ERA-40 Reanalysis Data Research Articles

Verification of ERA5 reanalysis data for interpretation of radiosonde measurements in 2009–2023 in western Siberia

Verification of ERA5 reanalysis data for interpretation of radiosonde measurements in 2009–2023 in western Siberia

Classification of North Atlantic and European extratropical cyclones using multiple measures of intensity

Abstract. The question of how to quantify the intensity of extratropical cyclones (ETCs) does not have a simple answer. To offer some perspective on this issue, we analyse multiple measures of intensity for North Atlantic and European ETCs for the extended winter season between 1979 and 2022 using ERA5 reanalysis data. The most relevant intensity measures are identified by investigating relationships between them and by performing a sparse principal component analysis on the set of measures. We show that dynamical intensity measures correlate strongly with each other, while correlations are weaker for impact-relevant measures. Based on the correlations and the sparse principal component analysis, we find that five intensity measures, namely 850 hPa relative vorticity, 850 hPa wind speed, wind footprint, precipitation, and a storm severity index, describe ETC intensity comprehensively and non-redundantly. Using these five measures as input, we objectively classify the ETCs with a cluster analysis based on a Gaussian mixture model. The cluster analysis is able to produce four clusters between which ETCs differ in terms of their intensity, life cycle characteristics such as deepening rate and lifetime, and geographical location. A fourth of all ETCs belong to the weakest cluster and occur mostly over Europe and in the Mediterranean area. Nearly half of all ETCs belong to the average-intensity cluster and occur mostly at the northeastern parts of the main North Atlantic storm track. A fifth of all ETCs belong to the second most intense cluster and occur mostly at the start of the North Atlantic storm track. Finally, less than a 10th of all ETCs belong to the most intense cluster and occur almost equally everywhere. This last cluster includes a clear majority of a set of investigated impactful storms (17 out of 21), which demonstrates the ability of the method to identify potentially damaging ETCs.

The impact of precipitation, temperature, and soil moisture on wheat yield gap quantification: evidence from Morocco

BackgroundClimate change has devastating impacts on agriculture, increasing the yield gap for most crops, especially in developing nations. This is likely to worsen food insecurity in some countries, calling for efforts to close the yield gap as much as possible. Estimating the yield gap and its drivers is essential for devising strategies to increase yields. This study quantifies the wheat yield gap in Morocco's five major wheat production regions. It analyzes the historical sensitivity of wheat yield to temperature, precipitation, and soil moisture, which are important factors affecting agricultural productivity. Furthermore, it evaluates how these yield gaps impact the revenue of producers in these regions. This analysis was conducted using datasets, including the Global Dataset of Historical Yield (GDHY) for yield gap assessment, soil moisture data, ERA5 reanalysis data, and CHIRPS datasets for climate assessment from 1982 to 2016. Pearson correlation and multiple linear regression analyses were employed to reflect the variation characteristics of wheat yield and to identify the impacts of precipitation, temperature, and soil moisture on wheat yield.ResultsHigh regional differences in wheat yield gaps were observed, with values ranging from 1.64 t/ha in Casablanca Settat to 4.12 t/ha in Marrakech Safi, and temporal variability ranging from 9 to 18%. Wheat yields were found to be strongly correlated with rainfall, particularly from December to March. Temperature fluctuations had a significant negative impact on wheat yield across the regions. Soil moisture was positively correlated with wheat yields throughout all growing periods, showing the strongest impacts during the early vegetative development phase. Additionally, losses due to wheat yield gaps were considerable, ranging between $ 194 and 891 per hectare. The revenue loss due to Yield Gap I ranged from 49 to 71%, while the loss due to Yield Gap II ranged from 240 to 444%, depending on the method used to calculate the wheat yield gap.ConclusionsResults reveal gaps in wheat yield, forming a basis for process-based modeling to understand crop yield gap drivers. Understanding yield gap drivers will play a pivotal role in evidence-based intervention strategies to enhance yields. By applying such strategies, it is possible to not only manage and reduce the variability in wheat production, but also ensure sustainable agricultural practices and achieve food security in Morocco and beyond.

