ERA5 Reanalysis Dataset Research Articles

Early Season Forecasting of Corn Yield at Field Level from Multi-Source Satellite Time Series Data

Crop yield forecasting during an ongoing season is crucial to ensure food security and commodity markets. For this reason, here, a scalable approach to forecast corn yields at the field-level using machine learning and satellite imagery from Sentinel-2 and Landsat missions is proposed. The model, evaluated on 1319 corn fields in the U.S. Corn Belt from 2017 to 2022, integrates biophysical parameters from Sentinel-2, Land Surface Temperature (LST) from Landsat, and agroclimatic data from ERA5 reanalysis dataset. Resampling the time series over thermal time significantly enhances predictive performance. The addition of LST to our model further improves in-season yield forecasting, through its capacity to detect early drought, which is not immediately visible to optical sensors such as the Sentinel-2. Finally, we propose a new two-stage machine learning strategy to mitigate early season partially available data. It consists in extending the current time series on the basis of complete historical data and adapting the model inference according to the crop progress.

Future offshore wind energy evolution in the Bay of Biscay

A relevant point near the Biscay Marine Energy Platform (BiMEP) has been chosen and data from the ERA5 reanalysis and CMIP6 datasets in future SSP5-85 scenario (wind and wave data) have been used to analyse the energy potential and its trends from 2015 to 2100 for wind energy. A 15 MW wind turbine has been chosen in line with current technological state-of-the-art and needs, and its energy production has been computed considering the corresponding power curve, and the wind speed at its hub height based on the variation of sea roughness due to the sea state (wave height and period). The results, although most of them have not been relevant, have shown a slightly downward trend mostly on winter in all parameters analysed: mean wave period (Tm), significant wave height (Hs), corresponding sea roughness (z0), and wind speed at hub height (Ux). The 15MW wind turbine simulation has therefore shown a small decrease in energy production.

Variability and trends of near-surface wind speed over the Tibetan Plateau: The role played by the westerly and Asian monsoon

Near-surface wind speed exerts profound impacts on many environmental issues, while the long-term (≥60 years) trend and multidecadal variability in the wind speed and its underlying causes in global high-elevation and mountainous areas (e.g., Tibetan Plateau) remain largely unknown. Here, by examining homogenized wind speed data from 104 meteorological stations over the Tibetan Plateau for 1961–2020 and ERA5 reanalysis datasets, we investigated the variability and long-term trend in the near-surface wind speed and revealed the role played by the westerly and Asian monsoon. The results show that the homogenized annual wind speed displays a decreasing trend (−0.091 m s−1 per decade, p < 0.05), with the strongest in spring (−0.131 m s−1 per decade, p < 0.05), and the weakest in autumn (−0.071 m s−1 per decade, p < 0.05). There is a distinct multidecadal variability of wind speed, which manifested in an prominent increase in 1961–1970, a sustained decrease in 1970–2002, and a consistent increase in 2002–2020. The observed decadal variations are likely linked to large-scale atmospheric circulation, and the correlation analysis unveiled a more important role of westerly and East Asian winter monsoon in modulating near-surface wind changes over the Tibetan Plateau. The potential physical processes associated with westerly and Asian monsoon changes are in concordance with wind speed change, in terms of overall weakened horizontal air flow (i.e., geostrophic wind speed), declined vertical thermal and dynamic momentum transfer (i.e., atmospheric stratification thermal instability and vertical wind shear), and varied Tibetan Plateau vortices. This indicates that to varying degrees these processes may have contributed to the changes in near-surface wind speed over the Tibetan Plateau. This study has implications for wind power production and soil wind erosion prevention in the Tibetan Plateau.

