ERA-40 Dataset Research Articles

Long-Term Prediction of Mesoscale Sea Surface Temperature and Latent Heat Flux Coupling Using the iTransformer Model

Mesoscale air–sea interaction, which is active in Western Boundary Currents (WBCs), has a non-negligible effect on mid-latitude climate variability. The analysis and prediction of the mesoscale air–sea interaction rely on high-resolution observation datasets and mesoscale-resolving climate models, which often require long processing times to estimate future changes and have several limitations. Therefore, in this study, we used a newly developed iTransformer model, which integrates mesoscale sea surface temperature anomaly (SSTa) and latent heat flux anomaly (LHFa) coupling coefficient data to predict future changes in SSTa–LHFa coupling. First, we individually trained the model using data corresponding to 1–15 past winters from ERA5 dataset. Thereafter, we used the trained model to predict SSTa–LHFa coupling coefficient for the next 10 winters. Compared with the predictions using only the coupling coefficient, the prediction yields 3.0% relative improvements when SST data were incorporated. The iTransformer model also showed the ability to reproduce the linear trend and mean value of mesoscale SSTa–LHFa coupling coefficients. Furthermore, we chose the optimal input length for each WBC and used the model to predict changes in mesoscale SSTa–LHFa coupling in the future. The results thus obtained were comparable to those obtained using mesoscale-resolving climate models, indicating that the iTransformer model showed satisfactory prediction performance. Therefore, it provides a novel pathway for exploring mesoscale air–sea interaction variations and predicting future climate change.

Chinese power structure in 2050 considering energy storage and demand response under high renewable power penetration ratio.

Energy storage and demand response offer critical flexibility to support the integration of intermittent renewable energy and ensure the stable operation of the power system. Using the ERA5 dataset and hourly power load data, this study develops an hourly-based dynamic optimization model to assess the roles of energy storage and demand response in Chinese (2050) power structure under high renewable energy penetration. The results revea; that: (1) Energy storage and demand response significantly contribute to reducing power transition cost, carbon emission, and power curtailment. Specifically, 2h storage duratin and 10% demand response capacity are found to reduce transition costs by 6.07 trillion CNY, carbon emissions by 11.38 billion tons, and annual power curtailment by 2.21 PWh. (2) By 2050, Chinese power structure will be dominated by wind and PV, with installed capacity exceeding 7000GW. Regional differences will be evident, and energy storage will account for 8%-20% of the total installed renewable power capacity, while facilitating the retirement of over 250GW of thermal power plants. (3) Complex substitution effects between energy storage, demand response, CCS, and renewable energy are observed, varying according to the duration of energy storage and the capacity of transmission lines across regions. (4) The operational mechanisms of energy storage and demand response align closely with PV generation patterns, showing high utilization from Feb to May. In contrast, thermal power generation and CCS mainly complement renewable power generation during the peak power demand period of Jul to Sep.

Divergent impacts of soil desiccation on atmospheric water vapor–temperature responses regulated by evapotranspiration

Abstract Climate warming induces temporally varying atmospheric water vapor, yet the spatial distribution of opposing trends across global land remains elusive. Here we use monthly ERA5 dataset to discern the responses of water vapor changes to rising air temperatures from 1982 to 2020. Simultaneous increase in both water vapor and air temperature over approximately three-quarters of global land, with a median of 0.21 mm·K–1, particularly evident in tropics. Strong positive responses are primarily influenced by increasing trends in evapotranspiration and low-elevation areas. About one-fifth of global land shows a decline in water vapor with a median of –0.62 mm·K–1, predominantly in southeastern South America and southwestern North America. Negative responses are also driven by evapotranspiration trends, where strong evapotranspiration enhances these effects that are less pronounced at high-altitude regions. The prevalence of positive response is highest during September-October-November (81%), while negative response observed most in December-January-February (35%). The spatial distribution of negative responses generally aligns with soil desiccation patterns; soil desiccation exacerbates negative responses in humid regions due to evaporative cooling but mitigates them in arid regions due to intensified warming. This study enhances our comprehension regarding divergent responses of atmospheric water vapor towards global warming.

