Longe-range meteorological prediction using teleconnectionsof sea surface temperature and ocean indices to regional and local climate in Thailand
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1 Longe-range meteorological prediction using teleconnectionsof sea surface temperature and ocean indices to regional and local climate in Thailand Department of Geohydraulics and Engineering Hydrology University of Kassel, Germany
2 CONTENTS Introduction Climate analysis / Teleconnections Application for climate prediction Summary / Conclusion 2
3
4 Study area: THAILAND NN NE CC EE Indian Ocean Pacific Ocean 121 meteorological stations SS ± Kilometers meteorological station province border Thailand borderline distributed into regions North (NN) Northeast (NE) Central (CC) East (EE) South (SS)
5 Ocean state indexes / oscillation Indian Pacific
6 Fine points Large grids Zonal influence Surface water Saturated Zone Groundwater
7 Objectives Identify telecconnection existence relationship local weather - regional index by employing statistical framework Predict local weather toward the impact of climate change on water/climate by using Goal prediction improvement by using regional indices To investigate of the climate change impact toward water resources
8
9 Change of climate behavior
10 Autocorrelation analysis NN NE CC SS ± NN Kilometers SS EE Scale legend MIN MAX meteorological station province border Thailand borderline Region/ Index Average evapora tion Average humidity Extrme max temp Extrme min temp Max rain in 1 day Mean max temp Mean min temp absolute correlation coef. Mean temp Mean daily rain Number of rainy day Monthly rainfall NN ep nina nina nina nina noi pdo pna setio soi swio wp wtio SS ep nina nina nina nina noi pdo pna setio soi swio wp wtio
11 Autocorrelation analysis (ENSO 1+2) absolute correlation coef. humidity Extrme max temp Extrme min temp Max rain in 1 day Mean max temp Mean min temp Mean temp Mean daily rain Numbr of rainy day Monthly rainfall NE EE CC SS max correlation Average evapora tion Average NN Region ENSO 1+2 (Nino 1.2) NN NE CC Thailand SETIO EE SWIO ± SS Kilometers Nino 3.4 Nino 4 Nino 3 WTIO meteorological station province border Thailand borderline SOI Nino1+2 Equator
12 Relationship of indexes to the local weather Cross-correlation of ENSOcompared with local climate /-1-2/ = Climate took 3 months later after ENSO
13 Auto-Correlation minimum temp vs Nina [NINA took 3 months before temp] ± Kilometers +0.7 Autocorrelation coeff (within -/+11 months) Mean min. temp. vs NINA Thailand borderline ± Kilometers +3 Time-lag (month) at best autocorrelative Mean min. temp. vs NINA Thailand borderline
14 Auto-Correlation monthly rainfall vs Nina [NINA took 3 months after rain] ± Kilometers Autocorrelation coeff (within -/+11 months) Monthly rainfall vs NINA Thailand borderline ± Kilometers -3 Time-lag (month) at best autocorrelative Monthly rainfall vs NINA Thailand borderline
15 Auto-Correlation mean temp vs EP/NP +0. EP / NP Autocorrelation coeff (within -/+11 months) Mean temp. vs EP EP / NP : Eastern Pacific Northern Pacific Oscillation ± Indian ocean side : inverse variation Pacific ocean side : direct variation Kilometers Thailand borderline
16 Pilot area to predict climate Rayong province : Seaboard Autocorrelation coeff (within -/+11 months) Mean min. temp. vs NINA Time lag -1 to -3 months from ENSO 0 El Nino 1 El Nino 34 El Nino ± Kilometers Thailand borderline Timg lag (month) -2-3 Min temp Max temp
17 Teleconnection for long-range prediction ARIMA model NS coef = Verification of minimum temperature prediction (6-year forward) based on calibration year ARIMA with ENSO 1+2 NS coef = 0.836
18 Teleconnections for long-range prediction The model accuracy in forward prediction by AR models No ENSO No ENSO calibration verification prediction (1 yr) with ENSO the Nash Sutcliffe coefficient MxT MnT Rn MxT MnT Rn MxT MnT Rn AR ARIMA ARIMA with external regression Model
19 Teleconnections for long-range prediction Nash Sutcliffe efficiency Nash-Sutcliffe model efficiency coefficient of monthly rainfall prediction in Khongyai Basin Calibration Verification (scenario A1B) Verification (scenario A2) trend analysis trend analysis Nash-Sutcliffe model efficiency coefficient of monthly maximum temperature prediction at station 4849 in Khongyai Basin Nash-Sutcliffe model efficiency coefficient of monthly minimum temperature prediction at station 4849 in Khongyai Basin Nash Sutcliffe efficiency trend analysis trend analysis Nash Sutcliffe efficiency trend analysis trend analysis using nino data Calibration Verification (scenario A1B) Verification (scenario A2) Calibration Verification (scenario A1B) Verification (scenario A2)
20 Summary : Teleconnections in long-range prediction (Nash-Sutclif. coef.)
21
22 The ENSO indices mostly relate to local Thai climate variables and local geography Teleconnection of SST and regional climate is able to improve the prediction of climate in Thailand, particularly the temperature Auto- and Co-regressive models allow to create a longrange time series of local climate along with the change of SST Using a combination of selected predictors in globalscale and regional/local climate indicators provides for better long-term forecasting
23 Questions & Answers
24 Water Resources System Research Unit, Chulalongkorn University, Thailand (WRSRU_CU) Royal Irrigation Department, Thailand (RID) Thai meteorological department, Thailand (TMD)
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