DOI: 10.5061/dryad.hqbzkh1vh
title: PRECIP 2022 mei-yu front SAMURAI analyses, estimated rain rate, and convective-stratiform partition data

description: 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.
description: # PRECIP 2022 mei-yu front SAMURAI analyses, estimated rain rate, and
 convective-stratiform partition data Dataset DOI:
 [10.5061/dryad.hqbzkh1vh](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 &lt;0,
 0&gt; 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](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](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](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.