The added value and potential of long-term radio occultation data for climatological wind field monitoring

Abstract. Global long-term stable 3D wind fields provide valuable information for climate-oriented analyses of the dynamics of the atmosphere. Their monitoring remains a challenging task given the shortcomings of available observations. One promising option for progress is the use of radio occultation (RO) satellite data, which enable deriving dynamics based on thermodynamic data. In this study we focus on three main goals, explored through the fifth version of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) and RO datasets, using monthly-mean January and July data over 2007–2020. Our focus is on a 2.5° × 2.5° spatial synoptic scale over the free troposphere to the mid-stratosphere (i.e. 800–10 hPa). First, by comparing ERA5-derived geostrophic and gradient wind speeds to the original ERA5 ones, we examine the regions of validity of the studied approximations at a given synoptic-scale resolution. Second, to assess the possible added value of the RO-derived climatic winds in terms of their long-term stability, we test their consistency with the corresponding ERA5-derived winds. Third, by comparing the RO climatic winds to the original ERA5 winds, we evaluate the potential benefit of RO as an additional dataset for wind analyses and climate monitoring. With this three-step analysis, we decompose the total wind speed bias into the contributions from the approximation and the systematic difference between the RO and ERA5 datasets. We find that the geostrophic approximation is a valid method to estimate winds in the free troposphere, while the gradient wind approximation works better in the lower stratosphere. Both approximations generally work well over the mentioned altitudes, within an accuracy of 2 m s−1 for the latitudes 5–82.5°. Exceptions are found in winter in the monsoonal area and above larger mountain ranges in the free troposphere, as well as above the northern polar regions in the mid-stratosphere. RO- and ERA5-derived geostrophic winds mostly showed good agreement (within 2 m s−1). However, temporal change in the systematic difference higher than 0.5 m s−1 per decade was found. This points to a possible impact of changes in the source of the assimilated data in ERA5. The overall high accuracy of the monthly-mean wind fields, backed by the long-term stability and fine vertical resolution of the underlying RO data, highlights the added value and potential benefit of RO-derived climatic winds for climate monitoring and analyses.

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 extratropical atmospheric circulation is influenced by Rossby waves propagating from the Indian Ocean toward 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, exploring potential reasons for a weak model response. Linear regression methods are employed to examine the extratropical links with the Indian Ocean dipole (IOD), both in isolation and in combination with 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 extratropical 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.

Spatio-Temporal Characteristics of Climate Extremes in Sub-Saharan Africa and Potential Impact of Oceanic Teleconnections

Sub-Saharan Africa (SSA) is a region vulnerable to extreme weather events due to its low level of adaptive capacity. In recent decades, SSA has been punctuated by more intense climatic phenomena that severely affect its population. Therefore, this study evaluates the performance of the ERA5 and CHIRPS datasets, and the spatio-temporal evolution of extreme weather indices and their potential relationship/response to climate variability modes in the Pacific, Indian, and Atlantic oceans, namely, the El Niño−Southern Oscillation, Indian Ocean Dipole, and Tropical Atlantic Variability (ENSO, IOD, and TAV). The CHIRPS dataset showed strong positive correlations with CPC in spatial patterns and similarity in simulating interannual variability and in almost all seasons. Based on daily CHIRPS and CPC data, nine extreme indices were evaluated focusing on regional trends and change detection, and the maximum lag correlation method was applied to investigate fluctuations caused by climate variability modes. The results revealed a significant decrease in total precipitation (PRCPTOT) in north−central SSA, accompanied by a reduction in Consecutive Wet Days (CWDs) and maximum 5-day precipitation indices (RX5DAYS). At the same time, there was an increase in Consecutive Dry Days (CDDs) and maximum rainfall in 1 day (RX1DAY). With regard to temperatures, absolute minimums and maximums (TNn and TXn) showed a tendency to increase in the center−north and decrease in the south of the SSA, while daily maximums and minimums (TXx and TNx) showed the opposite pattern. The IOD, TAV, and ENSO modes of climate variability influence temperature and precipitation variations in the SSA, with distinct regional responses and lags between the basins.

