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INTERANNUAL AND INTERDECADAL VARIABILITY OF THAILAND SUMMER MONSOON: DIAGNOSTIC AND FORECAST. NKRINTRA SINGHRATTNA CIVIL, ENVIRONMENTAL AND ARCHITECTURAL ENGINEERING DEPARTMENT UNIVERSITY OF COLORADO AT BOULDER 2003. MOTIVATION. THAILAND BACKGROUND - PowerPoint PPT Presentation
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INTERANNUAL AND INTERDECADAL VARIABILITY OF THAILAND SUMMER MONSOON:
DIAGNOSTIC AND FORECAST
NKRINTRA SINGHRATTNACIVIL, ENVIRONMENTAL AND ARCHITECTURAL
ENGINEERING DEPARTMENTUNIVERSITY OF COLORADO AT BOULDER
2003
MOTIVATION
THAILAND BACKGROUND• Location between 5-20
N latitudes and 97-106 E longitudes
• Population ~ 61.2 million• Major occupation:
agriculture (50%-60% of national economy)
• Agriculture depends on precipitation and irrigation that is dependent on precipitation to store in reservoirs as well
• “Precipitation” is crucial
MOTIVATION
SEASON OF RAINFALL• 80%-90% of annual
precipitation occurs during monsoon season (May-Oct)
• Runoff is stored in reservoirs for use until the next year’s monsoon
• Variability over inter-annual and decadal time scales– Need to understand
this variability• All of these serve as
“motivation” of this research
Total Annual Rainfall
600.0
800.0
1000.0
1200.0
1400.0
1600.0
1800.0
1950 1960 1970 1980 1990 2000
Year
Rain
fall (
mm
)
OUTLINE
1. THAILAND HYDROCLIMATOLOGY2. TRENDS3. INTERANNUAL/INTERDECADAL
VARIABILITY- RELATIONSHIP TO ENSO- PHYSICAL MECHANISM
4. PREDICTORS OF THAILAND RAINFALL
5. FORECASTING THAILAND MONSOON RAINFALL
6. CONCLUSIONS AND FUTURE WORK
DATA DETAILS
• http://hydro.iis.u-tokyo.ac.jp/GAME-T
• Thailand Meteorological Dept.
• Six rainfall stations (r ~ 0.51)
• Five temperature stations (r ~ 0.50)
• Atmospheric circulation variables such as SLPs, SSTs and vector winds: NCEP/NCAR Re-analysis (www.cdc.noaa.gov)
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
DATA DETAILS
• Correlation maps (CMAP and SATs) ensure their consistency
• Thus, average rainfall ~ “rainfall index”
average temperature ~ “temperature index”
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
MECHANISM OF CLIMATOLOGY
• Spring (MAM) temperatures set up land-ocean gradient driving the summer monsoon
• Summer monsoon (rainy season): Aug-Oct (ASO)
• Little peak in May: Due to Northward movement of ITCZ
• Enhanced MAM temperatures Enhanced ASO rainfall Decreasing monsoon seasonal (ASO) temperatures
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
MECHANISM OF CLIMATOLOGY
• ITCZ northward movement:- Cover Thailand in May- Move to China in June- Southward move to cover Thailand again in August
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
AM
SON
TRENDS• Decreasing MAM
temperature over decadal (-0.4 C)
• Decreasing ASO rainfall (-180 mm)
• Tend to cool land and atmosphere less Increasing ASO temperature
• Trends after 1980: Increasing MAM temperature Increasing ASO rainfall (IPCC 2001 report)
• Trends are part of global warming trends (IPCC 2001)
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
KEY QUESTION
“What drives the interannual and interdecadal variability of Thailand
summer monsoon?”
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
Schematic view of sea surface temperature and tropical rainfall in the the equatorial Pacific Ocean during normal, El Niño, and La Niña conditions
..
Global Impacts of ENSO
FIRST INVESTIGATION• 21-yr moving window correlation with SOI index: Strong
significant correlation only post-1980• Spectral Coherence with SOI index
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
FIRST INVESTIGATION
• Correlation maps (pre- and post-1980)
SS
TS
LPHydro
climatologyTrends
Interannual /Interdecadal
Predictors of Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
Pre-1980 Post-1980
FURTHER INVESTIGATION
• To understand nonlinear relationship: Composite maps (pre- and post-1980) of high and low rainfall years (3 highest and lowest years)
Hig
hLo
w
Pre-1980 Post-1980
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
INSPIRED QUESTIONS
“Why ENSO related post-1980 only?”“Is there change in ENSO after 1980?”
