Dataset for "Improving Heavy Precipitation Forecasts via Phase-Aware Bias Correction of All-Sky Infrared Radiances Using Cloud-Top Temperature"

Abstract This repository provides the Python implementation and sample datasets for the Phase-Aware Bias Correction method, as described in the manuscript: "Improving Heavy Precipitation Forecasts via Phase-Aware Bias Correction of All-Sky Infrared Radiances Using Cloud-Top Temperature" (submitted to Journal of Advances in Modeling Earth Systems (JAMES)). Key Methodology The provided code addresses systematic biases in all-sky infrared (IR) radiances within numerical weather prediction (NWP) systems (e.g., WRF/WRFDA). Unlike conventional methods, this approach utilizes Cloud-Top Temperature (CTT) to differentiate between: Cloud Formation Errors: Biases originating from misplaced cloud systems. Cloud Property Errors: Biases stemming from incorrect cloud microphysics or optical properties. The correction is performed using LOWESS (Locally Weighted Scatterplot Smoothing) to ensure a robust and physically consistent bias estimation across various weather scenarios, including extreme precipitation and tropical cyclones. bc_by_ctt.py: Core algorithm for phase-aware cloud top temperature (CTT) bias estimation and correction. bc_by_ca.py: Core algorithm for cloud amount(Ca) bias estimation and correction. requirements.txt: List of necessary Python libraries (xarray, wrf-python, etc.). sample: data/diags_himawari-8-ahi_2026032212.nc: Sample diagnostic file from WRFDA containing observed and background brightness temperatures for Himawari-8 AHI. data/wrfvar_input_d01_2026032212.nc: Sample WRF output file used to extract model-simulated Cloud-Top Temperature (CTT). data/GK-2A_CTPS_EA_2026032212.nc: Sample GK-2A (Cheollian-2A) Cloud Top Product used as the observational reference for CTT. data/latlon_sample.txt: A text file containing the latitude and longitude information for the 784x784 model grid. Contact & Affiliation Creator: Jiwon Hwang (Postdoctoral Researcher) Affiliation: Ulsan National Institute of Science and Technology (UNIST), South Korea Research Field: Atmospheric Science, Data Assimilation, Numerical Modeling.