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.

中文翻译：

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使用粒子滤波器对雨量计进行同化

公认的降雨观测的非线性和非高斯分布约束限制了其在高维数值天气预报（NWP）模型中的同化作用。在这项研究中，印度气象局（IMD）雨量计在印度陆地上观测到的降雨在使用颗粒过滤器的天气研究和预报（WRF）模型中被同化。在不完善的天气模型的框架中，使用各种模型物理学组合（即，积云参数化，微观物理学和行星边界层方案）创建了用于降雨预测的粒子（或集合体）。多个假设用于确定不同粒子的权重，这是将IMD降雨数据用于同化的步骤。进一步，使用随机动能反向散射方案（SKEBS）方法执行重采样步骤，以从高重量粒子中生成新粒子，其中，动力学变量被扰乱到模型物理中。根据降雨验证分数得出的结果表明，使用粒子过滤器对降雨进行同化可以改善对CNT运行（未加权颗粒；不进行同化）的降雨预测。此外，在24小时预报中，温度和水汽混合比（WVMR）和风速的表面和垂直剖面也得到了改善。这表明使用粒子过滤器对降雨进行同化可以改善对CNT运行（未加权粒子；不进行同化）的降雨预测。此外，在24小时预报中，温度和水汽混合比（WVMR）和风速的表面和垂直剖面也得到了改善。这表明使用粒子过滤器对降雨进行同化可以改善对CNT运行（未加权粒子；不进行同化）的降雨预测。此外，在24小时预报中，温度和水汽混合比（WVMR）和风速的表面和垂直剖面也得到了改善。