European Weather Research Articles

Differentiation of Geographic Origin of South African Wines from Austrian Wines by IRMS and SNIF-NMR.

Geographic origin and terroir are very important parameters for wine and significantly impact price. Incorrect declarations are known to occur intentionally to increase profit, thus, measures for control are required. Accompanying paperwork has been shown to be unreliable, thus, control of the product itself is required. Here we investigate and compare the stable isotope pattern of South African (Western Cape Province) wine, and evaluate its potential for discrimination from Central European/Austrian wine. The results show that the isotope values of the investigated South African wine samples differ significantly from the values of average Austrian (Central European) wines. Thus, a differentiation of the products from these two regions by stable isotope analysis is generally straightforward. However, the data suggest that vintages from years with exceptionally hot and dry summer weather in Europe may reduce the differentiation between these regions. Therefore, this method is a potent tool for the discrimination of Austrian (Central European) and South African wines under current climatic conditions, although drier and hotter summer weather in Europe, which is likely to occur more frequently due to global climate change, may require further method adjustments in the future.

Changes in complex bioclimatic indicators in Crimea since the middle of the 20th century

BACKGROUND: Observed global climate changes can significantly influence on the regional climate anomalies and recreational conditions over the Crimean peninsula.
 AIM: To study the features of complex bioclimatic indicators changes on the territory of Crimea over a multidecadal period (~70 years), paying special attention to each month of the year, and on bases of obtained results to show the possible way of recreational activities development associated with these changes.
 METHODS: For calculations, we used daily hydrometeorological data for each month from the archive of observations at the network of European weather stations E-OBS (v. 17.0) from 22 stations and NCEP/NCAR R1 reanalysis for the period of 19502018. The obtained arrays were verified in accordance with an open database of standard daily station observations in Crimea in 20052018. To assess the impact of climate change on the human body, the equivalent effective temperature, normal equivalent effective temperature, weight content of oxygen in the air (partial oxygen density) (O2), wet wind chill index Hill (Hw) and index Bodmann dry wind chiller (S) were calculated. The features of their linear trends for each month in the Crimean Peninsula were analysed.
 RESULTS: Monthwise analyses of the bioclimatic indicator trends demonstrated an increase or decrease in recreational comfort in certain seasons. The most pronounced positive normal equivalent effective temperature trends over Crimea were observed from winter to spring. This indicated improved complex recreational conditions in recent decades and the possibility to extend the duration of the holiday season. The pO2, Hw and S indices had opposite tendencies throughout the year (except for December). According to the pO2 index, in the period from August to October, a physiological oxygen deficiency may be experienced throughout Crimea. Windy and uncomfortable bioclimatic conditions tend to increase in the steppes of Crimea in December.
 CONCLUSION: The patterns of changes in bioclimatic indicators revealed through this study can be used as a regulatory factor for effectively organising and conducting recreational activities in the territory of Crimea and thus ensure the duration of the recreational period.

An Ice‐Ocean Model Study of the Mid‐2000s Regime Change in the Barents Sea

AbstractOver the satellite record, the Barents Sea winter maximum in sea ice extent has declined and was increasingly limited to areas north of the Polar Front after 2005 by warming Atlantic Water (AW) and Barents Sea Water (BSW). Sea ice extent here continues to garner interest, not least because it is associated with extreme winter weather in Europe and Asia. Previous model studies suggest there is a possibility that natural variability will cause southward re‐expansion of the lost sea ice cover but reducing uncertainties requires a better understanding of the processes driving BSW variability. To address questions about BSW variability, we used a high‐resolution model validated with observations over 1985–2014 to calculate the watermass transport, heat, and freshwater budgets within the central Barents Sea, south of the Polar Front. The model shows BSW volume minima events in years centering at 1990 and 2004, meaning a reduction in the Barents Sea's volume reservoir (also termed “memory”) of water that is consistent with historical BSW properties. Both events were preceded by extensive winter sea ice and substantial summer net sea ice melt. The event in 2004 was more extreme and led to warming AW occupying a greater volume in the Barents Sea after 2005. The reduced “memory” of BSW volume could impede a return to the more extensive winter sea ice regime and make further reduction in winter sea ice possible.

