bluuebunny/crossref_metadata_2025 huggingface dataset:
Solar resource assessment in Ireland, comparison of satellite data versus ground measurement
Authors: Chouleur Nicolas , Morandi Bianca , Martin Shane , Mau Stefan
Published: June 2021

Accurate solar resource assessments are essential to project a solar photovoltaic (PV) plant’s energy production – and ultimately forecast its revenue.

Solar resource assessments are the bedrock of the ‘Revenue’ line in PV financial models. In today’s competitive financing environment, the assumptions underlying solar resource assessment often have make-or-break impact on project valuations. It’s critical that investors trust the numbers provided.

To quantify solar resource, industry typically compares different irradiation databases derived from multiple physical sources – whether measurements or satellite images. There is always some level of scatter; in Western Europe this is often around 3%, after excluding outliers.  Satellite database are never as good as accurate ground measurement.  And the rather narrow variation observed is due to past calibration of satellite derived model with data from weather stations.  The reality can be different when it comes to Ireland. 

The solar sector is currently experiencing a rapid development in the Republic of Ireland, making the yield assessment and by extension the solar resource estimation key for the bankability of the projects.

The aim of our work was the validate the accuracy of different databases, available in Ireland.

The first step of this analysis will be to qualify our data sources. Everoze and Brightwind have access to measurement campaigns from multiple solar projects in Ireland. All the gathered dataset will be processed, applying state of the art quality control, to retain only trustable information.  The quality check will also include the sensors themselves, with a verification of the accuracy and calibration certificates of the different pieces of equipment.

In a second step, the qualified datasets will be used to compare satellite derived data.  We plan to use CAMS, SolarGIS and Meteonorm.  The intention is to categorise our results in regions, classified based on the difference in annual irradiation between different databases in order to reduce uncertainty – and ultimately boost investor confidence in energy yield assessments.
Assessment of nocturnal low-level jets during the FESSTVaL campaign 2020 for wind energy applications
Authors: Weide Luiz Eduardo , Fiedler Stephanie
Published: June 2021

Due to the increasing contribution of wind power to electricity in Europe, an exact wind characterization at the height of wind turbines is important. Nocturnal Low Level Jets (NLLJ) can influence the winds at typical blade heights and therefore influence the wind power production. However, due to the often missing measurements with a sufficient precision and resolution, the occurrence frequency and spatio-temporal characteristics of NLLJs are still poorly understood. The present work characterizes the properties of NLLJs, measured with a Doppler lidar at the Lindenberg Meteorological Observatory – Richard Aßmann Observatory (Germany), during the period of June–August 2020, and evaluates the representation of NLLJs in state-of-the-science re-analysis products. The vertical profiles of 10-minute mean winds from the lidar are statistically analysed using automated detection tools. These allow to determine the frequency of occurrence, height and wind speed in the core of NLLJs as well as the vertical wind shear and momentum transport with a high temporal resolution. We intercompare NLLJ results from different previously-used identification tools to estimate the uncertainty. Our automatic detections identified NLLJs in more than about 60% of the summer nights in 2020, with NLLJ cores between 70m and 500 m above ground level and a core speed of ~3–25 m/s. The prevailing wind direction in NLLJ cores is southwest. A considerable amount of NLLJ cores occurred at heights that are in the range of modern wind turbines and rotor sizes on land, with wind speeds of ~3-12 m/s. We use the measurements of NLLJs to evaluate their representation in the ERA5 re-analysis of the European Centre for Medium-Range Weather Forecasts and plan to compare the NLLJs to regional high-resolution re-analyses developed in the research area Climate Monitoring and Diagnostics in the Hans-Ertel Centre for Weather Research.  The first comparisons suggest a frequent co-occurrence of NLLJs in the measurements and ERA5 re-analysis, but the strength and height of NLLJ cores often differ. Possible reasons are the model’s vertical resolution and the parameterization of vertical mixing in the stable boundary layer. Future work includes extending the NLLJ analysis to more lidar measurements and other regional re-analysis data.
IWV retrieval from ship-borne GNSS receiver in the framework of the MAP-IO project
Authors: Bosser Pierre , Van Ballen Joël , Bousquet Olivier
Published: June 2021

In the framework of the research project “Marion Dufresne Atmospheric Program – Indian Ocean” (MAP-IO), which is aiming at collecting long-term atmospheric and marine biology observations in the under-instrumented Indian and Austral Oceans, a Global Navigation Satellite System (GNSS) receiver was installed on the research vessel (RV) Marion Dufresne in October 2020 to describe, and monitor, global moisture changes in these areas. GNSS raw data are recorded continuously and used to retrieve integrated water vapor contents (IWV) along the RV route.

