Search NASA⌕ Search

DOE OSTI · 2203692

SAIL-Net Raw and Post Corrected POPS Data Fall 2021 - Summer 2023

Abstract

SAIL-Net is a DOE funded project in the East River Watershed near Crested Butte, Colorado with the goal of advancing our understanding of aerosol-cloud interactions in complex, mountainous regions. Through the deployment of a network of six low cost microphysics nodes in Fall 2021 in the same domain at the SAIL campaign, SAIL-Net provides data on aerosol size distributions, cloud condensation nuclei (CCN), and ice nucleation particles (INP). This network enables the investigation of small-scale variations in complex terrain. Two datasets are provided - one containing raw data and the other containing post-corrected data. The raw dataset provides the raw data recorded from the POPS which were deployed at each of the six sites. These data are organized by site and broken down into daily data files. The six site names used here are: “gothic”, “irwin”, “cbtop”, “cbmid”, “pumphouse”, and “snodgrass”. These data are not cleaned or post-corrected, but some flags have been added. The data are reported at 1 second time resolution. The post-corrected dataset provides the post-corrected and cleaned data recorded from the POPS which were deployed at each of the six sites. This data are also organized by site (same as those found in the raw data) and broken down into daily data files. Unlike the raw POPS data, these data have already been cleaned to remove what we believe are bad values. These data should be ready to use with no cleaning. For a full description of the cleaning and post-correction process, see the readme.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gibson, Leah, Levin, Ezra. 2023-10-27. SAIL-Net Raw and Post Corrected POPS Data Fall 2021 - Summer 2023. https://doi.org/10.5439/2203692

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