Regional climate model is a scientific tool to monitor present climate change and to provide reliable estimation of future climate projection. In this study, the Regional Climate Model version 4.7 (RegCM4.7) developed by International Centre for Theoretical Physics (ICTP) has been adopted to simulate rainfall scenario of Bangladesh. The study examines model performance of rainfall simulation through the period of 1991-2018 with ERA-Interim75 data of 75 km horizontal resolution as lateral boundaries, downscaled at 25km resolution using the mixed convective precipitation scheme; MIT-Emanuel scheme over land and Grell scheme with Fritsch-Chappell closure over ocean. The simulated rainfall has been compared both at spatial and temporal scales (monthly, seasonal and annual) with observed data collected from Bangladesh Meteorological Department (BMD) and Climate Research Unit (CRU). Simulated annual rainfall showed that the model overestimated in most of the years. Overestimation has been observed in the monsoon and underestimation in pre-monsoon and post-monsoon seasons. Spatial distribution of simulated rainfall depicts overestimation in the southeast coastal region and underestimation in the northwest and northeast border regions of Bangladesh. Better estimation of rainfall has been found in the central and eastern parts of the country. The simulated annual rainfall has been validated through the Linear Scaling bias correction method for the years of 2016, 2017, and 2018 considering the rainfall of 1991-2015 as reference. The bias correction with linear scaling method gives fairly satisfactory results and it can be considered in the future projection of rainfall over Bangladesh.
In this study an attempt has been made to inspect the forecasting of thunderstorms based on two cases (1st case: 17th May, 2019 and 2nd case: 31st March, 2019) over Dhaka using WRF Model. The model is run for 72 hours with 03 nested domain of 09 km, 03 km and 01 km horizontal resolutions using 0.25º X 0.25º six hourly global data assimilation system. For model simulation, Milbrandt-Yau Double-Moment 7-class scheme (9) has been used as microphysics scheme in this study. The model performance is evaluated by calculating hourly instability indices (VTI, TTI, KI, CTI, MCAPE, MCIN, BRN, LI, SI, SWI) value and have been compared with the threshold value of indices. Different meteorological parameters such as MSLP, temperature, winds at upper (300 hPa) and lower (925 hPa) level, relative humidity along with vertical cross section are also studied by the model and compared with the favorable conditions for forming of thunderstorms. Area rage rainfall (hourly) value has been also calculated and compared with indices value to comprehend the nature of thunderstorms. Observing the indices value it is seen that all indices value increase sharply 5-6 hours before of thunderstorm occurring and MCAPE is giving more reliable result. Moreover, this study shows that inner two domains (3 and 1 km resolution) are giving better results than outer one and which indices are more probable in forecasting of thunderstorm for our country as well as giving less Root Mean square Error. From the simulated and validated results, it can be concluded that the model performance of instability indices can be used as forecasting of thunderstorms over Bangladesh.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.