Learning to predict spatiotemporal movement dynamics from weather radar networks

Abstract Weather radar networks provide wide‐ranging opportunities for ecologists to quantify and predict movements of airborne organisms over unprecedented geographical expanses. The typically sparse spatial distribution of radar measurements poses, however, a major challenge to spatiotemporal predictive modelling. We propose FluxRGNN, a recurrent graph neural network that is based on a generic mechanistic description of population‐level movements across the Voronoi tessellation of radar sites. The resulting hybrid model capitalises on local associations between environmental conditions and animal density as well as on spatiotemporal dependencies inherent to the movement process. We applied FluxRGNN to make 72‐h forecasts of nocturnal bird migration over Western Europe using simulated trajectories and measurements from the European weather radar network. For both datasets, FluxRGNN achieves higher predictive performance than baseline models based on environmental conditions alone. It effectively disentangles local take‐off and landing dynamics from aerial movements and correctly predicts migration directions with an accuracy of 87%. Continental‐scale forecasts of animal density and biomass fluxes have the potential to improve the impact and cost‐effectiveness of wildlife management and conservation efforts. With FluxRGNN this becomes feasible for nocturnal bird migration. In the future, other migration systems could benefit from applying the proposed method to similar static sensor networks.

A deep learning based classification of atmospheric circulation types over Europe: projection of future changes in a CMIP6 large ensemble

High- and low pressure systems of the large-scale atmospheric circulation in the mid-latitudes drive European weather and climate. Potential future changes in the occurrence of circulation types are highly relevant for society. Classifying the highly dynamic atmospheric circulation into discrete classes of circulation types helps to categorize the linkages between atmospheric forcing and surface conditions (e.g. extreme events). Previous studies have revealed a high internal variability of projected changes of circulation types. Dealing with this high internal variability requires the employment of a single-model initial-condition large ensemble (SMILE) and an automated classification method, which can be applied to large climate data sets. One of the most established classifications in Europe are the 29 subjective circulation types called Grosswetterlagen by Hess & Brezowsky (HB circulation types). We developed, in the first analysis of its kind, an automated version of this subjective classification using deep learning. Our classifier reaches an overall accuracy of 41.1% on the test sets of nested cross-validation. It outperforms the state-of-the-art automatization of the HB circulation types in 20 of the 29 classes. We apply the deep learning classifier to the SMHI-LENS, a SMILE of the Coupled Model Intercomparison Project phase 6, composed of 50 members of the EC-Earth3 model under the SSP37.0 scenario. For the analysis of future frequency changes of the 29 circulation types, we use the signal-to-noise ratio to discriminate the climate change signal from the noise of internal variability. Using a 5%-significance level, we find significant frequency changes in 69% of the circulation types when comparing the future (2071–2100) to a reference period (1991–2020).

Drought Risk Evaluation in Iran by Using Geospatial Technologies

A drought risk map has been developed at the national scale by using remote-sensing satellite data over Iran by combining output layers resulting from three main components of a risk-evaluation procedure including Hazard Quantification (HQ), Vulnerability Assessment (VA) and Identification of Elements at Risk (IER) in a GIS environment. In this respect, Drought Severity (DS) was calculated by using the monthly Normalized Difference Vegetation Index (NDVI) (over 31 years from 1986–2016). Iran landcover classification and a slope map, population density maps, and irrigated farm percentages at the provincial scale were utilized within the drought risk evaluation (DRE) process. The final risk map reveals that the northwest of the country, with a climate similar to the central European weather conditions, is exposed to the maximum drought risk. In contrast, the areas with an arid climate, mainly located in the middle of Iran, exhibits minimum risk against drought. Based on the risk map, the southern part of the Caspian Sea shows very low drought risk due to the moderate and subtropical climate in this region. The outputs of this research will provide advice and warnings to help decision makers reduce drought risk consequences after prioritizing risk areas at the administrative scale.

