To enhance the operational efficiency of the CMA-BJ3.0 regional numerical model and address the issue of short-term precipitation overforecasting caused by assimilating estimated saturated water vapor, this study investigates the assimilation of radar reflectivity mosaic data by optimizing the configuration of retrieved water vapor in the indirect assimilation scheme. A 1D (one-dimensional) Bayesian method was employed to retrieve and constrain water vapor from reflectivity observations, generating retrieved water vapor for assimilation to mitigate overforecasting biases. A case study of precipitation on 1 August 2022 was analyzed, with particular focus on comparing the innovation vector statistics, spatial patterns of analysis increments, and physical mechanisms underlying forecast differences across multiple data assimilation configurations. Results showed that an observation-background (O-B) statistical distribution closer to a Gaussian unbiased state indicated a better balance between observations and the background field. The optimized scheme corrected systematic positive biases in water vapor, curbed excessive increments, and effectively resolved the overforecasting issue by refining the initial water vapor field. Batch experiments quantitatively demonstrated that assimilating 1D Bayesian-retrieved water vapor significantly improved precipitation forecast scores, particularly for higher magnitudes (≥25.0 mm/3 h), and reduced the over-forecast within the first 6 h. While the study focused on improving short-term precipitation accuracy without considering hydrometeor impacts or convective dynamics, the 1D Bayesian method, despite its background-dependency, proved effective in correcting water vapor biases, making it a promising assimilation scheme.

A wind profiler radar detects fine spatiotemporal resolution dynamical information, enabling the capture of meso- and micro-scale systems. Experience gained from observing system experiments (OSEs) studies confirms that reasonable profiler assimilation techniques can achieve improved short-term forecasts. This study further applies the adjoint-based forecast sensitivity to observation (FSO) method to investigate the quantitative impact of a profiler data assimilation strategy on short-term forecasts, and the results are consistent with those obtained from OSEs, further demonstrating that FSO and OSEs can be used to evaluate the effect of data assimilation techniques from different perspectives. Considering the unique advantage that the FSO can quantify the interactions between various observing systems and the impact on improving the model forecasts according to specific needs without costly additional calculations, we further diagnose in detail the observation impacts from multiple perspectives, including the observation platform, observation variables, and spatial distribution. And the results show that dynamical variables are more significant in improving forecasts compared to the other observed variables. Meanwhile, the dense profiler observations resulted in a more significant impact when radiosonde observations were not detected. The upper-level single winds monitored by profiler radars play a more important role in improving forecast skill. The FSO method measures the impact of an individual observing system, which can be used to enrich the evaluation of data assimilation schemes, efficiently calculate the impacts of multisource observations, and contribute to future development in adaptive observation, observation quality control, and observation error optimization.

To address the nonlinear amplification of numerical weather prediction (NWP) errors and the difficult trade-off between coverage and sharpness in short-term offshore wind power forecasting, this paper proposes PRWind, a physics-guided framework for short-term probabilistic forecasting. Built upon a Transformer encoder, the framework integrates horizon-adaptively weighted power-curve, persistence, and wind-speed cubic-law baselines; propagates wind-speed uncertainty into a power distribution through a probabilistic wind-speed distribution with Monte Carlo integration; and employs spatial graph convolution to model inter-turbine correlations, together with online bias correction and segmented post hoc interval calibration. The framework is validated on two offshore wind farms in coastal China (8350 kW and 10,000 kW units), with all data analyzed at hourly resolution and forecasts issued for 23 hourly steps ahead. For point forecasting, the NMAE reaches 8.74% and 8.24%, with MAE reductions of 19.1% (95% CI: 18.1–19.9%) at Wind Farm A and 7.8% (95% CI: 7.3–8.4%) at Wind Farm B relative to the strongest baselines, both statistically significant under the Diebold–Mariano test (p < 0.001). For probabilistic forecasting, the PICP remains stable at 0.89, balancing coverage and sharpness. The error of PRWind accumulates markedly more slowly with the forecast horizon than that of the baselines, and ablation studies confirm the synergistic effect of the modules. Trained and evaluated independently on the two wind farms, PRWind performs consistently, demonstrating cross-wind-farm stability (rather than zero-shot generalization) conferred by physical guidance, and offering significant engineering value for intra-day and day-ahead offshore wind power dispatch.