ERA-40 Dataset Research Articles

Diurnal Cycles of Cloud Properties and Precipitation Patterns over the Northeastern Tibetan Plateau During Summer

In the context of rising temperatures and increasing humidity in Northwest China, substantial gaps remain in understanding the mechanisms of land–atmosphere cloud–precipitation coupling across the northeastern Tibetan Plateau (TP), Loess Plateau (LP), and Huangshui Valley (HV). This study addresses these gaps by investigating cloud properties and precipitation patterns utilizing the Fengyun-4 Satellite Quantitative Precipitation Estimation Product (FY4A-QPE) and ERA5 datasets. We specifically focus on Lanzhou, a pivotal city within the LP, and Xining, which epitomizes the HV. Our findings reveal that diurnal variations in precipitation are significantly less pronounced in the eastern regions compared to northeastern TP. This discrepancy is attributed to marked diurnal fluctuations in convective available potential energy (CAPE) and wind shear between 200 and 500 hPa. While both cities share similar wind shear patterns and moisture transport directions, Xining benefits from enhanced snowmelt and effective water retention in surrounding mountains, resulting in higher precipitation levels. Conversely, Lanzhou suffers from moisture deficits, with dry, hot winds exacerbating the situation. Notably, precipitation in Xining is strongly correlated with CAPE, influenced by diurnal variability, and intensified by valley and lake–land breezes, which drive afternoon convection. In contrast, Lanzhou’s precipitation exhibits a weak relationship with CAPE, as even elevated values fail to generate significant cloud formation due to insufficient moisture. The ongoing trends of warming and humidification may lead to improved precipitation patterns, especially in the HV, with potential ecological benefits. However, concentrated rainfall during summer afternoons and midnights raises concerns regarding extreme weather events, highlighting the susceptibility of the HV to geological hazards. This research underscores the need to further explore the uncertainties inherent in precipitation dynamics in these regions.

Research on filling missing GNSS precipitable water vapor time series data using the PSORF model combined with reanalysis datasets

Abstract The inversion of precipitable water vapor (PWV) using the Global Navigation Satellite System (GNSS) has advantages such as all-weather observation, high precision, low cost, and high temporal resolution. Currently, long-term GNSS-PWV data has become an important data source for studying climate change. However, due to factors such as equipment failures, observation technology limitations, and estimation model errors, missing data and outliers often occur in real-time or post-processed PWV time series data. Furthermore, the main sources of GNSS-PWV errors are influenced by the atmospheric weighted mean temperature (Tm) and surface meteorological data (pressure and temperature). The results indicate that the European Centre for Medium⁃Range Weather Forecasts Reanalysis v5 (ERA5) dataset exhibits high accuracy in the Chinese region, making it suitable for GNSS-PWV inversion. By utilizing ERA5 meteorological data to calculate hourly GNSS-PWV and conducting accuracy assessments, it is demonstrated that the PWV inverted based on GNSS and ERA5 meteorological parameters possesses high precision. Based on this, this study selects GNSS stations from the Crustal Movement Observation Network of China (CMONOC) where the proportion of missing measured data is less than 8%. By combining ERA5, random forest (RF), and particle swarm optimization (PSO) algorithms, a new model called PSORF is proposed to fill in missing values in GNSS-PWV time series data. The research findings reveal that the R2 and RMSE of PSORF-PWV are 0.98 and 2.16 mm, respectively. Additionally, GNSS stations with more than 8% missing measured data are utilized to validate the accuracy of the PSORF model. A comparative analysis is conducted between the results obtained through the PSORF model and the ERA5-PWV acquired via traditional interpolation methods. The MAE and RMSE of PSORF-PWV are reduced by 21% and 17%, respectively, indicating that the PSORF model excels in filling missing data and effectively enhances the accuracy and reliability of PWV time series analysis. This study not only presents an effective approach for processing missing PWV data but also evaluates the applicability and accuracy of the ERA5 dataset in PWV inversion. This provides crucial technical support and data security for climate change research, short-term humidity field forecasting, and studies in related fields.