New gridded dataset of rainfall erosivity (1950–2020) on the Tibetan Plateau

Abstract. The risk of water erosion on the Tibetan Plateau (TP), a typical fragile ecological area, is increasing with climate change. A rainfall erosivity map is useful for understanding the spatiotemporal pattern of rainfall erosivity and identifying hot spots of soil erosion. This study generates an annual gridded rainfall erosivity dataset on a 0.25∘ grid for the TP in 1950–2020. The 1 min precipitation observations at 1787 weather stations for 7 years and 0.25∘ hourly European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) precipitation data for 71 years are employed in this study. Our results indicate that the ERA5-based estimates have a marked tendency to underestimate annual rainfall erosivity when compared to the station-based estimates, because of the systematic biases of ERA5 precipitation data including the large underestimation of the maximum contiguous 30 min peak intensity and relatively slight overestimation of event erosive precipitation amounts. The multiplier factor map over the TP, which was generated by the inverse distance-weighted method based on the relative changes between the available station-based annual rainfall erosivity grid values and the corresponding ERA5-based values, was employed to correct the ERA5-based annual rainfall erosivity and then reconstruct the annual rainfall erosivity dataset. The multiyear average correction coefficient over the TP between the station-based annual rainfall erosivity values and the newly released data is 0.67. In addition, the probability density and various quantile values of the new data are generally consistent with the station-based values. The data offer a view of large-scale spatiotemporal variability in the rainfall erosivity and address the growing need for information to predict rainfall-induced hazards over the TP. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.271833; Chen, 2021).

Subpolar Atlantic Ocean mixed layer heat content variability is increasingly driven by an active ocean

Cold conditions in the upper layer of the subpolar North Atlantic Ocean, at a time of pervasive warming elsewhere, have provoked significant debate. Uncertainty arises both from potential causes (surface heat loss and ocean circulation changes) and characteristic timescales (interannual to multidecadal). Resolution of these uncertainties is important as cold conditions have been linked to recent European weather extremes and a decline in the Atlantic overturning circulation. Using observations, supported by high resolution climate model analysis, we show that a surprisingly active ocean regularly generates both cold and warm interannual anomalies in addition to those generated by surface heat exchange. Furthermore, we identify distinct sea surface temperature patterns that characterise whether the ocean or atmosphere has the strongest influence in a particular year. Applying these new insights to observations, we find an increasing role for the ocean in setting North Atlantic mixed layer heat content variability since 1960.

Deep learning based short term load forecasting with hybrid feature selection

The reliable and an economic operation of the power system rely on an accurate prediction of short term load. In this paper, a deep learning based Long Short Term Memory (LSTM) with hybrid feature selection namely RMR-HFS-LSTM, is proposed. The objective of this study is to reduce the curse of dimensionality, reduce the overfitting and improve the accuracy of short term load forecasting. The RMR-HFS is a combination of filter and wrapper feature selection introduced for identifying optimal subset of features. The instance based RReliefF and information theoretic based mutual information filter feature selection are utilized to reduce curse of dimensionality by finding and eliminating irrelevant features. The selected features of filter feature selection is tuned by using Recursive Feature Elimination (RFE) wrapper feature selection to reduce overfitting. The deep learning based LSTM improves the accuracy by handling uncertainty issues. The experiment was conducted on European weather and electricity load data using python on Tensorflow environment. The performance of the proposed RMR-HFS-LSTM model is compared against Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) in terms of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The result shows that the proposed RMR-HFS-LSTM model outperforms other models.

On the Evolution of a Sub-C Class Flare: A Showcase for the Capabilities of the Revamped Catania Solar Telescope

Solar flares are occasionally responsible for severe space-weather events, which can affect space-borne and ground-based infrastructures, endangering anthropic technological activities and even human health and safety. Thus, an essential activity in the framework of space-weather monitoring is devoted to the observation of the activity level on the Sun. In this context, the acquisition system of the Catania Solar Telescope has been recently upgraded in order to improve its contribution to the European Space Agency (ESA) – Space Weather Service Network through the ESA Portal, which represents the main asset for space weather in Europe. Here, we describe the hardware and software upgrades of the Catania Solar Telescope and the main data products provided by this facility, which include full-disk images of the photosphere and chromosphere, together with a detailed characterization of sunspot groups. As a showcase of the observational capabilities of the revamped Catania Solar Telescope, we report the analysis of a B5.4 class flare that occurred on 7 December 2020, simultaneously observed by the Interface Region Imaging Spectrograph and the Solar Dynamics Observatory satellites.

