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 & AffiliationCreator: Jiwon Hwang (Postdoctoral Researcher)Affiliation: Ulsan National Institute of Science and Technology (UNIST), South KoreaResearch Field: Atmospheric Science, Data Assimilation, Numerical Modeling.