After a data quality check that confirmed that a wise choice of location of the GNSS antenna on the RV is crucial to avoid mask, signal reflection and interference from other instruments that may degrade IWV retrieval, a first assessment of the GNSS analysis performances was carried out by comparing the vertical component of the estimated positions to sea surface height model. The differences are on the order of 20 to 30 cm; they are consistent with both the error budget for sea surface height determination using GNSS and the sea surface height model formal errors.

An evaluation of GNSS-derived IWV was conducted using IWV estimates from the ECMWF fifth ReAnalysis (ERA5) and ground-based GNSS reference stations located nearby the tracks of RV Marion Dufresne. Preliminary analyses show encouraging results with a mean root mean square error of ~2-3 kg m-2 between ERA5 and GNSS-derived IWV. The use of ultra-rapid GNSS orbit and clock product was also investigated to assess the performance of near real-time GNSS-derived IWV estimation for numerical weather prediction purposes.
Transformation of marine air masses in the Fennoscandian Boreal forest – changes in aerosol, humidity, and clouds
Authors: Räty Meri , Sogacheva Larisa , Keskinen Helmi-Marja , Kerminen Veli-Matti , Petäjä Tuukka , Ezhova Ekaterina , Kulmala Markku
Published: June 2021

Fennoscandian boreal forest is a region with commonly occurring particle formation, which benefits from the abundance of biogenic volatile organic compounds emitted by the vegetation. The same vegetation also regulates the exchange of water vapour between the ecosystem and the atmosphere. Thus, as the forest has the potential to provide the two components needed in cloud formation, i.e. condensation nuclei and humidity, there is reason to suspect consequent changes in air masses that are influenced by the forest below.

We investigated the link between boreal forest air mass transport and cloud related properties in air masses that arrived to the SMEAR II station (61°10’N, 24°17’E, 170m a.s.l.), Finland, from between western and norther directions. These selected air masses were originally marine and travelled only across a land area with relatively minor anthropogenic emissions sources, allowing us to focus on biogenic influences. The source region and the time each air mass spent above land before arrival, were determined from 96-hour long air mass back trajectories. We used a long-term comprehensive data sets, spanning up to 11 growing seasons (April-September, 2006-2016).

Air masses with short transport times over the forest, often coincided with measurements of particles in smaller size ranges. Higher numbers of larger cloud condensation nuclei sized particles became more common in air masses with longer transport times over the forest. Similarly, air masses that spent little time over land, were often relatively cool and carried less water vapour. Whereas, higher specific humidities were more likely in air masses with longer times spent over land, as associated warming had most likely facilitated an increased uptake of water vapour from plant evapotranspiration. We also observed corresponding moderate increases in satellite observed cloud optical thickness and in-situ measured precipitation. Air masses with very short transport times over land were an exception, as these fast-moving air masses are likely to be connected to weather fronts and therefore also have a high probability for clouds and precipitation. The reported differences between air masses more or less disappeared when the transport time over land reached approximately 60 hours, and any further increase in land transport time no longer caused a substantial change. This appears to be the time scale in which most of the forest environment’s influence on these cloud related properties is realised and a balance is reached.
Modeling irrigation effects on the regional climate in the "Greater Alpine Region" using a newly developed parameterization
Authors: Asmus Christina , Hoffmann Peter , Pietikäinen Joni-Pekka , Böhner Jürgen , Rechid Diana
Published: June 2021

Irrigation is a common land use practice to adapt agriculture to unsuitable climatic conditions. It is highly relevant to ensure food production. Due to the growing population and its food demand in the future, as well as due to climate change, the irrigated areas are expected to increase globally. Therefore, it is important to understand the effects of irrigation on the climate system. Irrigation of cropland alters the biogeophysical properties of the land surface and the soil. Due to the land-atmosphere interactions, these alterations have the potential to affect the atmosphere directly or through feedback processes. Various studies point out that the effects of irrigation, like temperature reduction, are particularly pronounced on local to regional scales where they bear a mitigation potential to regional climate change.

This study aims to investigate the effects of irrigation on the regional climate. To model these effects, we developed and implemented a new flexible irrigation parameterization into the regional climate model REMO. In our setup, REMO is interactively coupled to the mosaic-based vegetation module iMOVE, enabling the calculation of irrigation effects and feedbacks on land, vegetation, and atmosphere. Multiple simulations for specific climatic conditions with and without the new irrigation parameterization are conducted on 0.11° resolution for the ”Greater Alpine Region“, which includes some of Europe‘s most intensively irrigated areas like the Po valley in Northern Italy. The differences between these simulations are analyzed to identify and quantify irrigation effects on atmospheric processes.