Assessing the environmental and economic viability of floating PV-powered green hydrogen: A case study on inland ferry operations in Türkiye

This paper conducts a comprehensive analysis of the environmental impacts and costs associated with a floating photovoltaic (FPV)-powered green hydrogen production system for a ferry line in Türkiye, utilizing life cycle assessment (LCA) and life cycle cost assessment (LCCA) methods. The methodology involves regridding daily data from 13 Global Climate Models (GCMs) to a consistent 1° × 1° grid, comparing this data with ERA5 datasets using various statistical methods, and identifying the top four GCMs. These models are then used to forecast ambient temperature, wind speed, and solar radiation for 2023–2052. Based on these forecasts, the FPV system's component sizes are determined. The LCA, based on the GREET 2022 database, reveals that green hydrogen is the most environmentally friendly option, reducing global warming potential (GWP) by 77.5 % compared to marine diesel oil (MDO 0.1 % S), with more than 62 % of GWP for the hydrogen-powered ferry attributed to PV production. LCCA results indicate that, without subsidies, green hydrogen is not economically feasible for the selected inland ferry line. Carbon taxes are insufficient to bridge cost differences, highlighting the need for financial incentives. Reducing corporate tax to 10 % is identified as the most effective incentive, and the Production Tax Credit (PTC) option maximizes internal rates of return for green hydrogen producers. This study provides valuable insights for sustainable energy choices in maritime transport, emphasizing the importance of both environmental and economic considerations.

Climatology of Cirrus Clouds over Observatory of Haute-Provence (France) Using Multivariate Analyses on Lidar Profiles

This study aims to achieve the classification of the cirrus clouds over the Observatory of Haute-Provence (OHP) in France. Rayleigh–Mie–Raman lidar measurements, in conjunction with the ERA5 dataset, are analyzed to provide geometrical morphology and optical cirrus properties over the site. The method of cirrus cloud climatology presented here is based on a threefold classification scheme based on the cirrus geometrical and optical properties and their formation history. Principal component analysis (PCA) and subsequent clustering provide four morphological cirrus classes, three optical groups, and two origin-related categories. Cirrus clouds occur approximately 37% of the time, with most being single-layered (66.7%). The mean cloud optical depth (COD) is 0.39 ± 0.46, and the mean heights range around 10.8 ± 1.35 km. Thicker tropospheric cirrus are observed under higher temperature and humidity conditions than cirrus observed in the vicinity of the tropopause level. Monthly cirrus occurrences fluctuate irregularly, whereas seasonal patterns peak in spring. Concerning the mechanism of the formation, it is found that the majority of cirrus clouds are of in situ origin. The liquid-origin cirrus category consists nearly entirely of thick cirrus. Overall results suggest that in situ origin thin cirrus, located in the upper tropospheric and tropopause regions, have the most noteworthy occurrence over the site.

Characteristic analysis of India-Burma trough events and its impact on winter precipitation in South and East Asia: Based on objective identification

Abstract During the boreal winter, the India-Burma trough (IBT), a shortwave trough system primarily positioned over the northern Bay of Bengal, exerts a significant synoptic scale variation and impact on precipitation in South and East Asia. This study utilizes the 6-hourly ERA5 dataset to objectively identify and track 714 IBT events from 1981 to 2019. On average, IBT occurred about 54.7 days per year, with an average of 18.3 events annually and lasting around 2.5 days. IBT events are classified into two types: local and eastward-moving. Local IBTs manifest in the middle and lower troposphere with a vertical temperature structure of warm-over-cold, whereas the signals of the eastward-moving IBTs extend from the lower to the upper troposphere, exhibiting cold anomalies vertically. The impacts on winter precipitation in South and East Asia differ significantly between these two IBT types. Local IBTs are primarily linked with the eastward propagation of wave disturbances along the subtropical westerly jet, while eastward-moving IBTs are associated with robust disturbance sources from mid-high latitudes and jointly modulated by the consequent eastward propagation of Rossby wave trains along northern and southern branches of the westerly jet streams. Precipitation anomalies during local IBTs are typically positive (negative) in the Indochina Peninsula and southwestern China (Indian Peninsula), whereas eastward-moving IBTs correlate with more intense and widespread precipitation in South and East Asia, with a pronounced band of anomalies from the Indochina Peninsula to southern China.

Propagation pathways from meteorological to agricultural drought in different climatic basins in iran.

