Long-range and short-range prediction of Rainfall and Rainy days over northwestern part of Bangladesh during Monsoon Season

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1 Page 47 Long-range and short-range prediction of Rainfall and Rainy days over northwestern part of Bangladesh during Monsoon Season Md. Abdul Mannana 1,2*, Md. Abdul Mannan Chowdhury 3 and Samarendra Karmakar 4 1 SAARC Meteorological Research Centre (SMRC), Agargaon, Dhaka, Bangladesh, 2 Bangladesh Meteorological Department, Agargaon, Dhaka,Bangladesh, 3 Jahangirnagar University, Savar, Dhaka Bangladesh, 4 Bangladesh Centre for Advanced Studies (BCAS), Dhaka, Bangladesh * Corresponding Author: mannan_u23@yahoo.co.in Abstract: Heavy rainfalls during monsoon season are very common in northeastern, southeastern and central parts of Bangladesh but it is relatively rare over northwestern part of Bangladesh. Prediction of heavy rainfall over northwestern part of Bangladesh is a challenging job. Attempt is therefore made to derive the variability of rainfall, rainy days and frequencies of heavy rainfall with their trends over northwestern part of Bangladesh using the daily rainfall data recorded at Bogra, Dinajpur, Ishwardi, Rajshahi and Rangpur for the period of Long-range or seasonal forecasting is made through Principal Components Regression (PCR) mode Climate Predictability Tool (CPT) where ERSST3b data of is used as predictors. Short-range prediction has been made by simulating the heavy rainfall events occurred during 211 over northwestern part of Bangladesh using meso-scale non-hydrostatic WRF model with the resolution of 9 km grid space and six hourly initial and boundary conditions from NCEP. Investigation depicts that the variability of rainfall, rainy days and heavy rainfall over northwestern part of Bangladesh are very high during monsoon months as well as in monsoon season but the trends of rainfall, rainy days and heavy rainfall are -.6%, -.2 and -.27/ year respectively. The frequency of rainy day is the highest in June but the frequency of heavy rainfall is in July among the monsoon months. Both the frequencies of rainy day and heavy rainfall show negative trends in the monsoon months as well in monsoon season. Long-range prediction with -lead months overestimates the frequency of rainy days and heavy rainfall during monsoon season and the Root Mean Square Errors (RMSEs) are.3 and 9.3. WRF-ARW model simulates the heavy rainfall events reasonably well but the signatures of maximum model rainfall zone locate sometimes quite far away from the evidenced area. Key words: CPT, heavy rainfall, long-range prediction, short-range prediction, WRF Model. 1. INTRODUCTION Long-range or extended-range forecasts or prediction is defined as the forecast for the duration of more than 1 days to a season. But there is no rigid definition for long-range forecasting which may range from a monthly to a seasonal forecast. Similarly, short-range forecast is identified as the weather (mainly rainfall) in each successive 24 hr intervals may be predicted up to 3 days [1]. According to World Meteorological Organization (WMO), the prediction of weather for 1 days to the future is considered as long-range forecast but it is for the duration of 24 to 72 hrs is known as short-range forecast. The statistical approach to making seasonal forecasts from sea-surface temperatures has been used for a number of years at many National Meteorological Services (NMSs). Since the late 199s, these statistical forecasts have been combined to produce a consensus forecast, representing a patchwork of nationally-based forecasts for subcontinental areas, in Regional Climate Outlook Forums [2]. While such forums have been very successful in building the capacity to produce seasonal climate forecasts, a number of problems have emerged, and some systematic errors in the forecasts have been identified [3]. Climate prediction is very much important, as the climate is changing owing to natural and anthropogenic activities. Climate change has been aggravated in the recent past and projected to be even worsening in the future especially due to anthropogenic inputs to the living climate system. In view of this, climate change has a greater bearing on seasonal weather and therefore a suitable technique is desired for making seasonal weather forecasts for the benefits of stakeholders in various sectors such as agriculture, water resources, energy, etc. The scientific basis for seasonal predictions lies in the interaction of the atmosphere with slowly varying components of the climate system such as the ocean [4-]. To

