Recent Developments in Data Quality Control Techniques for Dropsondes and Radiosondes and Quantifying Uncertainty in Sonde Relative Humidity Measurements
In recent years, advancements in atmospheric research, technology and data assimilation techniques have contributed to driving the need for higher quality, higher resolution radiosonde and dropsonde data. 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 Sounding Group, within the In-Situ Sensing Facility (ISF), at the National Center for Atmospheric Research (NCAR) has developed an extensive, multi-step process of quality control (QC) to ensure that the highest quality of data are made available to the research community. Traditionally, this QC scheme has included individual examination of raw data profiles, processing of data through the Atmospheric Sounding Processing Environment (ASPEN) software, evaluating the data products using a variety of visualization tools and statistical methods, and applying corrections when necessary. These routine measures enable us to identify, characterize, and in many cases correct significant errors that could potentially impact research and analyses performed using these data.
Recently, additional error detection and correction methods have been implemented to further improve data quality. New procedures for statistical analysis of data quality have been developed for real-time use, and corrections for launch detect errors, persistent pressure offsets, and an altitude correction that converts ellipsoid height to geopotential altitude by taking into account both geoid height and latitude have been adopted. Additionally, strides are being made to characterize and quantify the uncertainty of relative humidity measurements from the hygrometer used in both the radiosonde and the dropsonde. The ultimate goal of quantifying this measurement error is to characterize sensor behavior and use this information to verify that manufacturer's claims are valid, and to investigate whether or not broader ranges, than originally thought, exist. It is crucial to the modeling community to know measurement uncertainties, and this information could ultimately be useful in developing new algorithms to improve the moisture profile.