bluuebunny/crossref_metadata_2025 huggingface dataset:
Diagnosing and calibrating the multi-century Sunspot Number Series
Authors: Bhattacharya Shreya , Lefevre Laure , Clette Frederic , Jansen Maarten
Published: June 2022

Visual sunspot observations form the longest scientific record of solar activity, spanning over four centuries. Long term solar studies  are crucial to predict the future evolution of the solar cycles and at improving our understanding of the solar influence on Earth climate change.

As an important step for the ongoing sunspot number recalibration throughout the entire solar physics community, (Lefevre et al., 2018), SN Version 2 was released in July, 2015 (Clette et al., 2016), which helped in shedding new light to long-term solar variations and instabilities of the 11-year solar cycle. However, uncertainties remain and errors in past historical data need to be further revised using the data at our disposal  for a robust long-term series.

In 1843, Professor Rudolf Wolf  who coined the term “Sunspot Number”, founded a journal called the "Mittheilungen der Naturforschenden Gesellschaft in Berne" where he published yearbooks with all of his findings, including sunspot observations as far back as Galileo (Wolf, 1861). The journal was maintained from 1848 (Wolf, 1848) until his death (Wolf and Wolfer, 1894). The Sunspot records collected by him from his European colleagues and his auxiliary observers are all tabulated in this journal. The Royal Observatory of Belgium (https://www.astro.oma.be/en/), particularly the WDC-SILSO, conducted a mission between 2017 and 2019 to digitize all the data contained in the published Mittheilungen.

In this study we exploit this database along with other available recounts of various observers to identify scale discrepancies or inhomogeneities that happened along the Sunspot number series over time. We also introduce statistical techniques to implement confidence bands or errors on daily Sunspot Numbers, an information that existing versions lack. The long-term aim is a complete reconstruction of the Sunspot Number series from the available raw data instead of a recalibration.
Factors behind local communities resilience to extreme weather events
Authors: Choryński Adam , Jeran Agniesz , Matczak Piotr , Pińskwar Iwona
Published: June 2022

In recent years one may notice an increasing number of extreme weather events occurrence, as well as their rising intensity. This becomes a threat for local communities and brings challenges for institutions responsible for risk management. Larger towns seem to have a different potential to deal with these risk, due to their resources (material, institutional, human). Large cities are also researched more often because of their larger exposure. On the other hand a research focus on extreme weather events resilience in small communities is lacking. Smaller towns also experience the effects of extreme meteorological events, and even if losses are not as high as in larger towns, on the local level they are they are very severe.

In this paper we analyze the issue of extreme weather events resilience in eight small municipalities from the Wielkopolska region in Poland. Main aim of this research is to study how local resilience arrangements formed in the studied municipalities acts during an another significant extreme meteorological event. Municipalities focus mostly on emergency services actions, but they differ in case of the second component of local resilience arrangements. These municipalities that were strongly affected by extremes concentrate more on structural – hard measures. Municipalities that have not faced so severe extreme weather events in the past, built resilience with a strong component of creativity resilience, where soft solutions related to education, training, organization of local risk management system complement emergency services activities.

This research is based on 40 in-depth interviews with local stakeholders, meteorological data, as well as information on firefighters operations since 2010, municipal budgets information. In order to analyze which factors affect resilience of local communities to extreme weather events based on analyzed case studies and which influence is rather limited qualitative comparative analysis (QCA) approach has been applied.

Acknowledgements: This research has been funded by the National Science Center of Poland under the grant number: 2018/31/B/HS4/03223
A climate storyline approach to inform private sector decisions on cross-border climate risks:A case study of the chocolate industry in Europe
Authors: Ercin Ertug , Kaune Alexander , Karaman Cagri
Published: June 2022

Europe is the major chocolate manufacturer in the world. With a market value of 40 billion euros, the sector not only serves the continent’s internal chocolate demand but supplies most of the world, accounting for more than 76% of global chocolate sales. Yet the sector is highly dependent on imports of chocolate’s main ingredient, cocoa, from beyond Europe, mainly from West Africa. This dependency means that the sector is vulnerable to climate extremes, and possible climate change impacts, that occur in cocoa exporting regions. This study uses the climate storyline approach to build links between extreme climatic events occurring in two major West African cocoa exporting countries and their potential consequences on European society and its economy. It aims to provide cross-border climate risk information to the chocolate industry in Europe.

