Observational records are more often than not influenced by residual non‐climatic factors which must be detected and adjusted for prior to their usage. In this work, we present a novel approach, named Radiosounding HARMonization (RHARM), providing a homogenized data set of temperature, humidity and wind profiles along with an estimation of the measurement uncertainties for 697 radiosounding stations globally. The RHARM method has been used to adjust twice daily (0000 and 1200 UTC) radiosonde data holdings at 16 pressure levels in the range 1,000–10 hPa, from 1978 to present, provided by the Integrated Global Radiosonde Archive. Relative humidity (RH) data are limited to 250 hPa. The applied adjustments are interpolated to all reported levels. RHARM is the first data set to provide homogenized time series with an estimation of the observational uncertainty at each sounding pressure level. By construction, RHARM adjusted fields are not affected by cross‐contamination of biases across stations and are fully independent of reanalysis data. Analysis of trends for temperature, RH and winds highlights increased geographical coherency of trends over 1978–2000 globally, but especially in the Northern Hemisphere and South America. RHARM shows warming trends of 0.39 K/decade at 300 hPa in the Northern Hemisphere and of 0.25 K/decade in the tropics. The RHARM adjustments also reduce differences with the European Centre for Medium‐Range Weather Forecast ERA5 reanalysis, with the strongest effect in the Northern Hemisphere for temperature and relative humidity. For wind speed, the comparison indicates a good agreement with ERA5 in the troposphere.

Trends in atmospheric boundary layer height may represent an indication of climate changes. The related modified interaction between the surface and free atmosphere affects both thermodynamics variables and dilution of chemical constituents. Boundary layer is also a major player in various feedback mechanisms of interest for climate models. This paper investigates trends in the nocturnal and convective boundary layer height at mid-latitudes in Europe using radiosounding profiles from the Integrated Global Radiosounding Archive (IGRA). Atmospheric data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis v5 (ERA5) and from the GCOS Reference Upper-Air Network (GRUAN) Lindenberg station are used as intercomparison datasets for the study of structural and parametric uncertainties in the trend analysis. Trends are calculated after the removal of the lag-1 autocorrelation term for each time series. The study confirms the large differences reported in literature between the boundary layer height estimates obtained with the two different algorithms used for IGRA and ERA5 data: ERA5 shows a density distribution with median values of 350 m and 1150 m for the night and the daytime data, respectively, while the corresponding IGRA median values are of 1150 m and 1750 m. An overall good agreement between the estimated trends is found for nighttime data, while daytime ERA5 boundary layer height estimates over Europe are characterized by a lower spatial homogeneity than IGRA. Parametric uncertainties due to missing data in both the time and space domain are also investigated: the former is not exceeding 1.5 m, while the latter are within 10 m during night and 17 m during the day. Recommendations on dataset filtering based on time series completeness are provided. Finally, the comparison between the Lindenberg data as processed at high-resolution by GRUAN and as provided to IGRA at a lower resolution, shows the significant impact of using high-resolution data in the determination of the boundary layer height, with differences from about 200 m to 450 m for both night and day, as well as a large deviation in the estimated trend.

Changes in the frequency of temperature extremes are often attributed to global warming. The recent availability of near‐surface temperature data records from reference networks, such as the U.S. Climate Reference Network (USCRN), enables the quantification of measurement uncertainties. Within an activity of the Copernicus Climate Change Service, the estimation of the measurement uncertainty has been provided for USCRN temperature data, using metadata made available by the National Oceanic and Atmospheric Administration (NOAA). In this paper, four climate extreme indices (Frost Days, Summer Days, Ice Days, Tropical Nights) and the related uncertainties are calculated for the period 2006–2020 from the USCRN data set and compared with traditional indices. Moreover, the asymmetric USCRN measurement uncertainties are propagated to estimate the uncertainties of climate indices. The comparison shows expanded uncertainties homogeneously distributed with the latitude and typically within 15 days per year for Frost Days and within 10 days for Ice Days, while smaller uncertainties are estimated for Summer Days and Tropical Nights, with values typically within six to seven days per year. Positive uncertainties are typically larger than negative ones for all the indices. The values of Frost and Ice Days with the related uncertainties for USCRN have also been compared with the corresponding values calculated from reanalyses data, showing differences typically within 60 days for median values, quite often smaller than USCRN and inconsistent within the related uncertainties, Overall, the results show that USCRN measurement uncertainties increase confidence in the estimation of climate extreme indices and decisions for adaptation.