The new irrigation parameterization will be introduced and the analysis of the irrigation effects on the regional climate in the “Greater Alpine Region” will be presented.
Local Land-Atmosphere Interaction: Exploring the Terrestrial Leg with “Little Omega”
Authors: Ek Michael , Holtslag Bert
Published: June 2021

Land-atmosphere coupling involves the interaction between the land-surface and the overlying atmospheric boundary layer, with effects on and by the free atmosphere above, and then with associated downstream impacts on clouds, convection and precipitation. We focus on the "terrestrial leg" of land-atmosphere coupling, that is, the near-surface land-atmosphere interaction where changing soil moisture affects the surface evapotranspiration. (The "atmospheric leg" of land-atmosphere coupling involves changes in surface fluxes and the effects on the atmospheric boundary layer, with those downstream impacts.) The change in surface evapotranspiration, or evaporative fraction, with changing soil moisture is an indicator of the strength of coupling between the soil/surface and the near-surface atmosphere, where for strong coupling, a given change in soil moisture yields a large change in the evaporative fraction, and for weak coupling, a given change in soil moisture yields a small change in the evaporative fraction. The strength of coupling depends on a number of different conditions and processes, i.e. the nature of the surface-layer turbulence, to what degree the surface is vegetated and by what type of vegetation, what the soil texture is, and how plant transpiration and soil hydraulic and soil thermal processes change with changing soil moisture. We examine this terrestrial leg of land-atmosphere coupling with an analytical development using the Penman-Monteith equation, then evaluate several years of fluxnet data sets from multiple sites to characterize these interactions on the local scale, contrasting different landscapes, e.g. grasslands versus forests, and other surface types. Initial findings show stronger coupling over forests. 
CSTools: the MEDSCOPE Toolbox for Climate Forecasts postprocessing
Authors: Pérez-Zanón Núria , Caron Louis-Philippe , Terzago Silvia , Van Schaeybroeck Bert , Batté Lauriane , Alvarez-Castro M. Carmen , Corti Susana , Dominguez Marta , Fabiano Federico , Gualdi Silvio , von Hardenberg Jost , Lledó Llorenç , Manubens Nicolau , Marson Paola , Materia Stefano , Sánchez Eroteida , Torralba Verónica , Verfaillie Deborah , Volpi Danila
Published: June 2021

Climate forecasts need to be postprocessed to obtain user-relevant climate information, to develop and implement strategies of adaptation to climate variability and to trigger decisions. Several postprocessing methods are gathered into CSTools (short for Climate Service Tools) for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. 

Besides an overview of the methods and documentation available in CSTools, a practical example is demonstrated. The objective of this practical example is to postprocess a seasonal forecast with a set of CSTools functions in order to obtain the required data to produce forecasts of mountain snow resources. Quantile mapping bias-correction and RainFARM stochastic downscaling methods are applied to raw seasonal forecast daily precipitation data to derive 1 km resolution fields. Bias-adjusted and downscaled precipitation data are then employed to drive a snow model, SNOWPACK, and generate snow depth seasonal forecasts at selected high-elevation sites in North-Western Italian Alps. 

The computational resources required by CSTools to process the forecasts will be discussed. This assessment is relevant given the memory requirements for the use case: while seasonal forecast data occupies ~10MB (8 x 8 grid cells, 215 forecast time steps for 30 different initializations with 25 members each), the data post-processed reaches ~1TB (the RainFARM downscaling requires a refinement factor 100 for the SNOWPACK model increasing the spatial resolution to 800 x 800 grid cells and creating 10 stochastic realizations for each ensemble member). In addition to one strategy using conventional loops, startR is introduced as an efficient alternative. startR is an R package that allows implementing the MapReduce paradigm, i.e. chunking the data and processing them either locally or remotely on high-performance computing systems, leveraging multi-node and multi-core parallelism where possible.
A precipitation climatology for the Arabian Peninsula: from droughts to extremes
Authors: Patlakas Platon , Stathopoulos Christos , Bartsotas Nikolaos S. , Flocas Helena , Kallos George
Published: June 2021