The increases in extreme climatic events over the northeastern Tibetan Plateau and their association with atmospheric circulation changes

In the context of global warming, increasing frequency of climate extremes poses a great challenge to natural systems and humankind. However, the spatial and temporal characteristics, as well as the mechanisms of extreme climate change, remain insufficiently understood, particularly in arid and semi-arid mountainous regions with high climate sensitivity and ecological fragility. In this study, we analyzed the spatial and temporal characteristics of 27 extreme climate indices (including intensity, duration, and frequency) in the Qilian Mountains, northeastern Tibetan Plateau, from 1961 to 2016 using the CN05.1 meteorological grid dataset, explored the relationship between the extreme climate indices and internal variability of the climate system (e.g., the North Atlantic Multi-decadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), and the Arctic Oscillation (AO)), and further probed into the mechanisms affecting climate extremes using ERA5 reanalysis dataset. The results showed that extreme temperatures in the Qilian Mountains were significantly enhanced in terms of intensity, frequency, and duration during 1961–2016. The decrease rates of frost days (FD0) and ice days (ID0) were − 4.4 d per decade and − 4.7 d per decade, respectively, and the increase rate of growing season length (GSL) was 2.9 d per decade. Extreme precipitation intensity increased significantly, with the intensity indices R95P and PRCPTOT showing the most significant change trends of 4 mm per decade and 14.9 mm per decade, respectively (p < 0.05). The number of consecutive dry days (CDD) displayed a significant downward trend, with a rate of −6 d per decade, the number of moderate precipitation days (R10) showed an increasing trend of 0.12 d per decade. Correlation analyses revealed significant associations between the increases in extreme climates and the AMO and PDO. Additionally, the enhancement of the AO contributed to the increased extreme precipitation. Enhanced anticyclonic circulation in Eurasia and geopotential height, as well as increased cloud cover in winter, were the main reasons for the increase in extreme temperatures in the Qilian Mountains, whereas the significant increase in water vapor transport entering the east boundary of the Qilian Mountains in summer mainly contributed to the increase in extreme precipitation. This study highlights internal oscillations in the climate system that regulate the extreme climate variability in the Qilian Mountains at various spatial-temporal scales.

Secular changes in the tropical stratospheric water vapour entry induced by the Indo-Pacific warm pool warming

A decreasing trend in the tropical (30°S ∼ 30°N) stratospheric water vapour (SWV) entry in recent four decades (from 1984 to 2020) is detected based on the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) measurements and the ERA5 reanalysis dataset using linear regression and Ensemble Empirical Mode Decomposition (EEMD) analysis. With the concurrent warming of the sea surface temperature (SST), the Indo-Pacific warm pool (IPWP) appears to be the most significant region among the tropical oceans based on correlation analysis. >43% of the decreasing tropical lower SWV trend is likely to be related to the IPWP SST warming. To validate this relationship, two groups of idealized runs are carried out with version 4 of the Whole Atmosphere Community Climate Model (WACCM4) and version 5 of the Community Atmosphere Model (CAM5). Both simulations agree with the observational-based linkage. The IPWP-SST-warming forced simulations show that the temperature in the tropical tropopause has decreased at the rate of around 0.318 K per decade in the coldest point region, as the tropical convection over the IPWP has become more vigorous and excited stronger equatorial waves to produce adiabatic cooling around tropopause. This cooling tropical tropopause leads to a dehydrating tropical lower stratosphere at the rate of 0.025 ppmv per decade, as expected by the freeze-drying mechanism. These results imply the substantial warming trend of IPWP is an important factor for the long-term trend of the tropical SWV entry under climate change, and a better representation of this relationship in the model is critical for the SWV projection under future climate scenarios.

Comparison of the Climatic Characteristics of Ozone Valley Over the Tibetan Plateau and the Rocky Mountains

AbstractThis study compares the ozone valleys over the Tibetan Plateau (TP) and the Rocky Mountains (RM) using the ERA5 reanalysis data set. The dynamical transport of the ozone over these two regions is analyzed using the Lorenz circulation decomposition method. The ozone content valley over TP is observed around 200–50 hPa (upper troposphere and lower stratosphere, or UTLS), and that over RM is around 300–100 hPa. It is shown that the TP ozone content is smaller than that over RM. By analyzing the spatiotemporal distribution of the ozone content and the general circulation, the anticyclone over Southern Asian (SAH) plays a significant role in existence of the TP ozone valley, and the ozone content flux reaches its maximum in July. Large‐scale terrain and related general circulation determine the ozone valley appearance. Further analysis suggests that stationary transport has a larger impact on the ozone valley formation than the transient transport. The transport by the zonal circulation nearly cancels out most of that by the meridional circulation, due to the fact that the zonal transport magnitude is nearly equal to the meridional transport. The transport center over the RM is much weaker than that over the TP. Furthermore, the contrasts between transient and stationary transports are less evident over RM than over TP. The eddy‐driven stationary ozone transport flux significantly impacts the development of the two low ozone centers across these large terrains.