2 Page 48 be useful for decision making, seasonal climate predictions need to be probabilistic and the capability of probability forecasts to provide valuable information needs to be assessed [6]. Climate forecasts are associated with uncertainty because of the stochastic nature of the climate system. The level of uncertainty can be conveyed in a quantitative way by using probabilities [7-1]. Owing to their ability to quantify the uncertainty, probabilistic forecasts are of potentially greater value to decision makers than deterministic forecasts [7, 11-12]. The level of uncertainty can be quantified in several ways. For example, it can be estimated subjectively by expert assessment. It can also be derived from the confidence intervals of statistical forecasts. The tropical climate is suggested to have potential for long-term prediction because a significant part of its long-term variability seems to be determined by slowly varying components of the climate system such as the sea surface temperature (SST) rather than by synoptic scale instabilities [13]. Predictors involving SST parameters are used in the statistical forecasts of seasonal mean monsoon rainfall over India issued every year by the India Meteorological Department [14]. However, the ability of SST in the Indian and Pacific Oceans to predict the seasonal mean monsoon has not been firmly established. The seasonal mean Indian rainfall has been shown, in observational and model studies, to have maximum correlation with the SST of the east Pacific Ocean when the monsoon leads the SST by four to six months [1]. The relation between intra-seasonal variability involving active and break phases of the monsoon and the SST is poorly understood. When the SST is contemporaneous with or leads the summer monsoon season, a strong relation between the SST and monsoon rainfall has not been detected. Recent advances in the application of climate prediction to agriculture suggest potential for improved risk management strategies, enabling producers to better tailor management decisions to the season [16]. Farmers can use site specific seasonal forecasts to mitigate unwanted impacts or take advantage of favourable conditions. By providing advance information with a sufficient lead time to adjust critical agricultural decisions, seasonal forecasts have significant potential to contribute to the efficiency of agricultural management and to food and livelihood security [17]. Integrating crop simulation models with seasonal climate forecast tools is a perceived opportunity to add value to seasonal climate forecasts for agriculture [18]. Considering all the aspects of the importance of seasonal/ monthly weather forecast, a reliable and user-friendly seasonal weather forecasting technique is an utmost urgency for Bangladesh. To comply with the above requirements, the Climate Predictability Tool (CPT), developed by the International Research Institute (IRI) for Climate and Society of University of Columbia, USA is made use of for making monthly and seasonal frequency of rainy days and heavy rainfall forecasts for northwestern part of Bangladesh. The short-range weather forecasting is to provide various users with information on the anticipated weather over forthcoming 1-3 days for the sites in an area of a few million square kilometers to take necessary precautions beforehand and thus to reduce the damage of adverse weather conditions, as well as to gain maximum advantage from those favourable for various kinds of the human activity. Recent investigations of convective-scale numerical forecasting using the Advanced Research Weather Research and Forecasting (WRF-ARW) Model have demonstrated the potential for improved forecasting of convective events which are associated with heavy rainfall but still there are lots of deficiencies related to these issues [19-21]. As such, WRF Model (version: 3.1.4) is used to simulate the heavy rainfall events occurred over northwestern part of Bangladesh WRF-ARW model simulates the heavy rainfall events occurred over northwestern part of Bangladesh during, 9, 1 and 17 August, 17 and 26 September of Climate Predictability Tool (CPT) CPT is a software package developed by the International Research Institute for Climate and Society (IRI), Columbia, USA designed for making seasonal climate forecasts. CPT is used to perform Canonical Correlation Analysis (CCA) or Principal Components Regression (PCR) on any pair of data sets for any application. This involves data that represent predictors, and data that represent what is to be predicted, i.e., predictands. Often, the predictor data is set up to occur earlier than the predictand data with each spanning a historical period, so that predictive relationships become detectable and describable, and can be used for real-time forecasts. Both, PCR and CCA techniques in which predictors and predictands are involved in making forecasts using Model Output Statistics (MOS) technique. In both techniques, the prediction rules are determined by analyzing the set of predictors and predictands over a historical period. In climate diagnostics and prediction, often each case of corresponding predictor (s) and predictand (s) come from one year for a specific season or month, so that there are as many cases as there are years. Short histories are less effective in identifying the best prediction rules, since every year contains extraneous or random variations; thus the more years that are available, the greater the likelihood that the consistent and robust relationships outweigh the random behaviors and appear clearly in the analysis results [22].

