The representation of precipitation microphysics remains a key uncertainty in numerical simulations of tropical maritime convection. This study addresses this challenge by using an observation-informed approach to diagnose and correct microphysics biases in a high-resolution Weather Research and Forecasting (WRF) model simulation. The case selected is a squall line observed on 29 July 2022 during the joint Prediction of Rainfall Extremes Campaign in the Pacific (PRECIP) / Taiwan-Area Heavy Rain Observation and Prediction Experiment (TAHOPE) / Japanese Tropical cyclones-Pacific Asian Research Campaign for Improvement of Intensity estimations/forecasts (T-PARCII) field campaign. A rigorous model-to-observation comparison framework is implemented. Simulated polarimetric radar data are generated using the Cloud-Resolving Model Radar Simulator (CR-SIM) and the APAR Observing Simulation, Processing, and Research Environment (AOSPRE) forward-operator pipeline. These simulated data are subjected to the same quality control and filtering procedures as observations from the Colorado State University Sea-Going Polarimetric (SEA-POL) C-band radar. This framework enables a fair comparison between a research model and observations.
The control simulation using the default Morrison two-moment microphysics scheme reveals a significant bias: an overestimation of raindrop size, evidenced by differential reflectivity values exceeding 4.5 dB where observations showed only up to 2.5 dB. This discrepancy stems from an underestimation of raindrop number concentration, a known sensitivity linked to the scheme's parameterization of raindrop breakup. Targeted sensitivity experiments demonstrate that increasing breakup efficiency and limiting the maximum allowable raindrop size substantially correct this bias, producing polarimetric signatures in much closer agreement with observations. This study highlights the critical need for applying consistent observational filters to simulated radar data for robust model microphysics evaluation, while also providing a refined microphysics parameter set that could offer guidance for improving quantitative precipitation forecasts in tropical maritime environments.
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