Satellite-Driven Hydrological Modelling in Ungauged Moroccan bassins: Case study Ouzoud river

Predicting streamflow is vital for flash-flood early warning systems and managing water resources under climate change. However, limited streamflow observations restrict advanced prediction techniques to gauged basins, leaving most of the world's ungauged basins at a disadvantage. Developing reliable prediction methods for ungauged basins (PUB) is therefore essential. Over the past two decades, satellite-driven products, such as ERA5, have become crucial for enhancing precipitation and meteorological measurements, especially in complex terrains and changing climatic conditions. This study focuses on Morocco's arid and semi-arid regions, where water resource management is critical for agriculture, urbanization, and economic stability. Using the ERA5 dataset, which provides high-resolution atmospheric information, the study evaluates satellite-derived precipitation against ground measurements from the Sgatt station at Bernat River on daily timescales. Various statistical metrics assess ERA5's performance in representing daily precipitation and its integration into the GR4J-CemaNeige model for flow simulation. Results highlight the potential of ERA5 in improving rainfall representation and hydrological modelling in ungauged basins, verifying its effectiveness in simulating rainfall events compared to ground-based observations. This work underscores the importance of satellite-driven products for enhancing hydrological predictions and supporting water management in data-scarce regions.

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 yr−1, 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 mid- 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.

Analysis of Dust Storm Intensity over Baghdad City

Background: Semi-arid regions of the world experience diverse climates and weather events, including dust storms. These storms suspend and transport particles in the atmosphere. Objective: The Iraqi Meteorological Organization and Seismology (IOM) is a key data source for researchers studying these dynamics, offering visibility data for Baghdad from several years (2005, 2008, 2009, 2011, 2018, 2022). Baghdad’s data is also included in the ERA-5 dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF), which provides precise hourly air temperature data at 2 meters above ground level. Methods: The study investigates the correlation between air temperature and dust storm severity in semi-arid regions, particularly during storm waves, which can constitute up to half of a storm’s duration. The severity of dust storms, influenced by temperature variations, is crucial in understanding storm characteristics. Results: The findings underscore the significance of air temperature in understanding dust storms’ behavior, aiding in refining forecasting models and emergency planning in vulnerable areas. The detailed spatial and temporal data from the study provide a foundation for in-depth research into the complexities of dust storms. Conclusions: By analyzing visibility and temperature records, the study emphasizes the role of climatic factors in shaping dust storm behavior in semi-arid regions.

Underestimation of Wave Energy from ERA5 Datasets: Back Analysis and Calibration in the Central Tyrrhenian Sea

Characterizing wave climate is crucial for coastal and offshore engineering applications. Reanalysis models, such as ERA5, are increasingly used due to their efficiency and lower costs compared to in situ measurements. However, their accuracy has not been thoroughly examined. This study addresses this gap by calibrating wave data from the ERA5 dataset with the available years of measurements from wave buoys in the Central Mediterranean Sea, specifically near Ponza, Cetraro, and Civitavecchia. A calibration approach was developed to adjust ERA5 wave data by aligning the model predictions closely with the co-located wave buoy observations. Results indicate that ERA5 systematically underestimates significant wave heights and periods, leading to an underestimation of wave power by up to 42% compared to buoy data. Calibration improved alignment between ERA5 and buoy measurements, enhancing wave energy representation and increasing estimated wave power by 35–48% annually. These findings underscore the importance of calibrating reanalysis datasets like ERA5 with in situ data to accurately assess wave energy potential, particularly in regions where model data may not fully capture local wave conditions. The outcomes provide valuable insights for wave energy projects in the Central Tyrrhenian Sea and similar semi-enclosed seas.

Development of a Diagnostic Algorithm for Detecting Freezing Precipitation from ERA5 Dataset: An Adjustment to the Far East

Freezing precipitation and the resultant ice glaze can have catastrophic impacts on urban infrastructure, the environment, forests, and various industries, including transportation, energy, and agriculture. In this study, we develop and evaluate regional algorithms for detecting freezing precipitations in the Far East, utilizing the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts, along with standard meteorological observations for 20 cold seasons (September–May) from 2004 to 2024. We propose modified diagnostic algorithms based on vertical atmospheric temperature and humidity profiles, as well as near-surface characteristics. Additionally, we apply a majority voting ensemble (MVE) technique to integrate outputs from multiple algorithms, thereby enhancing classification accuracy. Evaluation of detection skills shows significant improvements over the original method developed at the Finnish Meteorological Institute and the ERA5 precipitation-type product. The MVE-based method demonstrates optimal verification statistics. Furthermore, the modified algorithms validly reproduce the spatially averaged inter-annual variability of freezing precipitation activity in both continental (mean correlation of 0.93) and island (correlation of 0.54) regions. Overall, our findings offer a more effective and valuable tool for operational activities and climatological assessments in the Far East.

Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer.

Weather prediction is of great significance for human daily production activities, global extreme climate prediction, and environmental protection of the Earth. However, the existing data-based weather prediction methods cannot adequately capture the spatial and temporal evolution characteristics of the target region, which makes it difficult for the existing methods to meet practical application requirements in terms of efficiency and accuracy. Changes in weather involve both strongly correlated spatial and temporal continuation relationships, and at the same time, the variables interact with each other, so capturing the dynamic correlations among space, time, and variables is particularly important for accurate weather prediction. Therefore, we designed a spatiotemporal coupled prediction network based on convolution and Transformer for weather prediction from the perspective of multivariate spatiotemporal fields. First, we designed a spatial attention encoder-decoder to comprehensively explore spatial representations for extracting and reconstructing spatial features. Then, we designed a multi-scale spatiotemporal evolution module to obtain the spatiotemporal evolution patterns of weather using inter- and intra-frame computations. After that, in order to ensure that the model has better prediction ability for global and local hotspot areas, we designed a composite loss function based on MSE and SSIM to focus on the global and structural distribution of weather to achieve more accurate multivariate weather prediction. Finally, we demonstrated the excellent effect of STWPM in multivariate spatiotemporal field weather prediction by comprehensively evaluating the proposed algorithm with classical algorithms on the ERA5 dataset in a global region.

A Lightweight Transformer-Based Spatiotemporal Analysis Prediction Algorithm for High-Dimensional Meteorological Data

High-dimensional meteorological data offer a comprehensive overview of meteorological conditions. Nevertheless, predicting regional high-dimensional meteorological data poses challenges due to the vast scale and rapid changes. Apart from slow conventional numerical weather prediction methods, recently developed deep learning methods often fail to fully integrate spatial information of the high-dimensional data and require a significant amount of computational resources. This paper presents the spatiotemporal analysis fitting prediction algorithm (SA-Fit), an approximation algorithm for regional high-dimensional meteorological data prediction. SA-Fit proposes two key designs to achieve efficient prediction of the high-dimensional data. SA-Fit introduces a lightweight Transformer-based spatiotemporal analysis network to encode spatiotemporal information, which can integrate the interaction information between different coordinates in the data. Furthermore, SA-Fit introduces explicit functions with a lasso penalty to fit variations in high-dimensional meteorological data, achieving the prediction of a large amount of data with minimal prediction values. We performed experiments using the ERA5 dataset from the Shanghai and Xi’an regions. The experimental results show that SA-Fit is comparable to other advanced deep learning prediction methods in overall prediction performance. SA-Fit shortens training time and significantly reduces model parameters while using the Transformer structure to ensure prediction accuracy.

Associations between weather and Plasmodium vivax malaria in an elimination setting in Peru: a distributed lag analysis.

Plasmodium vivax ( Pv ) is the predominant malaria species in countries approaching elimination. In the context of climate change, understanding environmental drivers of transmission can guide interventions, yet evidence is limited, particularly in Latin America. We estimated the association between temperature and precipitation and Pv malaria incidence in a malaria elimination setting in Peru. We analyzed malaria incidence data from 2021-2023 from 30 communities in Loreto, Peru with hourly weather data from the ERA5 dataset and land cover data from MapBiomas. Predictors included average weekly minimum and maximum temperature, high heat (>90th percentile mean temperature), total weekly precipitation, and heavy rain (>90th percentile total precipitation). We fit non-linear distributed lag models for continuous weather predictors and generalized additive models for binary predictors and the lookback period was 2-16 weeks. Temperature models adjusted for total precipitation; precipitation models adjusted for maximum temperature. We performed subgroup analyses by season, community type, and distance to forest edge. The median vs. lowest values of weekly average minimum temperature was associated with 2.16 to 3.93-fold higher incidence 3-16 weeks later (5-week lag incidence ratio (IR) =3.93 [95% CI 2.18, 7.09]); for maximum temperature, the association was hump-shaped across lags, with protective associations at 1-2 and 15-16 week lags and 1.07-1.66-fold higher incidence at 6-13 week lags. High heat (>27.5°C) was associated with 1.23 to 1.37-fold higher incidence at 5--9 week lags (9-week lag IR = 1.25 [1.02, 1.53]). Associations between total precipitation and malaria incidence were hump-shaped across lags, with the strongest positive association at 750 mm of precipitation at a 9-week lag (IR=1.56; [1.27, 1.65]). Heavy rain (>186mm) was associated with 1.22-1.60-fold higher incidence at 2-10 week lags (9-week lag IR=1.23 [1.02, 1.49]). Higher temperatures and precipitation were generally associated with higher malaria incidence over 1-4 months.