3 Page METHODOLOGY AND DATA USED For monthly (sub-seasonal) and seasonal prediction recorded rainfall from the rain gauge locations of Dinajpur, Rajshahi, Rangpur, Bogra, Ishurdi and Sayedpur of Bangladesh Meteorological Department (BMD), Bangladesh for the period of 198 to 214 are used. Monthly rainfalls are calculated from the daily recorded rainfall at each station individually. On the basis of that representative rainfall of June, July, August, September and monsoon season (June to September) of northwestern part of Bangladesh is calculated by averaging out of the monthly and seasonal station rainfalls. Similarly, frequency of heavy rainfall (rainfall with the amount of greater than or equal to 44 mm/day) and rainy days (rainfall greater than 1 mm/ day) at each rain gauge station as well as for northwestern part of Bangladesh and their trends are calculated and analyzed. Finally, the time series of rainfall, frequency of heavy rainfall and rainy days during are prepared and used for seasonal prediction. For prediction of monthly and seasonal rainfall over northwestern part of Bangladesh historical monthly global Sea Surface Temperature [NOAA Extended Reconstructed Sea Surface Temperature (ERSST, version 3b)] data (defined as ERSST3b) with the grid resolution 1 x1 are collected through IRI Data Library. SST of the selected coverage area (3 S-3 N and E-27 E) of the Pacific and Indian Oceans (Fig. 1) of the previous month is used as predictors in Principal Component Regression (PCR) mode for the prediction of the frequency of rainy days and heavy rainfall using CPT software package (version ) for, 1, 2 and 3-months lead time. During the experiments, 3 years of data length has been taken to train the model and years has been considered to validate the model. Prediction of the frequency of heavy rainfall and rainy days has been conducted for the latest 1-years of Investigation reveals that the predicted result deviated from the observation with the increment of lead time but the errors are random. The errors are quiet reasonable and prediction skills are quiet high for zero month lead (e.g., prediction of heavy rainfall of June is conducted using the SST of May and so on). As such, -month lead prediction result has been formulated and analysis has been conducted. Mean absolute error (MAS) of the predicted result has also been calculated using equation (1) for the cases of monthly and seasonal prediction so that the predicted result can be utilized by the operational forecasters in a better way. Where, F i denotes the forecast value and O i denotes the observed value of any parameter Fig. 1: Coverage area of monthly ERSST3b data used as the predictors for prediction (colour scale indicates the Perason s correlation between ERSST3b data of May and rainy day of June over northwestern part of Bangladesh during ) Short range prediction for heavy rainfall has also been made using meso-scale non-hydrostatic model of Advanced Research Weather Research and Forecast (WRF-ARW: version 3.1.). Simulated results of related to the mentioned heavy rainfall events using WRF-ARW model have been summarized in section RESULT AND DISCUSSION 3.1 Variability of rainfall and rainy days in monsoon season over southwestern part of Bangladesh The variation of rainfall in June at Bogra lies between -69 to 136%. It varies between -74 to 114%, -64 to 11% and -63 to 1% in July, August and September respectively. As a result, monsoon rainfall fluctuates from - to 6% during the observed period. The deviation of rainfall in June at Dinajpur lies between -73 to 91%. It varies between -68 to 138%, -7 to 141% and -69 to 197% respectively in July, August and September. Consequently, monsoon rainfall fluctuates from -46 to 81% during the observed period. The disparity of rainfall in June at Ishwardi lies between -74 to 83%. It varies between -62 to 112%, -63 to 179% and -6 to 97%

4 Deviation (%) Deviation (%) Page respectively in July, August and September. Accordingly, monsoon rainfall changes from -49 to 4% at Ishwardi during the observed period. The discrepancy of rainfall in June at Rajshahi lies between -73 to 1%. It varies between -68 to 162%, -6 to 96% and -79 to 111% respectively in July, August and September. Therefore, monsoon rainfall changes from -2 to 72% at Rajshahi during the observed period. The difference of rainfall in June at Rangpur lies between -77 to 124%. It varies between -78 to 191%, -74 to 128% and -8 to 11% respectively in July, August and September. Hence, the monsoon rainfall changes from -48 to 73% at Rangpur. The variation of rainfall over northwestern part of Bangladesh in June, July, August and September are -1 to 69%, -6 to 12%, -6 to 92% and -46 to 9% respectively but it is from -37 to 48% during monsoon season. The trends of monsoon rainfall at Bogra, Dinajpur, Ishwardi, Rajshahi and Rangpur are -.8, -1.67, -3.2, and mm/year respectively and the deviations of monsoon rainfall of these rain gauge stations are -.47, -.71, -.31, -.63 and -1.11% /year. As a result, the trends of monsoon rainfall and its deviations over northwestern part of Bangladesh are mm/year and -.6%/year (Fig. 2a). Information related to the number of rainy days over northwestern part of Bangladesh is summarized in Table-1. It is found that the numbers of rainy days are the highest in July at all stations as well as over northwestern part of Bangladesh but it is the lowest in September. The number of the rainy days during monsoon season is the highest at Bogra followed by Rangpur and Dinajpur but it is the lowest at Ishwardi. The number of rainy days depicts negative trend during monsoon season at all rain gauge stations. The trends over northwestern part of Bangladesh during June, July, August and September are +.29, -.77, -.7 and -.84/ year but it is -.14/ year during monsoon season (Fig. 2b). 6 (a) 4 y = -.6x R 2 = (b) y = -.2x R 2 =.6 Fig. 2: Variability of (a) rainfall and (b) rainy days over northwestern part of Bangladesh during monsoon season. Station/ Area Table-1. Statistics related to the number of rainy days over northwestern part of Bangladesh June July August September Monsoon Mean Range Mean Range Mean Range Mean Range Mean Range Bogra Dinajpur Ishwardi Rajshahi Rangpur NW-BD Long-range prediction of the frequency of heavy rainfall and rainy days over northwestern part of Bangladesh during monsoon season Analysis reveals that the frequencies of predicted rainy days are higher than observation in monsoon months. It is higher by 1., 1.2,.6 and.8 respectively in June, July, August and September. It is also higher than observation by. in monsoon during The MAEs of the predicted rainy days in June, July, August and September are 2.1, 2.1, 3.1 and 1.7 respectively. Similarly, it is.3 in monsoon season (Fig. 3a).

