Search Results (42)

In this study, based on total column ozone observations from eight Antarctic stations, we evaluate the applicability of ERA5, C3S-MSR, MERRA-2, and JRA-55 reanalysis datasets and the NIWA-BS merged satellite dataset, in terms of interannual variation and long-term trend, using the correlation coefficient (R), root-mean-square error (RMSE), interannual variability skill score (IVS), and linear trend bias (TrBias). The results show that for interannual variation, C3S-MSR performs well at multiple stations, while JRA-55 performs poorly at most stations, especially Marambio, Rothera, and Faraday/Vernadsky, which are located at lower latitudes on the Antarctic Peninsula. Additionally, all datasets show significantly higher RMSE at Dumont D’Urville and Arrival Heights, which generally are located around the edge of the Antarctic stratospheric vortex where total column ozone values are more variable and on average larger than in the core of the vortex. The comprehensive ranking results show that C3S-MSR performs the best, followed by ERA5 and NIWA-BS, with MERRA-2 and JRA-55 ranking lower. For the long-term trend, each of the datasets has large bias values at Arrival Heights, and the absolute TrBias values of JRA-55 are larger at three stations on the Antarctic Peninsula. The overall averaged results show that C3S-MSR and NIWA-BS have the smallest absolute TrBias, and perform best in reflecting the Antarctic ozone trends, while ERA5 and JRA-55 significantly overestimate the Antarctic ozone recovery trend and perform poorly. Based on our analysis, the C3S-MSR dataset can be recommended to be prioritized when analyzing the interannual variations in Antarctic stratospheric ozone, and both the C3S-MSR reanalysis and NIWA-BS datasets should be prioritized for trend analysis. Full article

Precipitation is a fundamental component of the hydrologic cycle and is an extremely important variable in meteorological, climatological, and hydrological studies. Reliable climate information including accurate precipitation data is essential for identifying precipitation trends and variability as well as applying hydrologic models for purposes such as estimating (surface) water availability and predicting flooding. In this study, I compared precipitation rates from five reanalysis datasets and one analysis dataset—the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA-5), the Japanese 55-Year Reanalysis (JRA-55), the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis 1 (NCEP/NCAR R1), the NCEP/Department of Energy Reanalysis 2 (NCEP/DOE R2), and the NCEP/Climate Forecast System Version 2 (NCEP/CFSv2)—with the merged satellite and rain gauge dataset from the Global Precipitation Climatology Project in Version 2.3 (GPCPv2.3). The latter was taken as a reference due to its global availability including the oceans. Monthly mean precipitation rates of the most recent five-year period from 2019 to 2023 were chosen for this comparison, which included calculating differences, percentage errors, Spearman correlation coefficients, and root mean square errors (RMSEs). ERA-5 showed the highest agreement with the reference dataset with the lowest mean and maximum percentage errors, the highest mean correlation, and the smallest mean RMSE. The highest mean and maximum percentage errors as well as the lowest correlations were observed between NCEP/NCAR R1 and GPCPv2.3. NCEP/DOE R2 showed significantly higher precipitation rates than the reference dataset (only JRA-55 precipitation rates were higher), the second lowest correlations, and the highest mean RMSE. Full article

In this paper, we use ozone mixing ratio data from the MERRA-2, ERA-5 and JRA-55 reanalyses from 500 hPa to 1 hPa in the period 1980–2020 with the aim of assessing their suitability for trend analysis. We found that these data are not suitable for trend studies due to huge differences in trend values and large differences in the variance of the ozone mixing ratio between reanalyses, and due to strong discrepancies between the ozone mixing ratio from reanalyses and that from the reliable ozonesonde at Hohenpeissenberg. These large differences can be caused by satellite replacement or by the assimilation of imperfect homogeneous data. Full article

