Prediction of Monsoon Rainfall Using Large Scale Climate Signals: A Case Study

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1 Wrld Envirnmental and Water Resurces Cngress 2007: Restring Our Natural Habitat Predictin f Mnsn Rainfall Using Large Scale Climate Signals: A Case Study 1 Mhammad Karamuz, Prfessr, Schl f Civil Engineering, University f Tehran, Karamuz@ut.ac.ir 2 Sara Nazif, Ph.D. Candidate, Schl f Civil Engineering, University f Tehran, snazif@ut.ac.ir 3 Ali Mridi, Ph.D. Candidate, Schl f Civil and Envirnmental Engineering, Amirkabir University (Tehran Plytechnic), Tehran, Iran. Mridi@cic.aut.ac.ir 4. Ali Yazdanpanah, M.Sc in Civil and Envirnmental Engineering, Envirnmental and Sustainable Develpment Department, Tehran Municipality, Tehran, Iran. yazdanpanah@members.asce.rg. Abstract In this paper, a cmprehensive investigatin n applicatin Artificial Neural Netwrks in lng-term rainfall frecasting is presented. A variety f ANN-mdels have been develped t prcess and train a system with large scale climate signals fr the summer precipitatin spells. The summer mnsn is ne f the mst dynamic climate systems that cntrls rainfall variatin in sme Asian cuntries such as India and Pakistan and delivers a cmpnent f annual rainfall in these regins. The sum f rainfall during July, August and September is called summer mnsn rainfall. In rder t quantify the effects f large scale climate signals n the precipitatin in the study area, lng-term recrds f SST (Sea Surface Temperature) and SLP (Sea Level Pressure) ver Oman Sea, Arabian Sea and Indian Ocean have been examined. The SSTs ver west cast f India as well as the SSTs ver the Oman Sea shws a high crrelatin with mnsn rainfall in Iran. Als SLP ver the Nrthern part f India shws a significant crrelatin with recrded rainfall at different pints f the regin. These signals can be used as useful predictrs fr mnsn rainfall at the sutheastern part f Iran. The results shw that cnsidering a set f predictrs develped in this study culd significantly increase the accuracy f lng-lead precipitatin frecasting in the study area using ANN mdels. Keywrds: Mnsn Rainfall, Climate Signals, Rainfall Predictrs, SLP, SST, Frecasting, Artificial Neural Netwrk (ANN) 1- Intrductin 1

2 Wrld Envirnmental and Water Resurces Cngress 2007: Restring Our Natural Habitat The summer mnsn is ne f the dynamic climate systems and the livelihd f mre than 60 percent f the wrld's ppulatin depends n them. Furthermre, the Asian summer mnsn is a key cmpnent f the earth's climate system, having imprtant telecnnectins with glbal weather and climate in Iran and delivers a cmpnent f annual rainfall in the sutheastern part f Iran. The sum f rainfall during the mnth f July, August and September is the seasnal mnsn in sutheastern part f Iran. Many investigatins have been dne t understand the relatin between mnsns and ther glbal phenmenas and large scale climate signals such as El Nin suthern scillatin, (SST), (SLP) and ther large scale climate signals. (Karamuz et al., 2006) Fllwing the Great Indian Drught f 1877, Blanfrd, wh established the Indian Meterlgical Department in 1875, frecasted Indian mnsn rainfall in Later, in the early part f the 20th century, Walker initiated extensive studies f glbal telecnnectins which led him t the discvery f Suthern Oscillatin. Walker intrduced the cncept f crrelatin fr lng-range frecasting f Asian summer mnsn and his findings are relevant even tday (Krishna Kumar et al., 2004). The mdels established later by Gwariker et al. (1989), Thapliyal (1990), and Sahai et al. (2003) belng t the categry f Walker's studies. Amng these, the mdel f Sahai et al. (2003), which links glbal SST with Indian mnsn seasnal data, appears t be the mst successful. Artificial Neural Netwrks (ANNs) have prven t be an efficient alternative t traditinal methds fr mdeling qualitative and quantitative water resurce variables (Karunanithi et al. 1994; Smith and Eli 1995; Maier and Dandy 1996; Shamseldin 1997; Clair and Ehrman 1998). A successful wrk f ANNs applicatin in rainfall frecasting is the study dne by French et al. (1992), wh applied a neural netwrk t frecast ne-hur-ahead, tw-dimensinal rainfall fields n a regular grid. Tth et al. (2000) investigated the capability f ANNs in shrt-term rainfall frecasting using past rainfall depths as the nly input infrmatin. In the present study, the emphasis is n lng-lead rainfall frecasting which is referred t frecasts as 1 mnth t 6 mnth ahead frecast. Mst f ANNs applicatins in hydrlgy have used feed frward neural netwrks, namely the standard multilayer perceptrn (MLP) trained with the back-prpagatin algrithm (Culibaly et al., 1999). MLP is a static and memryless netwrk and even thugh it is the mst widely 2

