ERA-40 Data Research Articles

Impact of mountain-wave-induced temperature fluctuations on the occurrence of polar stratospheric ice clouds: a statistical analysis based on MIPAS observations and ERA5 data

Abstract. Temperature fluctuations induced by mountain waves can play a crucial role in the formation of polar stratospheric clouds (PSCs). In particular, the cold phase of the waves can lower local temperatures sufficiently to trigger PSC formation, even when large-scale background temperatures are too high. To provide new quantitative constraints on the relevance of this effect, this study analyzes a decade (2002–2012) of ice PSC detections obtained from Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) measurements and ERA5 data in the polar winter lower stratosphere. In the MIPAS observations, we find that approximately 52 % of the Arctic ice PSCs and 26 % of the Antarctic ice PSCs are detected at temperatures above the local Tice. Ice PSCs above Tice are concentrated around mountainous regions and their downwind directions. A backward-trajectory analysis is performed to investigate the temperature history of each ice PSC observation. The cumulative fraction of ice PSCs above Tice increases as the trajectory gets closer to the observation point. The most significant change in the fraction of ice PSCs above Tice occurs within the 6 h preceding the observations. At the observation point, the mean fractions of ice PSCs above Tice, taking into account temperature fluctuations along the backward trajectory, are 33 % in the Arctic and 9 % in the Antarctic. The results provide a quantitative assessment of the occurrence of ice PSCs above Tice in connection with orographic waves. Additionally, the observational statistics presented can be utilized for comparison with chemistry climate simulations.

Exploring Topography Downscaling Methods for Hyper‐Resolution Land Surface Modeling

AbstractHyper‐resolution land surface modeling provides an unprecedented opportunity to simulate locally relevant water and energy cycles. However, the available meteorological forcing data is often insufficient to fulfill the requirements of hyper‐resolution modeling. Here, we developed a comprehensive downscaling framework based on topography‐adjusted methods and automated machine learning (AutoML). With this framework, a 90 m and hourly atmospheric forcing data set was developed from ERA5 data at a 0.25° resolution, and the Common Land Model (CoLM) was then forced with the developed forcing data over two complex terrain regions (the Heihe River Basin and Upper Colorado River Basin). We systematically evaluated the downscaled forcing and the CoLM outputs against both in situ observations and gridded data. The ground‐based validation results suggested consistent improvements for all downscaled forcing variables with mean RMSE improved by 6.362%–95.86%. The downscaled forcings, which incorporated detailed topographic features, offered improved magnitude estimates, achieving a comparable level of performance to that of regional reanalysis forcing data. The downscaled forcing driving the CoLM model showed comparable or better skills in simulating water and energy fluxes, as verified by in situ validations. The hyper‐resolution simulations provided a detailed and more reasonable description of land surface processes and attained similar spatial patterns and magnitudes with high‐resolution land surface data, especially over highly elevated areas. Additionally, this study highlighted the benefits of using mountain radiation theory‐based shortwave radiation downscaling models and AutoML‐assisted precipitation downscaling models. These findings emphasized the significance of integrating topography‐based downscaling methods for hillslope‐scale simulations.

Long-Term Trends in Significant Wave Heights in The Mediterranean as an Indicator of Climate Change

The Mediterranean coastal zones, particularly along the Nile Delta, are increasingly vulnerable to the impacts of climate change. This study examines the historical changes in the significant wave height along the Nile Delta coast over the past 84 years (1940-2023) at a depth of 20 meters. By synthesizing a comprehensive long-term wave data series using ERA5 reanalysis data and the MIKE21-SW model, the study establishes a robust foundation for analysis. To ensure accuracy, the observed wind data were contrasted against ERA5 data, and they were found to be similar to a great extent. Furthermore, the model's accuracy was rigorously validated against the observational data, ensuring the reliability of the results. Three methods were used to obtain the trends for the maximum wave height to ensure robust and reliable trend assessments (i.e., linear regression, Theil-Sen, and ElasticNet). The results were obtained and analyzed, revealing a statistically significant increase in the maximum wave heights during the study period. Particularly, seasonal analysis revealed a positive trend in maximum wave heights during winter and spring (0.15 to 0.67 cm/year respectively). Furthermore, the monthly analysis showed a positive trend in the wave heights for all months except July, August, and September. Specifically, there were notable increases from September to December, ranging from 0.24 to 0.92 cm/year. These findings are consistent with the global trends of increasing wave heights due to climate change. It is essential for developing adaptive coastal management strategies and designing resilient structures. Consequently, the results will assist coastal authorities in mitigating the impacts of climate change on coastal zones.