Association of spring thermal forcing anomalies in the Tibetan Plateau with dust aerosol changes over the Taklamakan Desert.

The Tibetan Plateau (TP) is significantly influencing the climate and environmental evolution regionally and globally. Adjacent to the northwestern TP, the Taklimakan Desert (TD) experiences the unique pattern of dust aerosol variations due to the deep basin terrain. However, systematic studies on how TP climate change affects TD dust aerosol variations are lacking. This study employs MERRA-2 and ERA5 reanalysis data (1991-2020) to investigate the impact of springtime TP thermal forcing on TD dust aerosol. Results indicate that the interannual variation of dust column mass densities over the TD showed an increasing trend over 1991-2020, with a significant increase in the northern TD, where the maximum value exceeded 9.51mgm-2 per year. Thermal forcing strengthening on the northwestern TP are positively correlated with dust column mass density variations in the northern TD, while negatively correlated in the southern TD. Further comprehensive analyses indicate that strong northwestern TP thermal forcing has increased dust aerosol concentrations in northern TD, with a maximum 14mgm-3 increase at 700hPa, while decreasing them in the southern part. During strong SNTP-Q1 years, anticyclonic anomalies, strong updrafts, and the blocking effect of the Tian Shan Mountains, combined with high temperatures, result in dust aerosols suspended in the atmosphere over the northern TD. In contrast, during weak SNTP-Q1 years, cyclonic anomalies, strong downdrafts, and the large topography of the TP, along with inversion temperatures, contribute to dust aerosol accumulation in the southern TD. This study elucidates the mechanism of TP thermal forcing on TD dust aerosol variations, enhancing understanding of its effects on environmental and climatic changes in the TD and Central Asia.

АВТОМАТИЧЕСКАЯ ИДЕНТИФИКАЦИЯ НОВОЗЕМЕЛЬСКОЙ БОРЫ

The paper examines a type of downslope windstorm – the Novaya Zemlya bora. This is a insufficiently studied mesoscale phenomenon characterized by the presence of high wind speeds on the western slope of the archipelago, threatening the safety of port buildings and maritime navigation. The paper proposes an approach for automatic identification of bora, which will allow to make a climatic picture of this phenomenon and to identify its characteristic features. The developed approach was applied to long-term (2015–2023) high-resolution numerical simulation data obtained using the WRF atmospheric model, and to lower-resolution data – atmospheric reanalysis ERA5. Over a 9-year period, about 220 bora events were analyzed, and this was done not only for the entire archipelago (which is typical for most studies), but also for individual regions of Novaya Zemlya. It was shown that the qualitative climatic characteristics of bora do not depend on spatial resolution, which potentially allows us to apply the developed method to identify the bora on longer time periods, for example, the entire period covered by the ERA5 reanalysis data. However, on a quantitative level, high resolution showed a greater intensity and duration of the phenomenon, as expected.

Assessing the Reliability of Predicted Decadal Surface Temperatures in Southeast Asia

Climate predictions spanning 10-year periods, known as Decadal Climate Predictions (DCPs), have become an important aspect of the latest Coupled Model Intercomparison Project (CMIP6). These DCPs have the capability to capture the El Niño-Southern Oscillation (ENSO) phenomena, which affects heatwave frequency in Southeast Asia over years to decades. This research assesses the ability of six General Circulation Model (GCM) DCPs to predict surface temperature over the Southeast Asian region, using the dcpp-A hindcast as the main product. The metrics of Anomaly Correlation Coefficient (ACC) and Mean Error (ME) are employed to assess the model outputs, with 51 hindcast datasets spanning initial years from 1960 to 2010 and ERA5 reanalysis data serving as the reference. The evaluation reveals that DCP model skill varies across lead times and subregions, with no single model consistently outperforming the others. The highest correlation values are observed during the September-October-November (SON) season, and the ENSEMBLE model demonstrates the ability to increase correlation values compared to the individual DCP models. However, the ENSEMBLE approach is unable to effectively reduce ME values due to the contrasting errors among individual models. PBIAS metric aligns with the ME, consistently identifying similar areas of underestimation (mainland) and overestimation (maritime continent) across the models. Despite these challenges, the evaluation results highlight the potential of DCPs in predicting surface temperature variability for the Southeast Asian region over decadal periods, particularly in capturing ENSO-related signals. Further improvements in model initializations, internal variability representation, and bias reduction are necessary to enhance the utility of CMIP6 decadal predictions for heatwave preparedness and mitigation strategies in this vulnerable region.

