Download presentation

Presentation is loading. Please wait.

Published byBeverly Wells Modified over 3 years ago

1
Statistical downscaling of rainfall extremes for the Hawaiian Islands Oliver Elison Timm 1 Henry F. Diaz 2 Thomas Giambelluca 3 Mami Takahashi 3 1 International Pacific Research Center, University of Hawaii at Manoa, Honolulu, Hawaii 2 Earth System Research Laboratory, CIRES, NOAA, Boulder, Colorado 3 Department of Geography, University of Hawaii at Manoa, Honolulu, Hawaii In collaboration with John Marra, EWC Ocean Science Meeting, Portland, Feburary 26 th 2010 IT51C-04

2
Outline of the presentation Defining our goal: From IPCC scenarios to local extreme rainfall changes Data and methods: The statistical challenge of dealing with rare events The downscaling-scheme for daily mean rainfall extremes Results: Synoptic classifications Linkage between large-scale circulation and local rainfall Downscaling of IPCC AR4 scenario runs

3
IPCC's Fourth Assessment Report, 2007 precipitation change: likely to decrease but for Hawaii, no robust signals Models show a drier climate Models results inconsistentMost models: drier climateMost models: wetter climate No significant changeModels show a wetter climate

4
Extreme events: Changes in the tail of distribution Gaussian distribution Gamma distribution present 2046-2065 2081-2100 present 2046-2065 2081-2100

5
Hawaii's rainfall is controlled by large-scale modes in synoptic circulation Trade Wind Regime Kona Wind Regime 700hPa geopotential height and wind anomalies for days with precipitation above 90% quantile ( during wet season) Left: Station from southern part of Big Island Right: Hilo Airport Southern Big Island Eastern Big Island Na'ālehu (“the volcanic ashes”) Na'ālehu (“the volcanic ashes”) Hilo

6
Hawaii's rainfall is controlled by large-scale circulation pattern ‘Kona wind’ regime: Favourable condition for moisture-rich air masses from tropics

7
Horizontal anomalies linked to anomalies in vertical structure upward moisture flux downward moisture flux <30% of mean rainfall 30-100% of mean rainfall 100-220% of mean rainfall >220% of mean rainfall Island-wide station index for rainfall percentages relative to long-term climatological mean (134 stations) 95% percentile = 220% mean precipitation

8
Task: discriminate extreme events within the information from large-scale circulation Example illustration of the detection of extreme rainfall events using specific humidity and vertical velocity (omega) at 700hPa level. Red dots: Island-wide extreme rain events (daily data) (>95% percentile) find climate variables and pattern that provide best information for ‘hindcasting’ extreme events

9
From large-scale circulation to extreme event 'hindcast' We use the circulation anomalies that occur on days with extreme events to form a 'template pattern'. - + Projection pattern: typical circulation anomalies during extreme rain events P - + X(t) circulation anomaly: for a given day t X(t) P i(t) = time t i(t) extreme event (?)

10
'Prediction' of extreme events: Tasks: Find the subspace associated with extreme events in a high-dimensional large-scale climate space X P Estimate the transfer-function f(X1,X2,...) X1(X2) :daily projection index for large-scale projection pattern 1(2) X1 X2 precipitation PDF f(X1,X2) Large-scale climate information Local rainfall we use logistic regression to hindcast events

11
From large-scale circulation to local extreme events ('hindcast') Specific humidity anomalies and wind anomalies 700 hPa Projection pattern: typical circulation anomalies during extreme rain events at Naalehu (southern Big Island) P X(t) P i(t) = Resulting projection index and observed precipitation projection index (non-dimensional) rainfall (inches/day)

12
Measuring the skill of downscaled extreme events: contingency table hits false alarms missed events correct rejections 88/73/105 81/69/105 41/40/4 3572/3447/3415 sum= 122/109/109 81/69/105 sum= 3660/3520/3520 sum= 129/113/109 sum= 3653/3516/3520 sum= 3782/3629/3629 e = yes e = no h=no h=yes e: observed extreme event h: hindcasted event NCEP reanalysis – Station Naalehu 1958-1983/1984-2008/random guess