Drivers of Cold Frontal Hourly Extreme Precipitation: A Climatological Study Over Europe

AbstractIn the mid‐latitudes extreme precipitation events are strongly associated with cold fronts. By exploring drivers across different scales and relating them to precipitation, we aim to improve our understanding of processes influencing cold frontal extremes. Using hourly ERA5 data over Europe and the North Atlantic, cold fronts are detected and the associated conditions are identified. Quantile regression models are employed to find drivers of frontal precipitation and to quantify these relations. Additionally, we use composites to study the synoptic conditions and meso‐scale structure of extreme events. We find that humidity close to the detected fronts, convergence and the low level jet speed contribute most to the formation of extreme precipitation. Synoptic conditions favoring the formation of extreme events are also identified. These results improve our understanding of cold frontal processes leading to precipitation. Additionally, they provide the foundation for a process‐based evaluation of frontal dynamics in climate models.

Analysis of the Response Relationship Between PWV and Meteorological Parameters Using Combined GNSS and ERA5 Data: A Case Study of Typhoon Lekima

Precipitable water vapor (PWV) is a crucial parameter of Earth’s atmosphere, with its spatial and temporal variations significantly impacting Earth’s energy balance and weather patterns. Particularly during meteorological disasters such as typhoons, PWV and other meteorological parameters exhibit dramatic changes. Studying the response relationship between PWV and typhoon events, alongside other meteorological parameters, is essential for meteorological and climate analysis and research. To this end, this paper proposes a method for analyzing the response relationship between PWV and meteorological parameters based on Wavelet Coherence (WTC). Specifically, PWV and relevant meteorological parameters were obtained using GNSS and ERA5 data, and the response relationships between PWV and different meteorological parameters before and after typhoon events were studied in time–frequency domain. Considering that many GNSS stations are not equipped with meteorological monitoring equipment, this study interpolated meteorological parameters based on ERA5 data for PWV retrieval. In the experimental section, the accuracy of ERA5 meteorological parameters and the accuracy of PWV retrieval based on ERA5 were first analyzed, verifying the feasibility and effectiveness of this approach. Subsequently, using typhoon Lekima as a case study, data from six GNSS stations affected by the typhoon were selected, and the corresponding PWV was retrieved using ERA5. The WTC method was then employed to analyze the response relationship between PWV and meteorological parameters before and after the typhoon’s arrival. The results show that the correlation characteristics between PWV and pressure can reveal different stages before and after the typhoon passes, while the local characteristics between PWV and temperature better reflect regional precipitation trends.

The Zenith Total Delay Combination of International GNSS Service Repro3 and the Analysis of Its Precision

Currently, ground-based global navigation satellite system (GNSS) techniques have become widely recognized as a reliable and effective tool for atmospheric monitoring, enabling the retrieval of zenith total delay (ZTD) and precipitable water vapor (PWV) for meteorological and climate research. The International GNSS Service analysis centers (ACs) have initiated their third reprocessing campaign, known as IGS Repro3. In this campaign, six ACs conducted a homogeneous reprocessing of the ZTD time series spanning the period from 1994 to 2022. This paper primarily focuses on ZTD products. First, the data processing strategies and station conditions of six ACs were compared and analyzed. Then, formal errors within the data were examined, followed by the implementation of quality control processes. Second, a combination method is proposed and applied to generate the final ZTD products. The resulting combined series was compared with the time series submitted by the six ACs, revealing a mean bias of 0.03 mm and a mean root mean square value of 3.02 mm. Finally, the time series submitted by the six ACs and the combined series were compared with VLBI data, radiosonde data, and ERA5 data. In comparison, the combined solution performs better than most individual analysis centers, demonstrating higher quality. Therefore, the advanced method proposed in this study and the generated high-quality dataset have considerable implications for further advancing GNSS atmospheric sensing and offer valuable insights for climate modeling and prediction.