The propagation of meteorological drought (MD) to agricultural drought (AD) is influenced by various factors, particularly the climate type. This research examined the characteristics of drought propagation, encompassing propagation rates, lag time, and response time, from MD to AD within the context of Iran's diverse climate conditions. This was accomplished using three crucial meteorological and agricultural drought indices, namely the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), and Standardized Soil Moisture Index. The research data included in-situ and ERA5 datasets from 30 basins (catchments) across Iran in the 1979-2021 period. Based on reports from the Global Drought Observatory, the three most prominent MD events were identified in 1999-2002, 2008-2009, and 2017-2019. The correlation coefficients between MD and AD indices across various timescales, in climates ranging from hyper-arid to humid, exhibited a decline from 0.75 to 0.44. The response time, varying between 2.42 to 6.63months, was determined by the strong correlation between SPI (or SPEI) and SSI1 (or SSI2) within the studied basins. Furthermore, the lag time, which was affected by the onset of MD and AD events, fluctuated between 2 to 6months in hyper-arid and arid basins, revealing a 1-3month variation compared to humid basins. The findings on propagation rates highlighted heightened sensitivity or response from meteorological to agricultural drought in humid climates, as opposed to hyper-arid climates. In light of these outcomes, comprehending the transition from MD to AD holds substantial significance for predicting, issuing early warnings, and fortifying preparedness in managing drought risks.

A 1940-2020 spatiotemporal analysis of thermal discomfort days in Southeast Asian countries

Abstract The high temperature and humidity make Southeast Asia (SEA) one of the regions most susceptible to the occurrence of thermal discomfort days (TDDs) in the world. In the context of global warming, SEA’s rapid population growth and urban expansion further exacerbated the region’s exposure to TDD, posing greater risks in public health. However, there exists a significant knowledge gap in the understanding of the long-term spatiotemporal evolution of TDD, as well as its projection in the future. By utilizing the newly released ERA5 datasets of Universal Thermal Climate Index (UTCI) and Mean Radiant Temperature (MRT), this study presented the 81-year analysis of TDDs in SEA countries, spanning from 1940 to 2020, with projections extending to 2100. While the mean increase in UTCI was relatively modest, the corresponding rise in TDD likelihood was disproportionately larger, indicating a heightened risk of exposure. A distinct contrast was observed between continental and maritime regions, with maritime countries showing smaller absolute rises in both indices but larger trends in TDD frequency due to oceanic moderating effects. Seasonal analyses highlighted the dominant influence of the East Asia monsoon over SEA, and spatial analyses revealed a negative correlation between TDD occurrence and elevation, with low-lying areas being hotspots. Projections for 2050 suggest continued warming. These findings underscore the urgency of proactive measures to address climate change impacts, particularly in vulnerable maritime and low-lying areas, providing valuable insights for enhancing climate resilience and adaptation in SEA. Our findings reveal critical insights into the trends and future scenarios of thermal discomfort in the region, underscoring the urgent need for effective climate adaptation strategies.

The combined link of the Indian Ocean dipole and ENSO with the North Atlantic-European circulation during early boreal winter in reanalysis and the ECMWF-SEAS5 hindcast

Abstract During early boreal winter, the extra-tropical atmospheric circulation is influenced by Rossby waves propagating from the Indian Ocean towards the North Atlantic-European (NAE) regions, resulting in a North Atlantic Oscillation (NAO)-like pattern. The mechanisms driving these teleconnections are not well understood and are crucial for improving model skills. This study investigates these mechanisms using the ERA5 dataset and tests the predictive capabilities of the ECMWF-SEAS5 hindcast, exploring potential reasons for a weak model response. Linear regression methods are employed to examine the extra-tropical links with the Indian Ocean dipole (IOD), both in isolation and in combination with the El Niño-Southern Oscillation (ENSO). Our findings demonstrate a connection between October IOD sea surface temperature anomalies and December Indian Ocean precipitation patterns. Furthermore, a correlation between the October IOD and December NAO time series suggests a link between the IOD and NAE circulation. The early winter European response to a positive IOD is characterized by a north-south precipitation dipole and a large positive surface air temperature anomaly. Positive feedback from transient eddy forcing reinforces the wavenumber-3-like propagation across extra-tropical regions, with ENSO playing a minor role compared to the IOD. This phenomenon is particularly evident in regions such as the North Pacific and North Atlantic, where wave energy propagation is intensified. Although SEAS5 replicates the NAO response, its magnitude is significantly weaker. The model struggles to simulate the delayed rainfall dipole response to the IOD accurately and shows structural discrepancies compared to reanalysis data.