The Arabian Peninsula is an area of diverse climatic conditions due to its location and geomorphological characteristics. It is affected by the Indian monsoon in the South and the Mediterranean synoptic scale systems in the North. It has mostly a desert-type climate with extreme heat during the daytime and very low annual rainfall. Extreme precipitation events related to convective activities are not that rare though. Such events often lead to flooding and may pose threat to human life and activities. At the same time, their local nature makes the recording and forecasting very difficult but essential. Towards a better understanding of the spatiotemporal features, the links and the feedback mechanisms associated with precipitation, a thirty year regional climatic analysis has been prepared with the aid of the state-of-the-art modeling system RAMS/ICLAMS. Its two-way interactive nesting capabilities, explicit cloud microphysical schemes with seven categories of hydrometeors and the ability to handle dust aerosols as predictive quantities make it suitable in an area where dust is a dominant factor. Special focus is given to densely populated regions and in several cities in order to cover the climatic features of the Peninsula to a satisfactory extent. An extended evaluation based on in-situ measurements and satellite records is performed. A monthly climatic analysis is performed alongside with a trend analysis to better assess the rainfall patterns throughout the thirty year period. The extremes are studied under the principles of the extreme value theory focusing not only on the duration but also on the intensity of the events. To enrich the analysis of the precipitation climate an examination of droughts is also performed. Apart from the strict scientific interest, the outcome has an added value in civil protection and several industries such as constructions and reinsurance.
Data assimilation for an operational nowcasting tool
Authors: R. Gelpi Ivan , Diaz de Arcaya Aurelio , Pedruzo Xabier , Gaztelumendi Santiago
Published: June 2021

In the Basque Meteorology Agency (EUSKALMET), numerical weather prediction (NWP) models, adapted to the particular characteristics of the territory, are executed daily for many different purposes. In order to improve nowcasting and forecasting tasks, a WRFDA base data assimilation tool, was implemented. Assimilation of meteorological data combines the information provided by measured data with the information coming from numerical models, supplying the numerical representation more consistent with observations.

Working with continuous assimilation-forecast cycles of the assimilation system allows constant updating of limited area forecasts, improving nowcasting tasks, especially severe weather events. Nowadays, the tool is being executed routinely in operational basis. The assimilation system includes several datasets from different sources (surface and upper air data), available in the forecast domains: RAOB soundings, SYNOP, Buoy, METAR, Automatic weather stations and Radar. The Basque Country Weather Mesonet, managed by EUSKALMET, is a high-density network with more than 100 Automatic Weather Stations (AWS), representative of a territory of complex orography such as the Basque Country. Some observations registered in this network (ten-minute data) are included on the Data assimilation system. Euskalmet Radar is a METEOR 1500 Doppler Weather Radar with Dual polarization capabilities located on Kapildui mountain top (1174 m). Two volumetric scan are available each 10 minutes (range 300 km in reflectivity mode, range 150km in Doppler/Reflectivity mode). Reflectivity data is included in assimilation cycles.

The objective of this paper is to present the assimilation system included in the tool and to explain the results of some sensitivity experiments during high-impact weather events, to test the system's skill nowcasting extreme weather events. We present different validation analysis based on punctual and areal approaches. With a special focus on the use of datasets from the Basque Country Automatic Weather Station Mesonetwork and the available radar data.
Evaluation of high-resolution reanalyses for wind energy application in the North Sea and Baltic Sea
Authors: Spangehl Thomas , Borsche Michael , Niermann Deborah , Kaspar Frank , Tinz Birger
Published: June 2021

The exploitation of offshore wind energy is an essential part of the German energy transition (Energiewende). The planning of new offshore wind farms demands detailed information on wind conditions at turbine hub heights in the North Sea and Baltic Sea. High-resolution reanalyses which are based on state-of-the-art numerical weather prediction (NWP) models combined with data assimilation systems offer the required meteorological data which are suitable for climatological assessment.

The regional reanalysis COSMO-REA6 operated by Germany’s national meteorological service (Deutscher Wetterdienst, DWD) provides hourly data of 6 km horizontal resolution for 1995-2019/08 (Kaspar et al., 2020). Moreover, hourly data of 31 km horizontal resolution for 1950 to present are available from the global reanalysis ERA5 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). DWD delivers reanalysis data and statistical evaluation results to Bundesamt für Seeschifffahrt und Hydrographie (BSH) in order to facilitate offshore site tenders. Data and a report were recently published as part of the tenders for 2021 (https://pinta.bsh.de/).

Here we present an evaluation of the 100 m wind speed and direction from COSMO-REA6 and ERA5 based on a comprehensive statistical analysis. On the reference side the FINO measurements (Research platforms in the North Sea and Baltic Sea, https://www.fino-offshore.de/en/index.html) from FINO1 and FINO2 are used. The FINO measurements are not used by the data assimilation schemes of the two reanalyses and therefore constitute independent reference data. The focus is on episodes prior to the installation of wind farms in the direct vicinity of the FINO platforms to avoid wake effects. The quality of the two reanalyses is compared to other state-of-the-art reanalyses and wind atlas data.

