TRAMS: SVD-3DEnVar Simulations, Observation Data, and Plotting Scripts

This dataset contains the key results from two sets of assimilation experiments conducted with the Tropical Regional Atmospheric Model System (TRAMS) v3.0 as described in the paper "Improvement of the Computational Efficiency in SVD-3DEnVar Data Assimilation Scheme and Its Preliminary Application to the TRAMS Model." The data is organized into three directories: osse, sswa, and plot, each containing files relevant to specific aspects of the research.The osse directory contains data from the Observing System Simulation Experiment focused on Typhoon Khanun (2023). This includes forecast outputs from the control experiment initialized at 12:00 UTC on July 28, 2023, and from the assimilation experiment valid at 00:00 UTC on July 29, 2023. Initial condition files for the assimilation cycle show both the background state and the analysis state at 00:00 UTC on July 29, 2023. Observational data includes both the extracted pseudo-observations used in the assimilation and a file containing the full observation set valid at 00:00 UTC on July 29, 2023.The sswa directory contains results from the real-data assimilation experiments using multi-source satellite-derived sea surface wind fields for Typhoon Mocha (2024). The control forecast was initialized at 12:00 UTC on September 5, 2024, while assimilation forecasts are provided for three consecutive cycles valid at 00:00, 06:00, and 12:00 UTC on September 6, 2024. Corresponding initial condition files show both background and analysis states for each assimilation time. Observation files contain the satellite-derived sea surface wind data used in each assimilation cycle.The plot directory contains Python scripts for visualizing the experimental results. These scripts generate the primary figures presented in the associated paper, including visualizations of the model domain, perturbation fields, parallel computing performance, assimilation increments, and typhoon forecast comparisons. The scripts are designed to work with the provided NetCDF files and require standard scientific Python libraries.File naming follows a consistent pattern throughout the dataset. Forecast files are named as forecast_YYYYMMDDHH_experiment.nc, where the timestamp indicates the initialization time and the suffix (ctl for control or da/da1/da2/da3 for assimilation cycles) indicates the experiment type. Initial condition files follow the pattern input_YYYYMMDDHH_type, where type is either bk (background) or da (analysis). Observation files are named obs_YYYYMMDDHH.nc indicating the valid time of the observations.This dataset supports the reproducibility of the study's findings regarding the performance of the optimized SVD-3DEnVar scheme for typhoon data assimilation. It provides a valuable resource for researchers interested in evaluating and further developing data assimilation techniques for numerical weather prediction, particularly in the context of tropical cyclone forecasting.