Characterization of Surface Pressure Accuracy and Spatial and Temporal Variations of The GPT3 And HGPT2 Models in The Chinese Region

The accuracy of surface pressure is crucial for obtaining high-precision zenith hydrostatic delay(ZHD). In order to verify the accuracy of the Global Pressure and Temperature model (HGPT2 ) and the Global Pressure and Temperature (GPT3) in obtaining ground air pressure in China, as well as the spatiotemporal variation characteristics of the errors of the two models, this paper uses the ERA5 reanalysis dataset and combines it with actual meteorological data from weather stations to evaluate their performance. The results show that the results show that the HGPT2 model has better accuracy in simulating atmospheric pressure in China than the GPT3 model, with atmospheric pressure bias of -0.54 hPa and -2.09 hPa, and RMS of 4.42 hPa and 4.91 hPa, respectively. This article analyzes the spatial and temporal characteristics of model errors based on China's climate types. The performance of the GPT3 and HGPT2 models varies in different regions, and their accuracy in simulating atmospheric pressure changes is poorer in northern and northwestern China.

Unleashing the power of machine learning and remote sensing for robust seasonal drought monitoring: A stacking ensemble approach

Droughts cause significant economic losses in many regions around the world, highlighting a need to more accurately quantify implications of drought on production and water management. Remote sensing technologies and machine learning-based models offer promising solutions for timely and accurate regional drought monitoring, but the accuracy of such approaches is constrained by both the conceptual design and algorithms underpinning such approaches. We developed a machine learning stacking ensemble approach to overcome such limitations, drawing upon Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record (PERSIANN-CDR), Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing products, and climate zoning data to estimate 3-month scale SPEI (Standardized Precipitation Evapotranspiration Index, SPEI-3) in 9 sub-regions of China. We compared 19 individual machine learning models and used the stacking approach to select the most robust meta-model for SPEI-3 prediction. We found that CatBoost Regressor (CBR), Extra Trees Regressor (ETR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Random Forest (RF) were the top individual models for predicting drought, while using CBR as the stacked meta-model achieved the best performance. The R2 values for the stacking model with CBR as the meta-model were 0.9065 and 0.8218 in the eastern and western regions, respectively. We then employed the stacking model with CBR as the meta-model to generate seasonal drought maps based on SPEI-3 across different years and seasons. Our predicted SPEI-3 was compared with drought maps generated by the GPCC and ERA5 reanalysis datasets. We found a strong correlation between them with R2 exceeding 0.8 across multiple years and seasons, indicating our machine learning stacking ensemble approach had a good performance in monitoring seasonal drought conditions. The findings of our study establish a standardized protocol for predicting droughts in the agricultural sector, enabling its widespread application irrespective of variations in terrain and climate.

Near-Future Projection of Sea Surface Winds in Northwest Pacific Ocean Based on a CMIP6 Multi-Model Ensemble

Information about wind variations and future wind conditions is essential for a monsoon domain such as the Northwest Pacific (NWP) region. This study utilizes 10 Generalized Circulation Models (GCM) from CMIP6 to evaluate near-future wind changes in the NWP under various climate warming scenarios. Evaluation against the ERA5 reanalysis dataset for the historical period 1985–2014 reveals a relatively small error with an average of no more than 1 m/s, particularly in the East Asian Marginal Seas (EAMS). Future projections (2026–2050) indicate intensified winds, with a 5–8% increase in the summer season in the EAMS, such as the Yellow Sea, East Sea, and East China Sea, while slight decreases are observed in the winter period. Climate mode influences show that winter El Niño tends to decrease wind speeds in the southern study domain, while intensifying winds are observed in the northern part, particularly under SSP5-8.5. Conversely, summer El Niño induces higher positive anomalous wind speeds in the EAMS, observed in SSP2-4.5. These conditions are likely linked to El Niño-induced SST anomalies. For the application of CMIP6 surface winds, the findings are essential for further investigations focusing on the oceanic consequences of anticipated wind changes such as the ocean wave climate, which can be studied through model simulations.