Reference:

Kaspar et al. (2020): Regional atmospheric reanalysis activities at Deutscher Wetterdienst: review of evaluation results and application examples with a focus on renewable energy, Adv. Sci. Res., 17, 115–128, https://doi.org/10.5194/asr-17-115-2020.
Evolution of Köppen-Geiger's climate classification in the Basque Country in the context of climate change
Authors: Hernandez Roberto , Martija Maialen , Gomez de Segura Jose Daniel , Gaztelumendi Santiago
Published: June 2021

The Köppen-Geiger system classifies climate into five main classes and thirty sub-types, based on threshold values and seasonality of monthly air temperature and precipitation. Its aim is to map empirically biome distributions around the world. In this paper, we analyze the evolution of this climate classification in the Basque Country for the historical period 1971-2000 and for three periods of future conditions: 2011-2040, 2041-2070, 2071-2100 projected by climate change scenarios.

The baseline data consists of high spatial resolution climate data available in the Basque Country. Different results from the KLIMATEK project on Adaptation to Climate Change are used (promoted by IHOBE -Basque Government-). These results have been generated for the RCP4.5 and RCP8.5 experiments, based on simulations carried out with regional climate models within the framework of the Euro-CORDEX project. Once the indicators were obtained at a spatial resolution of 12km x 12km, they were also obtained at a resolution of 1km x 1km, using the delta method. This process is carried out for each of the Euro-CORDEX models, so that an averaged result is finally provided, together with a statistic on its dispersion.

The evolution of the Köppen-Geiger maps is accompanied by other bioclimatic indices (aridity/continentality) and diagrams, which reinforce the pre-diagnosis of future climate conditions in the Basque Country. In addition, the dispersion of the models used is taken into account in the analysis of results, showing the most and least unfavorable scenarios.

These indices are applicable in impact and vulnerability analysis studies in sectors such as agriculture and landscape. Although they have received less attention than other indices of climate extremes, they nevertheless reflect concepts that are relatively simple to understand by the general public and are therefore also useful in the task of disseminating the consequences of climate change.
A climate change adaptation measure through education and citizen science. The project FLOODUP
Authors: Llasat-Botija Montserrat , Llasat Maria Carmen
Published: June 2021

In this communication, FLOODUP, a strategy that combines a participatory methodology (citizen science) with educational activities, will be presented as a measure of adaptation to floods and their possible increase due to climate change. The adaptation strategies to climate change range from structural measures (building a dam, for example) to non-structural measures (legislation and improvement of risk assessment, for example). In recent years it has become clear that it is necessary to increase the risk awareness of the population in the face of extreme events such as floods, not only when they occur but also before and after (Hyogo Framework). The population's lack of awareness in front of natural hazards makes it difficult to take responsible decisions at the individual and community level. This is especially relevant considering the context of climate change, which also contributes with a perception of uncertainty. In this context, the development of innovative adaptation strategies based on the knowledge are needed. These strategies are also an opportunity to face the challenges associated with SDGs (Sustainable Development Goals).

The objective of FLOODUP project is double: (a) to improve the knowledge, capacitation and empowerment of the population in front of natural hazards and climatic change and (b) to collect information about their impact and management through citizen science. The tools developed in the project as a mobile application, questionnaires or educational materials will be presented. The main campaigns carried out will also be shown. Their aim is to generate spaces for reflection, especially after flood events. On the other hand, the itinerary of citizen science that is developed in secondary schools and its strengths and weaknesses will be presented. Finally, it will analyze how to respond to current challenges, such as those associated with the SDGs, from projects such as FLOODUP.

This work has been done in the framework of the M-CostAdapt (CTM2017-83655-C2-1&2-R) research project, funded by the Spanish Ministry of Science and Innovation (MICINN-AEI/FEDER, UE) and the PIRAGUA project EFA210/16 Interreg V Spain-France-Andorre Programme (POCTEFA 2014-2020, EU).
Long-term trends of precipitation in Europe in different datasets
Authors: Huth Radan , Vít Václav
Published: June 2021

It is already a well known fact that different types of climate datasets (station, gridded, reanalyses) and even individual datasets differ in how they describe statistical properties of climate variables. Here we compare precipitation trends in Europe between station data (taken from the ECA&D database), gridded data (E-OBS and CRU TS), and reanalyses (JRA-55 and NCEP/NCAR) for period 1961-2011, both annually and for individual seasons. Theil-Sen non-parametric trend estimator is used for the quantification of the trend magnitude; Mann-Kendall test is used to evaluate the significance of trends.

On the annual basis, station data indicate precipitation increases in northern Europe and decreases in southern and southeastern Europe. Whereas trends in the gridded datasets roughly agree with station data, reanalyses provide much more negative trends with a different geographical distribution. There is a tendency for reanalyses to overestimate precipitation in the beginning of the period at some places, whereas they underestimate precipitation near the end of the period elsewhere. The disagreement among different data types and datasets is larger in all seasonal analyses except winter. Particularly notable is an excessive drying trend in central, southwestern, and southeastern Europe in NCEP/NCAR in most seasons. Reanalyses thus do not appear to be suitable data sources for estimation of precipitation trends.  

