PRECIP 2022 mei-yu front SAMURAI analyses, estimated rain rate, and convective-stratiform partition data
Data files
Nov 24, 2025 version files 5.92 GB
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combined_analyses_v2.tar.gz
5.92 GB
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README.md
3.21 KB
Abstract
545 Multi-Doppler, estimated rainfall, and convective-stratiform partitioning analyses from research and operational radars during the PRECIP 2022 field campaign are used to examine the relationship between rotation, vertical motion, divergence, and rainfall intensity in two mei-yu frontal periods. Each NetCDF file contains SAMURAI-analyzed reflectivity, Cartesian wind components, vorticity, and divergence. The 1.5-km altitude estimated rain rate using the NSF NCAR Hybrid method is also provided along with the convective-stratiform partitioning results from ECCO. The dataset also includes a bad data mask that was used to remove data in the analysis.
Dataset DOI: 10.5061/dryad.hqbzkh1vh
Description of the data and file structure
This dataset contains analyzed kinematic, precipitation, and morphological data from two mei-yu frontal periods collected during the National Science Foundation (NSF)-supported Prediction of Rainfall Campaign in the Pacific (PRECIP), alongside the Taiwan-Area Heavy rain Observation and Prediction Experiment (TAHOPE), and Japan Tropical cyclones-Pacific Asian Research Campaign for Improvement of Intensity estimations/forecasts (T-PARCII) field campaigns in 2022. The goal of the NSF PRECIP campaign was to improve our understanding of the fundamental processes behind extreme rainfall.
Files and variables
File: combined_analyses_v2.tar.gz
Description:
The zipped tar file contains 545 files each named for the time of the analysis. The naming convention is PRECIP_meiyu_GRL_yyyymmddHHMM.nc, where yyyy, mm, dd, HH, and MM correspond to the year, month, day, hour, and minute, respectively. The time corresponds to the start of the SAMURAI analysis.
The dimensions are time, altitude, longitude, and latitude. The variables x and y correspond to the distance from the center of the analysis <0, 0> in kilometers. The variables included in the data are the SAMURAI Cartesian wind components (U, V, W; m/s), relative vorticity (VORT; 10-5 s-1), divergence (DIV; 10-5 s-1), Okubo-Weiss parameter (OW; 10-10 s-1), reflectivity (DBZ; dBZ), FRACTL condition number (FRACTL_CN; unitless), 1.5-km altitude estimated rain rate from S-Pol (RATE_HYBRID; mm hr^-1), ECCO precipitation type composite from S-Pol (ECCO_TYPE_COMP; integers correspond to categories described in Table D1 of Dixon and Romatschke 2022), and a bad data flag (bad_data; boolean where 1 indicates bad data). To apply the bad data flag to RATE_HYBRID or ECCO_TYPE_COMP, use the 1.5-km altitude level of bad_data.
References:
Dixon, M., and U. Romatschke, 2022: Three-Dimensional Convective–Stratiform Echo-Type Classification and Convectivity Retrieval from Radar Reflectivity. J. Atmos. Oceanic Technol., 39, 1685–1704, https://doi.org/10.1175/JTECH-D-22-0018.1.
NCAR/EOL S-Pol Team. 2024. PRECIP: NCAR S-Pol radar moments data. Version 2.0. NSF NCAR Earth Observing Laboratory. https://doi.org/10.26023/QA4M-CDHH-MY0Z. Accessed 23 Sep 2025.
Code/software
Any modern software language that can read NetCDF files can read the data (e.g., Python, MATLAB). Data visualization tools such as ncview can also be used to examine the data.
Access information
Data was derived from the following sources:
- S-Pol radar data (Version 2.0) are available through the EOL Field Catalog (https://doi.org/10.26023/QA4M-CDHH-MY0Z).
- Operational CWA radar data were provided by Pao-Liang Chang at Taiwan's Central Weather Administration.
- TEAM-R radar data came from Yu-Chieng Liou and Wei-Yu Chang from the National Central University TAHOPE team.