Boundary-layer jets (BLJs) in the South China Sea play an important role in heavy rainfall in South China, yet observations in maritime locations are still limited. This study examines the vertical structures and temporal evolutions of BLJs in the northern South China Sea using intensive radiosonde observations from a research vessel from 15 to 18 June 2022 and evaluates the performance of various reanalysis datasets in capturing these features. Observations identified BLJs with jet cores at altitudes of approximately 500–700 m. Wind speeds slightly decreased from 15 to 16 June and then significantly increased after 17 June, showing double peaks on 17 June below 1 km at altitudes of 250 and 700 m. Among the reanalysis datasets, ERA5 exhibited more accurate results on average, followed by MERRA2, both of which outperformed JRA55 and FNL. ERA5 and MERRA2 had mixed performances in depicting BLJ characteristics. ERA5 accurately captured the initial decrease in wind speeds and their subsequent enhancement, while MERRA2 initially faltered but improved later. On the diurnal scale, neither MERRA2 nor ERA5 accurately represented the wind speed peaks observed at 2300 and 1100 LST, whereas ERA5 roughly reflected the nocturnal acceleration of the BLJs. During the observation period, the intensification of BLJs in the northern SCS, influenced by an eastward-moving high-pressure system and a southward-moving low-pressure vortex, led to enhanced precipitation in South China that gradually moved northward from the coastline to inland regions. This study provides new insights into the detailed characteristics of marine BLJs based on direct observations. Full article

The Global Precipitation Measurement Mission (GPM) improved spaceborne precipitation data. The GPM dual-frequency precipitation radar (DPR) provides information on total precipitation (TP), snowfall precipitation (SF) and snowfall flags (surface snowfall flag (SSF) and phase near surface (PNS)), among other variables. Especially snowfall data were hardly validated. This study compares GPM DPR TP, SF and snowfall flags on the Tibetan Plateau (TiP) against TP and SF from six well-known model-based data sets used as ground truth: ERA 5, ERA 5 land, ERA Interim, MERRA 2, JRA 55 and HAR V2. The reanalysis data were checked for consistency. The results show overall high agreement in the cross-correlation with each other. The reanalysis data were compared to the GPM DPR snowfall flags, TP and SF. The intercomparison performs poorly for the GPM DPR snowfall flags (HSS = 0.06 for TP, HSS = 0.23 for SF), TP (HSS = 0.13) and SF (HSS = 0.31). Some studies proved temporal or spatial mismatches between spaceborne measurements and other data. We tested whether increasing the time lag of the reanalysis data (+/−three hours) or including the GPM DPR neighbor pixels (3 × 3 pixel window) improves the results. The intercomparison with the GPM DPR snowfall flags using the temporal adjustment improved the results significantly (HSS = 0.21 for TP, HSS = 0.41 for SF), whereas the spatial adjustment resulted only in small improvements (HSS = 0.12 for TP, HSS = 0.29 for SF). The intercomparison of the GPM DPR TP and SF was improved by temporal (HSS = 0.3 for TP, HSS = 0.48 for SF) and spatial adjustment (HSS = 0.35 for TP, HSS = 0.59 for SF). Full article

The summer Asian westerly jet (AWJ)’s shifting in latitudes is one important characteristic of its variability and has great impact on the East Asian summer climate. Based on the observed and reanalyzed datasets from the Hadley Center Sea Ice and Sea Surface Temperature dataset (HadISST), the Japanese 55-year reanalysis (JRA-55), and the fifth generation of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5), this study investigates the relationship between the spring tripole North Atlantic SST (TNAT) anomalies and the summer meridional shift of the AWJ (MSJ) for the period of 1958–2020. Through the method of correlation analysis and regression analysis, we show that the ‘+ - +’ TNAT anomalies in spring could induce a northward shift of the AWJ in the following summer. However, such a climatic effect of the spring TNAT anomalies on the MSJ is unstable, exhibiting an evident interdecadal strengthening since the early 1990s. Further analysis reveals that this is related to a strengthened intensity of the spring TNAT anomalies in the most recent three decades. Compared to the early epoch (1958–1993), the stronger spring TNAT anomalies in the post epoch (1994–2020) could cause a stronger pan-tropical climate response until the following summer through a series of ocean–atmosphere interactions. Through Gill responses, the resultant more prominent cooling in the central Pacific in response to the ‘+ - +’ TNAT anomalies induces a pan-tropical cooling in the upper troposphere, which weakens the poleward gradient of the tropospheric temperature over subtropical Asia. As a result, the AWJ shifts northward via a thermal wind effect. By contrast, in the early epoch, the spring TNAT anomalies are relatively weaker, inducing weaker pan-tropical ocean–atmosphere interactions and thus less change in the meridional shit of the summer AWJ. Our results highlight a strengthened lagged effect of the spring TNAT anomalies on the following summer MSJ and have important implications for the seasonal climate predictability over Asia. Full article