A novel approach for snow depth retrieval in forested areas by integrating horizontal and vertical canopy structures information

Snow cover significantly influences the Earth’s climate system and global hydrological cycle through its thermal insulation properties and high albedo, and is an important component of the cryosphere. Currently, the most efficient means of quantifying snow depth at both global and regional scales is through passive microwave remote sensing. However, the accuracy of passive microwave remote sensing inversion of snow depth in forested areas is affected by the forest canopy. In this study, a normalized maximum stem volume (NMSV) index was constructed by combining canopy height and tree cover data obtained through remote sensing techniques. The NMSV index was then incorporated into development of snow depth retrieval algorithm to improve accuracy of passive microwave snow depth estimation in forested areas. Compared to the Chang algorithm and the AMSR-E snow depth product, this study demonstrated higher accuracy in the mid- to high-latitude forested areas of Eurasia, with an R value approximately twice as high and a reduction in the overall root mean square error (RMSE) by 2.3 cm and 7.2 cm, respectively. The relative mean bias of this study in the Western Russia, the Eastern Siberian Mountains and the Northeast China is significantly reduced than that of the existing remote sensing algorithms. Against the ERA5 and GlobSnow datasets, with the exception of the Western Russia, the performance of this study in the mid- to high-latitude forested areas of Eurasia is comparable to the ERA5 dataset and superior to the other datasets. Based on the performance of the algorithms in different NMSV values, we observe a decline in the accuracy of this algorithm when the value exceeds 0.8, which was caused by small size of high NMSV values among the ground observation sites involved in the development of snow depth retrieval algorithm. Overall, the NMSV index proposed in this study, which integrates information from both the horizontal and vertical structures of forest, can better characterize the microwave radiation properties of sparse and moderately dense forests, facilitating improvements in the accuracy of passive microwave snow depth retrieval in global forested areas.

Characterizing Long-term Astroclimate Parameters at the Muztagh-Ata Site in the Pamir Plateau with ERA5 and MERRA-2 Data

Abstract The Muztagh-Ata site, an excellent high-altitude ground-based astronomical observing site was discovered and monitored in the eastern part of the Pamir Plateau in the southwestern Xinjiang Uygur Autonomous Region of China. This site has been systematically monitored using various observational parameters since the spring of 2017. Yet, the site lacks long-term monitoring and statistical characterization of key variables such as: precipitable water vapour (PWV) and air temperature. These factors directly influence whether a site is suitable for hosting large ground-based telescopes across optical, infrared, submillimeter, and millimeter wavelengths in later stages. In this study, we utilized atmospheric reanalysis datasets from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and ERA5, the fifth edition of the European Centre for Medium-Range Weather Forecasts (ECMWF). These datasets were combined with local observations from the weather station at the Muztagh-Ata site. Following the validation of ground-based and satellite data, we conducted a comparative analysis of PWV and temperature trends at the Muztagh-Ata site over a period of 24 years. The weighted annual mean nighttime temperature and PWV increase at rates of 0.18 ∼ 0.38 ○C decade−1 and 0.02 ∼ 0.15 mm decade−1, respectively. The nighttime PWV slightly decrease during the winter with rates of −0.01 ∼ −0.03 mm decade−1. This findings reveal that the PWV and temperature variation patterns at the Muztagh-Ata site are consistently stable, particularly in the results derived from the ERA5 dataset. The comprehensive conditions at this site are highly suitable and advantageous for hosting large optical, infrared, submillimeter, and millimeter wavelengths telescope installations.

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 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 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 R 2 and root mean square error (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.

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.

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.