3 Wrld Envirnmental and Water Resurces Cngress 2007: Restring Our Natural Habitat used fr water resurce variables predictin which ften yields subptimal slutins. In fact, the MLP mdel des nt perfrm tempral prcessing and the input vectr space des nt cnsider the tempral relatinship f the inputs (Giles et al. 1997). In this study, attempts have been made t explre the relatinship between Iran summer mnsn rainfall and SLP at certain pints in India, Arabian Sea, Oman Sea, Pakistan and Iran t find predictrs fr frecasting precipitatin in the study area. After finding predictrs f Iran summer mnsn rainfall, static and dynamic ANN mdels have been develped fr rainfall frecasting based n these predictrs. 2- Study area The study area (Suth Balchestan watershed) lies between 60 and 62 Nrthern latitude and 25 and 27 Eastern lngitude, in the suth-east part f Iran in an area f abut square kilmeters. There are 6 meterlgical statins at the regin including: Kagdar, Ghasre-Ghand, Rask, Bahkahalt, Pishin, Chabahar. The average precipitatin f the regin has been calculated using the Kridging methd. The average annual precipitatin in Suth Balchestan watershed is abut 180 mm. Figure1 shws the summer rainfall variatin in the study area during 1973 thrugh 2002 in which rainfall data are available with 3 mnth average f 22.8 mm Rainfal(m m ) Year Figure 1: Summer Rainfall Variatin in the Suth Balchestan 3. Large scale climate signals In rder t quantify the effects f large scale climate signals n the precipitatin, lng-term recrds f SST (Sea Surface Temperature) and SLP (Sea Level Pressure) ver Oman Sea, Arabian Sea and Indian Ocean have been examined. The study f 3

4 Wrld Envirnmental and Water Resurces Cngress 2007: Restring Our Natural Habitat SLP and SLP effects n precipitatin f study area is discussed in Karamuz et al. (2006) and its summary is presented in sectin 3.1. In the fllwing sectin, the crrelatin between mnsn precipitatin in the study area and SSTs ver a part f Indian Ocean and Oman Sea is discussed. Data f these signals are available frm 1973 t Sea level pressure (SLP) Karamuz et al. (2006) cnsidered Sutheastern parts f Iran, Oman Sea, Pakistan, nrthwest f India, the Arabian Sea and the Indian Ocean fr determining lng-lead rainfall predictrs. They investigated the relatin between lng-term recrds f SLP in the mnths July f previus year thrugh June f the fllwing year in selected lcatins and the ttal average area precipitatin in the mnths f July thrugh September. The result f the best crrelatin btained fr each studied climate signal and cnsidered water surfaces, are presented in Table 1. In this study the fllwing indexes cnsidered as indicatrs f summer precipitatin in the study area. Table 1: Crrelatin f each climate signals at different lcatins (Karamuz et al., 2006). Mnsn Rainfall Predictr characteristics Name f cnsidered area lcatin Mnth f effectiveness Latitude Lngitude signal CC * Central Iran (P1) April-May 0.53 Pakistan & nrthern India (P2) March-May Nrthern India (P3) March-May Signal SLP SLP difference Indian Ocean-Study area April-May 0.47 Indian Ocean-Nrthern India April-May 0.43 * Crrelatin Cefficient 3.2. Sea surface temperature (SST) The Oman Sea, Arabian Sea and the east f Oman Sea are selected fr determining lng-lead rainfall predictrs. The relatin between lng-term recrds f SST in the mnths f July t June f the fllwing year at selected pints and the ttal average precipitatin in the prceeding mnths f July thrugh September in the study area is investigated. The sea surface pressures (SSTs) f selected area have been determined ver 1 1 degree grids. As an example, Figure 2 shws the crrelatin map ver the Arabian Sea shwing the relatinship between mnsn rainfall f the study area and 4