The climate storyline approach is inspired by a drought event in West Africa in 1983, which resulted in cocoa production losses of up to 60% in the region. Consequently, cocoa prices spiked and there was a shortage of supply which affected prices for both chocolate producers and consumers in Europe. The storyline approach includes a modeling chain of these events, i.e., the impacts of the drought on cacao yields and cocoa production in the exporting region; consequent changes to the trade volume between the region and Europe, and to cocoa prices; and the cascading economic impacts of these changes on the wider chocolate industry. Furthermore, drought impacts on cacao yields and cocoa production under future climatic conditions are determined for the periods 2021-2040, 2041-2060 and 2081-2100 under RCP 2.6, 7.0 and 8.5 scenarios. Next, the storyline modeling chain is applied to the future climatic conditions to reveal how climate change in West Africa could impact European society and its economy, with a particular focus on chocolate manufacturing.
Explaining Heatwaves with Machine Learning
Authors: Buschow Sebastian , Keller Jan , Wahl Sabrina
Published: June 2022

The dynamic and thermodynamic drivers of spatially extended climate extremes are difficult to disentangle due to the earth system’s the high dimensionality, interconnectedness and non-linear relationships between individual processes. One approach in the literature relies on carefully selected case studies and uses dynamical models to obtain physical insights into the development of individual extreme episodes. Other studies focus on the statistical relationship between a class of extreme events and one specific driving mechanism. We aim to complement both of these approaches with a machine learning framework in three steps: firstly, the dimensionality of the predictand and a wide range of potential predictor variables is reduced using an appropriate change of basis functions. Secondly, their relationship is modeled by a statistical learner of intermediate complexity — powerful enough to represent the non-linear relationships but simple enough to allow for fast training and extensive experimentation. Lastly, the contribution of each variable to the overall model performance, and to the representation of individual events is assessed using recently developed methods of explainable machine learning.

As a first example application, we model European heatwaves in ERA5 data based on potential explanatory variables including geopotential, sea level pressure, soil moisture and the wind components of the jet stream. The predictors are summarized by classic principal component analysis (PCA); for the heatwave fields we rely on a specialized PCA for binary data. A simple neural network is capable of representing a large part of the variability in the reduced space. With the help of Shapley values, we can then quantitatively asses how much information on heatwaves is contained in each variable, and how individual heatwave events differ in terms of the variables by which the model recognizes them. This type of explanation explicitly allows for predictors with overlapping information and nonlinear interactions.  One advantage of our framework is its ability to represent the impact of all suspected drivers on any arbitrarily defined binary event in a single model.
Data rescue of historical wind observations in Sweden since the 1920s
Authors: Engström Erik , Azorin-Molina Cesar , Wern Lennart , Hellström Sverker , Zhou Chunlüe , Södling Johan , Joelsson Magnus , Chen Deliang
Published: June 2022

The ongoing climate change raises the question if, how and why the wind climate is changing. IPCC stated in AR6WG1 that the confidence in wind changes is “low” to “medium”; there is limited knowledge of historical wind changes and multidecadal variability. A fundamental challenge for the climate research community is to rescue old climate observations from weather archives at the National Weather Services and derive homogeneous and complete data series. At SMHI most of the meteorological data is available digitally since the 1960s, but before that only a minor part of the data is previously digitized.

Historical wind speed and direction observations from 1920th to 1940th from 13 stations in Sweden have been rescued and digitized making 165 additional station years of wind data available through Swedish Meteorological and Hydrological Institute (SMHI) open data portal. Stations with instrumental measurements of wind were selected and in the early 1900-century the accordingly equipped stations were mainly found at lighthouses along the coast and at airports. The dominant type of anemometer was of cup-type and different versions are described in the article. The work followed the protocol “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization. Along with digitize the wind observations meta data of the measurements done at the stations was collected and compiled as a support to the following quality control and homogenization of the wind data. The meta data showed that the most common identified possible homogeneity break was change of observer, but also change of instrument type and position were found in the records.

The presentation is a part of the first work package of the WINDGUST project, which is a collaboration between the SMHI, the University of Gothenburg - Regional Climate Group (GU-RCG) and the Spanish National Research Council (CSIC). The project aim is to fill the key gap of short availability (since 1939) and temporal inhomogeneity of wind datasets in Sweden. Especially, the results could contribute to futures studies on the causes driving the current “stilling” and “reversal” debate in a global warming climate.

Since previous presentations of the current work package, a data screening has been performed for the wind observations from the 13 digitized stations to visualize the data cover and monthly variability and wind speed range. Two distinct categories of stations with separate wind patterns can be established: coastal and inland stations where inland stations typically has a weak annual variation while coastal stationstypically experience the highest wind speed in November and December.
ICA&D: Climate services across borders
Authors: van den Besselaar Else , van der Schrier Gerard , van der Schee Marlies , Verver Ge , Baddour Omar , Jepsen Lisa-Anne , Suwondo Aris , Allen Teddy
Published: June 2022

The International Climate Assessment & Dataset (ICA&D) contributes to the provision of climate services in regions across the world. ICA&D contributes both to the provision of climate data and to climate monitoring, thereby supporting the WMO Regional Climate Centres in carrying-out their mandatory functions.