Year‐round sub‐seasonal forecast skill for Atlantic–European weather regimes

AbstractWeather regime forecasts are a prominent use case of sub‐seasonal prediction in the midlatitudes. A systematic evaluation and understanding of year‐round sub‐seasonal regime forecast performance is still missing, however. Here we evaluate the representation of and forecast skill for seven year‐round Atlantic–European weather regimes in sub‐seasonal reforecasts from the European Centre for Medium‐Range Weather Forecasts. Forecast calibration improves regime frequency biases and forecast skill most strongly in summer, but scarcely in winter, due to considerable large‐scale flow biases in summer. The average regime skill horizon in winter is about 5 days longer than in summer and spring, and 3 days longer than in autumn. The Zonal Regime and Greenland Blocking tend to have the longest year‐round skill horizon, which is driven by their high persistence in winter. The year‐round skill is lowest for the European Blocking, which is common for all seasons but most pronounced in winter and spring. For the related, more northern Scandinavian Blocking, the skill is similarly low in winter and spring but higher in summer and autumn. We further show that the winter average regime skill horizon tends to be enhanced following a strong stratospheric polar vortex (SPV), but reduced following a weak SPV. Likewise, the year‐round average regime skill horizon tends to be enhanced following phases 4 and 7 of the Madden–Julian Oscillation (MJO) but reduced following phase 2, driven by winter but also autumn and spring. Our study thus reveals promising potential for year‐round sub‐seasonal regime predictions. Further model improvements can be achieved by reduction of the considerable large‐scale flow biases in summer, better understanding and modeling of blocking in the European region, and better exploitation of the potential predictability provided by weak SPV states and specific MJO phases in winter and the transition seasons.

A prototypal high-vacuum integrated collector storage solar water heater: Experimentation, design, and optimization through a new in-house 3D dynamic simulation model

Integrated collector storage units are typically affected by convective and radiative heat losses which significantly reduce their energy performance. To enhance solar collection and heat retention, innovative techniques and novel design of such units are being more and more developed. In this framework, this paper focuses on the design and optimization of a prototypal high-vacuum integrated collector storage solar water heater. The proposed unit allows for reaching high temperatures of the stored water and for reducing the temperature drop during non-collection periods. This goal is achieved by suppressing the convective heat losses into the system by keeping the pressure into the related enclosure below 0.01 Pa and by applying a special selective coating to the solar absorber surface to drastically reduce radiative losses as well.In this paper, details about the system design and the conducted experimental tests are reported. In addition, a new mathematical model able to assess the energy performance of the innovative prototype is presented. This tool, based on a detailed 3-D transient finite-difference thermal network, was validated against the gathered experimental data. By such a tool the optical properties of the adopted selective coating are optimized for maximizing the system thermal efficiency. Finally, with the twofold aim of showing the potentiality of the developed code, as well as the effectiveness of the presented innovative solar collector prototype, a comprehensive case study referred to three different European weather zone is presented. Promising results in terms of energy savings (0.73 MWh/y) and CO2 emission reduction (149 kgCO2/y) are achieved.

An ice‐free Arctic: what could it mean for European weather?

An ice‐free Arctic: what could it mean for European weather?

On the Treatment of Soil Water Stress in GCM Simulations of Vegetation Physiology