Unravelling the performance of atmospheric radiative transfer schemes in the simulation of mean surface climate in Central Africa using the RegCM5 climate model

AbstractThe theory of radiative transfer in the atmosphere is crucial in the study of climate, because radiative exchanges are at the origin of the atmospheric dynamics. It is therefore important to evaluate this phenomenon in order to be able to take effective measures to tackle climate change. The objective of this work is to evaluate the capability of the RegCM5 climate model to reproduce radiative transfer over Central Africa. The analysis is carried out over a 10‐year period, from January 2002 to December 2011 preceded by 1 year as spin‐up. RegCM5 model were evaluated using the ERA5 dataset for the radiative transfer parameters (the shortwave radiation [SWR], longwave radiation [LWR], cloud cover [CLT], surface albedo [ALB] and surface temperature), as well as CHIRPS dataset for precipitation. Three subregions were identified for more specific analysis of the model, namely the Sahel, Congo basin and Cameroon highlands. Two radiative schemes were used: the radiative scheme of the community climate model (CCM) and Rapid Radiative Transfer Model (RRTM). The assessment of radiative transfer parameters was carried out by examining their seasonal variability and annual cycles using data from two RegCM5 experiments, RegCM5‐CCM3 and RegCM5‐RRTM. Before this assessment, a sensibility analysis to convective schemes carried out with the default RegCM5 radiative scheme (CCM3) shows that Grell scheme with Arakawa and Shulber closure is the best scheme to represent key radiation parameters (LWR and SWR). This convective scheme is therefore used for assessing the two Radiative transfer schemes. Results show that both RegCM5 experiments simulate relatively well the variables linked to radiative transfer for the four seasons of the year. However, RegCM5 with RRTM as radiative scheme depicts better performance over all subregions and seasons, suggesting that the choice of this scheme does not depend on land cover, topography and rainfall regimes in a complex region such as Central Africa.

Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models

AbstractThe Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years, it has shown significant utility across various climate-related tasks, ranging from forecasting and climate change research to renewable energy prediction, resource management, air quality risk assessment, and the forecasting of rare events, among others. Unfortunately, the availability of CERRA is lagging 2 years behind the current date, due to constraints in acquiring the requisite external data and the intensive computational demands inherent in its generation. As a solution, this paper introduces a novel method using diffusion models to approximate CERRA downscaling in a data-driven manner, without additional informations. By leveraging the lower resolution ERA5 dataset, which provides boundary conditions for CERRA, we approach this as a super-resolution task. Focusing on wind speed around Italy, our model, trained on existing CERRA data, shows promising results, closely mirroring the original CERRA. Validation with in-situ observations further confirms the model’s accuracy in approximating ground measurements.

Association of PM2.5 exposure in early pregnancy and maternal liver function: A retrospective cohort study in Shenzhen, China

ObjectiveStudies have shown that fine particulate matter (PM2.5) has adverse effects on the liver function, but epidemiological evidence is limited, especially regarding pregnant women. This study aims to investigate the association between PM2.5 exposure in early pregnancy and maternal liver function during pregnancy. MethodsThis retrospective cohort study included 13,342 pregnant participants. PM2.5 and Ozone (O3) exposure level, mean temperature, and relative humidity for each participant were assessed according to their residential address. The levels of serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total bilirubin (TBIL) were measured during the second and third trimesters. Data on PM2.5 and O3 exposure level were sourced from Tracking Air Pollution in China (TAP), while the mean temperature and relative humidity were obtained from the ERA5 dataset. The Generalized Additive Model (GAM) was used to analyze the associations between PM2.5 exposure and maternal liver function during pregnancy, adjusting for potential confounding factors. ResultsAccording to the results, each 10 μg/m3 increase in PM2.5 was associated with an increase of 3.57% (95% CI: 0.29%, 6.96%) in ALT and 4.25% (95% CI: 2.33%, 6.21%) in TBIL during the second trimester and 4.51% (95% CI: 2.59%, 6.47%) in TBIL during the third trimester, respectively. After adjusting for O3, these associations remained significant, and the effect of PM2.5 on ALT during the second trimester was further strengthened. No significant association observed between PM2.5 and AST. ConclusionsPM2.5 exposure in early pregnancy is associated with increasement of maternal ALT and TBIL, suggesting that PM2.5 exposure may have an adverse effect on maternal liver function. Although this finding indicates an association between PM2.5 exposure and maternal liver function, more research is needed to confirm our findings and explore the underlying biological mechanisms.