In WMO Regional Association VI (Europe and the Middle East), the European Climate Assessment & Dataset (ECA&D) fulfills this role and from this the ICA&D concept is derived. ICA&D has already been implemented in Indonesia as the Southeast Asian Climate Assessment & Dataset (SACA&D).

ICA&D facilitates the sharing of daily meteorological surface observations from countries in a given region with meteorological services in that region and with scientists worldwide, and deriving climate monitoring products from these observations. Examples are the climate indices of extremes, such as the number of warm or dry days, which can be monitored over time in view of climate change. ICA&D complements national meteorological databases in having only a daily resolution and containing information from multiple countries in the region in the same format.
 
With support from the EU-funded ClimSA project, under which WMO is implementing a EUR 5.5 million grant, ICA&D will be implemented in the Caribbean, the Pacific and 5 sub-regions in Africa. ICA&D will be set-up in selected Regional Climate Centres that form part of the WMO institutional network. Within the ClimSA project, the ICA&D website has seen an upgrade and has been made more user friendly. Furthermore, e-learning modules are being developed to aid the ICA&D maintainers in the region in updating their ICA&D database and website.
Projected extreme precipitation changes over the coastal-mountainous region of the eastern Adriatic and Dinaric Alps
Authors: Ivušić Sarah , Güttler Ivan , Horvath Kristian
Published: June 2022

The study aims to estimate the future climate change of extreme precipitation over the topographically complex coastal-mountainous region of the eastern Adriatic and Dinaric Alps, which is particularly vulnerable to climate change. A number of studies classify this region as an area with a "zero-change" line between the wetter north and drier south, which shifts northward towards the end of the century. However, the research on future extreme precipitation changes over this region is still limited. We use an unprecedented ensemble of ~140 regional climate model (RCM) simulations of future climate from the EURO-CORDEX ensemble at 0.11° resolution, to cover as many future conditions and sources of uncertainty as possible. The ensemble is comprised of 15 RCMs driven by 11 CMIP5 global climate models. The climate change signal is estimated for three different greenhouse gases concentration scenarios (RCP2.6, RCP4.5 and RCP8.5) and several future periods (2041-2070, 2071-2100) with respect to the historical period (1971-2000). We focused on heavy precipitation measures: the 99th percentile of all-day precipitation, number of heavy and very heavy precipitation days, maximum one-day and five-day aggregated precipitation sum. Additionally, we have applied the extreme value analysis, specifically the generalized extreme value theory, to assess extreme precipitation return levels associated with return periods between 10 and 100 years. The results are highly dependent on period, scenario, season and location. Overall,  results show an intensification of both heavy and extreme precipitation events, especially during cold seasons over the north-eastern areas for the far future period. For this projected change, models show high agreement as opposed to that in summer, when most of the examined indices display the aforementioned south-north gradient. A more detailed analysis is planned to quantify the climate change signal for several subdomains of interest.
Weather impacts on different types of road accidents
Authors: Becker Nico , Rust Henning W. , Ulbrich Uwe
Published: June 2022

Weather conditions can affect the probability of road accidents. However, the effect can be different, depending on the vehicle type, the type of collision or road characteristics at the location of the accident. The aim of this study is to quantify the combined effects of traffic volume and different meteorological parameters on hourly probabilities of different accident types using generalized additive models. These models are based on police reports, which are available at the level of administrative districts, as well as reanalysis data and radar-based precipitation. Using tensor product bases, we model non-linear relationships and combined effects of different meteorological parameters. We evaluate the increase in relative risk of different accident types in case of precipitation, sun glare and high wind speeds.