Spatiotemporal Characteristics of the Evaporation Duct in the Kuroshio Extension

The evaporation duct over the sea affects the propagation of electromagnetic waves, impacts the performance of electromagnetic systems accordingly, such as the shipborne radars. In order to reveal the spatiotemporal variations of the evaporation duct in the Kuroshio Extension (KE) region, where is one of the most intense air-sea interaction regions, the study utilized a 30-year ERA5 reanalysis dataset (from 1993 to 2022) and conducted statistical analysis on the monthly, seasonal variations and spatial distribution characteristics of the evaporation duct based on the NPS evaporation duct diagnostic model. The results enrich the database of maritime evaporation duct characteristics and improve the resolution and accuracy compared to previous studies.

Assessing the Performance of Water Vapor Products from ERA5 and MERRA-2 during Heavy Rainfall in the Guangxi Region of China

Precipitable water vapor (PWV) is a crucial factor in regulating the Earth’s climate. Moreover, it demonstrates a robust correlation with precipitation. Situated in a region known for the generation and development of tropical cyclones, Guangxi in China is highly susceptible to floods triggered via intense rainfall. The atmospheric water vapor in this area displays prominent spatiotemporal features, thus posing challenges for precipitation forecasting. The water vapor products within the MERRA-2 and ERA5 reanalysis datasets present an opportunity to overcome constraints associated with low spatiotemporal resolution. In this study, the PWV data derived from GNSS and meteorological measurements in Guangxi from 2016 to 2018 were used to evaluate the accuracy of MERRA-2 and ERA5 water vapor products and their relationship with water vapor variations during extreme rainfall. Using GNSS PWV as a reference, the average bias of MERRA-2 PWV and ERA5 PWV for heavy rainfall was −0.22 mm and 1.84 mm, respectively, with average RMSE values of 3.72 mm and 3.31 mm. For severe rainfall, the average bias of MERRA-2 PWV and ERA5 PWV was −0.14 mm and 2.92 mm, respectively, with average RMSE values of 4.28 mm and 4.01 mm. During heavy rainfall days from Days 178 to 184 in 2017, the average bias of MERRA-2 PWV and ERA5 PWV was 0.92 mm and 2.42 mm, respectively, with average RMSE values of 4.04 mm and 3.40 mm. The accuracy was highest at the Guiping and Hechi stations and lowest at the Hezhou and Rongshui stations. Furthermore, when comparing MERRA-2/ERA5 PWV with GNSS PWV and actual precipitation, the trends in the variations of MERRA-2/ERA5 PWV were generally consistent with GNSS PWV and aligned with the increasing or decreasing trends of actual precipitation. In addition, ERA5 PWV exhibited high accuracy. Before the onset of heavy rainfall, PWV has a sharp surge. During heavy rainfall, PWV reaches its peak value. Subsequently, after the cessation of heavy rainfall, PWV tends to stabilize. Therefore, the reanalysis data of PWV can effectively reveal significant changes in water vapor and actual precipitation during periods of heavy rainfall in the Guangxi region.

Recent warming trends of the Greenland ice sheet documented by historical firn and ice temperature observations and machine learning

