school of information technologies the university of sydney australia spatio-temporal analysis of...
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School of Information TechnologiesThe University of Sydney
Australia
Spatio-Temporal Analysis of the relationship between South American Precipitation Extremes and the El Niño
Southern Oscillation
Elizabeth Wu and Sanjay Chawla
SSTDM 2007 2
Overview School of Information TechnologiesThe University of Sydney
Australia
AimsMotivationBackgroundExperimentsFuture ResearchQuestions
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School of Information TechnologiesThe University of Sydney
Australia
To discover the spatial and temporal relationships of high precipitation extremes between regions over South America
To compare the spatial and temporal behaviour of high precipitation extremes to the weather phenomenon known as the El Niño Southern Oscillation (ENSO), which is said to have a teleconnection with rainfall patterns
Aims
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Why look at high precipitation extremes?High precipitation extremes can bring both devastation (destruction of property, disease, etc) and rejuvenation (replenish dry areas)
Why choose South America?Data is available from the NOAA since 1940South Americans are particularly vulnerable to the effects of flooding
Why compare the behaviour of precipitation extremes to the Southern Oscillation Index (SOI)?
Further understanding of the teleconnection between precipitation extremes and the El Niño Southern Oscillation (ENSO) is required.
Motivation
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Previous research has looked at the temporal nature of precipitation extremes and drawn qualitative spatial conclusions from their results
This research provides quantitative analysis of the spatial and temporal relationship of precipitation extremes
Motivation
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Provided by the NOAA (National Oceanic and Atmospheric Administration)NetCDF format Daily data2.5° grids – data is averaged for each day from all stations in the gridAbout 7900 stations
Background:South American Precipitation Data
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Considerations:(a)Extremes:
- Fixed threshold – doesn’t consider seasonal variations- xth-percentile
(b)Independent and Identical Distribution (iid)
- Daily data is not independent, so deseasonalised weekly maximum data is used instead
(c)Time Intervals
- Selected data from ‘strong’ (as classified by the NOAA) El Nino events from 1978-2004
(d)Locations
- Latitude 60°S to 15°N (31) - Longitude 85°W to 35°W (23)- Total number of regions: 713- from all stations in the grid
Background:South American Precipitation Data
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Considerations:(e) Deseasonalisation
- Consider a period eg. 1970-1989.- Take the weekly max of all weeks in that period- Subtract the period average of that particular week of the year
(between 1-53) from each week.- Average is calculated as the sum of all non-missing values for that
period divided by the total number of non-missing values.(f) Peak Over Threshold (POT) approach to selecting extreme values
- Rather than using a pre-defined threshold, we use the top 95th percentile of weekly maxima residuals
Background:South American Precipitation Data
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Australia
What are extreme precipitation values?Significant deviations from the normal rainfall for a particular time of year - must be deseasonalisedIn our study, they are the 95th percentile of precipitation values (top 5%)
How does EVT help to analyse them? What are the advantages over other techniques?
EVT only looks at the extreme values to understand past and future extremes, rather than looking at all of the data (ie. Looks at the tail of a distribution)
How is EVT applied to this study?EVT is used to model precipitation extremes over different periods for each gridFrom this, we obtain the parameters of the distributionsUse Moran’s I to determine the extent that the parameters from one region influence the parameter values of nearby regions
Background:Extreme Value Theory
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Australia
Moran’s I Statistic is a measure of spatial autocorrelation
Can be used to measure global and local correlation
Global models may not take into account spatial structural instability (large variations between regions), and so Local Indicators of Spatial Association (LISA) are best used for this purpose
Moran’s I may indicatePositive autocorrelation: an event in one region increases the likelihood of the same event in a neighbouring regionNegative autocorrelation: an event in one region decreases the likelihood of the same event in a neighbouring regionNo autocorrelation: an event in one region will have no effect on the likelihood of events in neighbouring region (random)
Background:Moran’s I Statistic
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A naturally occurring phenomenon consisting of two phases:El Niño (Warm)La Nina (Cold)
El Niño is often associated with heavy precipitation in South America due to the warming of the East Pacific Ocean
Three measures of ENSO phases and strengths are:1) Southern Oscillation Index (SOI) – atmospheric - measures
the difference in Sea Level Pressure (SLP) between Tahiti and Darwin relative to the ‘normal’ SLP.
2) Sea Surface Temperatures (SST) - oceanic3) Multivariate ENSO Index (MEI) – considers both atmospheric
and oceanic measures
Background:El Nino Southern Oscillation
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The relationship between parameters of the extreme value distributions were evaluated using the Local Moran I statistic
Compared El Niño Southern Oscillation Index (SOI) for several strong El Niño periods with the average Local Moran I values over South America
Experiments
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Strong El Nino periods obtained from the NOAA website:
Experiments
Event # El Nino Event Period Start Period End
1 1939-1941 01-Jan-1940 30-Jun-1949
2 1957-1959 01-Jul-1949 30-Jun-1966
3 1972-1973 01-Jul-1966 31-Dec-1977
4 1982-1983 01-Jan-1978 31-Dec-1986
5 1990-1993 01-Jan-1987 30-Jun-1995
6 1997-1998 01-Jul-1995 31-Jun-2000
7 Remaining 01-Jul-2000 31-Dec-2004
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Of the 713 periods, only some contain data:
Experiments
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Experiments
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Bootstrap Analysis
Experiments
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Compare the average Local Moran I to other ENSO indicators such as the SST and the MEI
Try other methods of spatial autocorrelation
Future Research
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School of Information TechnologiesThe University of Sydney
Australia
Compare the average Local Moran I to other ENSO indicators such as the SST and the MEI
Try other methods of spatial autocorrelation
Develop spatio-temporal data mining techniques to discover new and interesting patterns about extreme weather from data sets
Future Research
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Australia
Questions
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