The largest effect of snow is found in case of single-truck accidents, while rain has a larger effect on single-car accidents. Precipitation particularly increases the relative risk of run-off-road accidents, as well as accidents at curves and descending road sections. A increasing effect of sun glare on accident probability was found in case of multi-car accidents, in particular in case of rear-end crashes. High wind speeds increase the risk of single-truck accidents and, for all vehicle types, the risk of collisions with objects blown on the road. A comparison of the predictive skill of models with and without meteorological variables shows an improvement of scores of up to 24%. This makes the models suitable for applications in real-time traffic management or impact-based warning systems and have the potential to improve risk perception and behavior of warning recipients.
Shear-Convection flux decomposition in atmospheric boundary layer using neural network
Authors: Shamekh Sara , Gentine Pierre
Published: June 2022

 

The representation of turbulent mixing in climate models is challenging due to eddies' wide range of scales and modeling limitations thus

turbulent flux parameterization remains one major source of climate models inaccuracy. Most turbulent flux parameterizations decompose total flux to a diffusion term, which models the local small scale mixing and a non diffusive term  (called non-local) that represents the contribution from coherent structure. However, these two terms are not coupled and it is not clear how much each term should contribute to the total flux.

Given the availability of high resolution numerical simulations, one promising approach for more accurate representation of turbulent fluxes in climate models is the use of machine-learning (ML) based parameterization. The high resolution simulations such large eddy simulations (LES) have substantially less biases so that it provides a rich dataset for training ML models that have the potential to outperform conventional parameterizations. The use of neural networks in atmospheric science has proven promising due to its ability in emulating nonlinear physical systems.

 In this work, we use a novel neural network architecture that combines variational auto-encoders (VAE), a nonlinear dimensionality  reduction technique, with an encoder-decoder network to parameterize the turbulent fluxes. VAE maps high dimensional information into a very compact representation which is then used to predict turbulent fluxes. Our neural network is trained on coarse grained high resolution LES simulation of dry convective boundary layer. We show that by enforcing few physical constraints in the latent space of VAE, we can decompose scalar turbulent fluxes into two main modes of variability. These two modes represent the contribution of convection and shear to scalar turbulent flux. This finding can be used to inform conventional parameterization of turbulent fluxes.
Long-term change in the upper atmosphere and a possible effect on surface climate
Authors: Cnossen Ingrid , Lu Hua , Liu Hanli
Published: June 2022

Not only the climate in the troposphere is changing, also at higher altitudes long-term trends have been observed. The global mean middle and upper atmosphere have been cooling, resulting in atmospheric contraction and a decline in thermosphere density at fixed height, mainly driven by the increase in atmospheric CO2 concentration. The secular variation of the Earth’s magnetic field is an additional driver of long-term change in the upper atmosphere, causing trends that are strongly location-dependent. While magnetic field changes are most important for the ionosphere, they also affect the temperature and wind structure throughout the thermosphere, mainly via changes in the strength and geographic distribution of Joule heating. Simulations with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) suggest that perturbations induced by magnetic field changes in the lower thermosphere climate can further propagate downward via vertical dynamical coupling. Our results show a significant response in the zonal mean temperature and zonal wind in the Southern Hemisphere (SH) middle- to high-latitude troposphere, stratosphere, and mesosphere of up to ±2 K and ±2 m/s, as well as regionally significant changes in Northern Hemisphere (NH) polar surface temperatures of up to ±1.3 K, in December-January-February. In the SH, changes in gravity wave filtering in the thermosphere induce a change in the residual circulation that extends down into the upper mesosphere, where further changes in the zonal mean wind climatology are generated, together with changes in local planetary wave generation and/or amplification and gravity wave filtering. This induces an anomalous residual circulation that extends down into the troposphere. The NH middle atmosphere response is zonally asymmetric, consisting of a significant change in the positioning and shape of the upper stratospheric polar vortex, which is dynamically consistent with the surface temperature response. While the details of the lower and middle atmosphere responses may not be entirely accurate due to model limitations, the results from our simulations do indicate that dynamical coupling within the atmosphere can conceivably result in upper atmosphere processes having a significant effect on surface climate.
Storylines of future climate extremes in Switzerland
Authors: Mastai Alina , Wehrli Kathrin , Kotlarski Sven , Fischer Erich
Published: June 2022

Storylines provide a way to communicate climate change and especially changes in extreme events in a tangible way for the public and decision makers by putting the focus on associated risks and impacts. In this work, storylines are created for the 2003 summer heatwave in Switzerland. For this purpose, an analogous event is searched for in the already existing Climate Scenarios for Switzerland (CH2018). This event shall be as extreme in the future climate as 2003 was in its time. Building storylines for this future extreme event, we demonstrate how a 2003-like summer might be like by the end of the Century assuming no climate change mitigation. Specific indicators are chosen to develop four individual heat-related storylines and enabling a direct comparison between 2003 and the future hot summer. For the future summer, the number of hiking weather days in the Alps is projected to increase on the one hand, while on the other hand also fire danger increases massively. More and longer heatwaves may aggravate heat-related health issues and additional generations of agricultural pests could threaten agricultural yields.