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
CONVECTION
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
Pre-1980 Post-1980
corr
ela
tion
com
posi
te
El Nino-La Nina Pre-1980 El Nino-La Nina Post-1980
ENSO INVESTIGATIONS
• Composite maps of SSTs:
• Strong and eastward anomalies during post-1980
Pre-1980
Post-1980
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
HYPOTHESIS
“East Pacific centered ENSO reduces convections in Western Pacific regions (Thailand) while dateline centered ENSO decreases convections in Indian subcontinent”
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
Pre-1980
Post-1980
COMPARISON WITH INDIAN MONSOON
• To show changes in regional impacts of ENSO• 21-yr moving window correlation: Indian monsoon lose
its correlation with ENSO around post-1980• Thailand monsoon picks up correlation at the same time
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
CASE STUDIES
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
1997 2002
SS
TC
MA
P
SUMMARY (up to this point)
• Strong relationship with ENSO during post-1980
• Indian monsoon shows weakening relationship with ENSO at the same time
• Eastern Pacific centered ENSO (post-1980) contain descending branch over Western Pacific (included Thailand)
• Dateline Pacific centered ENSO (pre-1980) contain descending branch over Indian subcontinent
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
REQUIREMENTS FOR GOOD PREDICTORS
• Good relation with monsoon rainfall (post-1980)
• Reasonable lead-time to forecast before monsoon season
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
CORRELATED WITH STANDARD INDICES
• Significant correlations show 1-2 seasons lead-time
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
CORRELATION MAPS WITH LARGE-SCALE VARIABLES
MAM AMJ
MJJ
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
SATs
CORRELATION MAPS WITH LARGE-SCALE VARIABLES
MAM AMJ
MJJ
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
SLPs
CORRELATION MAPS WITH LARGE-SCALE VARIABLES
MJJ
AMJMAM
SSTs
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
TEMPORAL VARIABILITY OF PREDICTORS
• 21-yr moving window correlation with three seasons (MAM, AMJ, MJJ) of all predictors
• Indicate MJJ SLPs and MAM SSTs are the best predictors during post-1980
MAM
AMJ
MJJ
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
TRADITIONAL MODEL: LINEAR REGRESSION
• Y = a * SLP + b * SST + e• e = residual: normal distribution with mean =
0, variance = 2
• Variable assumed normally distributed• Relationship among variables assumed linear
relation• Drawbacks:
– unable to capture non-Gaussian/nonlinear features– High order fits require large amounts of data– Not portable across data sets
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
Modified K-nn
0
100
200
300
400
500
600
700
800
900
1000
0 2 4 6 8 10 12 14
x
y
NONPARAMETRIC MODEL: MODIFIED K-NN
• Y = (SLPs, SSTs) + e = local regression (residual: e are
saved)• Capture any arbitrary: Linear or
nonlinear• To forecast at any given “x*”, the
mean forecast “y*” obtained by local regression (first step)
• To generate ensemble forecasts: Resample residual (e) of neighbors “K” to “x*” by weighted assumption:– More weight to nearest
neighbor, less weight to farther neighbor
• Add residual to mean forecast “y*”• Be able to generate unseen values
in historical data
y*
x*
Resample “e” of neighbors
E1E2
E3
E4
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
NONPARAMETRIC MODEL: MODIFIED K-NN
• Number of neighbors for resampling residuals: “K” = (n-1) (n = # of variables)
• Weighted function: W = 1/j (1/j) (j = 1 to “K”)
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
MODEL EVALUATION
• Models are verified by cross validation• Data at given year is dropped out of the
model• Model generates ensemble forecasts of
the dropped year• Do for all years• Forecast will be evaluated by 3 criteria
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
CRITERIA OF MODEL EVALUATION• Correlation (r) between ensemble median and observed values:
Rx,y = cov(x,y) ,cov(x,y) = (1/n)(xi-x)(yi-y) , higher;better
x * y
• Likelihood (LLH): Evaluates skill in capturing the PDF N
[ Pf ]1/N
t=1
LLH = 0 ~ no skill N
[ Pc ] 1/N 1 – 3 ~ better to capture PDF
t=1
• Rank probability skill score (RPSS): Evaluates skill in capturing categorical probability
