Abstract Temperatures in the upper troposphere of the atmosphere, near the tropopause, play a key role in the evolution of tropical cyclones (TC) by controlling their potential intensity (PI), which describes the thermodynamically based maximum TC intensity that the environment will support. Accurately identifying past trends in PI is critical for understanding the causes of observed changes in TC intensity, but calculations of PI trends using different atmospheric reanalysis products can give very different results, largely due to differences in their representation of upper-tropospheric temperatures. Without a means to verify the fidelity of the upper-tropospheric temperatures, PI trends calculated from these products are very uncertain. Here, a method is introduced to validate the upper-tropospheric temperatures in the reanalysis products by using the TCs themselves as thermometers. Using a 30-yr global dataset of TC cloud-top temperatures and three widely utilized atmospheric reanalysis products—Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF interim reanalysis (ERA-Interim), and NCEP–NCAR Global Reanalysis 1—it is shown that storm-local upper-level temperatures in the MERRA and ERA-Interim data vary similarly to the TC cloud-top temperatures on both interannual and decadal time scales, but the NCEP–NCAR data have substantial biases that introduce an increasing trend in storm-local PI not found in the other two products. The lack of global storm-local PI trends is due to a balance between temporal increases in the mean state and the poleward migration of TCs into lower climatological PI, and it has significant implications for the detection and attribution of mean TC intensity trends.

Surface air temperature (t2m) data are essential for understanding climate dynamics and assessing the impacts of climate change. Reanalysis products, which combine observations with retrospective short‐range weather forecasts, can provide consistent and comprehensive datasets. ERA5 represents the state‐of‐the‐art in global reanalyses and supplies initial and boundary conditions for higher‐resolution regional reanalyses designed to capture finer‐scale atmospheric processes. However, these products require validation, especially in complex terrains like Italy. This study analyses the capability of different reanalysis products to reproduce t2m fields over Italy during the 1991–2020 period. The analyses encompass ERA5, ERA5‐Land, the MEteorological Reanalysis Italian DAtaset (MERIDA), the Copernicus European Regional ReAnalysis (CERRA), and the Very High‐Resolution dynamical downscaling of ERA5 REAnalysis over ITaly (VHR‐REA_IT). The validation we conduct pertains to both the spatial distribution of 30‐year seasonal and annual normal values and the daily anomaly records. Each reanalysis is compared with observations projected onto its respective grid positions and elevations, overcoming any model bias resulting from an inaccurate representation of the real topography. Key findings reveal that normal values in reanalyses closely match observational values, with deviations typically below 1°C. However, in the Alps, winter cold biases sometimes exceed 3°C and show a relation with the elevation. Similar deviations occur in the Apennines, Sicily, and Sardinia. Conversely, VHR‐REA_IT shows a warm bias in the Po Valley up to 3°C in summer. Daily anomalies generally exhibit lower errors, with MERIDA showing the highest accuracy and correlation with observational fields. Moreover, when aggregating daily anomalies to annual time scales, the errors in the anomaly records rapidly decrease to <0.5°C. The results of this study empower reanalysis users across multiple sectors to gain a more profound insight into the capabilities and constraints of different reanalysis products. The knowledge and the characterization of the reanalyses t2m bias against observations can indeed be crucial when incorporating these products into their research and practical applications.

Abstract. To advance our understanding of the stratosphere, high quality observational datasets of the upper atmosphere are needed. It is commonplace that reanalysis is used to conduct stratospheric studies. However the accuracy of the standard reanalysis at these heights is hard to infer due to a lack of in-situ measurements. Satellite measurements provide one source of temperature information. As some satellite information is already assimilated into reanalyses, the direct comparison of satellite temperatures to the reanalysis is not truly independent. Stratospheric lidars use Rayleigh scattering to measure density in the upper atmosphere, allowing temperature profiles to be derived for altitudes from 30 km (where Mie scattering due to stratospheric aerosols becomes negligible) to 80–90 km (where the signal-to-noise begins to drop rapidly). The Network for the Detection of Atmospheric Composition Change (NDACC) contains several lidars at different latitudes that have measured atmospheric temperatures since the 1970s, resulting in a long running upper-stratospheric temperature dataset. These temper1ature datasets are useful for validating reanalysis datasets in the stratosphere, as they are not assimilated into reanalyses. Here we take stratospheric temperature data from lidars in the northern hemisphere for winter months between 1990–2017 and compare them with the European Centre for ECMWF's ERA-interim and ERA-5 reanalyses. To give confidence in any bias found, temperature data from NASA's EOS Microwave Limb Sounder is also compared to ERA-interim and ERA-5 at points over the lidar sites. In ERA-interim a cold bias of −3 to −4 K between 10 hPa and 1 hPa is found when compared to both measurement 15 systems. Comparisons with ERA-5 found a small bias of magnitude 1 K which varies between cold and warm bias with height between 10 hPa and 3 hPa, indicating a good thermal representation of the upper atmosphere to 3 hPa. At heights above this, comparisons with EOS MLS yield a slight warm bias and the temperature lidar yield a cold bias. A further comparison is undertaken to see the effects of the assimilation of the Advanced Microwave Sounding Unit-A satellite data and the Constellation Observing System for Meteorology, Ionosphere, and Climate GPS Radio Occulation (COSMIC GPSRO) data on stratospheric temperatures. By comparing periods before and after the introduction of each data source it is clear that COSMIC GPSRO improves the cold bias in the 3 hPa to 0.5 hPa altitude range.

