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

Madonna, Fabio; Tramutola, Emanuele; Sy, Souleymane; Serva, Federico; Proto, Monica; Rosoldi, Marco; Gagliardi, Simone; Amato, Francesco; Marra, Fabrizio; Fassò, Alessandro; Gardiner, Tom; Thorne, Peter

doi:10.1029/2021jd035220

Cited by 18 publications

(24 citation statements)

References 69 publications

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“…Three upper-air homogenized data records are used in this study to better understand the performance of the reanalysis datasets: the Radiosonde Observation Correction using Reanalyses (RAOBCORE) (Haimberger, 2007), the Radiosonde Innovation Composite Homogenization (RICH) (Haimberger et al, 2012), and the Radiosounding HARMonization (RHARM) homogenized datasets (Madonna et al, 2022). RAOBCORE and RICH are both homogenized versions for upper air temperatures from the global radiosonde network.…”

Section: Homogenized Radiosonde Recordsmentioning

confidence: 99%

“…On the other hand, RHARM algorithm identifies breakpoints and estimates adjustments using a hybrid approach based on "reference measurements" (Thorne et al, 2017;Madonna et al, 2022). The RHARM algorithm works on each time series (i.e., station): data since 2004 (with starting time stationdependent) are obtained by post-processing each single radiosounding profile using a GRUAN-like algorithm (Dirsken et al, 2014); data before 2004 are homogenized at mandatory pressure levels using the cumulative sum test for detection of breakpoint and regulating trends using data after 2004 as a constraint.…”

Section: Homogenized Radiosonde Recordsmentioning

confidence: 99%

“…The usability of reanalyses data for long-term climate applications, including trend estimation, is controversial as discussed in the literature, for specific regions in previous reanalyses generations (e.g., Thorne, 2008;Thorne and Vose, 2010). Assessing both long-term temperature variability and trend using reanalyses proved to be challenging due to changes in the assimilation systems and observations that can introduce inhomogeneities, and to the documented difficulties with the representation of low-frequency variations (Simmons et al, 2017;Madonna et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Intercomparison of Atmospheric Upper-Air Temperature From Recent Global Reanalysis Datasets

Essa

Cagnazzo²,

Madonna³

et al. 2022

Front. Earth Sci.

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Atmospheric temperature is a key variable to detect and attribute climate change. Due to the relative sparseness of ground-based observations and heterogeneity of satellite data, global atmospheric reanalysis products are considered valuable datasets for studying and monitoring the climate, since these usually ensure spatially complete and continuous temporal coverage. Consequently, evaluating differences among the existing reanalyses is key to identifying inconsistencies. To this aim, the current study intercompares the climatological mean, variability, and linear trends for upper air temperature provided from four recent atmospheric reanalysis products (ERA5, ERA-Interim, MERRA-2, and JRA-55) The Reanalysis Multi-Model Ensemble-mean (RMME) is used as a comparator. Radiosonde observations are included for comparison on the regional scale (tropics). The results reveal that all evaluated reanalyses provide a consistent reproduction of the upper-air temperature profile. Temperature differences from RMME within ±0.25 K are found in both the troposphere and lower stratosphere, except for a few specific regions. Larger differences (>± 1.5 K) and discrepancies among the datasets are found in the upper stratosphere. Agreement between reanalyses increased after 1998. Differences in the temperature time series and seasonal cycle at the regional scales are smaller in the Northern Hemisphere mid-latitudes than in the tropics and Southern Hemisphere. A robust cooling trend in the lower stratosphere during the period of largest ozone depletion (1980-1997) and a warming trend in the troposphere for the period 1998-2018 are clearly found, at the global level, in all the datasets. Temperature trends and variability in the tropics are consistent in all reanalysis datasets with the homogenized radiosonde records from the lower to middle troposphere and in the lower stratosphere. However, large differences are found in the upper troposphere, tropical tropopause layer (TTL), and middle stratosphere. The well-known temperature variability in the lower stratosphere associated with Quasi-Biennial Oscillation (QBO) is captured in both reanalyses and observational datasets. Among the reanalyses, ERA5 shows the smallest temperature difference with respect to homogenized upper-air radiosoundings records.

