Assimilation of the Rain Gauge Measurements Using Particle Filter

Abstract

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.

Long‐Term High‐Resolution Gauge Adjusted Satellite Rainfall Product Over India

“…Kumar et al (2021) also presented the better performance of the MLE based method over direct method. Few methods (like Particle Filter) exist that are applicable for non-linear and non-Gaussian distribution (Kumar, 2020), but operational implementation of such methods are not feasible due to large computational requirements when emphasizing near real-time products. The large computational requirements may be mitigated in the future environment.…”

Assimilation of the rain-gauges measurements using Particle Filter

“…Additionally, due to limitations of the realistic representation of the nonlinear model physics as a tangent linear model, numerical models are not able to assimilate rainfall precisely using the four-dimensional variational (4D-Var) data assimilation method. Most of the previous studies based on variational method and ensemble Kalman filter (EnKF) assume/convert non-Gaussian distribution of rainfall to Gaussian error statistics which lead to suboptimal analysis (e.g., Posselt et al, 2014;Posselt & Bishop, 2012;Van Leeuwen, 2009, 2010. One well-known advantage of the particle filter over EnKF is that the particle filter can works for non-Gaussian distribution (Kumar & Shukla, 2019;Mattern et al, 2013;Ratheesh et al, 2016).…”