With these storylines of a similar future heatwave, we gain a better understanding of locally relevant processes, get insight into how extreme events quantitatively change with the warming climate, give examples of possible impacts, and finally try to stimulate public awareness for possible consequences of future climate change. We explore different ways of visualizing and communicating the results, which may find broader application to inform Swiss stakeholders about future climate change and its possible impacts.
Updated gridded datasets version 2022 provided by the Global Precipitation Climatology Centre (GPCC) 
Authors: Rustemeier Elke , Schneider Udo , Ziese Markus , Finger Peter , Hänsel Stephanie
Published: June 2022

Since its founding in 1989, the Global Precipitation Climatology Centre (GPCC) has been producing global precipitation analyses based on land surface in-situ measurements. In the now over 30 years the underlying database has been continuously expanded and includes a high station density and large temporal coverage. Due to the semi-automatic quality control routinely performed on the incoming station data, the GPCC database has a very high quality. Today, the GPCC holds data from more than 123,000 stations, about three quarters of them having long time series.

The core of the analyses is formed by data from the global meteorological and hydrological services, which provided their records to the GPCC, as well as national meteorological and hydrological services from all over the world.  In addition, the GPCC receives SYNOP and CLIMAT reports via the WMO-GTS. These form a supplement for the high quality precipitation analyses and the basis for the near real-time evaluations.

Quality control activities include cross-referencing stations from different sources, flagging of data errors, and correcting temporally or spatially offset data. This data then forms the basis for the following interpolation and product generation.

In near real time, the 'First Guess Monthly', 'First Guess Daily', 'Monitoring Product', ‘Provisional Daily Precipitation Analysis’ and the 'GPCC Drought Index' are generated. These are based on WMO-GTS data and monthly data generated by the CPC (NOAA).

With a 2-3 year update cycle, the high quality data products are generated with intensive quality control and built on the entire GPCC data base. These non-real time products consist of the 'Full Data Monthly', 'Full Data Daily', 'Climatology', and 'HOMPRA-Europe' and are now available in the 2022 version.

All gridded datasets presented in this paper are freely available in netcdf format on the GPCC website https://gpcc.dwd.de and referenced by a digital object identifier (DOI). The site also provides an overview of all datasets, as well as a detailed description and further references for each dataset.
Idealized simulations of orographically-induced thermal circulations triggering deep moist convection
Authors: Göbel Matthias , Serafin Stefano , Rotach Mathias W.
Published: June 2022

Processes on the meso-gamma scale play an essential role in triggering deep moist convection. In mountainous terrain, thermally-driven circulations–such as slope, valley, and plain-to-mountain winds–can provide the necessary trigger mechanism to lift air parcels above the level of free convection. Despite its relevance, the impact of thermally-driven circulations on convection initiation has so far not been systematically quantified.

We study the effect of the cross-valley circulation on convection initiation with idealized large-eddy simulations with the WRF model, considering quasi-2D mountain ranges of different heights and widths and different instability profiles. Despite being idealized by using simplified initial profiles, an idealized mountain geometry, and periodic lateral boundary conditions, the simulations employ a complete suite of physics parametrizations to achieve an adequate representation of the essential processes.

The vertical and horizontal redistribution of heat and moisture is quantified using the newly developed budget analysis tool WRFlux, and an attempt is made to determine how the initial stratification and terrain characteristics affect the time scale of convective destabilization. One distinctive finding is that steeper mountain ranges feature a later onset and lower intensity of deep moist convection due to a weaker thermal circulation.

We present different scaling arguments for the vertical profiles of vertical velocity and horizontal convergence above the mountain ridge. Surprisingly, classical velocity scales for the boundary layer over flat and horizontally homogeneous terrain work well also in our idealized mountainous terrain. In contrast, a framework that was specifically designed for estimating the strength of thermal circulations, the heat-engine framework, shows the poorest performance.
High resolution hybrid forecast based on the combination of satellite and an All Sky Imager (ASI) network forecasts
Authors: Lezaca Jorge , Hammer Annette , Lünsdorf Ontje
Published: June 2022

A method to combine a highly resolved All sky imager (ASI) network forecast with a satellite based forecast has been developed. The ASI network forecast input is based on the data from the DLR’s Eye2Sky network. This network is installed in North West Germany and includes 29 ASIs, ten Rotating Shadowband Irradiometers (RSIs) and two reference meteorological stations (based on thermal irradiometers) in an extent of 100 km². This network forecast was developed by our colleges from DLR-SF (publication in preparation). It has a forecast horizon of 30 minutes and a step of 1 min with an update of 30 seconds on a domain of 40 km². The second input is based on our operational satellite forecast at DLR-VE and has a horizon of 6 hours with a step and update of 15 minutes. The satellite domain is reduced to the same 40 km² area.