Effect of temperature on seasonal wind power and energy potential estimates in Nordic climates

AbstractA major obstacle standing in the way of full‐scale adoption of renewable energy sources is their intermittency and seasonal variability. To better understand the power generation dynamics, the effect of air density due to temperature on power and energy generation figures was modelled. The model uses historical ERA5 data and considers changes in weather patterns in a subarctic climate where seasonal changes are most pronounced. The power generation figures of using a mean and a dynamic air density value were compared and the results show that power generation estimates may be under‐ and overestimated by on average 5% up to 10% in winter and summer, respectively. This can have implications for the sizing of power transmission infrastructure and energy storage in both on‐grid and off‐grid applications as well as power availability. The topic is highly relevant in the Nordic countries where roughly 10% of new global added wind capacity is installed annually.

Typhoon Trajectory Prediction by Three CNN+ Deep-Learning Approaches

The accuracy in predicting the typhoon track can be key to minimizing their frequent disastrous effects. This article aims to study the accuracy of typhoon trajectory prediction obtained by combining several algorithms based on current deep-learning techniques. The combination of a Convolutional Neural Network with Long Short-Term Memory (CNN+LSTM), Patch Time-Series Transformer (CNN+PatchTST) and Transformer (CNN+Transformer) were the models chosen for this work. These algorithms were tested on the best typhoon track data from the China Meteorological Administration (CMA), ERA5 data from the European Centre for Medium-Range Weather Forecasts (ECMWF), and structured meteorological data from the Zhuhai Meteorological Bureau (ZMB) as an extension of existing studies that were based only on public data sources. The experimental results were obtained by testing two complete years of data (2021 and 2022), as an alternative to the frequent selection of a small number of typhoons in several years. Using the R-squared metric, results were obtained as significant as CNN+LSTM (0.991), CNN+PatchTST (0.989) and CNN+Transformer (0.969). CNN+LSTM without ZMB data can only obtain 0.987, i.e., 0.004 less than 0.991. Overall, our findings indicate that appropriately augmenting data near land and ocean boundaries around the coast improves typhoon track prediction.

Hydrological Cycle in the Arabian Sea Region from GRACE/GRACE-FO Missions and ERA5 Data

Hydrological Cycle in the Arabian Sea Region from GRACE/GRACE-FO Missions and ERA5 Data

Sensitivity of horizontal resolution and land surface model in operational WRF forecast for Online Nuclear Emergency Response System (ONERS)