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Section: Homogenized Radiosonde Recordsmentioning

confidence: 99%

Section: Homogenized Radiosonde Recordsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intercomparison of Atmospheric Upper-Air Temperature From Recent Global Reanalysis Datasets

Essa

Cagnazzo²,

Madonna³

et al. 2022

Front. Earth Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The detection of trends can be affected by data inhomogeneities and sudden changes in the time series due, for example, to changes in processing methods, observing networks, etc.. Depending on the ECV, modern statistical homogenization methods can deliver different breakpoint assessments (Coll et al, 2020, Madonna et al 2022. This is particularly important for merged satellite datasets produced by concatenation of successive, and sometimes overlapping, satellite platforms (Weatherhead et al, 2017).…”

Section: ) Trends Characterizationmentioning

confidence: 99%

Independent Quality Assessment of Essential Climate Variables: Lessons Learned from the Copernicus Climate Change Service

Yang

Cagnazzo

Artale

et al. 2022

Bulletin of the American Meteorological Society

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If climate services are to lead to effective use of climate information in decision-making to enable the transition to a climate-smart, climate-ready world, then the question of trust in the products and services is of paramount importance. The Copernicus Climate Change Service (C3S) has been actively grappling with how to build such trust; provision of demonstrably independent assessments of the quality of products, which was deemed an important element in such trust-building processes. C3S provides access to Essential Climate Variables (ECVs) from multiple sources to a broad set of users ranging from scientists to private companies and decision-makers. Here we outline the approach undertaken to coherently assess the quality of a suite of observation- and reanalysis-based ECV products covering the atmosphere, ocean, land and cryosphere. The assessment is based on four pillars: basic data checks, maturity of the datasets, fitness for purpose (scientific use cases and climate studies), and guidance to users. It is undertaken independently by scientific experts and presented alongside the datasets in a fully traceable, replicable and transparent manner. The methodology deployed is detailed, and example assessments are given. These independent scientific quality assessments are intended to guide users to ensure they use tools and datasets that are fit for purpose to answer their specific needs rather than simply use the first product they alight on. This is the first such effort to develop and apply an assessment framework consistently to all ECVs. Lessons learnt and future perspectives are outlined to potentially improve future assessment activities and thus climate services.

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“…Both are highly variable in space and time and the related time series are often prone to inhomogeneities due to changes in the instrumentation, calculation algorithms, station relocations and other factors, which must be adjusted to enable the identification of climate signals (Essa et al, 2022;Hausfather et al, 2016;Madonna et al, 2022). Moreover, observations over certain regions are sparse and implies large sampling uncertainties (Sy et al, 2021).…”

Section: -Introductionmentioning

confidence: 99%

Assessment of NA-CORDEX regional climate models, reanalysis, and in-situ gridded-observational against U.S. Climate Reference Network datasets

Madonna

Serva

et al. 2022

Preprint

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Climate models still need to be improved in their capability of reproducing the present climate at both global and regional scale. The assessment of their performance depends on the datasets used as comparators. Reanalysis and gridded (homogenized or not homogenized) observational datasets have been frequently used for this purpose. However, none of these can be considered a reference dataset. Here, for the first time, using in-situ measurements from NOAA U.S. Climate Reference Network (USCRN), a network of 139 stations with high-quality instruments deployed across the continental U.S, daily temperature, and precipitation from a suite of dynamically downscaled regional climate models (RCMs; driven by ERA-Interim) involved in NA-CORDEX are assessed. The assessment is extended also to the most recent and modern widely used reanalysis (ERA5, ERA-Interim, MERRA2, NARR) and gridded observational datasets (Daymet, PRISM, Livneh, CPC). Results show that biases for the different datasets are mainly seasonal and subregional dependent. On average, reanalysis and in-situ-based datasets are generally warmer than USCRN year-round, while models are colder (warmer) in winter (summer). In-situ-based datasets provide the best performance in most of the CONUS regions compared to reanalysis and models, but still have biases in regions such as the Midwest mountains and the Northwestern Pacific. Results also highlight that reanalysis does not outperform RCMs in most of the U.S. subregions. Likewise, for both reanalysis and models, temperature and precipitation biases are also significantly depending on the orography, with larger temperature biases for coarser model resolutions and precipitation biases for reanalysis.

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

Cited by 18 publications

References 69 publications

Intercomparison of Atmospheric Upper-Air Temperature From Recent Global Reanalysis Datasets

Intercomparison of Atmospheric Upper-Air Temperature From Recent Global Reanalysis Datasets

Independent Quality Assessment of Essential Climate Variables: Lessons Learned from the Copernicus Climate Change Service

Assessment of NA-CORDEX regional climate models, reanalysis, and in-situ gridded-observational against U.S. Climate Reference Network datasets

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