Reasons for the disagreement are varied and are conjectured by a detailed examination of station / point or regional time series: station series may suffer from inhomogeneities; gridded data may be affected by different sets of stations entering the interpolation procedure at different times; while reanalyses may be affected by different kinds of data being assimilated into them in different periods.
Seamless postprocessing of multi-model NWP surface wind forecasts with deep learning
Authors: Nerini Daniele , Bhend Jonas , Spirig Christoph , Moret Lionel , Liniger Mark
Published: June 2021

Hourly wind forecasts from numerical weather prediction models suffer from a range of systematic and random errors that are to a great extent related to limitations in the model grid resolution. To correct for such biases, statistical postprocessing and downscaling procedures are commonly applied so to leverage the information provided by automatic wind measurements at the surface. More recently, such techniques have been reformulated in a machine learning framework so to profit from the increased availability of data and computational resources. The results reported in the literature are promising and call for a serious evaluation of their potential for operational forecasting.

However, there remain several scientific and more applied challenges that need to be addressed before such methods can transition to real-world applications. One such challenge relates to the availability of multiple ensemble forecasts for the same point in time and space, which raises the question of how the information can be efficiently and optimally handled during postprocessing, so to provide added value to the end-user without adding technical debt to the operational system.

We propose an approach where a single deep learning model is trained to postprocess a combination of three ensemble forecasting systems, namely the high-resolution regional COSMO model with two configurations, and the ECMWF IFS ENS global ensemble forecasting system. We will show how the training is set up to provide a robust postprocessing model that can account for real time scenarios that include missing data and late model runs, while the quality of the forecasts remains comparable to a single-model approach. We found that the flexibility of the deep learning architecture translates into a robust automatic postprocessing solution that limits the maintenance burden and improves the system’s reliability.
Benefits and Challenges of distance Learning approaches for the Training of Climate Services Professionals in sub-Saharan Africa
Authors: Tarchiani Vieri , Bacci Maurizio , Rapisardi Elena , Parrish Patrick , Veek Luciane , Pasqui Massimiliano , Di Giuseppe Edmondo , Righini Guido , Simonetti Marco , Baldi Marina
Published: June 2021

Distance learning is assuming a greater value in professional training both because of its lower cost compared to face-to-face learning and for the opportunities it provides in periods when trainee displacement is hindered by socio-political or health emergencies (e.g. the recent covid-19 pandemic). Moreover, distance learning allows us to create lasting learning resources that otherwise would not be continuously available for trainees. During recent years, the World Meteorological Organization invested human and financial resources in the development of the Global Campus initiative, which has  virtual spaces where learning materials and opportunities are gathered and made available to users. As part of the Global Campus initiative, the Regional Training Center in Italy, with the support of the Italian Ministries of Foreign Affairs, developed several distance learning packages among which the TOPaCS (Training Operational Package For Climate Services) addresses the training needs of Climate Services Professionals in sub-Saharan Africa. TOPaCS builds on the competency-based training approach and on the application of up-to-date training solutions such as the integrated use of different multimedia supports and the use of Open Badges to certify learning. Synchronous distance learning was also tested for teaching soft skills related to climate services communication. This contribution analyzes pros and cons of distance learning approaches, comparing synchronous and asynchronous solutions and their suitability for hard and soft skills teaching. It highlights opportunities and constraints, critical points and key strategic choices in the development of distance learning. The results of the study highlight that flexible solutions and versatile approaches allow personalized training paths according to end-user needs. Asynchronous microlearning (very small units of study) can be helpful in this perspective but it may result in the atomization of the training modules and the fragmentation of education pathways. Synchronous distance learning is often more appropriate for soft skills but requires larger development  efforts and more efficient technical solutions (e.g. higher bandwidth), which can be an issue in some developing countries. 
The remote influence of the extra-tropical stratosphere and tropical troposphere on North Atlantic-European winter circulation
Authors: Maidens Anna , Knight Jeff R , Scaife Adam A
Published: June 2021

Many seasonal forecast systems show skill at monthly to seasonal timescales in predicting the winter North Atlantic Oscillation (NAO), the primary mode of variability in surface pressure over the North Atlantic and European sector.  This skill has practical benefit for prediction of winter conditions over Northern Europe, and arises from the representation of remote teleconnections within the prediction system, such as from the stratosphere or the tropical troposphere.  Despite skill in the NAO, most prediction systems have little skill in other patterns of North Atlantic winter circulation variability, such as East Atlantic Pattern (EAP – the second mode of regional winter surface pressure variability). This is despite the clear contribution that patterns such as the EAP make to European winter climate variability and their demonstrated role in the generation of extreme winter conditions.

