1. Toolsets for Airborne Data
Earth Science Information Partners – Summer Meeting
Aubrey Beach, Amanda Early, Lindsay Parker, Gao Chen
July 10, 2014

2. Airborne Data Description
Aircraft measurement of atmospheric trace species (e.g., trace gases or aerosol properties) can generally be classified as remote sensing and in-situ measurements
An in-situ measurement typically involves the collection of an air sample through an inlet mounted on the aircraft fuselage.
The air sample is then analyzed by instruments onboard the aircraft.
Data from recent studies reported in ICARTT format – a NASA standard
Bullet 2 - The data is reported along the flight path as functional time.

3. Why Use Airborne Data? High spatial and temporal resolutions
Large number of simultaneous measurements, which include a much broader array of variables than space and most ground-based long-term measurement sites
Used to characterize specific atmospheric processes
Often used as benchmarks for assessing and evaluating models and satellite observations

4. Complexities with Airborne Data
Each field study may involve multiple aircraft
Each aircraft may have instruments onboard, which can generate 100’s of variables and parameters
Ex. SEAC4RS (Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys)
3 aircraft, 53 research groups, 80+ instruments = 400 variables!
No consistent variable naming convention across field campaigns
Instruments often report data on different time scales and intervals
Ex. Aircraft navigational data reported continuously at a 1 second time interval, whereas a whole air sampler provides measurements at a ~60 second time interval
I would also discuss loose formatting rules

5. Idealized ICARTT Format
ICARTT Format Issues
Inconsistent variable naming
"Number Density for Particles Larger than 10 nm”
DISCOVER-AQ: "CN>10nm”
INTEX-B: "cold_aerosol_per_cm3"
Lack of units or description
Special characters in comments
Inconsistent delimiters
I think this is fairly self explanatory, so I’d just explain the issues and how we’ve developed an idealized format to try and fix this. We’re storing the files’ metadata in a postgreSQL database which allows us to link the missions through the common naming system
Database Schema

6. TAD – Motivation and Design
NASA has conducted airborne tropospheric chemistry studies for about three decades. These field campaigns have generated a great wealth of observations, including a wide range of the trace gases and aerosol properties. The ASDC Toolset for Airborne Data (TAD) is being designed to meet the user community needs for manipulating aircraft data for scientific research on climate change and air quality relevant issues.

7. Aircraft measurement metadata
Mission/Platform
Date/time
Instrument
Variable & definition
PI contact information
Data reporting conditions: STP or Ambient, mixing ratio or concentrations
Uncertainties, LODs
Data flags

8. Common Naming System Diagram? Common Naming System #:#:#(#)_name
Aerosol
Microphysical Properties (1:1:0)
Optical Properties
Composition
Trace Gases
Hydrogen & Oxygen
Nitrogen
Sulfur
Halocarbons & Halogens
Alkanes
Alkenes & Alkynes
Aromatics
Oxygenates & Acids
Other Gases
Cloud Properties
J values
Meteorological & Navigational
Meteorological
Navigational
Radiation Variable
Diagram?
Common Naming System
Level Two
Frequency Flag
Level One
#:#:#(#)_name
Variable Common Name
Level Three

9. Timebase Data Merge tpj Data ID Timebase Δtpj Merge Timebase Δtmi tmi
DISCOVER-AQ 2013 P3B Flight Path
Data ID time base differs across a flight. Merges allow user to get all data into a consistent time base, which can be continuous integer intervals or the time base for a specific data ID. Allows for geolocated data as well.

10. Weighted Average , if , if , if
PI data value
, if
Merged data value
, if
Wij is the weighting factor for each interval. You can point them back to the previous slide for the t’s and delta t’s, but t is the interval midpoint and delta t is the interval length. Any interval with no overlap will have a Wij = 0, any where the data ID interval is fully within the merge interval has Wij = 1, and any with partial overlap has Wij determined by the top equation (0 < Wij < 1). The weighted value is found by the equation at the right. Note that this is only for scalars. Vectors are handled vectorially (taking into account magnitude and direction, 2D). Wind is handled very differently, but I would suggest pointing them to Gao if they want details. It’s a complicated equation that I don’t fully get. It’s kind of like the vectors, but on a shifted plane.
Weighting factor (Wij) based on overlap between the PI measurement time interval (tpj) and the target time interval (tmi)
Merge module handles uncertainty propagations
Special handling of wind direction averages

11. User Interface – Search Function
OR SHOW DEMO IF POSSIBLE

12. User Interface – Merge/Download

13. Future Enhancements Data Set Upload Data Subsetting Tools
Allow user to directly submit data to TAD back-end database
Data Subsetting Tools
Temporal/Spatial subsetting
Visualization Tools
Examine specific parameters over geographic regions

14. Questions?