The assessment and optimization of the MIPA (Morphological Image Processing Approach) algorithm for the retrieval of Atmospheric Boundary Layer Height (ABLH) from Aerosol High-power Lidars (AHL) data are presented. MIPA has been developed at CNR-IMAA in the framework of ACTRIS, and it was tested on several lidar datasets, showing, in general, a good agreement with the traditional ABLH retrieval techniques. The main innovative feature of MIPA with respect to other approaches consists in applying optimized morphological filters and object-oriented analysis on lidar timeseries to obtain ABLH estimates. In this study, we carried out a robust MIPA validation effort based on a dedicated measurement campaign organized at CIAO (CNR-IMAA Atmospheric Observatory) in Spring 2024, where several lidar systems were operating continuously along with a quite complete set of other atmospheric sensors and two radiosounding systems. During the campaign, several case studies were considered for MIPA validation, each characterized by an intensive radiosonde schedule to ensure the establishment of a representative ABLH reference dataset. The ABLH retrieved by MIPA was compared against the corresponding ones obtained by radiosonde data. We observed a good overall agreement under different atmospheric conditions, ranging from intense dust events penetrating the ABL to cleaner atmospheric conditions. The best agreement between MIPA and reference dataset is obtained for longer wavelengths (532 nm and 1064 nm) and during daytime conditions.

This study describes a quite special and interesting atmospheric event characterized by the simultaneous presence of fresh and aged smoke layers. These peculiar conditions occurred on 16 July 2024 at the CNR-IMAA atmospheric observatory (CIAO) in Potenza (Italy), and represent an ideal case for the evaluation of the impact of aging and transport mechanisms on both the optical and microphysical properties of biomass burning aerosol. The fresh smoke was originated by a local wildfire about 2 km from the measurement site and observed about one hour after its ignition. The other smoke layer was due to a wide wildfire occurring in Canada that, according to backward trajectory analysis, traveled for about 5–6 days before reaching the observatory. Synergetic use of lidar, ceilometer, radar, and microwave radiometer measurements revealed that particles from the local wildfire, located at about 3 km a.s.l., acted as condensation nuclei for cloud formation as a result of high humidity concentrations at this altitude range. Optical characterization of the fresh smoke layer based on Raman lidar measurements provided lidar ratio (LR) values of 46 ± 4 sr and 34 ± 3 sr, at 355 and 532 nm, respectively. The particle linear depolarization ratio (PLDR) at 532 nm was 0.067 ± 0.002, while backscatter-related Ångström exponent (AEβ) values were 1.21 ± 0.03, 1.23 ± 0.03, and 1.22 ± 0.04 in the spectral ranges of 355–532 nm, 355–1064 nm and 532–1064 nm, respectively. Microphysical inversion caused by these intensive optical parameters indicates a low contribution of black carbon (BC) and, despite their small size, particles remained outside the ultrafine range. Moreover, a combined use of CIAO remote sensing and in situ instrumentation shows that the particle properties are affected by humidity variations, thus suggesting a marked particle hygroscopic behavior. In contrast, the smoke plume from the Canadian wildfire traveled at altitudes between 6 and 8 km a.s.l., remaining unaffected by local humidity. Absorption in this case was higher, and, as observed in other aged wildfires, the LR at 532 nm was larger than that at 355 nm. Specifically, the LR at 355 nm was 55 ± 2 sr, while at 532 nm it was 82 ± 3 sr. The AEβ values were 1.77 ± 0.13 and 1.41 ± 0.07 at 355–532 nm and 532–1064 nm, respectively and the PLDR at 532 nm was 0.040 ± 0.003. Microphysical analysis suggests the presence of larger, yet much more absorbent particles. This analysis indicates that both optical and microphysical properties of smoke can vary significantly depending on its origin, persistence, and transport in the atmosphere. These factors that must be carefully incorporated into future climate models, especially considering the frequent occurrences of fire events worldwide.

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)