Radiosounding HARMonization (RHARM): a new homogenized dataset of radiosounding temperature, humidity and wind profiles with uncertainty

Abstract

Abstract. Observational records are essential for assessing long-term changes in our climate. However, these records are more often than not influenced by residual non‐climatic factors which must be detected and adjusted prior to their usage. Ideally, measurement uncertainties should be properly quantified and validated. In the context of the Copernicus Climate Change Service (C3S), a novel approach, named RHARM (Radiosounding HARMonization), has been developed to provide a harmonized dataset of temperature, humidity and wind profiles along with an estimation of the measurement uncertainties for about 650 radiosounding stations globally. The RHARM method has been applied to IGRA daily (0000 and 1200 UTC) radiosonde data holdings on 16 standard pressure levels (from 1000 to 10 hPa) from 1978 to present. Relative humidity adjustment and data provision has been limited to 250 hPa owing to pervasive issues on sensors' performance in the upper troposphere and lower stratosphere. The applied adjustments are interpolated to all reported significant levels to retain information content contained within each individual ascent profile. Each historical station time series is harmonized using two distinct methods. Firstly, the most recent period of the records when modern radiosonde models have been in operation at each station (typically starting between 2004 and 2010 but varying on a station-by-station basis) are post-processed and adjusted using reference datasets from the GCOS Reference Upper Air Network (GRUAN) and from the 2010 WMO/CIMO (World Meteorological Organization/Commission for Instruments and Methods of Observation) radiosonde intercomparison. Subsequently, at each mandatory pressure level, the remaining historical data are scanned backward in time to detect structural breaks due to prolonged systematic effects in the measurements and then adjusted to homogenize the time series. This paper describes the dataset portion related to the adjustment of post-2004 measurements only. A step-by-step description of the algorithm is reported and comparisons with GRUAN and atmospheric reanalysis data for temperature and relative humidity data are discussed. The evaluation shows that the strongest benefit of RHARM compared to existing products is related to the substantive adjustments applied to relative humidity time series for values below 15 % and above 55 % as well as to the provision of the uncertainties for all variables. Uncertainties have been validated using the ECMWF reanalysis short-range forecast outputs. The RHARM algorithm is the first to provide homogenized time series of temperature, relative humidity and wind profiles alongside an estimation of the observational uncertainty for each single observation at each pressure level. A subset of RHARM dataisavailable at https://doi.org/10.5281/zenodo.3973353 (Madonnaet al., 2020a)

The New Radiosounding HARMonization (RHARM) Data Set of Homogenized Radiosounding Temperature, Humidity, and Wind Profiles With Uncertainties

“…In the most recent decades, a positive trend in RH was also identified by Madonna, Tramutola, et al. (2020) in Europe, in the SH and the tropics. Wind speed shows improvements especially in the NH.…”

“…For both temperature and RH, overall trends in the NH and SH observed at 300 hPa agree with trends at 850 hPa. In the most recent decades, a positive trend in RH was also identified by Madonna, Tramutola, et al (2020) in Europe, in the SH and the tropics. Wind speed shows improvements especially in the NH.…”

An Appraisal of the Progress in Utilizing Radiosondes and Satellites for Monitoring Upper Air Temperature Profiles

“…The quality of atmospheric temperature data is foundational for a wide range of applications in atmospheric sciences, climate research, weather forecasting, ecosystem management, human health, and policy development [145]. It underpins our understanding of climate change, weather patterns, and atmospheric processes, ultimately contributing to informed decisions and actions for a sustainable future [146]. Many studies illustrated that despite RS data quality being of paramount importance, there have been growing challenges such as data discrepancies [147], the lack of homogeneity (in time and space) [148], biases [149], and discontinuities [15] associated with current and historical climate data records obtained using these technologies.…”

“…Significant progress has been made to enhance upper air temperature records acquired utilizing in situ and satellite-based instruments to improve weather forecasting [171], the understanding of atmospheric boundary layer processes [172], and climate change studies [140]. Studies have demonstrated that RSs are still by far the most effective tools for in situ atmospheric temperature observations, but operational meteorological satellites are gradually closing the gap despite their reliance on in situ data for their calibration and validation [77,146,151]. For instance, there have been great advances in RS [141,173] and satellite technology [174].…”

Sensitivity of trends to estimation methods and quantification of subsampling effects in global radiosounding temperature and humidity time series

“…At the global scale, more than 1,000 radiosoundings are approximately launched at day and night time each day to characterize the state of the atmosphere (Ladstädter et al ., 2011), although with large sparse coverage in the SH and occasional launches performed on ships to fill the gap over the oceans. The use of radiosounding datasets for climate change studies implies the handling of inhomogeneities caused by frequent changes in instruments, sensor types, observation practices or change in data processing systems (Thorne et al ., 2005; Karl et al ., 2006; Miloshevich et al ., 2006; Simmons et al ., 2014; Madonna et al ., 2020a, 2020b). Radiosondes from different manufacturers are affected by systematic errors, which differ from one type to another (e.g., Dirksen et al ., 2014; Ingleby, 2017).…”