The goal of this study is to evaluate the ability of the state-of-the-art, higher-resolution, convection-permitting, weather research forecasting (WRF) model in predicting the changes in precipitation regimes which come in response to the seasonal changes in the large-scale environmental forcing. The simulation days are selected in the year 2009 and according to four environmental regimes defined by the daily flow direction (Ragi et al. (IEEE Trans Geosci Remote Sens 55:3466–3474, 2017)) using QuikSCAT scatterometer and the comparison of the same with National Center for Environmental Prediction (NCEP) final analysis (FNL) data. The observations used for analysis are from Indian Meteorological Department, Wyoming, TRMM satellite data, and NCEP-NCAR reanalysis data. This study finds that WRF is capable of reproducing the season-specific differences in the precipitating patterns that reflect the different phases of the monsoon. Extensive comparisons to observations point out that the model simulates reasonably well the temperature and the humidity fields, including their diurnal variability and vertical structure. However, the model-produced precipitation and winds do not compare so well, especially the winds. The simulated large-scale monsoon circulation and rainfall patterns indicate a wet bias in the model rainfall simulations than the TRMM rainfall observations over the selected region. In particular, WRF overestimates the rain. The base variables such as outgoing longwave radiation (OLR), latent and sensible heat fluxes, and convective available potential energy (CAPE) and convective inhibition energy (CIN) are nearly in agreement with the observations. In effect, WRF is skilled to represent the variability in different seasons and its spatial distribution, an important characteristic of the precipitation, especially concerning prediction of the monsoon onset. The disagreements between the observed and the model precipitation and winds can be due to the WRF model physics which generates different dynamics and different precipitating systems and initial conditions.

这项研究的目的是评估最新的，高分辨率的，允许对流的天气研究预报（WRF）模型在预测随季节变化而变化的降水状况方面的能力。在大规模的环境强迫中。在2009年中选择模拟天，并使用QuikSCAT散射仪根据每日流向定义的四种环境状况（Ragi等人（IEEE Trans Geosci Remote Sens 55：3466–3474，2017））进行比较，并进行比较。国家环境预测中心（NCEP）的最终分析（FNL）数据。用于分析的观测数据来自印度气象局，怀俄明州，TRMM卫星数据和NCEP-NCAR再分析数据。这项研究发现，WRF能够再现反映季风不同阶段的降水模式中特定于季节的差异。与观察结果的广泛比较表明，该模型可以很好地模拟温度和湿度场，包括它们的日变化和垂直结构。但是，模型产生的降水和风之间的比较不太好，尤其是风。模拟的大季风环流和降雨模式表明，在模型降雨模拟中，与在选定区域的TRMM降雨观测值相比，存在湿偏。特别是，WRF高估了降雨。基本变量，例如输出长波辐射（OLR），潜热通量和显热通量，对流有效势能（CAPE）和对流抑制能（CIN）与观测值基本一致。实际上，WRF熟练地代表了不同季节的变异性及其空间分布，这是降水的重要特征，尤其是与季风爆发的预测有关。WRF模型物理学产生了不同的动力学，不同的降水系统和初始条件，这是由于观测到的和模型的降水与风之间存在分歧。