Accurate Meteorological forecasts are crucial for the assessment of plume dispersion and dose prediction in nuclear power plant (NPP) sites. In this work the forecast sensitivity of the Weather Research and Forecasting (WRF) model is tested by running a series of forecast simulations for horizontal resolution, and land surface models (LSM) in the context of Online Nuclear Emergency Response System (ONERS) for Indian NPP sites. 72 h forecast simulations are made for three seasons viz. summer, southeast and northeast monsoon using the Global Forecast data. Three simulation experiments, namely 2 km-NOAH, 3 km-NOAH and 3 km-NOAHMP are conducted using two different nested domain configurations (18–6–2 km and 9–3 km) and two LSM schemes (NOAH and NOAH-MP) and tested at four different NPP sites. Forecast comparison of surface winds, relative humidity, temperature, heat fluxes and planetary boundary layer heights with data from meteorological tower, radiosonde and the Modern-Era Retrospective analysis for Research and Applications 2 (MERRA-2) shows 3 km-NOAH is equally capable in predicting surface parameters as well as vertical profiles compared to 2 km-NOAH with marginal differences. 3 km-NOAHMP shows less mean bias and better correlation for boundary layer height and heat fluxes. Comparison of spatial flow-field with 5th generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data shows synoptic scale seasonal winds, sea level pressure systems and temperature hot-spots are better captured by 3 km-NOAHMP compared to 6 km coarse domain in the 18–6–2 km configuration. The daily accumulated rainfall by all simulations is overestimated compared to ERA5 data. The predictions by 3 km-NOAHMP better agree with Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM-IMERGE) data whereas 2 km-NOAH predicts delayed rainfall occurrence. Dispersion simulations of hypothetical plume release from a coastal NPP site with all three forecasts properly show the influence of local scale diurnal land-sea breeze and seasonal winds on the plume movement. Therefore the 9–3 km domain with NOAHMP LSM is found to be a suitable choice for operational weather forecast in ONERS for Indian NPP sites.

Abnormal high water temperature prediction in nearshore waters around the Korean Peninsula using ECMWF ERA5 data and a deep learning model

The abnormally high-water temperature (AHWT) phenomena have caused the mass stranding of farmed fish in the Korean coastal waters, leading to a substantial monetary loss in recent decades. It is most important to predict the HWT occurrence and take responsive measures before the HWT arrival to prevent such loss, we proposed a methodology to predict HWT occurrences using a deep-learning technology. Firstly, we trained a long short-term memory (LSTM) deep-learning model using the sea surface temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 product to estimate future water temperature in advance. Secondly, we used the estimated water temperature data to predict HWT occurrences from 1 day to 7 days later. We computed root mean square error (RMSE), mean absolute percentage error (MAPE) metrics, and F1-scores to evaluate the accuracy of the proposed LSTM model. In the cases of 1-day and 7-day water temperature predictions, RMSE and MAPE values between the estimated data and the sea-truth data were 0.293 degrees Celsius with 1.313 % and 0.854 degrees Celsius with 4.175 %, respectively. The F1-scores of the classification algorithm of 1- and 7-day HWT predictions were 0.96 and 0.74, respectively. This study contributes to developing measures to reduce the monetary loss of HWT damage on fish farms.

Analysis of the Relationship between Upper-Level Aircraft Turbulence and the East Asian Westerly Jet Stream

The jet stream is a primary factor contributing to turbulence, especially for upper-level aircraft. This study utilized pilot reports and ERA5 data from 2023 to investigate the relationship between upper-level turbulence and the East Asian westerly jet (EAJ). The results indicate that approximately 45.9% of upper-level aircraft turbulence occurs within the jet stream, with the lowest proportion in August and the highest in January. Additionally, the strongest vertical wind shear (VMS) is found concentrated in the lower part of the jet stream core, particularly in the South–Down part of the jet stream, where upper-level aircraft turbulence occurs most frequently (27.1%). The most turbulent area is located between 30–40° N and 110–120° E, with the main air routes experiencing turbulence being the Henan sections of G212 and B208. From a seasonal perspective, there is less frequent occurrence of upper-level aircraft turbulence in summer and autumn but more in winter and spring. The EAJ volume increases with the strengthening of the jet core wind speed, with the jet core regions being most distinct at altitudes of 200~300 hPa. Meanwhile, the jet stream intensity index peaks at 70.6 m/s in January and reaches its lowest value of 7.1 m/s in August. The jet stream axis shifts southward in winter and northward in summer, reaching the southernmost position in December at 32.2° N and the northernmost position in August at 43.5° N. Furthermore, the VMS at turbulence points within the jet stream is higher than that at the turbulence points outside the jet stream, and the Richardson number (RI) is lower. Moreover, the temporal distribution of upper-level aircraft turbulence is primarily determined by the location and intensity of the jet stream, of which the jet stream intensity index provides guidance and thus serves as a reliable indicator.