The Influence of North Atlantic Dipole Sea Surface Temperature Anomalies on Autumn Drought Over Southwest China

ABSTRACTIn recent decades, there has been a notable increase in the frequency and severity of drought events in Southwest China (SWC), which have significantly impacted agriculture and the social economy. Using sea surface temperature (SST) data from the Hadley Centre, daily meteorological drought composite index grid data and ERA5 reanalysis data, we investigate the characteristics of autumn drought in SWC and discuss its possible causes using empirical orthogonal function (EOF) analysis. The results indicate a distinct ‘Northeast–Southwest’ dipole pattern of autumn drought over SWC in the past decade, which we define as the ‘Chuan‐Yu’‐type drought. A British–Okhotsk Corridor (BOC) pattern Rossby wave train, presenting over Eurasia, is identified as the key factor influencing the ‘Chuan‐Yu’‐type drought in SWC during the autumn. The BOC‐pattern Rossby wave train not only obstructs water vapour transport channels in SWC but also induces anomalous descending motions in the lower to middle troposphere over the region, leading to high temperatures and insufficient precipitation. Further investigation reveals that North Atlantic dipole SST anomalies trigger the BOC‐pattern Rossby wave train in autumn. The results of the linear baroclinic model sensitivity simulations support the above conclusion. These findings will make valuable contributions to autumn drought prevention and mitigation in SWC.

A global climatology of sting-jet extratropical cyclones

Abstract. Sting jets have been identified in the most damaging extratropical cyclones impacting northwest Europe. Unlike the cold conveyor belt and other long-lived cyclone wind jets, sting jets can lead to regions of exceptionally strong near-surface winds and damaging gusts over just a few hours and with much smaller wind “footprints”. They descend into the frontal-fracture region found in warm-seclusion cyclones. Previous research has focused almost exclusively on North Atlantic–European cyclones, but there are no known physical reasons why sting jets should not develop elsewhere, and recognition of their existence can inform weather nowcasting and wind warnings. We have produced the first climatology of sting-jet cyclones over the major ocean basins. A sting-jet precursor diagnostic has been applied to more than 10 000 warm-seclusion cyclones in the top intensity decile, tracked using 43 extended winters of ERA5 reanalysis data. Cyclones with sting-jet precursors are found to occur over the North Pacific and Southern oceans for the first time, and they are more prevalent in the Northern Hemisphere (27 % of all top decile cyclones) compared to the Southern Hemisphere (15 %). These cyclones have distinct characteristics to those without the precursor, including initiating closer to the Equator, deepening faster in mean-sea-level pressure (MSLP), and having stronger near-surface winds, even in the reanalysis data that are too coarse to fully resolve sting jets. Composite analysis reveals systematic differences in structural evolution, including in potential vorticity (PV) and jet crossing. These differences evidence the climatological consequences of strong diabatic cloud processes on cyclone characteristics, implying that sting jets are likely to be enhanced by climate change.