Abstract. Surface melt on the Greenland ice sheet has been increasing in intensity and extent over the last decades due to Arctic atmospheric warming. Surface melt depends on the surface energy balance, which includes the atmospheric forcing but also the thermal budget of the snow, firn and ice near the ice sheet surface. The temperature of the ice sheet subsurface has been used as an indicator of the thermal state of the ice sheet's surface. Here, we present a compilation of 4612 measurements of firn and ice temperature at 10 m below the surface (T10 m) across the ice sheet, spanning from 1912 to 2022. The measurements are either instantaneous or monthly averages. We train an artificial neural network model (ANN) on 4597 of these point observations, weighted by their relative representativity, and use it to reconstruct T10 m over the entire Greenland ice sheet for the period 1950–2022 at a monthly timescale. We use 10-year averages and mean annual values of air temperature and snowfall from the ERA5 reanalysis dataset as model input. The ANN indicates a Greenland-wide positive trend of T10 m at 0.2 ∘C per decade during the 1950–2022 period, with a cooling during 1950–1985 (−0.4 ∘C per decade) followed by a warming during 1985–2022 (+0.7 ∘ per decade). Regional climate models HIRHAM5, RACMO2.3p2 and MARv3.12 show mixed results compared to the observational T10 m dataset, with mean differences ranging from −0.4 ∘C (HIRHAM) to 1.2 ∘C (MAR) and root mean squared differences ranging from 2.8 ∘C (HIRHAM) to 4.7 ∘C (MAR). The observation-based ANN also reveals an underestimation of the subsurface warming trends in climate models for the bare-ice and dry-snow areas. The subsurface warming brings the Greenland ice sheet surface closer to the melting point, reducing the amount of energy input required for melting. Our compilation documents the response of the ice sheet subsurface to atmospheric warming and will enable further improvements of models used for ice sheet mass loss assessment and reduce the uncertainty in projections.

The Variation Characteristics of Stratospheric Circulation under the Interdecadal Variability of Antarctic Total Column Ozone in Early Austral Spring

Antarctic Total Column Ozone (TCO) gradually began to recover around 2000, and a large number of studies have pointed out that the recovery of the Antarctic TCO is most significant in the austral early spring (September). Based on the Bodeker Scientific Filled Total Column Ozone and ERA5 reanalysis dataset covering 1979–2019, the variation characteristics of the Antarctic TCO and stratospheric circulation for the TCO ‘depletion’ period (1979–1999) and the ‘recovery’ period (2000–2019) are analyzed in September. Results show that: (1) Stratospheric elements significantly related to the TCO have corresponding changes during the two eras. (2) The interannual variability of the TCO and the above-mentioned stratospheric circulation elements in the recovery period are stronger than those in the depletion period. (3) Compared with the depletion period, due to the stronger amplitude of the planetary wave 1, stronger Eliassen–Palm (EP) flux corresponds to EP flux convergence, larger negative eddy heat flux, and positive eddy momentum flux in the stratosphere during the recovery period. The polar temperature rises in the lower and middle stratosphere and the polar vortex weakens in the middle and upper stratosphere, accompanied by the diminished area of PSC. This contributes to the understanding of Antarctic ozone recovery.

Diagnosing Flavors of Tropospheric Rossby Wave Breaking and Their Associated Dynamical and Sensible Weather Features

Abstract Rossby wave breaking (RWB) can be manifested by the irreversible overturning of isentropes on constant potential vorticity (PV) surfaces. Traditionally, the type of breaking is categorized as anticyclonic (AWB) or cyclonic (CWB) and can be identified using the orientation of streamers of high potential temperature (θ) and low θ air on a PV surface. However, an examination of the differences in RWB structure and their associated tropospheric impacts within these types remains unexplored. In this study, AWB and CWB are identified from overturning isentropes on the dynamic tropopause (DT), defined as the 2 potential vorticity unit (PVU; 1 PVU = 10−6 K kg−1 m2 s−1) surface, in the ERA5 dataset during December, January, and February 1979–2019. Self-organizing maps (SOM), a machine learning method, is used to cluster the identified RWB events into archetypal patterns, or “flavors,” for each type. AWB and CWB flavors capture variations in the θ minima/maxima of each streamer and the localized meridional θ gradient (∇θ) flanking the streamers. Variations in the magnitude and position of ∇θ between flavors correspond to a diversity of jet structures leading to differences in vertical motion patterns and troposphere-deep circulations. A subset of flavors of AWB (CWB) events are associated with the development of strong surface high (low) pressure systems and the generation of extreme poleward moisture transport. For CWB, many events occurred in similar geographical regions, but the precipitation and moisture patterns were vastly different between flavors. Our findings suggest that the location, type, and severity of the tropospheric impacts from RWB are strongly dictated by RWB flavor. Significance Statement Large-scale atmospheric waves ∼15 km above Earth’s surface are responsible for the daily weather patterns that we experience. These waves can undergo wave breaking, a process that is analogous to ocean waves breaking along the seashore. Wave breaking events have been linked to extreme weather impacts at the surface including cold and heat waves, strong low pressure systems, and extreme precipitation events. Machine learning is used to identify and analyze different flavors, or patterns, of wave breaking events that result in differing surface weather impacts. Some flavors are able to generate notable channels of moisture that result in extreme high precipitation events. This is a crucial insight as forecasting of extreme weather events could be improved from this work.