Reconstruction of significant wave height distribution from sparse buoy data by using deep learning

Significant wave height plays a crucial role in influencing marine ecosystems, ocean shipping, and other maritime activities. The distribution of buoy observation data tends to be sparse. Gridded wave data obtained through numerical simulation typically offer broader applicability, albeit with higher computational demands. In this paper, a deep learning model based on Full Connected and Convolutional Neural Networks is proposed, utilizing sparse buoy observation data as input to reconstruct the distribution of significant wave height in the sea area. The model reconstruction results are validated using ERA5 data, demonstrating excellent performance. Additionally, we explore the influence of the model's spatial boundaries and the number of input buoys on reconstruction accuracy, as well as the adaptability of the model to different sea areas. This study provides a novel method and approach for the rapid and cost-effective retrieval of regional significant wave height.

Dynamic of upwelling variability in southern Indonesia region revealed from satellite data: Role of ENSO and IOD

The Southern Indonesian (SI) region is known for its high-intensity coastal upwelling caused by monsoonal wind. Interannual phenomena such as El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) also influence upwelling activity in this region. This study analyzed the relationship between upwelling intensity (UIsst) and those variables and their impact on oceanographic features such as Sea Surface Temperature (SST) and chlorophyll-a concentration. We used satellite imagery data, including SST from the National Oceanic and Atmospheric Administration (NOAA) and chlorophyll-a from MODIS, to analyze the aforementioned issue. To identify the impact of wind patterns on coastal upwelling, we analyzed using zonal wind stress from ERA-5 Data. Quantification of UIsst is defined as the SST gradient between the coastal and open ocean waters. Linear and partial correlation analysis between UIsst with the Ocean Niño Index (ONI) and Dipole Mode Index (DMI) were conducted to see the influence of ENSO and IOD phenomena. Anomaly analysis was also conducted on SST, chlorophyll-a concentration, zonal windstress and UIsst to see how large the values were during the years of the ENSO and IOD events. Upwelling in the SI region typically occurs during southeast monsoon (SEM) periods, starting earlier in the East side (Nusa Tenggara Islands) and moving towards the West side (South Coast of Java). The correlation analysis (both linear and partial) indicates that the IOD has a stronger influence on UIsst in the SI region compared to ENSO, especially during June to October (SEM periods). This finding is confirmed by anomaly analysis, which reveals significant changes in SST, chlorophyll-a concentration, zonal windstress, and UIsst during ENSO and IOD events. The magnitude of the anomalies is generally stronger during IOD events than those observed under ENSO conditions.

An improved method for developing the precipitable water vapor vertical correction global grid model

Precipitable Water Vapor (PWV) is a crucial parameter in climate research. However, obtaining high precision PWV data remains a problem. Discrepancies in water vapor elevation data from diverse observation sources pose challenges, emphasizing the need for precise vertical correction. In this paper, we proposed the precipitable water vapor model with systematic difference correction by using ERA5 (the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis) data and GNSS (Global Navigation Satellite System) data. In this proposed method, two vertical correction models, employing cubic polynomial and exponential functions, were developed. The exponential function demonstrated superior overall performance with the RMS error of 0.96 mm, effectively capturing vertical characteristics across diverse regions, while the cubic polynomial function had the RMS (Root Mean Square) error of 2.77 mm validated by ERA5 PWV. The cubic polynomial function was found more suitable for low-elevation regions, while the exponential function excelled in high-elevation regions. Validated by radiosonde PWV, the cubic polynomial function and the exponential function show a strong correlation with latitude. Both functions exhibit smaller RMS values at higher latitudes and larger RMS values at lower latitudes. Validated by GNSS PWV, the cubic polynomial function exhibits superior accuracy, with the RMS of 2.39 mm, compared to the exponential function, particularly in specific regions. Analyzing six years of data reveals significant systematic differences between ERA5 and GNSS PWV. This discrepancy exhibits a noticeable annual periodic variation. The global GNSS stations were organized into grids, with stations within the same region grouped together for correction. Models considering systematic differences exhibited substantial RMS reductions and better accuracy. These findings can provide reference in atmospheric correction and the study of extreme weather events.