: In recent years, a considerable body of research has demonstrated the suitability of global and regional reanalysis data for human-biometeorological applications. These applications include the assessment of the outdoor thermal environment and the investigation of its relation to human health, especially in areas where the spatial coverage of surface observational networks is sparse. Here, we present the first comprehensive evaluation of the most recent pan-European regional reanalysis, namely the Copernicus European Regional Reanalysis (CERRA) dataset at 5.5 km spatial resolution, in terms of simulating the observed human bioclimate, as expressed by the modified physiologically equivalent temperature (mPET) that is computed through the RayMan Pro model, and its meteorological drivers. The validation was performed over Greece using up to 11 years of records of 2 m air temperature and relative humidity, 10 m wind speed and global solar radiation derived from 35 sites of the nationwide network of surface weather stations operated by the METEO Unit at the National Observatory of Athens. The ERA5-Land dataset at ~9 km spatial resolution, which represents the current state-of-the-art reanalysis, was also compared against the same observations. Our findings show that the CERRA dataset performs significantly better compared to the ERA5-Land reanalysis with respect to the replication of the examined meteorological variables and mPET. The added value of the CERRA data is particularly evident during the warm period of the year and in regions that are characterized by complex topography and/or proximity to the coastline. Combining the CERRA dataset with population and mortality data, we further showcase its applicability for human-biometeorological and heat–health studies at a local scale, using the regional unit of Rethymno (Crete) as a pilot area for the analysis.

During the analysis - funded by The European Agricultural Fund for Rural Development (EAFRD) that has financed the EU’s contribution to rural development program in Umbria (Italy) – of meteorological data from automatic weather stations following the WMO requirements [1], it has become evident the need of using the newest global climatic data (ERA5, ECMWF [2]) to compute indices to perform an extended quality control over data and to give climate information to the end users. The peculiar regional environment with strong orographic modulation of the Umbria region and the consequent impact over the precipitation field together with the signal of a seasonal and intra-seasonal change of the temperature distribution, show a complex impact of the climate change over the Umbria region and broadly over Central Italy. This work is trying to show how to account for the impact of climate change over the phenology of vineyards using different indices as SPI , SPEI, and Winkler starting from the new reanalysis dataset with enhanced resolution in time and space. Moreover, within this project agronomists and plant pathologists are cooperating with meteorological scientists to analyze and relate weather epidemic development in order to reduce the economic impact and environmental effects of airborne plant disease epidemics and ultimately to make the end-users timely decisions about the effective and economical application of fungicides and about other tactics to manage plant diseases. [1] WMO. Guide to agricultural meteorological practices (WMO-no. 134). World Meteorological Organization: Geneva, Switzerland, 2010. [2] https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era5

Abstract Reference evapotranspiration is a key element in agricultural management, particularly in a changing global environment, and represents an important requirement for the effective planning, monitoring, and management of water resources. However, accurate evapotranspiration estimation requires spatially well-distributed continuous meteorological data to capture regional variations, and reanalysis datasets are valuable tools for this purpose. In this context, this study aimed to assess the performance of the Copernicus European Regional ReAnalysis (CERRA) dataset in the western Iberian Peninsula, focusing on Portugal and Galicia (Spain). Meteorological data (air temperature, relative humidity, solar radiation, and wind speed) from several surface stations were used to analyze the differences between the observations and CERRA hindcasts. The reference evapotranspiration ( ET o ) was then computed for both datasets to estimate CERRA’s consistency and accuracy. The results revealed that CERRA data strongly correlated with the observational data, accurately capturing the spatial and temporal atmospheric patterns. Daily air temperature was the most accurately represented variable, followed by relative humidity, solar radiation, and wind speed. ET o estimates from the CERRA dataset were closely aligned with observations. The high spatial resolution of CERRA enabled an accurate representation of the regional climatic variations, addressing the weaknesses found in other reanaly