Continuous and accurate precipitation data are critical to water resource management and eco-logical protection in water-scarce and ecologically fragile endorheic or inland basins. However, in typical data-scarce endorheic basins such as the endorheic basin of the Yellow River Basin (EBYRB) in China, multi-source precipitation products provide an opportunity to accurately capture the spatial distribution of precipitation, but the applicability evaluation of multi-source precipitation products under multi-time scales and multi-modes is currently lacking. In this context, our study evaluates the regional applicability of seven diverse gridded precipitation products (APHRODITE, GPCC, PERSIANN-CDR, CHIRPS, ERA5, JRA55, and MSWEP) within the EBYRB considering multiple temporal scales and two modes (annual/monthly/seasonal/daily precipitation in the mean state and monthly/daily precipitation in the extreme state). Furthermore, we explore the selection of suitable precipitation products for the needs of different hydrological application scenarios. Our research results indicate that each product has its strengths and weaknesses at different time scales and modes of coupling. GPCC excels in capturing annual, seasonal, and monthly average precipitation as well as monthly and daily extreme precipitation, essentially meeting the requirements for inter-annual or intra-annual water resource management in the EBYRB. CHIRPS and PERSIANN-CDR have higher accuracy in extreme precipitation assessment and can provide near real-time data, which can be applied as dynamic input precipitation variables in extreme precipitation warnings. APHRODITE and MSWEP exhibit superior performance in daily average precipitation that can provide data for meteorological or hydrological studies at the daily scale in the EBYRB. At the same time, our research also exposes typical problems with several precipitation products, such as MSWEP’s abnormal assessment of summer precipitation in certain years and ERA5 and JRA55’s overall overestimation of precipitation assessment in the study area. Full article

The Beijing Climate Center of the China Meteorological Administration (BCC/CMA) has developed a gauge-satellite-model merged gridded daily precipitation dataset with complete global coverage, called BCC Merged Estimation of Precipitation (BMEP). Using the unified rain gauge dataset from the CPC (CPC-U) as the independent benchmark, BMEP and the four most widely used global daily precipitation products, including the Global Precipitation Climatology Project one-degree daily (GPCP-1DD), the NCEP Climate Forecast System Reanalysis (CFSR), the Interim ECMWF Re-analysis (ERA-interim), and the 55 year Japanese Reanalysis Project (JRA-55), are evaluated over the global land area from January 2003 to December 2016. The results show that all gridded datasets capture the overall spatiotemporal variation of global daily precipitation. All gridded datasets can basically capture the overall spatiotemporal variation of global daily precipitation. However, CFSR data tend to overestimate precipitation intensity and exhibit a spurious positive trend after 2010, attributed to the transition from CFSR to NCEP’s Climate Forecast System Version 2 (CFSv2). On the other hand, JRA-55 and ERA-interim data demonstrate higher skill in characterizing spatial and temporal variations, bias, correlation, and RMSE. GPCP-1DD data perform well in terms of bias but show limitations in detecting the interannual variability and RMSE of daily precipitation. Among these evaluated products, BMEP data exhibit the best agreement with CPC-U data in terms of the spatiotemporal variation, pattern, magnitude of variability, and occurrence of rainfall events across different thresholds. These findings indicate that BMEP gridded precipitation data effectively capture the actual characteristics of daily precipitation over global land areas. Full article

Fog computing has become a hot topic in recent years as it provides cloud computing resources to the network edge in a distributed manner that can respond quickly to intensive tasks from different user equipment (UE) applications. However, since fog resources are also limited, considering the number of Internet of Things (IoT) applications and the demand for traffic, designing an effective offload strategy and resource allocation scheme to reduce the offloading cost of UE systems is still an important challenge. To this end, this paper investigates the problem of partial offloading and resource allocation under a cloud-fog coordination network architecture, which is formulated as a mixed integer nonlinear programming (MINLP). Bring in a new weighting metric-cloud resource rental cost. The optimization function of offloading cost is defined as a weighted sum of latency, energy consumption, and cloud rental cost. Under the fixed offloading decision condition, two sub-problems of fog computing resource allocation and user transmission power allocation are proposed and solved using convex optimization techniques and Karush-Kuhn-Tucker (KKT) conditions, respectively. The sampling process of the inner loop of the simulated annealing (SA) algorithm is improved, and a memory function is added to obtain the novel simulated annealing (N-SA) algorithm used to solve the optimal value offloading problem corresponding to the optimal resource allocation problem. Through extensive simulation experiments, it is shown that the N-SA algorithm obtains the optimal solution quickly and saves 17% of the system cost compared to the greedy offloading and joint resource allocation (GO-JRA) algorithm. Full article

