Vertical Wind Velocity Data from NSF and NASA Flight Campaigns

Overview: This dataset provides a unified, quality-controlled compilation of in-situ atmospheric measurements focused on vertical velocity at 1-second frequency. It consolidates near-global data from seven NSF and five NASA airborne field campaigns conducted between 2008 and 2016. The data are structured to support research on atmospheric turbulence, vertical velocity trends, and cloud processes.

Person of contact: Minghui Diao, [e-mail]

Dataset Composition
The collection consists of two NetCDF files:

1. File: NSF_sig_w_T_430s.nc
• Source: U.S. National Science Foundation (NSF) campaigns.
• Contains data from: (1) CONTRAST, (2) NSF-DC3, (3) HIPPO, (4) ORCAS, (5) PREDICT, (6) START08, and (7) TORERO.

2. File: NASA_sig_w_T_430s.nc
• Source: U.S. National Aeronautics and Space Administration (NASA) campaigns.
• Contains data from: (1) ATTREX, (2) MACPEX, (3) NASA-DC3, (4) POSIDON, and (5) SEAC4RS.

Data Variables & Structure
Each NetCDF file is organized with the following columns:
• Time (UTC)
• Campaign Label (Numerical identifier, see descriptions above)
• Research Flight Number
• Standard Deviation of Vertical Velocity (σw) (m/s)
• Temperature (°C)
• Pressure (Pa)

Processing Notes
• σw Calculation: The standard deviation of vertical velocity was computed using a 430-second moving average. This corresponds to a horizontal length scale of approximately 100 km based on aircraft true airspeed.
• Data Filtering: The beginning and ending segments of each flight are filtered out for the σw calculations (indicated by a campaign variable value of NaN).

Recommended Citation & Documentation
The DOI of this dataset is: Diao, Minghui; Maciel, Flor; Ngo, Derek; Patnaude, Ryan (2025), “Vertical Wind Velocity Data from NSF and NASA Flight Campaigns”, Mendeley Data, V1, doi: 10.17632/pr28vks52k.1

Please cite the following papers when using this dataset:

1. Primary Methodology & Campaign Details:
Maciel, F. V., Diao, M., and Patnaude, R.: Examination of aerosol indirect effects during cirrus cloud evolution, Atmos. Chem. Phys., 23, 1103–1129, https://doi.org/10.5194/acp-23-1103-2023, 2023.

2. Data Application Through a Machine Learning Approach:
Ngo, D., Diao, M., Patnaude, R. J., Woods, S., and Diskin, G.: Aerosol–cloud interactions in cirrus clouds based on global-scale airborne observations and machine learning models, Atmos. Chem. Phys., 25, 7007–7036, https://doi.org/10.5194/acp-25-7007-2025, 2025.

3. Data Application in Model Evaluation:
Patnaude, R., Diao, M., Liu, X., and Chu, S.: Effects of thermodynamics, dynamics and aerosols on cirrus clouds based on in situ observations and NCAR CAM6, Atmos. Chem. Phys., 21, 1835–1859, https://doi.org/10.5194/acp-21-1835-2021, 2021.

4. Data Application in Vertical Velocity Trend Analysis:
Barahona, D., Breen, K., Ngo, D., Maciel, F. V., Patnaude, R., and Diao, M.: Trends in vertical wind velocity variability reveal cloud microphysical feedback. Nature Communications, 2025.