The method consists of three blocks, forecasts homogenization, regression and prediction. In the homogenization block the satellite forecast is interpolated in space and time to the resolutions of the ASI network forecast. We applied linear interpolation for both resolutions as first test case. In the second block, a linear regression is applied to find the optimal weights of the linear combination of the forecast inputs, including a bias term. The regression is based on timeseries extracted from the historical forecasts (features) where the reference is taken from the historical timeseries of ground measurements (samples). Historical data is used in order to indirectly characterize the mean actual local weather conditions on the domain. It is important to note that the regression is performed independently for every lead time. In the third block, we use the optimized weights and biases along with the present (not historical) forecasts to produce the hybrid forecasts. The hybrid forecast resolutions are the same as the ASI based forecast. The output product can be given as maps or timeseries.

For the test case, we are limited from the ASI network side to a dataset of two full months of forecasts (July and August 2020). The highly resolved hybrid forecast was validated against the individual input sources and satellite persistence. We found that this newly developed forecast outperforms the RMSE of persistence and the individual input forecasts for all calculated lead times. It shows an improvement on RMSE of 5.1% to 14.0% with respect to satellite forecasts and 7.6% to 15.1% with respect to the ASI network forecast on lead times going from 5 to 30 minutes. It also shows a lower RMSE under high variability conditions.
A recurrent pan-Atlantic wave pattern driving concurrent wintertime extremes
Authors: Messori Gabriele , Kornhuber Kai
Published: June 2022

Wintertime extremes such as cold spells and heavy precipitation can have severe socioeconomic impacts, disrupting critical infrastructures and affecting human well-being. In this context, recurrent and persistent atmospheric patterns that favour the occurrence of extremes in specific regions are of particular interest for timely extreme event prediction and for understanding the underlying physical mechanisms. Furthermore, if these atmospheric patterns span large spatial scales, they may lead to concurrent weather extremes at geographically remote locations. Such spatially compounding extremes are of particular interest due to their potentially enhanced impacts compared to extremes occurring in isolation. We identify a quasi-hemispheric wave-4 pattern in the Northern Hemisphere winter atmosphere, which is most pronounced over the pan-Atlantic region and coincides with local and concurrent cold or wet wintertime extremes in North America and Europe. Wave-4 is the only wavenumber to exhibit recurrent and phase-locked behavior, such that the associated atmospheric circulation and surface anomalies re-occur over the same locations when the pattern's wave amplitude is high. Specifically, the wave-4 pattern we identify increases the probability of extreme cold or wet events by up to 300 % in certain areas of North America and Europe, as well as favouring their concurrence at different locations. High-amplitude wave-4 events have increased significantly in frequency over the past four decades (1979–2021). Wave-4 amplitude does not seem to be directly linked to changes in the hemispheric-scale meridional temperature gradient, but shows moderate correlations with some modes of variability in the Pacific. We conclude that the identified wave-4 pattern could provide useful insights for both statistical forecasting of regional and concurrent pan-Atlantic wintertime extremes and for an improved understanding of their future changes.
In Situ Based Performance of Satellite- and Reanalysis-derived Hourly Global Horizontal Irradiance Products over West Africa
Authors: sawadogo windmanagda , Bliefernicht Jan , Fersch Benjamin , Salack Seyni , Guug Samuel , Diallo Belko , Ogunjobi Kehinde.O. , Nacoulma Guillaume , Tanu Michael , Meilinger Stefanie , Kunstmann Harald
Published: June 2022

Global horizontal irradiance (GHI) is the primary driver for photovoltaic (PV) technology. For PV system design and monitoring, hourly and sub-hourly GHI from reanalysis and satellite-based data are frequently used, especially in data-poor regions like Sub-Saharan Africa. However, the use of these datasets is uncertain and need to be assessed in detail. In this study, we evaluated the performance of state-of-the-art reanalysis and satellite-based datasets (ERA5, CAMS, MERRA-2 and SARAH-2) with in situ measurements of hourly GHI for the main climatic zones (Guinea, Savannah, and Sahel) in West Africa. The in situ measurements come from novel regional and national meteorological networks consisting of 51 automatic weather stations in Burkina Faso and Ghana. The performance assessment was done for the year 2020 for different weather conditions (cloudy, clear and all sky). The effects of clouds and aerosols were also investigated. Moreover, a new overall performance measure is introduced for joint evaluation of different standard measures, such as the root-mean-square error and the index of agreement. The results show that the data from SARAH-2 performs best under cloudy-sky conditions, while ERA5 performs worse under all atmospheric conditions. The low performance under cloudy skies for all datasets is the result of a large bias observed during the Harmattan period, when the region has a high concentration of aerosols. The average diurnal variation of GHI shows good agreement between the in situ measurements, the satellite and the reanalysis data under clear and all-sky conditions, but an overestimation under cloudy skies at some stations. The new overall performance value clearly indicates hourly GHI from SARAH-2 is the best alternative for assessing solar energy in West Africa.