Aggregation–Disaggregation Cycles in ERA5 Reanalysis

Abstract Understanding convective aggregation is very important for understanding tropical climate and climate sensitivity. However, we still lack a full understanding of how aggregation evolves in the real world or what phenomena and scales are analogous to the self-aggregation observed in idealized models. In this study, we apply the moist static energy (MSE) variance budget framework to ERA5 reanalysis data to study the evolution of large-scale aggregation over tropical oceans at basinwide scales. Our novel phase space diagnostics focus on the variability of observed aggregation compared to most previous self-aggregation studies, which focus more on the aggregated mean state. We visualize observed aggregation to evolve anomalously around a mean state in a cyclical fashion forming aggregation–disaggregation cycles. We find horizontal advection of MSE to play the primary role in determining when the domain aggregates or disaggregates. In contrast, all advective, radiative, and surface flux feedbacks are found important for determining the magnitude of the aggregation anomalies. Surface fluxes and horizontal advection tend to dampen aggregation anomalies, while radiative fluxes and vertical advection tend to amplify aggregation anomalies. Looking deeper into the advection terms, we find that changes in vertical advection are dominated by enhanced low-level subsidence over the dry regions during the more aggregated states. This creates an anomalous drying tendency over the dry regions, which maintains aggregation anomalies. In contrast, horizontal advection changes are found to be dominated by increased moisture advection out of the moist columns with stronger aggregation. Significance Statement The purpose of this study is to characterize and understand the evolution of large-scale convective aggregation in the real world through reanalysis data. While most previous observational studies have focused on the evolution of clouds and cloud populations with aggregation, we focus on the energetics and the impact of aggregation on redistributing moisture throughout the domain. Our framework highlights that aggregation can be visualized as a continuously occurring cyclic feature at large scales in the tropics. Further, our work provides a deeper insight into the changes in large-scale circulation that accompany aggregation and characterizes the similarities and differences between the different regions in the tropics.

Impacts of Changing Atmospheric Circulation Patterns on Aviation Turbulence Over Europe

AbstractAddressing aviation turbulence not only enhances passenger comfort but also help to reduce fuel consumption and environmental impact, supporting aviation sustainability. By using ERA5 reanalysis data we explore how changing atmospheric circulation, monitored via sea level pressure trends, impacts aviation turbulence over Europe. Our results show coherent climate anomalies, with rising turbulence intensity over the UK and Northern Europe where most events involve clear air turbulence, which occurs unexpectedly and without warning, particularly at flight altitudes. We also highlight a clear seasonal patterns in moderate‐or‐greater turbulence encounters, most frequent and intense in winter, with a key role of wind shears due to the sub‐tropical jet position over Southern Mediterranean. Our approach adds to previous studies on the same topic by analyzing individual atmospheric circulation pattern changes and their effects on turbulence‐related factors. This offers insight into how climate change affecting atmospheric dynamics may contribute to increased aviation turbulence.

Tibetan Plateau Vortex Activity and Its Relationship With the Tibetan Plateau Summer Monsoon and Precipitation

ABSTRACTThe unique topography and location of the Tibetan Plateau (TP) often result in extreme weather events, which have led to disastrous consequences for the TP and its downstream regions. The TP summer monsoon (TPSM) and the TP vortex (TPV) play key roles in the transfer and redistribution of water vapour during the summer months on the TP and are increasingly active in summer and disappear in winter. However, it remains uncertain if a relationship between these systems. Understanding the relationship between these two systems is crucial for uncovering precipitation patterns on the TP, improving weather forecasting accuracy and reducing socioeconomic losses resulting from weather‐related disasters. On the basis of GLDAS and ERA5 reanalysis data from 1996 to 2022, the relationships between TPVs and the TPSM were explored in terms of their intensity and spatial characteristics, and their impacts on the spatial distributions of precipitation across the TP were examined in this study. The results indicated that the monthly mean TPSM index agreed very well with the TPV in terms of the annual number formed, duration and intensity, especially in July and August. The investigation of the movement of the center of the TPSM from May to October revealed that the center of the TPSM moves westward when the TPV is active and moves eastward when the TPV is less active. In years with a strong TPSM, the precipitation location generated by TPVs was biased toward the east. This finding could be attributed to the greater number of TPV events and the fact that the TPVs in years with a stronger TPSM moved eastward across a greater distance than those in years with a weaker TPSM. These findings highlightthe contribution of the joint relationship between the TPSM and TPV to seasonal circulation changes and could provide a new perspective for the study and prediction of precipitation distributions on the TP.

