A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in the Department of Physics, Khulna University of. Mahbub Alam, Professor, Department of Physics, Khulna University of Engineering and Technology, Khulna, for his kind guidance and supervision and for his constant encouragement throughout the research work.
Nomenclature
Abstract
Introduction
Pre Monsoon season
- Pre-monsoon rainfall
- Pre-monsoon wind
Due to the country's location in the tropical monsoon region, the amount of rainfall is very high. Average temperatures in July vary from around 27°C in the south-east to 29°C in the north-west part of the country.
Monsoon season
Of these, it is the pre-monsoon season when most of the severe local thunderstorms occur in different parts of Bangladesh at frequent intervals. The Bay of Bengal coastline is 716 km south of the country.
Post Monsoon season
With the arrival of the monsoon, the extreme temperatures of summer drop significantly across the country. Although the average temperature hardly drops by one degree, the maximum temperature drops by 2-5oC over most of the country except the coastal belts where the drop is by 5-6oC (WMO/UNDP/BGD.
Winter season
Weather Research & Forecasting Model
- Microphysics Schemes in WRF-ARW Model
- Kessler Scheme
- Lin et al. Scheme
- WRF Single Moment 3-class (WSM3) microphysics Scheme
- WRF Single Moment 5-class (WSM5) microphysics Scheme
- Ferrier Scheme
- WRF Single-moment 6-class microphysics Scheme (WSM6)
- Thompson Scheme
- WRF double-moment 6-class microphysics Scheme (WDM6)
The memory, i.e., the size of the fourth dimension in these arrays, is allocated depending on the needs of the selected scheme, and the species attachment is also applied to all those required by the microphysics option. The WRF-single-moment-6-class (WSM6) microphysics scheme has been one of the microphysics process options in the WRF model since August 2004.
Cumulus Parameterization
- Kain-Fritsch (KF) Scheme
- Betts-Miller-Janjic (BMJ) Scheme
In addition, the prognostic number concentrations of cloud and rainwater, together with CCN, are taken into account in the WDM6 scheme. The construction of the reference profiles and the specification of the relaxation time scale are two.
Planetary Boundary Layer (PBL)
- Yonsei University (YSU) scheme
The YSU scheme is a bulk scheme that expresses nonlocal mixing by convective large eddies. Nonlocal mixing is achieved by adding a nonlocal gradient fitting term to the local gradient.
Map Projection
- Mercator projection
Yonsei University (YSU) PBL is the next-generation MRF, non-local-K scheme with clear additive layer and parabolic K profile in the unstable mixed layer. On top of the PBL, the YSU scheme uses a clear treatment of the mantle layer, which is proportional to the surface layer flux (Shin and Hong 2011; As a result of these criticisms, modern atlases no longer use the Mercator projection for world maps or for areas far from the equator, preferring other cylindrical projections or projection forms with equal surface area.
The Mercator projection is still commonly used for areas near the equator, but where distortion is minimal.
Arakawa Staggered C-grids
Methodology
Model Setup
Model Domain and Configuration
Tropical Rainfall Measuring Mission (TRMM)- 3B42RT daily rainfall datasets were downloaded from their website (http://lake.nascom. nasa.gov) while daily rain gauge data were collected from 33 stations of Bangladesh Meteorological Department (BMD). all over Bangladesh. For this reason, we have added 8 more points in the Bangladesh map to collect rainfall data. We extracted convective and non-convective rainfall data from WRF Model output at 33 BMD station points with additional 8 points in the northeastern and southwestern regions of Bangladesh.
We also extracted TRMM rainfall data from the above 41 points during the monsoon season of 2010-2014. Txt format data from ctl file of WRF model output was found using Grid Analysis and Display System (GrADS). This txt data was converted into Microsoft Excel format and then drawn using SURFER software.
The RMSE and MAE of rainfall were calculated for 107 days forecast using Microsoft Excel and then plotted using SURFER Software during 2010-2014.
Root mean square error (RMSE)
Hourly RMSE and MAE of forecasted rainfall are also plotted using the same procedure for 2014. The CC between hourly and 107-day forecasted rainfall is obtained using Microsoft Excel and then plotted using SURFER software. The MAE measures the average size of the errors in a set of forecasts, regardless of their direction.
The MAE is the average of the absolute values of the differences between the prediction and the corresponding observation in the verification sample. In statistics, the mean absolute error is a quantity used to measure how close predictions or predictions are to the final results. As the name suggests, the mean absolute error is the average of the absolute errors where is the prediction and is the true value.
The mean absolute error is a common measure of forecast error in time series analysis, where the terms "mean absolute deviation" is sometimes used in confusion with the more standard definition of mean absolute deviation.
Coefficient of Correlation (CC)
The MAE is a linear score, meaning that all the individual differences are weighted equally in the average. The total variation of Y is defined as ( Y Y )2; that is, the sum of the squares of the deviations of the values of Y from the mean Y. Y 0 1 and a measure of the spread around the regression line of Y on X is provided by the quantity.
In this study, the Weather Research and Forecast model (WRF-ARW V3.5.1) was used to simulate the pre-monsoon rainfall during 2010–2014 for all of Bangladesh. The model was also run for 72 h with initial conditions every day at 0000 UTC for 94 days to forecast 24, 48, and 72 h advance rainfall in the premonsoon season of 2014. In this study, convective and nonconvective rainfall were simulated at a 3-h interval, and then were daily and monthly data on the total amount of precipitation for hours and 107 days in the studied period.