Evaluating heat stress and occupational risks in the Southern Himalayas under current and future climates

The southern Himalayas, characterized by its dense population and hot, humid summers, are confronted with some of the world’s most severe heat stress risks. This study uses the hourly ERA5 dataset (1979–2022) and CMIP6 projections (2005–2100) to evaluate past and future heat stress based on the Wet Bulb Globe Temperature (WBGT). This has significant implications for the management of occupational workloads in the southern Himalayas. Heat stress levels are classified into 6 categories (0 to 5) using WBGT threshold intervals of 23 °C, 25 °C, 28 °C, 30 °C, and 33 °C. With heat stress surpassing level 3 for almost half of the time, people are constrained to engage in less than moderate workloads to ensure their health remains uncompromised. Flow-analogous algorithm is employed to contextualize the unprecedented heat stress case in the summer of 2020 and the associated atmospheric circulation patterns from historical and future perspectives. The results show that over 80% of the time in 2020, heat stress levels were at 3 and 4. The identified circulation pattern explains 27.6% of the extreme intensity, and such an extreme would have been nearly impossible in pre-21st-century climate conditions under the identified pattern. Future projections under SSP2-4.5 and SSP5-8.5 scenarios indicate that heat stress similar to what was experienced in 2020 will likely become a common occurrence across the southern Himalayas. Under a similar circulation pattern, the heat stress levels by the end of the 21st century would be elevated by at least one category compared to the climatic baseline in over 70% of the region, leading to an additional 120.5 (420.1) million daily population exposed to the highest heat stress level under the SSP2-4.5 (SSP5-8.5) scenario.

Elevation‐dependent warming and possible‐driving mechanisms over global highlands

AbstractElevation‐dependent warming (EDW) has been a topic of intense debate due to limited observed data in global highland areas. This study aims to fill this gap by utilizing CRU and ERA5 datasets from 1981 to 2021 to explore the trends of climate change and its elevation dependency. The anomalies of temperature indicators (Tmean, Tmax, and Tmin) in both ERA5 and CRU showed significant warming trends over global highlands. Moreover, the response of temperature indicators to elevation across global highlands is not spatially uniform. The linear regression model based on the elevation showed significant warming signals for the temperature indicators at various elevations over the global highlands. On a regional scale, Tmean and Tmax predominantly showed linear EDW over EU highlands, while Tmean in Asian highlands exhibited EDW signals at 4–5 km. Tmin showed EDW at 2.5–5.5 km with ERA5 and 3–5 km with CRU. In the Andes, EDW was prominent at 2.5–4 km. Overall, EDW signals are evident in all studied regions but vary across them. While assessing the driving factors, the results of this study indicate that total column water vapour (TCWV), snow depth (SD), snow albedo, and normalized difference vegetation index (NDVI) correlated positively with the temperature indicators. These findings emphasize the significance of elevation‐specific interactions between environmental factors and temperature in forecasting temperature changes in mountainous areas. Additionally, temperature exhibited coherence with teleconnection indices from the Atlantic and Pacific Oceans. Asian and European (EU) highlands exhibited interzonal coherence with the Pacific and Atlantic Oceans, while North American (NA) highlands showed coherence, followed by South American (SA) highlands. These findings provide a comprehensive understanding of EDW and its implications for highland regions globally, including the potential for more severe depletion of snow/ice resources in a warmer future.

Creating High-Resolution Precipitation and Extreme Precipitation Indices Datasets by Downscaling and Improving on the ERA5 Reanalysis Data over Greece

The aim of this study was to construct a high-resolution (1 km × 1 km) database of precipitation, number of wet days, and number of times precipitation exceeded 10 mm and 20 mm over Greece on a monthly and on an annual basis. In order to achieve this, the ERA5 reanalysis dataset was downscaled using regression kriging with histogram-based gradient boosting regression trees. The independent variables used are spatial parameters derived from a high-resolution digital elevation model and a selection of ERA5 reanalysis data, while as the dependent variable in the training stages, we used 97 precipitation gauges from the Hellenic National Meteorological Service for the period 1980–2010. These stations were also used for validation purposes using a leave-one-out cross-validation methodology. The results of the study showed that the algorithm is able to achieve better R2 and RMSE over the standalone ERA5 dataset over the Greek region. Additionally, the largest improvements were noticed in the wet days and in the precipitation over 10 and 20 mm, where the ERA5 reanalysis dataset overestimates the number of wet days and underestimates precipitation over 10 and 20 mm, while geographically, the ERA5 dataset performs the worst in the island regions of Greece. This indicates that the ERA5 dataset does not simulate the precipitation intensity accurately over the Greek region, and using our methodology, we were able to increase the accuracy and the resolution. Our approach delivers higher-resolution data, which are able to more accurately depict precipitation in the Greek region and are needed for comprehensive climate change hazard identification and analysis.

