Open AccessAssimilation of the Rain Gauge Measurements Using Particle Filter
Author(s) -
Kumar Prashant
Publication year - 2020
Publication title -
earth and space science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.843
H-Index - 23
ISSN - 2333-5084
DOI - 10.1029/2020ea001212
Subject(s) - weather research and forecasting model , numerical weather prediction , meteorology , data assimilation , environmental science , ensemble kalman filter , resampling , particle filter , gaussian , atmospheric sciences , mathematics , physics , kalman filter , statistics , extended kalman filter , quantum mechanics
The well‐recognized constraint of nonlinear and non‐Gaussian distribution of rainfall observation limits its assimilation in the high‐dimensional numerical weather prediction (NWP) model. In this study, rainfall observed from Indian Meteorological Department (IMD) rain gauges over Indian landmass is assimilated in the Weather Research and Forecasting (WRF) model using particle filter. In the framework of imperfect weather models, particles (or ensembles) for rainfall predictions are created with various combinations of model physics (viz., cumulus parameterization, microphysics and planetary boundary layer schemes). The multiple hypotheses are used to determine the weights for different particles, and this is the step where IMD rainfall data are used for assimilation. Further, a resampling step is performed to generate new particles from high weight particles using stochastic kinetic‐energy backscatter scheme (SKEBS) method in which dynamical variables are perturbed into the model physics. Results, based on rainfall verification scores, suggest that the assimilation of the rainfall using particle filter could improve the prediction of rainfall over CNT runs (unweighted particles; without assimilation). Moreover, surface and vertical profile of temperature, water vapor mixing ratio (WVMR), and wind speed are also improved in 24‐hr forecasts.