Abstract
Profile measurements play a crucial role in operational weather forecasting across diverse scales and latitudes. However, assimilating tropospheric wind and temperature profiles remains a challenging endeavor. This study assesses the influence of profile measurements on numerical weather prediction (NWP) using the weather research and forecasting (WRF) model coupled to the parallel data assimilation framework (PDAF) system. Utilizing the local error-subspace transform Kalman filter (LESTKF), observational temperature and wind profiles generated by WRF are assimilated into an idealized tropical cyclone. The coupled WRF-PDAF system is adopted to carry out the twin experiments, which employ varying profile densities and localization distances. The results reveal that high-resolution observations yield significant forecast improvements compared to coarser-resolution data. A cost-effective balance between observation density and benefit is further explored through the idealized tropical cyclone case. According to diminishing marginal utility and increasing marginal costs, the optimal observation densities for U and V are found around 26–27%. This may be useful information to the meteorological agencies and researchers.
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