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When there is an obvious large‐scale bias between a regional simulation and its driving global analysis, the regional model will provide inaccurate background information for radar data assimilation, which may eventually yield location errors associated with predicted precipitation. A case study of a squall line over the Yangtze‐Huaihe river basin presents such a situation. In this regard, we propose an approach to incorporate a large‐scale constraint into radar data assimilation to mitigate the effects of large‐scale bias on analysis and forecast results, in which global analysis data are introduced into the regional model using the spectral nudging technique to improve the quality of the first guess and background error statistics in radar data assimilation. A series of experiments are conducted with the Weather Research and Forecasting model and its three‐dimensional variational system to investigate the effectiveness of the proposed approach to introduce a large‐scale constraint into radar data assimilation. The experimental results demonstrate that the introduction of global analysis data can effectively correct the large‐scale bias and significantly improve the forecast skill of large‐scale patterns and convection initiation. The background error covariance (BE) obtained with the large‐scale constraint plays an important role in improving the assimilation effect. The length scales of BE are reduced after the large‐scale bias is removed, which represents a partial solution to the overestimation of BE reported in previous studies. In addition, applying a larger nudging wave number to radar data assimilation domain is not appropriate because the use of a larger wave number can negatively impact the three‐dimensional variational analysis.

Doppler wind lidar has played an important role in alerting low-level wind shear (LLW). However, these high-resolution observations are underused in the model-based analysis and forecasting of LLW. In this regard, we employed the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-VAR) system to investigate the impact of lidar data assimilation (DA) on LLW simulations. Eight experiments (including six assimilation experiments) were designed for an LLW process as reported by pilots, in which different assimilation intervals, assimilation timespans, and model vertical resolutions were examined. Verified against observations from Doppler wind lidar and an automated weather observing system (AWOS), the introduction of lidar data is helpful for describing the LLW event, which can represent the temporal and spatial features of LLW, whereas experiments without lidar DA have no ability to capture LLW. While lidar DA has an obviously positive role in simulating LLW in the 10–20 min after the assimilation time, this advantage cannot be maintained over a longer time. Therefore, a smaller assimilation interval is favorable for improving the simulated effect of LLW. In addition, increasing the vertical resolution does not evidently improve the experimental results, either with or without assimilation.

Flow‐dependent errors in tropical analyses and short‐range forecasts are analysed using global observing‐system simulation experiments assimilating only temperature, only winds, and both data types using the ensemble Kalman filter (EnKF) Data Assimilation Research Testbed (DART) and a perfect model framework. The idealised, homogeneous observation network provides profiles of wind and temperature data from the nature run for January 2018 using the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) forced by the observed sea‐surface temperature. The results show that the assimilation of abundant wind observations in a perfect model makes the temperature data in the Tropics largely uninformative. Furthermore, the assimilation of wind data reduces the background errors in specific humidity twice as much as the assimilation of temperature observations. In all experiments, the largest analysis uncertainties and the largest short‐term forecast errors are found in regions of strong vertical and longitudinal gradients in the background wind, especially in the upper troposphere and lower stratosphere over the Indian Ocean and Maritime Continent. The horizontal error correlation scales are on average short throughout the troposphere, just several hundred km. The correlation scales of the wind variables in precipitating regions are half of those in nonprecipitating regions. In precipitating regions, the correlations are elongated vertically, especially for the wind variables. Strong positive cross‐correlations between temperature and specific humidity in the precipitating regions are explained using the Clausius–Clapeyron equation.