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Dataset results
7 results for “raindrop size distribution”
Raindrop size distribution and precipitation over Peruvian central Andes (12.0°S)
<p>The raindrop size distribution and precipitation over Peruvian central Andes (12.0°S) database consist of raindrop size distribution, raindrop fall velocity and precipitation data measured in the Huancayo Observatory of Peru.<br> Two Parsivel 2 and two tipping bucket rain gauge are used in this database which are operating together since 2018.</p> <p>The repository ULR:<br> https://scah.igp.gob.pe/sites/datos/PP_prods/</p> <p>There are two types of files, one NetCDF for the precipitation totals and<br> another NetCDF which contains each Parsivel2 raw data.</p> <p>The precipitation totals files are located in the subfolder:<br> https://scah.igp.gob.pe/sites/datos/PP_prods/PP_dia/</p> <p>And the Parsivel2 raw data are located in the sub folder:<br> https://scah.igp.gob.pe/sites/datos/PP_prods/PSV_nc/</p> <p>This repository is part of the Atmospheric Microphysics And Radiation Laboratory (LAMAR),<br> Huancayo Observatory (12.0S, 75.3W, 3113 m ASL) (https://scah.igp.gob.pe/laboratorios/lamar)</p>
JGR-Statistical Characteristics of Raindrop Size Distributions and Parameters in Central China during the Meiyu Seasons
<p>The dataset for the JGR article "Statistical Characteristics of Raindrop Size Distributions and Parameters in Central China during the Meiyu Seasons"</p>
Characteristics of summer season raindrop size distribution in western Pacific (Data source)
<p>Datailed information on the data sets used in our manuscript are provided, along with the main code in data process.</p>
Characteristics of Raindrop Size Distribution in Western and Eastern Parts of East Asian Summer Monsoon Rainband
<p>The datasets used in our manuscript are provided here, along with the main code.</p>
Dataset for "Polarimetric retrieval of raindrop size distribution: double-moment normalization approach and machine learning techniques"
<p>This is the dataset for the publication [1]. This dataset contains:<br>1) Training dataset (train_data.pkl)<br>2) Independent validation dataset (test_data.pkl)</p> <p>This dataset was also used for the publication [2]. The training and validation datasets were merged in [2].</p> <p> </p> <p>References</p> <p>[1] Shin, K., Kim, K., Song, J. J., & Lee, G. (2024). Polarimetric Retrieval of Raindrop Size Distribution: Double‐Moment Normalization Approach and Machine Learning Techniques. <em>Geophysical Research Letters</em>, <em>51</em>(1), e2023GL106057. <a href="https://doi.org/10.1029/2023GL106057">https://doi.org/10.1029/2023GL106057</a></p> <p>[2] Tapiador, F. J., Shin, K., Leganés, L. J., Lim, K.-S., Juárez, G., Bang, W., et al. (2025). A Physically Consistent Particle Size Distribution Modelling of the Microphysics of Precipitation for Weather and Climate Models. <em>Submitted</em>.</p>
Machine Learning Approaches for Characterizing the Raindrop Size Distributions in Western Pacific Tropical Cyclones
Open the record for dataset details and reuse information.
The dataset of raindrop size distribution and environmental properties during summer 2014 and 2015 in East China
<p>This dataset is used to make figures for the paper entitled “Observational Evidence of the Environmental Impacts on Raindrop Size Distribution in East China” submitted to Geophysical Research Letters in June 2022. It contains the disdrometer and automatic weather station observations in minute, hourly, daily scales, and also the training and testing samples for machine learning.</p>
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