Long-term homogeneous climate data records (CDRs) are essential to diagnose changes in our climate, understand its variability, and assess and contextualize future climate projections. When CDRs are used it is highly desirable to detect and adjust for all the known and quantifiable systematic inhomogeneities in the observational record, establish measurement traceability ideally to an absolute reference (Système international, SI), or community acknowledged “standard” through an unbroken chain of calibrations, each contributing to the measurement uncertainty; quantify measurement uncertainties in any data where traceability was not properly established; in such cases, uncertainties must be inferred from the available metadata, results of sensors' intercomparisons, or information about the measurement process. In practice, for historical in-situ observations, it is often not easy to fulfill the above list of requirements, especially for global baseline or comprehensive. A novel approach, named RHARM (Radiosounding HARMonization), has been developed to provide a homogenized dataset of upper-air temperature, humidity, and wind profiles along with an estimation of the related uncertainties  for a substantive subset of radiosounding stations globally distributed among those available from the Integrated Global Radiosonde Archive (IGRA,https:// www.ncdc.noaa.gov/data-access/weather-balloon/integrated-globalradiosonde-archive, ). The RHARM method can adjust twice daily (0000 and 1200 UTC) radiosonde data holdings at 16 pressure levels in the range 1,000–10 hPa, from 1978 to the present. Relative humidity (RH) data are limited to 250 hPa. RHARM is the first approach to implementing adjustments on single profiles and providing an uncertainty estimate. By construction, RHARM adjusted fields are not affected by cross-contamination of biases across stations and are fully independent of reanalysis data. This work discusses the algorithm adopted in RHARM for the uncertainty estimation based on the hybrid use of metadata, for data since 2004, and statistical methods, before 2004 when a limited amount of metadata are available from upper-air data records. Moreover, validation of uncertainties is also discussed showing an adaptation of the approach developed within the FIDUCEO (Fidelity and uncertainty in climate data records from Earth Observations) H2020 project.  Madonna, F., Tramutola, E., SY, S., Serva, F., Proto, M., Rosoldi, M., et al. (2022). The new Radiosounding HARMonization (RHARM) data set of homogenized radiosounding temperature, humidity, and wind profiles with uncertainties. Journal of Geophysical Research: Atmospheres, 127, e2021JD035220. https://doi.org/10.1029/2021JD035220.  Durre, I., X. Yin, R.S. Vose, S. Applequist, and J. Arnfield: Enhancing the Data Coverage in the Integrated Global Radiosonde Archive. J. Atmos. Oceanic Technol., 35, 1753–1770, 2018, https://doi.org/10.1175/JTECH-D-17-0223.1
Topic : Theme 1: Atmospheric Chemistry and Physics.
Reference : T1-A2
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