Source contributions to two super dust storms over Northern China in March 2021 and the impact of soil moisture

Two extremely devastating super dust storms (SDS) hit Mongolia and Northern China in March 2021, causing many deaths and substantial economic damage. Accurate forecasting of dust storms is of great importance for avoiding or mitigating their effects. One of the most critical factors affecting dust emissions is soil moisture, but its value in desert exhibits significant uncertainty. In this study, model experiments were conducted to simulate dust emissions using four soil moisture datasets. The results were compared with observations to assess the effects of soil moisture on the dust emission strength. The Integrated Source Apportionment Method (ISAM) was used to track the dust sources and quantify the contribution from each source region to the dust load over the North China Plain (NCP), Korea peninsula, and western Japan. The results show large differences in the dust load depending on the soil moisture datasets used. The high soil moisture in the NCEP dataset results in substantial underestimation of the dust emission flux and PM10 concentration. Despite a minor overestimation of PM10 concentrations in many Northern China cities, the ERA5 dataset yields the best simulation performance. During the two SDS events, about 7.5 Mt dust was released from the deserts in Mongolia and 2.8 Mt from the deserts in China. Source apportionment indicates that the Mongolian Gobi Desert is the dominant source of PM10 in the NCP, Korea peninsula, and western Japan, accounting for 60 %–80 %, while Inner Mongolia contributed 10 %–20 %.

Prediction and analysis of sea surface temperature based on LSTM-transformer model

This paper introduces a novel method for predicting sea surface temperature (SST) using a hybrid LSTM-Transformer model. The study utilizes the ERA5 dataset for SST prediction at six specific locations near China. The LSTM-Transformer model combines the temporal processing capability of LSTM with the efficient data processing power of Transformer, showing superior performance in reducing Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), as well as improving the R-squared (R²) value, compared to standalone LSTM, Transformer, and traditional Logistic Regression (LR) models. This is particularly evident in spring and autumn, indicating its robustness to seasonal changes. The model's performance varies across different geographic locations, with lower prediction errors in low-latitude and open sea areas, attributed to the less complex environmental dynamics compared to continental shelf areas. Overall, the LSTM-Transformer hybrid model presents a significant advancement in SST prediction, providing important implications for fisheries, meteorology, and climate change research.

ClimaMeter: contextualizing extreme weather in a changing climate

Abstract. Climate change is a global challenge with multiple far-reaching consequences, including the intensification and increased frequency of many extreme-weather events. In response to this pressing issue, we present ClimaMeter, a platform designed to assess and contextualize extreme-weather events relative to climate change. The platform offers near-real-time insights into the dynamics of extreme events, serving as a resource for researchers and policymakers while also being a science dissemination tool for the general public. ClimaMeter currently analyses heatwaves, cold spells, heavy precipitation, and windstorms. This paper elucidates the methodology, data sources, and analytical techniques on which ClimaMeter relies, providing a comprehensive overview of its scientific foundation. We further present two case studies: the late 2023 French heatwave and the July 2023 Storm Poly. We use two distinct datasets for each case study, namely Multi-Source Weather (MSWX) data, which serve as the reference for our rapid-attribution protocol, and the ERA5 dataset, widely regarded as the leading global climate reanalysis. These examples highlight both the strengths and limitations of ClimaMeter in expounding the link between climate change and the dynamics of extreme-weather events.