 
A deep learning approach for identifying coastal sea breezes globally
Authors: Bedoya-Valestt Shalenys , Rozas-Larraondo Pablo , Azorin-Molina Cesar , Cafaro Carlo , Gimeno Luis , Minola Lorenzo , Guijarro Jose A. , Dunn Robert , Aguilar Enric , Brunet Manola
Published: June 2022

Sea breezes can occur on any coast of the world affecting meteorological variables broadly used to detect sea breeze episodes. To date, there is no universal method to identify sea breezes that works all over the globe. Most existing methodologies develop their own selection methods based on thresholds related to the local characteristics of the study site: e.g. wind direction based on the coastline orientation, cloud cover, precipitation, humidity, land-sea air temperature difference, pressure amplitude, insolation, among others. However, detecting past episodes from well-defined criteria makes sea breeze identification dependent on these criteria. This makes most classified events on the same site differ from each other, as well as making extrapolation to other regions difficult. The scarcity of high-resolution observed historical data over land and sea surfaces has limited the sea breeze understanding in many coastal regions across the world. To address the need of developing a universal method applicable to any coastal region of the globe, here we explore deep learning techniques (e.g. deep convolutional neural networks). We train these models using a time-series of high-resolution physical reanalysis (e.g. ERA-5 Land) gridded and observed data, after identifying 5 years of sea breezes manually for random stations around the globe. Results from this study constitute the first historical sea breeze global database spanning approximately the last 40 years. The ability of machine learning models to detect sea breezes allows the development of a simple and universal approach, which will improve the understanding of sea breeze at spatial scales which has not been addressed before, as far as we know.
Towards seamless and high-resolution renewable energy forecasting by the combination of approaches and data sources
Authors: Camal Simon , Van Der Meer Dennis , Kariniotakis George
Published: June 2022

Operational forecasting models of Renewable Energy Sources (RES) are usually developed for specific time frames, using a reduced set of data sources as a function of the forecasting horizon. This results in discontinuities in predictions between the different time frames from the next minutes to the next days, which is detrimental to decision-making in power systems. Although advanced forecasting model may lower forecasting error, an alternative and maybe more impactful option consists in expanding the set of potential sources for RES forecasting, going beyond recent power measurements, classical Numerical Weather Prediction and information from satellites. It is therefore crucial for next-generation RES forecasting to propose continuous, seamless predictions that harvest the full potential of heterogeneous data sources. This presentation highlights the different solutions developed in the Horizon2020 project Smart4RES for seamless RES forecasting based on the combination of multiple data sources, including high-resolution weather measurements and forecasts.

A step towards high-resolution RES forecasting, i.e. temporal resolutions below 5 min and spatial resolutions at the scale of 100 m, is achieved by the integration of high-resolution into RES forecasting models. The use of lidar measurements for the minute-ahead power forecasting of wind turbines improves RMSE against persistence. The production of wind speed forecasts at a 100m-30s resolution thanks to Large Eddy Simulation (LES) at different wind farms of an isolated power systems enables to predict the total variability of wind power production on 10-min rolling intervals, with higher reliability than an approach based on traditional NWP that have a lower spatio-temporal resolution.

The combination of multiple data sources has proven to be efficient for the improvement of RES forecasting, especially at intraday horizons. Smart4RES proposes optimal combinations for both weather and RES forecasting. The combination of high-resolution irradiance maps derived from a network of All-Sky-Imagers (ASI) with satellite images outperforms the predictions of the ASI network only. Similarly, the combination of filter-based models for Wind and PV forecasting improves the forecasting scores of the individual models.
Seaming together a patchwork of knowledges: toward co-producing social and behaviourally informed climate services
Authors: Werner Micha , Masih Ilyas , Emerton Rebecca , Pechlivanidis Ilias , Schaafsma Marije , Pesquer Lluís , Di Baldassarre Giuliano , van den Homberg Marc , Mazzoli Paolo , Gamtkitsulashvili Megi , De Stefano Lucia , Gräler Benedikt , Bela Györgyi , Tzimas Apostolis
Published: June 2022

Recent decades have shown significant advance in weather forecasting; sub-seasonal to seasonal forecast and climate prediction, including advances in a more seamless integration across the spatial and temporal scales of the knowledges these contain. Despite these advances, barriers remain to the uptake of climate services and the realising of their full value proposition. One dimension to overcoming some of the barriers is to recognise that stakeholders and decision makers consider multiple knowledges when taking climate relevant decisions; including their own local knowledges, perceptions and preferences, as well as the knowledges provided by scientific climate data and projections.