5 Wrld Envirnmental and Water Resurces Cngress 2007: Restring Our Natural Habitat SST fr March. Crdinates f selected SST areas are presented in Table 2. This table als shws the maximum crrelatin between SST f studied regins and mnsn rainfall f the study area. Accrding t Table 2, the SST f Eastern part f the Oman Sea in the mnths f March and April has the highest crrelatin with Iran mnsn rainfall Figure 2: The crrelatin map f Arabian Sea shwing the relatinship between mnsn rainfall f the study area and SST fr March. Table 2: Characteristics f SST at selected pints and crrelatin with mnsn rainfall Area Maximum Crrelatin Name Latitude Lngitude Latitude Lngitude Perid CC 23.5 N E - Oman Sea 23.5 N March E N 64.5 E April 19 N - 55 E - Arabian Sea 16.5 N 55.5 E May N 65 E Eastern part f Oman 19.5 N E - Sea 20 N March E N 72 E April 3.3. Summer precipitatin Indicatrs Results shw that the fllwing indices can be cnsidered as indicatrs f summer precipitatin in the study area: 1. SLP f central Iran (P1) 2. SLP f Pakistan & Nrthern India (P2) 3. SLP f Nrthern India (P3) 4. SLP difference between Indian Ocean and study area, and 5. SLP difference between Indian Ocean and Nrthern India 6. SST f Oman Sea 7. SST f Arabian Sea 5

6 Wrld Envirnmental and Water Resurces Cngress 2007: Restring Our Natural Habitat 8. SST f Eastern part f Oman Sea The abve signals have high crrelatin with summer mnsn in the study area and cmbinatins f them are cnsidered as ANN mdel inputs. 4- Results f mnsn summer rainfall frecasting A thirty year time series f summer Mnsn rainfall and large scale signals fund as Mnsn predictrs (including SST, SLP and SLP), frm 1973 t 2002 are used fr develping an ANN mdel fr precipitatin frecasting in the study area. The first twenty tw years data frm 1973 t 1994 are used fr mdel calibratin and the remaining 8 years f data are used fr validatin f the ANN mdel. The ANN mdel inputs are time series f different cmbinatins f Mnsn predictrs and their utputs are the Mnsn rainfall time series. Several different types f ANN mdels including MLP, RNN and IDNN have been develped and cmpared t find the mdel with the best perfrmance. Each type f ANN mdels has been trained with different parameters and cmbinatin f signals until the best mdel has been btained. The best structure f each type f cnsidered ANN mdels has been shwn in Table 3. Input data f ANN mdels are signals discussed in sectin 3.3. T quantify the ANNs perfrmance, RMSE (Rt Mean Square Errr) and MAE (Mean Abslute Errr) have been calculated (Table 2). These tw statistics shw that MLP mdel has better perfrmance. Table 3: Structure f different ANN Mdels and their errrs ANN Mdel n. f neurns in hidden layer TDL (Time Delay Line) RMSE MAE MLP RNN IDNN The results f summer Mnsn rainfall frecasting with the selected ANN mdel, has been shwn in Figure 3. This figure shws the acceptable ability f these three ANN mdels fr rainfall predictin. But it must be nted that sme times mdels predictins are verestimated. T better understand mdel perfrmance, sme errr tlerances are defined. Fr this purpse, errr is calculated as fllws: Errr i bsi fri = bs i (1) 6

7 Wrld Envirnmental and Water Resurces Cngress 2007: Restring Our Natural Habitat where bsi and fri stand fr bserved and frecasted rainfall, respectively. The percentages f predicted Mnsn rainfalls which lie within different errr ranges are calculated and summarized in Table 4 fr different ANN mdels. 100 sum m erm nsn rainfall(m m ) O bserved IDNN M LP RNN year Figure 3: Cmparative frecasted and bserved summer Mnsn rainfall Accrding t Table 4, MLP can predict 50% f time within 30% f the bserved rainfall. This range culd be reduced fr the mnths f August and September nce the rainfall data fr July is bserved. Table 4: Percentage f ccurrence f predicted values within different errr ranges Tlerance ANN Mdel ± 10% ± 20% ± 30% Calibratin MLP Validatin RNN IDNN Calibratin Validatin Calibratin Validatin Cnclusin This study is an extensin f Karamuz et al. (2006) which studied the relatins between SLP and SLP with Iran mnsn rainfall. SST signals are cmbined with 7