Impact of global warming on wind power potential over East Asia

Wind power, which is the fastest–growing renewable energy source, is directly influenced by weather or climate conditions. This study examined the recent changes and future projections under different carbon emission levels in the wind power potential (Wpot) over East Asia using the ERA5 datasets and high–resolution multiple regional climate models (RCMs). Because wind conditions are constantly changing, we employed the wind velocity range–based approach for estimating the Wpot using the shortest time interval data available, which is the three–hourly wind velocity. Seasonal Wpot climatology of area–averaged Wpot over East Asia was the highest in spring. The average Wpot showed the highest values in northern China and southern Mongolia throughout all seasons, while recent changes in the Wpot over East Asia were characterized by overall increases in spring, autumn, and winter, but a decrease in summer. However, there were large regional variabilities. Northern China and southern Mongolia are expected to remain significant regions with high Wpot in the future. This is due to the large contribution of the frequency of optimal wind conditions, ranging from 12 m s−1 to 25 m s−1, to the Wpot in these regions, both currently and in the future. The average future Wpot in East Asia is expected to show no significant changes in both the low and the highest carbon emission scenarios compared to the present. However, it is projected that regional disparities in the Wpot will increase in a scenario with enhanced emission by the end of the 21st century.

The Development of a Hailstone Disdrometer and Its Preliminary Observation in Aksu, Xinjiang

Hailfall is a severe local weather event that can cause great economic losses as well as the loss of people’s property; however, it is still difficult for domestic meteorological stations to comprehensively observe hail, and domestic independently developed hail observation instruments are still scarce. To help enable better automatic hail observations, a new independently developed hailstone disdrometer based on the acoustic principle, which can be used to measure the hailstone number and particle size and to calculate the corresponding equivalent liquid precipitation of hailstones, is proposed in this paper. The characteristics of hailstones were preliminarily analyzed using observation data from two hailstone disdrometers installed in Aksu, Xinjiang, where three hail events were observed via the hailstone disdrometer in the summer of 2023. By analyzing the development of deep convection clouds using the Fengyun 4A satellite-based cloud-top brightness temperature, and synoptic conditions based on the fifth-generation global climate reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (the ECMWF ERA5 dataset), the hail formation mechanism was investigated in detail for one hailfall event. Accurate hail observations are an important basis for understanding spatiotemporal hail variation. The hailstone disdrometer proposed in this study offers a useful approach for domestic hail observation to provide first-hand hail information for the inspection of weather modification effects and disaster prevention and reduction.

Development of a monthly PM2.5 forecast model for Seoul, Korea, based on the dynamic climate forecast and a convolutional neural network algorithm

A convolutional neural network (CNN) model has been developed to predict monthly concentrations of particulate matter with a diameter ≤ 2.5 μm (PM2.5) in Seoul, Republic of Korea, during winter months (November through February). Seven meteorological variables influencing PM2.5 concentrations were selected as predictors: geopotential heights at 1000 and 500 hPa, zonal and meridional winds, relative humidity and temperature at 850 hPa, and boundary layer height. These predictors were obtained from the fifth generation of the European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) for model construction and from the Korea Polar Prediction System (KPOPS) dynamic forecasts for operational predictions. From the 11-year ERA5 dataset for 2008–2020, one year was allocated for testing, while the remaining 10 years were randomly assigned for either training (80%) or validation (20%). This process was repeated until all 11 years were allocated for testing, resulting in 11 CNN models. The benchmark which used the ERA5 dataset as the predictors yielded a mean bias error (MBE) of 0.05 μg m−3, root mean square error (RMSE) of 2.41 μg m−3, Pearson correlation coefficient (R) of 0.85, and 71% hit rate in predicting the months of high PM2.5 concentrations (≥ 30 μg m−3) for the testing periods. The 11-CNN-model ensemble predictions using the KPOPS forecasts resulted in an MBE of 0.1 μg m−3, RMSE of 3.19 μg m−3, R of 0.74, and hit rate of 62.5% for the period 2008–2020. Considering that the average temporal correlation coefficients between the ERA5 and the KPOPS forecasts for the seven predictors ranged from 0.20 to 0.66, the CNN-based PM2.5 prediction model has demonstrated good performance in overcoming the adverse effects of the KPOPS forecast errors on predicting PM2.5 concentrations. The CNN model showed limitations for unusual period such as the COVID-19 period, November 2020 through February 2022, when the emissions in East Asia were substantially below their climatological normal levels. In conclusion, the CNN model developed in this study showed potential applicability to operational monthly PM2.5 concentration predictions for the years of normal emissions using solely meteorological data. This can provide professionals in various fields, such as health care and environmental science, with valuable insights for long-term planning.