Develop statistical model to predict extreme precipitation through
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This document outlines a proposed study to predict extreme precipitation in Sri Lanka through analysis of the relationship with El Niño Southern Oscillation (ENSO). The objectives are to identify the relationship between extreme precipitation and ENSO, develop a predictive model, and validate it with recent data. The methodology will include collecting rainfall and ENSO data, defining a threshold for extreme precipitation, distribution and correlation analysis between stations and seasons, and time series analysis. The results are expected to show whether extreme precipitation days are more or less frequent during El Niño versus La Niña phases. The study will be conducted over 12 months with the support of Sri Lankan government agencies and universities.
Develop statistical model to predict extreme precipitation through
- 1. Predict Sri Lanka Extreme Precipitation through El Nino Southern Oscillation R.M.S.P. Ratnayake PGIS/SC/M.Sc./ APS/10/20 MSc in Applied Statistics Post Graduate Institute of Science/ University of Peradeniya
- 2. Over view • Introduction • Motivation and Background • Problem • Objectives • Hypothesis • Methodology • Organization • Time Frame
- 3. Introduction • Sri Lanka economy mainly depend on Agriculture Industry. • Sri Lankan Agriculture mainly depend on two monsoons. • Therefore extreme precipitation changes the natural agriculture cycle. • Expose to Disaster and Hazard potentials.
- 4. Problem • Extreme Precipitation requires extra effort beyond basic Statistical Analysis. • There is no proper model to predict Extreme Precipitation. • Heavy Precipitation is a result of multiple courses. • Sri Lanka climate data are spatially coherent. • Analysis required longer period precipitation data
- 5. Motivation and Background Case Study : Early 2011 rainfall No of Affected Families 268544 No of Affected People 990471 No of Reported Deaths 18 No of Injuries 24 No of Missing People 3 No of Fully Damaged Houses 4216 No of Partially Damaged Houses 22186 Department of Metrology : Sri alnka
- 6. Objectives • Identify Relationship between Extreme Precipitation and ENSO. • Develop a model to relate Extreme Precipitation and ENSO. • Validate defined model with recent data.
- 7. Hypothesis • Null hypothesis that “There is a significant relationship between extreme precipitation and ENSO behaviour.” • Against the alternative hypothesis that “There is no significant relationship between extreme precipitation and ENSO behaviour. ”
- 8. Others Work • 2009 – Comparative analysis of indices of extreme rainfall events: variations and trends from Mexico • 2008 - Predictability of Sri Lankan rainfall based on ENSO • 1998 – ENSO influence on Intraseasonal Extreme Rainfall and Temperature Frequency in the Contiguous United State: Implications for Long Range Predictability • 2011 – Research on the Relationship of ENSO and the Frequency of Extreme Precipitation Events in China
- 9. Methodology : Overview • Data Collection • Defining Threshold value • Analysis – Distribution of Data – Identifying Extreme Percentile – Spatial Distribution of Extreme Precipitation – Correlation Analysis – Time Series Analysis
- 10. Methodology : Data Collection • Quarterly Cumulative Rainfall data • At least 50 years • 11 out of 21 Stations • Treating missing rainfall data : By Multiplying each year value by multiplying N/(N-m) • NINO 3.4 – monthly data from 1951 to 2002
- 11. Methodology : Threshold value • Gamma Distribution is used. • Rainfall above 95% percentile. • Separately calculated to Individual Stations and All Island.
- 12. Methodology : Analysis • Distribution of Data – Histogram – Normality Test
- 13. Methodology : Analysis • Correlation Analysis between ENSO and Seasons January - March April - June July - September October - December
- 14. Methodology : Analysis • Correlation Analysis between ENSO and Different Stations and All Island Anuradhapura Mannar Batticoloa Nuwara Eliya Colombo Puttalam Hambanthota Ratmalana Kankasanthure Trincomalee Katunayake
- 15. Expected Results End of the Research • In JFM/ AMJ/ JAS/ OND Extreme Precipitation days in Anuradhapura/ Batticoloa/ Colombo/ Hambanthota/ Kankasanthure/ Katunayake/ Mannar/ Nuwara Eliya/ Puttalam/ Ratmalana/ Trincomalee/ All Island are significantly More or Less Frequent in El Nino than La Nino
- 16. Statistical Software • R • Excel
- 17. Organization • Irrigation Department • Department of Meteorology of Sri Lanka • Foundation of Environment and Climate Technology • Institute of Post Graduate Studies – University of Peradeniya.
- 18. Time Line Require Data Data Study Analyzing Developing Testing Report Presentation ment Gathering Arranging Existing Model and preparation Analysis Approaches Validating Week1 Week2 Week3 Week4 Week5 Week6 Week7 Week8 Week9 Week10 Week11 Week12
- 19. Acknowledgement • Dr. Lareef Zubair at Foundation of Environment and Climate Technologies, Dhigana. • Eng. R.M.W. Ratnayake at Director (Water Resources) Ministry of Irrigation and Water Resource Management. • Post Graduate Institute of Science University of Peradeniya
- 20. Thanking you Weather is a great metaphor for life - sometimes it's good, sometimes it's bad, and there's nothing much you can do about it but carry an umbrella. ~Terri Guillemets