Neural network algorithm for precipitation estimation from atms radiometer data

The paper presents a neural network method for precipitation estimation using microwave measurements from ATMS radiometer on board Suomi NPP and NOAA-20/21 satellites. The algorithms based on two fully-connected neural networks, the first one is used to detect precipitation clouds and the other one is used to quantify precipitation rate. When training the neural networks, the reference source of information was an array of measurements simulated using the fast radiation transfer model RTTOV in the bands of ATMS instrument and the corresponding precipitation rates were taken from ECMWF ERA5 reanalysis data. Validation of the obtained precipitation estimates was carried out using the results of the MIRS and GPROF algorithms for satellite radiometer ATMS, as well as ground-based radar observations from NIMROD. The results of the validation showed a high accuracy level consistent with many others works in this research field. The validation was carried out for land and water surface separately. The comparison with MIRS algorithm showed the correlation coefficient was more 0.9, and the RMSE error was approximately 0.78 mm/h for water and 0.84 mm/h for land surface. The same metrics for GPROF algorithm showed the correlation coefficient was ~0.8, and the RMSE error was approximately 1.27 mm/h and 0.9 for water and land surface, respectively. When compared with ground-based NIMROD radar data, the correlation and the RMSE were 0.47 and 1.37 mm/h, respectively. The results of the validation confirm the performance of the presented neural network method for precipitation estimation. In addition, further minor refinement of the presented algorithm will make it possible to apply it to measurements of other microwave satellite instruments, including Russian ones, such as MTVZA-GY, installed on Meteor-M satellites.

Assessment of the CORDEX-CORE Models in Simulating the Length of Rainy Season (LRS) over Northeast India

Abstract The Length of Rainy Season (LRS) in Northeast India (NEI) is longer than that over other parts of the country and contributes to the seasonal quantum of rainfall, a hallmark of the Indian Summer Monsoon. Low-resolution global general circulation models (GCMs) underestimate rainfall annual cycle over NEI due to unresolved regional orography requiring downscaling with the Coordinated Regional Climate Downscaling Experiment (CORDEX) models for South Asia. However, the uncertainty in CORDEX simulations of the annual cycle of rainfall and its intensity of over NEI due to biases in simulating the LRS is unknown. This study evaluates performance of CORDEX climate models, focusing on their ability to simulate the LRS. Utilizing an objective metric based on the change of sign in a north-south gradient of tropospheric temperature, we define LRS and employ ERA5 reanalysis data as a proxy for observations to establish a benchmark for the historical period of 1979-2005. Our analysis reveals that the CORDEX models when forced by ERA-Interim data, exhibit a consistent positive bias of approximately 6 days, while models forced by General Circulation Models (GCMs) display a negative bias of 10 days. Additionally, both model sets indicate a reduction in bias over inter-annual timescales. Further, we have observed that RCMs forced by ERA-Interim boundary conditions tend to overestimate the thermodynamic index of the Indian summer monsoon (TISM) compared to RCMs forced by GCMs. Our findings suggest that significant discrepancies in the simulation of the upper atmospheric conditions over the South Asia domain by regional models contribute to the pronounced biases in LRS estimates over NEI forced either by observations or by GCM outputs. This study underscores the complexities of accurately simulating the LRS using regional climate models, emphasizing the need for improved methodologies to precisely forecast LRS. Such improvements are crucial for reliable climate change impact assessments over NEI.

Identification of compound flood-heatwave extremes in the Yangtze River Basin and their socio-economic exposure

Compound flood-heatwave extremes are sequential events where a heatwave closely follows a flood, posing significant threats to public health, well-being, and infrastructure. This study, based on ERA5 reanalysis data and the NEX-GDDP-CMIP6 statistical downscaling dataset with 25 climate models, developed an index to quantify the magnitude of these compound events. The study compared independent extreme events with compound flood-heatwave events, examining the differences in maximum duration and average magnitude between regular heatwaves and those occurring within compound events. It also analyzed the effects of warming and humidification on the trends of compound flood-heatwave events. Furthermore, the study explored the atmospheric dynamics of these events and quantified the impact of risk changes on population and GDP under shared socioeconomic pathways. The results show that the frequency, magnitude, and probability of compound flood-heatwave extremes have significantly increased. Compared to single extreme events, compound events present a much greater increase in risk. Under future scenarios, there is a particularly high risk of short-duration, high-intensity heatwaves occurring after floods. The rise in Wet Bulb Globe Temperature is identified as a key factor driving the increase in compound flood-heatwave events. Return period calculations suggest a growing future risk, with population exposure in the Yangtze River basin expected to rise by 2.5 to 3.5 times, and economic exposure projected to increase by 13 to 42 times. Atmospheric dynamic analysis also indicates that cyclones are closely linked to compound flood-heatwave extremes.

