This document discusses tools and methods for analyzing large volumes of climate data to extract useful information. It introduces the open-source tool "esd" for retrieving, processing, analyzing and visualizing climate data. Esd uses simple algorithms to distill information from data sources like temperature records, reanalysis data and climate model results. The document provides examples of using esd to analyze patterns in temperature and precipitation trends over time. It emphasizes extracting the essence of the data through techniques like principal component analysis to understand changing climate patterns and how climate models compare to observations.

1. Brussels 2015-06-15
Climate analysis & Big data
Rasmus E. Benestad
Abdelkader Mezghani, & Kajsa M. Parding
esd for retrieving, processing, dissecting, analysing, and visualisation

2. How do I find useful climate
information
in large volumes of data?

3. Information & Data & Knowledge
● Information
"...in its most restricted technical sense, is a sequence of
symbols that can be interpreted as a message".
(wikipedia)
● Data are measurements, observations, calculations
● Knowledge is expectation about causalities,
dependencies, and why?

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The ultimate objective is to
find the answers.
The data is the source and
the means finding answers.
Analytical tools designed for
data analysis/statistics.
What is the question?

5. Big data - simple algorithms
Fast “distillation” of information

6. Open source tool ‘esd’
http://github.com/metno/esd

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esd: open-source and free

8. Data access
Source of information

9. Temperature
ECA&D:
~1.1 Gb
-temperature
-precipitation

10. Temperature
ECA&D +
GHCN +
MET
Norway
-temperature

11. Precipitation

12. Reanalyses and satellite data

13. Climate model results

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Make use of information hidden
in vast archives of climate
model results and observations

15. Example
Extracting information embedded in global climate models
and observations

16. Climate model results

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What is hidden behind the results?

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Building block
Empirical-statistical
downscaling
Dependencies &
connections
Redundancy

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Extension of the results to regions

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High one-in-twenty mean
summer temperature in 2100

23. Infographics
Exposing different aspects

24. Different sides to information

25. Changes in rainfall statistics?

26. Quick look at temperatures

27. Maps
Correlation

28. Analysis
Patterns of behaviour

29. Anomaly with respect to latitude

30. How similar are the reanalyses? Correlation maps

31. How good are the models?

32. Using mathematics to make
sense of the data
Is there a change in the precipitation?

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How much does it rain?

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Global rain gauge data
= huge
volume
35,000 rain
gauges with
daily data
Best way to
mine hidden
information?

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Understand the data
Exponential
distribution?
Vast number
of data points
on top of each
other

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Extract the essence (cleverly)
Principal
component
analysis:
two main
characteristics

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E.g. The wet-day 95-percentile
for 24-hr precipitation
Calculated Observed

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Test results

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Changing rainfall patterns

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Changing rainfall patterns

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Aggregate
Principal Component Analysis (PCA)
Regression analysis
Quick search
Combination of sources
Mapping/gridding
Statistical distributions
Main instruments

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Tricks for efficiency
Structured and standardised metadata
Common information model (CIM)
Data reference syntax (DRS)
classes and ‘S3’ methods
Fast algorithms making use knowns

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• Facilitate intercomparisons
• Sharing of generic methods
• Traceability and replicability
• Promotes community building & discussions
Benefits of common standards &
structures

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Summary
●esd - “easy and simple data” or empirical-
statistical downscaling
●Statistics - information from the data
●Aim to address specific questions

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Thanks for your attention!