Current land surface schemes in weather and climate models make use of the so-called coupled photosynthesis–stomatal conductance (A–gs) models of plant function to determine the surface fluxes that govern the terrestrial energy, water and carbon budgets. Plant physiology is controlled by many environmental factors, and a number of complex feedbacks are involved, but soil moisture control on root water uptake is primary, particularly in sub-tropical to temperate ecosystems. Land surface models represent plant water stress in different ways, but most implement a water stress factor, β, which ranges linearly (more recently also curvilinearly) between β = 1 for unstressed vegetation and β = 0 at the wilting point, expressed in terms of volumetric water content (θ). β is most commonly used to either limit A or gs, and hence carbon and water fluxes, and a pertinent research question is whether these treatments are in fact interchangeable. Following Egea et al. (Agricultural and Forest Meteorology, 2011, 151 (10), 1,370–1,384) and Verhoef et al. (Agricultural and Forest Meteorology, 2014, 191, 22–32), we have implemented new β treatments, reflecting higher levels of biophysical complexity in a state-of-the-art LSM, Joint UK Land Environment Simulator, by allowing root zone soil moisture to limit plant function non-linearly and via individual routes (carbon assimilation, stomatal conductance, or mesophyll conductance) as well as any (non-linear) combinations thereof. The treatment of β does matter to the prediction of water and carbon fluxes: this study demonstrates that it represents a key structural uncertainty in contemporary LSMs, in terms of predictions of gross primary productivity, energy fluxes and soil moisture evolution, both in terms of climate means and response to a number of European droughts, including the 2003 heat wave. Treatments allowing ß to act on vegetation fluxes via stomatal and mesophyll routes are able to simulate the spatiotemporal variability in water use efficiency with higher fidelity during the growing season; they also support a broader range of ecosystem responses, e.g., those observed in regions that are radiation limited or water limited. We conclude that current practice in weather and climate modelling is inconsistent, as well as too simplistic, failing to credibly simulate vegetation response to soil water stress across the typical range of variability that is encountered for current European weather and climate conditions, including extremes of land surface temperature and soil moisture drought. A generalized approach performs better in current climate conditions and promises to be, based on responses to recently observed extremes, more trustworthy for predicting the impacts of climate change.

Comparison between some meteorological elements measured by ECMWF and Earth stations in different regions in Iraq

The aim of this paper is to validate the data of three meteorological elements Air Temperature (Ta) , Relative Humidity (RH), Wind Speed (WS) from the European Center For-Medium Range Weather Forecasting (ECMWF) , against ground stations data using several Models at six stations well distributed in Iraq (Mosul, Kirkuk, Baghdad, Kut, Nasiriya, and Basra).
 Due to the difficulties which experienced by the ground climate stations in Iraq from a shortage of devices and equipment for measuring the various climatic elements, which led to a huge shortage of data throughout time for political, economic and natural disasters. It is found that researchers can adopt the data of satellite stations to monitor the climate because let’s found that there is a highly significance Correlations between the data of these stations and the data of the ground stations for climate monitoring
 Five Mathematical Models were used for that [Linear Models, Quadratic Models, Exponential Models, Logarithmic Models, and Power Models]. The performance of these models were evaluated by comparing the calculated (Ta, RH, WS) from earth stations.
 Those mathematical correlations help to be able to calculate the ground data in state of there is no ground climate stations data.
 Several statistical tests Correlation Coefficient (R), Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) were used to control the validation and goodness of these Models.
 The R2 obtained from these Models were very high in all stations. This means that, there is a highly significance Correlations between (Ta, RH, WS) estimated and [Ta, RH, WS] measured in all station

Quantifying year-round nocturnal bird migration with a fluid dynamics model.

To understand the influence of biomass flows on ecosystems, we need to characterize and quantify migrations at various spatial and temporal scales. Representing the movements of migrating birds as a fluid, we applied a flow model to bird density and velocity maps retrieved from the European weather radar network, covering almost a year. We quantified how many birds take-off, fly, and land across Western Europe to (1) track bird migration waves between nights, (2) cumulate the number of birds on the ground and (3) quantify the seasonal flow into and out of the study area through several regional transects. Our results identified several migration waves that crossed the study area in 4 days only and included up to 188 million (M) birds that took-off in a single night. In spring, we estimated that 494 M birds entered the study area, 251 M left it, and 243 M birds remained within the study area. In autumn, 314 M birds entered the study area while 858 M left it. In addition to identifying fundamental quantities, our study highlights the potential of combining interdisciplinary data and methods to elucidate the dynamics of avian migration from nightly to yearly time scales and from regional to continental spatial scales.

Isotope Analysis (13C, 18O) of Wine From Central and Eastern Europe and Argentina, 2008 and 2009 Vintages: Differentiation of Origin, Environmental Indications, and Variations Within Countries

In this study, we compare the stable isotope composition of oxygen and carbon of wines from four Central and Southeastern European countries and from Argentina to study the similarities and differences in the isotope signatures and, thus, the potential of differentiation of the various wine-growing countries. We observe similar trends for wines from Austria, Slovenia, and Romania with respect to the vintages 2008 and 2009, which are absent in the Montenegrin and Argentinean samples. It is speculated that the weather develops similarly for Austria, Slovenia, and Romania, as these countries are positioned at a similar latitude and not too far away from each other (general central and eastern European weather situation), whereas Montenegro is not influenced by the latter being situated farther south and dominantly influenced by the Adriatic Sea. Investigations on further vintages are needed to test this assumption.

