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T argeted T raining A ctivity: S easonal P redictability in T ropical R egions: R esearch and A pplications. Seasonal Predictability in East Asian Region.
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Targeted Training Activity: Seasonal Predictability in Tropical Regions: Research and Applications Seasonal Predictability in East Asian Region 『 East Asian Group』Juhyun Park (Republic of Korea) Yanju Liu (China), qiaoping Li (China)N. Jyothi (India), A. P. Dimri (India)
Table of Contents 1. Introduction 2. Climatological map of DEMETER 7 models 3. Deterministic Forecast skill in DMME 4. Probabilistic Forecast skill in DMME 5. Conclusion
1. Introduction • Period • 1980 yr ~ 2001 yr (Summer mean/Winter mean) • Region • Lon. : 40E~160E , Lat. : 20S~60N • Variable • Precipitation , 2m Temperature
2. Climatological structure of DEMETER models 850 hPawind climatology (JJA) CERF ECMW INGV LODY MAXP METF UKMO DMME OBS
2. Climatological structure of DEMETER models 2m Temperature climatology (JJA) CERF ECMW INGV LODY MAXP METF UKMO DMME OBS
2. Climatological structure of DEMETER models Precipitation climatology (JJA) CERF ECMW INGV LODY MAXP METF UKMO DMME OBS
observation and MME of Precipitation over Mid-lower Yangtze River basins
2. Climatological structure of DEMETER models Effect of MME : Mean Bias reductionCMAP climatology – ECMW model climatology CMAP climatology – Multi-model climatology
3. Deterministic Forecast skill in MME Correlation skill between Observation and Multi-Model PRCP TA2M MSLP JJA JJA JJA DJF DJF DJF
3. Deterministic Forecast skill in MME JJA Indian ocean index The Indian Ocean Region Lon. : 40 E ~ 110 E Lat. : 15 S ~ 10 N Var. : SST anomaly DJF Black line : Observation index Red line : the index in DMME
3. Deterministic Forecast skill in MME East Asia Summer Monsoon index The East Asia Region Black line : Observation index Green line : the index in DMME
Correlation between EASM Index and Precipitation Observation(left) MME(right)
Precipitation in strong monsoon year(1997) Observation(left) MME(right)
Precipitation in weak monsoon year(1998) Observation(left) MME(right)
Below normal (BN) Near normal (NN) Above normal (AN) 4. Probabilistic Forecast skill in DMME • First Step : Climatological Probability Distribution Function - Normalizing all the forecast value Climatological PDF Ensemble PDF of particular time BN NN AN 0 -Xc Xc • In the 3 category case, -Xc and Xc make the below area separate 1/3 value each. • Second Step : Probability Forecast for particular time / For Above normal case / Non-parametric approach Parametric approach m : Ensemble mean value for particular year
4. Probabilistic Forecast skill in DMME 1982 winter mean TS2M PRCP 1985 winter mean
4. Probabilistic Forecast skill in DMME • Reliability diagram : graphically represent the performance of probability forecasts of dichotomous events for each category • The plot of observed relative frequency as a function of forecast probability : • The 1:1 diagonal perfect reliability line : • A summary of the frequency of use of each forecast value Above Normal Category ( Global Region ) Precipitation / JJA Black, dashed line : Each model Red, solid line : DMME
4. Probabilistic Forecast skill in DMME • Brier Score (B) • Brier skill score (BSS) n : the number of realizations of the forecasts over which the validation is preformed For each realization i , pi : forecast probability of the occurrence of the event vi : a value equal to 1 or 0 depending on the event occurred / not. Bref : a reference forecast (taken to be the low-skill climatological forecasts) BSS = 1 : a perfect forecast system BSS = 0 (negative) : performs like (poorer than) the reference system
4. Probabilistic Forecast skill in DMME Above Normal Category Precipitation / JJA CERF ECMW INGV LODY MAXP METF UKMO DMME
4. Probabilistic Forecast skill in DMME • Economic value * pt : threshold probability V = 1 : a perfect forecast system V = 0 : performs like the reference system
4. Probabilistic Forecast skill in DMME Above Normal Category Precipitation / JJA CERF ECMW INGV LODY MAXP METF UKMO DMME
5. Conclusion • The Multi-model shows the better predictability than the single model following this study. • But, the forecast skill is different about the variables and the target region. This is the same results as in the deterministic forecast. • Probability forecasts show more informationfor users about future climate than deterministic forecast. Because this contains the uncertainty in the forecast problem.