8 Wrld Envirnmental and Water Resurces Cngress 2007: Restring Our Natural Habitat SLP signals in this paper t develp a frecasting mdel. Three regins including the Oman Sea, Arabian Sea and Eastern parts f the Oman Sea are selected and SST signals have been studied. Each regin is divided int 1 1 degree grids and the crrelatin f SST in each zne with average rainfall f the study area has been estimated. Different ANN mdels including MLP, RNN and IDNN with different architectures and different cmbinatins f input predictrs have been develped fr Iran mnsn rainfall predictin. The mdels are tested and cmpared using MAE and RMSE statistics. The MLP mdel has the best perfrmance and can predict rainfall patterns with acceptable accuracy. 6. References Alijani, B., (1999), Effects f Variatins in 500 hpa Geptential height n the Middle-east and Mediterranean Regin and als Climate f Iran in the Years f , Reprt prvided t the Natinal Center fr Climatlgy, Iran Meterlgical Organizatin, Iran Barry R. J. and Carletn A. M. (2001), "Synptic and Dynamic Climatlgy", Rutledge publicatins, New Yrk. Gwariker V., Thapliyal V., Sarker R. P., Mandal G. S. and Sikka D. R., (1989), "Parametric and pwe regressin mdels: new apprach t lng range frecasting f mnsn in India", Mausam, 42, Iyengar R. N. and Raghu Kanth S. T. G., (2005) "Intrinsic mde Functins and a strategy fr frecasting Indian mnsn rainfall", Meterl Atms phys, 90, PP Karamuz M., Yazdanpanah A., Mridi A. and Nazif, S. (2006), "Effects f Large scale climate signals n Iran Mnsn Rainfall.", EWRI 2006 cnference, India, Krishna Kumar K., Sman M. K. and Rupa Kumar K. (2004), "Seasnal Frecasting f Indian Summer Mnsn Rainfall: A Review", Indian Institute f Trpical Meterlgy. guhathakurta P., Rajeevan M. and Thapliyal V. (1999), Lng Range Frecasting Indian Summer Mnsn Rainfall by a Hybrid Principal Cmpnent Neural Netwrk Mdel, J. Meterlgy and Atmspheric Physics, 71, pp Lim Y. K. (2004). " Diagnsis f The Asian Summer Mnsn Variability And The Climate Predictin Of Mnsn Precipitatin Via Physical Decmpsitin" Ph.D. thesis. Flrida State University, Cllege f Arts and Sciences. Nazemsadat M. J. and Crdery I, (2000), "On the relatinships between ENSO and Autumn Rainfall in Iran", Internatinal Jurnal f Climatlgy, Vl. 1, Sahai A. K., Grrimm A. M., Satyan V. and Pant G. B., (2003), "Lng lead predictin f Indian Summer mnsn rainfall frm glbal SST evlutin", climate Dyn, 20, Singh O. P. (2000), " Multivariate ENSO index and Indian mnsn rainfall: relatinships n mnthly and subdivisinal scales", Meterlgy and Atmspheric Physics, Vlume 78, Issue 1/2, pp 1-9 8

9 Wrld Envirnmental and Water Resurces Cngress 2007: Restring Our Natural Habitat Shukla, J. and Palin, J.A. (1983), The suthern scillatin and the lng-range frecasting f summer mnsn rainfall ver India,. Mn. Wea. Rev., 111, Tapliyal V., (1990). "Lng range predictin f summer mnsn rainfall ver India: Evlutin and develpment f new mdels." Mausam, 91, Yasunari, T., (1990), "Impact f Indian mnsn n the cupled Atmsphere/Ocean in the trpical Pacific." Meterl. Atms. Phys., 44, pp

SNOW AND AVALANCHES FORECASTING OVER THE ANDES MOUNTAlNS. Jose A. Vergara* Departamento de Geoflsica, Universidad de Chile

SNOW AND AVALANCHES FORECASTING OVER THE ANDES MOUNTAlNS. Jose A. Vergara* Departamento de Geoflsica, Universidad de Chile SNOW AND AVALANCHES FORECASTNG OVER THE ANDES MOUNTAlNS Jse A. Vergara* Departament de Geflsica, Universidad de Chile Rene len, Camins y Nieve, ANDNA-CODElCO, CHLE ABSTRACT: During the last three years