North Atlantic Extratropical Cyclone Tracks and Lagrangian-Derived Moisture Uptake Dataset

Extratropical cyclones (ETCs) are the focus of many open questions, spanning from weather to climate and from local to global scales. The relationship between moisture uptake and precipitation over different mesoscale ETC structures remains poorly understood. The availability of moisture parameters to the scientific community through tracer dispersion models benefits the representation of the processes involving moisture, the dynamics of ETCs and the understanding of the associated meteorological fields. In this study, we present a database for North Atlantic ETC tracks and a Lagrangian-Derived Moisture Uptake Dataset, both of which are derived from dynamically downscaled ERA5 reanalysis data. We provide moisture parameters such as the total moisture uptake and its vertical distribution by layer, and shapefiles of ETC structures, to facilitate further studies in this area. Our data have been thoroughly validated to ensure that the storm tracks and their characteristics are accurately represented in the model without distortion. Additionally, we conduct a series of experiments to demonstrate the rigour and quality of the methods employed to generate the data.

Weather Regimes in Northern Eurasia: Statistics, Predictability and Associated Weather Anomalies

A weather regimes approach is applied to study large-scale circulation patterns over the North Eurasian sector (NE, defined as 0–180° E and 40–80° N domain) during the 1940–2022 period using ERA5 reanalysis data. We identified four NE weather regimes (WRs) both for boreal winter and summer seasons using a k-means standard cluster analysis method for daily geopotential height fields (z500). Whereas the winter NE WRs do not have any significant long-term changes in occurrence, three summer NE WRs are found to have statistically significant linear trends of occurrence over the studied period, the most notable of which is a regime associated with anticyclonic activity centered over the Ural Mountains called Summer Ural High, which has a statistically significant trend of +2.4 days of seasonal occurrence per decade. Loops of statistically significant likely transitions are found for the NE WRs in both seasons, showing that the NE WRs evolution follows preferred paths and is not purely random. NE WRs’ seasonal occurrence is found to change depending on the phase of the El Nino oscillation and Northern Hemisphere sea-ice area anomalies in preceding seasons. El Nino events in autumn are associated with an increased occurrence of the Winter Scandinavian Blocking regime and a decreased occurrence of the Winter North Eastern High regime. Negative sea-ice area anomalies in autumn are associated with an increased occurrence of the Winter Ural High and a decreased occurrence of the Winter Scandinavian Low regime. NE WR’s are associated with considerable, up to several times, changes in probability of temperature and precipitation extremes over the NE region.

Exploring the integration of a global AI model with traditional data assimilation in weather forecasting

Abstract Recent advancements in artificial intelligence (AI) have profoundly transformed weather forecasting, challenging traditional reliance on numerical weather prediction (NWP) models. Despite notable progress, AI models still depend heavily on traditional NWP systems and data assimilation methods to generate analysis fields, a dependency that increases computational demands and might limit forecast accuracy. This study explored the integration of Gridpoint Statistical Interpolation (GSI) with the Pangu-Weather AI forecasting model (GSI-Pangu), and assessed the potential for AI models to autonomously generate forecasts by leveraging mature data assimilation systems. Our experiments commenced by adopting ERA5 reanalysis data for the initial cycle, and then involved assimilation of simulated observations in subsequent cycles, spanning a month-long period. Results demonstrated notable enhancements in forecast accuracy, with reductions in the root mean square error across various atmospheric variables compared with the results of a control experiment without data assimilation. Additionally, the results highlighted GSI-Pangu’s ability to predict large-scale circulation patterns of extreme precipitation events, together with its effectiveness in driving regional models to accurately forecast precipitation intensity and distribution. Successful implementation of GSI within the Pangu-Weather framework underscores the transformative potential of hybrid forecasting systems, which merge conventional meteorological techniques with AI innovations, thereby facilitating accelerated adoption of AI in weather forecasting.

