tcoourpulnedexopceenasn iv -a etm en ossepmhbelrees . mIofdseol , s w to ecpo re u d ld ic tuslea rg th e e -l doiscst . i on Tsh , ebasskeid ll in seasonal rainfall and f scale indices such as the SOI, then use statistical generated the v o al nuethferoEmlN us iinnog -S ta ocu ti tchaelrnmaOnsrcoisltl at p io rne , ­ v re alra ia ti bolne ships between these forec agement. fic locat siowne . nAele te drn to at p iv reeldyi , ctw , esuccohuaasstraii ndices and the This study demonstrated that the skill obtainable in models to ld us nefatlhleatcaosuppelceid ­ A gr uosu tr nad li s a , th weaisrussueffiinc ie c n ro tptmo an ju asg ti efmy, enot. n Pr eecsounmoam bl iycp en re s d em ic b te lderpurnesdiocftasneaas tm ur o fa scpehetreimc peratures, then use these forecasts could also be useful in drought-dicti sea surface temperature msotdoelpfro ep rc aerdew pr it e h ­ mmiannaatg io enmeonftadpep ci rsoiporniam te aksitnogc , kifnogrirnas te ta sncoen in padse to te ra r l ­ tChoenbsoi ns edse of trsatbrlera ate tihno fa gy ulglhatnadndotthee st rinvga riables of interest. properties (McKeon et al. 1990). prediction. for using models iisnnseeead so endaltoc li s m el aetcet

Droughts ◽  
2016 ◽  
pp. 78-78
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Seasonal Rainfall

scholarly journals Wavelet Analysis of Seasonal Rainfall Variability of the Upper Blue Nile Basin, Its Teleconnection to Global Sea Surface Temperature, and Its Forecasting by an Artificial Neural Network

2014 ◽  
Vol 142 (5) ◽  
pp. 1771-1791 ◽  
Author(s):  
Mohamed Helmy Elsanabary ◽  
Thian Yew Gan
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
River Basin ◽  
Principal Component ◽  
Sea Surface ◽  
Seasonal Rainfall ◽  
Blue Nile ◽  
Artificial Neural ◽  
Mean Square Errors ◽  
Global Sea Surface Temperature

Abstract Rainfall is the primary driver of basin hydrologic processes. This article examines a recently developed rainfall predictive tool that combines wavelet principal component analysis (WPCA), an artificial neural networks-genetic algorithm (ANN-GA), and statistical disaggregation into an integrated framework useful for the management of water resources around the upper Blue Nile River basin (UBNB) in Ethiopia. From the correlation field between scale-average wavelet powers (SAWPs) of the February–May (FMAM) global sea surface temperature (SST) and the first wavelet principal component (WPC1) of June–September (JJAS) seasonal rainfall over the UBNB, sectors of the Indian, Atlantic, and Pacific Oceans where SSTs show a strong teleconnection with JJAS rainfall in the UBNB (r ≥ 0.4) were identified. An ANN-GA model was developed to forecast the UBNB seasonal rainfall using the selected SST sectors. Results show that ANN-GA forecasted seasonal rainfall amounts that agree well with the observed data for the UBNB [root-mean-square errors (RMSEs) between 0.72 and 0.82, correlation between 0.68 and 0.77, and Hanssen–Kuipers (HK) scores between 0.5 and 0.77], but the results in the foothills region of the Great Rift Valley (GRV) were poor, which is expected since the variability of WPC1 mainly comes from the highlands of Ethiopia. The Valencia and Schaake model was used to disaggregate the forecasted seasonal rainfall to weekly rainfall, which was found to reasonably capture the characteristics of the observed weekly rainfall over the UBNB. The ability to forecast the UBNB rainfall at a season-long lead time will be useful for an optimal allocation of water usage among various competing users in the river basin.

scholarly journals Assessment of Better Prediction of Seasonal Rainfall by Climate Predictability Tool Using Global Sea Surface Temperature in Bangladesh

2019 ◽  
pp. 1-13 ◽  
Author(s):  
Md. Zakaria Hossain ◽  
Md. Abul Kalam Azad ◽  
Samarendra Karmakar ◽  
Md. Nazrul Islam Mondal ◽  
Mohan Das ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
South Asian ◽  
Rainy Season ◽  
Regional Cooperation ◽  
Sea Surface ◽  
Seasonal Rainfall ◽  
Research Centre ◽  
Positive Deviation ◽  
Climate Predictability

This study was conducted to determine better prediction result of seasonal rainfall. To evaluate the better prediction of seasonal rainfall of rainy season (15 June-15 August) by Climate Predictability Tools (CPT) in the context of using sea surface temperature (SST) of starting month of rainy season compare to using SST of one month before the rainy season. The study was carried out at the South Asian Association for Regional Cooperation Meteorological Research Centre, Dhaka; Bangladesh between January and December, 2010. A correlation between rainfall at Rangpur, Dhaka, Barisal and Sylhet and global SST of different areas of the world was studied by using the both data of 1975- 2008 years with the help of the CPT to find more positive correlated SST with observed rainfall and use as predictor for giving the prediction of the year 2009. The statistical method applied using CPT which is canonical correlation analysis. Using SST of one month before rainy season as predictor, the positive deviation of predicted rainfall from observed rainfall was 1.34 mm/day at Sylhet and 0.9 mm/day at Dhaka. The negative deviation of mean rainfall was 1.16 mm/day at Rangpur and 1.10 mm/day at Barisal. Again, using of starting one month SST of rainy season as predictor, positive deviation of predicted rainfall from observed rainfall was 4.03 mm/day at Sylhet. The positive deviation of daily mean rainfall was found 6.58 mm/day at Dhaka and 6.23 mm/day over southern Bangladesh. The study reveals that SST of one month before rainy season was better predictor than SST of starting month of rainy season.