AMO modulation of interdecadal background of persistent heavy rainfall in summer over the Huaihe River Basin

This study used observed rainfall, ERA5 reanalysis, and CMIP6 model datasets to investigate the interdecadal variation and underlying mechanism of persistent heavy rainfall (PHR) over the Huaihe River Basin (HRB) in China during July–August, and to examine the role of the Atlantic Multidecadal Oscillation (AMO) in modulating such rainfall. The results indicate that PHR over the HRB exhibited a marked interdecadal variation. The interdecadal increase in PHR was found attributable primarily to interdecadal enhancement of ascending motion, which might have been due to external forcing by the AMO. The AMO can trigger upper-tropospheric mid–high-latitude Rossby wave trains that can lead to an upper-tropospheric anticyclone and a lower-tropospheric cyclone configuration over the HRB. Increased warm advection and greater transport of water vapor from lower latitudes, caused by enhanced southerlies in the middle–lower troposphere over the HRB, can also lead to enhanced ascending motion over the HRB. Meanwhile, an anomalous cyclone over northeastern China triggered by AMO can transport cold air from higher latitudes to the HRB, boosting convection and promoting the development and duration of PHR. By affecting sea surface temperature in the western North Pacific Ocean, the AMO can also indirectly cause a meridional teleconnection pattern in the lower troposphere, which increases the transport of water vapor to the HRB. Numerical model experiments can reproduce both the interdecadal variations in PHR and the mechanisms of the influence of the AMO on PHR, providing a reliable foundation for understanding and forecasting PHR over the HRB.

The value of wake steering wind farm flow control in US energy markets

Abstract. Wind farm flow control represents a category of control strategies for achieving wind-plant-level objectives, such as increasing wind plant power production and/or reducing structural loads, by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated – for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm flow control tool with historical electricity price data for 15 existing US wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis dataset. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.4 % and 1.7 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual revenue of production, ARP) 4 % higher than the AEP gains. For most wind plants, ARP gain was found to exceed AEP gain. But the ratio between ARP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool – the region with the highest wind penetration analyzed (31 %) – the increase in ARP from wake steering is 11 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.

Evaluation of ERA5 and MERRA-2 Reanalysis Datasets over the Aegean Region, Türkiye

Reanalysis products are among the most-used datasets in the atmospheric sciences since they comprehensively describe the observed climate at sub-daily intervals in a region. Two reanalysis datasets, namely, the fifth generation of European Centre for Medium-range Weather Forecast (ECMWF) atmospheric reanalysis of global climate (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2), were evaluated for the representation of air temperature at 2 m, mean sea level pressure and wind speed over the Aegean Region of Türkiye during the period 1963–2020. Hourly reanalysis data were compared with observations in 19 meteorological stations in the region. Several statistical parameters, such as root mean square error (RMSE), correlation coefficient (R), and mean bias error (MBE), were used to evaluate the performances of the datasets. The results indicated that air temperature and mean sea level pressure are generally better represented by the MERRA-2 reanalysis in the region, whereas the ERA5 reanalysis dataset better represents wind speed. MERRA-2 had lower RMSE and slightly better performance at 11 stations with high R (&amp;gt;0.98) for mean sea level pressure. The MERRA-2 reanalysis dataset had a high overall R (&amp;gt;0.94) for air temperature and performed better at 12 stations. The overall regional R-value for the ERA5 wind speed dataset was 0.58, and ERA5 showed better performance at 13 individual stations for wind speed. Our results guide which reanalysis dataset better represents the regional climate characteristics for selected parameters.