Research on the refinement of atmospheric weighted average temperature model in Xi’an based on machine learning

One of the most important parameters for calculating the atmospheric precipitable water vapor (PWV) is the atmospheric weighted mean temperature (Tm). Meteorological parameters that are measured typically constrain the accuracy and regional applicability of commonly used empirical models. When precipitable water is inverted using the Global Navigation Satellite System (GNSS), it results in erroneous Tm being obtained. Thus, in this paper, a small-scale Tm model for Xi’an is proposed by least squares method based on data provided by the European Centre for Mesoscale Weather Forecasts (ECMWF) from 2019 to 2022, while improving the functional model’s capacity to fit nonlinear residuals through the Random Forest method and enabling it to predict peaks more accurately. It is based on a random forest to confirm the weighted average temperature model’s correctness (RF Tm). When comparing the RF Tm model’s projected Tm accuracy to the GPT3, UNB3, and regional Bevis models, using the 2023 ERA5 data as the experimental data, the improvements are 23.6 %, 18.6 %, and 11.4 %, respectively. With the sounding station data as the reference value, the BIAS of RF Tm is only 1.51 K. In determining the atmospheric weighted average temperatures, the two models’ accuracy was comparable to that of the Long Short-Term Memory neural network model (LSTM). However, the LSTM model requires short-term forward data for practical usage, the RF Tm model compensated for the shortfall. The RF Tm model is used to invert the atmospheric precipitable water vapor at the appropriate monitoring stations using data from GNSS monitoring stations in Xi’an. It was demonstrated that the RF Tm model’s PWV inversion accuracy outperformed the PWV inverted by the GPT3 model by at least 30 %. Moreover, Xi’an rainfall data integration shows that the PWV inverted by the RF Tm model reacts to brief rainfall episodes efficiently. To sum up, this study’s RF Tm model provides a dependable and effective approach to meteorological observation and climate research.

Characteristics of Gravity Waves in Opposing Phases of the QBO: A Reanalysis Perspective with ERA5

Abstract Gravity waves dispersing upward through the tropical stratosphere during opposing phases of the QBO are investigated using ERA5 data for 1979–2019. Log–log plots of two-sided zonal wavenumber–frequency spectra of vertical velocity, and cospectra representing the vertical flux of zonal momentum in the tropical lower stratosphere, exhibit distinctive gravity wave signatures across space and time scales ranging over two orders of magnitude. Spectra of the vertical flux of momentum are indicative of a strong dissipation of westward-propagating gravity waves during the easterly phase and vice versa. This selective “wind filtering” of the waves as they disperse upward imprints the vertical structure of the zonal flow on the resolved wave spectra, characteristic of (re)analysis and/or free-running models. The three-dimensional structures of the gravity waves are documented in composites of the vertical velocity field relative to grid-resolved tropospheric downwelling events at individual reference grid points along the equator. In the absence of a background zonal flow, the waves radiate outward and upward from their respective reference grid points in concentric rings. When a zonal flow is present, the rings are displaced downstream relative to the source and they are amplified upstream of the source and attenuated downstream of it, such that instead of rings, they assume the form of arcs. The log–log spectral representation of wind filtering of equatorial waves by the zonal flow in this paper can be used to diagnose the performance of high-resolution models designed to simulate the circulation of the tropical stratosphere.

