1. Computation of skill of a mesoscale model in forecasting thunderstorm using radar reflectivity
- Author
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Someshwar Das, Devajyoti Dutta, and Abhijit Sarkar
- Subjects
010504 meteorology & atmospheric sciences ,Meteorology ,Computation ,Mesoscale meteorology ,Weather forecasting ,010502 geochemistry & geophysics ,computer.software_genre ,01 natural sciences ,law.invention ,Data assimilation ,law ,Weather Research and Forecasting Model ,Thunderstorm ,Initial value problem ,Weather radar ,Computers in Earth Sciences ,Statistics, Probability and Uncertainty ,General Agricultural and Biological Sciences ,computer ,0105 earth and related environmental sciences ,General Environmental Science ,Mathematics - Abstract
An attempt is made to determine skill of a mesoscale model through calculation of distance, intensity and time errors with respect to observed maximum reflectivity data (maxdBZ) of Doppler Weather Radar (DWR). A new approach is adopted to calculate distance, intensity and time errors of the model simulated thunderstorm events. Five cases of pre-monsoon thunderstorms over the eastern part of India are simulated by Advanced Research WRF (ARW) model and the model simulated maxdBZ is validated against DWR data. In all the cases the model is run from two different initial conditions. The global model forecast of National Centre for Medium Range Weather Forecasting (NCMRWF), India is directly used as the initial condition for the control run (Exp1) while in the other run (Exp2) initial condition is prepared by assimilating local synoptic data with 6 h model forecast taken as background. The spatial distributions of model simulated and observed maxdBZ show that the newly adopted method is capable of capturing the model skill through computation of distance error. This newly adopted method is used to investigate whether the model simulation with data assimilation is more skillful in determining distance, intensity and time errors. In all the cases, except one, experiments with data assimilations have shown comparatively less distance error than control experiments in the initial hours of the days of the events. Intensity errors of experiments with data assimilations are marginally better than those of control experiment for two cases. Experiments with data assimilation show less time errors than that of control experiment in some cases but it is not always true.
- Published
- 2018