PRECIP 2022 mei-yu front SAMURAI analyses, estimated rain rate, and convective-stratiform partition data

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., , # 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 PR...,