Towards sustainable energy: integrating ERA5 data for offshore wind farm planning in India

India’s pursuit of offshore wind energy development, particularly along the Tamil Nadu coastline within its Exclusive Economic Zone (EEZ), faces significant spatial and economic challenges due to existing industrial activities and water depth limitations. Motivated by the need to optimize offshore wind resources and bridge the gap between onshore and offshore wind energy achievements, this study hypothesizes that ERA5 reanalysis wind data can be relied upon for assessing offshore wind resources and optimizing wind farm layouts, especially in the absence of measured data. The research employs methods such as comparing ERA5 data with empirical wind measurements, generating wind rose diagrams for layout optimization, and conducting a comprehensive financial analysis to estimate the Levelized Cost of Energy (LCoE). The results confirm the efficacy of ERA5 data, identifying an optimal wind farm layout with a capacity density of 5.17 MW per square kilometer, turbine spacing of 7.22 meters, and an LCoE of Rs. 8.84 per kWh. The study concludes that significant financial support and strategic policy interventions are essential for minimizing wake losses, optimizing capacity density, and fostering the long-term growth of India’s offshore wind energy sector, providing critical insights for policymakers, developers, and investors. The novelty of this research lies in its detailed exploration of spatial optimization and cost-effective strategies to enhance the techno-economic viability of offshore wind projects in India.

GNSS Real-Time ZTD/PWV Retrieval Based on PPP with Broadcast Ephemerides

GNSS precise point positioning (PPP) plays an important role in retrieving atmospheric water vapor values and performing numerical weather prediction. However, traditional PPP relies on real-time orbits and clocks, which require continuous internet or satellite communication. Improved broadcast ephemerides of GNSSs offer new opportunities for PPP with broadcast ephemerides (BE-PPP) instead of using precise ephemeride products. Therefore, we investigated the feasibility of utilizing BE-PPP for retrieving zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data. We processed the GPS/Galileo observations from 80 IGS stations during a 30-day experiment to retrieve ZTD values using both real-time PPP (RT-PPP) and BE-PPP solutions. Then, we processed observations from 20 EUREF Permanent GNSS Network (EPN) stations to retrieve PWV data. The IGS final tropospheric products were used to validate the ZTD, and radiosonde (RDS) and ERA5 data were used to validate the PWV. The results show that the real-time ZTD from BE-PPP agrees well with that from RT-PPP: the standard deviation (STD) of the ZTD is 1.07 cm when using BE-PPP and 0.6 cm when using RT-PPP. Furthermore, the STD of the PWV is 1.69 mm when using BE-PPP, and 0.96 mm when using RT-PPP, compared to the ERA5-PWV. Compared to the RDS-PWV, the STD is 3.09 mm when using BE-PPP and 1.39 mm when using RT-PPP. In conclusion, the real-time ZTD/PWV products obtained using the BE-PPP solution are consistent with those of RT-PPP and meet the requirements of NWP, so this method can be used as an effective complement to RT-PPP to expand its application potential.

Global-scale ERA5 product precipitation and temperature evaluation

ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis Fifth Generation) is a significant dataset in climate reanalysis. Conducting a global-scale accuracy assessment and spatiotemporal distribution characteristic study of its temperature and precipitation data is crucial for gaining in-depth understanding of extreme climate changes. This study, based on CRU TS v4.04 (Climatic Research Unit Time-Series version 4.04) gridded data, employs multiple statistical indicators and detection metrics to assess the accuracy of the ERA5 temperature and precipitation dataset at a monthly scale over the past 70 years. Additionally, it utilizes Empirical Orthogonal Functions (EOF) to explore its spatiotemporal distribution characteristics. The results indicate that: (1) ERA5 demonstrates good consistency in most regions globally; however, the influence of uneven station density distribution results in weaker correlation in areas with sparse station density. (2) The accuracy of ERA5 data exhibits certain differences between the two periods (1950–1979, 1979–2019); with the overall performance of ERA5 temperature data near the Earth’s surface being better than that of precipitation data. (3) ERA5 precipitation data in the mid and low latitudes of the Southern and Northern Hemispheres exhibit characteristics of positive–negative distribution, while temperature data show an opposite distribution in the Northern and Southern Hemispheres (with a predominant pattern of warmer temperatures in the north and colder temperatures in the south). Additionally, the typical patterns for both variables occur in January and July each year. The above study will provide reference and insights for future climate change prediction and adaptation efforts.