13
Measuring the skill in 2-d joint probability distribution p(e,h) hits false alarms missed events correct rejections 2%/2%/3% 81/69/105 1%/1%/0.1% 95%/95%94% p(e=1)= 3%/3%/3% 2%/2%2.9% p(e=0)=97%/97%/97% p(h=1)=3%/3%/3.1% p(h=0)=97%/97%/96.9% 100%/100%/100% e = yes e = no h=no h=yes e: observed extreme event h: hindcasted event NCEP reanalysis – Station Naalehu 1958-1983/1984-2008/random guess

14
Measuring the skill in terms of conditional probabilities p(e|h) p(e|h)=p(e,h)/p(e) p(e=yes|h=yes) : 33% / 33% / 3% p(e=yes|h=no) : 2% / 2%/ 3% p(e=no|h=yes) : 66% / 66% / 97% p(e=no|h=no) : 98% / 98% / 97% Probability of Detection Probability of False Alarm calibration/validation/random guess with p(h=1)=p(e=1)

15
33% chance of extreme rain given the specific humidity field specific humidity anomalies 700 hPa (contours) Projection pattern: typical circulation anomalies during extreme rain events at Naalehu (southern Big Island) P X(t) P i(t) = Resulting projection index and observed precipitation projection index (non-dimensional) rainfall (inches/day)

16
ECHAM 4 MPI SRESA1B scenario simulation Probability Density Function 700-hPa specific humidity projection Index NCEP 1958-1983 ECHAM 20 th cent. ECHAM 2046-2065 ECHAM 2081-2100

17
Projected changes: present / 2046-2065 / 2081-2100 (based on one AR4 model (MPI_ECHAM5 SRESA1B scenario) hits false alarms missed events correct rejections 41/40/4 2%/4%/6% 81/69/105 1%/2%/3% 95%/92%89% p(e=yes)= 3%/4%/5% 2%/2%/2% p(e=no)=97%/97%/97% p(h=yes)=3%/6%/9% p(h=no)=97%/94%/91% 100%/100%/100% e = yes e = no h=no h=yes

18
Projected changes: expected changes in the contingency table for an average winter season present / 2046-2065 / 2081-2100 hits false alarms missed events correct rejections 41/40/4 4/8/10 81/69/105 2/4/5 170/165/162 p(e=yes)= 6/7/8 4/3/3 p(e=no)=174/173/172 p(h=yes) 6/12/15 p(h=no) 174/168/165 days 180/180/180 e = yes e = no h=no h=yes

19
Conclusions Large-scale circulation provides information to downscale individual extreme rain events! Projection-pattern method and logistic regression applicable for Hawaii's rainfall Model scenarios: downscaled onto the large-scale climate pattern, they provide quantitative estimates of the expected changes in number of extreme events Future improvements: – incorporate more large-scale information – multi-model scenario analysis

20
What is the limit of the prediction skill? TRMM satellite rainfall estimates 11-Dec-1999 NCEP Reanalysis rainfall estimates 11-Dec-1999 Reanalysis products have their own uncertainty State of large-scale circulation and projection indices contain errors ERA40 Reanalysis data and TRMM rainfall estimates 11-Dec-1999

22
Extreme events are local features: spatial correlation is low TRMM GPCP GOES ERA40 NCEP different rainfall estimates for Hawaii wet season Nov 1999 – Apr 2000 data source: Asia-Pacific Data-Research Center (APDRC) http://apdrc.soest.hawaii.edu

23
How to update the contingency table information? Contingency table equivalent to joint distribution p(e,h) Conditional p(e|h)=p(e,h)/p(h) Assumption: p(e|h) does not change under changing climate p(e,h)=p(e|h)*p(h) → p(e) can be obtained by marginalization Moreover we can use p(e,h) to estimate the estimated hit rate, missed events and false alarm rate.

Similar presentations

OK

NAME SWG-7.5 30 th Annual NOAA Climate Diagnostics and Prediction Workshop State College, Pennsylvania Oct. 28, 2005.

NAME SWG-7.5 30 th Annual NOAA Climate Diagnostics and Prediction Workshop State College, Pennsylvania Oct. 28, 2005.

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on power line communication journal Ppt on 555 timer monostable Web technology books free download ppt on pollution Ppt on bluetooth broadcasting device Ppt on fundamental rights and duties Ppt on interest rate risk Ppt on nuclear family and joint family system Ppt on non ferrous minerals in the body Ppt on obesity management dog Ppt on nature and history of democracy in nepal