We have compared these data with the observed precipitation at 33 meteorological stations of BMD and TRMM precipitation.
Pre-monsoon Rainfall distribution 2010-2014
- Distribution of Observed, TRMM and Model Simulated rainfall for 2010
- Distribution of Observed, TRMM and Model Simulated rainfall for March 2010 From the distribution of observed rainfall for March 2010 (Fig 4.1.1a), the maximum rainfall
- Distribution of Observed, TRMM and Model Simulated rainfall for 2011
- Distribution of Observed, TRMM and Model Simulated rainfall for 2012
- Distribution of Observed, TRMM and Model Simulated rainfall for April 2012 From the distribution of observed rainfall for April 2012 (Fig.4.1.3d), the maximum rainfall
- Distribution of Observed, TRMM and Model Simulated rainfall for 2013
- Distribution of Observed, TRMM and Model Simulated rainfall for 2014
- RMSE of Rainfall for March 2010-2013
- RMSE of Rainfall for May 2010-2013
- Model simulated RMSE of rainfall for March 2014
- RMSE of Rainfall for April 2014
- RMSE of Rainfall for May 2014
The distribution of TRMM rainfall for May 2010 (Figure 4.1.1h) shows that the maximum rainfall is recorded in the northeastern and south-eastern parts of Bangladesh. From the 107 days of forecast rainfall for March 2011 (Fig.4.1.2c), the maximum rainfall is simulated in the northwestern region near Bogra station, which is more than 120 mm. The minimum rainfall is simulated in the southern and southeastern regions, which is about 20-45 mm.
From the distribution of 107 days of rainfall forecast for March 2013 (Fig. 4.1.4c), the maximum rainfall simulated in the central region at Chandpur station is more than 30 mm. But in the southeast region, the maximum rainfall is simulated at Sitakunda, which is approximately 12 mm. Minimum rainfall has also been found in the southeast region at Rangamati station which is about 1 mm.
From this figure, the minimum rainfall is simulated in the northwestern region at Bogra station which is close to about 130 mm. The RMSE of rainfall is found maximum in the northeastern region at Sylhet station and its value is 40 mm. The RMSE of rainfall is found to be maximum in the western, northeastern and southern regions at Chuadanga, Sylhet and M.
Mean Absolute Error (MAE)
- MAE of Rainfall for March 2010-2013
- MAE of Rainfall for April 2010-2013
- MAE of Rainfall for March 2014
- MAE of Rainfall for April 2014
- MAE of Rainfall for May 2014
The highest value of MAE of rainfall was also found in the south-eastern region i.e. the minimum value of MAE of rainfall is also found in the western and northwestern part, ie the daily MAE of rainfall is found maximum in the northeastern region at Sylhet is 2.6 mm.
The daily MAE rainfall maximum is found in the central region of Faridpur and its value is 70 mm. The forecast of the MAE of precipitation for 107 days for March 2014 (Fig. 4.3.5d) is minimally simulated in the southeastern region, i.e. the daily MAE of precipitation is found maximum in the northeastern region at Sylhet and amounts to 3.6 mm.
The daily MAE of precipitation for the 107-day forecast for May 2014 (Figure 4.3.6d) is the simulated minimum in the southeastern region, i.e.
Correlation coefficients (CC)
- Distribution of Correlation Coefficients (CC) between the simulated and observed rainfall for March 2014
- Distribution of Correlation coefficients between the simulated and observed rainfall for April 2014
- Distribution of Correlation coefficients between the simulated and observed rainfall for May 2014
CC is achieved maximum in the northern, western, central, southwestern and southeastern regions. Minimum CC is found in the north-west, north-east and south-west regions of the country. Distribution of CC between observed and 72 h predicted rainfall for April 2014 has been presented in fig.
The minimum CC is found in the northwestern, northeastern, southern, southwestern and southeastern regions of the country. The minimum CC is found in the central to northern, northeastern, southwestern and southeastern regions of the country. The minimum CC is found in central to northern, western, southern and southeastern regions of Bangladesh.
The distribution of CC between observed and 72-hour forecast rainfall for May 2014 is shown in Figure 2.
Conclusions
The 24-, 48- and 72-hour forecast rainfall is similar to that observed during the pre-monsoon season in the central, southwestern and northeastern regions. The rainfall forecast for 107 days will be maximum in central to northeastern and southeastern regions and minimum in western, southwestern and northwestern regions of the country. The RMSE is found minimal in the northwestern, western and southwestern regions for 24, 48 and 72 hours of predictable rainfall and their values are very low for March, April and May.
The patterns of RMSE of precipitation for 24, 48 and 72 hours lead time and forecast of 107 days during the month of March, April and May are almost similar, but the value of RMSE for forecast of 107 days is simulated much higher than that of 24, 48 and 72 hours and for the month of March and April 2014, rainfall and maximum value of RMSE have been observed in northeastern, central and southern regions. The minimum value of MAE of March, April and May precipitation for 24, 48 and 72 hour lead time prediction is within 0.0 and 0-5 mm respectively and the maximum value is found in the central and northeastern region of Bangladesh. The maximum CC has been shown in the southern and southeastern regions and the minimum CC has been found in the northern, northwestern and southwestern regions of the country.
Fukushima, A., 2012: Determination of premonsoon period and annual variations of premonsoon rainfall in the Himalayan foothills, J.