Assessing the Monthly Trends in Precipitable Water Vapor over the Indian Subcontinent

This study estimates the trends in precipitable water vapor (PWV), atmospheric moisture budget (AMB), and the factors influencing them: air temperature, evapotranspiration (ET), convective available potential energy (CAPE), and vertical velocity (Omega) over the Indian subcontinent using ERA5 reanalysis data sets between 1980 and 2020. PWV is examined across three atmospheric layers (1000–850 hPa: lower layer; 850–500 hPa: middle layer; 500–300 hPa: upper layer), and the entire atmospheric column (EAC; 1000–300 hPa). The observed PWV trends exhibit variability within the EAC, ranging from −0.53 to 1.25 mm/decade across the study area, with the middle layer showing the most pronounced variation (–0.44 to 0.83 mm/decade), followed by the lower layer (0.10 to 0.45 mm/decade), and the upper layer (–0.02 to 0.23 mm/decade). These fluctuations in PWV are attributed to changes in air temperature, ET, CAPE, and Omega. This investigation, however, underscores the necessity of delving into the impacts of these influencing factors on PWV using finer resolution data, to enhance our comprehension of its spatial and temporal dynamics in the region. Furthermore, the annual AMB analysis reveals a declining trend in the study region. These findings collectively contribute to the understanding of regional water-energy cycles and the recent shifts in atmospheric dynamics.

The Characteristics of Tropical Cyclones and Associated High Winds in Shanghai from 2005 to 2020

The characteristics of 21 tropical cyclones (TCs) and associated high winds in Shanghai from 2005 to 2020 are analyzed based on the TC best track data, surface observations, and ERA5 reanalysis dataset. Results indicate that 95% of the TCs causing high winds in Shanghai were generated during July to September, and located to the south of Shanghai for ~80% of the impact time. Divided into four categories according to tracks, the TCs show significant discrepancies in genesis month and location, intensity, size, and duration. The findings of high winds in Shanghai highlight the combination effect of TC intensity, track, and the distance between TCs and Shanghai normalized by TC size (NDSH-TC). The maximum 10‐min sustained (Vmax) and extreme 3‐s wind speeds (Vex) in Shanghai only increase slightly with TC intensity, due to the fact that the stronger TCs are usually farther away from Shanghai. The Vmax and Vex differ in response to different TC tracks and reach the maximum value under TCs moving northward across the land west of Shanghai and the sea east of Shanghai, respectively. The urban and coastal wind speeds are the smallest and the largest respectively, indicating that the largest underlying surface roughness is associated with the most rapid urban construction. The wind speeds under the influence of TCs in four quadrants of Shanghai also present different variations with the NDSH-TC. Additionally, the wind direction in Shanghai is regularly related to the relative position between the TC center and Shanghai. A case study combined with TC In‐Fa (2106) verified the validity of the conclusions, which can provide a reference for TC forecasters to make more accurate wind field predictions.

Influence of climate variability modes over wind-sea and swell generated wave energy

Wave energy is one of the cleanest renewable sources of energy generated through the motion of wind-sea and swell waves. This study examines the influence of different climate variability modes over global wave energy generated from wind-sea (PW) and swell waves (PS) during 1950–2021 using the ERA5 reanalysis dataset. The major climate modes, such as El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD), and Southern Annular Mode (SAM) are considered during four boreal seasons. Wave energy extremes are determined using a non-stationary generalized extreme value analysis. The significant positive influence of ENSO on mean and extreme PS is evident over the northeast and central Pacific Ocean (PO) during December–February (DJF), and weakens during March–May (MAM). The strongest NAO response to PW and PS is evident during DJF as a tripolar and dipolar pattern over the north Atlantic Ocean, respectively. Further, PW responses due to IOD include significant increases over the Bay of Bengal and eastern Indian Ocean (IO) during JJA, which extend towards the central IO during SON. Lastly, SAM influence over PW occurs as a dipole pattern in the Southern Ocean (SO), i.e., positive anomalies in between 55°S and 65°S and negative anomalies in between 30°S and 55°S.