Interdecadal Variations in the Spatial Pattern of the Arctic Oscillation Arctic Center in Wintertime

AbstractArctic Oscillation (AO) is the dominant mode of atmospheric circulation in the extratropical Northern Hemisphere regions. The spatial pattern of the AO Arctic center affects the extent of polar cold air outbreaks southward. However, the underlying nature and causes of its interdecadal variation remain unclear. Utilizing ERA5 reanalysis data, this study identifies two distinct spatial patterns of the wintertime AO Arctic center through the K‐means clustering method, which alternate over different decade periods. The Double‐trough pattern generates a tripolar temperature pattern of “cold Arctic‐warm Eurasia‐cold Tibetan Plateau” through a Rossby wave train during 1960–1997/2013–2024. While the Single‐trough pattern leads to a dipolar temperature pattern in 1998–2012. Furthermore, interdecadal variations in North Atlantic sea surface temperature meridional gradient act as an influencing factor in shaping the spatial pattern of the AO Arctic center. This research aids the understanding and prediction of climate anomalies using AO signals within various decadal contexts.

High-resolution assessment of wind energy potential in the Hami region of Northwestern China

Abstract Wind energy plays a pivotal role in the global effort to mitigate climate change, with China emerging as a leader in renewable energy adoption. The Hami region in northwestern China stands out as a crucial area for wind power development, given its substantial wind resources and strategic importance in China’s energy landscape. However, existing studies on wind energy potential vary widely and involve large uncertainties due to sparse measurements and coarse resolution, highlighting the need for more precise assessments to guide policy decisions and optimize energy utilization. This study leverages high-resolution ERA5 reanalysis data and advanced wind turbine technology to assess wind energy potential in the Hami region, taking into account factors including wind speed patterns, turbine heights, and geographical constraints. The comparison with in-situ data demonstrated that high-resolution ERA5 reanalyzed wind speeds enable to capture multi-year wind speed variations in this region. We find substantial potential for wind energy in Hami, with energy densities exceeding 200 W m−2 in the high-potential wind zones. Importantly, this study identifies a new high-potential area in eastern Hami, termed the East Wind Zone. Our high-resolution assessment of wind energy potential at different heights over the past two decades reveals long-term trends and seasonal variations. Increasing the hub height from 95 m to 140 m raises the average wind power generation potential across Hami by 31.3 GWh yr−1. Our findings highlight the importance of strategic wind farm placement to maximize renewable energy output and provide insights for policy and industry, supporting China’s renewable energy goals.

Investigation of thunderstorm characteristics with severe lightning events over NE region of India

Abstract. The north eastern region (NER) of India is among the hotspot regions for thunderstorm events leading to substantial loss of lives and properties every year. A reliable thunderstorm early warning system can prevent these losses. As these events are highly dynamic, prediction becomes equally challenging. Diagnostic studies of such events could provide more understanding of lightning and storms over a region which could then be used as input for the prediction of such events. Improving the efficiency of the prediction of associated meteorological parameters could improve the prediction of thunderstorm. In this study, two thunderstorm events have been identified with high lightning flash counts, using observations from the INSAT-3D satellite and lightning detection sensors. These observations revealed that one of the events was caused by a cloud system that originated elsewhere and travelled over the area of impact while the other event was an isolated convective system that occurred over a small region. The characteristic features of thunderstorms, like vertical wind velocity, cloud ice water content, relative humidity, cloud fraction and radar reflectivity, etc. were simulated using the WRF model. These simulations were validated with the ERA-5 reanalysis data and observations from Radar and satellite data. Validation results suggest that the WRF model could predict these features associated with thunderstorm events quite reliably. The study helped to improve the severe weather warning over NER of India and the stakeholders are trained on effective utilization of such warning.