 

We examine the role of the tropical troposphere and extra-tropical stratosphere in driving North Atlantic and European winter circulation patterns, with a focus on teleconnections to the EAP.  We use relaxation experiments, in which a set of seasonal-length hindcasts are run with the atmospheric conditions within the relaxation region constrained to be similar to reanalysis.  These are then compared with an initialised, but otherwise freely evolving, hindcast set, and with reanalysis, in regions outside the relaxation region. The aim is to assess how better prediction of the relaxation regions would influence the skill in prediction of winter atmospheric circulation in the North Atlantic-European sector.

 

We find that both regions play a role in influencing regional circulation. Tropical tropospheric relaxation in particular increases the reproduction of winter surface pressure anomalies. A key part of this improvement is in the EAP, which is very well reproduced. It is shown that forcing of the EAP occurs via propagating Rossby waves linked to convective anomalies in the tropical Atlantic. In addition, we find that teleconnections from either the tropics or stratosphere lead to reproduction of observed large-scale surface pressure patterns in most winters.  In contrast, the diagnosed response to tropical forcing is rarely matched in the hindcast without relaxation, despite a similar rate of matches with the response to stratospheric forcing. This suggests that while winter stratospheric influences are well represented in the prediction system, tropical influences are under-represented.  The results suggest that the improvement of tropical Atlantic predictability could lead to improvements for European winter predictability, and should be an important focus for future work.
Drought assessment and the impacts on the agriculture and the forestry in Slovakia in 2020
Authors: Turňa Maroš , Ivaňáková Gabriela , Krčová Ivana , Mrekaj Ivan , Ridzoň Jakub
Published: June 2021

Weather conditions were quite remarkable in Slovakia during the whole year of 2020. In terms of annual mean temperature, year 2020 was very warm, with temperature very much above normal and deviations ranging mostly from +1.3 to +1.6 ° C from the normal 1981-2010 (in mountain regions even more than +1.7 ° C). With the exception of spring 2020, all other seasons were significantly above normal in terms of seasonal temperature, especially the winter 2019/2020. From the point of view of the amount of atmospheric precipitation, last year has mostly the above-normal precipitation. This was mainly due to the frequent occurrence of thunderstorm torrential rainfalls during the summer and due to the exceptionally above-normal precipitation in October, when extreme widespread rainfalls occurred in some regions of western and eastern Slovakia between 11 and 15 October. The second half of the year was thus significantly different from the point of view of the precipitation balance in comparison with the period from 10 March to approximately 15 May, when exceptionally dry conditions prevailed in Slovakia. The drought in Slovakia in year 2020 was dominant mostly in the spring, mainly in April and in the beginning of May. The lack of soil moisture was caused by the very low level of the snow cover in the previous winter months and the weather was also influenced by the long-term anticyclone in April 2020. This month was very dry in the major part of Slovakia and the extreme drought hit 24.3 % of the area of Slovakia. The unfavourable situation was registered in various regions by our reporters, in both the agricultural and forestry sectors. In summer the situation was stable and the severe drought didn´t occurred. In September the mild drought was only in the eastern part of Slovakia. October was humid in several regions. The severe and extreme soil drought in November was expressed in the first half of December in the north-western and middle part of Slovakia.

 Key words: extreme drought, April 2020
From climate information to climate services: the need for a Belgian climate service centre
Authors: De Troch Rozemien , Termonia Piet
Published: June 2021

The Belgian expertise with respect to climate research is very fragmented and led by both federal (like the Royal Meteorological Institute of Belgium, hereafter RMI) as well as regional scientific research institutions and universities. Furthermore, both in societal, research as well as policy context there is an increasing need for detailed, quantitative, reliable and consistent climate information and services, in particular linked to the expected climate changes and its impacts in different sectors (e.g. water, agriculture, energy, health, transport).

To make all this scientific expertise and climate information available in a coordinated and, above all, user-friendly way, a Belgian climate centre or a one-stop shop for climate research and services would fully address the needs. With its long-standing scientific expertise and service provision, RMI plays a crucial role in fulfilling this growing need for climate information and services.

Hence, RMI has elaborated a proposal on the creation of a Belgian climate centre. For the creation of such centre it is preferable to use existing collaborations from previous or ongoing research and service-provision activities in Belgium, requiring a structural cooperation in which each members’ expertise can be fully deployed. In the context of a previous research project, a large consortium of Belgian research institutions active in regional climate modelling, calculated for the first time a consistent set of high-resolution climate projections and sectoral impacts for Belgium. Nevertheless, it should be emphasized that the number of models used was far too limited to develop reliable information of the future climate. Moreover, this was a one-time project and there is currently an urgent need to update the climate projections to meet the new scientific requirements of the IPCC's new sixth Assessment Report.