The Greenland Sea is a key player in the Atlantic Meridional Overturning Circulation (AMOC), crucial for forming dense waters through open-water convection and influencing global climate dynamics. Recent changes, such as decreasing sea ice concentration (SIC) and the shoaling of the mixed layer depth (MLD), have spurred detailed research into their impact on the AMOC. Our study, using the latest TOPAZ reanalysis, explores these changes from 1991 to 2021.To strengthen our findings, we meticulously compare a 10-year observational dataset, validating TOPAZ's ability to reproduce processes like dense water formation and MLD evolution in the Greenland Sea. We find notable agreement, with the MLD reaching intermediate depths, and TOPAZ's overflow water density aligning with observations. Results show a decrease in SIC and a shallowing of the MLD, linked to rising surface water temperatures.While our results indicate a similar trend, we're not ready to draw final conclusions. Further analysis is needed to understand how observational data compares to TOPAZ findings. Although reanalysis data provides valuable insights, it's crucial to validate everything with observational data. The comprehensive dataset and almost daily temporal resolution of our observational platforms significantly bolster the reliability of our conclusions.Understanding Greenland Sea variability is vital not only for decoding its role in the AMOC but also for grasping broader implications for the global climate s

To advance our understanding of the stratosphere, high-quality observational datasets of the stratosphere are needed. It is commonplace that reanalysis datasets are used to conduct stratospheric studies. However, the accuracy of these reanalyses at these heights is hard to infer due to a lack of in situ measurements. Satellite measurements provide one source of temperature information. As some satellite information is already assimilated into reanalyses, the direct comparison of satellite temperatures to the reanalysis is not truly independent. Stratospheric lidars use Rayleigh scattering to measure density in the middle and upper atmosphere, allowing temperature profiles to be derived for altitudes from 30 km (where Mie scattering due to stratospheric aerosols becomes negligible) to 80–90 km (where the signal-to-noise ratio begins to drop rapidly). The Network for the Detection of Atmospheric Composition Change (NDACC) contains several lidars at different latitudes that have measured atmospheric temperatures since the 1970s, resulting in a long-running upper-stratospheric temperature dataset. These temperature datasets are useful for validating reanalysis datasets in the stratosphere, as they are not assimilated into reanalyses. Here, stratospheric temperature data from lidars in the Northern Hemisphere between 1990–2017 were compared with the European Centre for Medium-Range Weather Forecasts ERA-Interim and ERA5 reanalyses. To give confidence to any bias found, temperature

Due to limited observational data, there remains considerable uncertainty in the estimation and spatiotemporal variations of land surface evapotranspiration (ET). Reanalysis products, with their advantages of high spatiotemporal resolution, global coverage, and long-term data availability, have emerged as powerful tools for studying ET. Nevertheless, the accuracy of reanalysis ET products varies among different products and the reasons for these accuracy differences have not been thoroughly investigated. This study evaluates the ability of different reanalysis ET products to reproduce the spatiotemporal patterns and long-term trends of ET in China, using remote sensing and water-balance-derived ET as reference. We investigate the possible reasons for their disparity by analyzing the three major climatic factors influencing ET (precipitation, solar radiation, and temperature). The findings reveal that compared to the water balance ET, the Global Land Evaporation Amsterdam Model (GLEAM) product is capable of reproducing the mean, interannual variability, and trends of ET, making it suitable for validating reanalysis ET products. In comparison to GLEAM ET, all reanalysis ET products exhibit consistent climatology and spatial distribution but show a clear overestimation, with multi-year averages being overestimated by 16–40%. There are significant differences among the reanalysis products in terms of interannual variability, long-term trends, and attribution. Within the common pe

Meteorological sensors deployed on ocean buoys frequently suffer from data loss or outliers due to electromagnetic interference and component failures caused by harsh weather and environmental conditions. Accurate reconstruction of corrupted buoy data remains a significant challenge, as conventional interpolation and imputation methods often fail to capture the inherent spatio-temporal dependencies in marine meteorological variables. To address this issue, this paper proposes a novel deep learning model that integrates Transformer and Graph Attention Network (GAT) architectures-termed the Spatio-Temporal Dual-Attention Network (ST-DAN). The model uses parallel computing to capture two aspects of the data: on one hand, it captures temporal dependencies through a Transformer enhanced by position encoding; on the other, it models inter-variable spatial correlations with a Graph Attention Network (GAT) based on a physically informed adjacency matrix, which dynamically adjusts the influence weights between variables to significantly enhance reconstruction accuracy. To evaluate the ST-DAN model, extensive experiments were conducted leveraging the ERA5 reanalysis dataset and in-situ observations from a Qingdao buoy, focusing on the reconstruction of temperature and wind speed data. The experiment result shows that ST-DAN outperformed baseline models (e.g., ARIMA, RNN, Bi-LSTM, and Transformer) across metrics including MAE, MSE, RMSE, and R². It indicates that the proposed model (ST-DAN) is off high robustness and achieves high-precision interpolation and anomaly correction for meteorological data.