Understanding East Asian monsoon (EAM) has been a crucial issue due to its socio-economic effects on one-fifth of the world’s population and its interactions with the global climate system. However, the reliabilities of climate reanalysis data are still uncertain at varying temporal and spatial scales. In this study, we examined the correlations and differences for climate reanalyses with weather observations and suggested the best climate reanalysis for the EAM region. The three reanalyses of ERA5, JRA55, and NCEP2 along with a gridded observation (CRU) were evaluated using the correlation coefficients (Pearson, Spearman, and Kendall), difference statistics (RMSE and bias), and Taylor diagrams, comparing their annual and seasonal temperatures and precipitations with those from the total of 537 weather stations across China, North Korea, South Korea, and Japan. We found that ERA5 showed the best performance in reproducing temporal variations in temperature with the highest correlations in annual, summer, and autumn, and the smallest RMSEs and biases for all seasons and annually. For precipitation, among the three reanalysis datasets, ERA5 had the highest correlations, annually and in four seasons, with the smallest RMSEs, annually and in spring, summer and autumn, and the smallest biases, annually and in summer and autumn. Regarding spatial variations, ERA5 was also the most suitable reanalysis data in representing the annual and seasonal climatological averages. Full article

The analysis of actual evapotranspiration (ETa) changes is of great significance for the utilization and allocation of water resources. In this study, ETa variability in northern China (aridity index < 0.65) is investigated based on the average of seven datasets (GLEAM, GLASS, a complementary relationship-based dataset, CRA-40, MERRA2, JRA-55, and ERA5-Land). The results show that ETa increases significantly from 1982 to 2017. Limited by water supply, ETa is significantly correlated with precipitation (R = 0.682), whereas the increase in precipitation is insignificant (p = 0.151). Spatially, the long-term trend of ETa is also not completely consistent with that of precipitation. According to a singular value decomposition (SVD) analysis, the trend of ETa is mainly related to the first four leading SVD modes. Homogeneous correlation patterns indicate that more precipitation generally leads to high ETa; however, this relationship is modulated by other factors. Overall, positive potential evapotranspiration anomalies convert more surface water into ETa, resulting in a higher increase in ETa than in precipitation. Specifically, ETa in the northern Tibetan Plateau is associated with meltwater generated by rising temperatures, and ETa in the Badain Jaran Desert is highly dependent on the wet-day frequency. Under global warming, the inconsistency between ETa and precipitation changes has a great impact on water resources in northern China. Full article

Evapotranspiration (ET) is an important part of the surface energy balance and water balance. Due to imperfect model parameterizations and forcing data, there are still great uncertainties concerning ET products. The validation of land surface ET products has a certain research significance. In this study, two direct validation methods, including the latent heat flux (LE) from the flux towers validation method and the water balance validation method, and one indirect validation method, the three-corned hat (TCH) uncertainty analysis, were used to validate and compare seven types of ET products in the Haihe River Basin in China. The products evaluated included six ET products based on remotely-sensed observations (surface energy balance based global land evapotranspiration [EB-ET], Moderate Resolution Imaging Spectroradiometer [MODIS] global terrestrial evapotranspiration product [MOD16], Penman–Monteith–Leuning Evapotranspiration version 2 [PML_V2], Global Land Surface Satellite [GLASS], global land evaporation Amsterdam model [GLEAM], and Zhangke evapotranspiration [ZK-ET]) and one ET product from atmospheric re-analysis data (Japanese 55-year re-analysis, JRA-55). The goals of this study were to provide a reference for research on ET in the Haihe River Basin. The results indicate the following: (1) The results of the six ET products have a higher accuracy when the flux towers validation method is used. Except for MOD16_ET and EB_ET, the Pearson correlation coefficients (R) were all greater than 0.6. The root mean square deviation (RMSD) values were all less than 40 W/m². The GLASS_ET data have the smallest average deviation (BIAS) value. Overall, the GLEAM_ET data have a higher accuracy. (2) When the validation of the water balance approach was used, the low values of the MOD16_ET were overestimated and the high values were underestimated. The values of the EB_ET, GLEAM_ET, JRA_ET, PML_ET, and ZK_ET were overestimated. According to the seasonal variations statistics, most of the ET products have higher R values in spring and lower R values in summer, and the RMSD values of most of the products were the highest in summer. (3) According to the results of the uncertainty quantification based on the TCH method, the average value of the relative uncertainties of the GLEAM_ET data were the lowest. The relative uncertainties of the JRA_ET and ZK_ET were higher in mountainous areas than in non-mountainous area, and the relative uncertainties of the PML_ET were lower in mountainous areas. The performances of the EB_ET, GLEAM_ET, and MOD16_ET in mountainous and non-mountainous areas were relatively equal. The relative uncertainties of the ET products were significantly higher in summer than in other periods, and they also varied in the different sub-basins. Full article