One of the activities of the centre would thus be the coordination of the climate research based on scientific research projects. Furthermore, in order to ensure the continuous development and provision of climate services based on this objective and scientifically based expertise coming from Belgian climate scientists, the climate centre would carry out two main core tasks: (i) the production and storage of climate information and (ii) the support for the development of climate services.

The financing and implementation of a Belgian climate centre, would provide a structural framework for climate research and services, establishing partnerships with the various regions and communities, at both policy and research levels. In this way, RMI, as national meteorological institute and at the start of the climate services value chain, can guarantee a continuous scientific expertise and respond to the major needs for climate information and services at national and international level.
The interaction between cold fronts and convection in the mid-latitudes
Authors: Pacey George , Pfahl Stephan , Schielicke Lisa
Published: June 2021

Cold fronts provide an environment favourable for convective initiation in the mid-latitudes. Some studies also show the presence of a cold front can increase the chance of certain convective hazards, such as hail and heavy rain. Convection initiates in three locations in respect to cold fronts: ahead of the cold front in the warm sector of the cyclone, directly at the cold frontal boundary and also behind the cold front. Previous literature has typically focused on each initiation location independently, thus a comprehensive study investigating the link between cold fronts and convection is currently lacking from literature. This study seeks to better understand the climatology, scale interactions and forcing mechanisms of convection at each initiation location relative to the front (i.e., behind, at, ahead).

Automatic front detection methods are applied to reanalysis data and a convective cell-tracking dataset from the German Weather Service is used to build a climatology of cold fronts and convection between April–September. Convective cells are found to initiate most commonly 200–300km ahead of the cold front during late afternoon. Cells behind the front primarily initiate in north-western Germany and exhibit a strong diurnal cycle. On the contrary, cells at and ahead of the front initiate most frequently in southern Germany and exhibit a less prominent diurnal cycle, especially for cells at the frontal boundary. Lightning probability decreases with closing proximity to the cold front and the average number of cell initiations per day is significantly higher on days with cold fronts opposed to days without. The next stages of research will investigate the relative importance of various forcing mechanisms on the development of convective cells at different cell-front positions.
New Developments in Ensemble-based Probabilistic Forecasting of Solar Radiation: The WRF-Solar Ensemble Prediction System
Authors: Sengupta Manajit , Jimenez Pedro , Yang Jaemo , Kim Ju-Hye , Xie Yu
Published: June 2021

The demand for increased accuracy in predicting solar power has grown considerably over recent years due to a rapid growth in grid-tied solar generation both utility scale and distributed. To increase confidence in forecasting solar power there is a need to provide reliable probabilistic solar radiation information that also minimizes error and uncertainty. Funded by the U.S. Department of Energy, the Weather Research and Forecasting (WRF)-Solar ensemble prediction system (WRF-Solar EPS) has been recently developed by a collaboration between the National Renewable Energy Laboratory and the National Center for Atmospheric Research. The WRF-Solar EPS is now ready to be disseminated to support the integration of solar generation resources and improve accuracy of day-ahead and intraday probabilistic solar forecasts. The first stage of our framework in developing WRF-Solar EPS required a specially designed method using a tangent linear (TL) sensitivity analysis to efficiently investigate uncertainties of WRF-Solar variables in forecasting clouds and solar irradiance. For the second stage, we applied a methodology to introduce stochastic perturbations in 14 key variables ascertained through the TL sensitivity analysis in generating ensemble members. A user-friendly interface is provided in WRF-Solar EPS, in which the parameters of stochastic perturbations can be controlled by configuration files. Lastly, we implemented an analog technique as an ensemble post-processing method to improve the performance of ensemble solar irradiance forecasts. This presentation will summarize the work performed in the past 3 years to understand the interactions between cloud physics, land surface, boundary layer and radiative transfer models through the development of a probabilistic cloud optimized day-ahead forecasting system based on WRF-Solar. For evaluation of forecasts, we adapt and use satellite-derived solar radiation data, e.g., the National Solar Radiation Data Base (NSRDB) as well as ground-measured observations. A comprehensive analysis to assess gridded model outputs over the Contiguous U.S is performed. The importance of evaluation of the WRF-Solar model with the NSRDB lies in the fact that the knowledge of the cloud-caused uncertainties in predicting solar irradiance over a wide range of regions provides model developers a detailed understanding of model strength and weaknesses in predicting clouds. Overall, we will present the detailed research steps that resulted in the development of the WRF-Solar EPS. We will also present a detailed validation demonstrating the improvements provided by this model. Moreover, we will also introduce the user’s guide for WRF-Solar EPS and future extension of this research.