questions regarding climate change consequences?
  2. Why do different projections vary?
    • methods for distilling information from multiple sources/dimensions (e.g. common PCA)
    • statistics to explore uncertainties (adding more information & regression)
    • statistics to gauge the model skill & evaluation
    • extremes
    • statistics to visualise information
    • various ways of using statistics in downscaling



  • Complex algorithms for multiple-station long-term data processing as a first step for optimal probability estimates mapping - I. Osetinskoys
  • Inter-variable relations in regional climate model outputs - R. Wilcke
  • An R-package Designed for Climate and Weather Data Analysis, Empirical-Statistical Downscaling, and Visualisation - A. Mezghani, R. E. Benestad, and K. Paring