There is a large number of grid-based climate datasets available which differ in terms of their data source, estimation procedures, and spatial and temporal resolutions. This study evaluates the performance of diverse meteorological datasets in terms of representing spatio-temporal climate variabilities based on a national-scale domain over South Korea. Eleven precipitation products, including six satellite-based data (CMORPH, MSWEP, MERRA, PERSIANN, TRMM, and TRMM-RT) and five reanalysis-based data (ERA5, JRA-55, CPC-U, NCEP-DOE, and K-Hidra) and four temperature products (MERRA, ERA5, CPC-U, and NCEP-DOE) are investigated. In addition, the hydrological performance of forty-four input combinations of climate datasets are explored by using the Variable Infiltration Capacity (VIC) macroscale model. For this analysis, the VIC model is independently calibrated for each combination of input and the response to each combination is then evaluated with in situ streamflow data. Our results show that the gridded datasets perform differently particularly in representing precipitation variability. When a diverse combination of the datasets are used to represent spatio-temporal variability of streamflow through the hydrological model, K-Hidra and CPC-U performed best for precipitation and temperature, followed by the MERRA and ERA5 datasets, respectively. Lastly, we obtain only marginal improvement in the hydrological performance when utilizing multiple climate datasets after comparing it to a single hydrological simulation with the best performing climate dataset. Overall, our results indicate that the hydrological performance may vary considerably based on the selection of climate datasets, emphasizing the importance of regional evaluation studies for meteorological datasets. Full article

Ozone is a very important trace gas in the stratosphere and, thus, we need to know its time evolution over the globe. However, ground-based measurements are rare, especially in the Southern Hemisphere, and while satellite observations provide broader spatial coverage generally, they are not available everywhere. On the other hand, reanalysis data have regular spatial and temporal structure, which is beneficial for trend analysis, but temporal discontinuities might exist in the data. These discontinuities may influence the results of trend studies. The aim of this paper is to detect discontinuities in ozone data of the following reanalyses: MERRA-2, ERA-5 and JRA-55 with the help of the Pettitt, the Buishand, and the Standard Normal Homogeneity tests above the 500 hPa level. The share of discontinuities varies from 30% to 70% and they are strongly layer dependent. The share of discontinuities is the lowest for JRA-55. Differences between reanalyses were found to be larger than differences between homogeneity tests within one reanalysis. Another aim of this paper is to test the ability of homogeneity tests to detect the discontinuities in 2004 and 2015, when changes in versions of satellite data took place. We showed the discontinuities in 2004 are better detected than those in 2015. Full article

Precipitation is a key component of the hydrological cycle and one of the most important variables in weather and climate studies. Accurate and reliable precipitation data are crucial for determining climate trends and variability. In this study, eleven different precipitation datasets are compared, six reanalysis and five observational datasets, including the reanalysis datasets ERA5 and WFDE5 from the ECMWF family, to quantify the differences between the widely used precipitation datasets and to identify their particular strengths and shortcomings. The comparisons are focused on the common time period 1983 through 2016 and on monthly, seasonal, and inter-annual times scales in regions representing different precipitation regimes, i.e., the Tropics, the Pacific Inter Tropical Convergence Zone (ITCZ), Central Europe, and the South Asian Monsoon region. For the analysis, satellite-gauge precipitation data from the Global Precipitation Climatology Project (GPCP-SG) are used as a reference. The comparison shows that ERA5 and ERA5-Land are a clear improvement over ERA-Interim and show in most cases smaller biases than the other reanalysis datasets (e.g., around 13% high bias in the Tropics compared to 17% for MERRA-2 and 36% for JRA-55). ERA5 agrees well with observations for Central Europe and the South Asian Monsoon region but underestimates very low precipitation rates in the Tropics. In particular, the tropical ocean remains challenging for reanalyses with three out of four products overestimating precipitation rates over the Atlantic and Indian Ocean. Full article