In-Situ Atmospheric Sounding Data Lifecycle from Data Collection, Analysis and Quality Control to Documentation, Archival and Tracking

Abstract:

In-situ measurement systems are used to monitor the atmosphere whereby instruments are located in the area of interest and are in direct contact with what is being measured. Dropsondes and radiosondes are instruments used to collect high-vertical-resolution profiles of the atmosphere. The dropsondes are deployed from aircraft and, as they descend, they collect pressure, temperature and humidity data at a half-second rate, and GPS wind data at a quarter-second rate. Radiosondes are used to collect high-resolution measurements of the atmosphere, from the ground to approximately 30 kilometers. Carried by a large helium-filled balloon, they ascend upward through the atmosphere measuring pressure, temperature, relative humidity, and GPS winds at a one-second rate.

Advancements in atmospheric research, technology and data assimilation techniques have contributed to driving the need for higher quality, higher resolution radiosonde and dropsonde data at an increasingly rapid rate. These data most notably represent a valuable resource for initializing numerical prediction models, calibrating and validating satellite retrieval techniques for atmospheric profiles, and for climatological research. The In-Situ Sensing Facility, at NCAR, has developed an extensive, multi-step process of quality control (QC). Traditionally, QC has been a time intensive process that involves evaluating data products using a variety of visualization tools and statistical methods. With a greater need for real-time data in the field and a reduced turn-around time for final quality controlled data, new and improved procedures for streamlining statistical analysis and QC are being implemented.

Improvements have also been made on two fronts regarding implementation of a comprehensive data management plan. The first was ensuring ease of data accessibility through an intuitive centralized data archive system, that both keeps a record of data users and assigns digital object identifiers to each unique data set. The second improvement was to define appropriate criteria needed for documentation and metadata so that data users have all of the relevant information needed to properly use and understand the complexities of these measurements.