In this contribution we present the recently initiated EU-H2020 I-CISK research and innovation project. Through trans-disciplinary research this project aims to develop a co-production framework within which social and behaviourally informed climate services are co-designed, co-created, co-implemented, and co-evaluated with their intended users, as well as with other actors in the climate services value chain, including climate services purveyors, providers and data producers. We present initial results from the project in which multi-actor platforms are established in the seven living labs that are central to our research approach, and the design of the co-production framework. Pre-operational climate services will be co-created in these that showcase the framework in seaming together the patchwork of multiple scientific and local knowledges that meet identified climate information needs across multiple sectors and climate hazards; and that help foster the translation of climate services provided data into actionable information while considering reinforcing and balancing feedback loops associated to users’ decisions.
Trends in intraseasonal temperature variability in Europe, 1961 - 2018
Authors: Krauskopf Tomas , Huth Radan
Published: June 2022

While long-term changes in measures of central tendency of climate elements, that is, mean temperature, are well acknowledged, studies of trends in measures of their variability are much less common. This is despite the fact that trends in variability can have higher effect on climate extremes than trends in mean. Four measures of intraseasonal variability are examined: (a) standard deviation of mean daily temperature, (b) the range between the 90th and 10th quantile of mean daily temperature, (c) mean absolute value of day-to-day temperature change, and (d) one-day lagged temporal autocorrelation. ECA&D daily data from 168 stations and linear regression method are utilized to calculate trends of these characteristics in period from 1961 to 2018. Significant trends (positive and negative) are revealed with substantial differences between seasons, regions and measures. The most considerable decreases in temperature variability were recorded in winter, for temporal autocorrelation in eastern Europe and for variance-based measures in northern Europe. For example, the standard deviation has decreased by more than 10% in the Arctic Ocean. This can indicate a decrease in the frequency of cold extremes in Scandinavia. On the contrary, increasing persistence may suggest a greater likelihood of cold extremes in the East European Plain in winter. Increases in variability prevail only in summer, but not for all measures and not as clearly as decreases in winter. However, the increase in variance-based measures in central and eastern Europe is noticeable. Trends in temporal autocorrelation and day-to-day change appear to be sensitive to data issues, such as inhomogeneities and changes in observational procedure.
Worldwide solar radiation benchmark of modelled surface irradiance
Authors: Forstinger Anne , Wilbert Stefan , Jensen Adam R , Kraas Birk , Fernández Peruchena Carlos , Gueymard Chris A , Ronzio Dario , Yang Dazhi , Collino Elena , Polo Martinez Jesús , Ruiz-Arias Jose A , Hanrieder Natalie , Blanc Philippe , Saint-Drenan Yves-Marie
Published: June 2022

Modelled irradiance data based on satellite products is frequently used in solar energy applications and atmospheric sciences. This kind of data is offered by many different institutional or commercial providers, and currently it is not possible for users to independently identify the best provider for their specific application and location. This work presents a benchmark of satellite-derived global horizontal irradiance (GHI) as well as direct normal irradiance (DNI) at 129 ground-based radiation measurement stations distributed globally. High temporal resolution data (1 min) from these stations from between 2015 and 2020 has been quality controlled by a team of experts using a comprehensive set of best practices and newly implemented quality control procedures. The ground stations provide measurements of GHI and DNI and/or diffuse horizontal irradiance (DIF) from 25 different providers or networks. The 129 stations are spread out worldwide with 31 stations in Africa, 31 in Asia, 27 in North America, 20 in Europe, 13 in Australia, 5 in South America and 2 in Antarctica. GHI and DNI data from eleven different commercial or open-access radiation models is compared against these stations’ high-quality ground data. Additionally, one common measurement year is used to perform a site adaptation of the model datasets. The site adaptation is based on the empirical quantile mapping method and is the same for all test data sets. The comparison of the raw model data as well as the site adapted data is conducted at both 60-min and 15-min temporal resolutions. The performance of the raw and site-adapted data is analysed with respect to different regions and climate zones. Users can rely on the results of this work to make an informed decision about which surface radiation model(s) and data providers are most suited for their application.