5 Frequency of heavy rainfall RMSE of heavy rainfall Frequency of rainy day' RMSE of rainy day' Page (a) Frequency (Obs) Frequency (Prd) MAE (b) Frequency (Obs) Frequency (Prd) MAE Jun Jul Aug Sep Mon Jun Jul Aug Sep Mon Fig. 3: Comparison of observed and predicted frequency of (a) heavy rainfall and (b) rainy days during over northwestern part of Bangladesh during monsoon season. Investigation also reveals that the frequency of predicted heavy rainfall is higher than observation in June, August and September but it is much higher in July. Similarly, the predicted rainfall frequency during monsoon season is quiet higher than observation. The MAEs of the predicted rainfall frequency in June, July, August and September are 3.,.7, 3.6 and 2.9 but it is 9.3 in monsoon season (Fig. 3b). 3.3 Short-range prediction of heavy rainfall over northwestern part of Bangladesh The occurrences of heavy rainfall over northwestern part of Bangladesh are mainly caused by the initiation of cyclonic vortex within monsoon trough over sub-himalayan West Bengal and adjoining northwestern part of Bangladesh. The systems then become significant due to vertical development and intensification over the same places. Fig. 4: Vorticity at 9 hpa for (a) August, (b) 9 August, (c) 1 August, (d) 17 August, (e) 17 September and (f) 26 September 211 Accordingly, the favourable atmospheric conditions of (a) moisture incursion to the system throughout lower troposphere from the Bay of Bengal, (b) increment of moisture content within the system and its surrounding, (c) strengthening of the convergence field, and (d) intensification of CAPE field extended from the Bay of Bengal as observed are the main significant features associated with the heavy rainfall events over northwestern part of Bangladesh.

6 Page 2 From the investigation it is also found that the systems starts to move east/ east-southeastward to northeastern part of India across northwestern part of Bangladesh during their matured stage (Fig. 4). Strong convergence field in the lower troposphere is mainly responsible accumulating moisture from the Bay of Bengal is also accountable for potential amounts of rainfall over northwestern part of Bangladesh. Model simulates the rainfall reasonably well but the zones of maximum rainfalls are quiet far away from the observed maximum rainfall in most of the cases (Fig. ). Fig. : Simulated rainfall during (a) August, (b) 9 August, (c) 1 August, (d) 17 August, (e) 17 September and (f) 26 September CONCLUSION From the investigation the following attributes are extracted: (i) (ii) The trends of monsoon rainfall at Bogra, Dinajpur, Ishwardi, Rajshahi and Rangpur are negative with the magnitude of -.8, -1.67, -3.2, -6.6 and mm/year respectively. The deviations of monsoon rainfall of these stations are also negative. The trends of monsoon rainfall and its deviations over northwestern part of Bangladesh are mm/year and -.6 %/year. The number of rainy days during monsoon season at all stations under this study depict negative trend. The trends rainy days over northwestern part of Bangladesh during June, July, August and September are +.29, -.77, -.7 and -.84/ year but the trend during monsoon season is -.14/ year. (iii) The frequencies of the predicted rainy days over northwestern part of Bangladesh are higher than observation by. during monsoon season of The MAE of the predicted rainy days in monsoon season is.3. (iv) The frequencies of the predicted heavy rainfall are higher than observation in each monsoon months. It is higher by 6.6 in monsoon season. The MAE of the predicted heavy rainfall frequency during monsoon season is 9.3. (v) The occurrences of heavy rainfall over northwestern part of Bangladesh are mainly due to the initiation of cyclonic vortex within monsoon trough over sub-himalayan West Bengal and adjoining northwester part of Bangladesh with other favourable atmospheric conditions. The zones of simulated maximum rainfall associated with heavy rainfall are sometimes quiet far away from the actual.

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