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Utskifting av bakgrunnsbilde:
- Høyreklikk på lysbildet og velg «Slide» -> «Set Background Picture for Slide...»
- Velg ønsket bilde og klikk «Open»
Brussels 2015-06-15
Climate analysis & Big data
Rasmus E. Benestad
Abdelkader Mezghani, & Kajsa M. Parding
esd for retrieving, processing, dissecting, analysing, and visualisation
How do I find useful climate informationin large volumes of data?
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?
Norwegian Meteorological Institute
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?
Big data - simple algorithmsFast “distillation” of information
Open source tool ‘esd’http://github.com/metno/esd
Norwegian Meteorological Institute
esd: open-source and free
Data accessSource of information
Temperature
ECA&D:~1.1 Gb-temperature-precipitation
Temperature
ECA&D +GHCN +MET Norway-temperature
Precipitation
Reanalyses and satellite data
Climate model results
Norwegian Meteorological Institute
Make use of information hidden in vast archives of climate model results and observations
ExampleExtracting information embedded in global climate models and observations
Climate model results
Norwegian Meteorological Institute
Norwegian Meteorological Institute
Norwegian Meteorological Institute
What is hidden behind the results?
Norwegian Meteorological Institute
Building block
Empirical-statistical downscaling
Dependencies & connections
Redundancy
Norwegian Meteorological Institute
Extension of the results to regions
Norwegian Meteorological Institute
High one-in-twenty mean summer temperature in 2100
InfographicsExposing different aspects
Different sides to information
Changes in rainfall statistics?
Quick look at temperatures
Maps
Correlation
AnalysisPatterns of behaviour
Anomaly with respect to latitude
How similar are the reanalyses? Correlation maps
How good are the models?
Using mathematics to make sense of the data
Is there a change in the precipitation?
Norwegian Meteorological Institute
How much does it rain?
Norwegian Meteorological Institute
Global rain gauge data
= huge volume
35,000 rain gauges with daily data
Best way to mine hidden information?
Norwegian Meteorological Institute
Understand the data
Exponential distribution?
Vast number of data points on top of each other
Norwegian Meteorological Institute
Extract the essence (cleverly)
Principal component analysis: two main characteristics
Norwegian Meteorological Institute
E.g. The wet-day 95-percentile for 24-hr precipitation
Calculated Observed
Norwegian Meteorological Institute
Test results
Norwegian Meteorological Institute
Changing rainfall patterns
Norwegian Meteorological Institute
Changing rainfall patterns
Norwegian Meteorological Institute
Aggregate
Principal Component Analysis (PCA)
Regression analysis
Quick search
Combination of sources
Mapping/gridding
Statistical distributions
Main instruments
Norwegian Meteorological Institute
Tricks for efficiency
Structured and standardised metadataCommon information model (CIM)Data reference syntax (DRS)classes and ‘S3’ methodsFast algorithms making use knowns
Norwegian Meteorological Institute
• Facilitate intercomparisons
• Sharing of generic methods
• Traceability and replicability
• Promotes community building & discussions
Benefits of common standards & structures
Norwegian Meteorological Institute
Summary
●esd - “easy and simple data” or empirical-statistical downscaling
●Statistics - information from the data
●Aim to address specific questions
Meteorologisk institutt
Thanks for your attention!