Different from the aircraft TC reconnaissance flight missions before, a tropical cyclone (TC) field campaign project with a Doppler radar equipped on an airship that could hang over on the top of a TC (about 20 km) has been recently carried out in China. To understand the impact of airship-borne radar radial wind observations in TC forecasting, this work conducted quick observation simulation system experiments (QuickOSSE) by assimilating simulated airship-borne Doppler radar radial winds with an Ensemble Kalman Filter (EnKF) algorithm. The results show that airship-borne radial winds assimilation reproduces the forecasted track and minimum sea level pressure of the nature run. The forecast of dynamic and thermodynamic TC structures, such as tangential wind, secondary circulation, and warm core, are also improved. In addition, two determining factors, the radar depression angle (D-ang) and the distance from the airship to the TC center (DIS), are found to primarily affect the forecast of the TC track and intensity, respectively. Benefiting from a larger horizontal coverage, observations under a smaller D-ang improved the track more significantly. Meanwhile, the intensity forecast error with a DIS around the radius of the maximum wind is the smallest among several sensitive experiments, which may because the peak-velocity winds representing the TC’s intensity could be observed by radar. The results are expected to help establish an observational strategy for upcoming airship flight missions in practice.

While research on forecasting tropical cyclone (TC) intensity via statistical methods is relatively abundant, studies on TC track forecasting using such methods remain scarce. Existing statistical TC track forecasts primarily rely on time‐series factors of TC motion; however, their efficacy diminishes significantly during TC track deflections, largely because most statistical methods lack physically meaningful factors governing TC motion. To address this, this study focuses on short‐term TC track forecasting by examining two physically meaningful factors—ventilation flow and steering flow—as predictors. It clarifies ventilation flow calculation procedures, develops a forecasting model integrating these flows and other factors, and uses 1990–2021 JTWC, ERA5, and SHIPS data sets. Analyzing their characteristics across varying radii, vertical layers, and calculation methods (with/without subtracting TC translation speed), performance is evaluated via linear regression and 6 artificial intelligence (AI) models. Results show ventilation flow (without subtracting translation speed) aligns better with actual TC movement; mid‐upper and whole‐layer average airflow correlates more strongly with TC motion. Incorporating steering/ventilation flow into time‐series and SHIPS factors improves forecasts. Among AI models, XGB performs best though some exhibit overfitting. This work provides a new indicator for short‐term TC track forecasting, clarifies ventilation flow's utility, and suggests integrating it into large AI meteorological models to enhance TC track simulation.

The selection of appropriate physical parameterization schemes (PPSs) is crucial for improving the simulation accuracy of numerical weather prediction models. This study evaluates the sensitivity of landfalling Tropical Cyclones (TCs), to PPSs in the Weather Research and Forecasting model. We are focusing on the TCs of Lekima (2019), Hagupit (2020), and In‐Fa (2021). For each of the three TC cases, 42 sensitivity experiments are conducted, resulting in a total of 126 simulations across three categories, which are planetary boundary layer parameterization scheme (PBLPS), cumulus convection parameterization scheme (CCPS), and cloud microphysics parameterization scheme (CMPS). Among the three categories, the PBLPS category achieves the best simulation on TC intensity, including landfall intensity. Both of TC track and intensity are the most sensitive to CMPS, and CMPS impacts TC track more during the post‐landfall stage than pre‐landfall stage. TC track is insensitive to PBLPS, especially during the post‐landfall stage, because of the influence of large‐scale forcing. PBLPS category has the smallest average track error. Furthermore, both landfall position and intensity have local sensitive to PBLPS. On average within the categories, the CMPS category shows the best performance in the simulation of TCs landfall position, while the PBLPS category has the minimum error in the simulation of TCs landfall intensity. Finally, the optimal schemes for TC track and intensity can respectively provide reference for the simulation of landfalling TCs in the future.