scholarly journals Spatiotemporal variability and trends of rainfall and its association with Pacific Ocean Sea surface temperature in West Harerge Zone, Eastern Ethiopia

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Getachew Bayable ◽  
Gedamu Amare ◽  
Getnet Alemu ◽  
Temesgen Gashaw
Keyword(s):  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Rainfall Variability ◽  
Sea Surface ◽  
Seasonal Rainfall ◽  
Spatiotemporal Variability ◽  
Annual Rainfall ◽  
Eastern Ethiopia ◽  
Enso Events

Abstract Background Rainfall variability exceedingly affects agriculture in Ethiopia, particularly in the eastern region where rainfall is relatively scarce. Hence, understanding the spatiotemporal variability of rainfall is indispensable for planning mitigation measures during high and low rainfall seasons. This study examined the spatiotemporal variability and trends of rainfall in the West Harerge Zone, eastern Ethiopia. Method The coefficient of variation (CV) and standardized anomaly index (SAI) were used to analyze rainfall variability while Mann-Kendall (MK) trend test and Sen’s slop estimator were employed to examine the trend and magnitude of the rainfall changes, respectively. The association between rainfall and Pacific Ocean Sea Surface Temperature (SST) was also evaluated by Pearson correlation coefficient (r). Results The annual rainfall CV during 1983–2019 periods is between 12 and 19.36% while the seasonal rainfall CV extends from 15–28.49%, 24–35.58%, and 38–75.9% for average Kiremt (June–September), Belg (February–May), and Bega (October–January) seasons, respectively (1983–2019). On the monthly basis, the trends of rainfall decreased in all months except in July, October, and November. However, the trends were not statistically significant (α = 0.05), unlike in November. On a seasonal basis, the trends of mean Kiremt and Belg seasons rainfall decreased while it increased in Bega season although it is not statistically significant. Moreover, the annual rainfall showed a non-significant decreasing trend. The findings also revealed that the correlation between rainfall and Pacific Ocean SST was negative for Kiremt while positive for Belg and Bega seasons. Besides, annual rainfall and Pacific Ocean SST was negatively correlated. Conclusions High spatial and temporal rainfall variability was observed at the monthly, seasonal, and annual time scales. Seasonal rainfall has high inter-annual variability in the dry season (Bega) than other seasons. The trends in rainfall were decreased in most of the months. Besides, the trend of rainfall decreased in the annual, Belg and Kiremt season while increased in the Bega season. The study also indicated that the occurrence of droughts in the study area was associated with ENSO events like most other parts of Ethiopia and East Africa.

scholarly journals Monitoring Water Resources over the Kotmale Reservoir in Sri Lanka Using ENSO Phases

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Sewwandhi Chandrasekara ◽  
Venkatraman Prasanna ◽  
Hyun-Han Kwon
Keyword(s):  
Sri Lanka ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Sea Surface ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Average Rainfall ◽  
Enso Phases

In this study, the El Nino Southern Oscillation (ENSO) phase index is used for water management over the Kotmale reservoir in Sri Lanka. Daily rainfall data of 9 stations over the Kotmale catchment during 1960–2005 June-September (JJAS) season is investigated over the Kotmale catchment. The ENSO phases are identified based on the 0.5°C sea surface temperature (SST) anomaly over Nino 3 region. The study has brought out few stations showing increasing and a few decreasing seasonal rainfall trends for JJAS season, while there is no change in the annual rainfall for the catchment. Monthly and seasonal rainfall of all the selected stations showed negative correlation with the sea surface temperature (SST) over the Nino-3 region index during JJAS season with varying magnitudes. During the warm phase of ENSO, below average rainfall is prominent for JJAS season over many stations. The rainfall especially during early September showed a significant below average rainfall during the warm ENSO phase. The seasonal rainfall during neutral and cold ENSO phases does not experience similar significant changes as seen during warm ENSO phase. Inflow of the Kotmale reservoir shows decreasing trend for the period of 1960–2005 in the observation from all stations collectively.

scholarly journals Assessment of Better Prediction of Seasonal Rainfall by Climate Predictability Tool (CPT) using Global Sea Surface Temperature in Bangladesh

2019 ◽  
Author(s):  
Zakaria Hossain ◽  
Md. Abul Kalam Azad ◽  
Samarendra Karmakar ◽  
Md. Nazrul Islam Mondal ◽  
Mohan Kumar Das ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Rainy Season ◽  
Sea Surface ◽  
Seasonal Rainfall ◽  
Negative Deviation ◽  
Positive Deviation ◽  
Climate Predictability ◽  
The World ◽  
Global Sea Surface Temperature

The main objective of this study is to search better prediction result of rainy seasonal rainfall (15 June-15 August). A correlation between rainfall of Bengali rainy seasons at Rangpur, Dhaka, Barisal and Sylhet and global sea surface temperature (SST) of different areas of the world was studied by using the both data of 1975- 2008 years with the help of the Climate Predictability Tool (CPT) to find more positive correlated SST with observed rainfall and use as predictor for giving the prediction of the year 2009. Using SST of one month before rainy season as predictor, the positive deviation of predicted rainfall from observed rainfall was 1.34 mm/day at Sylhet and 0.9 mm/day at Dhaka. The negative deviation of mean rainfall was 1.16 mm/day at Rangpur and 1.10 mm/day at Barisal. Again, using of starting one month SST of rainy season as predictor, positive deviation of predicted rainfall from observed rainfall was 4.03 mm/day at Sylhet. The positive deviation of daily mean rainfall was found 6.58 mm/day at Dhaka and 6.23 mm/day over southern Bangladesh. The study reveals that sea surface temperature (SST) of one month before rainy season was better predictor than SST of starting month of rainy season.

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