k i j
RPS = 1 [ ( Pn - dn)2] , RPSS = 1 – RPS (forecast)
i=1 n=1 n=1 RPS (standard)
k –1- < RPSS < +1 ; bad skill to perfect skill
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
MODEL SKILL
better skill: Higher r; LLH > 1; RPSS ~ +1
ALL YEARS WET YEARS DRY YEARS
R = 0.65
llh = 2.09
RPSS = 0.79
llh = 2.85 llh = 1.90
RPSS = 0.98 RPSS = 0.22
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
SCATTER PLOTS
Better forecasting in post-1980
ALL YEARS PRE-1980 POST-1980
R = 0.21 R = 0.19
R = 0.65
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
PDFs
• PDF obtain exceedence probability for extreme events (wet: >700 mm and dry: <400 mm) show good skill (especially for wet scenarios)
Year Cl imatol ogy K-nn1983 10.0% 89.0%1988 10.0% 82.9%1995 10.0% 25.1%
WET YEARSYear Cl imatol ogy K-nn1984 90.0% 84.1%1987 90.0% 100.0%1994 90.0% 39.5%
DRY YEARS
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
CATEGORICAL PROBABILITY FORECAST
• When cannot obtain large-scale variables
• Use only forecasted categorical probability of ENSO
• Quick and simple technique
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
PROBABILITY RELATIONSHIP
• Conditional probability theorem: P(BA) = P(AB)
P(A)
• Total probability theorem: P(B(A1A2A3) = P(BA1)*P(A1)+P(BA2)*P(A2)+P(BA3)*P(A3)
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
CATEGORICAL PROBABILITY MODEL: ALGORITHM
• Monsoon rainfall and standard index (SOI) are divided into 3 categorizes: Low, neutral, high (by 33rd and 66th percentile)
• Estimate conditional probabilities
• Forecasted categorical of SOI (P(SL),P(SN),P(SH)) are issued Estimate total categorical probability of monsoon rainfall (P(RL),P(RN),P(RH))
• To generate ensemble forecasts: Bootstrap historical data by total categorical probability
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
CASE STUDY
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
Rl Rn Rh
Sl 0.57 0.29 0.14
Sn 0.37 0.25 0.38
Sh 0.00 0.57 0.43P(Sl) P(Sn) P(Sh)
0.70 0.15 0.15
Conditional Probability
Forecasted Probability of SOI
P(Rl) P(Rn) P(Rh)
0.45 0.33 0.22
Total Categorical Probability of Rainfall
PDFs
• Exceedence of extreme events (wet: >700 mm and dry: <400 mm) show considerate skill
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
Year Cl imatol ogy ModelWet 1982 10.0% 14.2%
1997 10.0% 14.8%Dr y 1982 90.0% 85.6%
1997 90.0% 77.5%
El Nino Year sYear Cl imatol ogy Model
Wet 1988 10.0% 15.1%2000 10.0% 16.9%
Dr y 1988 90.0% 89.0%2000 90.0% 91.0%
La Nina Year s
CONCLUSIONS
• Decreasing trend in MAM SATs and monsoon rainfall during 1950-2001 – slight increase during post 1980s
• Thailand Monsoon rainfall shows strong relationship with ENSO in post-1980 while Indian monsoon lose its ENSO association in the same period
• ENSO related Anomalies over eastern equatorial Pacific Walker circulation subsidence largely in the pacific region impacting south east Asia. Vice-versa for the Indian subcontinent.
• Shifts in enso patterns post 1980• Pre-monsoon SSTs and SLPs the tropical indian and pacific
regions are identified as predictors of Thailand monsoon rainfall
• Results from both statistical models show good skill at 1-4 months lead time
• Significant implications to water (resource) management and planning
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
FUTURE WORK
• Forecast improvement: Forecasts in this research is based on data during post-1980 only
- Need to test this on data from other regions- Need to test this on earlier periods (e.g., pre
1950) on longer data sets• Streamflow forecasting : Obtain good quality
streamflow data repeat analyses results directly impact reservoir operations and management
• Causes for ENSO shifts: Area of active research (modeling and observational analysis)
• Statistical-physical forecasting models: Combined statistical and physical watershed models can potentially improve the forecasts
• Decision support system: to evaluate various decision options in light of the forecasts
Hydro climatology
TrendsInterannual
/InterdecadalPredictors of
Thailand Rainfall
Forecast Thailand Monsoon Rainfall
Conclusions /Future Work
ACKNOWLEDGEMENT
• My sponsor: Public Works Department• Balaji Rajagopalan and all members of
committee• Somkeit Apipattanavis• Katrina Grantz• Krishna Kumar
THANK YOU