Speed loss analysis and rough wave avoidance algorithms for optimal ship routing simulation of 28,000-DWT bulk carrier

Restrictions on greenhouse gas emissions and the existence of developing countries has diversified ship routes and ship types, making the ship operation more complex. Many types of simulation methods have come up in the last decade due to the enforcement of the energy efficiency design index. However, few studies that validated their simulations with actual data. This study performs the optimal ship routing of a 28,000-DWT-class bulk carrier to evaluate the optimal ship route with the speed loss analysis. The simulation results are compared with the measurement data of the bulk carrier in conditions including rough sea voyage in the Pacific Ocean. For comparison, a modified version of the speed loss analysis algorithm is developed. These algorithms are combined with the isochrone method. The differences in the simulation results with the American and European weather databases without higher wave avoidance and deliberate speed reduction are obvious. The simulated routes are closer to the measured ship trajectory if higher wave avoidance is considered. There are a couple of remarkable speed drops in the measured result, and the deliberate speed reduction is considered. Finally, some factors to control the reproducibility of ship routing including in rough sea voyages are summarized.

Evaluation of the dual-polarization weather radar quantitative precipitation estimation using long-term datasets

Abstract. Accurate, timely, and reliable precipitation observations are mandatory for hydrological forecast and early warning systems. In the case of convective precipitation, traditional rain gauge networks often miss precipitation maxima, due to density limitations and the high spatial variability of the rainfall field. Despite several limitations like attenuation or partial beam blocking, the use of C-band weather radar has become operational in most European weather services. Traditionally, weather-radar-based quantitative precipitation estimation (QPE) is derived from horizontal reflectivity data. Nevertheless, dual-polarization weather radar can overcome several shortcomings of the conventional horizontal-reflectivity-based estimation. As weather radar archives are growing, they are becoming increasingly important for climatological purposes in addition to operational use. For the first time, the present study analyses one of the longest datasets from fully operational polarimetric C-band weather radars; these are located in Estonia and Italy, in very different climate conditions and environments. The length of the datasets used in the study is 5 years for both Estonia and Italy. The study focuses on long-term observations of summertime precipitation and their quantitative estimations by polarimetric observations. From such derived QPEs, accumulations for 1 h, 24 h, and 1-month durations are calculated and compared with reference rain gauges to quantify uncertainties and evaluate performances. Overall, the radar products showed similar results in Estonia and Italy when compared to each other. The product where radar reflectivity and specific differential phase were combined based on a threshold exhibited the best agreement with gauge values in all accumulation periods. In both countries reflectivity-based rainfall QPE underestimated and specific differential-phase-based product overestimated gauge measurements.

North Atlantic midwinter storm track suppression and the European weather response in ERA5 reanalysis

In this study, we analyse the influence of North Atlantic midwinter storm track suppressions on European synoptic temperature and precipitation anomalies to determine the large-scale conditions relevant for the so-called Christmas thaw. We diagnose this relation in daily ERA5 reanalysis data in the spatial resolution of 0.25∘ between 1979 and 2018. To access synoptic time scales, a 3–10-day band-pass filter is applied. An index for the suppression is defined by the upper tropospheric Eddy Kinetic Energy (EKE) anomalies in the North Atlantic. We define the strong jet stream years as the year exceeding the 75% of the winter seasonal values at 250 hPa. In winters with strong jet activity, the storm track suppression is found, in agreement with the barotropic governor mechanism. Composites of European surface temperature and precipitation for low index values reveal weakly warmer conditions during winter (DJF) in Central Europe and the British Isles and a distinct cooling in Northern Europe. In the 1-month interval during December 15 to January 15, the warming is more pronounced. The clearest signal is the precipitation increase with a magnitude of 1 mm/day in the Mediterranean region.